Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : PainSupportLink (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : University College Dublin — Pays : Irlande — Rôle : PI
Acronyme de l'entité : UCD
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1747001.0 €

This project will advance chronic pain treatment by developing a personalised support package tailored to patients' uniqu. e social, biological, and psychological drivers of pain. Chronic pain affects billions and costs Europe €440 billion annually. Socially disadvantaged individuals, especially those of low socioeconomic status, are disproportionately affected, but current treatments primarily target biological and psychological factors, overlooking social elements. This gap may explain the modest and short-lived effects of current treatments, particularly in vulnerable groups. I hypothesise that adding personalised community support—an approach not yet explored for pain—will enhance outcomes and propose four work packages: WP1 Identify causal pathways through which biological, psychological, and social factors mediate the effects of social disadvantage on pain to identify support targets, using latent class mediation analysis of longitudinal cohort data. WP2 Build a personalised support package tailored to individuals' needs and targets identified in WP1, using a scoping review, qualitative study, and feasibility trial. WP3 Evaluate the support package's effects on pain, disability and quality of life through a randomised clinical trial. WP4 Investigate whether selected biological (inflammation, nervous system sensitisation, physical activity), psychological (mental health, self-efficacy), and social (support, isolation) mechanisms mediate the support package’s effect on clinical outcomes using a causal mediation analysis. Multiomics blood profiling, computerised cuff pressure algometry, activity accelerometers, and questionnaires will be employed. This project will advance understanding of how bio-psycho-social mechanisms link social disadvantage to pain and transform pain outcomes through a personalised, socially sensitive, equity-driven framework that addresses the full range of social, biological, and psychological factors, surpassing current treatments.

Mots-clés : chronic musculoskeletal pain social disadvantage social prescribing link workers self management randomised controlled trial psychologically informed exercise and education
Projet ERC : VISIONSAFE (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université de Saragosse — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1497873.0 €

Clinical eye images could be explored in an entirely new way to provide early detection of serious eye diseases that currently affect thousands worldwide yet could be preventable. The objective of VISIONSAFE is to extract macro- and microstructure biomarkers from real-world clinical images depicting the retina, lens, and cornea, thereby facilitating early disease detection and personalised management. While macrostructural biomarkers related to ocular shape are well-established, microstructural biomarkers related to tissue remain largely unexplored. The project's foundation rests on the preliminary work of the PI, which demonstrated the feasibility of deriving disease-related microstructure biomarkers from low-resolution clinical eye images. The approach combines data science, advanced image processing, and rigorous experimental validation to better understand the interplay between macro- and microstructural biomarkers in prevalent sight-threatening eye diseases, including age-related macular degeneration (AMD), glaucoma, cataracts, and corneal ectasia, using efficient, cost-effective, and accessible tools. VISIONSAFE covers several ground-breaking aspects: clinical eye data is already available but underused (can we use data science for vision preservation?); the acquisition and combination of macro- and microstructural biomarkers from clinical images have not yet been considered (is this combination a key for the early diagnosis of eye diseases? Are we missing out essential information from already available data?). The project's potential gains are significant. It enhances knowledge on eye disease biomarkers accessible through routine eye exams, shifts the paradigm toward macro- and microstructural biomarker synergy, and represents an important step towards a future where eye diseases are identified and managed earlier, offering hope and improved quality of life to countless individuals.

Mots-clés : ophthalmology
Projet ERC : H2TRAP (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université Nicolas-Copernic — Pays : Pologne — Rôle : PI
Acronyme de l'entité : UMK
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1923238.75 €

Due to its simplicity, H2 constitutes a perfect tool for testing fundamental physics: testing quantum electrodynamics, determining fundamental constants, or searching for new physics beyond the Standard Model. H2 has a huge advantage over the other simple calculable systems (such as H, He, or HD+) of having a set of a few hundred ultralong living rovibrational states, which implies the ultimate limit for testing fundamental physics with H2 at a relative accuracy level of 10^-24. The present experiments are far from exploring this huge potential. The main reason for this is that H2 in its ground electronic state extremely weakly interacts with electric and magnetic fields; hence, H2 is not amenable to standard techniques of molecule slowing, cooling, and trapping. In this project, we propose a completely new approach for H2 spectroscopy. For the first time, we will trap a cold sample of H2. We will consider two approaches: superconducting magnetic trap and ultrahigh-power optical dipole trap (with trap depths of the order of 1 mK). T = 5 K will be achieved with a standard refrigeration technique, and the trap will be filled in situ with the 5 K thermal distribution of the H2 sample. Presently, there is no technology available to cool down the H2 gas sample from 5 K to 1 mK; hence, the only option is to directly capture the coldest fraction. The majority of the molecules that initially fill the trap zone will be lost. However, the high initial H2 density will allow us to trap up to 600 000 molecules. We will do infrared-ultraviolet double resonance H2 spectroscopy referenced to the optical frequency comb and primary frequency standard. The ability to do spectroscopy using a cold and trapped sample will eliminate the sources of uncertainty that have limited previous best approaches and will allow us to improve the accuracy by at least two orders of magnitude. The H2 traps will open up a new way for further long-term progress in the metrology of H2 rovibrational lines.

Mots-clés : precision laser spectroscopy,cold molecules,magnetic trap,optical dipole trap,quantum electrodynamics for molecules
Projet ERC : COLINA (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Donostia International Physics Center — Pays : Espagne — Rôle : Partner
Acronyme de l'entité : DIPC
Type d'entité : Recherche
Financement ERC de l'entité : 50000.0 €

Coherent elastic neutrino-nucleus scattering (CEνNS) is a recently demonstrated novel process of neutrino interaction. It provides numerous avenues to advance our sensitivity to new physics beyond the Standard Model and enables a miniaturization of otherwise massive neutrino detectors, opening up the possibility of technological applications.  The development the development of an innovative single-phase noble liquid time projection chamber (TPC) to detect CEνNSis proposed. Two distinct ideas are combined to maximize the potential of the technique. 1) The signal will be amplified through electroluminiscence (EL). 2) The TPC will be shaped as a conical frustum. Single-phase EL is unaffected by charge trapping which is the major deterrent of dual-phase noble liquid TPCs for CEνNS searches at shallow depths. However, it requires extremely high electric fields. Such fields can be reached by using very thin wires – μm-scale diameter. This is an impediment to produce large amplification regions. Common TPC shapes are thus limited in size and target mass. The conical shape allows to maximize the mass by drifting all charges towards a small amplification region at the smaller circle of the cone. Such scheme allows for good coverage with few sensors.  COLINA, a conical TPC capable of holding ∼50 kg of LXe, will be developed and deployed at the largest spallation neutrino source, the European Spallation Source. Simulations point to a conservative energy threshold as low as ∼0.525 keVnr. The detector will allow for operation with different noble gases. The increase in density of liquid-phase, compared to gaseous-phase, results in large CEνNS rate with rather small detectors. In fact, COLINA will produce the larger CEνNS statistics in all the considered isotopes, Xe, Kr and Ar, and will do so in unexplored energy regions for the process, where the physics relevance is maximal.

Mots-clés : neutrino,liquid noble gas,time projection chamber,coherent elastic neutrino nucleus scattering
Projet ERC : Learn2Predict (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Université d'Édimbourg — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1941819.0 €

To perform sensory guided behaviors, animals need to distinguish self-generated and externally generated sensory inputs. Predictive processing theories propose that the brain does this by learning to predict sensations caused by self-motion. The key signals thought to drive this learning are prediction errors: differences between predicted and actual sensory input. My previous work shows that neurons in the primary visual cortex (V1) compute visuomotor prediction errors, and that prediction errors activate the locus coeruleus, a brain-wide neuromodulatory system. We will now investigate the circuit and neuromodulatory mechanisms underlying the learning of sensory predictions, using V1 as a model. I hypothesize that input to V1 from higher order cortical areas undergoes plasticity during self-generated sensory feedback. This plasticity should be driven by prediction errors in V1 activity, modulated by locus coeruleus output, and improve detection of externally generated visual flow during self-motion. We will test this hypothesis using innovative methods, including a multimodal virtual reality system and a novel object detection task, combined with in vivo whole cell recordings, two-photon imaging, and optogenetics. The specific aims are to (1) investigate how prediction errors are communicated between the locus coeruleus and the cortex, (2) decipher the mechanisms of predictive plasticity within the V1 circuit, and (3) assess the behavioral relevance of this plasticity. The knowledge gained will have a fundamental impact on our mechanistic understanding of predictive learning in the cortex and the role of neuromodulation in this process, which will have significance for 1) understanding conditions in which the processing of self-generated sensory feedback is thought to be disrupted (e.g., neurodevelopmental conditions and psychosis), and 2) development of AI and brain-machine interfaces that deal with self-generated sensor feedback (e.g., prostheses).

Mots-clés : learning,plasticity,neocortex,sensorimotor integration,visual cortex,neuromodulation
Projet ERC : GLP1RES (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Université de Parme — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have revolutionized the treatment of type 2 diabetes mellitus (T2DM). However, a subset of patients does not respond to these agents or develop treatment resistance, which poses a significant clinical challenge. Emerging evidence from our work in humans and mice shows that endogenous amidated hormones synergize with GLP-1RAs to reduce glycated hemoglobin, and their absence promotes GLP-1RA resistance. This proposal explores the intricate mechanisms by which endogenous amidated hormones affect endogenous GLP-1 secretion, modulate its function, and influence the effectiveness of GLP-1RAs. Furthermore, it will investigate which characteristics predict the GLP-1RA effectiveness in patients, and test potential therapeutic approaches to prevent detrimental GLP-1 resistance mechanisms with the goal of ameliorating T2DM treatment and outcomes. A plethora of approaches will be used to elucidate amidated hormone-induced GLP-1 resistance. We will study L-cell functions and GLP-1 endogenous secretion in vitro and in vivo in the absence of amidated hormones. With tissue-specific genetically engineered mice, we will selectively deplete key amidated hormones that counteract the effects of GLP-1RAs and dissect their contribution in physiological readouts of GLP-1RA resistance. Lastly, we will leverage machine learning approaches to clinical data analysis on large deeply phenotyped biomedical cohorts to select for patient characteristics that determine the likelihood of developing GLP-1RA resistance, and to assess the combinatorial contribution of amidated hormones on T2DM susceptibility. By combining experimental and computational approaches, this project aims to gain a deeper understanding of the complex interplay between endogenous hormones (and their combinatorial effects) and GLP-1 signaling, to develop more effective and personalized therapies, and ultimately to improve clinical outcomes in individuals with T2DM.

Mots-clés : glp 1 receptor agonists,type 2 diabetes mellitus,amidated hormones,glp1 ra
Projet ERC : MMSCancer (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Institut chypriote de neurologie et de génétique — Pays : Chypre — Rôle : PI
Acronyme de l'entité : CING
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 913562.5 €

Inefficient drug delivery to tumors can reduce dramatically treatment efficacy and thus, affect negatively the life of cancer patients. This is particularly evident in desmoplastic cancers where interactions among cancer cells, stromal cells and the fibrotic matrix cause tumor stiffening and accumulation of mechanical forces that compress tumor blood vessels. Indeed, in subsets of pancreatic cancers and sarcomas, 95% of intratumoral blood vessels may be compressed and up to 80% totally collapsed leading to reduced blood flow (hypo-perfusion) and drug delivery. Hypo-perfusion also leads to hypoxia that helps cancer cells evade the immune system and increase their invasive and metastatic potential. Use of mechanotherapeutics and ultrasound sonopermeation are two mechano-modulation strategies that separately have been employed to treat vascular abnormalities in tumors. Even though these strategies have reached the clinic, their promise has yet to be realized by cancer patients owning to limitations of the methods. My hypothesis is that these strategies can uniquely complement each other and have not only additive, but highly multiplicative synergistic effects on modulating the desmoplastic tumor microenvironment and improving the efficacy of the promising but still of limited use nano-immunotherapy. However, it is crucial that specific guidelines should be developed. To achieve this ground-breaking goal, I will employ a mixture of cutting-edge computational and experimental techniques. I will perform in vivo mice studies in pancreatic cancers and sarcomas to investigate under what conditions these mechano-modulation strategies can be optimally combined to improve treatment efficacy, prevent metastasis and increase survival. In parallel, I will develop new mathematical models to provide useful guidelines for optimizing the experimental protocol. MMSCancer will introduce novel therapeutic strategies for the treatment of drug resistant tumors leading to better therapies.

Mots-clés : bioengineering,biomechanics,computational models,mechanical properties,tumor pathophysiology,drug delivery,animal tumor models,ultrasound shear wave elastography
Projet ERC : Persistence (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Université Ca' Foscari de Venise — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 664850.0 €

This project proposes a novel econometric approach suited for hypothesis testing and confidence interval construction in the presence of generic time series regressors with arbitrary persistence degree. The project will develop inference for a large class of regressor processes commonly encountered in macroeconomic and financial data, ranging from stationary, local-to-unit-root, explosive, long memory, time-varying parameter and other nonstationary processes as well as multivariate systems containing mixed components. The key idea behind the approach is to build a new explanatory variable from the data which conforms to a standard central limit theory even when the original regressor does not. The resulting instrumental variable estimators based on this endogenously constructed instrument are shown to be asymptotically mixed-Gaussian regardless of the true stochastic nature of the regressor, implying standard inference for any IV-based self-normalised test. The main contribution of the project is to place a large class of nonstandard processes with a wide range of dynamics and memory properties under a common econometric framework which delivers standard inference regardless of the regressor's stochastic properties. The asymptotic development of the procedure requires fundamental theoretical contributions such as a novel Granger-Johansen type representation theory for multivariate time series with mixed stochastic components and the asymptotic analysis of time series with different persistence types. The novel procedure is shown to be valid uniformly across persistence regimes and automatically delivers asymptotically correct inference without a priori knowledge of the regressor's true stochastic nature. In addition to its generality and theoretical coherence, the approach has the added advantage of ease of implementation (with closed-form estimators and tests that employ standard critical values), thus making it suitable for general practical application.

Mots-clés : time series,uniform inference,confidence intervals,central limit theory,autoregression,predictive regression,instrumentation,mixed gaussianity,t statistic
Projet ERC : PolyphonicPhilosophy (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Université Ca' Foscari de Venise — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498215.0 €

This project aims at an innovative, interdisciplinary approach to Latin logical commentaries from the Long Twelfth-Century (c. 1070-1220). My contention is that this little-explored topic presents us with a unique "Polyphonic Philosophy" that can open up a new horizon in our understanding of philosophical commentaries, on the one hand, and help to reshape some core ideas in the history of philosophy, on the other - at a time when both are urgently needed. A team of four postdoctoral researchers led by the PI will actualise the potential of some extraordinary preliminary material to study a wealth of 12th-century manuscripts of mainly unpublished texts with novel concepts. We will have three objectives: 1. A comprehensive study of 12th-century logical manuscripts as the main units for accessing this complex field, in which the notions of unitary text and author have proven challenging. 2. A study of mainly unpublished logical texts from a "polyphonic" perspective that will follow a comparative, archaeological and analytical approach and develop some pioneering digital scholarly editions of texts to model their polyphony. 3. Meta-analysis of the concepts used from an interdisciplinary and historiographical perspective, offering new insights into the key concepts of the project and non-solo paradigms to reconstruct philosophy's past. The project will require interdisciplinary expertise in digital medieval studies, digital textual criticism, codicology, the history of education, the history of literacy, the social history of logic, the history of philosophy, and the historiography of philosophy. Following a concrete-to-abstract highly integrated research design, taking advantage of recent methodologies, this project has the ambitious goal of opening up an entirely new horizon for the history of philosophy, while also impacting on several other areas, from medieval studies, to the history of universities, to the study of commentaries more generally.

Mots-clés : philosophical commentaries,latin medieval logic,long twelfth century,peter abelard,boethius,manuscripts,edition of latin logical texts,historiography of philosophy,twelfth century schools
Projet ERC : OLAF (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Study of the Human Past
Entité participante : Université de l'Algarve — Pays : Portugal — Rôle : PI
Acronyme de l'entité : UALG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Tool production is intrinsically tied to the evolution of our species as it allows us to occupy nearly every environment on the planet. The widespread production of sharp-edged tools known as the Oldowan, 2.6 million years ago, is regarded as a major adaptive leap as it may have fundamentally changed the ecology of our ancestors. This major adaptative shift resulted in a change in diet, enhanced ecological versatility for a range of environments, and, ultimately, the proliferation of the human lineage across the globe. However, it remains unclear if the emergence of Oldowan technologies resulted from a watershed innovation or if it represents a technological continuity of the tool repertoire of apes. The OLAF project aims to determine the adaptive significance of the appearance of the Oldowan by implementing a new set of theoretical tools while shifting the scale of archaeological analysis from the site to a broader landscape level. OLAF will use agent-based modeling to directly investigate the relationship between stone tool use, environmental factors, and site formation processes at the landscape scale and generate concurrent expectations for how hominin-environment interactions produce patterns in the archaeological record. In parallel, we will reconstruct the portions of the Ledi Geraru (Ethiopia) research area where sediments have preserved the 2.6-million-year-old artifact and fossil-rich paleolandscape, over an area of 33 square kilometers. Agent-based models that are tailored to local data, will validate, or reject predictions concerning mobility, diet, or space use. OLAF will thus provide a comprehensive understanding of the tool-mediated foraging behaviors of the earliest Oldowan tool makers, allowing us to examine the adaptive benefits of tool use at the dawn of humanity.

Mots-clés : landscape archaeology,computational modeling,early stone age