Oléoduc Keystone

Résumé

L’oléoduc Keystone, en anglais Keystone Pipeline, exploité par l'entreprise TC Énergie (anciennement TransCanada), est long de et transporte essentiellement des hydrocarbures synthétiques et du bitume dilué issus de sables bitumineux. En projet depuis 2005, les deux tronçons de l'oléoduc (en marron et orange sur la carte ci-contre) sont opérationnels depuis 2011. Partant de la région des sables bitumineux de l'Athabasca, dans le Nord-Est de la province canadienne de l'Alberta, ils desservent plusieurs destinations aux États-Unis, dont la raffinerie de Wood River, ainsi que les dépôts pétroliers de Patoka (dans le Sud de l'Illinois) et de Cushing (en Oklahoma). Deux autres tronçons, formant un tout nommé Keystone XL, sont proposés en 2008. Le premier (, bleu et violet sur la carte), correspondant au segment sud et qui rejoint la côte texane du golfe du Mexique , est en majeure partie en exploitation depuis . Le second tronçon (, en vert sur la carte), correspondant au segment nord,

Source officielle

https://fr.wikipedia.org/wiki/Ol%C3%A9oduc_Keystone

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Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

Christian Büchel, Maxime Descoteaux, Nawal Noëlle Kinany, Maryam Seif, Dimitri Nestor Alice Van De Ville

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/. The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.

NATURE PORTFOLIO2021

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Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.

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Modeling infectious disease dynamics towards informed public health interventions, with applications on COVID-19 and cholera

Joseph Chadi Benoit Lemaitre

Emerging and existing infectious diseases pose a constant threat to individuals and communities across the world. In many cases, the burden of these diseases is preventable through public health interventions. However, taking the right decisions and designing effective policies is an intricate task: epidemics are complex phenomena resulting from the interaction between the environment, pathogens, individuals, and societies. Modeling offers a principled way to reason about infectious disease dynamics from scarce and biased information and to guide decision-makers towards effective policies. This thesis tackles selected topics in cholera and COVID-19 modeling towards informed public-health decisions. These two contrasting diseases were associated by a twist of fate, but also through the lens of a common modeling approach: compartmental, SIR-based, models are conditioned on the available evidence using computer-age statistical inference frameworks. A set of five models is developed, each tackling a different facet of the spread and control of these two infectious diseases. Each model aims at answering questions related to either the understanding of the mechanisms behind disease transmission, the projection of the future dynamics under different scenarios, or the assessment of the effectiveness of past interventions. Moreover, a novel application of epidemiological models to the formal design of control policies is proposed. Optimal control provides a rigorous framework to identify the most effective control measures under a set of operational constraints, providing a benchmark on what it is possible to achieve with the available resources.The results presented in this thesis range from scientific insight on the relationship between cholera and rainfall in Juba, South Sudan to the COVID Scenario Pipeline which produces reports used to inform the response to the COVID-19 pandemic of different governmental entities. Furthermore, the effectiveness of the non-pharmaceutical interventions against COVID-19 in Switzerland is evaluated; and so is the probability of eliminating cholera from Haiti under different scenarios of mass vaccination campaigns. Finally, the development of an optimal control framework towards the effective spatial allocation of vaccines against SARS-CoV-2 in Italy closes this conversation of models.The present thesis demonstrates how infectious disease modeling enables informed decision-making by projecting the uncertainties under the light of the available evidence. It also highlights the effort needed to tailor the models and inference methods to the specificities of the transmission setting and the research question considered. From insights on transmission pathways to weekly reports aimed at decision-makers, it explores different applications of infectious disease modeling. Methods developed along the way enrich the toolbox available to modelers, to guide policy decisions further towards a reduction of the burden of infectious diseases on communities.

EPFL2021

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Modeling infectious disease dynamics towards informed public health interventions, with applications on COVID-19 and cholera

Joseph Chadi Benoit Lemaitre

EPFL2021