Effet du canal sur la reconnaissance automatique de la parole

Les variations dans les caractéristiques du canal augmentent le nombre d’erreurs dans la reconnaissance automatique de la parole. Une nouvelle méthode, utilisant la transformée en paquets d’ondelettes, est présentée dans ce travail. Cette transformée est utilisée pour approximer les bandes critiques du système auditif humain. On calcul ensuite des coefficients cepstraux perceptuels à spectre relatif pour caractériser la parole. Les résultats de tests utilisant ces coefficients ont montré une amélioration de la robustesse aux variations de canal comparativement aux coefficients obtenus à partir d’une batterie de filtres basée sur la transformée de Fourier

A Cross-Species Neuroimaging Study of Sex Chromosome Dosage Effects on Human and Mouse Brain Anatomy.

All eutherian mammals show chromosomal sex determination with contrasting sex chromosome dosages (SCDs) between males (XY) and females (XX). Studies in transgenic mice and humans with sex chromosome trisomy (SCT) have revealed direct SCD effects on regional mammalian brain anatomy, but we lack a formal test for cross-species conservation of these effects. Here, we develop a harmonized framework for comparative structural neuroimaging and apply this to systematically profile SCD effects on regional brain anatomy in both humans and mice by contrasting groups with SCT (XXY and XYY) versus XY controls. Total brain size was substantially altered by SCT in humans (significantly decreased by XXY and increased by XYY), but not in mice. Robust and spatially convergent effects of XXY and XYY on regional brain volume were observed in humans, but not mice, when controlling for global volume differences. However, mice do show subtle effects of XXY and XYY on regional volume, although there is not a general spatial convergence in these effects within mice or between species. Notwithstanding this general lack of conservation in SCT effects, we detect several brain regions that show overlapping effects of XXY and XYY both within and between species (cerebellar, parietal, and orbitofrontal cortex), thereby nominating high priority targets for future translational dissection of SCD effects on the mammalian brain. Our study introduces a generalizable framework for comparative neuroimaging in humans and mice and applies this to achieve a cross-species comparison of SCD effects on the mammalian brain through the lens of SCT.SIGNIFICANCE STATEMENT Sex chromosome dosage (SCD) affects neuroanatomy and risk for psychopathology in humans. Performing mechanistic studies in the human brain is challenging but possible in mouse models. Here, we develop a framework for cross-species neuroimaging analysis and use this to show that an added X- or Y-chromosome significantly alters human brain anatomy but has muted effects in the mouse brain. However, we do find evidence for conserved cross-species impact of an added chromosome in the fronto-parietal cortices and cerebellum, which point to regions for future mechanistic dissection of sex chromosome dosage effects on brain development

Genetic variation in the odorant receptors family 13 and the mhc loci influence mate selection in a multiple sclerosis dataset

<p>Abstract</p> <p>Background</p> <p>When selecting mates, many vertebrate species seek partners with major histocompatibility complex (MHC) genes different from their own, presumably in response to selective pressure against inbreeding and towards MHC diversity. Attempts at replication of these genetic results in human studies, however, have reached conflicting conclusions.</p> <p>Results</p> <p>Using a multi-analytical strategy, we report validated genome-wide relationships between genetic identity and human mate choice in 930 couples of European ancestry. We found significant similarity between spouses in the MHC at class I region in chromosome 6p21, and at the odorant receptor family 13 locus in chromosome 9. Conversely, there was significant dissimilarity in the MHC class II region, near the <it>HLA-DQA1 </it>and -<it>DQB1 </it>genes. We also found that genomic regions with significant similarity between spouses show excessive homozygosity in the general population (assessed in the HapMap CEU dataset). Conversely, loci that were significantly dissimilar among spouses were more likely to show excessive heterozygosity in the general population.</p> <p>Conclusions</p> <p>This study highlights complex patterns of genomic identity among partners in unrelated couples, consistent with a multi-faceted role for genetic factors in mate choice behavior in human populations.</p

Neural conditional random fields for natural language understanding

This thesis presents work on Neural Conditional Random Fields (NeuroCRFs), a combination of neural network and conditional random field, applied to chunking and named entities recognition (NER), two information extraction tasks. Information extraction is a subfield of natural language understanding (NLU), the study of the automatic processing of natural language utterances in order to obtain the information they contain in a form suitable for further automatic processing. NER is the recognition and classification of the named entities found in an utterance, while chunking is the syntactic segmentation of an utterance. In both cases, information contained in an utterance is extracted in the form of segments, composed of successive words, and an attached class. In this thesis, chunking and NER are approached through sequence labelling, the assignment of a label to each element in an input sequence. This transforms the natural language utterance into a structured sequence of labels that can easily be interpreted to extract the required information. NeuroCRFs are models composed of a neural network (NN) used for feature extraction and a conditional random field (CRF), used to factorize the complex distribution of output labels conditioned by the input utterance into simpler factor functions that are based on the NN. CRFs rely on a set of features than can be extracted from the natural language utterance. Once the set of features is defined, machine learning algorithms can be used to learn the relation between those features and the correct output sequence. Feature engineering is the main challenge of CRF, and requires extensive work by a human expert. NeuroCRFs use the feature learning capability of NNs to reduce, and even remove, the need for feature engineering. This thesis includes three major contributions. The first is an extension of NeuroCRFs, where the NNs is used to learn and extract features corresponding to transitions between label in the output sequence, instead of the usual emission features. The second contribution is a continuation of this concept. NeuroCRFs use a NN to learn factor functions corresponding to events in the output sequence. Label emissions and label transitions are only one form of such events. We extended this concept to add factor functions shared by multiple events. This improved performance at the cost of reintroducing some feature engineering. We also improved performance by combining those shared features with a large margin model training algorithm. Performance was further improved by combining NNs obtained with different initializations into a single ensemble model. Finally, the third contribution addresses the limitations of the feed forward NNs (FFNNs) used in the previous experiments. FFNNs are limited by their input, a sliding window overthe natural language utterance. The model is forced to assume that labels are independent of the input outside of this limited window. Recurrent layers, such as long short term memory (LSTM) layers, do not have this limitation. LSTM based NeuroCRFs, a new addition to the NeuroCRFs family, significantly improved performance over FFNN based NeuroCRFs. Bi-directional LSTM layers were found to remove the need for the sliding window.Cette thèse présentera des travaux portant sur les NeuroCRFs, une combinaison de réseaux de neurones et de champs markoviens conditionnels (CRF), dans le contexte du chunking et de la reconnaissance d'entités nommées (NER), deux tâches d'extraction d'information. L'extraction d'information est un sous-domaine de la compréhension du langage naturel(NLU), l'étude du traitement automatique de phrases en langage naturel afin d'obtenir l'information contenue dans cette phrase dans une forme structurée compatible avec un traitement automatique subséquent. La NER consiste à reconnaitre et classifier les entités nommées présentes dans une phrase. Le chunking est la segmentation sémantique d'une phrase. Dans les deux cas, l'information est extraite sous forme de segments, composés de mots consécutifs, auxquels est attaché une classe. Dans cette thèse, ces tâches sont approchées par l'étiquetage de séquence, où une étiquette est appliquée à chaque élément d'une séquence. Cela transforme la phrase en une séquence d'étiquettes structurée, qui peut être interprétée facilement afin d'extraire l'information désirée. Les NeuroCRFs sont des modèles composés d'un réseau de neurones (NN), utilisé pour extraire des caractéristiques, et d'un CRF qui va factoriser une complexe distribution d'étiquettes, conditionnée par la phrase, en un produit de plus simple fonctions, qui sont obtenues à partir des sorties du NN. Les CRFs dépendent d'un ensemble de caractéristiques qui peuvent être extraites d'une phrase en langage naturel. Une fois que cet ensemble est défini, les algorithmes d'apprentissage automatique permettent d'apprendre la relation entre ces caractéristiques et la séquence d'étiquettes désirée. L'ingénierie des caractéristiques est la principale difficulté d'un CRF, et demande l'attention d'un expert humain. Les NeuroCRFs exploitent la capacité d'apprentissage de caractéristiques des NNs afin de réduire ce travail d'ingénierie. Cette thèse inclue trois contributions majeures. La première est une extension des NeuroCRFs, où le NN est utilisé pour apprendre et extraire des caractéristiques correspondant aux transitions entre deux étiquettes, plutôt qu'à l'émission d'une seule étiquette. La seconde contribution est un prolongement de ce concept. Les NeuroCRFs utilisent leur NN afin d'apprendre des fonctions correspondant à des événements dans la séquence d'étiquettes. Les transitions entre étiquettes et l'émission d'une étiquette ne sont que deux formes d'événements. Nous étendons ce concept en ajoutant des fonctions qui sont partagées par plusieurs événements. Ceci améliora les performances, au prix d'efforts supplémentaires d'ingénierie des caractéristiques. Des améliorations supplémentaires ont été obtenues avec un algorithme d'apprentissage maximisant la marge de la séquence correcte, et en combinant des NNs obtenues avec différentes initialisations dans un large modèle-ensemble. Finalement, la troisième contribution adresse les limitations des NNs utilisés dans les expériences précédentes. Ces NNs sont limités par leur entrée, une fenêtre glissée sur la phrase en langage naturel. Le modèle doit supposer que les étiquettes sont indépendantes de l'entrée en dehors de cette fenêtre. Les couches de neurones récurrentes, par exemple des couches à longue mémoire à court terme (LSTM), n'ont pas cette limitation. Des NeuroCRFs basés sur des couches LSTM, un nouveau membre de la famille des NeuroCRFs, ont des performances significativement améliorées comparées aux NeuroCRFs sans récursion. L'ajout d'une récursion bidirectionnelle peut même remplacer la fenêtre glissée sur la phrase en langage naturel

Serotonin regulation of behavior via large-scale neuromodulation of serotonin receptor networks

Although we understand how serotonin receptors function at the single-cell level, what role different serotonin receptors play in regulating brain-wide activity and, in turn, human behavior, remains unknown. Here, we developed transcriptomic–neuroimaging mapping to characterize brain-wide functional signatures associated with specific serotonin receptors: serotonin receptor networks (SRNs). Probing SRNs with optogenetics–functional magnetic resonance imaging (MRI) and pharmacology in mice, we show that activation of dorsal raphe serotonin neurons differentially modulates the amplitude and functional connectivity of different SRNs, showing that receptors’ spatial distributions can confer specificity not only at the local, but also at the brain-wide, network level. In humans, using resting-state functional MRI, SRNs replicate established divisions of serotonin effects on impulsivity and negative biases. These results provide compelling evidence that heterogeneous brain-wide distributions of different serotonin receptor types may underpin behaviorally distinct modes of serotonin regulation. This suggests that serotonin neurons may regulate multiple aspects of human behavior via modulation of large-scale receptor networks

Hindu manners, customs and ceremonies.

Mode of access: Internet

Comparative neuroimaging of sex differences in human and mouse brain anatomy

In vivo neuroimaging studies have established several reproducible volumetric sex differences in the human brain, but the causes of such differences are hard to parse. While mouse models are useful for understanding the cellular and mechanistic bases of sex-specific brain development, there have been no attempts to formally compare human and mouse neuroanatomical sex differences to ascertain how well they translate. Addressing this question would shed critical light on the use of the mouse as a translational model for sex differences in the human brain and provide insights into the degree to which sex differences in brain volume are conserved across mammals. Here, we use structural magnetic resonance imaging to conduct the first comparative neuroimaging study of sex-specific neuroanatomy of the human and mouse brain. In line with previous findings, we observe that in humans, males have significantly larger and more variable total brain volume; these sex differences are not mirrored in mice. After controlling for total brain volume, we observe modest cross-species congruence in the volumetric effect size of sex across 60 homologous regions (r=0.30). This cross-species congruence is greater in the cortex (r=0.33) than non-cortex (r=0.16). By incorporating regional measures of gene expression in both species, we reveal that cortical regions with greater cross-species congruence in volumetric sex differences also show greater cross-species congruence in the expression profile of 2835 homologous genes. This phenomenon differentiates primary sensory regions with high congruence of sex effects and gene expression from limbic cortices where congruence in both these features was weaker between species. These findings help identify aspects of sex-biased brain anatomy present in mice that are retained, lost, or inverted in humans. More broadly, our work provides an empirical basis for targeting mechanistic studies of sex-specific brain development in mice to brain regions that best echo sex-specific brain development in humans

Pyrovanadolysis, a Pyrophosphorolysis-like Reaction Mediated by Pyrovanadate, Mn2+, and DNA Polymerase of Bacteriophage T7

DNA polymerases catalyze the 3'–5'-pyrophosphorolysis of a DNA primer annealed to a DNA template in the presence of pyrophosphate (PPi). In this reversal of the polymerization reaction, deoxynucleotides in DNA are converted to deoxynucleoside 5'-triphosphates. Based on the charge, size, and geometry of the oxygen connecting the two phosphorus atoms of PPi, a variety of compounds was examined for their ability to carry out a reaction similar to pyrophosphorolysis. We describe a manganese-mediated pyrophosphorolysis-like activity using pyrovanadate (VV) catalyzed by the DNA polymerase of bacteriophage T7. We designate this reaction pyrovanadolysis. X-ray absorption spectroscopy reveals a shorter Mn-V distance of the polymerase-VV complex than the Mn-P distance of the polymerase-PPi complex. This structural arrangement at the active site accounts for the enzymatic activation by Mn-VV. We propose that the Mn2+, larger than Mg2+, fits the polymerase active site to mediate binding of VV into the active site of the polymerase. Our results may be the first documentation that vanadium can substitute for phosphorus in biological processes.