摘要
Hydraulic shear has been widely accepted as one of the essential factors modulating phytoplankton growth. Previous experimental studies of algal growth have been conducted at the macroscopic level, and direct observation at the cell scale has been lacking. In this study, an algal-cell dynamic continuous observation platform (ACDCOP) is proposed with a parallel-plate flow chamber (PPFC) to capture cellular growth images which are then used as input to a computer vision algorithm featuring a pre-trained backpropagation neural network to quantitatively evaluate the volumes and volumetric growth rates of individual cells. The platform was applied to investigate the growth ofScenedesmus quadricaudacells under different hydraulic shear stress conditions. The results indicated that the threshold shear stress for the development ofScenedesmus quadricaudacells was 270 µL min−1(5.62 × 10−5m2s−3). Cellular growth was inhibited at very low and very high intensities of hydraulic shear. Among all the experimental groups, the longest growth period for a cell, from attachment to PPFC to cell division, was 5.7 days. Cells with larger initial volumes produced larger volumes at division. The proposed platform could provide a novel approach for algal research by enabling direct observation of algal growth at the cell scale, and could potentially be applied to investigate the impacts of various environmental stressors such as nutrient, temperature, and light on cellular growth in different algal species.
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