Molecular epidemiology of DFNB1 deafness in France

BACKGROUND: Mutations in the GJB2 gene have been established as a major cause of inherited non syndromic deafness in different populations. A high number of sequence variations have been described in the GJB2 gene and the associated pathogenic effects are not always clearly established. The prevalence of a number of mutations is known to be population specific, and therefore population specific testing should be a prerequisite step when molecular diagnosis is offered. Moreover, population studies are needed to determine the contribution of GJB2 variants to deafness. We present our findings from the molecular diagnostic screening of the GJB2 and GJB6 genes over a three year period, together with a population-based study of GJB2 variants. METHODS AND RESULTS: Molecular studies were performed using denaturing High Performance Liquid Chromatograghy (DHPLC) and sequencing of the GJB2 gene. Over the last 3 years we have studied 159 families presenting sensorineural hearing loss, including 84 with non syndromic, stable, bilateral deafness. Thirty families were genotyped with causative mutations. In parallel, we have performed a molecular epidemiology study on more than 3000 dried blood spots and established the frequency of the GJB2 variants in our population. Finally, we have compared the prevalence of the variants in the hearing impaired population with the general population. CONCLUSION: Although a high heterogeneity of sequence variation was observed in patients and controls, the 35delG mutation remains the most common pathogenic mutation in our population. Genetic counseling is dependent on the knowledge of the pathogenicity of the mutations and remains difficult in a number of cases. By comparing the sequence variations observed in hearing impaired patients with those sequence variants observed in general population, from the same ethnic background, we show that the M34T, V37I and R127H variants can not be responsible for profound or severe deafness

The concept of 'the everyday': ephemeral politics and the abundance of life

Against the background of a continuing interest in the everyday in international relations, this article asks what kind of analytics upon and within the world mobilises one through the concept of the everyday and what consequences this may have for thinking about politics. In particular, it explores a conception of the the everyday that foregrounds the abundance of human life and ephemeral temporalities. The abundance of life invites a densification of politics combined with an emphasis on displacing levels or scales by associative horizontal relations. The ephemeral introduces a conception of temporality that foregrounds the political significance of fleeting practices and the emergent nature of life. When applied to politics, this conception of the everyday performs politics as emergent, as possibilities that are not already defined by fixing what politics can possibly be. The order of politics is then understood as an immanently precarious succession of situations and practices in which lived political lives remain inherently aleatory, momentary and emergent rather than as an order of mastering the political. The concept of the everyday, thus draws attention to the immanent elusiveness and fragility of politics as it loses its ground, its referent

Multi-State Predictive Neural Networks For Textindependent Speaker Recognition

Both Hidden Markov Models and Neural Networks have already been used as production systems for speaker identification or verification. Recently [9] has shown that ergodic multi-state hidden Markov Models do not outperform one-state "hidden" Markov Models, i.e. Gaussian Mixture Models, for speaker recognition. She put in evidence that the important characteristic of these models is the total number of mixtures and not the number of states. These HMMs are thus unable to make use of temporal information for performing speaker recognition. On the other hand, recent experiments have shown that, for neural predictive systems, modelization of non stationarity allowed to significantly improve the performances [6]. We are interested here in the development of such models which will be refereed to as multi-state predictive neural networks (MSPNNs). We study the ability of these systems for speaker identification and discuss the superiority of multi-state upon one-state models. We provide results..