Stroke but no hospital admission: Lost opportunity for whom?

International audienceTo counter the spread of COVID-19, the French government imposed several stringent social and political measures across its entire population. We hereto assess the impact of these political decisions on healthcare access in 2020, focusing on patients who suffered from an ischemic stroke. We divide our analysis into four distinct periods: the pre-COVID-19 pandemic period, the lockdown period, the “in-between” or transitional period, and the shutdown period. Our methodology involves utilizing a retrospective dataset spanning 2019–2020, an exhaustive French national hospital discharge diagnosis database for stroke inpatients, integrated with income information from the reference year of 2019. The results reveal that the most affluent were more likely to forgo medical care, particularly in heavily affected areas. Moreover, the most disadvantaged exhibited even greater reluctance to seek care, especially in the most severely impacted regions. The data suggest a loss of opportunity for less severely affected patients to benefit from healthcares during this lockdown period, regardless of demographic, location, and socioeconomic determinants. Furthermore, our analysis reveals a notable discrepancy in healthcare-seeking behavior, with less affluent patients and seniors (over 75 years old) experiencing slower rates of return to healthcare access compared to pre-pandemic levels. This highlights a persistent gap in healthcare accessibility, particularly among socioeconomically disadvantaged groups, despite the easing of COVID-19 restrictions

Quantifying the mixing of trade‐wind cumulus during the NEPHELAE‐EUREC4A field campaign with remotely piloted aircraft

International audienceAbstract During the Network for studying Entrainment and microPHysics of cLouds using Adaptive Exploration (NEPHELAE)–ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte (EUREC4A) field campaign in January and February 2020 in Barbados, remotely piloted aircraft (RPA) were implemented to characterise the structures of trade‐wind cumulus for a total of 40 flights. Two observation methods were tested: one making racetracks to get statistics on the cumulus clouds and a new sampling strategy using sensor‐driven adaptive sampling to track an individual cloud autonomously throughout its lifetime. It appears from the statistics that there are two types of cohabiting cloud population, with small‐diameter clouds (transect lengths less than 500 m) being less buoyant than larger clouds (transect lengths greater than 500 m). Also, this statistical study shows that cumulus clouds do not have an adiabatic core. These results are compared with individual clouds tracked by adaptive sampling, which also show that the core of the cumulus clouds is diluted by the environment. A comparison with high‐resolution large‐eddy simulations shows that these numerical studies tend to underestimate mixing in the whole cloud

Use of Large-Eddy simulations to design an adaptive sampling strategy to assess cumulus cloud heterogeneities by Remotely Piloted Aircraft

Trade wind cumulus clouds have a significant impact on the earth's radiative balance, due to their ubiquitous presence and significant coverage in subtropical regions. Many numerical studies and field campaigns have focused on better understanding the thermodynamic and macroscopic properties of cumulus clouds with ground-based and satellite remote sensing as well as in-situ observations. Aircraft flights have provided a significant contribution, but their resolution remains limited by rectilinear transects and fragmented temporal data of individual clouds. To provide a higher spatial and temporal resolution, Remotely Piloted Aircraft (RPA) can now be employed for direct observations, using numerous technological advances, to map the microphysical cloud structure and to study entrainment mixing. In fact, the numerical representation of mixing processes between a cloud and the surrounding air has been a key issue in model parameterizations for decades. To better study these mixing processes as well as their impacts on cloud microphysical properties, the manuscript aims to improve exploration strategies that can be implemented by a fleet of RPAs. Here, we use a Large-Eddy simulation (LES) of oceanic cumulus clouds to design adaptive sampling strategies. An implementation of the RPA flight simulator within high-frequency LES outputs (every 5 s) allows to track individual clouds. A Rosette sampling strategy is used to explore clouds of different sizes, static in time and space. The adaptive sampling carried out by these explorations is optimized using one ors two RPAs and with or without Gaussian Process Regression (GPR) mapping, 1by comparing the results obtained with those of a reference simulation, in particular the total liquid water content (LWC) and the LWC distributions in a horizontal cross section. Also, a sensitivity test of lengthscale for GPR mapping is performed. The results of exploring a static cloud are then extended to a dynamic case of a cloud evolving with time, to assess the application of this exploration strategy to study the evolution of cloud heterogeneities

Study of thermodynamic properties of trade-wind cumulus clouds with Remotely Piloted Aircrafts during the EUREC4A field campaign

International audienceTrade wind cumulus clouds have a significant impact on the earth's radiative balance, due to their extensive coverage in subtropical regions but due to their characteristic size are still parameterized.<br>The feedback of low clouds on the climate system as well as biases still existing in their representation of Global Climate Models (GCMs) results in a climatic response with relatively large uncertainty and induce a significant divergence in GCMs. Many studies and campaigns have focused on a better understanding of the thermodynamic and macroscopic properties of cumulus clouds with ground-based and satellite-based remote sensing<br>and also in-situ observations from aircraft flights, but few provide information on the three-dimensional properties of individual cumulus clouds. Our understanding of cumulus clouds is also based on high-resolution numerical simulations (LES: 25m, 5m of resolution) that reproduce the<br>average characteristics of cumulus clouds fairly reliably, yet these simulations still depend on parametrizations (turbulence and microphysics).<br>The development of a fleet the sampling of RPAs (Remotely Piloted Aircraft) contributes to the increase in the resolution of the sampling of the evolution of cloud microphysical properties. Recent studies have permitted to have an autonomous adaptive sampling and a mapping using Gaussian<br>Process Regression to interpolate missed values during exploration.<br>An experimental strategy has been developed and tested in a cumulus cloud field simulated in a LES simulation with the Meso-NH model by implementing a simulator of RPA flights. During the EUREC4A field campaign in Barbados in January-February, more than forty RPAs flights have been conducted and thermodynamic properties of cumulus clouds were studied in three dimensions using miniaturized instruments installed on-board (PTU probe, cloud sensor). We validate first the results of cloud sensor with an other microphysics instrument. Several clouds were followed for about ten minutes and their thermodynamic evolution have been compared to cumulus clouds simulated in the LES

Study of thermodynamic properties of cumulus clouds: from strategy design in numerical simulation to application during a measurement campaign

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Field report: deployment of a fleet of drones for cloud exploration

International audienceDrones are commonly used for many civil applications and the procedures to operate them have evolved during the past years to make them accessible to those with limited piloting skills in several scenarios. However, the deployment of a fleet in the context of scientific research can lead to complex situations that require an important preparation in terms of logistics, permission to fly from authorities, and coordination during the flights. This paper is a field report of the flight campaign held end of January 2020 at the Barbados Island as part of the NEPHELAE project. The main objectives of the project were to fly into trade wind cumulus clouds to understand the microphysical processes involved in their evolution, as well as to provide a proof of concept of sensor-based adaptive navigation patterns to optimize the data collection. After presenting the overall flight strategy and the context of operation, the main challenges and the solutions to address them will be presented, to conclude with the evaluation of some technical evolution developed from these experiments

Experimental flights of adaptive patterns for cloud exploration with UAVs

International audienceThis work presents the deployment of UAVs for the exploration of clouds, from the system architecture and simulation tests to a real-flight campaign and trajectory analyzes. Thanks to their small size and low altitude, light UAVs have proven to be adapted for in-situ cloud data collection. The short life time of the clouds and limited endurance of the planes require to focus on the area of maximum interest to gather relevant data. Based on previous work on cloud adaptive sampling, the article focuses on the overall system architecture, the improvements made to the system based on preliminary tests and simulations, and finally the results of a field campaign. The Barbados experimental flight campaign confirmed the capacity of the system to map clouds and to collect relevant data in dynamic environment, and highlighted areas for improvement

Multimarker proteomic profiling for the prediction of cardiovascular mortality in patients with chronic heart failure.

Risk stratification of patients with systolic chronic heart failure (HF) is critical to better identify those who may benefit from invasive therapeutic strategies such as cardiac transplantation. Proteomics has been used to provide prognostic information in various diseases. Our aim was to investigate the potential value of plasma proteomic profiling for risk stratification in HF. A proteomic profiling using surface enhanced laser desorption ionization - time of flight - mass spectrometry was performed in a case/control discovery population of 198 patients with systolic HF (left ventricular ejection fraction <45%): 99 patients who died from cardiovascular cause within 3 years and 99 patients alive at 3 years. Proteomic scores predicting cardiovascular death were developed using 3 regression methods: support vector machine, sparse partial least square discriminant analysis, and lasso logistic regression. Forty two ion m/z peaks were differentially intense between cases and controls in the discovery population and were used to develop proteomic scores. In the validation population, score levels were higher in patients who subsequently died within 3 years. Similar areas under the curves (0.66 - 0.68) were observed for the 3 methods. After adjustment on confounders, proteomic scores remained significantly associated with cardiovascular mortality. Use of the proteomic scores allowed a significant improvement in discrimination of HF patients as determined by integrated discrimination improvement and net reclassification improvement indexes. In conclusion, proteomic analysis of plasma proteins may help to improve risk prediction in HF patients

Circulating Long Noncoding RNA, LIPCAR, Predicts Survival in Patients With Heart Failure

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