Exposure to arsenic during pregnancy and newborn mitochondrial DNA copy number: A birth cohort study in Wuhan, China

This is an accepted manuscript of an article published by Elsevier in Chemosphere on 11/11/2019, available online: https://www.sciencedirect.com/science/article/abs/pii/S0045653519325755?via%3Dihub The accepted version of the publication may differ from the final published version.Background: Arsenic (As) is a widely distributed environmental chemical with potentially different toxicities. However, little is known about the impact of maternal As exposure on newborn mitochondrial DNA copy number (mtDNAcn), which may lie on the pathway linking As exposure to adverse health impacts. Objectives: We aimed to explore whether maternal As exposure was associated with newborn mtDNAcn. Methods: We conducted a birth cohort study of 762 mother-infant pairs in Wuhan, China, 2013-2015. Cord blood mtDNAcn was determined using qPCR. Maternal urinary As levels in each trimester were quantified by ICP-MS. Multiple informant models were used to examine the associations of repeated urinary As levels with cord blood mtDNAcn. Results: The median urinary As levels in the first, second, and third trimesters were 17.2 g/L, 16.0 g/L and 17.0 g/L respectively. In the multivariate model, each doubling increase in the first-trimester urinary As level was associated with a 6.6% (95% CI: -12.4%, -0.5%) decrease in cord blood mtDNAcn. The highest versus lowest quintile of first-trimester urinary As level was related to a 19.0% (95% CI: -32.9%, -2.2%) lower cord blood mtDNAcn. There was significant association of urinary As levels in the second and third trimesters with cord blood mtDNAcn. The inverse relationship between first-trimester urinary As level and cord blood mtDNAcn was more pronounced among female infants. Conclusions: First-trimester As exposure was associated with decreased cord blood mtDNAcn. The potential health impacts of decreased mtDNAcn in early life need to be further clarified

Climate Predictability Tool version 15.7.14

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The Climate Predictability Tool (CPT) is a software package for constructing a seasonal climate forecast model, performing model validation, and producing forecasts given updated data. Its design has been tailored for producing seasonal climate forecasts using model output statistic (MOS) corrections to climate predictions from general circulation model (GCM), or for producing forecasts using fields of sea-surface temperatures or similar predictors. Although the software is specifically tailored for these applications, it can be used in more general settings to perform canonical correlation analysis (CCA), principal components regression (PCR), or multiple linear regression (MLR) on any data, and for any application

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The Climate Predictability Tool (CPT) is a software package for constructing a seasonal climate forecast model, performing model validation, and producing forecasts given updated data. Its design has been tailored for producing seasonal climate forecasts using model output statistic (MOS) corrections to climate predictions from general circulation model (GCM), or for producing forecasts using fields of sea-surface temperatures or similar predictors. Although the software is specifically tailored for these applications, it can be used in more general settings to perform canonical correlation analysis (CCA), principal components regression (PCR), or multiple linear regression (MLR) on any data, and for any application