Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : BugTheDrug (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University of Luxembourg — Pays : Luxembourg — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Precision medicine is an emerging paradigm that aims at maximizing the benefits and minimizing the harm of drugs. Realistic mechanistic models are needed to understand and limit heterogeneity in drug responses. Consequently, novel approaches are required that explicitly account for individual variations in response to environmental influences, in addition to genetic variation. The human gut microbiota metabolizes drugs and is modulated by diet, and it exhibits significant variation among individuals. However, the influence of the gut microbiota on drug failure or drug side effects is under-researched. In this study, I will combine whole-body, genome-scale molecular resolution modeling of human metabolism and human gut microbial metabolism, which represents a network of genes, proteins, and biochemical reactions, with physiological, clinically relevant modeling of drug responses. I will perform two pilot studies on human subjects to illustrate that this innovative, versatile computational modeling framework can be used to stratify patients prior to drug prescription and to optimize drug bioavailability through personalized dietary intervention. With these studies, BugTheDrug will advance mechanistic understanding of drug-microbiota-diet interactions and their contribution to individual drug responses. I will perform the first integration of cutting-edge approaches and novel insights from four distinct research areas: systems biology, quantitative systems pharmacology, microbiology, and nutrition. BugTheDrug conceptually and technologically addresses the demand for novel approaches to the study of individual variability, thereby providing breakthrough support for progress in precision medicine.

Mots-clés : gut microbiota,nutrition,therapeutic stratification,precision medicine,drug metabolism,human metabolism,colorectal cancer,parkinson s disease,physiology based pharmacokinetic modeling
Projet ERC : NitroScission (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2744880.0 €

Present ammonia synthesis, via the Haber-Bosch process, occurs in centralised facilities above 150 bar and above 400 C; it consumes a colossal 1% of our global fossil fuel consumption. Electrolytic ammonia synthesis, i.e. below 100 C and at atmospheric pressures, could be far more attractive: it would be powered by renewable energy and would take place at the point-of-consumption. I have recently made a breakthrough, by demonstrating the first unambiguous and quantitative evidence that dinitrogen electroreduction is possible under ambient conditions on a solid electrode, albeit at low efficiency My aim for NitroScission is to elucidate pathways —at a molecular level— to catalyse the reaction at high efficiency. However, only the most reactive metal or metal nitride surfaces bind to dinitrogen. Such surfaces will bind even stronger to hydrogen or oxygen from water or air. To circumvent these constraints, I will use three strategies: (i) I will tailor the access of protons to the electrode-electrolyte interface, via in-situ deposited ionic interphases, exploiting recent advances in controlling the reactivity of electrolytes. (ii) I will tailor the binding to dinitrogen through oxygen-free fabrication and testing of metals and metal nitrides electrodes. By preventing air exposure, my team will gain access to a class of highly reactive electrodes, never previously tested in an electrochemical cell. (iii) I will use electrochemical looping, to dynamically separate dinitrogen adsorption from its subsequent hydrogenation. These experiments will be enabled by a novel method that allows us to observe gas evolution in real time. I will combine advanced thin film preparation methods, electrochemical tests, and in operando and ex-situ spectroscopy to establish the design principles for this important reaction. Guided by these unique tools and my scientific leadership, my team will shed unique insight into how to tailor electrode-electrolyte interfaces.

Mots-clés : electrochemistry,electrocatalysis,electrolysis
Projet ERC : AssemblySkills (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Technical University of Darmstadt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Present-day industrial robots are made for the purpose of repeating several tasks thousands of times. What the manufacturing industry needs instead is a robot that can do thousands of tasks, a few times. Programming a robot to solve just one complex motor task has remained a challenging, costly and time-consuming task. In fact, it has become the key bottleneck in industrial robotics. Empowering robots with the ability to autonomously learn such tasks is a promising approach, and, in theory, machine learning has promised fully adaptive control algorithms which learn both by observation and trial-and-error. However, to date, learning techniques have yet to fulfil this promise, as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of collaborative robots. The goal of the AssemblySkills ERC PoC is to validate an autonomous skill learning system that enables industrial robots to acquire and improve a rich set of motor skills. Using structured, modular control architectures is a promising concept to scale robot learning to more complex real-world tasks. In such a modular control architecture, elemental building blocks – called movement primitives, need to be adapted, sequenced or co-activated simultaneously. Within the ERC PoC AssemblySkills, our goal is to group these modules into an industry-scale complete software package that can enable industrial robots to learn new skills (particularly in assembly tasks). The value proposition of our ERC PoC is a cost-effective, novel machine learning system that can unlock the potential of manufacturing robots by enabling them to learn to select, adapt and sequence parametrized building blocks such as movement primitives. Our approach is unique in the sense that it can acquire more than just a single desired trajectory as done in competing approaches, capable of save policy adaptation, requires only little data and can explain the solution to the robot operator.

Mots-clés : autonomous learning system,motor primitives,motor skills,modular control architecture
Projet ERC : SILK-EYE (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University of Valladolid — Pays : Espagne — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 216680.35 €

Prevalent eye diseases, such as myopia, presbyopia, and corneal disease affect millions worldwide, but for now cannot be prevented. Surgical interventions of these conditions are turning to additive surgery, exemplified by corneal implants or the replacement of the natural crystalline lens by (or addition of) an intraocular lens, as it reduces complications of tissue removal surgeries. Current eye treatments involving adding tissue or lenses exist in the form of amnion bandages, corneal inlays, and intraocular lenses. However, those approaches suffer from a number of shortcomings: corneal haze or rejection; risk of disease transmission, short lifespan, need of cryopreservation and donor tissue; lack of compliance of lens designs and biomaterials. In particular, no material has been found that fully meets the requirements for mechanical properties, transparency, biocompatibility and versatility for applications in the cornea and in accommodating intraocular lenses. In recent years, silk fibroin derived from silkworm cocoons has emerged as a protein polymer for biomaterial applications. SILK-EYE will develop a new generation of corneal and intraocular implants, using silk-based materials tuned to each specific application and light enabling procedure. The silk-based implants will feature both the accessibility advantages of synthetic materials and the structural and biocompatibility properties of allografts, capitalizing on silk’s unique potential for transparency, controllable stiffness and degradability, refractive index and permeability, and their potential for light-induced cross-linking and bonding in the eye. SILK-EYE will design radically novel corneal dressings and implants, and accommodating intraocular lenses that are more biocompatible and functional than current synthetic implants, and are safer, more tunable, accessible and affordable than donor allografts, potentially revolutionizing how the major corrective procedures in ophthalmology are performed.

Mots-clés : ophthalmology,silk,ocular prosthesis,implants,intraocular lens,refractive error,presbyopia,corneal injury,biomaterials,optics,photonics
Projet ERC : SILK-EYE (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Spanish National Research Council — Pays : Espagne — Rôle : PI
Acronyme de l'entité : CSIC
Type d'entité : Recherche
Financement ERC de l'entité : 2282929.65 €

Prevalent eye diseases, such as myopia, presbyopia, and corneal disease affect millions worldwide, but for now cannot be prevented. Surgical interventions of these conditions are turning to additive surgery, exemplified by corneal implants or the replacement of the natural crystalline lens by (or addition of) an intraocular lens, as it reduces complications of tissue removal surgeries. Current eye treatments involving adding tissue or lenses exist in the form of amnion bandages, corneal inlays, and intraocular lenses. However, those approaches suffer from a number of shortcomings: corneal haze or rejection; risk of disease transmission, short lifespan, need of cryopreservation and donor tissue; lack of compliance of lens designs and biomaterials. In particular, no material has been found that fully meets the requirements for mechanical properties, transparency, biocompatibility and versatility for applications in the cornea and in accommodating intraocular lenses. In recent years, silk fibroin derived from silkworm cocoons has emerged as a protein polymer for biomaterial applications. SILK-EYE will develop a new generation of corneal and intraocular implants, using silk-based materials tuned to each specific application and light enabling procedure. The silk-based implants will feature both the accessibility advantages of synthetic materials and the structural and biocompatibility properties of allografts, capitalizing on silk’s unique potential for transparency, controllable stiffness and degradability, refractive index and permeability, and their potential for light-induced cross-linking and bonding in the eye. SILK-EYE will design radically novel corneal dressings and implants, and accommodating intraocular lenses that are more biocompatible and functional than current synthetic implants, and are safer, more tunable, accessible and affordable than donor allografts, potentially revolutionizing how the major corrective procedures in ophthalmology are performed.

Mots-clés : ophthalmology,silk,ocular prosthesis,implants,intraocular lens,refractive error,presbyopia,corneal injury,biomaterials,optics,photonics
Projet ERC : bioPCET (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Stockholm University — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1153863.1 €

Primary energy conversion in nature is powered by highly efficient enzymes that capture chemical or light energy and transduce it into other energy forms. These processes are catalyzed by coupled transfers of protons and electrons (PCET), but their fundamental mechanistic principles are not well understood. In order to obtain a molecular-level understanding of the functional elements powering biological energy conversion processes, we will study the catalytic machinery of one of the largest and most intricate enzymes in mitochondria and bacteria, the respiratory complex I. This gigantic redox-driven proton-pump functions as the entry point for electrons into aerobic respiratory chains, and it employs the energy released from a chemical reduction process to transport protons up to 200 Å away from its active site. Its molecular structure from bacteria and eukaryotes was recently resolved, but the origin of this remarkable action-at-a-distance effect still remains unclear. We employ and develop multi-scale quantum and classical molecular simulation techniques in combination with de novo-protein design methodology to identify and isolate the functional elements that catalyze the long-range PCET reactions in complex I. To fully understand the natural PCET-elements, we will further engineer central parts of this machinery into artificial protein frameworks, with the goal of designing modules for redox-driven proton pumps from first principles. The project aims to establish a fundamental understanding of nature's toolbox of catalytic elements, to elucidate how the complex biochemical environment contributes to the catalytic effects, and to provide blueprints that can guide the design of man-made enzymes for sustainable energy technology.
Projet ERC : SELMA (Horizon 2020, appel 2018, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University Medical Center Utrecht — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UMC
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149979.0 €

Problem: Cerebral small vessel disease (SVD) is a hallmark of serious brain diseases such as acute stroke and dementia. It has a major impact on society, since it affects quality of life of many ageing individuals, and increasingly challenges the healthcare resources. Current image-based biomarkers do not reflect SVD itself, but rather macroscopic, irreversible brain damage, secondary to the abnormalities of the small vessels. This greatly hampers development of new drugs as clinical trials lack effective biomarkers directly reflecting SVD. Solution: The SmallVesselMRI ERC project (337333), yielded 7T MRI methods with the potential to offer a paradigm shift in SVD research. We have developed new MRI measures that, for the first time, allow non-invasive assessment of the condition of the small vessels themselves, including blood flow velocity pulsation in small perforating vessels, which informs on vessel stiffness. The current proof-of-concept project ‘trial-ready Small vEsseL MRI MArkers (SELMA) aims to make these novel promising vascular parameters ready for use in phase II/III clinical trials. Our ambition is to bridge the gap between high end imaging research platforms and widespread clinical MRI, in order to enable drug-target engagement studies in clinical trials for newly developed drugs. SELMA will: 1) Port the small-vessel 7T MRI pulse sequences to clinically ubiquitous 3T MRI scanners. 2) Develop a software tool that can provide robust, automated data analysis. 3) Determine the reproducibility of the SELMA biomarkers in a group of 35 healthy individuals with scan-rescan experiments. 4) Provide clinical proof of concept by a pilot study in 15 patients with SVD, measuring the vascular parameters before and after administration of a pharmaceutical agent. Using results of our proof of concept study, we will develop a business plan to approach the pharmaceutical industry to change the paradigm in drug development for SVD treatment.

Mots-clés : cerebral small vessel disease,biomarkers,drug target engagement
Projet ERC : PTEROSOR (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1895636.0 €

Processes related to electronically excited states are central in chemistry, physics, and biology, playing a key role in ubiquitous processes such as photochemistry, catalysis, and solar cell technology. However, defining an effective method that reliably provides accurate excited-state energies remains a major challenge in theoretical chemistry. In PTEROSOR, we aim at developing a totally novel approach to obtain excited-state energies and wave functions in molecular sys- tems thanks to the properties of non-Hermitian Hamiltonians. Our key idea is to perform an analytic continuation of conventional computational chemistry methods. Indeed, through the complex plane, ground and excited states can be naturally connected. In a non-Hermitian complex picture, the energy levels are sheets of a more complicated topological manifold called Riemann surface and they are smooth and continuous analytic continuation of one another. PTEROSOR’s main goal is to develop a new theoretical approach allowing to connect, through the complex plane, electronic states. Instead of Hermitian Hamiltonians, we propose to use a more general class of Hamiltonians which have the property of being PT-symmetric, i.e., invariant with respect to combined parity reflection P and time reversal T. This weaker condition ensures a real energy spectrum in unbroken PT-symmetric regions. PT-symmetric Hamiltonians can be seen as analytic continuation of conventional Hermitian Hamiltonians. Using PT-symmetric quantum theory, an Hermitian Hamiltonian can be analytically continued into the complex plane, becoming non-Hermitian in the process and exposing the fundamental topology of eigenstates. Our gateway between ground and excited states are provided by exceptional points which lie at the boundary between broken and unbroken PT-symmetric regions.
Projet ERC : TERI (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Delft University of Technology — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU Delft
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499894.0 €

Programming and re-programming robots is extremely time-consuming and expensive, which presents a major bottleneck for new industrial, agricultural, care, and household robot applications. My goal is to realize a scientific breakthrough in enabling robots to learn how to perform manipulation tasks from few human demonstrations, based on novel interactive machine learning techniques. Current robot learning approaches focus either on imitation learning (mimicking the teacher’s movement) or on reinforcement learning (self-improvement by trial and error). Learning even moderately complex tasks in this way still requires infeasibly many iterations or task-specific prior knowledge that needs to be programmed in the robot. To render robot learning fast, effective, and efficient, I propose to incorporate intermittent robot-teacher interaction, which so far has been largely ignored in robot learning although it is a prominent feature in human learning. This project will deliver a completely new and better approach: robot learning will no longer rely on initial demonstrations only, but it will effectively use additional user feedback to continuously optimize the task performance. It will enable the user to directly perceive and correct undesirable behavior and to quickly guide the robot toward the target behavior. In my previous research I have made ground-breaking contributions to the existing learning paradigms and I am therefore ideally prepared to tackle the three-fold challenge of this project: developing theoretically sound techniques which are at the same time intuitive for the user and efficient for real-world applications. The novel framework will be validated with generic real-world robotic force-interaction tasks related to handling and (dis)assembly. The potential of the newly developed teaching framework will be demonstrated with challenging bi-manual tasks and a final study evaluating how well novice human operators can teach novel tasks to a robot.
Projet ERC : AnalogCreate (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : KU Leuven — Pays : Belgique — Rôle : PI
Acronyme de l'entité : KUL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

Progress in semiconductor technology and in intelligent data processing are converging today, opening the door to countless smart ICT applications through the Cloud and Internet of Everything, to the people’s benefit in years to come. Applications that interact with the physical world (e.g. environmental sensing, healthcare, autonomous vehicles, etc.), also need analog integrated circuits in the cyber-physical or edge layer. But while digital circuits are largely synthesized automatically through software, the analog circuits are mainly still handcrafted in industry with low design productivity. This results in long and error-prone design cycles, and the high development costs jeopardize many potential new ICT applications from ever being realized (e.g. solutions for rare diseases). It becomes even more problematic when moving to advanced technologies below 16 nm CMOS, that come with way more design and layout rules to be dealt with. The showstopper for state-of-the-art analog synthesis tools is that they require design heuristics and constraints to be entered explicitly by designers in order to handle the humongous solution space and to steer the circuit and layout optimizations towards acceptable solutions. The proposed disruptively new approach is to use the self-learning capabilities of advanced machine learning algorithms to self-learn and then exploit the design expertise and constraints from the many available successfully completed designs. Also a true circuit topology synthesis approach will be developed to create a proper (possibly novel) schematic from the target specifications, as well as an innovative formal analog design verification approach based on Quick Error Detection. These innovations will enable for the first time ever to truly autonomously create analog circuits from specifications to fully verified layout without direct input from any designer in the loop, and therefore enable the affordable implementation of many promising ICT applications.

Mots-clés : analog and mixed signal integrated circuits,design automation,autonomous synthesis,design creation,self learning