A new study published in Carbon Research has uncovered the complex and contradictory role that dying aquatic plants play in the health of shallow lakes. Using controlled mesocosm experiments, a team of scientists tracked the full life cycle of the floating-leaved macrophyte Trapa bispinosa, revealing that its decline simultaneously poses a risk of eutrophication while enhancing the lake’s ability to sequester carbon through a process known as the microbial carbon pump (MCP).

The research demonstrates that as these plants decay, they release substantial amounts of nitrogen and phosphorus into the water. This nutrient pulse can fuel algal blooms and create hypoxic (low-oxygen) "dead zones," posing a significant threat to water quality. However, this decay process also releases a flood of dissolved organic matter (DOM), the primary food source for aquatic microbes. The study found this isn't just a release of waste; it’s a fundamental shift in the lake’s carbon chemistry.

The National Institutes of Health has renewed support for Artificial Intelligence for Alzheimer’s Disease, or AI4AD. The new $12.6 million award to advance the project’s next phase, AI4AD2, brings its total investment in AI4AD to $30.7 million. Led by Paul M. Thompson, PhD, associate director of the USC Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) at the Keck School of Medicine of USC, the multi-institutional initiative will develop artificial intelligence (AI) tools to uncover the biological causes of Alzheimer’s and related dementias, improve predictions of disease progression, and help develop more precise treatment options. AI4AD2 unites 10 investigators and 23 co-investigators from 10 institutions in pursuit of four interconnected research goals. The consortium will analyze large-scale datasets, including whole-genome sequencing, brain imaging, cognitive testing, and other biological data, to advance the diagnosis and treatment of dementia. This work builds on the original AI4AD initiative launched in 2020, which developed AI tools to detect Alzheimer’s-related patterns in brain scans and showed how machine learning can link imaging findings to underlying genetic risk. AI4AD2 will also develop new “genomic language models,” a type of AI inspired by the same broad family of technology used in language-based artificial intelligence systems. Instead of analyzing words, these models will analyze genomic sequences to identify combinations of DNA changes associated with Alzheimer’s disease, disease progression, and key biomarkers. The project will train and evaluate these methods using data from over 58,000 participants across 57 cohorts. In practical terms, that involves teaching AI to search vast genetic datasets for patterns that traditional methods could not identify.