Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : MR O MICS (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Institut du cancer de Montpellier — Pays : France — Rôle : PI
Acronyme de l'entité : ICM
Type d'entité : Recherche
Financement ERC de l'entité : 537000.0 €

High-grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer. The early rapid onset of chemoresistance poses a major clinical challenge. Recent research highlighted high tumor heterogeneity (TH) and identified key mutational signatures that associate with prognosis and may aid in individualized treatment selection. Since HGSOC often presents with multisite disease and distinct populations of malignant cells, multi-region analysis of multimodal data is essential to study HGSOC. Single time point, single site biopsies cannot assess entire tumor while multiple biopsies at several time points are not feasible in clinical routine. Limitations of invasive sampling may be addressed with non-invasive imaging that captures morphologic and functional information about the entire tumor in space and, if repeated, in time. Radiomics has the potential for “whole tumour virtual sampling” using a single or serial non-invasive examinations in place of biopsies. To date, no study has evaluated the value of radiomics at macroscopic (in vivo 1.5T/3TMRI) and microscopic (ex vivo 9.4TMRI) scales to better understand the black box of radiomics features (explainable radiomics) or examine whether radiomics habitats are underpinned by tumour biology to provide a better insight into TH of HGSOC. Specifically, in this project, I will implement a model to capture imaging-based TH with multiscale radiomics approach by obtaining the mirror tumor image at in vivo MRI, ex vivo MRI and at histology. I will evaluate if imaging-based TH reflects underlying tumor histology by testing associations with imaging features and tumor-stroma proportion, infiltrative lymphocytes density and HRD status. Finally, I will unravel HGSOC TH biology through habitat imaging combined to an integrated Multi-O-Mics approach. Successful completion of this proposal will lead to a comprehensive characterization of HGSOC TH which will be then accessible non-invasively through radiomics.

Mots-clés : mri,radiomics,ovarian cancer,genomics,proteomics,immune tumor microenvironment,tumor habitat
Projet ERC : EVINE (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : Prussian Cultural Heritage Foundation — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : SPK
Type d'entité : Recherche
Financement ERC de l'entité : 356788.75 €

Language leaves no trace in the fossil record. However, an important component of the human language capacity, symbolic combinatoriality, might have “fossilized” after all. In the Paleolithic, hominins have embarked on their journey from Africa into the rest of the world. On their way, they have left artefacts which provide a window into their mind. Some of these bear early examples of visual information encoding: geometric signs. In the Middle Paleolithic, when Homo neanderthalensis roamed the landscape, evidence for geometric signs is scarce. It is not before the Middle Stone Age in Africa, typically associated with Homo sapiens, that the first systematic industries emerge. By the time Homo sapiens arrives in Central Europe – in the Upper Paleolithic – the practice of using stones, beads, bone fragments, and figurines as information carriers has become part of everyday life. In fact, the abundance of geometric signs in these assemblages is only gradually coming to light via large-scale collection efforts. This project proposes to marry the growing body of archaeological data with state-of-the-art tools from empirical linguistics to assess the Evolution of Visual Information Encoding (EVINE) in the human lineage. To this end, statistical measures based on information theory, quantitative linguistic laws, as well as classification algorithms need to be developed, and applied to sequences of paleolithic signs, ancient writing, and modern writing. Two core hypotheses are to be tested: First, paleolithic signs are statistically distinct from ancient and modern writing. Second, there was a combinatorial transition in the Upper Paleolithic of around 35 000 to 15 000 years ago. If the latter is verified, we would witness a major step towards modern visual information encoding – 10 000 years before the advent of ancient writing.

Mots-clés : language diversity,information theory
Projet ERC : CODE-PFAS (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
Entité participante : Institut fédéral suisse des sciences et technologies aquatiques — Pays : Suisse — Rôle : Partner
Acronyme de l'entité : Eawag
Type d'entité : Autres
Financement ERC de l'entité : 1080673.0 €

Per- and polyfluoroalkyl substances (PFAS) are persistent and pervasive environmental contaminants. Long-chain PFAS bioaccumulate in plants, humans, and wildlife, amplifying their toxicity and health risks, leading to increasing governmental regulations. Shorter-chain PFAS are being phased in by industry to replace longer-chain PFAS, but their toxicity is less known and they are harder to detect, highlighting the need for increased monitoring. The overall situation calls for rapid, accessible, and on-site methods to detect, differentiate, and break down diverse chain-length PFAS. To address this challenge, I propose to investigate ‘fluoro-selectivity’: the capacity of enzymes to differentiate between PFAS of varying chain-lengths and fluorinated versus non-fluorinated compounds. Strikingly, fluoro-selectivity already occurs in enzymes in nature. Here, I propose to use a combination of bioluminescence and machine learning to decipher the fluoro-selectivity code of natural enzymes that undergo conformational changes in response to different PFAS. In tandem, I will establish a new method using a fluoro-selective resistance mechanism to discover environmental enzymes capable of breaking down fluorinated compounds. Finally, I will take advantage of the close structural similarity between firefly luciferases - the enzymes that enable fireflies to glow - and natural PFAS-activating enzymes known as acyl-CoA ligases. Leveraging this evolutionary relationship, I will construct fluoro-selective, bioluminescent enzymes to enable the design of real-time, in situ, and chain length-specific PFAS biosensors. Mapping the first blueprint of enzymatic fluoro-selectivity will enable precision applications in environmental PFAS monitoring and remediation. More fundamentally, this work aims to reveal the underlying biochemical and evolutionary drivers of fluoro-selectivity from bacteria to fireflies, uncovering paths for the emergence of metabolic traits across the tree of life.

Mots-clés : per and polyfluoroalkyl substances pfas enzyme specificity firefly luciferase coenzyme a ligase biotransformation biosensor ion exporter metagenomic library protein engineering wastewater microbiome
Projet ERC : AI4SYN (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Integrative B iology : from Genes and Genomes to S ystems
Entité participante : Centro de Regulación Genómica — Pays : Espagne — Rôle : PI
Acronyme de l'entité : CRG
Type d'entité : Recherche
Financement ERC de l'entité : 1499653.0 €

Our abilities to predict and engineer complex biological systems are in their infancy. In the context of gene regulation, we cannot design artificial promoters with specificity to arbitrary cell states, and we cannot arbitrarily trans- and de-differentiate somatic cells, although such abilities would be of high biotechnological and biomedical value. To achieve these ambitious goals, we require quantitative, predictive models of gene regulatory elements (GREs) and gene regulatory networks (GRNs), respectively. Here I propose that the combination of deep learning and single-cell genetic screens is ideally suited to obtain such models, and, in particular, the amounts of highly informative data required for their training. Working with an ex vivo model of hematopoietic stem cell differentiation, we will first screen the activity of hundreds of thousands of synthetic GREs throughout the hematopoietic differentiation landscape. Thereby, we will obtain models of cell type specific GRE activity that can predict new, synthetic GREs with activity in any cell state of interest. Second, we will screen hundreds of thousands of combinatorial GRN perturbations and their effect on cell state. Thereby, we will derive models of GRNs that can predict combinatorial perturbation strategies to achieve arbitrary de- or trans-differentiation events. In sum, work on this project will yield a quantitative model of gene regulation in hematopoiesis at various scales of complexity while introducing a novel, AI-guided concept for biological engineering.

Mots-clés : single cell genomics,single cell genetic screens,machine learning
Projet ERC : MEMBRAIN (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Istituto Nazionale di Ricerca Metrologica — Pays : Italie — Rôle : PI
Acronyme de l'entité : INRIM
Type d'entité : Recherche
Financement ERC de l'entité : 1487500.0 €

Artificial Intelligence needs a hardware revolution to sustain the ever-growing demand of computing power in our society, where the huge energy consumption and environmental impact of computation with current technologies is unsustainable. In the race toward future computing, bioinspired technologies have been shown as promising hardware solutions for computing beyond the Turing model and the classical von Neumann architectures. Going beyond transistor-centred hardware solutions, the research community is exploring new device concepts and architectures that leverage physical phenomena for computing “in materia” with physical laws to emulate the effectiveness of information processing capabilities of our brain. While arrays of memristive devices realised with a top-down approach represent emerging solutions for the hardware realisation of artificial neural networks, these systems do not emulate the topology and emergent behaviour of biological neuronal circuits where the principle of self-assembly and self-organisation regulates both structure and functions, providing adaptability, efficiency, and robustness. Tackling main challenges of neuromorphic computing, the MEMBRAIN project aim to develop a radically new concept of physically grounded computing nanoarchitecture based on self-organising memristive nanonetworks of dendrites, able to efficiently process information and to store knowledge on the same physical substrate at the matter level through physical laws. Overcoming the concept of nanotechnology as a simple advancement of microtechnology, the ambition is to compute like nature – thermodynamically – to push computation near fundamental limits of efficiency. By establishing a hardware-software codesign framework at the crossroads of material science, machine learning and neuroscience, the aim is to retarget the original goal of neuromorphic computing of creating general-purpose truly intelligent systems that endow dynamic learning and multitasking capability.

Mots-clés : neuromorphic systems,neuromorphic computing
Projet ERC : DYNASTY (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Institut national de recherche en sciences et technologies du numérique — Pays : France — Rôle : PI
Acronyme de l'entité : Inria
Type d'entité : Recherche
Financement ERC de l'entité : 1498410.0 €

The recent advances in deep learning (DL) have transformed many scientific domains and have had major impacts on industry and society. Despite their success, DL methods do not obey most of the wisdoms of statistical learning theory, and the vast majority of the current DL techniques mainly stand as poorly understood black-box algorithms. Even though DL theory has been a very active research field in the past few years, there is a significant gap between the current theory and practice: (i) the current theory often becomes vacuous for models with large number of parameters (which is typical in DL), and (ii) it cannot capture the interaction between data, architecture, training algorithm and its hyper-parameters, which can have drastic effects on the overall performance. Due to this lack of theoretical understanding, designing new DL systems has been dominantly performed by ad-hoc, 'trial-and-error' approaches.  The main objective of this proposal is to develop a mathematically sound and practically relevant theory for DL, which will ultimately serve as the basis of a software library that provides practical tools for DL practitioners. In particular, (i) we will develop error bounds that closely reflect the true empirical performance, by explicitly incorporating the dynamics aspect of training, (ii) we will develop new model selection, training, and compression algorithms with reduced time/memory/storage complexity, by exploiting the developed theory.  To achieve the expected breakthroughs, we will develop a novel theoretical framework, which will enable tight analysis of learning algorithms in the lens of dynamical systems theory. The outcomes will help relieve DL from being a black-box system and avoid the heuristic design process. We will produce comprehensive open-source software tools adapted to all popular DL libraries, and test the developed algorithms on a wide range of real applications arising in computer vision, audio/music/natural language processing.

Mots-clés : machine learning deep learning theory dynamical systems
Projet ERC : CATNEMI (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Film University Babelsberg KONRAD WOLF — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1337744.0 €

Cinema is not just a representational but also an operational medium. Its global success is indisputably tied to its ability to embed its audience in affectively charged, atmospheric worlds and filmmakers carefully design their narrative worlds through ambient soundscapes, music, colorations, filters, camera movements, production design, mise en scène, rhythmical editing, and countless other features that arrange the emotional and affective experiences of the audience. Marginalized in film studies due to their ontologically indeterminate nature, cinematic atmospheres operate affectively and prior to cognitive, critical, and analytical reflection. This project examines cinematic atmosphere production in all its experiential, aesthetic, and ethical implications as an attractive but also problematic, even harmful, and ideologically loaded enterprise. It raises critical awareness of the basic atmospheric operations of cinema in a time when the medium is relocated into new media environments and its techniques are reused within ecologies that are not cinematic per se such as news broadcasting, videoconferencing, sports refereeing, and even terrorist recruitment. This project i) develops a broad conceptual understanding of cinematic atmospheres as a way of organizing the perceptive, affective, and cognitive epistemic access to the world of the film, and ii) mobilizes this understanding to develop a method for the study of the operations and techniques that constitute cinematic atmosphere production before iii) expanding this method to deal with the question of how these cinematic operations and techniques for atmosphere production are no longer the property of cinema alone but increasingly employed outside of the narrative-aesthetic domain at the service of larger socio-cultural, economic, and political forms of organizing perception, cognition, and affect from art installations to social media.

Mots-clés : atmosphere,embodiment,film worlds,media anthropology,operative ontology,affect,film phenomenology,enactivism,gernot b hme,ecology of film,relocation of cinema,mood,stimmung,environmental film studies
Projet ERC : KI-NET (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and Analytical Chemical Sciences
Entité participante : Fondation Jean-Marie Lehn — Pays : France — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 640000.5 €

Endergonic processes are central to Life. They are achieved by enzymes, that change conformation during their catalytic cycle. Thus, biological non-equilibrium processes are catalysis-driven. The realization of catalysis-driven processes in artificial systems proved challenging. It remains limited to synthetically demanding interlocked structures, which were upgraded with catalytic features affecting ring sliding motion.  With KI-NET, I want to develop a general biomimetic strategy enabling endergonic processes driven by chemical catalysis. I plan to invert the current approach by introducing defined conformational freedom into simple catalytic units. KI-NET scientific objectives go beyond state of the art in chemically-driven non-equilibrium systems, with the aim to: (i) establish an unconventional theoretical approach based on “effective transition states”, that guides experiments and reveals common underlying principles for catalysis-driven processes and chemical oscillations; (ii) realize endergonic conformation changes powered by catalytic processes, including ATP hydrolysis; (iii) promote endergonic assembly reactions, that will reveal how energy consumption directs chemical adaptation; (iv) realize an artificial synthase: a catalyst that harvests energy from one reaction and uses it to drive a different one.  I will implement a theory-guided experimental approach at the interface between systems chemistry and molecular machines. Leveraging my broad chemistry background, I will address questions that expand towards physics – in terms of formalizing models – and biology – in terms of operating systems to be imitated and unraveled. Realizing KI-NET allows overcoming thermodynamic boundaries. Unforeseen opportunities become possible in material science and energy management, such as the realization of artificial mitochondria. Indeed, KI-NET pioneers a largely unexplored area of science at the roots of dissipative systems, complex phenomena, and –ultimately, Life.

Mots-clés : systems chemistry,non equilibrium chemical systems,non equilibrium self assembly,chemical reaction networks,molecular machines
Projet ERC : 2D-sandwich (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : FZU  Institute of Physics of the Academy of Sciences of the Czech Republic — Pays : Rép. Tchèque — Rôle : PI
Acronyme de l'entité : FZU AV ČR;FZU CAS;FZÚ AV ČR
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1975955.0 €

The possibility to create new materials bottom-up was enhanced via the stacking of atomically thin layers of two-dimensional (2D) materials with van der Waals interactions (vdWIs). Unfortunately, the downside of vdWIs is the, in general, weak electronical and mechanical interaction between the individual layers. This hinders the creation of multiferroic materials working at ambient conditions, which are despite extensive research still very scarce, from stacked layers with vdWIs.  This research aims to create a new 2D building block: the 2D sandwich. This is a layered heterostructure with a strong interaction between the sandwich's individual layers mediated via an ionic or multivalent bond, whereas the interaction with other layers is still solely due to the vdWIs. As the prototype functional material, a 2D magnetoelectric multiferroic sandwich composed of layered transition metal chalcogenides, oxides or iodides will be grown using the modulated elemental reactants method. To tackle the possible sample degradation in the sandwiches, ultra-high vacuum optical surface science spectro-microscopy techniques will be developed to optically probe the 2D multiferroic sandwiches for both magnetism, ferroelectricity and the coupling between them. The concept of open samples will be introduced, facilitating the scientific community with the straightforward verification of the data and accelerate the development of this new class of 2D sandwiches.  This project will provide material design strategies that can remove the stringent lattice matching criteria to stack different classes of layered materials and provide these class of layered materials with similar stacking freedom as layered materials with vdWIs. These insights will allow the interbreeding of different classes of layered quantum materials, such as complex oxides, 2D layered crystals with vdWIs, cuprate superconductors or topological insulators or semimetals.

Mots-clés : heterostructure,multiferroic,van der waals interactions,ionic bonding,multivalent bonding,modulated elemental reactants method
Projet ERC : RELIC (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Study of the Human Past
Entité participante : Natural History Museum Vienna — Pays : Autriche — Rôle : Partner
Acronyme de l'entité : NHMW
Type d'entité : Recherche
Financement ERC de l'entité : 378000.0 €

From the 10th c. onwards, new polities emerged on the periphery of the Holy Roman Empire (HRE), the new centre of Christendom. Endowing Christianity as an institutional system was integral to the emperor’s power, expanding his influence and securing his rule in the new kingdoms. Previous narratives have been generally constructed on the basis of limited written accounts, which mainly concern the higher echelons of society, emphasising the role of secular and ecclesiastical elites. However, the ecclesiastical and secular administrative organisation of the rural population could not be reconstructed satisfactorily from these sources, despite their importance for the stability of both State and Church. RELIC proposes a complex, comparative analysis and contextualisation of archaeological and historical remains of the rural population living on the eastern fringes of the HRE during the Ottonian and Salian periods (10th -12th c.), exploring the influences of centres and networks of secular and ecclesiastical lords, of the natural environment, and of the economic infrastructure. Investigating this often-overlooked segment of the population, its hitherto unexplored or neglected role allows us to study how (top-level) changes in political and ecclesiastical organisations can be reflected in the evidence concerning the lower levels of society and of the local church network; how different strategies worked in different political settings, and what role local initiatives/agencies could have played in religious and political shifts. The religiopolitics of the HRE is a well-researched topic. Still, investigating the involvement of the rural population in these processes is a new approach, and so is the applied methodology and primary data: proximity, network and catchment analysis on archival archaeological data and historical evidence. Individually, such evidence provides little information, but together it forms a significant and unexplored dataset of the rural population.

Mots-clés : social archaeology,modelling religious processes in archaeology and history,gis analysis and big data in archaeology and history