Illegal cannabis cultivation leaves lasting chemical footprint on California’s public lands
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Updates every hour. Last Updated: 7-Nov-2025 16:11 ET (7-Nov-2025 21:11 GMT/UTC)
A new report led by researchers at Pennington Biomedical Research Center underscores the growing potential of precision medicine to transform how obesity is prevented, diagnosed and treated, while also illuminating key gaps and challenges that must be addressed.
Published in September in Obesity, the paper, “Precision Prevention, Diagnostics and Treatment of Obesity,” synthesizes the proceedings of a recent Pennington-Louisiana Nutrition Obesity Research Center, or NORC, scientific workshop that was convened to review current evidence on tailoring obesity interventions to individual biology, environment, behavior and social factors.
It’s a scene fit for a nature documentary: In the frigid ocean surrounding Antarctica, the water boils over as seabirds dive from above and marine animals like seals and whales rise from the depths to all feast on krill. But zoom out and this flurry of activity is just a tiny speck in a desolate seascape. Scientists have been puzzled by how these various species are all able to find the same food source at the same time. In research published October 6 in the journal Proceedings of the National Academy of Sciences, Duke University and UC Davis scientists tease out how multiple species of Antarctic seabirds forage together – with takeaways for conservation and for crowd behavior — and shows that flocks find food better when they rely on each other’s senses.
Regression analysis is essential in biomedical research for exploring relationships between phenotypic or clinical outcomes and diverse predictors. However, constructing multiple univariate and multivariate models is often hindered by the lack of robust tools for batch regression in R, forcing researchers to rely on custom scripts. To address this gap, we developed bregr, an open-source R package built in the tidyverse style, leveraging the object-oriented programming strategy for enhanced extensibility. bregr streamlines batch processing of diverse regression models, including generalized linear, Cox proportional hazards, and mixed-effects, using native R pipes. It provides tidy outputs, integrated visualization, parallel computing capabilities, and a cohesive workflow, enabling efficient execution of hundreds of models with structured results for downstream analysis. Available on CRAN, bregr enhances efficiency, reproducibility, and scalability in biomedical research and beyond.