Within urban areas, the management of mobility, both the mobility of people and urban logistics and the management of energy, from its production to its consumption, including its distribution and storage, can no longer be thought of. independently, in a logic of silos, if these territories want to achieve the objectives of controlling the energy, environmental and social impact which are those of the Smartcities. This is in particular the case for the deployment of infrastructures and electrical transport equipment supplied by territorial electricity networks of the Smartgrid type where each actor has several roles (consumption, production, storage). To this end, the scientific project of the future HYPHES laboratory is to propose and validate systemic models of Smartcities and algorithms for the joint management of these networks. The urban territory is considered here as a set of players (homes, businesses, administrations, etc.) interconnected by a set of networks of different kinds (mobility and transport networks and infrastructures, energy and fluid distribution networks, waste networks). These networks support flows (energy, mobility of people, logistics flows) and must achieve quality of service specific to each area. This quality of service must be considered above all from the point of view of the user (time, cost, comfort, health, inclusiveness, etc.), but also transversally in terms of maintenance costs and environmental impact. The systemic aspect is induced by the interaction of these different networks with each other, and with the different actors of the territory. An example of interaction between Smartgrids and electric vehicles is on the one hand the mobility of electric vehicles to reach energy distribution points, and on the other hand the possible use of the batteries of these vehicles as storage of electric energy. temporary for the actors who provide parking without degrading the mobility service they provide. The heart of the scientific project of the HYPHES laboratory is therefore a systemic vision of urban networks. In addition, the existing collaborations with territorial and private actors from DAVID and DCBrain will allow a definition of the metrics and objectives to be retained and the use cases and experiments which will make it possible to validate and enhance the work of the laboratory's scientific project. Finally, the scientific project will be built on a realistic vision of the availability of industrial and territorial data, considering the need for the proposed methods to be able to adapt to the quantity, quality and heterogeneity of the available data by controlling the quality and the reliability of the results obtained. The first objective of the laboratory's scientific project will thus be to propose and validate a systemic modeling of the interaction of the urban networks mobilized, and their uses. This modeling activity will be continuous and will evolve during the various projects carried out. The lab will initially focus on energy, mobility and logistics networks. He will lead the theoretical and analytical study of these problems and their resolution through the cooperation of different approaches: algorithmic theory of graphs and games, combinatorial optimization, AI and Machine Learning. This work will be validated on the use cases selected, and through experimentation within partner territories. The design and production of first prototypes of software solutions will finally be produced. The research program will consist of the systemic modeling of urban networks and the definition of real use cases, the integration of territorial data, IA and RO resolution methods for the management and resilience of these networks, and finally the implementation of innovative software prototypes.

Sepsis and COVID-19 are both placing a major burden on societies and populations worldwide. Deregulated host response to infection is the hallmark supporting the routine use of corticosteroids (CS), a low-cost and highly efficient class of immuno-modulators, in sepsis/COVID-19. Stratifying patients based on individual immune response may improve the balance of benefit to risk of CS treatment. This proposal will integrate different approaches to define the CS sensitivity/resistance of individual patients. The partners will elaborate signatures from different characterizations of biological systems in patients with sepsis/COVID-19. Targeted approaches will define whether characteristics at the level of DNA, RNA, proteins such as cytokines and hormones, or metabolite compounds, support predicting individual patient’s CS responsiveness. Methods of artificial intelligence will integrate the high dimensional multi-level data from previous studies of this consortium and from data to be newly generated. An exploratory adaptive trial will include patients with sepsis/COVID-19 in multiple arms based on novel CS responsiveness signatures to be tested. Within each biomarker-defined cohort, patients will be randomized to receive corticosteroids or placebo allowing the evaluation of the efficiency of signatures elaborated by the partners. Signatures of CS responsiveness will be integrated for predictive enrichment of CS sensitivity and resistance of each individual patient defining personalized treatment rules, and thereby improving their chance to survive in good health. We will test the robustness of the personalized corticotherapy across subsets of patients based on gender, social categories and ethnicity. We will also ensure that the proposed personalized corticotherapy for sepsis/COVID-19 can be accessed for routine care of patients in low- and middle-income countries.

The quality of the air we breathe is a central concern of individuals living in urban and suburban areas. Millions of people are exposed every day to air pollution at high levels. The impact of such pollution on the human health is extremely alarming. Particularly, WHO and IARC have classified air pollution, including fine particles, as certain carcinogenic. Understanding the totality of exposures to air pollutants over the course of our daily life is a key concern to reduce the risk of some major diseases. However, the ability to acquire high-quality, relevant, and useful individual’s exposure data is challenging. Currently available air pollution fixed station networks allow to only account for background air pollution and less frequently proximity air pollution from road traffic. As a result, the measurements made through this kind of network typically provide the average exposure to air pollution in a specific geographical zone. In particular, they fall short to quantify the real individual’s exposure with respect to his/her indoor/outdoor daily life activities in different settings, such as transport, work, dwellings, etc. Nowadays, an increasing number of wearable and lightweight environmental sensors have emerged, enabling a continuum measurement of the real personal exposure anywhere at anytime. Such an evolution has been the main enabler of providing new solutions for data acquisition, namely community-based participatory sensing where citizens contribute data to the system with the purpose of sharing events of interest within the community. This technology has recently gained a great interest among the actors of environmental science in public, associative, and private sectors, while stimulating a wide range of research projects worldwide. Building on top of such a technology evolution, Polluscope aims at bringing together experts from environmental, metrology, epidemiological, and data sciences while providing methodologies, techniques, and tools – expected to drastically change the way individual’s exposure and exposure variability are measured, perceived, and evaluated. Such measurements will not only consider gaseous pollutants (Ozone, NO2), but also particulates (via particulate matter and black carbon) and those typical of indoor environments (VOC) – providing a representative overview of the air pollution. Gaining such enriched insights into individual’s exposure will contribute towards reducing individual risks of some diseases by changing their behavior. This will end up in a solid, invaluable, and vital societal impact namely, saving life and improving the individual well-being. To achieve these objectives, a novel infrastructure for real individual’s exposure data acquisition, processing, and analysis will be develope. For this to be done, several scientific and technical challenges come into the picture. The data are collected at a high frequency and might be massive and noisy. Therefore, the system must be able to process them efficiently, while taking into account both their velocity and their uncertainty. More importantly, it has to offer microenvironment and user’s activity recognition, through integration with external spatiotemporal resources. An efficient data collection and analysis will provide an insightful knowledge on individual’s exposure over his/her daily life activities, and will enable conducting analytical queries, novel risk assessment modeling, mining and comparing profiles of pollution exposures, and so on. Therefore, it is evident that a robust, efficient, and powerful data science technology is crucial. Lastly, Polluscope will be evaluated under real-world use cases. Several type of population will be targeted by the data acquisition campaign. Both diseased and healthy subjects will be involved to conduct an epidemiological study relating air pollution exposure to health on the one hand, and volunteer participants for the crowd sensing on the other hand.