Programme Hospitalier Recherche Clinique, Institut Pasteur, Inserm, French Public Health Agency.

The substantial improvement in survival in France for newborns born at 25 through 31 weeks' gestation was accompanied by an important reduction in severe morbidity, but survival remained rare before 25 weeks. Although improvement in survival at extremely low gestational age may be possible, its effect on long-term outcomes requires further studies. The long-term results of the EPIPAGE-2 study will be informative in this regard.

Both underweight and obesity have been associated with increased mortality1,2. Underweight, defined as body mass index (BMI) ≤ 18,5 kg/m2 in adults 3 and ≤ −2 standard deviations (SD) in children4,5, is the main sign of a series of heterogeneous clinical conditions such as failure to thrive (FTT) 6–8, feeding and eating disorder and/or anorexia nervosa9,10. In contrast to obesity, few genetic variants underlying these clinical conditions have been reported 11, 12. We previously demonstrated that hemizygosity of a ~600 kb region on the short arm of chromosome 16 (chr16:29.5–30.1Mb), causes a highly-penetrant form of obesity often associated with hyperphagia and intellectual disabilities13. Here we show that the corresponding reciprocal duplication is associated with underweight. We identified 138 (132 novel cases) duplication carriers (108 unrelated carriers) from over 95,000 individuals clinically-referred for developmental or intellectual disabilities (DD/ID), psychiatric disorders or recruited from population-based cohorts. These carriers show significantly reduced postnatal weight (mean Z-score −0.6; p=4.4×10−4) and BMI (mean Z-score −0.5; p=2.0×10−3). In particular, half of the boys younger than 5 years are underweight with a probable diagnosis of FTT, while adult duplication carriers have an 8.7-fold (p=5.9×10−11; CI_95=[4.5–16.6]) increased risk of being clinically underweight. We observe a significant trend towards increased severity in males, as well as a depletion of male carriers among non-medically ascertained cases. These features are associated with an unusually high frequency of selective and restrictive feeding behaviours and a significant reduction in head circumference (mean Z-score −0.9; p=7.8×10−6). Each of the observed phenotypes is the converse of one reported in carriers of deletions at this locus, correlating with changes in transcript levels for genes mapping within the duplication but not within flanking regions. The reciprocal impact of these 16p11.2 copy number variants suggests that severe obesity and being underweight can have mirror etiologies, possibly through contrasting effects on eating behaviour.

TH slows the evolution of diffusion abnormalities on MRI following HIE in term infants.

Paroxysmal extreme pain disorder is a highly distinctive sodium channelopathy with incompletely carbamazepine-sensitive bouts of pain and sympathetic nervous system dysfunction. It is most likely to be misdiagnosed as epilepsy and, particularly in infancy, as hyperekplexia and reflex anoxic seizures.

PMM2-CDG clinical phenotype is heterogeneous in terms of clinical course, with no clear division between neurological and visceral presentations.

The purpose of super-resolution approaches is to overcome the hardware limitations and the clinical requirements of imaging procedures by reconstructing high-resolution images from low-resolution acquisitions using post-processing methods. Super-resolution techniques could have strong impacts on structural magnetic resonance imaging when focusing on cortical surface or fine-scale structure analysis for instance. In this paper, we study deep three-dimensional convolutional neural networks for the super-resolution of brain magnetic resonance imaging data. First, our work delves into the relevance of several factors in the performance of the purely convolutional neural network-based techniques for the monomodal super-resolution: optimization methods, weight initialization, network depth, residual learning, filter size in convolution layers, number of the filters, training patch size and number of training subjects. Second, our study also highlights that one single network can efficiently handle multiple arbitrary scaling factors based on a multiscale training approach. Third, we further extend our super-resolution networks to the multimodal super-resolution using intermodality priors. Fourth, we investigate the impact of transfer learning skills onto super-resolution performance in terms of generalization among different datasets. Lastly, the learnt models are used to enhance real clinical low-resolution images. Results tend to demonstrate the potential of deep neural networks with respect to practical medical image applications.

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