A Bayesian binary algorithm for root mean squared-based acoustic signal segmentation.

Changepoint analysis (also known as segmentation analysis) aims to analyze an ordered, one-dimensional vector in order to find locations where some characteristic of the data changes. Many models and algorithms have been studied under this theme, including models for changes in mean and/or variance, changes in linear regression parameters, etc. This work is interested in an algorithm for the segmentation of long duration acoustic signals; the segmentation is based on the change of the root-mean-square power of the signal.

A Sequential Algorithm for Signal Segmentation.

The problem of event detection in general noisy signals arises in many applications; usually, either a functional form of the event is available, or a previous annotated sample with instances of the event that can be used to train a classification algorithm. There are situations, however, where neither functional forms nor annotated samples are available; then, it is necessary to apply other strategies to separate and characterize events. In this work, we analyze 15-min samples of an acoustic signal, and are interested in separating sections, or segments, of the signal which are likely to contain significant events.

A multi-exonic SPG4 duplication underlies sex-dependent penetrance of hereditary spastic paraplegia in a large Brazilian pedigree.

SPG4 mutations are the most frequent cause of autosomal-dominant hereditary spastic paraplegia (HSP). SPG4 HSP is characterized by large inter- and intrafamilial variability in age at onset (AAO) and disease severity. The broad spectrum of SPG4 mutations has recently been further extended by the finding of large genomic deletions in SPG4-linked pedigrees negative for 'small' mutations.