Clinical and molecular delineation of the 17q21.31 microdeletion syndrome

Background: The chromosome 17q21.31 microdeletion syndrome is a novel genomic disorder that has originally been identified using high resolution genome analyses in patients with unexplained mental retardation. Aim: We report the molecular and/or clinical characterisation of 22 individuals with the 17q21.31 microdeletion syndrome. Results: We estimate the prevalence of the syndrome to be 1 in 16 000 and show that it is highly underdiagnosed. Extensive clinical examination reveals that developmental delay, hypotonia, facial dysmorphisms including a long face, a tubular or pear-shaped nose and a bulbous nasal tip, and a friendly/amiable behaviour are the most characteristic features. Other clinically important features include epilepsy, heart defects and kidney/urologic anomalies. Using high resolution oligonucleotide arrays we narrow the 17q21.31 critical region to a 424 kb genomic segment (chr17: 41046729-41470954, hg17) encompassing at least six genes, among which is the gene encoding microtubule associated protein tau (MAPT). Mutation screening of MAPT in 122 individuals with a phenotype suggestive of 17q21.31 deletion carriers, but who do not carry the recurrent deletion, failed to identify any disease associated variants. In five deletion carriers we identify a <500 bp rearrangement hotspot at the proximal breakpoint contained within an L2 LINE motif and show that in every case examined the parent originating the deletion carries a common 900 kb 17q21.31 inversion polymorphism, indicating that this inversion is a necessary factor for deletion to occur (p< 10(25)). Conclusion: Our data establish the 17q21.31 microdeletion syndrome as a clinically and molecularly well recognisable genomic disorder.

A WAVELET-BASED SPEAKER VERIFICATION ALGORITHM

This paper presents a study on wavelets and their characteristics for the specific purpose of serving as a feature extraction tool for speaker verification (SV), considering a Radial Basis Function (RBF) classifier, which is a particular type of Artificial Neural Network (ANN). Examining characteristics such as support-size, frequency and phase responses, amongst others, we show how Discrete Wavelet Transforms (DWTs), particularly the ones which derive from Finite Impulse Response (FIR) filters, can be used to extract important features from a speech signal which are useful for SV. Lastly, an SV algorithm based on the concepts presented is described.