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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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 9%
Unknown 31 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 24%
Student > Ph. D. Student 6 18%
Student > Master 6 18%
Student > Bachelor 4 12%
Lecturer 2 6%
Other 2 6%
Unknown 6 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 41%
Environmental Science 6 18%
Biochemistry, Genetics and Molecular Biology 2 6%
Earth and Planetary Sciences 2 6%
Computer Science 1 3%
Other 3 9%
Unknown 6 18%
Attention Score in Context

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
#20,180,477
of 22,694,633 outputs
Outputs from BMC Bioinformatics
#6,827
of 7,254 outputs
Outputs of similar age
#249,468
of 282,530 outputs
Outputs of similar age from BMC Bioinformatics
#129
of 137 outputs
Altmetric has tracked 22,694,633 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.