163 resultados para Space Vector


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How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model's prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.

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The dual-stream model of auditory processing postulates separate processing streams for sound meaning and for sound location. The present review draws on evidence from human behavioral and activation studies as well as from lesion studies to argue for a position-linked representation of sound objects that is distinct both from the position-independent representation within the ventral/What stream and from the explicit sound localization processing within the dorsal/Where stream.

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UNLABELLED: NYVAC, a highly attenuated, replication-restricted poxvirus, is a safe and immunogenic vaccine vector. Deletion of immune evasion genes from the poxvirus genome is an attractive strategy for improving the immunogenic properties of poxviruses. Using systems biology approaches, we describe herein the enhanced immunological profile of NYVAC vectors expressing the HIV-1 clade C env, gag, pol, and nef genes (NYVAC-C) with single or double deletions of genes encoding type I (ΔB19R) or type II (ΔB8R) interferon (IFN)-binding proteins. Transcriptomic analyses of human monocytes infected with NYVAC-C, NYVAC-C with the B19R deletion (NYVAC-C-ΔB19R), or NYVAC-C with B8R and B19R deletions (NYVAC-C-ΔB8RB19R) revealed a concerted upregulation of innate immune pathways (IFN-stimulated genes [ISGs]) of increasing magnitude with NYVAC-C-ΔB19R and NYVAC-C-ΔB8RB19R than with NYVAC-C. Deletion of B8R and B19R resulted in an enhanced activation of IRF3, IRF7, and STAT1 and the robust production of type I IFNs and of ISGs, whose expression was inhibited by anti-type I IFN antibodies. Interestingly, NYVAC-C-ΔB8RB19R induced the production of much higher levels of proinflammatory cytokines (tumor necrosis factor [TNF], interleukin-6 [IL-6], and IL-8) than NYVAC-C or NYVAC-C-ΔB19R as well as a strong inflammasome response (caspase-1 and IL-1β) in infected monocytes. Top network analyses showed that this broad response mediated by the deletion of B8R and B19R was organized around two upregulated gene expression nodes (TNF and IRF7). Consistent with these findings, monocytes infected with NYVAC-C-ΔB8RB19R induced a stronger type I IFN-dependent and IL-1-dependent allogeneic CD4(+) T cell response than monocytes infected with NYVAC-C or NYVAC-C-ΔB19R. Dual deletion of type I and type II IFN immune evasion genes in NYVAC markedly enhanced its immunogenic properties via its induction of the increased expression of type I IFNs and IL-1β and make it an attractive candidate HIV vaccine vector. IMPORTANCE: NYVAC is a replication-deficient poxvirus developed as a vaccine vector against HIV. NYVAC expresses several genes known to impair the host immune defenses by interfering with innate immune receptors, cytokines, or interferons. Given the crucial role played by interferons against viruses, we postulated that targeting the type I and type II decoy receptors used by poxvirus to subvert the host innate immune response would be an attractive approach to improve the immunogenicity of NYVAC vectors. Using systems biology approaches, we report that deletion of type I and type II IFN immune evasion genes in NYVAC poxvirus resulted in the robust expression of type I IFNs and interferon-stimulated genes (ISGs), a strong activation of the inflammasome, and upregulated expression of IL-1β and proinflammatory cytokines. Dual deletion of type I and type II IFN immune evasion genes in NYVAC poxvirus improves its immunogenic profile and makes it an attractive candidate HIV vaccine vector.

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Laboratory and field experiments have demonstrated in many cases that malaria vectors do not feed randomly, but show important preferences either for infected or non-infected hosts. These preferences are likely in part shaped by the costs imposed by the parasites on both their vertebrate and dipteran hosts. However, the effect of changes in vector behaviour on actual parasite transmission remains a debated issue. We used the natural associations between a malaria-like parasite Polychromophilus murinus, the bat fly Nycteribia kolenatii and a vertebrate host the Daubenton's bat Myotis daubentonii to test the vector's feeding preference based on the host's infection status using two different approaches: 1) controlled behavioural assays in the laboratory where bat flies could choose between a pair of hosts; 2) natural bat fly abundance data from wild-caught bats, serving as an approximation of realised feeding preference of the bat flies. Hosts with the fewest infectious stages of the parasite were most attractive to the bat flies that did switch in the behavioural assay. In line with the hypothesis of costs imposed by parasites on their vectors, bat flies carrying parasites had higher mortality. However, in wild populations, bat flies were found feeding more based on the bat's body condition, rather than its infection level. Though the absolute frequency of host switches performed by the bat flies during the assays was low, in the context of potential parasite transmission they were extremely high. The decreased survival of infected bat flies suggests that the preference for less infected hosts is an adaptive trait. Nonetheless, other ecological processes ultimately determine the vector's biting rate and thus transmission. Inherent vector preferences therefore play only a marginal role in parasite transmission in the field. The ecological processes rather than preferences per se need to be identified for successful epidemiological predictions.

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The impact of transnational private regulation on labour standards remains in dispute. While studies have provided some limited evidence of positive effects on 'outcome standards' such as wages or occupational health and safety, the literature gives little reason to believe that there has been any significant effect on 'process rights' relating primarily to collective workers' voice and social dialogue. This paper probes this assumption by bringing local contexts and worker agency more fully into the picture. It outlines an analytical framework that emphasizes workers' potential to act collectively for change in the regulatory space surrounding the employment relationship. It argues that while transnational private regulation on labour standards may marginally improve workers access to regulatory spaces and their capacity to require the inclusion of enterprises in them, it does little to increase union leverage. The findings are based on empirical research work conducted in Sub-Saharan Africa.

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Modelling the shoulder's musculature is challenging given its mechanical and geometric complexity. The use of the ideal fibre model to represent a muscle's line of action cannot always faithfully represent the mechanical effect of each muscle, leading to considerable differences between model-estimated and in vivo measured muscle activity. While the musculo-tendon force coordination problem has been extensively analysed in terms of the cost function, only few works have investigated the existence and sensitivity of solutions to fibre topology. The goal of this paper is to present an analysis of the solution set using the concepts of torque-feasible space (TFS) and wrench-feasible space (WFS) from cable-driven robotics. A shoulder model is presented and a simple musculo-tendon force coordination problem is defined. The ideal fibre model for representing muscles is reviewed and the TFS and WFS are defined, leading to the necessary and sufficient conditions for the existence of a solution. The shoulder model's TFS is analysed to explain the lack of anterior deltoid (DLTa) activity. Based on the analysis, a modification of the model's muscle fibre geometry is proposed. The performance with and without the modification is assessed by solving the musculo-tendon force coordination problem for quasi-static abduction in the scapular plane. After the proposed modification, the DLTa reaches 20% of activation.

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Abstract This work studies the multi-label classification of turns in simple English Wikipedia talk pages into dialog acts. The treated dataset was created and multi-labeled by (Ferschke et al., 2012). The first part analyses dependences between labels, in order to examine the annotation coherence and to determine a classification method. Then, a multi-label classification is computed, after transforming the problem into binary relevance. Regarding features, whereas (Ferschke et al., 2012) use features such as uni-, bi-, and trigrams, time distance between turns or the indentation level of the turn, other features are considered here: lemmas, part-of-speech tags and the meaning of verbs (according to WordNet). The dataset authors applied approaches such as Naive Bayes or Support Vector Machines. The present paper proposes, as an alternative, to use Schoenberg transformations which, following the example of kernel methods, transform original Euclidean distances into other Euclidean distances, in a space of high dimensionality. Résumé Ce travail étudie la classification supervisée multi-étiquette en actes de dialogue des tours de parole des contributeurs aux pages de discussion de Simple English Wikipedia (Wikipédia en anglais simple). Le jeu de données considéré a été créé et multi-étiqueté par (Ferschke et al., 2012). Une première partie analyse les relations entre les étiquettes pour examiner la cohérence des annotations et pour déterminer une méthode de classification. Ensuite, une classification supervisée multi-étiquette est effectuée, après recodage binaire des étiquettes. Concernant les variables, alors que (Ferschke et al., 2012) utilisent des caractéristiques telles que les uni-, bi- et trigrammes, le temps entre les tours de parole ou l'indentation d'un tour de parole, d'autres descripteurs sont considérés ici : les lemmes, les catégories morphosyntaxiques et le sens des verbes (selon WordNet). Les auteurs du jeu de données ont employé des approches telles que le Naive Bayes ou les Séparateurs à Vastes Marges (SVM) pour la classification. Cet article propose, de façon alternative, d'utiliser et d'étendre l'analyse discriminante linéaire aux transformations de Schoenberg qui, à l'instar des méthodes à noyau, transforment les distances euclidiennes originales en d'autres distances euclidiennes, dans un espace de haute dimensionnalité.

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Ticks transmit more pathogens to humans and animals than any other arthropod. We describe the 2.1 Gbp nuclear genome of the tick, Ixodes scapularis (Say), which vectors pathogens that cause Lyme disease, human granulocytic anaplasmosis, babesiosis and other diseases. The large genome reflects accumulation of repetitive DNA, new lineages of retro-transposons, and gene architecture patterns resembling ancient metazoans rather than pancrustaceans. Annotation of scaffolds representing ∼57% of the genome, reveals 20,486 protein-coding genes and expansions of gene families associated with tick-host interactions. We report insights from genome analyses into parasitic processes unique to ticks, including host 'questing', prolonged feeding, cuticle synthesis, blood meal concentration, novel methods of haemoglobin digestion, haem detoxification, vitellogenesis and prolonged off-host survival. We identify proteins associated with the agent of human granulocytic anaplasmosis, an emerging disease, and the encephalitis-causing Langat virus, and a population structure correlated to life-history traits and transmission of the Lyme disease agent.

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This thesis develops a comprehensive and a flexible statistical framework for the analysis and detection of space, time and space-time clusters of environmental point data. The developed clustering methods were applied in both simulated datasets and real-world environmental phenomena; however, only the cases of forest fires in Canton of Ticino (Switzerland) and in Portugal are expounded in this document. Normally, environmental phenomena can be modelled as stochastic point processes where each event, e.g. the forest fire ignition point, is characterised by its spatial location and occurrence in time. Additionally, information such as burned area, ignition causes, landuse, topographic, climatic and meteorological features, etc., can also be used to characterise the studied phenomenon. Thereby, the space-time pattern characterisa- tion represents a powerful tool to understand the distribution and behaviour of the events and their correlation with underlying processes, for instance, socio-economic, environmental and meteorological factors. Consequently, we propose a methodology based on the adaptation and application of statistical and fractal point process measures for both global (e.g. the Morisita Index, the Box-counting fractal method, the multifractal formalism and the Ripley's K-function) and local (e.g. Scan Statistics) analysis. Many measures describing the space-time distribution of environmental phenomena have been proposed in a wide variety of disciplines; nevertheless, most of these measures are of global character and do not consider complex spatial constraints, high variability and multivariate nature of the events. Therefore, we proposed an statistical framework that takes into account the complexities of the geographical space, where phenomena take place, by introducing the Validity Domain concept and carrying out clustering analyses in data with different constrained geographical spaces, hence, assessing the relative degree of clustering of the real distribution. Moreover, exclusively to the forest fire case, this research proposes two new methodologies to defining and mapping both the Wildland-Urban Interface (WUI) described as the interaction zone between burnable vegetation and anthropogenic infrastructures, and the prediction of fire ignition susceptibility. In this regard, the main objective of this Thesis was to carry out a basic statistical/- geospatial research with a strong application part to analyse and to describe complex phenomena as well as to overcome unsolved methodological problems in the characterisation of space-time patterns, in particular, the forest fire occurrences. Thus, this Thesis provides a response to the increasing demand for both environmental monitoring and management tools for the assessment of natural and anthropogenic hazards and risks, sustainable development, retrospective success analysis, etc. The major contributions of this work were presented at national and international conferences and published in 5 scientific journals. National and international collaborations were also established and successfully accomplished. -- Cette thèse développe une méthodologie statistique complète et flexible pour l'analyse et la détection des structures spatiales, temporelles et spatio-temporelles de données environnementales représentées comme de semis de points. Les méthodes ici développées ont été appliquées aux jeux de données simulées autant qu'A des phénomènes environnementaux réels; nonobstant, seulement le cas des feux forestiers dans le Canton du Tessin (la Suisse) et celui de Portugal sont expliqués dans ce document. Normalement, les phénomènes environnementaux peuvent être modélisés comme des processus ponctuels stochastiques ou chaque événement, par ex. les point d'ignition des feux forestiers, est déterminé par son emplacement spatial et son occurrence dans le temps. De plus, des informations tels que la surface bru^lée, les causes d'ignition, l'utilisation du sol, les caractéristiques topographiques, climatiques et météorologiques, etc., peuvent aussi être utilisées pour caractériser le phénomène étudié. Par conséquent, la définition de la structure spatio-temporelle représente un outil puissant pour compren- dre la distribution du phénomène et sa corrélation avec des processus sous-jacents tels que les facteurs socio-économiques, environnementaux et météorologiques. De ce fait, nous proposons une méthodologie basée sur l'adaptation et l'application de mesures statistiques et fractales des processus ponctuels d'analyse global (par ex. l'indice de Morisita, la dimension fractale par comptage de boîtes, le formalisme multifractal et la fonction K de Ripley) et local (par ex. la statistique de scan). Des nombreuses mesures décrivant les structures spatio-temporelles de phénomènes environnementaux peuvent être trouvées dans la littérature. Néanmoins, la plupart de ces mesures sont de caractère global et ne considèrent pas de contraintes spatiales com- plexes, ainsi que la haute variabilité et la nature multivariée des événements. A cet effet, la méthodologie ici proposée prend en compte les complexités de l'espace géographique ou le phénomène a lieu, à travers de l'introduction du concept de Domaine de Validité et l'application des mesures d'analyse spatiale dans des données en présentant différentes contraintes géographiques. Cela permet l'évaluation du degré relatif d'agrégation spatiale/temporelle des structures du phénomène observé. En plus, exclusif au cas de feux forestiers, cette recherche propose aussi deux nouvelles méthodologies pour la définition et la cartographie des zones périurbaines, décrites comme des espaces anthropogéniques à proximité de la végétation sauvage ou de la forêt, et de la prédiction de la susceptibilité à l'ignition de feu. A cet égard, l'objectif principal de cette Thèse a été d'effectuer une recherche statistique/géospatiale avec une forte application dans des cas réels, pour analyser et décrire des phénomènes environnementaux complexes aussi bien que surmonter des problèmes méthodologiques non résolus relatifs à la caractérisation des structures spatio-temporelles, particulièrement, celles des occurrences de feux forestières. Ainsi, cette Thèse fournit une réponse à la demande croissante de la gestion et du monitoring environnemental pour le déploiement d'outils d'évaluation des risques et des dangers naturels et anthro- pogéniques. Les majeures contributions de ce travail ont été présentées aux conférences nationales et internationales, et ont été aussi publiées dans 5 revues internationales avec comité de lecture. Des collaborations nationales et internationales ont été aussi établies et accomplies avec succès.

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The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.