Computer-Generated Ovaries to Assist Follicle Counting Experiments
- Submitting institution
-
The University of Kent
- Unit of assessment
- 12 - Engineering
- Output identifier
- 5910
- Type
- D - Journal article
- DOI
-
10.1371/journal.pone.0120242
- Title of journal
- PLoS ONE
- Article number
- e0120242
- First page
- -
- Volume
- 10
- Issue
- 3
- ISSN
- 1932-6203
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2015
- URL
-
https://kar.kent.ac.uk/47814/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Precise estimation of follicle numbers in mammalian ovaries is of key importance in the field of reproductive biology. The approach we developed and presented in this paper provides information on how to select the necessary number of ovaries and sampling frequency to be used in follicle counting experiments to achieve a desirable degree of accuracy. This helps to minimise use of animal tissue and laboratory time. Information provided in our paper has been used by the Metabolic Research Laboratories group (University of Cambridge) for their follicle counting experiments (Aiken et. FASEB J. 30, 1548–1556, 2016).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
