Semi-Supervised Learning Under General Causal Models.

Semi-supervised learning (SSL) aims to train a machine learning (ML) model using both labeled and unlabeled data. While the unlabeled data have been used in various ways to improve the prediction accuracy, the reason why unlabeled data could help is not fully understood. One interesting and promising direction is to understand SSL from a causal perspective.

Cord blood lipid correlation network profiles are associated with subsequent attention-deficit/hyperactivity disorder and autism spectrum disorder symptoms at 2 years: a prospective birth cohort study.

Background: Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental conditions with early life origins. Alterations in blood lipids have been linked to ADHD and ASD; however, prospective early life data are limited. This study examined (i) associations between the cord blood lipidome and ADHD/ASD symptoms at 2 years of age, (ii) associations between prenatal and perinatal predictors of ADHD/ASD symptoms and cord blood lipidome, and (iii) mediation by the cord blood lipidome.