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

Caribou conservation and recovery in Ontario: development and implementation of the Caribou Conservation Plan

The range of Ontario’s woodland caribou (Rangifer tarandus caribou) (forest-dwelling ecotype) has receded northward substantially over many decades, leading to its current Threatened designation. Ontario released its Caribou Conservation Plan (CCP) in the fall of 2009. This policy responded to public input and recommendations from the Ontario Woodland Caribou Recovery Team and the Caribou Science Review Panel, and outlines conservation and recovery actions to conserve and recover caribou. Within an adaptive management framework, the CCP builds upon a recent history of managing at large landscape scales in Ontario to implement a range management approach as the basis for recovery actions. These commitments and actions include enhanced research and monitoring, improved caribou habitat planning at the landscape scale, an integrated range analysis approach using advanced assessment tools to evaluate thresholds of habitat amount, arrangement and disturbance, the assessment of probability of persistence, consideration of cumulative effects, meeting forest management silvicultural performance requirements, consideration of caribou recovery implications when managing other wildlife, an initial focus on the southern edge of caribou distribution where threats are most significant, improved outreach and stewardship, and consideration of Aboriginal Traditional Knowledge in recovery actions. Implementation of the CCP signifies a long-term provincial commitment to caribou recovery, initially focusing on identified priorities within the CCP

Multicenter trial of one HLA-DR–matched or mismatched blood transfusion prior to cadaveric renal transplantation

Multicenter trial of one HLA-DR–matched or mismatched blood transfusion prior to cadaveric renal transplantation.BackgroundThe beneficial effect of blood transfusions before cadaveric renal transplantation on allograft survival, although previously well documented, has become controversial in light of their adverse effects. Recently, it has been suggested that their clinical benefits are due to HLA-DR sharing between the blood donor and recipient.MethodsIn this prospective study, 144 naive patients were randomly assigned to receive one unit of blood matched for one-HLA-DR antigen (N = 49), or one unit of mismatched blood (N = 48), or to remain untransfused (N = 47). Graft survival and acute rejection rate were analyzed in 106 cadaveric renal allograft recipients receiving the same immunosuppressive protocol.ResultsGraft survival was similar in the three groups at one and five years: 91.7 and 80% in untransfused patients, 90.3 and 79.3% in patients transfused with one DR-antigen–matched unit, and 92.3 and 83.7% in patients transfused with HLA-mismatched blood. The difference in the incidence of six-month post-transplant acute rejections was not statistically significant in the three groups: 12 out of 36, 33.3% in nontransfused patients; 6 out of 31, 19.4% in patients transfused with one DR-matched blood; and 13 out of 39, 33.3% in patients transfused with mismatched blood.ConclusionThe results of our prospective randomized trial showed that in a population of naive patients, one transfusion mismatched or matched for one HLA-DR antigen given prior to renal transplantation had no significant effect on the incidence and severity of acute rejection, and did not influence overall long-term graft outcome. Considering the potentially deleterious adverse effects of blood transfusions, the costs, and the considerable logistical efforts required to select and type blood donors, such a procedure cannot be recommended in a routine practice for patients awaiting cadaveric kidney transplantation

Separate and combined analysis of successive dependent outcomes after breast-conservation surgery: recurrence, metastases, second cancer and death

<p>Abstract</p> <p>Background</p> <p>In the setting of recurrent events, research studies commonly count only the first occurrence of an outcome in a subject. However this approach does not correctly reflect the natural history of the disease. The objective is to jointly identify prognostic factors associated with locoregional recurrences (LRR), contralateral breast cancer, distant metastases (DM), other primary cancer than breast and breast cancer death and to evaluate the correlation between these events.</p> <p>Methods</p> <p>Patients (n = 919) with a primary invasive breast cancer and treated in a cancer center in South-Western France with breast-conserving surgery from 1990 to 1994 and followed up to January 2006 were included. Several types of non-independent events could be observed for the same patient: a LRR, a contralateral breast cancer, DM, other primary cancer than breast and breast cancer death. Data were analyzed separately and together using a random-effects survival model.</p> <p>Results</p> <p>LRR represent the most frequent type of first failure (14.6%). The risk of any event is higher for young women (less than 40 years old) and in the first 10 years of follow-up after the surgery. In the combined analysis histological tumor size, grade, number of positive nodes, progesterone receptor status and treatment combination are prognostic factors of any event. The results show a significant dependence between these events with a successively increasing risk of a new event after the first and second event. The risk of developing a new failure is greatly increased (RR = 4.25; 95%CI: 2.51-7.21) after developing a LRR, but also after developing DM (RR = 3.94; 95%CI: 2.23-6.96) as compared to patients who did not develop a first event.</p> <p>Conclusion</p> <p>We illustrated that the random effects survival model is a more satisfactory method to evaluate the natural history of a disease with multiple type of events.</p

An update of the Worldwide Integrated Assessment (WIA) on systemic insecticides. Part 2: impacts on organisms and ecosystems

New information on the lethal and sublethal effects of neonicotinoids and fipronil on organisms is presented in this review, complementing the previous WIA in 2015. The high toxicity of these systemic insecticides to invertebrates has been confirmed and expanded to include more species and compounds. Most of the recent research has focused on bees and the sublethal and ecological impacts these insecticides have on pollinators. Toxic effects on other invertebrate taxa also covered predatory and parasitoid natural enemies and aquatic arthropods. Little, while not much new information has been gathered on soil organisms. The impact on marine coastal ecosystems is still largely uncharted. The chronic lethality of neonicotinoids to insects and crustaceans, and the strengthened evidence that these chemicals also impair the immune system and reproduction, highlights the dangers of this particular insecticidal classneonicotinoids and fipronil. , withContinued large scale – mostly prophylactic – use of these persistent organochlorine pesticides has the potential to greatly decreasecompletely eliminate populations of arthropods in both terrestrial and aquatic environments. Sublethal effects on fish, reptiles, frogs, birds and mammals are also reported, showing a better understanding of the mechanisms of toxicity of these insecticides in vertebrates, and their deleterious impacts on growth, reproduction and neurobehaviour of most of the species tested. This review concludes with a summary of impacts on the ecosystem services and functioning, particularly on pollination, soil biota and aquatic invertebrate communities, thus reinforcing the previous WIA conclusions (van der Sluijs et al. 2015)

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

Application of CP-MAS and liquid-like solid-state NMR experiments for the study of the ripening-associated cell wall changes in tomato

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Caractérisation par RMN des biopolymères d'origine végétale, de la molécule à l'organisation supramoléculaire

National audienceNMR investigations of plant biopolymers, from the molecule to the supramolecular organisation. Various NMR techniques have been used to characterize plant biopolymers, isolated or in interaction with other macromolecules in complex composites. In our investigations, 1H and 13C NMR spectroscopy is used to study, at the molecular scale, complex biochemical processes as those involved during fruit ripening. NMR informs also about the fine structure of biopolymers, about their polymorphism and their crystallinity. At supramolecular scales, measurements of self-diffusion coefficients complement the low-field relaxometry approaches in order to estimate pore sizes in composite systems of biopolymers

NMR of cell walls : a multi-scale approach

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