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Mendeley readers
Attention Score in Context
| Title |
Automatic Morphological Subtyping Reveals New Roles of Caspases in Mitochondrial Dynamics
|
|---|---|
| Published in |
PLoS Computational Biology, October 2011
|
| DOI | 10.1371/journal.pcbi.1002212 |
| Pubmed ID | |
| Authors |
Jyh-Ying Peng, Chung-Chih Lin, Yen-Jen Chen, Lung-Sen Kao, Young-Chau Liu, Chung-Chien Chou, Yi-Hung Huang, Fang-Rong Chang, Yang-Chang Wu, Yuh-Show Tsai, Chun-Nan Hsu |
| Abstract |
Morphological dynamics of mitochondria is associated with key cellular processes related to aging and neuronal degenerative diseases, but the lack of standard quantification of mitochondrial morphology impedes systematic investigation. This paper presents an automated system for the quantification and classification of mitochondrial morphology. We discovered six morphological subtypes of mitochondria for objective quantification of mitochondrial morphology. These six subtypes are small globules, swollen globules, straight tubules, twisted tubules, branched tubules and loops. The subtyping was derived by applying consensus clustering to a huge collection of more than 200 thousand mitochondrial images extracted from 1422 micrographs of Chinese hamster ovary (CHO) cells treated with different drugs, and was validated by evidence of functional similarity reported in the literature. Quantitative statistics of subtype compositions in cells is useful for correlating drug response and mitochondrial dynamics. Combining the quantitative results with our biochemical studies about the effects of squamocin on CHO cells reveals new roles of Caspases in the regulatory mechanisms of mitochondrial dynamics. This system is not only of value to the mitochondrial field, but also applicable to the investigation of other subcellular organelle morphology. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 2% |
| Portugal | 1 | <1% |
| Netherlands | 1 | <1% |
| Finland | 1 | <1% |
| United Kingdom | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 126 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 36 | 27% |
| Researcher | 29 | 22% |
| Student > Master | 17 | 13% |
| Student > Bachelor | 9 | 7% |
| Student > Postgraduate | 5 | 4% |
| Other | 17 | 13% |
| Unknown | 20 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 38 | 29% |
| Biochemistry, Genetics and Molecular Biology | 28 | 21% |
| Engineering | 10 | 8% |
| Computer Science | 9 | 7% |
| Medicine and Dentistry | 8 | 6% |
| Other | 18 | 14% |
| Unknown | 22 | 17% |
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 17 May 2019.
All research outputs
#17,649,940
of 25,870,940 outputs
Outputs from PLoS Computational Biology
#7,555
of 9,061 outputs
Outputs of similar age
#104,480
of 147,852 outputs
Outputs of similar age from PLoS Computational Biology
#84
of 126 outputs
Altmetric has tracked 25,870,940 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,061 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one is in the 11th percentile – i.e., 11% 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 147,852 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.