222 research outputs found

    Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides

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    Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level with high spatiotemporal resolution but may suffer from high estimation bias since they lack physical and chemical knowledge about air pollution dynamics. Chemical transport models (CTMs) leverage this knowledge; however, accurate predictions of ground-level concentrations typically necessitate extensive post-calibration. Here, we present a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints to jointly predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures fine-scale transport of NO2 and NOx, generates robust spatial extrapolation, and provides explicit uncertainty estimation. The framework fuses knowledge-driven physicochemical principles of CTMs with the predictive power of ML for air quality exposure, health, and policy applications. Our approach offers significant improvements over purely data-driven ML methods and has unprecedented bias reduction in joint NO2 and NOx prediction

    Prenatal metal(loid) mixtures and birth weight for gestational age: A pooled analysis of three cohorts participating in the ECHO program

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    Background: A growing number of studies have identified both toxic and essential metals which influence fetal growth. However, most studies have conducted single-cohort analyses, which are often limited by narrow exposure ranges, and evaluated metals individually. The objective of the current study was to conduct an environmental mixture analysis of metal impacts on fetal growth, pooling data from three geographically and demographically diverse cohorts in the United States participating in the Environmental Influences on Child Health Outcomes program. Methods: The pooled sample (N = 1,002) included participants from the MADRES, NHBCS, and PROTECT cohorts. Associations between seven metals (antimony, cadmium, cobalt, mercury, molybdenum, nickel, tin) measured in maternal urine samples collected during pregnancy (median: 16.0 weeks gestation) and birth weight for gestational age z-scores (BW for GA) were investigated using Bayesian Kernel Machine Regression (BKMR). Models were also stratified by cohort and infant sex to investigate possible heterogeneity. Chromium and uranium concentrations fell below the limits of detection for most participants and were evaluated separately as binary variables using pooled linear regression models. Results: In the pooled BKMR analysis, antimony, mercury, and tin were inversely and linearly associated with BW for GA, while a positive linear association was identified for nickel. The inverse association between antimony and BW for GA was observed in both males and females and for all three cohorts but was strongest for MADRES, a predominantly low-income Hispanic cohort in Los Angeles. A reverse j-shaped association was identified between cobalt and BW for GA, which was driven by female infants. Pooled associations were null for cadmium, chromium, molybdenum, and uranium, and BKMR did not identify potential interactions between metal pairs. Conclusions: Findings suggest that antimony, an understudied metalloid, may adversely impact fetal growth. Cohort- and/or sex-dependent associations were identified for many of the metals, which merit additional investigation

    When a birth cohort grows up: challenges and opportunities in longitudinal developmental origins of health and disease (DOHaD) research

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    High-quality evidence from prospective longitudinal studies in humans is essential to testing hypotheses related to the developmental origins of health and disease. In this paper, the authors draw upon their own experiences leading birth cohorts with longitudinal follow-up into adulthood to describe specific challenges and lessons learned. Challenges are substantial and grow over time. Long-term funding is essential for study operations and critical to retaining study staff, who develop relationships with participants and hold important institutional knowledge and technical skill sets. To maintain contact, we recommend that cohorts apply multiple strategies for tracking and obtain as much high-quality contact information as possible before the child's 18th birthday. To maximize engagement, we suggest that cohorts offer flexibility in visit timing, length, location, frequency, and type. Data collection may entail multiple modalities, even at a single collection timepoint, including measures that are self-reported, research-measured, and administrative with a mix of remote and in-person collection. Many topics highly relevant for adolescent and young adult health and well-being are considered to be private in nature, and their assessment requires sensitivity. To motivate ongoing participation, cohorts must work to understand participant barriers and motivators, share scientific findings, and provide appropriate compensation for participation. It is essential for cohorts to strive for broad representation including individuals from higher risk populations, not only among the participants but also the staff. Successful longitudinal follow-up of a study population ultimately requires flexibility, adaptability, appropriate incentives, and opportunities for feedback from participants

    Evaluation of pediatric epigenetic clocks across multiple tissues

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    BackgroundEpigenetic clocks are promising tools for assessing biological age. We assessed the accuracy of pediatric epigenetic clocks in gestational and chronological age determination.ResultsOur study used data from seven tissue types on three DNA methylation profiling microarrays and found that the Knight and Bohlin clocks performed similarly for blood cells, while the Lee clock was superior for placental samples. The pediatric-buccal-epigenetic clock performed the best for pediatric buccal samples, while the Horvath clock is recommended for children's blood cell samples. The NeoAge clock stands out for its unique ability to predict post-menstrual age with high correlation with the observed age in infant buccal cell samples.ConclusionsOur findings provide valuable guidance for future research and development of epigenetic clocks in pediatric samples, enabling more accurate assessments of biological age
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