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X Demographics
Mendeley readers
Attention Score in Context
| Title |
CeleST: Computer Vision Software for Quantitative Analysis of C. elegans Swim Behavior Reveals Novel Features of Locomotion
|
|---|---|
| Published in |
PLoS Computational Biology, July 2014
|
| DOI | 10.1371/journal.pcbi.1003702 |
| Pubmed ID | |
| Authors |
Christophe Restif, Carolina Ibáñez-Ventoso, Mehul M. Vora, Suzhen Guo, Dimitris Metaxas, Monica Driscoll |
| Abstract |
In the effort to define genes and specific neuronal circuits that control behavior and plasticity, the capacity for high-precision automated analysis of behavior is essential. We report on comprehensive computer vision software for analysis of swimming locomotion of C. elegans, a simple animal model initially developed to facilitate elaboration of genetic influences on behavior. C. elegans swim test software CeleST tracks swimming of multiple animals, measures 10 novel parameters of swim behavior that can fully report dynamic changes in posture and speed, and generates data in several analysis formats, complete with statistics. Our measures of swim locomotion utilize a deformable model approach and a novel mathematical analysis of curvature maps that enable even irregular patterns and dynamic changes to be scored without need for thresholding or dropping outlier swimmers from study. Operation of CeleST is mostly automated and only requires minimal investigator interventions, such as the selection of videotaped swim trials and choice of data output format. Data can be analyzed from the level of the single animal to populations of thousands. We document how the CeleST program reveals unexpected preferences for specific swim "gaits" in wild-type C. elegans, uncovers previously unknown mutant phenotypes, efficiently tracks changes in aging populations, and distinguishes "graceful" from poor aging. The sensitivity, dynamic range, and comprehensive nature of CeleST measures elevate swim locomotion analysis to a new level of ease, economy, and detail that enables behavioral plasticity resulting from genetic, cellular, or experience manipulation to be analyzed in ways not previously possible. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 20% |
| Russia | 1 | 10% |
| United Kingdom | 1 | 10% |
| Unknown | 6 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 60% |
| Scientists | 2 | 20% |
| Science communicators (journalists, bloggers, editors) | 2 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 140 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 2 | 1% |
| Italy | 1 | <1% |
| United Kingdom | 1 | <1% |
| Spain | 1 | <1% |
| Japan | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 133 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 33 | 24% |
| Student > Master | 24 | 17% |
| Researcher | 20 | 14% |
| Student > Bachelor | 10 | 7% |
| Professor > Associate Professor | 10 | 7% |
| Other | 15 | 11% |
| Unknown | 28 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 28 | 20% |
| Engineering | 18 | 13% |
| Biochemistry, Genetics and Molecular Biology | 14 | 10% |
| Neuroscience | 13 | 9% |
| Computer Science | 9 | 6% |
| Other | 23 | 16% |
| Unknown | 35 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 29. 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 15 April 2024.
All research outputs
#1,357,625
of 25,721,020 outputs
Outputs from PLoS Computational Biology
#1,119
of 9,025 outputs
Outputs of similar age
#12,495
of 228,173 outputs
Outputs of similar age from PLoS Computational Biology
#15
of 161 outputs
Altmetric has tracked 25,721,020 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,025 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.8. This one has done well, scoring higher than 87% 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 228,173 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 161 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.