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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Dendrite and Axon Specific Geometrical Transformation in Neurite Development
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, January 2016
|
| DOI | 10.3389/fncom.2015.00156 |
| Pubmed ID | |
| Authors |
Vasily I. Mironov, Alexey V. Semyanov, Victor B. Kazantsev |
| Abstract |
We propose a model of neurite growth to explain the differences in dendrite and axon specific neurite development. The model implements basic molecular kinetics, e.g., building protein synthesis and transport to the growth cone, and includes explicit dependence of the building kinetics on the geometry of the neurite. The basic assumption was that the radius of the neurite decreases with length. We found that the neurite dynamics crucially depended on the relationship between the rate of active transport and the rate of morphological changes. If these rates were in the balance, then the neurite displayed axon specific development with a constant elongation speed. For dendrite specific growth, the maximal length was rapidly saturated by degradation of building protein structures or limited by proximal part expansion reaching the characteristic cell size. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Russia | 1 | 4% |
| Unknown | 24 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 24% |
| Researcher | 6 | 24% |
| Student > Ph. D. Student | 3 | 12% |
| Student > Doctoral Student | 2 | 8% |
| Professor | 2 | 8% |
| Other | 5 | 20% |
| Unknown | 1 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 9 | 36% |
| Engineering | 4 | 16% |
| Biochemistry, Genetics and Molecular Biology | 3 | 12% |
| Agricultural and Biological Sciences | 2 | 8% |
| Unspecified | 1 | 4% |
| Other | 4 | 16% |
| Unknown | 2 | 8% |
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 28 January 2016.
All research outputs
#20,303,950
of 22,842,950 outputs
Outputs from Frontiers in Computational Neuroscience
#1,159
of 1,343 outputs
Outputs of similar age
#333,477
of 396,721 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#25
of 30 outputs
Altmetric has tracked 22,842,950 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 1,343 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 1st percentile – i.e., 1% 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 396,721 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 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.