Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : LUMABS (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : Eindhoven University of Technology — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU/e
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149750.0 €

Therapeutic antibodies represent an important class of newly introduced drugs and have been particularly successful in cancer therapy and the treatment of inflammatory diseases. The clearance rates of therapeutic antibodies are known to differ per individual and correlate with survival rates. Being able to define a personalized dose per patient based on continuous monitoring of therapeutic antibody levels would therefore be beneficial both for the individual patient and for the hospital’s budget. Our group recently developed a new platform of bioluminescent sensor proteins for antibody detection that allow antibody detection directly in blood plasma using the camera of a smart phone as the sole piece of equipment. The goal of this ERC Proof-of-Concept is to establish LUMABS as a unique, low cost, and easily accessible point-of-care technology for monitoring therapeutic antibodies. To enable clinical and commercial translation a product portfolio will be established that includes the development and validation of a prototype point-of-care assay for the therapeutic antibody Cetuximab. To establish LUMABS as a broadly applicable platform technology, we will use recently developed epitope screening technology to develop specific LUMABS sensors targeting Infliximab and biosimilar antibodies that are used to treat inflammatory conditions such as Crohns’ disease and rheumatoid arthritis. Based on an in-depth market analysis and the feedback we receive from external stakeholders on the performance of our technology, a realistic strategy will be developed for further translation and commercialization. During the project we will continuously assess opportunities for new patent applications. Our IP strategy will be guided by the outcome of our market examination and analysis of the most interesting business opportunities.

Mots-clés : point of care diagnostics,therapeutic antibodies,therapeutic drug monitoring,biosensor,bioluminescence
Projet ERC : SecondCANCERinKIDS (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Princess Máxima Center — Pays : Pays-Bas — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 2000000.0 €

Therapy-related malignancies are a major cause of long-term mortality among childhood cancer survivors. However, it is unclear how exposure to chemo- and/or radiotherapy early in life induces carcinogenesis. My aim is to determine the mechanisms and rate-limiting steps underlying the genesis of second malignancies in childhood cancer survivors. For this, we will focus on studying the etiology of therapy-related myeloid malignancies (t-MNs). I have pioneered methods to characterize mutation accumulation in single stem cells and study clonal lineages in the human hematopoietic system. My lab is embedded in Europe’s largest childhood cancer center, providing the opportunity to apply our techniques to unique patient material. In Objective 1, we will dissect the life history of t-MN and study its cellular origin. Our key question is: Was the original t-MN clone already present before chemotherapy exposure, or generated as a consequence thereof? We will address this by tracking back clonal lineages in the hematopoietic tissue of patients using the mutations present in their second cancers. In Objective 2, we will study the mutational consequences of chemotherapy in normal hematopoietic cells of children before and after they received treatment. Our key question is: Is enhanced mutagenesis rate limiting for t-MN development? To address this, we will perform in-depth mutational analyses and in vitro validations. In Objective 3, we will determine phenotypic effects of chemotherapy on population dynamics of blood. Our key question is: how does chemotherapy affect selection dynamics and clonal composition of blood? To address this, we will integrate clonal histories and lineage contributions using somatically acquired mutations. Our unique methodology and anticipated novel insights will not contribute to improved survival of children with cancer, but also to increased fundamental knowledge on the origin of cancer.

Mots-clés : therapy related malignancies,cancer etiology,hematopoietic stem cells,somatic mutations,phylogenetic tree
Projet ERC : BELIEFS (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : University of Bonn — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1450243.25 €

There are large differences in earnings between men and women. Recent work highlights the importance of parenthood for the existence of gender inequality in the labour market. Estimates of the long-run ‘child penalty’, i.e. the impact of having children on women’s relative to men’s earnings, are large and vary substantially across countries. Neither the existence of child penalties nor the striking cross-country variation in child penalties is well understood. BELIEFS will collect a representative dataset of 80,000 individuals in the 28 EU Member States to study the role of several factors in explaining the cross-country differences in child penalties. It will examine the role of (i) beliefs about the benefits/costs to fertility and labour supply decisions, (ii) preferences for having children and for work/leisure, (iii) constraints, and (iv) social norms. BELIEFS will explore different dimensions of heterogeneity and study the individual-level (gender, age etc.) and country-level (labour regulations, family policies etc.) determinants of these factors. It will study whether there are misperceptions of norms and identify whether informing individuals of prevalent social norms shifts their beliefs about the benefits/costs to men/women working and their support for public policies. BELIEFS examines educational, fertility and labour supply decisions in a dynamic life-cycle framework and explores the role of beliefs, preferences, constraints and norms in those decisions. The dynamic framework will also be used to study the role of perceived child penalties in explaining fertility and educational choices. The project is highly ambitious in its scope and it is highly innovative in its combination of research methods. Ultimately, this research agenda will shed light on what drives gender gaps in labour market outcomes as well as which policies may be effective in narrowing these gaps.

Mots-clés : beliefs,gender inequality,child penalties,education,fertility,labour supply
Projet ERC : PPP (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : International Institute for Applied Systems Analysis — Pays : Autriche — Rôle : PI
Acronyme de l'entité : IIASA
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

There has been tremendous progress in artificial intelligence (AI) in many different fields due to improved machine learning algorithms, advances in computing power and the availability of big data. With machine learning platforms like TensorFlow (Google), PyTorch (Facebook), Azure (Microsoft) or CoreML (Apple), researchers and application developers can now use AI for applications such as computer vision. While there are many image databases available such as Imagenet, which can be used to train machine learning algorithms to recognize a set of generic categories, e.g., cats, there is still a lack of image databases containing more specific features of interest, e.g., crop types. Moreover, there is currently no platform that offers a streamlined, standardized approach to setting up and running an image classification campaign to ingest the millions of photographs currently collected by many people around the world. The value proposition of the ERC PoC Picture Pile Platform (PPP) is to develop an innovative, commercially self-sustaining platform that uses the crowdsourcing game Picture Pile to rapidly classify images for machine learning purposes. After a pile of images has been sorted, the image classifications will be made publicly available. A number of premium services will be added to the Picture Pile Platform, which will make the platform self-sustaining. Campaign creators will be able to pay the crowd (with a small share paid to the Picture Pile Platform) for sorting the images. Paid advertisements will be possible through our social media channels as well as paid for space on the main Picture Pile page to attract more users. For a small fee, users will be able to use the Picture Pile Cloud for specific computer vision tasks using the machine learning models that will be built and trained with the Picture Pile data sets. The Picture Pile Platform has the potential to become a self-sustaining hub for crowdsourcing image classifications for machine learning.

Mots-clés : crowdsourcing,artificial intelligence,image classification,picture pile,machine learning
Projet ERC : CIC (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : University of Exeter — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1998722.0 €

There has been vast improvement in workplace gender equality, but there remain marked differences in the roles in which women and men work. Explanations for this inequality have focused on the barriers women face. However, as women begin to enter male-dominated roles, a new explanation has arisen: that remaining gender inequality must reflect fundamental differences between women and men, including differences in (a) ambition and desire for power, (b) needs for work-life balance, and (c) willingness to take career risks. Central to this analysis is the assumption that the glass ceiling is broken and thus inequality must be due to women’s active choices. This explanation downplays the fact that social context continues to be a barrier to women’s success and places responsibility for gender inequality on women themselves. Indeed, there has arisen the suggestion that gender equality necessitates women overcoming ‘internal obstacles’, ‘leaning-in’ and altering their choices (Sandberg, 2013), rather than challenging the status quo. I argue that diametrically contrasting structural barriers with women’s choices is unhelpful. Instead, I suggest that women’s choices are shaped and constrained by the gendered nature of organisational and social contexts and how women see themselves within these contexts. I propose a programme of research, across 3 integrated streams, that investigates how social and organisational structures define identities and constrain women’s choices in relation to ambition, work-life balance, and career risk-taking. I have four key objectives: (1) to clarify how organisational and social contexts define identity and constrain women’s choices, (2) to use an interdisciplinary, multi-methodological approach, to produce innovative theory and data, (3) to work collaboratively with stakeholders, and (4) to inform practical interventions designed to facilitate the increase of women’s participation in hitherto male-dominated roles.

Mots-clés : social identity,gender equality
Projet ERC : Color3.0 (Horizon 2020, appel 2019, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : University of Giessen — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : JLU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2497787.5 €

There have been tremendous advances in color science. The absorption of photons by three types of photoreceptors is known at the molecular genetic level. Human cone fundamentals are tabled to several decimal places and color opponency is understood at the neural and computational level. Yet, all this knowledge is based on extremely restrictive assumptions with a colored light in the dark (Color 1.0) or flat, matte surfaces in a uniformly colored context (Color 2.0). But which mechanisms mediate perception of colors in the real world– when looking at a field of flowers or searching for a certain product in the supermarket? Arguably, the most important function of color is the processing of information about objects in scenes. It is the tight link to objects through which color helps us see things quicker and remember them better. This proposal, Color 3.0, is based on an active observer dealing with three-dimensional objects in natural environments. It deals with the dimensions relevant for the main purpose of color perception – intensity, hue and saturation. The goal is to fundamentally rethink color science around real world objects and natural tasks. We will gain a deep understanding of the circuitry underlying color perception in real and virtual worlds, a Deep Neural Network model of color processing that can be traced through the brain, a new colorimetry based on natural object colors rather than flat, matte patches of light, and last but not least a better measure for luminous intensity that can deal with objects of different color. This could lead to a revision of how we study the early visual system, better color reproduction and better lighting systems. Our use of real-time raytracing in VR could cause a paradigm shift in vision science, away from a passively viewing observer pushing buttons, towards an active observer situated in a virtual world and performing a natural task.

Mots-clés : color vision,luminance,color appearance,color constancy
Projet ERC : PRE-IMAGE (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University Medical Center Groningen — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UMCG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499961.0 €

There is a considerable shortage of deceased donor kidneys. Hence, more organs of marginal quality need to be considered for transplantation. Transplant centers are increasingly utilizing ex vivo normothermic machine perfusion to better preserve donor kidneys prior to transplantation. Little is known about molecular pathways that are active while the organ is perfused ex vivo. Also, there is hardly any data on which molecular processes are relevant to assess organ quality during perfusion. I aim to determine the molecular mechanisms of ex vivo kidney perfusion prior to renal transplantation in order to develop breakthrough pre-transplant perfusion-based diagnostic markers that can indicate kidney transplant outcomes. First, a series of normothermic ex vivo porcine kidney perfusions will be conducted with repeated tissue and perfusate sampling. Ex vivo measurements will be contrasted with the contralateral kidney that remains in vivo. Genomics, transcriptomics, proteomics and metabolomics, as well as ex and in vivo magnetic resonance imaging followed by radiomics will be employed. Distinct molecular pathways will be identified which characterize an ex vivo perfused kidney, compared to the organ’s behaviour in vivo. Second, the discovered molecular pathways will be validated for human donor kidneys by performing ex vivo perfusions of discarded human organs followed by the same multi-omics approach. Finally, a prospective clinical study will be conducted with human kidneys that are perfused ex vivo prior to transplantation. With artificial intelligence analysis, tissue and perfusate multi-omics measurements and standard clinical variables will be associated with transplant results, to create advanced prediction models for post-transplant outcome. The high gain of my project will be a better understanding of molecular mechanisms during ex vivo kidney perfusion and advanced, personalized pre-transplant prediction models for post-transplant outcome.

Mots-clés : kidney transplantation,ex vivo machine perfusion,multi omics analysis,prediction models,artificial intelligence
Projet ERC : SUMMIT (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Nantes Université — Pays : France — Rôle : PI
Acronyme de l'entité : NU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999768.0 €

There is a high demand to design approaches capable of tracking the origin of biomarkers in complex biological environments, in the areas of life, environmental, food and forensic sciences. Metabolomics and Fluxomics show great promises towards this aim, and a high potential arises from their combination with Isotopic fingerprinting at natural abundance. The resulting isotopomics approach requires cutting-edge analytical tools, and Nuclear Magnetic Resonance (NMR) is currently the only generic technique giving access to the site-specific isotope content at natural abundance. The detection of very small relative variations between samples originating from different (bio)chemical pathways is possible through 13C isotopic NMR, which can however only be applied to simple and concentrated samples, due to its low sensitivity. Consequently, numerous applications are out of reach. To tackle the current limitations of 13C isotopic analysis, SUMMIT will develop a groundbreaking analytical workflow relying on two of the most powerful NMR methods: dissolution dynamic nuclear polarization and ultrafast 2D NMR. This cutting-edge approach will allow the simultaneous measurement of 13C fingerprints from multiple low-concentrated biomarkers in complex mixtures, which is impossible with existing methods. The high potential of this analytical strategy will be demonstrated on a relevant biological study, the investigation of breast cancer cell metabolism, through applications with gradually increasing risk levels. These approaches will make it possible to identify (i) new biomarkers to discriminate between cell lines expressing different hormonal receptors; (ii) novel potential therapeutic targets from the elucidation of metabolic pathways. Beyond this application, the project will have a high impact on a wide community of academic and industrial researchers, covering unmet needs from life sciences, food industry and forensic analysis.

Mots-clés : nmr spectroscopy,isotopic analysis,dynamic nuclear polarization,metabolomics
Projet ERC : SIZE (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Radboud University Nijmegen — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1670406.0 €

There is a major scientific and societal challenge in quantifying and reducing ecological footprints of products. Ecological footprint calculations suffer severely from a limited availability of data, such as the amount of energy and materials associated with the production, use and disposal of products. Furthermore, ecological footprints pertaining to biodiversity are typically biased towards a limited number of well-known species with a focus on relative species richness, leaving out ecosystem service attributes of biodiversity. As it is virtually impossible to collect all the empirical data required for all species, there is an urgent need to develop an operational framework to derive representative ecological footprints with limited data requirements. I propose to develop a novel framework based on a set of unifying scaling principles related to the production size of products and the body size of species. These scaling principles will be developed to predict key characteristics of biofuel production, such as energy return of investment, agricultural land requirements and greenhouse gas emissions, as well as global impact indicators, such as species extinction risks. The focus of the research is on (1) liquid biofuel production (bioethanol and biodiesel) from various first and second generation feedstock as an important but controversial renewable energy source (2) vascular plant diversity, as the common basis of all terrestrial ecosystems, and (3) habitat destruction and climate change, as important drivers of global change. Together with the PI, two PhD students, two Postdocs and a technical assistant will work on different components of the new predictive models, substantially enhancing the scientific understanding of how to provide reliable ecological footprints in practice.
Projet ERC : TechChild (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Trinity College Dublin — Pays : Irlande — Rôle : Partner
Acronyme de l'entité : TCD
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 956915.31 €

There is an increasing number of children with complex healthcare needs who require continuous technological support to sustain their lives. This technology dependence is initiated in an environment of medical interventionism, with potential for discrete discrimination. A scarcity of empirical data on the influences and interactions at the point of the initiation of technology dependence means that clinical, legal and ethical deliberations are driven more by opinion than empirical evidence. An evidence-based theoretical construct is required to articulate and contextualise the levers and penalties of the initiation of this technology. TechChild asks just because we can, should we? and how are the influences on the initiation of technology dependence understood in contrasting health, legal, and socio-political systems? Serendipitous findings from my research indicates parental concern regarding an absence of transparency, and parents are questioning patterns of family characteristics, when technology dependence is initiated. TechChild will be a step change in how we understand the coexistence of humans with an increasing availability of technological augmentations. This is urgent in a society where this debate predominantly happens in the public domain with limited opportunity for healthcare professionals to offer their perspective. TechChild will revolutionise how we conceive access to care and offers a research horizon that questions cultural relativism in a cyborg era. This is a scholarly ambitious project involving Paediatric Intensive Care Units in four international sites using a Bayesian framework to elicit the probability of factors likely to influence the initiation of technology dependence, leading to the development of a theory of technology initiation. This ground-breaking exploration will inform technology initiation across the lifespan with implications for healthcare, bioethics, education, parenting, policy making, and legal.

Mots-clés : child health,critical care,mixed methods,multi disciplinary,technology dependence,access to care