Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : Re-Start PD (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : German Center for Neurodegenerative Diseases — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : DZNE
Type d'entité : Recherche
Financement ERC de l'entité : 933750.0 €

Parkinson’s disease (PD) is the most common neurodegenerative movement disorder, but current diagnostic criteria identify individuals late, impeding the development of neuroprotective therapies: There is the critical yet unmet need to identify the disease onset timely and to define the inception of pathology. Advancing recognition of early motor deterioration and coupling it with biological evidence of pathological α-synuclein will overcome present barriers and tackle the questions when and where PD starts. Isolated REM sleep behavior disorder, confidently detectable by polysomnography, signals an early α-synucleinopathy. Overseeing a large cohort, I will integrate multimodal motor data on speech, facial expression, hand movements, gait, and long-term actigraphy within a machine learning framework, permitting a tablet-based application for highly mobile uses. This earliest PD motor-fingerprint, common to PD in general, will enable prospective large-scale screening from an unselected population. Motor phenotype detection will be paired with biological confirmation of pathological α-synuclein species in the cerebrospinal fluid using precise seed aggregation assays (SAA), capturing imminent PD earlier than ever before. This universal identification of the earliest phase of PD will allow to explore the initial spread of α-synuclein pathology, elucidating variations of pathology initiation: I will employ α-synuclein SAA on biosamples from the olfactory epithelium (brain-first) and the peripheral autonomic nervous system (body-first) to decode fundamental subtypes. Re-Start PD pairs general PD characteristics - its primal motor phenotype and its biological α-synuclein signatures - outlining the earliest phase of PD prospectively and being readily transferable for broad application to kickstart timely therapeutic innovations. Complementary biobanking, brain-imaging, and follow-up of this unique cohort will foster further biomarker development and subtype determination.

Mots-clés : parkinson s disease,rem sleep behaviour disorder,machine learning,early diagnosis,motor behavior,alpha synuclein,seed amplification assay
Projet ERC : TUCLA (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université d'Aarhus — Pays : Danemark — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999288.0 €

Machine learning has evolved from being a relatively isolated discipline to have a disruptive influence on all areas of science, industry and society. Learning algorithms are typically classified into either deep learning or classic learning, where deep learning excels when data and computing resources are abundant, whereas classic algorithms shine when data is scarce. In the TUCLA project, we expand our theoretical understanding of classic machine learning, with a particular emphasis on two of the most important such algorithms, namely Bagging and Boosting. As a result of this study, we shall provide faster learning algorithms that require less training data to make accurate predictions. The project accomplishes this by pursuing several objectives:  1. We will establish a novel learning theoretic framework for proving generalization bounds for learning algorithms. Using the framework, we will design new Boosting algorithms and prove that they make accurate predictions using less training data than what was previously possible. Moreover, we complement these algorithms by generalization lower bounds, proving that no other algorithm can make better use of data.  2. We will design parallel versions of Boosting algorithms, thereby allowing them to be used in combination with more computationally expensive base learning algorithms. We conjecture that success in this direction may lead to Boosting playing a more central role also in deep learning.  3. We will explore applications of the classic Bagging heuristic. Until recently, Bagging was not known to have significant theoretical benefits. However, recent pioneering work by the PI shows that Bagging is an optimal learning algorithm in an important learning setup. Using these recent insights, we will explore theoretical applications of Bagging in other important settings.

Mots-clés : learning theory,machine learning,algorithms,lower bounds,generalization bounds,sampling,randomized algorithms,boosting,bagging
Projet ERC : BioRIcON (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
Entité participante : Université d'Aarhus — Pays : Danemark — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999799.0 €

Developing molecules that display protein-like functions and efficiently operate in complex biological environments, e.g. cells, is a grand challenge in chemistry. The aim of BioRIcON is to develop a novel class of biohybrid interlocked molecules (BIMs) that mirrors the function of proteins and enables precise biomolecular regulation. Despite the advances of interlocked molecules, often compared to biological motors, their use in cellular or in vivo settings pales compared to their natural counterparts. BioRIcON comprises an entirely new concept in artificial biology and biomolecular control by yielding BIMs that contain dynamic components ultimately designed to efficiently actuate in cells with high spatio-temporal resolution. I will develop molecular switches and motors using small cyclic oligonucleotides (cONs) that will be mechanically bound – like links in a chain - to other biomolecules to regulate their function. The resulting BIMs merge the best of two worlds: i) the programmability and biocompatibility features of oligonucleotides, and ii) the robustness and scalable production of synthetic mechanically interlocked systems. The goals of BioRIcON include: i) developing BIM-based shuttles and motors that can operate effectively in biological environments, integrate, and react to cellular cues and signaling; ii) exploring mechanical interlocking for reversible molecular display and protection against degradation; iii) implementing BIMs in biomolecular regulation, focused on reversible oligonucleotide-based therapies. BioRIcON will significantly advance the toolbox of biomimetic systems and enable their integration in processes such as gene regulation and protein expression. BIMs use mechanical interlocking to reversibly protect and control biomolecular function in an adaptative manner, with a focus on RNA regulation which will help unravel the mechanisms of fundamental biological processes and hold enormous innovative potential in biosensing and biomedicine.
Projet ERC : OUTOFTHEBLUE (Horizon Europe, appel 2022, Consolidator grants)
Entité participante : Muséum national d'histoire naturelle — Pays : France — Rôle : Partner
Acronyme de l'entité : MNHN
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 187059.81 €

This project will bridge gaps between micro and macro evolution by ascertaining the evolutionary feedbacks between trait and species diversification, focusing on closely-related species living in sympatry. Sympatric species often differ in suites of traits involved in niche partitioning: how do ecological interactions induce the sequential evolution of series of traits? In turn, how does phenotypic divergence open up new niches and fuel sympatric speciation? The project focuses on the neo-tropical butterfly genus Morpho where multiple behavioural and morphological traits strikingly differ between sympatric species living in the canopy vs. understorey. Studying trait variations within and among these closely-related species living in sympatry allows reconstructing the evolutionary steps leading to the divergence in suites of traits linked to niche specialization. Within the understorey clade, striking parallel geographic variations are observed among sympatric species, resulting in repeated local convergences in iridescent blue wing patterns, that may be driven by mimicry among these fast-flying, conspicuous butterflies. Such evasive mimicry may induce costly reproductive interferences, favouring segregation of circadian activities between species and thus contributing to the speciation process. The project relies on the original combination of both up-to-date and field-based approaches: (1) cutting-edge phenotypic characterisation of complex traits (eg. iridescence, flight) (2) empirical estimations of selective forces with wild butterflies (3) machine learning-based population genomics applied to demographic inferences (4) and mathematical modelling of density-dependent processes with stochasticity. These innovative approaches will shed light on unrevealed ecological interactions between species, impacting diversification of traits and species, therefore bringing major scientific breakthrough and attracting society attention on biodiversity loss in Amazonia.

Mots-clés : mimicry,speciation,phenotypic diversification,mate choice,reproductive interference
Projet ERC : EDUCHANGE (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université d'Aarhus — Pays : Danemark — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 128692.5 €

EDUCHANGE aims to become one of the first ever projects to conduct simultaneous field experiments in four strategically selected countries (Denmark, Germany, Hungary, Iceland) with the goal to reduce inequality at the educational transition from compulsory to secondary education and at the transition to higher education. While experimental intervention studies have become more common in sociology and economics in recent years, EDUCHANGE will address five critical limitations in this literature: 1) No information or career guidance experiment with the goal to reduce inequality at educational transitions has ever been implemented in different countries with a harmonized design. This is puzzling given the importance of institutional context for the generation of inequalities in education 2) The majority of experiments focus on the transition to higher education while neglecting earlier transitions which are particularly relevant in tracked European education systems 3) Previous experiments have hardly considered the role and knowledge of professional counsellors 4) Previous interventions have mainly focused on updating students’ biased perceptions of costs and returns while neglecting other psychological and social barriers 5) Advances in multimedia technologies have only to a limited extent been integrated in previous experiments.  Key outcomes of EDUCHANGE will include: (a) A more nuanced conceptual understanding of how career guidance and information provision can help to reduce inequalities (b) A comparative study of students’ subjective evaluation of costs, returns and probabilities of success and psychological barriers they attach to different educational alternatives in a comparative perspective (c) Knowledge about to what degree institutional context has an impact on whether information provision and career guidance can reduce inequality at two key educational transitions – and potentially affect persistence at the next level.

Mots-clés : social inequality in education,field experiments,educational transitions,comparative research
Projet ERC : MAGIX (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Life Science
Panel : Cell Biology, Development, Stem Cells and R egeneration
Entité participante : Université de Fribourg-en-Brisgau — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1995921.0 €

Cell type compositions determine tissue morphologies and functions, are adapted during evolution, and their changes cause diseases. However, we do not understand how cell type compositions can be controlled and modified to predictively achieve different organ functions. The goal of the MAGIX project is to understand the developmental mechanisms controlling cell type compositions, and to mechanistically link variations of cell type compositions to physiological properties of tissues in development and disease. We will use mucociliary epithelia as they provide an optimal system to study how adaptations of cell type compositions are regulated and how they determine organ functions. They build on a conserved gene-regulatory network to generate epithelia with different functions across species and organs derived from all three germ layers. We hypothesize that key epigenetic, patterning and remodeling mechanisms are used in a modified fashion to generate different functions and morphologies in different organs. In this project, we will establish two new vertebrate mucociliary model systems in the Xenopus foregut and the mouse esophagus, multi-modal resources, and integratively analyze data across models. We will then test a series of hypotheses experimentally to gain insights into the mechanisms regulating mucociliary cell type compositions, their adaptations, and to model mucociliary patterning in silico. Lastly, the developmental functions of foregut mucociliary epithelia in Xenopus and mice will be investigated to reveal potential contributions to gut microbiome establishment, thereby uncovering a new role for mucociliary epithelia in vertebrate development. Using a range of state-of-the-art techniques, the MAGIX project will study mucociliary adaptations to resolve fundamental questions in cell and developmental biology, and to provide translational insights into human diseases that kill >8 Mio. people per year (chronic airway diseases, cancers, motile ciliopathies).

Mots-clés : xenopus,mucociliary epithelia,endoderm,microbiome
Projet ERC : WildfireTwins (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université Christian Albrecht de Kiel — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1986200.0 €

Wildfires have a devastating impact on the environment, infrastructure, animals, and human lives. The complex physical dynamics paired with their unpredictable development makes wildfires a dangerous natural phenomenon that is difficult to counteract. Recently, digital twins have emerged as a concept that combines geometric modeling, image synthesis, and physical simulation with broad applications in predicting real world processes. This proposal aims to develop digital twins for wildfires – 3D models of ecosystems coupled with physical simulations – and to build AI-based tools for wildfire assessment. A wildfire twin with realistic physical simulations will have ground-breaking implications for firefighting services who will be able to use my framework as part of their decision making, resulting in an improved ability to combat wildfires, keep human lives safe and protect the environment. In contrast to existing wildfire simulations, a novel digital twin will provide real-time simulations of wildfires and complex 3D representations of ecosystems. By relying on state-of-the-art computer graphics technology, a digital twin will support generating photorealistic images and videos of wildfires. Synthetically generated data with high visual fidelity will profoundly accelerate the development and adoption of AI-based solutions for managing wildfires, as it addresses the bottleneck of capturing and curating expensive real-world training datasets. A digital twin that supports fast and accurate simulations of wildfires will have major impact on the analytical investigation conducted by the scientific community interested in understanding wildfires by accelerating the evaluation of hypotheses. Additionally, I plan to use the digital twin as a RobotGym – a virtual training environment for autonomous agents. A risk of this proposal is that a digital twin may not attain the required degree of realism – in which case it is still possible to identify options for higher quality.

Mots-clés : digital twin,wildfire modeling and simulation,tree combustion,neural networks for wildfire prevention,synthetic data generation
Projet ERC : PROTAC-PDAC (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Université Christian Albrecht de Kiel — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999401.0 €

Pancreatic ductal adenocarcinoma (PDAC) is a life-threatening disease with few treatment options. Recently, the chemical space for targeted therapeutics was significantly increased by the development of compounds that induce protein degradation (PROTACs), but so far, they are unavailable for PDAC. The main motivation of PROTAC-PDAC is to develop novel targeted therapies for PDAC. The project is based on our accomplishments in three crucial areas. First, my group identified – as target candidates – a series of transcription factors that are absolutely required for PDAC growth in vivo. Second, we developed dozens of potent, specific PROTACs, thereby gaining deep insight into the underlying design principles and synthesis procedures. Third, we established genetic models in mice for simulating PROTAC function and discovering ideal target–E3 ligase pairs. In PROTAC-PDAC, we will develop compounds that degrade key oncogenic transcription factors and inhibit pancreatic tumor growth with minimal toxicity in healthy tissues. We will start by implementing the Auxin degron system in a murine PDAC model, to induce degradation of the target candidates and select four priority targets with the highest therapeutic index (Aim I). Then, we will assess their oncogenic functions by analyzing molecular, cellular and organismic consequences of acute depletion in mice (Aim II). In parallel, we will develop pharmaceutical PROTACs to induce target degradation and analyze their efficacy in PDAC models (Aim III). Finally, with the aim of developing less toxic PDAC therapy, we will identify pancreatic E3 ligases for the design of the first tissue-specific PROTACs that induce target degradation primarily in pancreatic tumor cells (Aim IV). While the main goal of PROTAC-PDAC is to develop new PROTAC-based therapeutic strategies, we will also establish groundbreaking methods and models leading to fundamental discoveries in PDAC biology.

Mots-clés : pancreatic ductal adenocarcinoma pdac,protacs,targeted protein degradation,non conventional cancer targets,auxin degron system aid,targeted therapy,pdac mouse models,tissue specific e3 ubiquitin ligases
Projet ERC : pMolEvol (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Université Christian Albrecht de Kiel — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2112500.0 €

Plasmids are autonomously replicating extra-chromosomal elements that play a major role in prokaryote ecology and evolution. The study of plasmid evolution has so far focused mostly on their biodiversity and effect on their host ecology and evolution. Nonetheless, the fundamentals of plasmid genome evolution remain to a large extent uncharted territory. Plasmids reside within a host cell that supplies their environmental, energetic and metabolic requirements. Consequently, natural selection operates on plasmids in two hierarchical levels: one component is the plasmid replication and inheritance within the host cell, and the other is the host fitness within the population. Studying the determinants of plasmid evolutionary success thus requires the expansion of classic population genetics theory to populations of plasmids within bacterial cells. The overarching aim of pMolEvol is to create a novel unified framework for plasmid molecular and genome evolution. This includes: 1) to quantify the effect of multilevel drift and selection on plasmid genetic diversity, 2) supply a framework for quantification of plasmid fitness and its effect on plasmid evolution, 3) characterize patterns and rate of plasmid genome evolution. Our conceptual framework will be based on empirical data from plasmid evolution experiments and reconstruction of past events from genomic information. The conceptually novel measures and approaches developed in pMolEvol open up avenues for the extension of classic population genetic theory to prokaryotic organisms. Across all domains of life, genetic information is stored, replicated and translated by similar mechanisms. Focusing on plasmids, we aim to uncover the general principles that govern the evolution of autonomously replicating genetic elements. Our ultimate goal is to describe plasmid evolution from emergence to extinction.

Mots-clés : prokaryote evolution,plasmid biology and evolution,experimental evolution,microbial genome evolution,molecular evolution,microbial genetics,extrachromosomal genetic elements,phylogenetics,lateral gene transfer,population genetics
Projet ERC : BOREAUSTRALIS (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université de Göteborg — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2094176.0 €

According to temperature reconstructions based on natural climate proxy archives (ice cores, lake sediments, tree rings etc.), Northern and Southern Hemisphere (NH, SH) temperature histories differ in important features over the past millennia. This stands in contrast to the broad hemispheric agreement found in corresponding climate model simulations.  I hypothesize that a new tree-ring proxy, based on quantitative wood anatomy, can be used to improve temperature reconstructions in both hemispheres, and by this reduce differences between reconstructions and simulations. Preliminary studies show such results at regional scales in Eurasia, and Hemispheric scale networks based on quantitative wood anatomy are thus expected to confirm my hypothesis. These advances will be novel and highly relevant to communities engaged in climate modelling, climate reconstructions, and human history research.  To test my hypothesis, the project is divided into the following aims:  1. Create Boreal and Austral networks of quantitative wood anatomy data from tree rings from already sampled, as well as newly sampled trees growing in extra-tropical regions. 2. Use the new proxy data to examine volcanic-related responses specifically in the SH, where model simulations and existing proxy reconstructions clearly disagree. 3. Investigate if intra- and inter-hemispheric patterns of long-term temperature changes are more coherent among the new proxy records, and less different from corresponding model simulations.  I am convinced that the results of this project will substantially improve upon the quality of both NH and SH temperature reconstructions, but if improved NH and SH reconstructions remain distinct from corresponding simulations, this may constitute an important basis for revisiting system physics implemented in current climate models. The outcome of these investigations will thus likely challenge conventional wisdom in at least one, and possibly several research communities.

Mots-clés : southern hemisphere,northern hemisphere,climate change,tree ring anatomy,earth system models,volcanic cooling,late holocene,current warming