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The COVID-19 pandemic is having a dramatic impact on societies and economies around the world. With various measures of lockdowns and social distancing in place, it becomes important to understand emotional responses on a large scale. In this paper, we present the first ground truth dataset of emotional responses to COVID-19. We asked participants to indicate their emotions and express these in text. This resulted in the Real World Worry Dataset of 5,000 texts (2,500 short + 2,500 long texts). Our analyses suggest that emotional responses correlated with linguistic measures. Topic modeling further revealed that people in the UK worry about their family and the economic situation. Tweet-sized texts functioned as a call for solidarity, while longer texts shed light on worries and concerns. Using predictive modeling approaches, we were able to approximate the emotional responses of participants from text within 14% of their actual value. We encourage others to use the dataset and improve how we can use automated methods to learn about emotional responses and worries about an urgent problem.

International audience; The queen conch, Strombus (Lobatus) gigas (Linnaeus 1758) is a marine gastropod mollusk. This is an endemic species from the Caribbean Sea, it is present in the water of 37 countries of the Caribbean Sea. It is a valuable marine benthic invertebrate of significant commercial importance in the Caribbean, with an important export trade worth millions of US dollars. The main international markets for conch meat are the USA (79% of the trade) and the French West Indies, with 20%). Also, conch is also consumed locally and its shell and other products sold as curios to visiting tourists in the Caribbean. However, populations have been depleted throughout the Wider Caribbean by overfishing. This species is indexed in Apendix II of CITES, the Convention on International Trade in Endangered Species of Wild Fauna and Flora). The aim of this work was to show results of a scientific collaboration in the Caribbean with various Universities, showing the effect of climatic change on abundance of larvae in this region, in the process of shell calcification and concentration of pollution by microplastics in various sites from the Caribbean.

Interleukin‐7 (IL7) plays a nonredundant role in T cell survival and homeostasis, which is illustrated in the severe T cell lymphopenia of IL7‐deficient mice, or demonstrated in animals or humans that lack expression of either the IL7Rα or γ c chain, the two subunits that constitute the functional IL7 receptor. Remarkably, IL7 is not expressed by T cells themselves, but produced in limited amounts by radio‐resistant stromal cells. Thus, T cells need to constantly compete for IL7 to survive. How T cells maintain homeostasis and further maximize the size of the peripheral T cell pool in face of such competition are important questions that have fascinated both immunologists and mathematicians for a long time. Exceptionally, IL7 downregulates expression of its own receptor, so that IL7‐signaled T cells do not consume extracellular IL7, and thus, the remaining extracellular IL7 can be shared among unsignaled T cells. Such an altruistic behavior of the IL7Rα chain is quite unique among members of the γ c cytokine receptor family. However, the consequences of this altruistic signaling behavior at the molecular, single cell and population levels are less well understood and require further investigation. In this regard, mathematical modeling of how a limited resource can be shared, while maintaining the clonal diversity of the T cell pool, can help decipher the molecular or cellular mechanisms that regulate T cell homeostasis. Thus, the current review aims to provide a mathematical modeling perspective of IL7‐dependent T cell homeostasis at the molecular, cellular and population levels, in the context of recent advances in our understanding of the IL7 biology.

Copernicus is the European programme for monitoring the Earth. It consists of a set of systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data and information processed and disseminated puts Copernicus at the forefront of the big data paradigm, giving rise to all relevant challenges, the so-called 5 Vs: volume, velocity, variety, veracity and value. In this short paper, we discuss the challenges of extracting information and knowledge from huge archives of Copernicus data. We propose to achieve this by scale-out distributed deep learning techniques that run on very big clusters offering virtual machines and GPUs. We also discuss the challenges of achieving scalability in the management of the extreme volumes of information and knowledge extracted from Copernicus data. The envisioned scientific and technical work will be carried out in the context of the H2020 project ExtremeEarth which starts in January 2019

International audience; Les ouvrages de protection contre la houle ou contre les submersions couvrent 17% des côtes françaises, soit 1210 km. Compte tenu du changement climatique, notamment l'élévation du niveau de la mer, et de la présence démographique importante, les enjeux sur le littoral sont considérables. Dans ce contexte, le projet de recherche collaborative DIGUE 2020 a été conçu de façon à construire une plateforme de recherche sur les digues maritimes en associant, en parallèle, des travaux de recherche sur l'auscultation et la durabilité de l'ouvrage, ainsi qu'une étude sur la perception du risque de submersion marine. Les connaissances développées et partagées au moyen de la plateforme de recherche envisagée ont pour objectif, à moyen terme, le développement de projets de confortement ou d'élaboration de nouvelles digues d'une conception durable et, sur le long terme, le développement d'une culture commune sur le risque de submersion marin

International audience

Inflammation of the preterm lungs is key to the pathogenesis of bronchopulmonary dysplasia (BPD), whether it arises as a consequence of intrauterine inflammation or postnatal respiratory management. This review explores steroidal and non-steroidal therapies for reducing neonatal pulmonary inflammation, aimed at treating or preventing BPD.

Genetic differences contribute to variations in the immune response mounted by different individuals to a pathogen. Such differential response can influence the spread of infectious disease, indicating why such diseases impact some populations more than others. Here, we study the impact of population-level genetic heterogeneity on the epidemic spread of different strains of H1N1 influenza. For a population with known HLA class-I allele frequency and for a given H1N1 viral strain, we classify individuals into sub-populations according to their level of susceptibility to infection. Our core hypothesis is that the susceptibility of a given individual to a disease such as H1N1 influenza is inversely proportional to the number of high affinity viral epitopes the individual can present. This number can be extracted from the HLA genetic profile of the individual. We use ethnicity-specific HLA class-I allele frequency data, together with genome sequences of various H1N1 viral strains, to obtain susceptibility sub-populations for 61 ethnicities and 81 viral strains isolated in 2009, as well as 85 strains isolated in other years. We incorporate these data into a multi-compartment SIR model to analyse the epidemic dynamics for these (ethnicity, viral strain) epidemic pairs. Our results show that HLA allele profiles which lead to a large spread in individual susceptibility values can act as a protective barrier against the spread of influenza. We predict that populations skewed such that a small number of highly susceptible individuals coexist with a large number of less susceptible ones, should exhibit smaller outbreaks than populations with the same average susceptibility but distributed more uniformly across individuals. Our model tracks some well-known qualitative trends of influenza spread worldwide, suggesting that HLA genetic diversity plays a crucial role in determining the spreading potential of different influenza viral strains across populations., Author summary Levels of immunity to strains of H1N1 influenza can vary, depending on the individual. This strongly influences how the disease spreads in a population. Accounting for such variations is a major challenge for the epidemiology of infectious diseases. We study the effect of population-level genetic heterogeneity on the epidemic spread of different strains of H1N1 influenza. We model the immune response of specific ethnicities to a number of H1N1 viral strains, using this information to study disease spread for these (ethnicity, viral strain) epidemic pairs. Our results show that larger genetic diversity at the level of immune response, leading to the presence of susceptibility sub-populations with a broad distribution of susceptibilities, protects against the spread of influenza in a population. We also show that populations with a small number of highly susceptible individuals, but with a large number of less susceptible ones, should exhibit smaller outbreaks than populations with the same average susceptibility but where it is more uniformly distributed. Our work captures some qualitative trends of influenza spread worldwide, providing a first attempt at understanding how susceptibility heterogeneities arising from variations in immune response determine disease spread in populations.