Evaluation of gestational nonpersistent pesticide exposure with newborn size and gestational length in rural Ghana using a novel time-varying extension of multiple informant models.

Background: Gestational pesticide exposure may negatively affect newborn outcomes. Prior results evaluating nonpersistent pesticides are inconsistent.

Objective: To examine associations between gestational exposure to nonpersistent pesticides and newborn outcomes and identify critical windows of susceptibility.

Gestational exposure to PM, NO, and sex steroid hormones: Identifying critical windows of exposure in the Rochester UPSIDE Cohort.

Background: Sex steroid hormones are critical for maintaining pregnancy and optimal fetal development. Air pollutants are potential endocrine disruptors that may disturb sex steroidogenesis during pregnancy, potentially leading to adverse health outcomes.

Methods: In the Environmental influences on Child Health Outcomes Understanding Pregnancy Signals and Infant Development pregnancy cohort (Rochester, NY), sex steroid concentrations were collected at study visits in early-, mid-, and late-pregnancy in 299 participants.

Inference on the Eigenvalues of the Normalized Precision Matrix.

Recent developments in the spectral theory of Bayesian Networks has led to a need for a developed theory of estimation and inference on the eigenvalues of the normalized precision matrix, . In this paper, working under conditions where and remains fixed, we provide multivariate normal asymptotic distributions of the sample eigenvalues of under general conditions and under normal populations, a formula for second-order bias correction of these sample eigenvalues, and a Stein-type shrinkage estimator of the eigenvalues. Numerical simulations are performed which demonstrate under what generative conditions each estimation technique is most effective.

Spectral Bayesian network theory.

A Bayesian Network (BN) is a probabilistic model that represents a set of variables using a directed acyclic graph (DAG). Current algorithms for learning BN structures from data focus on estimating the edges of a specific DAG, and often lead to many 'likely' network structures. In this paper, we lay the groundwork for an approach that focuses on learning global properties of the DAG rather than exact edges.

Pulmonary and systemic bacterial co-infections in severe RSV bronchiolitis.

In 127 infants admitted to intensive care for RSV bronchiolitis, concomitant bacterial sepsis was a rare event. However, in the subgroup of intubated patients the incidence of bacterial pneumonia was 43.9% (95% CI 31.