Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : POSTCELL (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Pompeu Fabra University — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UPF
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1876846.0 €

POSTCELL aims at laying the foundation for future generations of wireless networks as they move past the reigning cell-centric paradigm and into the post-cellular era. This entails the definition of a new architecture for such networks and the characterization of the ensuing performance. For the future of wireless communications, the implications would be far-reaching. The growth of wireless traffic is relentless, and it is actually gaining new momentum on account of fresh mechanisms: smartphones, cloud computing, and machine-to-machine communication. As a result, the volume of wireless traffic is poised to increase to truly staggering levels and, to face this challenge, wireless networks need to enter a new stage. There is a fledging awareness that this challenge can only be fended off by a process of network massification, with two views about it. In the first view, densification is the only strategy through which dramatic improvements can be attained hereafter; this leads to a vision where base stations become tiny and exceedingly abundant. The second view, in turn, is built on the idea of dramatically scaling the number of colocated antennas per base station from the current handful to possibly hundreds. One of the seeds of POSTCELL is that, since neither form of massification can by itself resolve the challenge facing wireless systems, the two forms will have to end up coexisting. Reconciling these two forms of massification and enabling a truly phenomenal scaling calls for an entirely new architecture where cells and physical base stations become things of the past, replaced by dynamically defined virtual base stations, powerful caches, and the possibility of device clustering, among other leaps forward. The signal processing needs to shift away from base stations, which become deconstructed, so as to gather at new places. POSTCELL seeks to drive this transformation and to gauge the performance of post-cellular wireless networks.

Mots-clés : wireless,communications,information theory,communication theory,radio
Projet ERC : PROTEGO (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of London — Pays : Royaume-Uni — Rôle : Partner
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 395972.76 €

PROTEGO arises out of a fundamental claim: procedural regulatory instruments have causal effects on the performance of political systems because they trigger accountability towards different stakeholders. The mix of policy instruments may be functional or dysfunctional, depending on how accountability mechanisms are combined. This project provides a theoretical rationale to capture the accountability effects by adopting an extension of delegation theory that considers multiple stakeholders. The theoretical framework will allow us to identify the key social mechanisms that make bureaucracies internalize the preferences of accountees. We will test the observable implications of the framework on outcomes that are crucial to the performance of political systems, such as trust in government, ease of business, control of corruption, sustainability. Empirically, we will collect, validate and analyze original data across the EU and its 28 Member States for the period 2000-2015, distinguishing between instruments that cover central departmental activity and independent regulatory agencies. The new dataset will cover administrative procedure acts, freedom of information, notice and comment, judicial review, impact assessment, environmental appraisal, and non-financial instruments for public spending accountability. These are the procedural instruments that enfranchise accountees. The bivariate relationship between a single instrument and outcomes explains little – because it’s the overall ecology or mix of instruments that produces causal effects. These ecologies combine in different sequences and paths associated with the outcome. Hence we will draw on a suitable methodological approach - Qualitative Comparative Analysis (QCA). Causality and diffusion across time will be also explored via event-history analysis and principal component analysis. PROTEGO will contribute to theories of regulation and accountability, and provide a robust operational model of data and analysis.

Mots-clés : policy instruments,public administration,regulation
Projet ERC : PROTEGO (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Exeter — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1124338.25 €

PROTEGO arises out of a fundamental claim: procedural regulatory instruments have causal effects on the performance of political systems because they trigger accountability towards different stakeholders. The mix of policy instruments may be functional or dysfunctional, depending on how accountability mechanisms are combined. This project provides a theoretical rationale to capture the accountability effects by adopting an extension of delegation theory that considers multiple stakeholders. The theoretical framework will allow us to identify the key social mechanisms that make bureaucracies internalize the preferences of accountees. We will test the observable implications of the framework on outcomes that are crucial to the performance of political systems, such as trust in government, ease of business, control of corruption, sustainability. Empirically, we will collect, validate and analyze original data across the EU and its 28 Member States for the period 2000-2015, distinguishing between instruments that cover central departmental activity and independent regulatory agencies. The new dataset will cover administrative procedure acts, freedom of information, notice and comment, judicial review, impact assessment, environmental appraisal, and non-financial instruments for public spending accountability. These are the procedural instruments that enfranchise accountees. The bivariate relationship between a single instrument and outcomes explains little – because it’s the overall ecology or mix of instruments that produces causal effects. These ecologies combine in different sequences and paths associated with the outcome. Hence we will draw on a suitable methodological approach - Qualitative Comparative Analysis (QCA). Causality and diffusion across time will be also explored via event-history analysis and principal component analysis. PROTEGO will contribute to theories of regulation and accountability, and provide a robust operational model of data and analysis.

Mots-clés : policy instruments,public administration,regulation
Projet ERC : PULSE (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2462015.0 €

PULSE will develop a new generation of ultrasound imaging capabilities to revolutionize the use of this low-cost and portable imaging technology across clinical medicine worldwide. The greatest barrier to the universal implementation of ultrasound (US) in clinical medicine today is the need to train sonographers to the highest level to ensure diagnostic images are of consistently high quality and fit for purpose. Unfortunately, the non-expert finds US images very difficult to interpret by eye alone. Perception Ultrasound by Learning Sonographic Experience (PULSE) is an innovative inter-disciplinary project designed to eliminate the need for highly skilled operators of the technology. It is motivated by the observation that sonographers find it easier to interpret their own scans than review those taken by others. The innovation in PULSE is to apply the latest ideas from machine learning and computer vision to build, from real world training video data, computational models that describe how an expert sonographer performs a diagnostic study of a subject from multiple perceptual cues. Novel machine-learning based computational models will be derived based on probe and eye motion tracking, image processing, and knowledge of how to interpret real-world clinical images and videos acquired to a standardised protocol. By building models that more closely mimic how a human makes decisions from US images we believe we will build considerably more powerful assistive interpretation methods than have previously been possible from still US images and videos alone. Software demonstrators will be developed and evaluated on real world obstetric US data in collaboration with clinical experts and novices to demonstrate the new approach and its potential to move routine US scanning services from hospitals into the community which would have clear economic, healthcare and social benefits across Europe and beyond.

Mots-clés : ultrasound imaging,biomedical image analysis
Projet ERC : REDSTEM (Horizon 2020, appel 2014, Proof of concept grants)
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 148307.0 €

Palms are some of the most successfully traded plants, with numerous species sold for indoor, outdoors and as crops. The kentia palm (Howea forsteriana), is one of the most widely exported houseplants in the world and is worth over seven million euros per year in the Dutch horticultural industry alone. Wild kentias are only found on a 12km2 island in the middle of the Tasman Sea, Lord Howe Island (LHI). LHI is a unique ecosystem. It is the focus of the applicant's ERC Advanced Grant, who is using the kentia palms as a model to tackle 'Darwin's mystery of mysteries'. During our expeditions on LHI, we have also discovered a new mutant variety of the kentia palm: instead of having the typical dark green colouration, it has a bright red stem. Red-stemmed varieties of palms are known, but have had very limited commercialisation due to climatic restrictions: these palms are tropical species, requiring high humidity and temperature. In contrasts, kentias are very hardy and grow well under low light and cool temperatures, hence their immense success as decorative items across innumerable indoor public venues and households. In addition, red is an auspicious colour in Asia – and we know our new red kentia palm has tremendous horticulture potential. Building on our genetic research and experiments with these kentia palms, we propose to disentangle the biological underpinning of the red stem. In parallel, we will conduct a market research. Examining our IPR position and strategy, we will take the necessary steps to protect our variety, including its registration as Ornamental Crop to the Community Plant Variety Office. Finally, we will strengthen our links with various industries and private stakeholders to develop a business plan for commercialisation of red kentias

Mots-clés : plant breeders rights,horticulture,palm,asia
Projet ERC : PancObese (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : The Francis Crick Institute — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 2500000.0 €

Pancreatic cancer is one of the most lethal malignancies, showing a high rate of metastatic spread and a dismal prognosis. With almost no effective treatment options, new therapies to treat this devastating disease are urgently needed. Such advances will depend on a better understanding of the mechanisms that drive pancreas cancer progression. Previous work has revealed two interesting features of pancreas cancer on which this proposal is built. Firstly, pancreas cancers contain an extremely high stromal component – normal cells that become part of the tumour mass and contribute to the control of malignant progression. Secondly, obesity – a rapidly growing epidemic worldwide – is associated with increased pancreas cancer development. This proposal aims to understand the mechanisms underlying the link between obesity and pancreas cancer, focusing on the metabolic cross talk between the adipocytes in the tumour environment and the tumour cells themselves. This is a pioneering project that pulls together critical aspects of pancreas cancer biology, building on a solid platform of existing knowledge around the importance of metabolic alterations in cancer and the role of other types of tumour associated stromal cells. State of the art metabolomic analyses and in vivo models will be used to identify novel aspects of metabolic control of the tumour cell environment, how these changes promote pancreas cancer progression and the impact of obesity on these responses. Focusing on the importance of adipocytes, the modulation of reactive oxygen species and the metabolite formate, we will apply our extensive expertise to a new area of study, determining novel mechanisms that underpin pancreas cancer progression. Ultimately, the insights developed by this proposal will provide a rational basis for the development of new therapeutic options. These studies will therefore have far-reaching impact on one of the world’s most devastating diseases.
Projet ERC : ExoSonic (Horizon 2020, appel 2017, Proof of concept grants)
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149996.0 €

Pancreatic cancer is the fourth-leading cause of cancer related mortality and is predicted to be the second leading cause of cancer death by 2030. Widely regarded as a death sentence, the 5-year survival rate is only 3% in the UK and this figure has not changed over the past four decades due to lack of specific therapies and inability to detect it early. Several years often elapses from the beginning of the disease to the patient’s diagnosis, suggesting a window of opportunity for early detection. Recently, tiny nanometre-sized vesicles (exosomes) shed by the tumour in the bloodstream, have emerged as powerful circulating biomarkers possessing extremely high sensitivity and specificity, thus paving the way for a new era of non-invasive cancer diagnostics. However, currently the process of exosome isolation and detection is not only highly inefficient, but also technically challenging and inaccessible to hospital laboratories, clinical facilities and resource-poor settings. To address technological constraints of exosome utilisation, we are developing a microfluidic device that leverages cutting-edge microfabrication technology to enable isolation and detection of tumour exosomes from the blood for screening and detecting early-stage pancreatic cancer with unprecedented precision. This chip will have two components, a separation module where exosomes will be isolated from all other blood particles, and a detection chamber that will incorporate technology to achieve ultralow resolution. The first stage of development will be concerned with the fabrication of all components necessary to harness the sensitivity of the exosome biomarker. Following initial testing, we plan to take steps towards commercialisation of the device.

Mots-clés : exosomes isolation and purification,microfluidic chip,acoustophoresis,graphene based sensor,liquid biopsy technology,early detection of pancreatic cancer
Projet ERC : MechanoGPER (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: Biological Mechanisms, Structures and Functions
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, is the fourth-leading cause of cancer related mortality and is predicted to be the second leading cause of cancer death by 2030. Widely regarded as a death sentence, the 5-year survival rate is less than 5% and this figure has not changed over the past four decades due to lack of effective therapies. PDAC develops in a fibrotic setting that hampers drug delivery and modulates immune response & tumour growth/dissemination. Pancreatic stellate cells (PSCs) are the main resident cells in the tumour microenvironment and the drivers of fibrosis. We have recently identified two pathways that mechanically reprogram PSCs and the tumour microenvironment to physiological conditions to inhibit fibrosis, cancer cell invasion, and modulate immune response. One of these mechanisms involves the G protein-coupled estrogen receptor (GPER). Modulating GPER mediated mechanosignalling has emerged as a powerful target in PDAC paving the way to implement new therapeutic approaches, which are so urgently needed. Building on our published data, we plan to demonstrate the efficacy of newly-synthesized compounds to target mechanotransduction of cancer and stromal cells as a therapeutic strategy in PDAC. Within the group of stromal cells, we will focus on: (i) PSCs to target fibrosis, and (ii) tumour associated macrophages, which are known to have tumour-promoting properties. Following this, we want to investigate the efficacy of self-assembling peptide hydrogels to deliver these compounds to pancreatic organoids. The first stage of each work package will be focused on the experimental verification of the efficacy of these compounds to target the different PDAC cell types and how these compounds can be more effectively delivered using hydrogels. Following validation of the proof of concept, we plan to take steps to commercialise these compounds.

Mots-clés : pancreatic cancer therapeutics,mechanotransduction,gper agonists,fibrosis,immune supression,pancreatic stellate cells
Projet ERC : EYELETS (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Karolinska Institutet — Pays : Suède — Rôle : PI
Acronyme de l'entité : KI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

Pancreatic islet transplantation is essential for diabetes treatment. Outcome varies due to transplantation site, quality of islets and the fact that transplanted islets are affected by the same challenges as in situ islets. Tailor-making islets for transplantation by tissue engineering combined with a more favorable transplantation site that allows for both monitoring and local modulation of islet cells is thus instrumental. We have established the anterior chamber of the eye (ACE) as a favorable environment for long term survival of islet grafts and the cornea as a natural body window for non-invasive, longitudinal optical monitoring of islet function. ACE engrafted islets are able to maintain blood glucose homeostasis in diabetic animals. In addition to studies in non-human primates we are performing human clinical trials, the first patient already being transplanted. Tissue engineering of native islets is technically difficult. We will therefore apply genetically engineered islet organoids. This allows us to generate i) standardized material optimized for transplantation, function and survival, as well as ii) islet organoids suitable for monitoring (sensor islet organoids) and treating (metabolic islet organoids) insulin-dependent diabetes. We hypothesize that genetically engineered islet organoids transplanted to the ACE are superior to native pancreatic islets to monitor and treat insulin-dependent diabetes. Our overall aim is to create a platform allowing monitoring and treatment of insulin-dependent diabetes in mice that can be transferred to large animals for validation. The objective is to combine tissue engineering of islet cell organoids, transplantation to the ACE, synthetic biology, local pharmacological treatment strategies and the development of novel micro electronic/micro optical readout systems for islet cells. This regenerative medicine approach will follow our clinical trial programs and be transferred into the clinic to combat diabetes.
Projet ERC : AGNOSTIC (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : University of Luxembourg — Pays : Luxembourg — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1843447.0 €

Parameterized mathematical models have been central to the understanding and design of communication, networking, and radar systems. However, they often lack the ability to model intricate interactions innate in complex systems. On the other hand, data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of flexibility. These approaches need labelled data, representing all the facets of the system interaction with the environment. With the aforementioned systems becoming increasingly complex with intricate interactions and operating in dynamic environments, the number of system configurations can be rather large leading to paucity of labelled data. Thus there are emerging networks of systems of critical importance whose cognition is not effectively covered by traditional approaches. AGNOSTIC uses the process of exploration through system probing and exploitation of observed data in an iterative manner drawing upon traditional model-based approaches and data-driven discriminative learning to enhance functionality, performance, and robustness through the notion of active cognition. AGNOSTIC clearly departs from a passive assimilation of data and aims to formalize the exploitation/exploration framework in dynamic environments. The development of this framework in three applications areas is central to AGNOSTIC. The project aims to provide active cognition in radar to learn the environment and other active systems to ensure situational awareness and coexistence; to apply active probing in radio access networks to infer network behaviour towards spectrum sharing and self-configuration; and to learn and adapt to user demand for content distribution in caching networks, drastically improving network efficiency. Although these cognitive systems interact with the environment in very different ways, sufficient abstraction allows cross-fertilization of insights and approaches motivating their joint treatment.

Mots-clés : signal processing,statistical signal processing,wireless communications,mimo communications,radar,mimo radar,cognitive radio,cognitive radar