Isolated neonatal MRI punctate white matter lesions in very preterm neonates and quality of life at school age

International audienceOBJECTIVE: To study the quality of life at school age of very preterm infants presenting isolated punctate periventricular white matter lesions (IPWL) on late-preterm or term magnetic resonance imaging (MRI). METHODS: In 1996-2000, 16 of the 131 very preterm neonates explored by MRI were found to have IPWL. At the age of 9-14, 12 children from the IPWL group were compared with 54 children born preterm but with a normal MRI (no lesion). Quality of life (Health Status Classification System Pre School questionnaire), school performance, and motor outcome were investigated. RESULTS: Overall quality of life did not differ between the groups (classified as perfect in 2/12 of the IPWL vs 20/54 in the no-lesion). The sub-items mobility and dexterity differed significantly between the two groups, with impairment in the IPWL group (p < 0.001 and p < 0.05). This group also displayed higher levels of motor impairment: they began walking later [20(4) vs. 15(3) months, p < 0.01], had higher frequencies of cerebral palsy (6/12 vs. 2/54, p < 0.05), and dyspraxia (4/12 vs. 0/54, p < 0.001). The rate of grade retention did not differ between the groups (3/12 in the IPWL group vs. 17/54 in the no-lesions group) but, as expected, was higher than that of the French general population (17.4%) during the study period. CONCLUSION: This long-term follow-up study detected no increase in the risk of subsequent cognitive impairment in very preterm infants with IPWL, but suggests that these children may have a significantly higher risk of dyspraxia, and motor impairment. © 2017 IOS Press and the authors. All rights reserved

Blind Spectral-GMM Estimation for Underdetermined Instantaneous Audio Source Separation

International audienceThe underdetermined blind audio source separation problem is often addressed in the time-frequency domain by assuming that each time-frequency point is an independently distributed random variable. Other approaches which are not blind assume a more structured model, like the Spectral Gaussian Mixture Models (Spectral-GMMs), thus exploiting statistical diversity of audio sources in the separation process. However, in this last approach, Spectral-GMMs are supposed to be learned from some training signals. In this paper, we propose a new approach for learning Spectral-GMMs of the sources without the need of using training signals. The proposed blind method significantly outperforms state-of-the-art approaches on stereophonic instantaneous music mixtures