Differences in immune cell profiles around the time of islet autoimmunity seroconversion in children with and without type 1 diabetes: Immune cell changes around islet autoimmunity.

Seroconversion (SV) marks the initiation of islet autoimmunity (IA) and pre-clinical phase of type 1 diabetes, yet the contributions of immune cells beyond cytotoxic T cells remain unclear. We applied high-resolution immune cell-type deconvolution using peripheral blood DNA methylation data from nested case-control samples of the Diabetes Autoimmunity Study in the Young (DAISY; n=151) and The Environmental Determinants of Diabetes in the Young (TEDDY; n=166) to estimate immune cell proportions across pre-SV and SV timepoints and construct functional ratios, such as the neutrophil-to-lymphocyte ratio (NLR). Using linear models, we evaluated differences between type 1 diabetes cases and controls at pre-SV, SV, and the change across timepoints.

Dust Storms and Emergency Department Visits in 3 Southwestern States Using NWS Storm Reports.

Importance: Dust storms are projected to increase with climate change. The short-term health outcomes associated with dust storms in the US are not well characterized, especially for morbidity outcomes.

Objective: To estimate associations between dust storms and diagnosis-specific emergency department (ED) visits during 2005 to 2018.

Dust storms and cardiorespiratory emergency department visits in three Southwestern United States: application of a monitoring-based exposure metric.

Climate change is projected to increase the risk of dust storms, particularly in subtropical dryland, including the southwestern US. Research on dust storm's health impacts in the US is limited and hindered by challenges in dust storm identification. This study assesses the potential link between dust storms and cardiorespiratory emergency department (ED) visits in the southwestern US.

Evaluating Data Product Exposure Metrics for Use in Epidemiologic Studies of Dust Storms.

Dust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio-temporal coverage, resolution, and accuracy of current data sets.

A nested machine learning approach to short-term PM prediction in metropolitan areas using PM data from different sensor networks.

Many predictive models for ambient PM concentrations rely on ground observations from a single monitoring network consisting of sparsely distributed sensors. Integrating data from multiple sensor networks for short-term PM prediction remains largely unexplored. This paper presents a machine learning approach to predict ambient PM concentration levels at any unmonitored location several hours ahead using PM observations from nearby monitoring sites from two sensor networks and the location's social and environmental properties.