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What is the optimal fuel for space flight? Efficiency, cost, and environmental impact

Kapitonova et al. | Dec 28, 2023

What is the optimal fuel for space flight? Efficiency, cost, and environmental impact
Image credit: NASA

Here, the authors sought to investigate the efficiency, cost, and environmental impact of several possible propellants that are or could be used for space flight. By deriving three novel equations, they identified harm, energy, and cost scores for each fuel, suggesting that considering each factor will be essential to the ongoing growth of the space industry.

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How are genetically modified foods discussed on TikTok? An analysis of #GMOFOODS

Basch et al. | Nov 20, 2023

How are genetically modified foods discussed on TikTok? An analysis of #GMOFOODS
Image credit: Camilo Jimenez

Here, the authors investigated engagement with #GMOFOODS, a hashtag on TikTok. They hypothesized that content focused on the negative effects of genetically modified organisms would receive more interaction driven by consumers. They found that the most common cateogry focused on the disadvantages of GMOs related to nutrition and health with the number of views determining if the video would be provided to users.

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Antibacterial activity by Dombeya wallichii plant extracts obtained by ultrasound-assisted extraction

Herur et al. | Nov 13, 2023

Antibacterial activity by <em>Dombeya wallichii</em> plant extracts obtained by ultrasound-assisted extraction

Medicinal plants could be a good source of medication to combat antibiotic resistance. Dombeya wallichii, which is commonly called Pink Ball Tree in the family Sterculiaceae, has been documented to have medicinal potential. We observed the highest antibacterial activity in the stem extracts, followed by leaf and bark extracts. The extracts were more effective against tested Gram-positive bacteria when compared with Gram-negative strains.

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A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood

Adami et al. | Sep 20, 2023

A novel approach for predicting Alzheimer’s disease using machine learning on DNA methylation in blood
Image credit: National Cancer Institute

Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.

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