Report on demo mission and dissemination pathways of obtained data based on different observational platforms

This document describes the deployment of instrumentation in the Eastern tropical Atlantic area and shows the preliminary data acquired

Development of a Multivariate Prediction Model for Early-Onset Bronchiolitis Obliterans Syndrome and Restrictive Allograft Syndrome in Lung Transplantation.

Chronic lung allograft dysfunction and its main phenotypes, bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS), are major causes of mortality after lung transplantation (LT). RAS and early-onset BOS, developing within 3 years after LT, are associated with particularly inferior clinical outcomes. Prediction models for early-onset BOS and RAS have not been previously described. LT recipients of the French and Swiss transplant cohorts were eligible for inclusion in the SysCLAD cohort if they were alive with at least 2 years of follow-up but less than 3 years, or if they died or were retransplanted at any time less than 3 years. These patients were assessed for early-onset BOS, RAS, or stable allograft function by an adjudication committee. Baseline characteristics, data on surgery, immunosuppression, and year-1 follow-up were collected. Prediction models for BOS and RAS were developed using multivariate logistic regression and multivariate multinomial analysis. Among patients fulfilling the eligibility criteria, we identified 149 stable, 51 BOS, and 30 RAS subjects. The best prediction model for early-onset BOS and RAS included the underlying diagnosis, induction treatment, immunosuppression, and year-1 class II donor-specific antibodies (DSAs). Within this model, class II DSAs were associated with BOS and RAS, whereas pre-LT diagnoses of interstitial lung disease and chronic obstructive pulmonary disease were associated with RAS. Although these findings need further validation, results indicate that specific baseline and year-1 parameters may serve as predictors of BOS or RAS by 3 years post-LT. Their identification may allow intervention or guide risk stratification, aiming for an individualized patient management approach

Integration of in situ and satellite multi-platform data (estimation of carbon flux for trop. Atlantic)

This report presents the results of task 7.3 on “Quantification of improvements in carbon flux data for the tropical Atlantic based on the multi-platform and neural network approach”. To better constrain changes in the ocean’s capture and sequestration of CO2 emitted by human activities, in situ measurements are needed. Tropical regions are considered to be mostly sources of CO2 to the atmosphere due to specific circulation features, with large interannual variability mainly controlled by physical drivers (Padin et al., 2010). The tropical Atlantic is the second largest source, after the tropical Pacific, of CO2 to the atmosphere (Landschützer et al., 2014). However, it is not a homogeneous zone, as it is affected by many physical and biogeochemical processes that vary on many time scales and affect surrounding areas (Foltz et al., 2019). The Tropical Atlantic Observing System (TAOS) has progressed substantially over the past two decades. Still, many challenges and uncertainties remain to require further studies into the area’s role in terms of carbon fluxes (Foltz et al., 2019). Monitoring and sustained observations of surface oceanic CO2 are critical for understanding the fate of CO2 as it penetrates the ocean and during its sequestration at depth. This deliverable relies on different observing platforms deployed specifically as part of the EuroSea project (a Saildrone, and 5 pH-equipped BGC-Argo floats) as well as on the platforms as part of the TAOS (CO2-equipped moorings, cruises, models, and data products). It also builds on the work done in D7.1 and D7.2 on the deployment and quality control of pH-equipped BGC-Argo floats and Saildrone data. Indeed, high-quality homogeneously calibrated carbonate variable measurements are mandatory to be able to compute air-sea CO2 fluxes at a basin scale from multiple observing platforms

Development of BGCArgo data quality validation based on an integrative multiplatform approach

This report presents the results of Task 7.3 on “Development of BGC-Argo data quality validation based on an integrative multiplatform approach”. Observing changes in ocean conditions on the spatiotemporal scales necessary to constrain carbon uptake is a challenge. Defined as an Essential Ocean Variable (EOV) by the Global Ocean Observing System (GOOS, e.g., Tanhua et al., 2019), pH is relevant to assess numerous crucial questions regarding the oceanic evolution in response to the current global changes. However, the large spatiotemporal variability of this carbonate system parameter requires sustained observations to decipher trends and punctual events. Within this scope, numerous pH sensors suitable for deployments both on autonomous observing tools and fixed stations have been developed. Nevertheless, as interpreting changes relies on accurate data, and because offsets or drifts in pH data might appear in response to changes in the sensor k0 constant, a consistent and rigorous correction procedure to quality-control and process the data has been implemented. This report presents the application of this method to pH data acquired by BGC-Argo floats launched in the Tropical Atlantic area

A Regional Neural Network Approach to Estimate Water-Column Nutrient Concentrations and Carbonate System Variables in the Mediterranean Sea: CANYON-MED

International audienceA regional neural network-based method, "CANYON-MED" is developed to estimate nutrients and carbonate system variables specifically in the Mediterranean Sea over the water column from pressure, temperature, salinity, and oxygen together with geolocation and date of sampling. Six neural network ensembles were developed, one for each variable (i.e., three macronutrients: nitrates (NO − 3), phosphates (PO 3− 4) and silicates (SiOH 4), and three carbonate system variables: pH on the total scale (pH T), total alkalinity (A T), and dissolved inorganic carbon or total carbon (C T), trained using a specific quality-controlled dataset of reference "bottle" data in the Mediterranean Sea. This dataset is representative of the peculiar conditions of this semi-enclosed sea, as opposed to the global ocean. For each variable, the neural networks were trained on 80% of the data chosen randomly and validated using the remaining 20%. CANYON-MED retrieved the variables with good accuracies (Root Mean Squared Error): 0.73 µmol.kg −1 for NO − 3 , 0.045 µmol.kg −1 for PO 3− 4 and 0.70 µmol.kg −1 for Si(OH) 4 , 0.016 units for pH T , 11 µmol.kg −1 for A T and 10 µmol.kg −1 for C T. A second validation on the ANTARES independent time series confirmed the method's applicability in the Mediterranean Sea. After comparison to other existing methods to estimate nutrients and carbonate system variables, CANYON-MED stood out as the most robust, using the aforementioned inputs. The application of CANYON-MED on the Mediterranean Sea data from autonomous observing systems (integrated network of Biogeochemical-Argo floats, Eulerian moorings and ocean gliders measuring hydrological properties together with oxygen concentration) could have a wide range of applications. These include data quality control or filling gaps in time series, as well as biogeochemical data assimilation and/or the initialization and validation of regional biogeochemical models still lacking crucial reference data. Matlab and R code are available at https:// github.com/MarineFou/CANYON-MED/

A neural network approach to estimate water-column nutrient concentrations and carbonate system parameters in the Mediterranean Sea: CANYON-MED

International audienc

A neural network approach to estimate water-column nutrient concentrations and carbonate system variables in the Mediterranean Sea: CANYON-MED

International audienc

High resolution sampling of Submesoscale Coherent Vortices transporting newly formed deep water across the NW Mediterranean Sea thanks to glider

International audienc

Existe-t-il des disparités rural/urbain en fonction du sexe chez les patients thrombolysés suite à un AVC ?

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Sex-Related Differences in Management and Outcome of Acute Ischemic Stroke in Eligible Patients to Thrombolysis

International audienceBACKGROUND: Literature has highlighted sex-based differences in the natural course of stroke and in response to treatment with intravenous tissue plasminogen activator (tPA).OBJECTIVES: We aimed to compare the management and outcome of acute ischemic stroke (AIS) among women and men on a French registry based on a federated network of emergency physicians and neurologists.METHOD: We included 2,790 patients received tPA between 2010 and 2016 from the stroke centers in the RESUVal area. We provided age-adjusted analysis and multivariate models for determining the role of sex in the outcome measures.RESULTS: After age-adjustment, women presented more moderate to severe stroke at admission with more proximal occlusions. Among tPA eligible patients, the therapeutic strategy and in-hospital hemorrhagic complications were proportionally identical whatever the sex. The total ischemic time from onset symptom to thrombolysis did not differ from women to men. Age-adjusted 3-month mortality did not differ between women and men, and the determinants of mortality were age (relative risk [RR] 1.56 [1.37-1.78], p < 0.0001), proximal occlusion (RR 2.5 [1.88-3.33], p < 0.0001), and at least one complication (RR 2.43 [1.89-3.13], p < 0.0001). The determinants of poor functional outcome at 3 months were the sex (RR 1.22 [1.01-1.48] for women, p = 0.0385) and the occurrence of onset symptom in rural landscape (RR 1.26 [1.03-1.55], p = 0.0219) compared to urban landscape.CONCLUSIONS: We provided an exhaustive overview and real-life professional practices conditions in thrombolyzed AIS. Despite a later prehospital management in neurovascular units and more severe strokes at admission, women and men had both similar outcomes at hospital discharge and in 3-month survival, but women were associated to worst functional outcome at 3 months