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Temperatures of 20°C Produce Increased Net Primary Production in Chlorella sp.

Biddinger et al. | Feb 25, 2020

Temperatures of 20°C Produce Increased Net Primary Production in <em>Chlorella sp.</em>

Chlorella sp. are unicellular green algae that use photosynthesis to reduce carbon dioxide into glucose. In this study, authors sought to determine the temperature that Chlorella sp. is maximally efficient at photosynthesis, and therefore removing the most carbon dioxide from the system. This activity could be harnessed to naturally remove carbon dioxide from the environment, fighting the effects of climate change.

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Pressing filtration for extraction of cabbage dietary fiber and soluble components

Xie et al. | Nov 16, 2025

Pressing filtration for extraction of cabbage dietary fiber and soluble components
Image credit: Shelley Pauls

Here the authors introduce pressing filtration as a novel, efficient, and low-energy method for extracting dietary fiber from cabbage, which successfully retains heat-sensitive nutrients and achieves a high fiber yield. The study demonstrates the scalability and economic viability of this technique for commercial use, highlighting that the resulting high-fiber cabbage powder can be incorporated into familiar foods like hamburger buns and beef patties without compromising taste or sensory quality.

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The effect of music on teenagers in combatting stress and improving performance

Josyula et al. | Nov 16, 2024

The effect of music on teenagers in combatting stress and improving performance
Image credit: Stefany Andrade

Here, the researchers investigated how exposure to active versus passive music affects a teenager's ability to perform a challenging task, namely a Sudoku puzzle, under stressful conditions. Following testing 75 high school teenagers split into two group, the researchers found that singing in a choir (active music) yielded a greater improvement in performance compared to passive listening for brief time periods.

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Enhancing marine debris identification with convolutional neural networks

Wahlig et al. | Apr 03, 2024

Enhancing marine debris identification with convolutional neural networks
Image credit: The authors

Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.

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