Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : ReaDMe (Horizon 2020, appel 2014, Advanced grants)
Domaine scientifique : Life Science
Panel : Integrative Biology: from Genes and Genomes to Systems
Entité participante : Friedrich Miescher Institute — Pays : Suisse — Rôle : PI
Acronyme de l'entité : FMI
Type d'entité : Recherche
Financement ERC de l'entité : 2136969.0 €

DNA and chromatin modifications are essential for proper control of gene expression during development. How these marks alter transcriptional programs and modulate binding patterns of sequence specific transcription factors (TF) remains poorly understood. This currently limits our interpretation of epigenomic maps towards their incorporation into predictive models of gene regulation. ReaDMe has the ambitious goal to systematically define the sensitivity of TFs to local levels of DNA methylation in vivo. We will use a combination of genomics, genome editing and proteomics tools to comprehensively identify transcriptional regulators that respond to DNA methylation. As a first approach, we will interrogate changes in the global TF binding landscape when DNA methylation is ablated from the genome. Using both embryonic stem cells and somatic cells, these experiments are aimed at identifying sites that are occupied by TFs in a DNA methylation dependent manner within different cellular context. Secondly, we will combine parallelized chromosomal insertions with targeted footprinting to determine the link between DNA sequence context, methylation density and TF binding. In a third approach we will define the global chromatin proteome as a function of DNA methylation. Through the use of a novel and orthogonal proteomics assay, we will characterize DNA methylation sensitive changes in the chromatin-bound proteome. Candidate factors predicted from all approaches will be validated and functionally characterized through direct genome-wide mapping as well as loss of function analysis. ERC funding would enable ReaDMe to develop an integrated setup to in vivo identify and characterize where DNA methylation influences the cis-regulatory landscape by modulating binding profiles of trans-acting factors. This goal represents a crucial step towards comprehensive understanding of the genomic readout of DNA methylation and its impact on gene regulation.

Mots-clés : dna methylation,transcription factor,genome engineering
Projet ERC : NUCDDR (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: Biological Mechanisms, Structures and Functions
Entité participante : University of Patras — Pays : Grèce — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499525.0 €

DNA lesions can impose serious threats to genome integrity and cell viability. Whereas DNA damage may occur anywhere in the genome, it is increasingly recognized that certain genomic loci rich in repetitive sequences display increased susceptibility to damage and are linked to chromosomal rearrangements and malignancy. Clusters of ribosomal DNA gene (rDNA) repeats, present on five different chromosomes, constitute the most heavily transcribed area of the human genome and are organized in a nuclear membrane-less organelle, the nucleolus. So far, putative links between rDNA damage and malignant processes have not been rigorously assessed. We will address the hypothesis that rDNA repeats represent a major hub of genomic instability contributing to malignant transformation. Using state-of-the-art experimental systems that allow enrichment for nucleolar DNA damage, we will explore: (i) hypothesis-driven and mass spectrometry-based approaches to define regulators of the rDNA damage response; (ii) live imaging and advanced molecular biology tools to uncover how histone epigenetic changes and formation of RNA:DNA hybrids impact on nucleolar chromatin, nucleolar organization, rDNA transcription and repair ; (iii) cell models that recapitulate malignant transformation caused by inducible oncogene expression or epigenetic inactivation of tumour suppressors, to assess replication stress in rDNA repeats as a primary source of genomic instability and pertinent to hallmarks of cancer. The proposed research is expected to yield novel insights into the signaling networks and biological processes regulating rDNA damage and repair within the nuclear environment and define how these mechanisms are corrupted during neoplastic transformation. This knowledge could be directly applicable to the design of new diagnostic or therapeutic strategies for cancer.

Mots-clés : dna damage response,nucleolus,hippo pathway,genome stability,epigenetics,rassf1a
Projet ERC : BREATHSENS (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Vrije Universiteit Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : VU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Dairy products accounts for some 5% of the global food intake, with dairy industry revenues in 2019 of around €675 billion. This dietary component is important in the global challenge of feeding the increasing human population that is expected to reach 9.7 billion in 2050. To attain peak productivity and efficiency, the health and well-being of each dairy cow is of paramount importance. We aim to use a non-invasive and automated spectroscopic sensor applied on breath, so that illness in individual cows can be detected well before clinical symptoms manifest. The technology is derived from the extremely sensitive and ultra-high spectral resolution cavity-enhanced spectroscopic methods developed in fundamental investigations on molecular hydrogen. We will first target ketosis, which is a common metabolic deficiency that causes estimated losses of more than €1 billion per year for the EU dairy farming industry. The continuous monitoring and early illness detection would enable timely and tailored feed adjustments by farmers to prevent or alleviate disease progression. The goal of this project is to make a portable prototype and explore opportunities of building a sustainable business around it. This novel and unique method will not only help reduce economic costs and minimize environmental emissions, but will help ensure a better quality of life for the farm animals.

Mots-clés : breath analysis,biomarker detection,pre clinical illness detection,molecular spectroscopy
Projet ERC : SCALAR (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
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 : 1033508.66 €

Damage associated with climate change is a core benchmark in science and policy. Macro Integrated Assessment Models estimating damages are criticized for neglecting risk distribution, adaptation dynamics and the possible collapse of regional economies. Micro-level social science studies contain substantial knowledge on individual behavior, decisions under risk and autonomous climate adaptation, and go beyond monetary losses by focusing on resilience. This knowledge can ameliorate theoretical and empirical flaws in current macro assessments, if adequate scaling up methods were to exist. SCALAR aims to bridge the gap between micro and macro research traditions by modeling the behavioral aspects of autonomous adaptation processes of heterogeneous agents, and integrating them into macro level climate policy models. The project focuses on floods. Its innovative nature allows to revisit the classic micro-macro aggregation problem through a unique combination of: 1) New behavioral data on climate adaptation decisions collected in multiple survey waves using mobile applications, going beyond a snapshot to uncover evolving decision processes; 2) Advances in agent-based modeling to scale up adaptation decisions of heterogeneous households and firms to a regional economy while including land use and hazard data; 3) Cutting-edge ways of integrating micro-simulation models with traditional macro models to synergize the two approaches for developing new theory- and data-grounded macro damage assessments. SCALAR will drive a major breakthrough in integrating behavioral aspects of human decision-making into macro climate policy models. It will enable the quantitative exploration of cross-scale damage cascades, the identification of thresholds over which autonomous adaptation impacts the macro level, and the tracing of the emergence of socio-economic resilience as climate change unfolds. The methodological advancements will have impact far beyond the domain of climate adaptation.

Mots-clés : climate change adaptation,flood risk,agent based modeling,coupled human natural systems,coupled social ecological systems,resilience,model integration
Projet ERC : DMAP (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Sapienza University of Rome — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1137500.0 €

Data Mining algorithms are a cornerstone of today's Internet-related services and products. We aim to tackle some of the most important problems in Data Mining --- our goal is to develop a systematic theoretical understanding of certain simple algorithms that, in spite of being at the core of today's web industry, are not yet well understood in terms of their properties and performances, and to develop new simple algorithms for fundamental problems in this domain that have so far escaped a satisfactory solution.

Mots-clés : data mining,linear algorithms
Projet ERC : SECURITY FLOWS (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
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 : 1897826.0 €

Datafication, the process transforming our everyday lives into quantifiable digital data, is also transforming borders today. Data collection, exchange and interoperability have become key for EU border security. How does data enact border security in the digital age? What are the political and ethical implications of these processes of datafication? This project proposes to develop a novel interdisciplinary framework to understand how data is generated, exchanged and contested in border encounters, and to investigate the complex epistemic, practical, political and ethical implications of these transformations. Starting from a socio-material reconceptualisation of datafication as the production of data forms, flows and frictions, the project advances an innovative theorisation of the (i)epistemic effects of datafication as producing both knowledge and ignorance. It will shed light on how data forms make things intelligible or unintelligible, and how digital data flows and frictions redistribute knowledge and ignorance among border security actors, NGOs and irregular migrants. To trace the (ii)practical implications of datafication, the project will devise a multi-modal methodology for 'following the data' along the Eastern, Central and Mediterranean routes as well as the routes leading to these from Morocco, Niger and Turkey, and finally along return routes. (iii)Politically, the project investigates how data reconfigures the worlds of actors involved in the governance of border security by enacting new power relations between these actors and reshaping decision-making. Finally, the project also advances a socio-material approach to (iv)ethics to account for how data protection and the rights of both citizens and non-citizens are transformed by datafication. Through its ambitious theoretical and methodological innovations, which will shape an emergent field of research, the project will have long-lasting impact for border and security studies.

Mots-clés : critical security studies,border studies,science and technology studies,data studies,digital methods
Projet ERC : DLT (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 1500000.0 €

Deep Learning is one of the most vibrant areas of contemporary machine learning and one of the most promising approaches to Artificial Intelligence. Deep Learning drives the latest systems for image, text, and audio processing, as well as an increasing number of new technologies. The goal of this project is to advance on key open problems in Deep Learning, specifically regarding the capacity, optimization, and regularization of these algorithms. The idea is to consolidate a theoretical basis that allows us to pin down the inner workings of the present success of Deep Learning and make it more widely applicable, in particular in situations with limited data and challenging problems in reinforcement learning. The approach is based on the geometry of neural networks and exploits innovative mathematics, drawing on information geometry and algebraic statistics. This is a quite timely and unique proposal which holds promise to vastly streamline the progress of Deep Learning into new frontiers.

Mots-clés : deep learning,neural networks,information geometry,algebraic statistics,reinforcement learning,representational power,optimization,regularization
Projet ERC : DeepSPIN (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Unbabel unipessoal, lda — Pays : Portugal — Rôle : Partner
Acronyme de l'entité : Unbabel
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 100000.0 €

Deep learning is revolutionizing the field of Natural Language Processing (NLP), with breakthroughs in machine translation, speech recognition, and question answering. New language interfaces (digital assistants, messenger apps, customer service bots) are emerging as the next technologies for seamless, multilingual communication among humans and machines. From a machine learning perspective, many problems in NLP can be characterized as structured prediction: they involve predicting structurally rich and interdependent output variables. In spite of this, current neural NLP systems ignore the structural complexity of human language, relying on simplistic and error-prone greedy search procedures. This leads to serious mistakes in machine translation, such as words being dropped or named entities mistranslated. More broadly, neural networks are missing the key structural mechanisms for solving complex real-world tasks requiring deep reasoning. This project attacks these fundamental problems by bringing together deep learning and structured prediction, with a highly disruptive and cross-disciplinary approach. First, I will endow neural networks with a "planning mechanism" to guide structural search, letting decoders learn the optimal order by which they should operate. This makes a bridge with reinforcement learning and combinatorial optimization. Second, I will develop new ways of automatically inducing latent structure inside the network, making it more expressive, scalable and interpretable. Synergies with probabilistic inference and sparse modeling techniques will be exploited. To complement these two innovations, I will investigate new ways of incorporating weak supervision to reduce the need for labeled data. Three highly challenging applications will serve as testbeds: machine translation, quality estimation, and dependency parsing. To maximize technological impact, a collaboration is planned with a start-up company in the crowd-sourcing translation industry.

Mots-clés : statistical machine translation,neural networks
Projet ERC : BeyondBlackbox (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Freiburg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1495000.0 €

Deep neural networks (DNNs) have led to dramatic improvements of the state-of-the-art for many important classification problems, such as object recognition from images or speech recognition from audio data. However, DNNs are also notoriously dependent on the tuning of their hyperparameters. Since their manual tuning is time-consuming and requires expert knowledge, recent years have seen the rise of Bayesian optimization methods for automating this task. While these methods have had substantial successes, their treatment of DNN performance as a black box poses fundamental limitations, allowing manual tuning to be more effective for large and computationally expensive data sets: humans can (1) exploit prior knowledge and extrapolate performance from data subsets, (2) monitor the DNN's internal weight optimization by stochastic gradient descent over time, and (3) reactively change hyperparameters at runtime. We therefore propose to model DNN performance beyond a blackbox level and to use these models to develop for the first time: 1. Next-generation Bayesian optimization methods that exploit data-driven priors to optimize performance orders of magnitude faster than currently possible; 2. Graybox Bayesian optimization methods that have access to -- and exploit -- performance and state information of algorithm runs over time; and 3. Hyperparameter control strategies that learn across different datasets to adapt hyperparameters reactively to the characteristics of any given situation. DNNs play into our project in two ways. First, in all our methods we will use (Bayesian) DNNs to model and exploit the large amounts of performance data we will collect on various datasets. Second, our application goal is to optimize and control DNN hyperparameters far better than human experts and to obtain: 4. Computationally inexpensive auto-tuned deep neural networks, even for large datasets, enabling the widespread use of deep learning by non-experts.

Mots-clés : bayesian optimization,deep learning,hyperparameter optimization,meta learning,learning across datasets,parameter control,reinforcement learning,performance prediction
Projet ERC : SUCCESS (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Bergen — Pays : Norvège — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2335750.0 €

Democracies are under pressure worldwide: The political landscape is rapidly changing, with new parties emerging and old ones suffering, as voters turn away from established parties. We also witness a significant gender-gap in political endurance, meaning that women’s careers as elected representatives in parliaments tend to end quicker than men’s. SUCCESS argues that questions of women’s political endurance are of uttermost importance for the quality and legitimacy of democratic processes and therefore demand scholarly attention. SUCCESS will advance a novel theory of inclusion that explains gender-gaps in endurance, and provide much-needed new empirical evidence on why, how and under what conditions women’s political careers are likely to endure. SUCCESS will investigate the multifaceted relationships between a) the agency of elected representatives, i.e. their choice of strategies to get re-elected b) popular opinions about representatives’ gender, political experience and their career strategy, and c) the specific institutions and contexts that underpin gender-gaps in political endurance across time, regions and political systems. SUCCESS thus offers a comprehensive perspective, highlighting the interconnections between these three aspects in a novel theory of political inclusion: Rather than focusing on what excludes women from political office, the project explores what contributes to their success. SUCCESS will employ a unique multi-methods approach to collect data in five core country cases and a large-N dataset, including qualitative interviews, survey experiments, conjoint analysis, simulation study and anthropological film. These efforts yield unprecedented opportunities for a new turn towards understanding how gender-gaps in political endurance affect political inequalities. The project is pioneering empirical studies on why and how increased electoral volatility in new and old democracies disrupts or reinforces gendered power hierarchies in politics.

Mots-clés : gender equality,political representation,parliaments,elections
Projet ERC : DCRIDDLE (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Life Science
Panel : Cellular, Developmental and Regenerative Biology
Entité participante : Vlaams Instituut voor Biotechnologie — Pays : Belgique — Rôle : PI
Acronyme de l'entité : VIB
Type d'entité : Recherche
Financement ERC de l'entité : 1999196.0 €

Dendritic cells (DCs) play a crucial role as gatekeepers of the immune system, coordinating the balance between protective immunity and tolerance to self antigens. What determines the switch between immunogenic versus tolerogenic antigen presentation remains one of the most puzzling questions in immunology. My team recently discovered an unanticipated link between a conserved stress response in the endoplasmic reticulum (ER) and tolerogenic DC maturation, thereby setting the stage for new insights in this fundamental branch in immunology. Specifically, we found that one of the branches of the unfolded protein response (UPR), the IRE1/XBP1 signaling axis, is constitutively active in murine dendritic cells (cDC1s), without any signs of an overt UPR gene signature. Based on preliminary data we hypothesize that IRE1 is activated by apoptotic cell uptake, orchestrating a metabolic response from the ER to ensure tolerogenic antigen presentation. This entirely novel physiological function for IRE1 entails a paradigm shift in the UPR field, as it reveals that IRE1’s functions might stretch far from its well-established function induced by chronic ER stress. The aim of my research program is to establish whether IRE1 in DCs is the hitherto illusive switch between tolerogenic and immunogenic maturation. To this end, we will dissect its function in vivo both in steady-state conditions and in conditions of danger (viral infection models). In line with our data, IRE1 has recently been identified as a candidate gene for autoimmune disease based on Genome Wide Association Studies (GWAS). Therefore, I envisage that my research program will not only have a large impact on the field of DC biology and apoptotic cell clearance, but will also yield new insights in diseases like autoimmunity, graft versus host disease or tumor immunology, all associated with disturbed balances between tolerogenic and immunogenic responses.

Mots-clés : unfolded protein response,ire1,ridd,dendritic cell biology,tolerogenic maturation