Untranslated regions of mRNAs

Gene expression is finely regulated at the post-transcriptional level. Features of the untranslated regions of mRNAs that control their translation, degradation and localization include stem-loop structures, upstream initiation codons and open reading frames, internal ribosome entry sites and various cis-acting elements that are bound by RNA-binding proteins

R\'enyi Information Measures for Spectral Change Detection

Change detection within an audio stream is an important task in several domains, such as classification and segmentation of a sound or of a music piece, as well as indexing of broadcast news or surveillance applications. In this paper we propose two novel methods for spectral change detection without any assumption about the input sound: they are both based on the evaluation of information measures applied to a time- frequency representation of the signal, and in particular to the spectrogram. The class of measures we consider, the R\'enyi entropies, are obtained by extending the Shannon entropy definition: a biasing of the spectrogram coefficients is realized through the dependence of such measures on a parameter, which allows refined results compared to those obtained with standard divergences. These methods provide a low computational cost and are well-suited as a support for higher level analysis, segmentation and classification algorithms.Comment: 2011 IEEE Conference on Acoustics, Speech and Signal Processin

Regularized Least Squares Cancer Classifiers from DNA microarray data

BACKGROUND: The advent of the technology of DNA microarrays constitutes an epochal change in the classification and discovery of different types of cancer because the information provided by DNA microarrays allows an approach to the problem of cancer analysis from a quantitative rather than qualitative point of view. Cancer classification requires well founded mathematical methods which are able to predict the status of new specimens with high significance levels starting from a limited number of data. In this paper we assess the performances of Regularized Least Squares (RLS) classifiers, originally proposed in regularization theory, by comparing them with Support Vector Machines (SVM), the state-of-the-art supervised learning technique for cancer classification by DNA microarray data. The performances of both approaches have been also investigated with respect to the number of selected genes and different gene selection strategies. RESULTS: We show that RLS classifiers have performances comparable to those of SVM classifiers as the Leave-One-Out (LOO) error evaluated on three different data sets shows. The main advantage of RLS machines is that for solving a classification problem they use a linear system of order equal to either the number of features or the number of training examples. Moreover, RLS machines allow to get an exact measure of the LOO error with just one training. CONCLUSION: RLS classifiers are a valuable alternative to SVM classifiers for the problem of cancer classification by gene expression data, due to their simplicity and low computational complexity. Moreover, RLS classifiers show generalization ability comparable to the ones of SVM classifiers also in the case the classification of new specimens involves very few gene expression levels

Sound context modulates perceived vocal emotion

International audienceMany animal vocalizations contain nonlinear acoustic phenomena as a consequence of physiological arousal. In humans, nonlinear features are processed early in the auditory system, and are used to efficiently detect alarm calls and other urgent signals. Yet, high-level emotional and semantic contextual factors likely guide the perception and evaluation of roughness features in vocal sounds. Here we examined the relationship between perceived vocal arousal and auditory context. We presented listeners with nonverbal vocalizations (yells of a single vowel) at varying levels of portrayed vocal arousal, in two musical contexts (clean guitar, distorted guitar) and one non-musical context (modulated noise). As predicted, vocalizations with higher levels of portrayed vocal arousal were judged as more negative and more emotionally aroused than the same voices produced with low vocal arousal. Moreover, both the perceived valence and emotional arousal of vocalizations were significantly affected by both musical and non-musical contexts. These results show the importance of auditory context in judging emotional arousal and valence in voices and music, and suggest that nonlinear features in music are processed similarly to communicative vocal signals