Katholieke Universiteit Leuven

About: Katholieke Universiteit Leuven is a education organization based out in Leuven, Belgium. It is known for research contribution in the topics: Population & Context (language use). The organization has 61109 authors who have published 176584 publications receiving 6210872 citations.

Interleukin 7 receptor α chain (IL7R) shows allelic and functional association with multiple sclerosis

Abstract: Multiple sclerosis is a demyelinating neurodegenerative disease with a strong genetic component. Previous genetic risk studies have failed to identify consistently linked regions or genes outside of the major histocompatibility complex on chromosome 6p. We describe allelic association of a polymorphism in the gene encoding the interleukin 7 receptor α chain ( IL7R ) as a significant risk factor for multiple sclerosis in four independent family-based or case-control data sets (overall P = 2.9 × 10−7). Further, the likely causal SNP, rs6897932, located within the alternatively spliced exon 6 of IL7R, has a functional effect on gene expression. The SNP influences the amount of soluble and membrane-bound isoforms of the protein by putatively disrupting an exonic splicing silencer.

Functional Diversity of Protein Phosphatase-1, a Cellular Economizer and Reset Button

Abstract: Ceulemans, Hugo, and Mathieu Bollen. Functional Diversity of Protein Phosphatase-1, a Cellular Economizer and Reset Button. Physiol Rev 84: 1–39, 2004; 10.1152/physrev.00013.2003.—The protein serin...

Automated model-based bias field correction of MR images of the brain

Abstract: The authors propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method the authors propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration. The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. The authors have validated the bias correction algorithm on simulated data and they illustrate its performance on various MR images with important field inhomogeneities. They also relate the proposed algorithm to other bias correction algorithms.