Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : SPANDRELS (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université Charles-III de Madrid — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

The closed-loop control of unsteady turbulent flows requires efficient strategies to sense the flow state. Despite the challenge posed by the non-linearities and the large range of scales of turbulent flows, their ubiquitous nature motivates unabated research efforts. Over the last years, we have developed linear and non-linear flow estimation tools, with relevant laboratory applications. Nevertheless, the state of the art requires an intractable number of sensors, making the data acquisition and analysis unfeasible in a practical scenario. Moreover, the current paradigm of flow control requires continuous sensing and action in time, leading to very large data rates. Strangely, this seems at odds with what nature does. Insects estimate the flow surrounding them with a few event-based sensors embedded in their wings. Algorithms for event-based signal processing avoid aliasing without the need for high-frequency periodic sampling, reducing the amount of data needed to estimate complex temporal series. This could enable flow estimation with easy-to-handle and cheap-to-compute data. Furthermore, our recent findings show that many complex flows can be represented on low-dimensional manifolds. The availability of a reduced set of coordinates for state representation is a key enabler for the choice of a sparse set of sensors in space. This project will develop a novel framework for the estimation of turbulent and unsteady flows coupling manifold learning and event-based sensors. Tackling selected relevant laboratory problems, with and without control, we will reduce problem dimensionality and represent turbulent unsteady flows on low-dimensional manifolds, identify parsimonious methods for sensor choice and location in complex flows, and define a theoretical framework for turbulent-flow measurements from event sensors. Such a framework will be a key enabler for flow control and will open a novel research path in fluid mechanics.

Mots-clés : flow control,flow estimation,manifold learning,scientific machine learning,event based sensors
Projet ERC : Cancer-Harakiri (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Helmholtz-Gemeinschaft — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HGF
Type d'entité : Recherche
Financement ERC de l'entité : 1992235.0 €

Many aggressive cancers, such as pediatric high-risk Ewing sarcoma and alveolar rhabdomyosarcoma, are fueled by and addicted to chimeric oncogenic transcription factors (OTFs). So far, all attempts to suppress these potent OTFs largely failed, wherefore they are classified as undruggable. This proposal challenges this dogma with a novel strategy to leverage, rather than to suppress, the exquisite neomorphic DNA-binding preferences of OTFs to express therapeutic genes specifically in cancer cells. In pursuing three ambitious aims, this project goes far beyond an original proof-of-concept study in which we drove pediatric cancer cells in preclinical models into suicide (CANCER-HARAKIRI) via viral transfer of therapeutic genes induced by the specific interaction of an OTF with its neomorphic DNA-binding motif:  In Aim 1, we will genetically engineer and functionally test a conceptionally advanced viral vector in vivo with optimized general design, oncotropism, and therapeutic payload focusing on immuno-oncological aspects. In Aim 2, we will comprehensively map the regulome of OTFs using novel reporter cell lines, single-cell sequencing, RNAi-screens, and CRISPR interference to identify and validate OTF-inhibiting genes that can be co-targeted in our vector to prevent escape variants with low OTF-activity. In Aim 3, we will integrate insights from Aims 1&2 into our new vector design, translate our therapeutic approach from our main model entity, Ewing sarcoma, to other OTF-driven cancers, and set the stage for GMP-compliant virus production and initiation of a clinical trial.  This integrative approach combines expertise and technology across disciplines with original experimental tools to afford a comprehensive understanding of the OTF-regulome, and to rationally design and leap ahead an innovative therapeutic concept, which has the potential to revolutionize the therapy of metastatic OTF-driven cancers. Thus, we will exploit the undruggable to promote CANCER-HARAKIRI.

Mots-clés : ewing sarcoma,pediatric oncology,virotherapy,oncolytic viruses,oncogenic fusions,neomorphic dna binding preferences
Projet ERC : APPAC IV (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Varsinais-suomen hyvinvointialue — Pays : Finlande — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1186310.0 €

Acute appendicitis is one of the most common surgical emergencies worldwide. For over a century, surgical removal of the appendix has been considered the only treatment option. Landmark trials by our experienced APPAC team have shown that imaging confirmed uncomplicated acute appendicitis is not a surgical emergency defining antibiotics to safe and effective also with only oral antibiotics avoiding unnecessary surgeries resulting in major cost and resource savings. However, the role of antibiotics remains a major knowledge gap. Our pilot APPAC III showed that even symptomatic treatment (placebo) may be sufficient warranting a larger noninferiority trial to assess whether antibiotics are needed in the treatment of uncomplicated acute appendicitis. APPAC IV is a randomized double-blind multicenter clinical trial comparing oral moxifloxacin with placebo in an outpatient setting. APPAC IV aims to evaluate whether antibiotics and hospitalization or both can be omitted in the treatment of uncomplicated appendicitis further significantly increasing cost savings and patient satisfaction; almost 2/3 of all appendectomies (approx. 550.000 operations in Europe), the associated hospital stays, and potentially also antibiotics could be avoided. The growing global health issue of antibiotic resistance threatens effective prevention and treatment of infections. Studies on rational use of antibiotics are warranted and especially in the changing treatment paradigm of such a common disease. The translational MAPPAC II (Microbiology APPAC) substudy includes serum, rectal and fecal samples of the APPAC IV trial patients pre- and post-therapy. The effects of antibiotics on gut microbiota, gut health and correlation to serum cytokines are evaluated in this unique real-life randomized patient cohort. The development of antibiotic resistance and the underlying bacterial defence mechanisms will be evaluated in E. coli and microbiome using microbial single-cell technologies and metagenomics.

Mots-clés : appendicitis uncomplicated acute appendicitis antibiotic therapy symptomatic therapy placebo appendiceal microbiota antibiotic resistance gut microbiota dysbiosis
Projet ERC : AI-Next (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Helmholtz-Gemeinschaft — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : HGF
Type d'entité : Recherche
Financement ERC de l'entité : 264649.16 €

Medical artificial intelligence (AI) holds immense promise for transforming radiology by introducing advanced diagnostic capabilities. Yet, traditional AI models face challenges like extensive data annotation needs and are task-specific with limited generalizability to different scenarios. This problem of robust and label-efficient generalization continues to be a key translational challenge for medical AI models and has prevented their broad uptake in real world healthcare settings. AI-Next will build on the recent advent of foundation models, providing a unique opportunity to rethink the development of medical AI and overcome the challenges of traditional AI models. At the core of AI-Next stands the development of a state-of-the-art visual foundation model (VFM) that learns generalizable representations from unlabelled radiology scans and provides a basis for label-efficient model adaptation in several applications. The VFM will focus on neuro-radiology and be trained with data at unprecedented scale including >200,000 brain computed tomography and magnetic resonance imaging scans from population-based and disease specific cohorts, leveraging self-supervised learning. The VFM will be adapted with a limited set of explicit labels to a range of tasks with clinical significance. This includes the screening and triage of scans for emergency findings, longitudinal disease activity assessment, risk prediction of dementia, forecasting of disease evolution and finally automated radiology reporting. Moreover, the VFM will be employed for image processing tasks, including generating super-resolution images to enhance diagnostic capabilities. Overall, AI-Next will represent a crucial step towards more generalisable, accurate and label-efficient AI in neuro-radiology, offering significant potential for improving diagnostics, clinical decision-making as well as patient outcomes, and its open-source innovations will serve as a blueprint for the broader field of radiology.

Mots-clés : radiology neuroradiology neuroimaging artificial intelligence foundation models
Projet ERC : Unstable Genome (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Helmholtz-Gemeinschaft — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HGF
Type d'entité : Recherche
Financement ERC de l'entité : 2000000.0 €

Cancer starts years to decades before diagnosis. Yet, early stages of tumor development are poorly understood. In particular, the precancer landscape of chromosome instability (CIN) is understudied, due to the scarcity of precursor lesions and methods to analyze CIN in single cells. The research presented here will address how the instability rate of the genome, followed by selection, determines whether a CIN-driven tumor arises. I hypothesize that the onset of CIN is the tipping point that governs the fate of a precancer clone. Using spatially aware and single-cell multiome methods that we developed, we will dissect essential factors that lead to malignancy via CIN, including extrachromosome circular DNAs. We will analyze two solid tissues at opposite ends of the cell turnover spectrum, which deeply diverge in regeneration capacity, clonality and constraints, namely the brain and the gut.  Aim 1 will assess the instability rate of the human genome using the postmortem brain as a model tissue. CIN patterns vary greatly in frequency and type across brain tumors; we will compare the background incidence of fundamental types of CIN in normal cells to CIN signatures after selection has acted, in diagnosed tumors of the same lineages.  Aim 2 will dissect CIN in space and time from the cell of origin, to identify what discriminates nascent clones that may cause cancer from those that do not. Timecourses in mouse models will reveal how CIN contributes to selective advantages and how nascent CIN-positive clones escape elimination by immune cells.  Aim 3 leverages human precursor lesions to probe the role of CIN as a central gatekeeper between tumor initiation and growth, using the gut as a longitudinal in vivo system to directly capture the tipping point to cancer. Unstable Genome will provide the first systematic quantification of the early development of chromosomally unstable lesions and reveal the principles that govern CIN-driven clone expansion in vivo.

Mots-clés : chromosome instability,genome instability,chromothripsis,cancer,pre cancer,early cancer
Projet ERC : VAPOURISE (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Université de Cambridge — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2965779.0 €

Disruptive new technologies for generation, storage and use will be essential to drive the green energy transition and progress society. Such technologies must be developed with sustainable, high-throughput manufacturing, and maintain high-performance over long-term use. VAPOURISE will provide solutions by developing ground-breaking technologies for electricity generation (high-performance photovoltaics), storage (photoelectrochemical production of high-value chemicals) and use (quantum computing) based on scalable and modular perovskite semiconductors. These applications will be enabled by development of scalable vapour-based deposition methods to deposit epitaxial layers of 3D, 2D and 3D/2D halide perovskites, and low-temperature chalcogenide perovskites. Halide perovskite configurations will include multi-quantum-well stacks and functional spacer molecules imparting passivation of defects, optical activity and/or chirality. The development of these materials will be uniquely driven by fundamental understanding of properties through cutting-edge multi-modal operando toolkits and automated machine-learning driven analyses. These approaches will unveil unprecedented connections between the photophysical, chemical, structural and device performance/stability properties on different length (nanometre to centimetre) and time (femtosecond to months) scales, in turn guiding fabrication. This unique combination of controlled processing and fundamental understanding will allow VAPOURISE to generate highly stable, scalable, and efficient triple-junction photovoltaics (efficiency >35%), triple-junction photo-electrodes driving CO2 reduction to ethylene (>10%), and reliable electrically pumped single-photon emitter arrays. The ambitious, interdisciplinary programme will revolutionise semiconductor development in which science-led understanding rapidly drives device engineering, leading to disruptive breakthroughs in energy, photo-electrochemistry and quantum technologies.

Mots-clés : halide perovskites,photovoltaics,optical spectroscopy,photo electrochemical cells,single photon emission,vacuum deposition,quantum computing
Projet ERC : EcoIslam (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Université d'Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UvA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

In Indonesia, the world’s most populous Muslim country, a so-called mediated “eco-jihad” (Zbidi 2013) has emerged. Today, Islamic ‘green’ pop musicians, eco-YouTube-imams, films, and social media (campaigns) find inspiration in religious scriptures to encourage Muslims to engage in environmental protection. For eco-conscious Muslims, the Qur’an is “the ultimate green-guide”. (Zaufishan 2013) The Qur’an is read by Muslims for spirituality, but the Qur’an also provides countless examples that could be read as “an Islamic call to combat climate change”. (Zaufishan 2013) Indonesian cultural producers are now “acting on these historic edicts with an eye toward the consequences of global warming for the world’s most populous Muslim country”. (Bodetti 2018) Taking Indonesia as a case study and focusing on the main outlets of Islamic eco-cultural discourse (film, TV, popular music, social media), this project aims to investigate an Islamic eco-cultural approach to climate change. EcoIslam asks: What does an Indonesian Islamic eco-cultural approach to climate change entail and how is it developed, negotiated, and contested? Through a multi-method research design and a conceptual approach that draws on an ‘affective eco-governmentality of care’, the project analyzes three levels (institutions, texts, publics) through four work packages. The project analyzes (level 1, work package 1: institutions) how Islamic institutions play a key role in the development of Islamic eco-media; (level 2, work package 2: texts) how subjects are affectively addressed as ‘caring’ eco-conscious Muslim citizens – as khala’ifa of the earth – by these media ‘texts’ (i.e. media products); (level 3, work package 3: publics) how people negotiate specific ‘green’ ‘Islamic’ subject positions through their engagement with media texts; and (synthesis, work package 4: relations) how institutions, texts, and publics relate.

Mots-clés : media,popular culture,indonesia,islam,climate change,environment
Projet ERC : UNIYARNS (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Madrid Institute for Advanced Studies — Pays : Espagne — Rôle : PI
Acronyme de l'entité : IMDEA
Type d'entité : Recherche
Financement ERC de l'entité : 1999338.0 €

Yarns are a natural architecture to assemble small building blocks into macroscopic objects and are thus woven in our history, from fabrics of natural fibres in ancient times to fibres of synthetic polymers developed in the 20th century for lightweight applications. Humankind’s new building blocks are nanomaterials, with superlative properties in all areas (optoelectronic, catalytic, transport, structural) relevant for global challenges related to energy use, storage and conversion. UNIYARNS proposes a new universal route for gas-phase assembly of one-dimensional nanomaterials into kilometric yarns, applicable to materials central to energy applications (metal oxides, semiconductors and semi-metals), and reaching high volume fractions without use of processing solvents or polymers. The strategy is to grow ultra-long nanomaterials by atmospheric-pressure floating catalyst chemical vapour deposition (FCCVD) at sufficiently high concentration for them to entangle and form aerogels suspended in the gas phase that can then be directly drawn as continuous, macroscopic yarns. The first objective of the project is to demonstrate the generality of the FCCVD synthesis process, with a particular focus on metal oxide nanowires. A further objective is to study the kinetics and reaction paths in 1D nanomaterials synthesis with floating catalyst in order to understand the exceptionally fast growth rate inherent to this synthesis mode and to explore its boundaries of selectivity and conversion. The next objective is to describe aerogel formation by determining factors at the aerogel network level and at the molecular-scale level that govern gas-phase assembly. The final objective is to establish clear structure-property relations for nanostructured yarn systems to overcome the current envelope of materials properties through the low charge transport resistance and high toughness of their network structure.

Mots-clés : yarn,network material,chemical vapour deposition,nanowire
Projet ERC : EDGE (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : London School of Economics and Political Science — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : LSE
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1901095.0 €

The EDGE project is a philosophical study of the concept of an edge or boundary as it relates to the universe on the largest scale. But what does an edge refer to when the region is the entire universe? Despite widespread use of this boundary, there is little agreement about how to interpret it: some have called it “heuristic”; others “incoherent’; and still others a “law of nature”. There is no unified perspective on the general nature of boundary conditions in physics, and no agreed-upon conceptual foundation for how they apply to the entire universe.  We propose an ambitious but credible programme to overcome this challenging problem. We begin with a classification of the properties, uses, and epistemology of boundaries as they are generally used in physics, from the separation of mind and world to the interaction of two coupled systems, and over a wide range of theories. We then turn to cases in which the boundary of the entire universe plays a substantial role in the frontiers of physics. These threads will form the basis for a general framework for understanding boundaries in physics, establishing shared features as well as the dimensions along which they differ. This will finally allow us to produce the first general analysis of what the edge of the universe means in physics: its ontology, its uses, and how we know about it.  The pay-off of this project is a path-breaking new analysis in philosophy, which establishes a major new account of our universe at its farthest fringes. In physics, it stands to help clarify the pathway for those physicists actively developing the frontiers of science by using boundaries of this kind. More generally, it will establish a novel, accessible account of our place in the universe in comparison to the farthest reaches of our imagination. The ambition of the EDGE project is to produce such an account that is philosophically rigorous, scientifically sound, and still manages to capture a sense of wonder about the cosmos.

Mots-clés : philosophy of science,philosophy of physics,boundary conditions,cosmology
Projet ERC : CulturAL (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université d'Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UvA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1998926.0 €

My research project focuses on natural language processing (NLP), an area of artificial intelligence concerned with automatic interpretation and generation of human language. NLP is well-known for its widely-used applications, such as machine translation (MT), text mining, question answering or dialogue systems. Much of this technology is now powered by large language models (LLMs), such as ChatGPT, which take a natural language instruction or question as input, and generate a fluent response. Due to their impressive performance in a range of tasks and their ease of use, in less than a year they received a wide adoption in many societal contexts (education, journalism, healthcare and others), and became the central paradigm in the field of NLP. The development of LLMs, however, requires access to a vast amount of data and resources in a given language, as well as considerable computational infrastructure. As a result, these models are in practice limited to a handful of widely-spoken languages, leaving over 6,000 of the world’s languages and dialects without access to language technology. Furthermore, research on LLM alignment, which aims to ensure the safety of their use, has been almost exclusively directed toward the English-speaking world. Taken together, these problems lead to a major inequity in today’s language technology and artificial intelligence more broadly.  Taking a step towards a more inclusive and equitable language technology, this project will develop a novel methodology for cross-lingual transfer of LLMs to a wide-range of (low-resource, understudied) languages and dialects, and their alignment to diverse cultural contexts. The project will, therefore, advance multilingual NLP technology, extending its reach to populations currently underserved by NLP and making it safe for them to use.