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A novel application of motion analysis for detecting stress responses in embryos at different stages of development

Overview of attention for article published in BMC Bioinformatics, February 2013
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Title
A novel application of motion analysis for detecting stress responses in embryos at different stages of development
Published in
BMC Bioinformatics, February 2013
DOI 10.1186/1471-2105-14-37
Pubmed ID
Authors

Oliver Tills, Tabitha Bitterli, Phil Culverhouse, John I Spicer, Simon Rundle

Abstract

Motion analysis is one of the tools available to biologists to extract biologically relevant information from image datasets and has been applied to a diverse range of organisms. The application of motion analysis during early development presents a challenge, as embryos often exhibit complex, subtle and diverse movement patterns. A method of motion analysis able to holistically quantify complex embryonic movements could be a powerful tool for fields such as toxicology and developmental biology to investigate whole organism stress responses. Here we assessed whether motion analysis could be used to distinguish the effects of stressors on three early developmental stages of each of three species: (i) the zebrafish Danio rerio (stages 19 h, 21.5 h and 33 h exposed to 1.5% ethanol and a salinity of 5); (ii) the African clawed toad Xenopus laevis (stages 24, 32 and 34 exposed to a salinity of 20); and iii) the pond snail Radix balthica (stages E3, E4, E6, E9 and E11 exposed to salinities of 5, 10 and 15). Image sequences were analysed using Sparse Optic Flow and the resultant frame-to-frame motion parameters were analysed using Discrete Fourier Transform to quantify the distribution of energy at different frequencies. This spectral frequency dataset was then used to construct a Bray-Curtis similarity matrix and differences in movement patterns between embryos in this matrix were tested for using ANOSIM.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 %
United Kingdom 3 12%
Unknown 23 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 27%
Student > Ph. D. Student 5 19%
Student > Master 5 19%
Student > Bachelor 3 12%
Professor > Associate Professor 1 4%
Other 1 4%
Unknown 4 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 42%
Environmental Science 5 19%
Biochemistry, Genetics and Molecular Biology 3 12%
Philosophy 1 4%
Earth and Planetary Sciences 1 4%
Other 1 4%
Unknown 4 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 01 February 2013.
All research outputs
#11,546,854
of 14,571,674 outputs
Outputs from BMC Bioinformatics
#4,458
of 5,418 outputs
Outputs of similar age
#171,536
of 243,471 outputs
Outputs of similar age from BMC Bioinformatics
#170
of 190 outputs
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So far Altmetric has tracked 5,418 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
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