Environmental health is becoming increasingly important in the broader context ofclimate change. This article aims to analyze the factors most impactful in determining human health, which was done by performing correlational analysis on a myriad of environmental factors. Each of these factors were evaluated by looking at counties or states as a whole and analyzing the strength of correlation between life expectancy in a county/state to a specific metric measuring environmental health, whether that be natural disasters, air quality, average temperature, exercise rate, water quality, walk score, or food security. The strength of correlation is in the form of an r-squared value, which is a metric that gives a measure of how much of a particular independent variable impacts the dependent variable, which here is life expectancy. The results show that lifestyle factors like food and exercise have a larger impact than expected, while factors such as air and water quality have a less significant impact than expected.

Abnormal tau inclusions are hallmarks of Alzheimer's disease and predictors of clinical decline. Several tau PET tracers are available for neurodegenerative disease research, opening avenues for molecular diagnosis in vivo. However, few have been approved for clinical use. Understanding the neurobiological basis of PET signal validation remains problematic because it requires a large-scale, voxel-to-voxel correlation between PET and (immuno) histological signals. Large dimensionality of whole human brains, tissue deformation impacting co-registration, and computing requirements to process terabytes of information preclude proper validation. We developed a computational pipeline to identify and segment particles of interest in billion-pixel digital pathology images to generate quantitative, 3D density maps. The proposed convolutional neural network for immunohistochemistry samples, IHCNet, is at the pipeline's core. We have successfully processed and immunostained over 500 slides from two whole human brains with three phospho-tau antibodies (AT100, AT8, and MC1), spanning several terabytes of images. Our artificial neural network estimated tau inclusion from brain images, which performs with ROC AUC of 0.87, 0.85, and 0.91 for AT100, AT8, and MC1, respectively. Introspection studies further assessed the ability of our trained model to learn tau-related features. We present an end-to-end pipeline to create terabytes-large 3D tau inclusion density maps co-registered to MRI as a means to facilitate validation of PET tracers.