Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : AI-Comp (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Libera Università Internazionale degli Studi Sociali — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1877793.0 €

AI algorithms are increasingly being used in markets. It is generally agreed that they create a wealth of value by facilitating economic interactions. However, there are also concerns that when applied to market environments, they could lessen competition and harm consumers. This proposal sets forth an important first step of an articulated research program on AI and Competition. The proposal is structured as three organic projects focusing on two important classes of AI algorithms. The first class, price-setting algorithms, are tools that are used by online merchants to set retail prices. As the algorithms of each merchant interact in a common market, they can mutually learn to defeat competition. We plan to study how these collusive conducts are learned to propose a direction for potential regulation. The second class, recommender systems (RS), are ubiquitous online algorithms that suggest products and content to consumers that are based on their current and past behaviour. For example, Amazon.com uses recommender systems to rank products on search result pages, or to suggest other products that the user may be interested in. The aim of the proposed research project is to assess whether RSs are pro-competitive or anti-competitive by studying how firms endogenously react to RSs in terms of price, product design and entry/exit decisions. Methodologically, these projects rigorously combine ingredients from economics and computer science (CS), departing from the stylized modelling approach of the literature and instead are aimed at analysing state-of- the-art algorithms developed in the CS literature. Algorithms and their impact on market outcomes naturally require an interdisciplinary approach. Dealing with actual algorithms calls for new tools, to supplement the economist’s traditional analytical toolbox. For this reason, an essential part of this research proposal is the creation of a joint research agenda via a “research pod” with an associated scientific program.

Mots-clés : competing with ai,recommender systems,algorithmic pricing,algorithmic collusion,
Projet ERC : HyperModeLex (Horizon Europe, appel 2021, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Université de Bologne — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2494509.0 €

In 2018, the Government of New Zealand started a project called “Rules as Code”. In 2020, it proposed to OECD-OPSI the adoption of coding methodology (see Cracking Code report) to create a macro-schema of Law, legally binding, that generates legal text in natural language. It resembles a reverse engineering approach with respect to the predominant method. It is backed by legal theory and AI&Law literature, where the digitalization of Legal Sources is performed from the legal provisions, expressed in natural language, to its formal-logic representation (AI&Law, LegalXML). MIT, Stanford CodeX, Australia & Canada governments are investigating this new direction using language programming (e.g., Java, Python). The intuition seems fascinating, especially in the infosphere where digital artefacts (e.g., IoT, smart contract, AI) need consumable Law to take rapid decisions (e.g., COVID-19 pandemic) often without human intervention (e.g., bots). However, such an approach can jeopardize legal heritage, democratic principles, institutional foundations, in the context of civil-law theory and EU Law & Human Rights traditions. This approach seems to neglect 30 years of AI&Law literature, legal theory foundations, philosophy of law and language, to foster a model of technocracy and efficiency. As the topic calls for timely actions, we aim to create a solid legal theoretical framework to allow the serialization of Law in machine-consumable format while preserving legal soundness. The output is a digital legal system framework (HyperModeLex) that produces a traced process of digital law-making system, in machine-consumable format (XML, RDF, coding), legally binding, executable, suitable for connected infosphere artefacts (IoT, smart contract, software, bot) and in the meantime explicable to human, using dialogic legal design approach. We need an interdisciplinary ground-breaking project to assemble various competencies, different disciplines from human and computer sciences.

Mots-clés : legal informatics,ai and law,legal design,legalxml,legal semantic web,open government data,legal open access,legal interpretation theory,legal theory
Projet ERC : Mind-the-NEXUS (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Collegio Carlo Alberto — Pays : Italie — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1053750.0 €

Since 2015, mediatised episodes of protest and hostility towards refugees in rural areas in Europe have contributed to the perception of an alleged nexus between reception and the electoral success of far-right populist parties. However, despite the increasing interest of academic research, a patchy understanding of the factors and mechanisms driving the emergence of an asylum-far right nexus in rural areas persists. Whereas public opinion surveys emphasise the nationalistic orientation of rural Europe, research on policymaking has unravelled cases of welcoming and pragmatism. Mind-the-NEXUS will address this puzzle by leading the first systematic and comprehensive study of asylum politics in rural areas that in the last decade have been facing multiple challenges due to the arrival of different waves of refugees in times of reduction of public services (e.g., schools, hospitals etc.). It will do so by focusing on five European countries – France, Germany, Italy, Sweden and the UK – where municipalities represent key actors in reception policy. By adopting an innovative cross-country/cross-locality research design, Mind-the-Nexus will deliver a new understanding of the rural politics of asylum centred on the notion of Rural Political Arena (RPA), intended as the multilevel relational sphere where reception and integration policies take shape. Mind-the-NEXUS will deeply innovate scholarly and policy-oriented debates by expanding our understanding of opposition to refugees and FR politicization in still under-researched rural area. Through a mixed-methods approach combining Large-N quantitative studies on a newly compiled database, qualitative research on no less than 60 rigorously selected RPAs, and the first comparative rural elites survey, Mind-the-NEXUS will generate a ‘gold mine’ of data and break new ground in theorizing the meso-level institutional and political factors and mechanisms driving the emergence of an asylum-FR nexus in rural Europe.

Mots-clés : asylum,refugee reception,local government,local policymaking
Projet ERC : ModularBioMachine (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: B iological  Mechanisms , Structures  and Functions
Entité participante : Université de Cambridge — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2444452.0 €

Complex I (NADH:ubiquinone oxidoreductase), one of the largest membrane-bound enzymes in the mammalian cell, uses the reducing potential of NADH to drive protons across the inner mitochondrial membrane and power ATP synthesis. It is a keystone of metabolism, crucial for survival of human cells, and a primary site of mitochondrial dysfunctions and clinical disease. Complex I is also an efficient energy-converting biomachine, which shares conserved subunits, a molecular architecture, and modular design with prokaryotic type-4 hydrogenases, such as formate hydrogenlyase (FHL), that contribute to microbial H2 metabolism and geochemical cycles. Detailed cryo-electron microscopy (cryoEM) structures have defined the physical framework of the catalytic machinery in complex I, but there are wide knowledge gaps in central aspects of how it works, and no firm foundation of functional data to distinguish competing mechanistic proposals. ModularBioMachine will combine advanced strategies for structure, nanoassembly, function and genetics to transform knowledge of the reversible energy-transducing mechanism of complex I and FHL, exploiting their similarities and differences for enhanced insights and impact. It will exploit two powerful complex I platforms, complex I-containing proteoliposomes, which enable cryoEM under precisely defined conditions during energy-linked catalysis, and complex I from Paracoccus denitrificans, which combines cryoEM, genetics and advanced functional analyses in a single system – and extend both to study FHL. Focussing on three crucial knowledge gaps, the power stroke at the heart of energy conversion, the clockwork mechanism that defines the moving parts, and the proton wiring diagrams, ModularBioMachine will deliver a coherent, integrated understanding of the shared engineering principles and molecular mechanism of energy conversion in these evolutionary-linked biomachines at an intersection of the mitochondrial and microbial worlds.

Mots-clés : oxidative phosphorylation,electron transport chain,complex i,formate hydrogenlyase,cryoem,proteoliposome,enzyme mechanism,redox enzyme,proton pumping enzyme
Projet ERC : SMART (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Université de Cambridge — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1888696.0 €

Across the animal kingdom, closely-related species can differ in their learning ability, as can different individuals of the same species, but the underlying causes of these differences are poorly understood. Differences in learning could be due to different structural and functional properties of learning circuits which in turn result from different patterns of gene expression. However, identifying homologous learning circuit neurons in different species for comparing their synaptic connectivity, cellular-resolution functional properties and transcriptomes is challenging in larger brains.  We have identified a strain of Drosophila melanogaster larvae that learns faster (after fewer training trials) than others and a closely related Drosophilid species that learns faster than the D. melanogaster strains. We will use this tractable genetic model system to compare learning circuits in faster/better and slower/worselearner strains and species, in particular, their synaptic resolution connectomes; cellular-resolution activity maps; and transcriptomes. We will then screen the differentially expressed genes to identify those that improve learning and determine the way in which they affect connectivity and functional properties of learning circuits.  This project will reveal the architectural features of learning circuits that enhance learning and memory, as well as the molecules that can transform slow/worse- into fast/better-learners and their mechanisms of action. Uncovering the underlying structural, functional and genetic causes of variability in learning will not only have a major impact on neuroscience but could also potentially inspire the development of better architectures and algorithms for artificial intelligence and provide new avenues for treating learning and memory deficits.

Mots-clés : connectomics functional imaging of neural activity transcriptomics learning enhancing learning drosophila larva mushroom body comparison of structure and function across strains and distinct drosophilid species
Projet ERC : LungDeCode (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Life Science
Panel : Cell Biology, Development, Stem Cells and R egeneration
Entité participante : Université de Cambridge — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499986.0 €

In chronic adult lung diseases both tissue homeostasis and repair are disrupted, regeneration mechanisms fail, and healthy cells are replaced with aberrant cells. My long-term vision is to overcome these problems in patients with early-stage lung disease by in vivo reprogramming of cell fate to induce regeneration and restore cellular homeostasis. To achieve this we will move beyond the current state-of-the-art for human lung cell culture and human lineage-tracing, employing new physiologically-mimetic culture methods optimised in my lab which allow the ecosystem of cell states present within a tissue to be interrogated together. Focusing on interstitial lung diseases, which are primarily caused by epithelial dysregulation, we will acquire the biological framework to test the hypothesis that intrinsic cell reprogramming can promote human lung regeneration. We will determine how the lineage-dynamics of complex airway epithelia vary with airway generation in development, homeostasis and chronic hypoxia, which is a consequence of multiple lung diseases. This crucial information will be pivotal for inducing cell fate transitions that follow naturally occurring cell lineages, which will be the most efficient and stable routes for reprogramming in the adult. In parallel, we will ‘decode’ lung cell fate by defining the gene regulation networks controlling differentiation, proliferation and quiescence of human lung epithelial cell types. This will allow us to predict, and functionally test, factors sufficient for cell fate reprogramming. These experiments will combine innovative human tissue models, novel genetic tools and systematic analyses of scRNA-seq data. These analyses of precise cell lineages and transcription factor codes will allow us to test whether in vivo reprogramming of cell fate to induce regeneration is feasible. Our data will also provide an essential framework for building advanced human tissue models.

Mots-clés : lung,epithelial,lineage tracing,scrna seq
Projet ERC : DISORDERED-SPEECH (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : Université de Cambridge — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2010394.0 €

Language problems are common in children, with ~7% incidence of significant spoken language problems (Developmental Language Disorder, DLD) and a further ~7% for written language problems (Developmental Dyslexia, DD). While there can be overlap in behavioural indices of DLD and DD (eg prosodic difficulties), no accepted causal theories satisfactorily explain such overlaps, and early identification remains difficult: only half of “late talkers”, toddlers who are not speaking by age 2, go on to develop DLD. Here we combine our innovative neural theory of child language acquisition (temporal sampling [TS] theory) with a novel speech production task to open a new research front in developmental language disorders, based on speech rhythm and the accurate production of prosodic patterns. We test both pre-school children aged 2.5 – 6 years and children with DD and DLD in 4 languages varying in rhythm type, English, French, Spanish and Basque. We apply our in-house state-of-the-art methods for measuring the similarity between the child’s speech amplitude envelope (AE) and pitch contour (PC) of multisyllabic items to oral targets. Prior English data show impaired AE and intact PC for DD, impaired AE and impaired PC for DLD, and consistent links between AE/PC production in typically-developing (TD) children and phonology/vocabulary. We test whether similar patterns characterise TD, DD and DLD across languages. Our focus on the accurate production of the AE/PC is entirely novel, exploits an automatic aspect of linguistic processing, applies to all languages, and may suggest new avenues for remediation. The speech production data acquired from >1000 children will further enable a machine learning approach to early diagnosis based on AE/PC. Our goal is to develop new tools for identifying DD and DLD which can apply across European languages. The frontier vision is that as AEs and PCs describe words in all languages, our approach may reveal new universals in language disorders.

Mots-clés : speech production,developmental dyslexia,developmental language disorder,machine learning
Projet ERC : XXL-NMR (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and Analytical Chemical Sciences
Entité participante : Commissariat à l’énergie atomique et aux énergies alternatives — Pays : France — Rôle : Partner
Acronyme de l'entité : CEA
Type d'entité : Recherche
Financement ERC de l'entité : 999681.0 €

The cell is a collection of dynamic molecular machines. NMR spectroscopy is the method of choice to observe, at atomic resolution, complex conformational changes, transient interactions and dynamics of proteins. Introduction of methyl specific labelling technology has enabled solution NMR studies of protein assemblies of several hundred kDa. However, this strategy is mostly restricted to symmetrical and thermostable protein assemblies, precluding applications to large medically relevant biological complexes. The increases of linewidths and the high number of signal overlaps hamper the NMR spectra analysis of most hetero-oligomeric large assemblies studied at room temperature. In this project, we will develop two complementary concepts to significantly simplify both NMR spectra and corresponding site specific analysis. We will build a multi-site specific labelling method enabling observation of NMR signals only for the sites of interest in a large protein complex. We will invent a combinatorial strategy to reduce the time required for site-specific identification of each individual NMR signal from a few months to a few hours. New 1H-frequencies edition schemes will be introduced to enhance significantly the NMR spectra’s resolution of very slow tumbling biological particles. These groundbreaking NMR methods will be validated using therapeutic antibodies (150 kDa), ribosome (2.4 MDa) samples, and will be used directly to capture the mechanisms of ATP-fueled human chaperonin (1 MDa) in complex with the aggregation prone form of Huntingtin. This project will provide new technological breakthroughs to push biological applications of NMR significantly beyond its current boundaries. We anticipate that the simplification of the NMR analysis resulting from the new developed NMR routes will transform the solution NMR spectroscopy in a very competitive method to study large medically relevant biomolecular assemblies and molecular machines, so far considered as untargetable.

Mots-clés : nmr spectroscopy,large protein assemblies,isotopic labelling,cell free
Projet ERC : ILCADAPT (Horizon Europe, appel 2021, Advanced grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and I mmunotherapy
Entité participante : Hôpital universitaire de la Charité de Berlin — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : Charité
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2379266.0 €

Border surfaces of vertebrate organisms are in constant exchange with their habitat. Host cell networks consisting of epithelial cells, stroma and tissue-resident immune cells continuously integrate signals from the environment, a process by which an organism becomes better suited to its habitat (i.e., adaptation). Our understanding of the molecular underpinnings of such homeostatic processes is in its infancy, yet they are decisive for proper organ function. Recently, we and others have identified homeostatic circuitry involving tissue-resident immune cells in the small intestine facilitating adaptation to changes in nutrient cargo. The goal of my laboratory is to capture these molecular networks and to investigate how their failure predisposes to diseases. This proposal aims to systematically define the role of mucosa-associated group 3 innate lymphoid cells (ILC3) and of the IL-22:IL-22BP module in tissue adaptation to changes in metabolic need via their interaction with intestinal stem cells, stromal cells and enteroendocrine cells (EEC). Answering these questions demands both focused and global experimental approaches as well as the use of new genetic models for targeting of ILC3 and for recording epithelial cell differentiation. Based on these considerations and on strong preliminary data, we have devised 3 Specific Aims that will allow an unprecedented analysis of ILC3-controlled tissue adaptation: (1) to understand intestinal ILC3-epithelial cell units regulating nutrient handling and systemic metabolism, (2) to test the role of ILC3 in intestinal adaptation to increased metabolic demand during pregnancy, and (3) to characterize a new immune-epithelial module consisting of EEC and IL-22-producing ILC3. We and others have demonstrated that ILC3 effector programs are highly conserved and are operative in humans. Thus, the anticipated results will carry tremendous implications for the prevention and therapy of metabolic diseases.

Mots-clés : innate lymphoid cells,interleukin 22,ilc epithelial cell units
Projet ERC : Decode-And-Treat (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Fondazione per la ricerca biomedica avanzata ets — Pays : Italie — Rôle : Partner
Acronyme de l'entité : VIMM
Type d'entité : Recherche
Financement ERC de l'entité : 1000000.0 €

One third of cancer death are due to cachexia, a metabolic syndrome characterized by fat and lean mass loss. The loss of muscle mass and strength is considered the most important clinical feature of cancer cachexia, and a key predictor of poor outcomes. We and others have shown that prevention of muscle wasting but not fat loss is beneficial for life span in tumour bearing animals. Despite several attempts to develop therapeutic approaches, cachexia is still orphan of any specific drug/treatment mainly because our understanding of the insights of this syndrome are largely unknown. We have started to approach the complexity of cancer cachexia by collecting blood plasma samples, muscle and fat biopsies from cancer patients and age-matched gender-balanced controls. By combining data from mouse models with human biopsies we have recently made exciting observations related to motoneurons involvement in cachexia and an unexpected neurotrophic action of Bone and Morphogenetic Proteins (BMPs) that inspired this application. By using interdisciplinary approaches and developing cutting edge technologies we have designed an experimental plan that will:1) define how gene networks and cellular landscapes in muscles are modified by tumour growth and dissect their relevance in cachexia onset and survival; 2) establish how much gene/chromatin signatures and cellular/cytokines landscapes overlap or diverge between humans and murine models, males and females cancer patients, colon and pancreatic cancer types; 3) develop patients derived organoids-on-chip to generate an in vitro pathology model that recapitulates the human cachexia complexity, 4) develop a personalized multi-target RNA-based therapeutic approach that will preserve muscle mass, force generation and survival. These ambitious objectives will pave the way for an innovative therapy that will match with the concept of precision and personalized medicine and could be broadly applied to many other muscle wasting diseases