| Title |
A unified deep-learning network to accurately segment insulin granules of different animal models imaged under different electron microscopy methodologies
|
|---|---|
| Published in |
Protein & Cell, October 2018
|
| DOI | 10.1007/s13238-018-0575-y |
| Pubmed ID | |
| Authors |
Xiaoya Zhang, Xiaohong Peng, Chengsheng Han, Wenzhen Zhu, Lisi Wei, Yulin Zhang, Yi Wang, Xiuqin Zhang, Hao Tang, Jianshe Zhang, Xiaojun Xu, Fengping Feng, Yanhong Xue, Erlin Yao, Guangming Tan, Tao Xu, Liangyi Chen |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 3 | 20% |
| Student > Ph. D. Student | 2 | 13% |
| Student > Bachelor | 2 | 13% |
| Other | 1 | 7% |
| Unknown | 7 | 47% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 2 | 13% |
| Biochemistry, Genetics and Molecular Biology | 1 | 7% |
| Nursing and Health Professions | 1 | 7% |
| Immunology and Microbiology | 1 | 7% |
| Agricultural and Biological Sciences | 1 | 7% |
| Other | 2 | 13% |
| Unknown | 7 | 47% |
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 15 October 2018.
All research outputs
#18,651,503
of 23,106,390 outputs
Outputs from Protein & Cell
#564
of 747 outputs
Outputs of similar age
#264,667
of 346,055 outputs
Outputs of similar age from Protein & Cell
#8
of 15 outputs
Altmetric has tracked 23,106,390 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 747 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one is in the 10th percentile – i.e., 10% 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 346,055 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.