Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : Post-REALM (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Université Heinrich Heine de Düsseldorf — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1873963.0 €

The study of pan-European medieval literature faces two significant problems. The first is so obvious that it is rarely given much thought: only a fraction of the texts created in this period have survived. The second problem is similarly easy to overlook when working on any one text, which is that the content and reception history of those texts that do survive are usually studied separately within the ‘national’ philologies, whose linguistic and cultural borders make it difficult to gain insights into the broader patterns of creation, dissemination, and reception arising out of these fragments of cross-regional text traditions. At the same time, the linguistic and temporal limitations of analogue study make it all but impossible to gain a comprehensive overview of text traditions spanning different languages and large numbers of text versions.  The interdisciplinary Post-REALM project will combine digital analysis with close reading and study of material context in order to focus on 26 versions of the late medieval pan-European 'Floire and Blancheflor' narrative. Drawing on a broad range of analogue and digital forms of analysis, we will develop and refine a new mixed-method template for how to approach widespread cross-lingual text traditions from the past. The aim of the project is not simply to create an entirely new understanding of how this popular late medieval narrative was written, adapted, and disseminated across different regions of Western Europe, but to inspire a step change in how the study of medieval literature is approached. Doing so will provide new conceptual frameworks and resource formats within which to relate the different surviving versions of historical text traditions to each other and new ways of understanding and visualising their contents, material context and paths of dissemination.

Mots-clés : mixed method digital philology,pan european medieval literature,material texts,manuscripts,close reading,stylometry,plot feature analysis,medieval romance,translation
Projet ERC : BESPOKE (Horizon Europe, appel 2025, Consolidator grants)
Entité participante : Hannover Medical School — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : MHH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999675.0 €

Antimicrobial resistance (AMR) is a major health challenge and predicted to be associated with 8-10 million deaths per year by 2050. Gram-positive pathogens from the genus Enteroccocus and Gram-negative extra-intestinal pathogenic Escherichia coli (ExPEC) are critical multidrug-resistant bacteria for which effective therapies are urgently needed. Both pathogens cause various types of infections, can survive in different biological niches, and can even be a beneficial part of the human gut microbiome. Glycoconjugate vaccines have proven highly effective for the prevention of bacterial infections. However, their development for Enterococcus spp. and ExPEC infections is hindered by the lack of knowledge about (i) the diversity, structure and identity of cell surface polymers expressed on Enterococcus spp. and ExPEC, (ii) how these polymers determine the biological niche a strain inhibits or the type of infection it causes, and (iii) what polymers to target to distinguish between beneficial and harmful strains. In BESPOKE we will explore and exploit bacterial cell surface polymers of Enterococcus spp. and ExPEC in an unprecedented manner. Using a bottom-up approach - from genome, to enzyme, to polymer – we aim to synthesize and structurally characterize the cell surface polymers in a pathogen-free way to elucidate the structural diversity that is presented to the human immune system during various types of infection. In addition, we will explore and biochemically investigate the as yet poorly understood cell wall polymer biosynthesis machineries on genome and enzyme level to make them available as marker for the surveillance of local epidemiology and as synthesis tools for biotechnological exploitation. Finally, we aim to build a highly versatile mix-and-match platform for the synthesis of glycoconjugate vaccines against bacterial infections that will help fight AMR and allow to respond quickly to local changes in epidemiology.

Mots-clés : enzymatic synthesis of polysaccharides and vaccine antigen,characterization of biosynthesis pathway and structure of bacterial surface polysaccharides
Projet ERC : MAMELI (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université de Milan — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2993251.0 €

The concept of “exposome” encompasses all human disease determinants encountered during the life course. The exposome induces epigenetic modifications (e.g., DNA methylation). Several studies have investigated how repetitive elements (REs) can be switched on in response to environmental stimuli, but have started from the assumption that altered RE methylation is always detrimental to the health of the affected individual. MAMELI proposes an alternative hypothesis that some REs are plastic entities that respond physiologically to the environment; consequently, the ability of DNA to adapt to environmental triggers can be monitored. To test this hypothesis, the MAMELI project will enroll 6200 subjects in the city of Legnano (Italy). The first-level (“discovery”) will apply third-generation sequencing methylation assessment on 200 subjects at two-time points: T0 (baseline) and T1 (6 months after T0). The discovery level will allow me to define a pool of REs (“differential REs”) whose methylation can change without impacting genome stability. The second level (“tuning”) will be conducted on 2700 subjects (200 subjects of the discovery level + 2500 additional subjects) and will allow me to develop an algorithm (“MAMELI algorithm”) to predict the relationship between exposome and RE methylation status (“RE methylation signature”). In the third level (“validation”), I will apply the MAMELI algorithm on the second set of the study cohort (n=3500) to calculate the difference between the measured RE methylation signature and the values predicted by the algorithm. In addition, to test the reversibility of RE methylation following lifestyle modifications and its preventive potential, I will design an intervention study embedded in the cohort. MAMELI would represent a breakthrough in the understanding of individual susceptibility to environmental pressures, enabling the development of preventive strategies to avoid disease.

Mots-clés : repetitive elements exposome dna methylation
Projet ERC : ACT-PC (Horizon Europe, appel 2023, Proof of concept grants)
Entité participante : Universitätsklinikum Hamburg-Eppendorf — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : UKE
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

The prognosis with metastatic pancreatic ductal adenocarcinoma PDAC is simply terrible, especially for those that do not respond to the chemotherapy, which is regarded as the standard therapy in most patients. More than half of the patients are non-responders and eventually pass away within months. It is therefore urgently needed to understand the differences among responder and non-responder patients, to develop alternative therapies that overcome the resistance and potentially further extend the survival for both groups. A recent hypothesis proposes the key role of microbiome and of the immune response in regard to tumor initiation, progression, as well as response to therapies. Diet, as a variable, introduces an additional layer of complexity, given its multifaceted impact on both the human microbiome and immune system, as our recent manuscript show. The metabolites produced by gut microbiota are influenced not only by diet, but also by a host of other factors. In ACT-PC, we propose the use of one or a combination of different microbiota-derived metabolites as a treatment to increase the efficacy of chemotherapy and at the same time to boost anti tumor immunity for PDAC patients. The overall goal of ACT-PC is to initiate the preclinical validation of specific microbiota-derived metabolites for improving treatment outcomes in PDAC patients. This project is designed to take our Technology Readiness Level from 2 to 4, enabling us to finalise the preclinical development within a subsequent EIC Transition and initiate clinical validation and development soon after. In implementing the project’s results we will leverage the PI’s unique network that includes commercial partners and patient organisations.
Projet ERC : RENOTREAT (Horizon Europe, appel 2024, Proof of concept grants)
Entité participante : University Hospital Heidelberg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Chronic kidney disease (CKD) is a major burden on society, causing significant morbidity and mortality. A typical symptom of CKD is the loss of protein into the urine, also termed proteinuria, which is usually associated with a poor prognosis. In most cases, a defective glomerulus leads to proteinuria, resulting in damage of the neighboring proximal tubular cells due to protein and lipid overload. In RENOTREAT, we aim to treat Alport syndrome with a natural amino acid, that is known to inhibit the tubular protein uptake receptors cubilin and megalin. This amino acid is already available as a safe treatment in humans, but has never been used for kidney disease. Alport syndrome is a rare disease with underestimated prevalence. It is caused by defective collagen fibers due to pathogenic genetic variants in collagen IV genes. The lack of functional collagen fibers leads to a more permeable glomerular basement membrane, resulting in proteinuria with subsequent proximal tubular damage. In our therapeutic strategy, we will induce tubular proteinuria by blocking protein uptake into the proximal tubule, overturning the paradigm that the degree of proteinuria correlates with kidney damage. First, we will test dosing regimens and bioavailability for three different formulations of the amino acid with regard to the induction of a stable tubular proteinuria in wild-type mice. Using a murine model of Alport syndrome, we will then evaluate these treatments for their potential to slow CKD progression and prolong the survival of the mutant mice. In parallel, we will conduct a pilot trial in healthy human volunteers with the already available form of the amino acid to prepare for a future clinical trial in Alport patients. In summary, we will verify the innovation potential of a novel therapeutic approach of managing Alport syndrome and potentially other proteinuric kidney diseases by performing proof-of-concept preclinical and clinical trials.
Projet ERC : ROCAP (Horizon Europe, appel 2022, Proof of concept grants)
Entité participante : Université d'Utrecht — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Image captioning is the process of mapping a visual scene to a short textual description. Automating this process is vital for many computer applications, including information retrieval from visual data, computerized assistance to visually impaired people, and automatic tour guiding. State-of-the-art captioning systems are limited by their heavy reliance on visual contents. As a result, generated captions are often purely descriptive and miss important information that is needed in order to understand the image. This PoC project develops a captioning tool that will be useful for knowledge-intensive areas like Geography, Radiology or Art History, where captions need to include information that cannot be extracted from images alone. It builds on results of the ROCKY ERC AdG project, whose innovative captioning system integrates external knowledge into the captioning process. This allowed the ROCKY project to employ standard methods of image captioning, with a deep convolutional neural network (CNN) for image understanding and a Transformer network for language generation. Thanks to the external knowledge integration, the ROCKY captioning prototype gets substantially closer to human-generated captions than standard captioning systems that do not take external knowledge into account. This PoC project will use this result by implementing a knowledge-aware captioning system that is scalable for practical purposes. The project examines the feasibility of the ROCKY captioning method for Medical Imaging and Art History and implements it for one of these domain as a use case. The project will engage with experts in these domains, specify a practical captioning system, implement it as an open-source tool and test it in realistic situations. The anticipated value of this effort is in the development of a general method that would allow one open-source platform to be multi-purpose, thereby cost-effectively adjustable to needs of different domains.
Projet ERC : GeoAI_LULC_Seg (Horizon Europe, appel 2022, Proof of concept grants)
Entité participante : Université Koç — Pays : Turquie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Rural depopulation, agricultural land abandonment, and deforestation are massive concerns for Europe and elsewhere today and our planet's future. These interlinked phenomena can be analysed using land use and land cover (LULC) maps combined with dynamics of population geography, especially regarding urban sprawl. Modern LULC and spatially disaggregated population datasets go back to the 1980s and 1970s. Although we have earlier population data, these are not geomatched to locations in LULC maps. Earlier LULC maps are either not very reliable (extracted from historical maps) or limited in their geographical coverage (based on selected aerial photos or satellite imagery). These are severe limitations to developing longer and deeper perspectives and understanding the root causes of these detrimental changes in population geography and land use practices in large territories. GeoAI_LULC_Seg will develop an advanced, modular, and customizable geospatial artificial intelligence-based land use land cover segmentation process to accurately map LULC conditions for around 30,000 km2 in a border region between Bulgaria and Turkey, including the cities Edirne, Istanbul, and Plovdiv, from historical aerial photographs and early reconnaissance satellite images (dating back to the 1950s and the 1970s respectively) by pairing them with geotagged historical population census data. Our methodological novelties are not limited to GeoAI-based object segmentation and super-resolution applications for panchromatic imagery for our research area. Our project will create transferable knowledge and scalable methods for global applications for the 1970s, thanks to worldwide coverage of high-spatial-resolution satellite imagery we will process. Furthermore, we will build long-term LULC maps series commensurable with current satellite data (1950-2020), allowing us to improve predictions for future population geography and LULC changes.
Projet ERC : BRCA-PREVENT (Horizon Europe, appel 2022, Proof of concept grants)
Entité participante : Université d'Innsbruck — Pays : Autriche — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Women with germline mutations in the BRCA1 genes face a 40-fold increased risk of developing breast and ovarian cancer. As a result, and due to a lack of other options, many women with a BRCA1 mutation choose to undergo drastic risk reducing surgery (removal of both breasts, ovaries and Fallopian Tubes) when they are still very young. Drugs currently recommended for prevention of breast cancer, such as tamoxifen or aromatase inhibitors, do not prevent those cancers with the poorest prognosis, such as triple negative breast cancer. Methods to prevent breast and ovarian cancers in these women are therefore a critical unmet medical need. We and others have identified progesterone as one of the key drivers for triple negative breast cancer development. We have shown that progesterone is consistently elevated during the luteal phase of the menstrual cycle in BRCA1 mutation carriers compared to women without such a mutation, and preliminary data point to progesterone receptor antagonists, such as mifepristone, as a promising class of drugs for primary prevention. Clinical trials of primary cancer prevention are extremely challenging due to the time-gap between the start of the cancer-preventive treatment and the primary endpoints (i.e., cancer incidence and cancer death). Current tools do not predict the likelihood of future cancer development with sufficient accuracy to be used to determine the preventive efficacy of a new treatment. We have developed DNA methylation markers reflecting tissue age and cell type that are increased in cancer tissue as well as in normal tissue from women with a BRCA1 mutation and could be used to monitor preventive efficacy.  Clinical trials are now required to further investigate the use of antiprogestins for the prevention of breast and ovarian cancer. This ERC Proof of Concept grant aims to bring consensus among the scientific, patient and regulatory/ethical communities regarding the design and conduct of an initial clinical trial.
Projet ERC : FormalWeb3 (Horizon Europe, appel 2023, Proof of concept grants)
Entité participante : Université d'Innsbruck — Pays : Autriche — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Mathematics is today on the verge of a revolution. Formal, computer-verified proofs become ubiquitous and largely adopted as the new means of communication and collaboration. This led to landmark proofs, such as Hales's verification of the Kepler conjecture and Scholze's condensed mathematics recently validated by computers, generating considerable momentum in the mathematical community. Simultaneously, there is growing industrial demand for formal proofs of software and hardware. Similarly, Web3 constitutes a transformation of how data and collaboration are realized in a decentralized, intelligent, and interconnected way.  In this project, we will develop a Web3 platform for formal proofs, designed to establish connections between mathematicians, researchers in formalization, communities, and businesses. This platform will also integrate state-of-the-art verification, AI, and autoformalization tools to assist in the creation of computer-understandable proofs. The proposed work will include the development of a Wiki for formal mathematics with integrated blockchain-based tokens that reward users for their contributions. We will provide translations for various logics and proof systems, and investigate the safety and security of the protocols. On the business side, we will conduct market research, engage potential customers, clarify intellectual property rights and formulate licensing solutions.  The project builds on the success of our ERC project SMART, where we have already developed software that harnesses artificial intelligence for advanced logical reasoning and proposed novel approaches to decentralizing proof verification using blockchain technology. In the current proof of concept project, we aspire to expand these software capabilities into a holistic Web3 platform that will serve as a collaborative hub, enabling diverse stakeholders to collaboratively address practical verification and formalization challenges while rewarding users for their contributions.
Projet ERC : MOSQITO (Horizon Europe, appel 2024, Proof of concept grants)
Entité participante : Austrian Institute of Technology — Pays : Autriche — Rôle : PI
Acronyme de l'entité : AIT
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

The advent of quantum computing and its unprecedented computational power has called the sustainability of today’s widely established encryption methods into question. Even though quantum physics addresses this emerging threat by offering quantum key distribution (QKD) at the same time, the complexity inherent to QKD systems constitutes a roadblock for their practical introduction, especially when addressing telecommunication segments that are subject to commodity-like applications. MOSQITO is an ERC Proof-of-Concept study that aims to proof the applicability of a novel monolithic silicon integration approach that is touted as a trailblazer to greatly simplify quantum communication circuits while further providing a graceful migration path towards a seamless integration of microelectronics. We will build on a recently demonstrated silicon optical power supply, which enables light emission through a quasi-direct silicon bandgap structure – thus alleviating silicon photonic integrated circuits (PIC) from the burden of complex hetero-integration of III-V materials. MOSQITO will conduct (i) the world’s first demonstration of a PIC-based QKD transmitter for BB84 polarization-encoded key exchange, where every element has been realized exclusively on a silicon basis, (ii) an evaluation of this greatly simplified QKD hardware for commodity applications such as 6G, residential access, the Industrial Internet or massively-parallel data interconnects – all of them being segments where traditional QKD implementations cannot enter the market due to cost and size concerns, and (iii) strategic clustering activities with silicon platform partners to lay inroads for refining the silicon light emitter with respect to further performance scaling and manufacturing readiness level.

Mots-clés : photonics,quantum communication,photonic integrated circuits,quantum key distribution