Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : IXSI3D (Horizon 2020, appel 2014, Consolidator 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 : 1999000.0 €

I propose to research, build and evaluate Interventional X-ray and Scintigraphy Imaging (IXSI). This will provide for the first time real-time, multimodality imaging during medical interventions by combining live x-ray and live nuclear imaging simultaneously from an identical viewpoint. The hybrid x-ray/nuclear imaging device will enable surgeons and interventional radiologists to exploit the power of molecular imaging in the operating theatre and intervention room through i) live guidance using 2D imaging and ii) 3D quantitative evaluation (IXSI3D). Systems, like the successful PET/CT and SPECT/CT, have revolutionized diagnostic medical imaging; however they acquire x-ray (anatomical information) and nuclear images (molecular information) in sequence. Our new technology brings live, hybrid imaging to operations and interventions. This will have a broad and powerful impact, particularly in oncological applications, including internal radiotherapy, tumor resection and biopsies. For combined X-ray/nuclear imaging, an x-ray tube, an x-ray detector and a gamma camera with collimator are required. Our concept relies on placing these three elements in one line, thus enabling imaging of the same field-of-view. However, straight-forward combination of these elements would block the line of views. Inspired by how eyes see around the nose, I invented a gamma camera geometry that sees around the x-ray tube. I have created a prototype, and using a provisional set-up based on this novel concept (patent pending), I have demonstrated IXSI’s basic principles. This proposal describes the quantum leap in medical imaging: clinical realization of IXSI for guidance, and the development of IXSI3D that enables intra-operative quantitative evaluation. I will develop algorithms and hardware, build a mobile system and prove it’s potential in a clinical research protocol. This will be the start of a new era in image-guided intervention.

Mots-clés : nuclear medicine physics  x ray imaging physics  interventional imaging physics
Projet ERC : AutoRecon (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Life Science
Panel : Cellular, Developmental and Regenerative Biology
Entité participante : University of Vienna — Pays : Autriche — Rôle : PI
Acronyme de l'entité : Univie
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999640.0 €

I propose to study how eukaryotic cells generate autophagosomes, organelles bounded by a double membrane. These are formed during autophagy and mediate the degradation of cytoplasmic substances within the lysosomal compartment. Autophagy thereby protects the organism from pathological conditions such as neurodegeneration, cancer and infections. Many core factors required for autophagosome formation have been identified but the order in which they act and their mode of action is still unclear. We will use a combination of biochemical and cell biological approaches to elucidate the choreography and mechanism of these core factors. In particular, we will focus on selective autophagy and determine how the autophagic machinery generates an autophagosome that selectively contains the cargo. To this end we will focus on the cytoplasm-to-vacuole-targeting pathway in S. cerevisiae that mediates the constitutive delivery of the prApe1 enzyme into the vacuole. We will use cargo mimetics or prApe1 complexes in combination with purified autophagy proteins and vesicles to reconstitute the process and so determine which factors are both necessary and sufficient for autophagosome formation, as well as elucidating their mechanism of action. In parallel we will study selective autophagosome formation in human cells. This will reveal common principles and special adaptations. In particular, we will use cell lysates from genome-edited cells in combination with purified autophagy proteins to reconstitute selective autophagosome formation around ubiquitin-positive cargo material. The insights and hypotheses obtained from these reconstituted systems will be validated using cell biological approaches. Taken together, our experiments will allow us to delineate the major steps of autophagosome formation during selective autophagy. Our results will yield detailed insights into how cells form and shape organelles in a de novo manner, which is major question in cell- and developmental biology.

Mots-clés : autophagy
Projet ERC : BACKUP (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : University of Trento — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499825.0 €

I will address the fundamental question of which is the role of neuron activity and plasticity in information elaboration and storage in the brain. I, together with an interdisciplinary team, will develop a hybrid neuro-morphic computing platform. Integrated photonic circuits will be interfaced to both electronic circuits and neuronal circuits (in vitro experiments) to emulate brain functions and develop schemes able to supplement (backup) neuronal functions. The photonic network is based on massive reconfigurable matrices of nonlinear nodes formed by microring resonators, which enter in regime of self-pulsing and chaos by positive optical feedback. These networks resemble human brain. I will push this analogy further by interfacing the photonic network with neurons making hybrid network. By using optogenetics, I will control the synaptic strengthen-ing and the neuron activity. Deep learning algorithms will model the biological network functionality, initial-ly within a separate artificial network and, then, in an integrated hybrid artificial-biological network. My project aims at: 1. Developing a photonic integrated reservoir-computing network (RCN); 2. Developing dynamic memories in photonic integrated circuits using RCN; 3. Developing hybrid interfaces between a neuronal network and a photonic integrated circuit; 4. Developing a hybrid electronic, photonic and biological network that computes jointly; 5. Addressing neuronal network activity by photonic RCN to simulate in vitro memory storage and retrieval; 6. Elaborating the signal from RCN and neuronal circuits in order to cope with plastic changes in pathologi-cal brain conditions such as amnesia and epilepsy. The long-term vision is that hybrid neuromorphic photonic networks will (a) clarify the way brain thinks, (b) compute beyond von Neumann, and (c) control and supplement specific neuronal functions.

Mots-clés : hybrid photonics,photonic microring resonators,reservoir computing network,deep learning,optogenetics,neurons,interface between living and condensed matter,memory engrams,neuronal excitability
Projet ERC : SPACE (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Alfred Wegener Institute for Polar and Marine Research — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : AWI
Type d'entité : Recherche
Financement ERC de l'entité : 1499082.0 €

I will determine and use the space-time structure of climate change from years to millennia to test climate models, fundamentally improve the understanding of climate variability and provide a stronger basis for the quantitative use of paleoclimate records. The instrumental record is only a snapshot of our climate record. Two recent advances allow a deeper use of the paleo-record: 1.increased availability and number of paleoclimate records, 2.major advances in the understanding of climate proxies. In a recent PNAS paper, we showed that consistent estimates of regional temperature variability across instruments and proxies can now be obtained by inverting the process by which nature is sampled by proxies. Empirical evidence and physics suggest an intrinsic link between time scale and the associated spatial scale of climate variations: While fast variations such as weather are regional, glacial-interglacial cycles appear to be globally coherent. I will quantify this presumed tendency of the climate system to reduce its degrees of freedom on longer time scales and use it to constrain the sparse, noisy and at times contradictory evidence of past climate changes. By systematically analyzing instrumental and paleo-records, I will 1. determine the space-time structure of climate changes on annual to millennial time scales. This provides the prerequisite for mapping past climate changes and will allow me to confront climate models with robust estimates of climate variability across spatial scales; 2. provide a clearer separation of internal and external forced climate variability, by leveraging their distinct space-time structures; 3. examine the past relationship between mean-state and climate variability to predict how variability will change in a warmer future. This will provide a key step forward to transform paleoclimate science from describing data to using the data as a quantitative test for models and system understanding in order to see more clearly into the future

Mots-clés : climatology and climate change
Projet ERC : HIV B Cell Function (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Life Science
Panel : Immunity, Infection and Immunotherapy
Entité participante : University of London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499269.0 €

I will establish how abnormal B cell behaviour contributes to the generation of HIV neutralising antibodies by studying B cell biology: Previous studies show that broadly neutralising HIV antibodies are the most highly-mutated human antibodies ever isolated, with unusually long binding loops and polyreactivity, suggestive of changes to B cell tolerance and germinal centre selection mechanisms. These remarkable antibodies develop only during HIV infection, concurrent with immune dysfunction, not following vaccination. Crucially, the cellular processes whereby these antibodies arise need to be elucidated and I hypothesize that doing so using RNAseq, flow cytometry and imaging B cell activation will answer why only a minority of HIV+ individuals develop neutralization breadth. Moreover, B cell dysfunction is associated to a range of malignancies and autoimmune diseases and, while much has been learnt from model antigens in mouse models, HIV infection provides a uniquely well-characterised human model to study B cell regulation. Another fundamental question is whether strain-specific antibodies are a stepping-stone to breadth, or a detour that must be eliminated in vaccination. I have shown that the best serum neutralization activity induced via stabilized HIV envelope protein immunization predominantly targets a strain-specific glycan hole, similar to many early responses observed in infection before broad neutralising activity develops. Therefore, I will determine whether non-neutralising and strain-specific antibodies affect the development of breadth by isolating a range of antibodies raised during viral infection from individual donors and comparing binding affinities, clonal ontogenies and B cell receptor activation. Defining the impact of competition between antibody specificities during HIV infection will be directly translatable to developing vaccines for other pathogens where cross-reactivity is a key factor such as Influenza, Dengue and Zika viruses.

Mots-clés : antibody,b cell,neutralization
Projet ERC : Spiders (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : University of Manchester — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1995655.76 €

I will use an innovative coupled approach to facilitate a paradigm shift in the use of neutron stars as a probe of high energy physics. The compactness, rapid rotation and large magnetic fields of neutron stars make them, along with black holes, the most extreme objects found in our Universe. Neutron stars are therefore formidable laboratories to study fundamental physics, as they allow us to probe regimes simply unavailable to Earth-based experiments. However, in spite of their potential utility, there are still a number of important outstanding questions in this field. Key amongst these are: What is the neutron star equation of state? How do neutron stars become the fastest rotating stellar objects in the Universe? How do millisecond pulsars affect their environment? Answering these questions has a broad multi-disciplinary impact, far beyond the field of neutron star research alone; however, at present our ability to answer these questions is limited by cherry picking individual objects for study. In this proposal I describe how I will move away from the biases inherent in the idiosyncratic nature of particular sources to a comprehensive population analysis, and specifically, how I will use a particular class of neutron star systems, known as “spider binaries”, to discover the most massive and the fastest spinning neutron stars that exist. Such extreme cases are of crucial importance for constraining the neutron star equation of state and thus determining the boundary for stellar mass black hole formation. This knowledge in turn can tell us about possible phase transitions of matter at high densities and provide observable quantities that can then be tested against Earth-based experiments, feeding back into high energy physics theories. I will achieve these goals through a transformational observational approach to finding new spider systems and drawing on innovative techniques from other fields to improve parameter estimation.

Mots-clés : neutron stars,pulsars,binary systems,optical photometry and spectroscopy,radio interferometry,high energy physics
Projet ERC : IASI-FT (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Sorbonne Université — Pays : France — Rôle : Partner
Acronyme de l'entité : Sorbonne U
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 460000.0 €

IASI - Flux and temperature July 2016 was Earth's warmest month on record. The first six months of 2016 were also the warmest six-month period since modern meteorology observations began. This, along with the recent so-called “hiatus” in the warming trend, and the Paris climate agreement, all attracted scientific and public attention as to how reliable the historical temperature record is, and to the level of confidence in future model climate projections. Although the role of satellites in observing the variability and change of the Earth system has increased in recent decades, remotely-sensed observations are still underexploited to accurately assess climate change fingerprints. The IASI - Flux and Temperature (IASI-FT) project aims at providing new benchmarks for top-of-atmosphere radiative flux and temperature observations using the calibrated radiances measured twice a day at any location by the IASI instrument on the suite of MetOp satellites. The main challenge is to achieve the stringent accuracy and stability necessary for climate studies, particularly for climate trends. Building upon the expertise accumulated by my group during the last 10 years, I propose the development of innovative algorithms and statistical tools to generate climate data records at the global scale, of (1) spectrally resolved outgoing radiances, (2) land and sea skin surface temperatures, and (3) temperatures at selected altitudes. Time series of these quantities will be compared with in situ and other satellite observations if available, atmospheric reanalyses, and climate model simulations. The observed trends will be analyzed at seasonal and regional scales in order to disentangle natural (weather/dynamical) variability and human-induced climate forcings. This project, while clearly research-oriented, will lead towards an operational integrated observational strategy for the Earth climate system, given that the IASI program started in 2006 and will last until 2040 at least.

Mots-clés : thermal infrared remote sensing,remote sensing spectroscopy,radiative flux,surface temperature,stratospheric temperature,ozone,climate forcings,iasi
Projet ERC : INSCONS (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : University of London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 757452.25 €

INSCONS is a groundbreaking, large-scale examination of the organisational dynamics of international scientific consortia (ISCs) and the interactions of these consortia with broader scientific communities, national bureaucracies, and industry. ISCs are very complex organisations with work being carried out at geographically dispersed sites, and involving international stakeholder groups from across the realms of science, policy, and industry. As these ISCs are becoming more important in efforts to address global challenges in areas such as health, the environment, and clean energy, our understanding of the distinctive organizational dynamics governing these consortia has lagged behind. Accordingly, there is a pressing need for novel organisational theory and frameworks that will advance our understanding of ISCs. INSCONS is an ambitious effort to address this need, using a comparative, interdisciplinary approach. Three case studies of large, international ISCs in nuclear fusion research, biomedicine, and the geosciences are at the core of INSCONS. The INSCONS project will examine four aspects of these ISCs. It will 1) Map the internal organisational dynamics of ISCs using interviews, bibliometric network analyses, and ethnographic field studies on everyday work in ISCs. 2) It will study ISCs’ interactions with the broader scientific community by conducting a survey among researchers in the scholarly fields ISCs operate in, and by analysing these fields’ co-authorship networks. 3) It will examine the (inter)national political processes and bureaucratic wrangling shaping ISCs. 4) It will examine relationships of dependency and influence between ISCs and industry through case studies as well as analyses of patenting and publication activities. Taken together, the project outputs of INSCONS will bring into clear focus the sociology and politics, as well as the operational complexities that govern this important, new organisational form in contemporary science.
Projet ERC : PALGLAC (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : University of Sheffield — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2425298.75 €

Ice sheets regulate Earth’s climate by reflecting sunlight away, enabling suitable temperatures for human habitation. Warming is reducing these ice masses and raising sea level. Glaciologists predict ice loss using computational ice sheet models which interact with climate and oceans, but with caveats that highlight processes are inadequately encapsulated. Weather forecasting made a leap in skill by comparing modelled forecasts with actual outcomes to improve physical realism of their models. This project sets out an ambitious programme to adopt this data-modelling approach in ice sheet modelling. Given their longer timescales (100-1000s years) we will use geological and geomorphological records of former ice sheets to provide the evidence; the rapidly growing field of palaeoglaciology. Focussing on the most numerous and spatially-extensive records of palaeo ice sheet activity - glacial landforms - the project aims to revolutionise understanding of past, present and future ice sheets. Our mapping campaign (Work-Package 1), including by machine learning techniques (WP2), should vastly increase the evidence-base. Resolution of how subglacial landforms are generated and how hydrological networks develop (WP3) would be major breakthroughs leading to possible inversions to information on ice thickness or velocity, and with key implications for ice flow models and hydrological effects on ice dynamics. By pioneering techniques and coding for combining ice sheet models with landform data (WP4) we will improve knowledge of the role of palaeo-ice sheets in Earth system change. Trialling of numerical models in these data-rich environments will highlight deficiencies in process-formulations, leading to better models. Applying our coding to combine landforms and geochronology to optimise modelling (WP4) of the retreat of the Greenland and Antarctic ice sheets since the last glacial will provide ‘spin up’ glaciological conditions for models that forecast sea level rise.

Mots-clés : numerical and mathematical modelling,machine learning
Projet ERC : CompHematoPathology (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Helmholtz Zentrum München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HMGU
Type d'entité : Recherche
Financement ERC de l'entité : 1981213.0 €

Identifying hematologic malignancies still relies on the time-consuming and subjective visual assessment of images. Every day, cytologists and pathologists are confronted with rare diagnostic cells, ever-increasing image data, and heterogeneous disease manifestations. Although we understand blood better than any other human tissue, we are unable to quantitatively predict a patient’s blood dynamics from a measurement. Diagnosis thus depends on rough staging schemes and the expertise and intuition of the clinician. In my proposal, I address these challenges by establishing computational hematopathology, a combination of artificial intelligence algorithms and mathematical models that will boost the currently prevailing manual assessment. Based on my experience in using these methods for scrutinizing stem cell differentiation I will combine the power of deep learning and mathematical modeling with digitized and expertly annotated image data. My unique approach enables me to design and parametrize a data-driven model to predict hematopoietic dynamics in health and disease. Since the interpretation of digitized slides is becoming the clinical standard, novel algorithms for standardized disease classification and improved diagnosis are critically needed now. This interdisciplinary project merges methods from digital pathology, machine learning, image processing, and mathematical modeling. ComHematoPathology will provide novel approaches and software tools for automated classification of hematopathology image data, allowing for reproducible and precise diagnosis at an unprecedented level. This will increase throughput and standardize the diagnosis of blood diseases and will thus improve the treatment of patients suffering from hematologic malignancies.

Mots-clés : computational pathology