Aged and Obscured Wildfire Smoke Associated with Downwind Health Risks.

Fine-mode particulate matter (PM) is a highly detrimental air pollutant, regulated without regard for chemical composition and a chief component of wildfire smoke. As wildfire activity increases with climate change, its growing continental influence necessitates multidisciplinary research to examine smoke's evolving chemical composition far downwind and connect chemical composition-based source apportionment to potential health effects. Leveraging advanced real-time speciated PM measurements, including an aerosol chemical speciation monitor in conjunction with source apportionment and health risk assessments, we quantified the stark pollution enhancements during peak Canadian wildfire smoke transport to New York City over June 6-9, 2023.

A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images.

Characterizing the anatomical structure and connectivity between cortical regions is a critical step towards understanding the information processing properties of the brain and will help provide insight into the nature of neurological disorders. A key feature of the mammalian cerebral cortex is its laminar structure. Identifying these layers in neuroimaging data is important for understanding their global structure and to help understand the connectivity patterns of neurons in the brain.

Increasing Contributions of Temperature-Dependent Oxygenated Organic Aerosol to Summertime Particulate Matter in New York City.

As part of the summer 2022 NYC-METS (New York City metropolitan Measurements of Emissions and TransformationS) campaign and the ASCENT (Atmospheric Science and Chemistry mEasurement NeTwork) observational network, speciated particulate matter was measured in real time in Manhattan and Queens, NY, with additional gas-phase measurements. Largely due to observed reductions in inorganic sulfate aerosol components over the 21st century, summertime aerosol composition in NYC has become predominantly organic (80-83%). Organic aerosol source apportionment via positive matrix factorization showed that this is dominated by secondary production as oxygenated organic aerosol (OOA) source factors comprised 73-76% of OA.

Deep convolutional neural networks for regular texture recognition.

Regular textures are frequently found in man-made environments and some biological and physical images. There are a wide range of applications for recognizing and locating regular textures. In this work, we used deep convolutional neural networks (CNNs) as a general method for modelling and classifying regular and irregular textures.