Unisex and Sex-Specific Prescriptive Fetal Growth Charts for Improved Detection of Small-for-Gestational-Age Babies in a Low-Risk Population: A post hoc Analysis of a Cluster-Randomized Study.

Introduction: Our aim was to develop and evaluate the performance of population-based sex-specific and unisex prescriptive fetal abdominal circumference growth charts in predicting small-for-gestational-age (SGA) birthweight, severe SGA (sSGA) birthweight, and severe adverse perinatal outcomes (SAPO) in a low-risk population.

Methods: This is a post hoc analysis of the Dutch nationwide cluster-randomized IRIS study, encompassing ultrasound data of 7,704 low-risk women. IRIS prescriptive unisex and IRIS sex-specific abdominal circumference (AC) fetal growth charts were derived using quantile regression.

Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants.

Blood-level oxygenation-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher-order connectivity.

Impaired Awareness in People with Severe Alcohol-Related Cognitive Deficits Including Korskoff's Syndrome: A Network Analysis.

Background: Impaired awareness of one's own functioning is highly common in people with Korsakoff's syndrome (KS). However, it is currently unclear how awareness relates to impairments in daily functioning and quality of life (QoL).

Methods: We assessed how impaired awareness relates to cognitive, behavioral, physical, and social functioning and QoL by applying a network analysis.

Penalized estimation of the Gaussian graphical model from data with replicates.

Gaussian graphical models are usually estimated from unreplicated data. The data are, however, likely to comprise signal and noise. These two cannot be deconvoluted from unreplicated data.

Analysis of Twitter data with the Bayesian fused graphical lasso.

We propose a method to simplify textual Twitter data into understandable networks of terms that can signify important events and their possible changes over time. The method allows for common characteristics of the networks across time periods and each period can comprise multiple unknown sub-networks. The networks are described by Gaussian graphical models and their parameter values are estimated through a Bayesian approach with a fused lasso-type prior on the precision matrices of the underlying mixtures of the sub-models.