Villeneuve-Saint-Germain – Le Fond du Ham

Le projet MASSIV-ANV de l’ASAVA a débuté en 2007 par une année probatoire. L’année 2010 a constitué la dernière année du programme triennal initialement formalisé. On rappellera qu’il s’agit d’une enquête sur les « fossés en croix » du site, qui vise à préciser les propriétés intrinsèques et extrinsèques de ces structures si originales. Les fossés servent de « fil rouge » à ce projet afin d’appréhender, après les fouilles des années 1970 et 1980, un site dont on a, au passage, porté la superf..

Ian Hodder (dir.), Archaeological Theory Today, Polity, Cambridge

Il est bien révolu ce temps de l’archéologie incarné par le Old Timer dans la parabole proposée par K. Flannery en 1982 « The golden Marshalltown » : qu’on le regrette ou non, on ne peut plus affirmer comme lui que la théorie en archéologie est anthropologique ou n’est pas. La seconde édition d’Archaeological Theory Today (ATT ensuite) dirigée par I. Hodder en apporte une nouvelle et utile démonstration. La comparaison avec l’édition de 2001 laisse apparaître une révision sensible des contenu..

RDF: A Reconfigurable Dataflow Model of Computation

International audienceDataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the first and most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF and most of its variants lack the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment. We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how and when the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and present its implementation and some experimental results

RDF : un modèle flot de données reconfigurable(version étendue)

Dataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF (and most of its variants) lacks the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment.We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and outline its implementation.Les modèles de calcul (MoCs) flot de données synchrones sont très utilisés dans les systèmes embarqués pour les applications multimédia, de traitement du signal, de télécommunication et de contrôle automatique. Dans ce style de modèle, une application est spécifiée par un graphe d’acteurs connectés par des liens FIFO de communication. Un des MoCs les plus connus, SDF (pour Synchronous Dataflow), permet des analyses statiques qui garantissent l’exécution enmémoire bornée et l’absence d’interblocage, propriétés clés pour les systèmes embarqués. Néanmoins, SDF (et la plupart de ses variantes) ne permet pas d’exprimer la dynamicité requise par les applications embarquées modernes. En particulier, ces applications ont souvent besoin de se reconfigurer pour s’adapter aux changements (par ex., de débit ou de qualité) du flot d’entrée, des objectifs de contrôle ou de l’environnement.Afin de répondre à ce besoin, nous proposons le MoC RDF (pour Reconfigurable DataFlow) qui étend SDF avec des règles de transformations spécifiant comment la topologie et les acteurs du graphe peuvent être reconfigurés dynamiquement. En considérant un graphe SDF initial et un ensemble de règles de transformation, un nombre arbitraire de nouveaux graphes peuvent être produits. La principale qualité de RDF est qu’il peut être analysé statiquement pour garantir que tous les graphes générés dynamiquement s’exécuteront en mémoire bornée et sans interblocage.Nous présentons le modèle RDF, décrivons les analyses statiques associées et décrivons brièvementson implémentation

RDF: Un modèle de calcul flot de données reconfigurable

Dataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the first and most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF and most of its variants lacks the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment. We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how and when the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and present its implementation and some experimental results.Les modèles de calcul (MoCs) flot de données synchrones sont très utilisés dans les systèmes embarqués et les applications multimédia, de traitement du signal, de télécommunication et de contrôle automatique. Dans ce style de modèle, une application est spécifiée par un graphe d’acteurs connectés par des liens FIFO de communication. Un des MoCs les plus connus, SDF (pour Synchronous Dataflow), permet des analyses statiques qui garantissent l’exécution en mémoire bornée et l’absence d’interblocage, propriétés clés pour les systèmes embarqués. Néanmoins, SDF (et la plupart de ses variantes) ne permet pas d’exprimer la dynamicité requise par les applications embarquées modernes. En particulier, ces applications ont souvent besoin de se reconfigurer pour s’adapter aux changements (par ex., de débit ou de qualité) du flot d’entrée, des objectifs de contrôle ou de l’environnement. Afin de répondre à ce besoin, nous proposons RDF (pour Reconfigurable DataFlow) un MoC qui étend SDF avec des règles de transformations spécifiant comment la topologie du graphe flot de données peut être reconfiguré dynamiquement. En considérant un graphe SDF initial et un ensemble de règles de transformation, un nombre arbitraire de nouveaux graphes peuvent être produits. La principale qualité de RDF est qu’il peut être analysé statiquement pour garantir que tous les graphes générés dynamiquement s’exécuteront en mémoire bornée et sans interblocage. Nous présentons le modèle RDF, les analyses statiques associées, sa mise en oeuvre et quelques expérimentations

Regulation of microRNA biogenesis and turnover by animals and their viruses

Item does not contain fulltextMicroRNAs (miRNAs) are a ubiquitous component of gene regulatory networks that modulate the precise amounts of proteins expressed in a cell. Despite their small size, miRNA genes contain various recognition elements that enable specificity in when, where and to what extent they are expressed. The importance of precise control of miRNA expression is underscored by functional studies in model organisms and by the association between miRNA mis-expression and disease. In the last decade, identification of the pathways by which miRNAs are produced, matured and turned-over has revealed many aspects of their biogenesis that are subject to regulation. Studies in viral systems have revealed a range of mechanisms by which viruses target these pathways through viral proteins or non-coding RNAs in order to regulate cellular gene expression. In parallel, a field of study has evolved around the activation and suppression of antiviral RNA interference (RNAi) by viruses. Virus encoded suppressors of RNAi can impact miRNA biogenesis in cases where miRNA and small interfering RNA pathways converge. Here we review the literature on the mechanisms by which miRNA biogenesis and turnover are regulated in animals and the diverse strategies that viruses use to subvert or inhibit these processes

Allergic Rhinitis and its Impact on Asthma (ARIA) Guidelines - 2016 Revision

BACKGROUND: Allergic rhinitis (AR) affects 10% to 40% of the population. It reduces quality of life and school and work performance and is a frequent reason for office visits in general practice. Medical costs are large, but avoidable costs associated with lost work productivity are even larger than those incurred by asthma. New evidence has accumulated since the last revision of the Allergic Rhinitis and its Impact on Asthma (ARIA) guidelines in 2010, prompting its update. OBJECTIVE: We sought to provide a targeted update of the ARIA guidelines. METHODS: The ARIA guideline panel identified new clinical questions and selected questions requiring an update. We performed systematic reviews of health effects and the evidence about patients' values and preferences and resource requirements (up to June 2016). We followed the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) evidence-to-decision frameworks to develop recommendations. RESULTS: The 2016 revision of the ARIA guidelines provides both updated and new recommendations about the pharmacologic treatment of AR. Specifically, it addresses the relative merits of using oral H1-antihistamines, intranasal H1-antihistamines, intranasal corticosteroids, and leukotriene receptor antagonists either alone or in combination. The ARIA guideline panel provides specific recommendations for the choice of treatment and the rationale for the choice and discusses specific considerations that clinicians and patients might want to review to choose the management most appropriate for an individual patient. CONCLUSIONS: Appropriate treatment of AR might improve patients' quality of life and school and work productivity. ARIA recommendations support patients, their caregivers, and health care providers in choosing the optimal treatment

Allergen immunotherapy for allergic rhinoconjunctivitis:protocol for a systematic review

The European Academy of Allergy and Clinical Immunology (EAACI) is in the process of developing the EAACI Guidelines for Allergen Immunotherapy (AIT) for the Management of Allergic Rhinoconjunctivitis. We seek to critically assess the effectiveness, cost-effectiveness and safety of AIT in the management of allergic rhinoconjunctivitis.status: publishe