Transition from paediatric to adult care: what makes it easier for parents?

To assess differences between parents of adolescents with chronic illness (CI) going through a self-reported easy or difficult transfer. Seventy-two parents of CI youths who had already transferred to adult care were divided according to whether they considered that the transfer had been easy (n = 45) or difficult (n = 27). We performed a bivariate analysis comparing both groups and variables with a significance level < .1 were included in a logistic regression. Results are presented as adjusted odds ratio (aOR). Over one third of parents (27/72) reported a difficult transfer. At the multivariate level, higher socioeconomic status (aOR: 7.74), parents feeling ready for transfer (aOR: 6.54) and a good coordination between teams (aOR: 7.66) were associated with an easy transfer. An easy transfer for parents is associated with feeling ready and considering that the coordination between teams is good. Health providers should consider these requisites for a successful transfer

Accelerating Image Retrieval Using Factorial Correspondence Analysis on GPU

International audienceWe are interested in the intensive use of Factorial Correspondence Analysis (FCA) for large-scale content-based image retrieval. Factorial Correspondence Analysis, is a useful method for analyzing textual data, and we adapt it to images using the SIFT local descriptors. FCA is used to reduce dimensions and to limit the number of images to be considered during the search. Graphics Processing Units (GPU) are fast emerging as inexpensive parallel processors due to their high computation power and low price. The G80 family of Nvidia GPUs provides the CUDA programming model that treats the GPU as a SIMD processor array. We present two very fast algorithms on GPU for image retrieval using FCA: the first one is a parallel incremental algorithm for FCA and the second one is an extension of the filtering algorithm in our previous work for filtering step. Our implementation is able to scale up the FCA computation a factor of 30 compared to the CPU version. For retrieval tasks, the parallel version on GPU performs 10 times faster than the one on CPU. Retrieving images in a database of 1 million images is done in about 8 milliseconds