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Zooplankton size is a crucial indicator in marine ecosystems, reflecting demographic structure, species diversity and trophic status. Traditional methods for measuring zooplankton size, which involve direct sampling and microscopic analysis, are laborious and time-consuming. In situ imaging systems are useful sampling tools; however, the variation in angles, orientations, and image qualities presented considerable challenges to early machine learning models tasked with measuring sizes.. Our study introduces a novel, efficient, and precise deep learning-based method for zooplankton size measurement. This method employs a deep residual network with an adaptation: replacing the fully connected layer with a convolutional layer. This modification allows for the generation of an accurate predictive heat map for size determination. We validated this automated approach against manual sizing using ImageJ, employing in-situ images from the PlanktonScope. The focus was on three zooplankton groups: copepods, appendicularians, and shrimps. An analysis was conducted on 200 individuals from each of the three groups. Our automated method's performance was closely aligned with the manual process, demonstrating a minimal average discrepancy of just 1.84%. This significant advancement presents a rapid and reliable tool for zooplankton size measurement. By enhancing the capacity for immediate and informed ecosystem-based management decisions, our deep learning-based method addresses previous challenges and opens new avenues for research and monitoring in zooplankton.

Summary: Haematococcus pluvialis, the richest bioresource for natural astaxanthin, encounters a challenge of achieving high growth rate when it comes to mass biomass production. Based on the substrate consumption model and Redfield ratio, rapid algae growth benefits from a proper carbon supply. However, the conventional cultivation schemes with limited carbon dioxide (CO2) supply and inefficient carbon mass transfer could have constrained the carbon capture and growing ability of H. pluvialis. We hypothesize that optimal H. pluvialis growth improvement may be achieved by efficient CO2 supply. Here, in this study, we first identified the carbon consumption of H. pluvialis during exponential growth. Then, a novel microbubble-driven photobioreactor (MDPBR) was designed to satisfy the carbon demand. The novel microbubble photobioreactor improves the CO2 supply by reducing bubble size, significantly elevating the CO2 mass transfer. With only 0.05 L min−1 of gas flow rate, higher cell growth rate (0.49 d−1) has been achieved in MDPBR.

The unicellular microalgae Haematococcus lacustris is an astaxanthin-rich organism that is widely used for commercial cultivation, but its main limitation is its relatively low biomass yield. It is widely accepted that the use of appropriate high light intensity could promote algal growth; however, H. lacustris is very sensitive to high-intensity light, and its growth can be readily arrested by inappropriate illumination. To exploit the growth-promoting benefit of higher light intensities while avoiding growth arrestment, we examined the growth of H. lacustris under high light intensities using various light profiles, including different light colors and light/dark cycles. The results show that light color treatments could not alleviate cellular stress under high light intensities; however, it was interesting to find that red light was favored the most by cells out of all the colors. In terms of the light/dark cycle, the 2/2 h light/dark cycle treatment was shown to lead to the highest specific growth rate, which was 46% higher than that achieved in the control treatment (18 μmol/m2·s light intensity, white light). Therefore, in further experiments, the 2/2 h light/dark cycle with red-light treatment was examined. The results show that this combination enabled a significantly higher specific growth rate, which was 66.5% higher than that achieved in the control treatment (18 μmol/m2·s light intensity, white light). These findings offer new strategies for the optimization of illumination for the growth of H. lacustris and lay the foundations for more reasonable lighting utilization for the cultivation of commercially valuable algal species.