Low maternal vitamin D status in pregnancy increases the risk of childhood obesity.

BACKGROUND: Vitamin D may modulate adipogenesis. However, limited studies have investigated the effect of maternal vitamin D during pregnancy on offspring adiposity or cardiometabolic parameters with inconclusive results. OBJECTIVES: The objective of this study is to examine the association of maternal 25(OH)-vitamin D [25(OH)D] status with offspring obesity and cardiometabolic characteristics in 532 mother-child pairs from the prospective pregnancy cohort Rhea in Crete, Greece. METHODS: Maternal 25(OH)D concentrations were measured at the first prenatal visit (mean: 14 weeks, SD: 4). Child outcomes included body mass index standard deviation score, waist circumference, skin-fold thickness, blood pressure and serum lipids at ages 4 and 6 years. Body fat percentage was also measured at 6 years. Body mass index growth trajectories from birth to 6 years were estimated by mixed effects models with fractional polynomials of age. Adjusted associations were obtained via multivariable linear regression analyses. RESULTS: About two-thirds of participating mothers had 25(OH)D concentrations <50 nmol L-1 . Offspring of women in the low 25(OH)D tertile (<37.7 nmol L-1 ) had higher body mass index standard deviation score (ß 0.20, 95% CI: 0.03, 0.37), and waist circumference (ß 0.87 95% CI: 0.12, 1.63) at preschool age, compared with the offspring of women with higher 25(OH)D measurements (≥37.7 nmol L-1 ), on covariate-adjusted analyses. The observed relationships persisted at age 6 years. We found no association between maternal 25(OH)D concentrations and offspring blood pressure or serum lipids at both time points. CONCLUSIONS: Exposure to very low 25(OH)D concentrations in utero may increase childhood adiposity indices. Given that vitamin D is a modifiable risk factor, our findings may have important public health implications.

Vision-based absence seizure detection.

In order to diagnose epilepsy, neurologists rely on their experience, performing an equal assessment of the electroencephalogram and the clinical image. Since misdiagnosis reaches a rate of 30% and more than one-third of all epilepsies are poorly understood, a need for leveraging diagnostic precision is obvious. With the aim at enhancing the clinical image assessment procedure, this paper evaluates the suitability of certain facial expression features for detecting and quantifying absence seizures. These features are extracted by means of time-varying signal analysis from signals that are gained by applying computer vision techniques, such as face detection, dense optical flow computation and averaging background subtraction. For the evaluation, video sequences of four patients with absence seizures are used. The classification performance of a C4.5 decision tree shows accuracies of up to 99.96% with a worst percentage of incorrectly classified instances of 0.14%.