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Previous work has shown that spherical CuO nanomaterials show negative effects on cell and animal physiology. The biological effects of Cu 2 O materials, which possess unique chemical features compared to CuO nanomaterials and can be synthesized in a similarly large variety of shapes and sizes, are comparatively less studied. Here, we synthesized truncated octahedral Cu 2 O particles and characterized their structure, stability, and physiological effects in the nematode worm animal model, Caenorhabditis elegans . Cu 2 O particles were found to be generally stable in aqueous media, although the particles did show signs of oxidation and leaching of Cu 2+ within hours in worm growth media. The particles were found to be especially sensitive to inorganic phosphate (PO 4 3− ) found in standard NGM nematode growth medium. Cu 2 O particles were observed being taken up into the nematode pharynx and detected in the lumen of the gut. Toxicity experiments revealed that treatment with Cu 2 O particles caused a significant reduction in animal size and lifespan. These toxic effects resembled treatment with Cu 2+ , but measurements of Cu leaching, worm size, and long-term behavior experiments show the particles are more toxic than expected from Cu ion leaching alone. These results suggest worm ingestion of intact Cu 2 O particles enhances their toxicity and behavior effects while particle exposure to environmental phosphate precipitates leached Cu 2+ into biounavailable phosphate salts. Interestingly, the worms showed an acute avoidance of bacterial food with Cu 2 O particles, suggesting that animals can detect chemical features of the particles and/or their breakdown products and actively avoid areas with them. These results will help to understand how specific, chemically-defined particles proposed for use in polluted soil and wastewater remediation affect animal toxicity and behaviors in their natural environment. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.