Browse Articles

Plasmid Variance and Nutrient Regulation of Bioluminescence Genes

Uhler et al. | Dec 09, 2014

Plasmid Variance and Nutrient Regulation of Bioluminescence Genes

Numerous organisms, including the marine bacterium Aliivibrio fischeri, produce light. This bioluminescence is involved in many important symbioses and may one day be an important source of light for humans. In this study, the authors investigated ways to increase bioluminescence production from the model organism E. coli.

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A model for angle evolution in conical piles formed using the fixed funnel method

Capaldi et al. | Jul 20, 2026

A model for angle evolution in conical piles formed using the fixed funnel method

When granular materials are poured onto a surface, they form conical piles whose slopes increase before reaching a stable angle of repose. We found that this angle evolution follows a previously unrecognized two-phase exponential growth pattern that is conserved across granular materials with diverse particle properties. The parameters of this model correlate with particle friction and are influenced by deposition conditions, providing a quantitative framework for describing pile formation.

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Innovative fake health news detection: Integrating emotional features into graph neural networks

Wang et al. | Jul 03, 2026

Innovative fake health news detection: Integrating emotional features into graph neural networks
Image credit: Wang and Wang

This manuscript tackles a major social issue in the health news sector, with social media being one of the primary sources of information and a prime spot to propagate fake news. The author proposes X-HND , which is a unique architecture that combines emotional and contextual analysis in a Graph Neural Network to accurately detect fake news. This was a multi-step process which involved the creation of a custom health news dataset (HNDataset), and an emotional variant that uses RoBERTa to extract emotion. These dataset were then used to prove the hypothesis that accuracy increases when the custom dataset is used to train the model and that with the integration of emotion capture, the detection accuracy increases further.

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