Bui, C. V. | Search bioRxiv

bioRxiv2023

Density effects on a tropical copepod Acartia sp.: implications as live feed in aquaculture

Calanoid copepod Acartia species are major live feeds for the early stages of economically important marine fish in hatcheries in Southeast Asian countries. However, rearing Acartia copepods at high densities to increase productivity remains a major challenge. To address the issue, we conducted two experiments on 1) Acartia sp. nauplii (1000, 3000, 6000, 9000, 12000, and 15000 individuals L-1) and 2) adults (1000, 1500, 2000, and 2500 individuals L-1). We assessed key parameters for biomass production: development, survival, and egg production. In general, increased density resulted in longer development time, and lowered survival and egg production, but did not affect the size of adult males and females. Despite survival to adulthood decreasing at higher stocking nauplii densities, the number of surviving adults was highest at a stocking density of 12000 ind L-1. Egg production was very low which may be the result of high egg predation. The total eggs harvested were highest at the lowest adult density. These results are essential for the biomass production of Acartia sp. in central Vietnam.

zoology↗

bioRxiv2020

Cell segmentation using deep learning: comparing label and label-free approaches using hyper-labeled image stacks

Deep learning provides an opportunity to automatically segment and extract cellular features from high-throughput microscopy images. Many labeling strategies have been developed for this purpose, ranging from the use of fluorescent markers to label-free approaches. However, differences in the channels available to each respective training dataset make it difficult to directly compare the effectiveness of these strategies across studies. Here we explore training models using subimage stacks composed of channels sampled from larger, hyper-labeled, image stacks. This allows us to directly compare a variety of labeling strategies and training approaches on identical cells. This approach revealed that fluorescence-based strategies generally provide higher segmentation accuracies but were less accurate than label-free models when labeling was inconsistent. The relative strengths of label and label-free techniques could be combined through the use of merging fluorescence channels and out-of-focus brightfield images. Beyond comparing labeling strategies, using subimage stacks for training was also found to provide a method of simulating a wide range of labeling conditions, increasing the ability of the final model to accommodate a greater range of experimental setups.

bioengineering↗