RESUMO
Keutel syndrome is caused by mutations in the matrix gamma-carboxyglutamic acid (MGP) gene (OMIM 154870) and is inherited in an autosomal recessive fashion. It is characterized by brachydactyly, pulmonary artery stenosis, a distinctive facial phenotype, and cartilage calcification. To date, only 36 cases have been reported worldwide. We describe clinical and molecular findings of the first Brazilian patient with Keutel syndrome. Keutel syndrome was suspected based on clinical and morphological evaluation, so we sequenced the MGP gene using the TruSight One Sequencing Panel (Illumina). The obtained MGP gene sequence was then validated by Sanger sequencing. We identified a novel pathogenic homozygous variant of the MGP gene (c.2T>C; p.Met1Thr) confirming Keutel syndrome. Proper diagnosis of this syndrome is important for clinical management and is an indication for genetic counseling. Keutel syndrome should be suspected in patients with cartilage calcifications and brachydactyly when associated with a distinctive facial phenotype and pulmonary artery stenosis.
RESUMO
Recently, some online kernel principal component analysis (KPCA) techniques based on the generalized Hebbian algorithm (GHA) were proposed for use in large data sets, defining kernel components using concise dictionaries automatically extracted from data. This brief proposes two new online KPCA extraction algorithms, exploiting orthogonalized versions of the GHA rule. In both the cases, the orthogonalization of kernel components is achieved by the inclusion of some low complexity additional steps to the kernel Hebbian algorithm, thus not substantially affecting the computational cost of the algorithm. Results show improved convergence speed and accuracy of components extracted by the proposed methods, as compared with the state-of-the-art online KPCA extraction algorithms.