Accumulating evidence indicates the developing central nervous system (CNS) is a target of air pollution toxicity. Epidemiological reports increasingly demonstrate that exposure to the particulate matter (PM) fraction of air pollution during neurodevelopment is associated with an increased risk of neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD). These observations are supported by animal studies demonstrating prenatal exposure to concentrated ambient PM induces neuropathologies characteristic of ASD, including ventriculomegaly and aberrant corpus callosum (CC) myelination. Given the role of the CC and cerebellum in ASD etiology, this study tested whether prenatal exposure to concentrated ambient particles (CAPs) produced pathological features in offspring CC and cerebella consistent with ASD. Analysis of cerebellar myelin density revealed male-specific hypermyelination in CAPs-exposed offspring at postnatal days (PNDs) 11-15 without alteration of cerebellar area. Atomic absorption spectroscopy (AAS) revealed elevated iron (Fe) in the cerebellum of CAPs-exposed female offspring at PNDs 11-15, which connects with previously observed elevated Fe in the female CC. The presence of Fe inclusions, along with aluminum (Al) and silicon (Si) inclusions, were confirmed at nanoscale resolution in the CC along with ultrastructural myelin sheath damage. Further, RNAseq and gene ontology (GO) enrichment analyses revealed cerebellar gene expression was significantly affected by sex and prenatal CAPs exposure with significant enrichment in inflammation and transmembrane transport processes that could underlie observed myelin and metal pathologies. Overall, this study highlights the ability of PM exposure to disrupt myelinogenesis and elucidates novel molecular targets of PM-induced developmental neurotoxicity.

This study is among the first to compare different levels of collaborative care on practice procedures. Understanding how we can best integrate between behavioral health and primary care services will optimize outcomes for children and families. (PsycINFO Database Record

Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization, and statistical analysis, little attention has been given to qPCR non-detects -those reactions failing to produce a minimum amount of signal. While most current software replaces these non-detects with a value representing the limit of detection, recent work suggests that this introduces substantial bias in estimation of both absolute and differential expression. Recently developed single imputation procedures, while better than previously used methods, underestimate residual variance, which can lead to anti-conservative inference. We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of relevant model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. In the proposed modeling framework, there are three sources of uncertainty: parameter estimation, the missing data mechanism, and measurement error. All three sources of variability are incorporated in the multiple imputation and direct estimation algorithms. We demonstrate the applicability of these methods on three real qPCR data sets and perform an extensive simulation study to assess model sensitivity to misspecification of the missing data mechanism, to the number of replicates within the sample, and to the overall size of the data set. The proposed methods result in unbiased estimates of the model parameters; therefore, these approaches may be beneficial when estimating both absolute and differential gene expression. The developed methods are implemented in the R/Bioconductor package nondetects. The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments, providing more confidence in generating scientific hypotheses and performing downstream analysis..

Background Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. An important aspect of qPCR data that has been largely ignored is the presence of non-detects: reactions failing to exceed the quantification threshold and therefore lacking a measurement of expression. While most current software replaces these non-detects with a value representing the limit of detection, this introduces substantial bias in the estimation of both absolute and differential expression. Single imputation procedures, while an improvement on previously used methods, underestimate residual variance, which can lead to anti-conservative inference. Results We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. We assess the proposed methods via simulation studies and demonstrate the applicability of these methods to three experimental data sets. We compare our methods to mean imputation, single imputation, and a penalized EM algorithm incorporating non-random missingness (PEMM). The developed methods are implemented in the R/Bioconductor package . Conclusions The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments in the presence of non-detects, providing increased confidence in downstream analyses.

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