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Learning-guided automatic three dimensional synapse quantification for drosophila neurons

Overview of attention for article published in BMC Bioinformatics, May 2015
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  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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5 tweeters
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1 Facebook page

Citations

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7 Dimensions

Readers on

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28 Mendeley
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Title
Learning-guided automatic three dimensional synapse quantification for drosophila neurons
Published in
BMC Bioinformatics, May 2015
DOI 10.1186/s12859-015-0616-y
Pubmed ID
Authors

Jonathan Sanders, Anil Singh, Gabriella Sterne, Bing Ye, Jie Zhou

Abstract

The subcellular distribution of synapses is fundamentally important for the assembly, function, and plasticity of the nervous system. Automated and effective quantification tools are a prerequisite to large-scale studies of the molecular mechanisms of subcellular synapse distribution. Common practices for synapse quantification in neuroscience labs remain largely manual or semi-manual. This is mainly due to computational challenges in automatic quantification of synapses, including large volume, high dimensions and staining artifacts. In the case of confocal imaging, optical limit and xy-z resolution disparity also require special considerations to achieve the necessary robustness. A novel algorithm is presented in the paper for learning-guided automatic recognition and quantification of synaptic markers in 3D confocal images. The method developed a discriminative model based on 3D feature descriptors that detected the centers of synaptic markers. It made use of adaptive thresholding and multi-channel co-localization to improve the robustness. The detected markers then guided the splitting of synapse clumps, which further improved the precision and recall of the detected synapses. Algorithms were tested on lobula plate tangential cells (LPTCs) in the brain of Drosophila melanogaster, for GABAergic synaptic markers on axon terminals as well as dendrites. The presented method was able to overcome the staining artifacts and the fuzzy boundaries of synapse clumps in 3D confocal image, and automatically quantify synaptic markers in a complex neuron such as LPTC. Comparison with some existing tools used in automatic 3D synapse quantification also proved the effectiveness of the proposed method.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 4%
Italy 1 4%
Unknown 26 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 25%
Researcher 7 25%
Student > Master 5 18%
Professor > Associate Professor 3 11%
Other 2 7%
Other 3 11%
Unknown 1 4%
Readers by discipline Count As %
Neuroscience 7 25%
Agricultural and Biological Sciences 6 21%
Computer Science 5 18%
Biochemistry, Genetics and Molecular Biology 5 18%
Engineering 2 7%
Other 2 7%
Unknown 1 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 28 June 2015.
All research outputs
#2,033,279
of 5,285,691 outputs
Outputs from BMC Bioinformatics
#1,411
of 2,975 outputs
Outputs of similar age
#65,084
of 175,450 outputs
Outputs of similar age from BMC Bioinformatics
#75
of 125 outputs
Altmetric has tracked 5,285,691 research outputs across all sources so far. This one has received more attention than most of these and is in the 60th percentile.
So far Altmetric has tracked 2,975 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 49th percentile – i.e., 49% 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 175,450 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 61% of its contemporaries.
We're also able to compare this research output to 125 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.