Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

EVALUATION AND COMPARISON OF TWO DEEP-LEARNING STRATEGIES FOR ON-LINE X-RAY COMPUTED TOMOGRAPHY

International audienceX-ray Computed Tomography (CT) has been increasinglyused in many industrial domains for its unique capability of con-trolling both the integrity and dimensional conformity of parts.Still, it fails to be adopted as a standard technique for on-line mon-itoring due to its excessive cost in terms of acquisition time. Thereduction of the number of projections, leading to the so-calledsparse-view CT strategy, while maintaining a sufficient recon-struction quality is therefore one of the main challenges in thisfield. This work aims to evaluate and compare the performancesof two deep learning strategies for the sparse-view reconstructionproblem. As such, we propose an extensive study of these meth-ods, both in terms of data regime and angular sparsity duringtraining. The two strategies present quantitative improvementsover a classical FBP/FDK approach with a PSNR improvementvarying between 11 and 16 dB (depending on the angular spar-sity) ; showing that efficient CT inspection can be performed fromonly few dozens of image

EVALUATION AND COMPARISON OF TWO DEEP-LEARNING STRATEGIES FOR ON-LINE X-RAY COMPUTED TOMOGRAPHY

International audienceX-ray Computed Tomography (CT) has been increasinglyused in many industrial domains for its unique capability of con-trolling both the integrity and dimensional conformity of parts.Still, it fails to be adopted as a standard technique for on-line mon-itoring due to its excessive cost in terms of acquisition time. Thereduction of the number of projections, leading to the so-calledsparse-view CT strategy, while maintaining a sufficient recon-struction quality is therefore one of the main challenges in thisfield. This work aims to evaluate and compare the performancesof two deep learning strategies for the sparse-view reconstructionproblem. As such, we propose an extensive study of these meth-ods, both in terms of data regime and angular sparsity duringtraining. The two strategies present quantitative improvementsover a classical FBP/FDK approach with a PSNR improvementvarying between 11 and 16 dB (depending on the angular spar-sity) ; showing that efficient CT inspection can be performed fromonly few dozens of image

Neural fields for sparse-view 3D X-ray imaging: preliminary work

International audienceX-ray Computed Tomography has been increasingly used in many industrial domains for its unique capability of controlling both the integrity and dimensional conformity of parts. Still, it fails to be adopted as a standard technique for on-line monitoring due to its excessive cost in terms of acquitision time. In this work we develop a hybrid methodology, which combines state of the art techniques to perform CT reconstruction from few views using deep learning on 3D cone-beam data.La tomographie par rayons X est de plus en plus utilisée dans de nombreux domaines industriels pour sa capacitéunique a contrôler l’intégrité et la conformité dimensionnelle des pieces. Pourtant, elle ne parvient pas à être adoptée comme technique standard de contrôle en ligne en raison de son cout excessif en temps d’acquisition. Dans ce travail, nous developpons une méthodologie hybride, qui combine differentes techniques d’apprentissage profond pour effectuer une reconstruction tomographique a partir de peu de vues sur des donnees 3D à faisceau conique

Neural fields for sparse-view 3D X-ray imaging: preliminary work

International audienceX-ray Computed Tomography has been increasingly used in many industrial domains for its unique capability of controlling both the integrity and dimensional conformity of parts. Still, it fails to be adopted as a standard technique for on-line monitoring due to its excessive cost in terms of acquitision time. In this work we develop a hybrid methodology, which combines state of the art techniques to perform CT reconstruction from few views using deep learning on 3D cone-beam data.La tomographie par rayons X est de plus en plus utilisée dans de nombreux domaines industriels pour sa capacitéunique a contrôler l’intégrité et la conformité dimensionnelle des pieces. Pourtant, elle ne parvient pas à être adoptée comme technique standard de contrôle en ligne en raison de son cout excessif en temps d’acquisition. Dans ce travail, nous developpons une méthodologie hybride, qui combine differentes techniques d’apprentissage profond pour effectuer une reconstruction tomographique a partir de peu de vues sur des donnees 3D à faisceau conique

Utilisation de méthodes basées sur l'Intelligence Artificielle pour le contrôle non destructif par rayons X

International audienceCes dernières années, l'intelligence artificielle (IA), notamment au travers du développement des méthodes d'apprentissage profond, a permis de considérablement améliorer la résolution de nombreuses tâches, en particulier en application à des problématiques de vision par ordinateur. Ceci a été possible principalement grâce à l'augmentation de la puissance des ordinateurs, et l'accès à de grandes bases de données. L'application des méthodes ainsi développées à un contexte industriel, comme celui du contrôle non destructif, apporte de nouvelles problématiques liées à la difficulté et au coût d'obtention de bases de données de taille suffisante, ainsi qu'à une annotation fiable et précise de celles-ci. La simulation de méthodes d'acquisition et de la physique qui y est associée, permise par des logiciels de simulation tels que CIVA, nous permet de pallier à ce manque de données. Nous proposons ici de présenter des développements effectués dans un cadre de contrôle non destructif par rayons X (RX), en radiographie et tomographie, à travers trois applications.Une première application concerne la détection de défauts sur des radiographies d'objets obtenus par coulage par méthode supervisée. Par ailleurs, la possibilité de correction d'artefacts en tomographie est envisagée. La correction du rayonnement diffusé sur les images de radiographies permet notamment d'améliorer la qualité de la reconstruction tomographique correspondante. Enfin, dans un objectif de mise en place de contrôles tomographiques sur ligne de production, des approches IA sont couplées aux algorithmes de reconstruction pour assurer une bonne qualité d'image reconstruite dans une configuration à faible nombre de projections. Nous présentons ici une méthode de pré-traitement, qui permet de compléter les mesures radiographiques, ainsi qu'une méthode de post-traitement, qui réduit les artefacts de reconstruction liés au manque de vues

cf_xarray

cf_xarray provides an accessor (DataArray.cf or Dataset.cf) that allows you to interpret Climate and Forecast metadata convention attributes present on xarray objects.If you use this software, please cite it using these metadata

cf_xarray

cf_xarray provides an accessor (DataArray.cf or Dataset.cf) that allows you to interpret Climate and Forecast metadata convention attributes present on xarray objects.If you use this software, please cite it using these metadata