Automated Egg-Counting Approaches for Aedes aegypti Oviposition Experiments.
Nicholas Kristoff TochorYunusa Garba MohammedBenjamin J MatthewsPublished in: Cold Spring Harbor protocols (2023)
Egg-laying preferences of mosquitoes can reveal key neurosensory mechanisms informing the decision-making process for this critical behavior. A single blood meal results in a gravid female Aedes aegypti mosquito laying more than 100 eggs. Therefore, egg counting represents a potentially time-consuming and laborious component to behavioral assays, such as those that measure oviposition preference or fecundity. Automated algorithms that count eggs from images can dramatically reduce the time required for this step of data processing and analysis and increase reproducibility associated with having multiple human observers count the eggs. Here, we present two distinct approaches for counting melanized Ae. aegypti eggs laid on white filter paper.
Keyphrases
- aedes aegypti
- deep learning
- zika virus
- machine learning
- dengue virus
- decision making
- high throughput
- endothelial cells
- heat stress
- artificial intelligence
- peripheral blood
- big data
- genome wide
- electronic health record
- single cell
- induced pluripotent stem cells
- gene expression
- optical coherence tomography
- dna methylation