Data-Gouv-ML/pcri-actions-marie-curie-participations-et-projets-laureats huggingface dataset:
Projet PredAlgoBC (Horizon 2020, appel à projets 2018)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : CLOSED

Breast cancer is the cancer with the highest incidence in women worldwide, and is the leading cause of cancer-related death, mainly due to treatment resistance. Recently, tumor heterogeneity has been described as one of the key driver in treatment failure. Indeed, tumor is not a homogeneous entity to treat, but a complex association of subclonal populations driven by their own genetic alterations, and immune and stromal cells from microenvironment. Breast cancer subtypes and tumor heterogeneity advocate for the development of tailored, personalized treatments, but so far, the discovery of efficient predictive markers has been compromised by the lack of adapted biological models and methodological tools. The recent developments of high-throughput methods for bulk and single-cell analyses has generated large ‘omics’ datasets from patients, stored in open access databases (ArrayExpress, GEO). Combining these numerous datasets will grant a sufficient statistical power to reveal a comprehensive overview of tumor complexity. However, this data mining is currently limited by methodological challenges like cross-platform normalization and the difficulty to analyze complex data structure with high dimension observations. To overcome these issues, I propose to implement a multidisciplinary project at the interface between mathematics, biology, and information technologies. With the support of the mathematicians and bioinformaticians from the Bioinfomics unit of the regional comprehensive cancer center (ICO), I will develop and implement machine-learning algorithms in the search of predictive biomarkers for breast cancer treatment. This innovative strategy will lead to personalized medicine in breast cancer by guiding clinicians in the selection of the optimal therapeutic option. Moreover, this generated pipeline for predictive marker discovery could be further adapted for the treatment of other cancer types.

Mots clés : breast cancer   mathematical oncology, machine learning, predictive markers   predictive algorithm   bulk and single cell analysis, transcriptomic

Participants :
- Institut de Cancérologie de l'Ouest (France) — Coordinator, beneficiary, Recherche, subvention : 184707.84 €
Projet PRO-ORATICA (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : CLOSED

Breast cancer is the most common cancer among women worldwide. Radiotherapy (RT) is an essential component of the treatment for the majority of BC patients. Although the therapeutic benefit of RT is well-proven, it may be offset by secondary effects, such as cardiac toxicity, arising from the inevitable irradiation of healthy tissue. Increased cardiac morbidity and mortality rates are well documented after breast cancer RT, but the exact underlying mechanisms behind cardiac toxicity is still unclear. Such information is crucial to develop accurate predictive models of cardiotoxicity and to devise new strategies for personalized RT treatments, either by optimizing the existing protocols or by considering emerging RT techniques, such as proton beam therapy. The goal of PRO-ORATICA project, carried out by the researcher Eugenia Mylona, is to devise decision-making tools aimed at proposing integrative and feasible strategies for reducing radiation-induced cardiac complications after breast cancer RT. Novel image processing and analysis techniques will be applied to identify radio-sensitive cardiac regions, while artificial intelligence will be used to develop multivariable subregion-driven predictive models of cardiac toxicities. Strategies will be proposed for transferring the developed models to the clinics, offering optimized personalized treatments with reduced cardiac complications. This research project can be practice-changing as it may define new improved standards for the treatment of breast cancer, potentially enhancing patients’ quality-of-life. The fellowship will take place at Universidad Carlos III de Madrid (UC3M) under the supervision of Dr Javier Pascau, Associate Professor at the Bioengineering and Aerospace Engineering Department. As part of this project, the researcher will carry out a secondment in the Clínica Universidad de Navarra (CUN) in Madrid, under the co-supervision of Dr Felipe Calvo, head of the Radiation Oncology Department.

Mots clés : breast cancer, cardiac toxicity, predictive modeling, artificial intelligence, image segmentation

Participants :
- Carlos III University of Madrid (Espagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 160932.48 €
Projet ConscBreathDynamics (Horizon 2020, appel à projets 2018)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : CLOSED

Breathing is perhaps the most vital physiological function, and it is occurring mainly unconsciously. Yet, despite the deceptive simplicity of breathing, it is an intricate behaviour influence by conscious top-down control and vigilance state. How consciousness shape breathing? What are the neural mechanisms underlying conscious control of breathing? Can breathing dynamics indicate on conscious awareness? We aim to address these questions by employing basic and clinical neuroscience approaches in combination with electrophysiology, neuroimaging and advanced computational tools. Disorders of consciousness (DOC) including vegetative state (VS)/unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) offer a unique model to investigate the relationship between consciousness and breathing, by providing a rare opportunity for cross-sectional and longitudinal tracking of breathing dynamics during recovery of consciousness. In the proposed research, we first aim to characterize the interaction between breathing and consciousness state. To do this we will record continues respiration during 24-hour from healthy individuals and DOC patients. Next, we intend to elucidate the neural mechanism underlying conscious regulation of breathing, by recording electroencephalogram (EEG) and functional resonance magnetic imaging (fMRI) activity during a multimodal (olfaction, audition and vision) paradigm designed to consciously and unconsciously trigger respiratory change. Last, we plan to investigate whether breathing dynamics can be used as a diagnostic and potentially prognostic tool to detect awareness in DOC patients, by developing an automated respiratory-based classifier differentiating between UWS and MCS patients. The significance of this study is two-fold; addressing fundamental questions in breathing regulation, and utilizing the interplay between basic physiological function and consciousness to better human condition.

Mots clés : disorders of consciousness, consciousness, breathing, olfaction, fmri, vegetative state, minimally conscious state

Participants :
- Institut du cerveau (France) — Coordinator, beneficiary, Recherche, subvention : 168477.04 €
Projet BridgeScan (Horizon 2020, appel à projets 2017)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : CLOSED

Bridges on Europe’s transportation network are at risk due to increased freight demands, aging, environmental impact and climate change. Deficiencies of bridges must be assessed in a timely manner for planning critical maintenance, repairs and expansions. However, with bi-annual general inspections of bridges, damage may not be documented timely to prevent catastrophic collapse. Moreover, visual inspection is the predominant method that has many downsides: subjective results, slow and expensive procedures, high safety risks for inspectors and requirements of experienced inspectors, and traffic closures. Recently, although unmanned aerial vehicle (UAV)-based image has been developed for bridge inspection, results based UAV-images are of relatively low accuracy, which can be exaggerated when the images subject to shadows and noise. While several pieces of methods have been proposed to process data (both images and a point cloud), these approaches are not automatic, scalable and robust. Thus, to address those issues, the project “Laser Scanning for Automatic Bridge Assessment” called BridgeScan proposes the framework to use laser scanning integrated into UAV to acquire 3D topographic data points of bridges and to automatically process data for bridge assessment purpose. This project will involve (i) investigate a flight path and scanning parameters to scan a bridge structure by UAV with an integrated laser scanning sensor, (ii) develop automatic, robust, efficient methods to process laser scanning data to identify structural deficiencies for bridge condition rating and (iii) develop automatic methods for reconstructing a 3D bridge model for bridge assessment based on finite element analysis. Success of the BridgeScan project will be fundamental for further deploying UAV with laser scanning for monitoring and assessment of other infrastructures. Moreover, the proposed methods can be extended to reconstruct other objects for urban modelling and construction management.

Mots clés : bridge inspection, bridge assessment, bridge monitoring, bridge conditional rating, lidar, laser scanning, point cloud, structural deficiency, geometric modelling

Participants :
- Delft University of Technology (Pays-Bas) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 177598.8 €
Projet GLUCOBAT (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Life Sciences
Statut du projet : SIGNED

Brown adipose tissue (BAT) has a high capacity to dissipate chemical energy generated from glucose and lipids as heat, a process typically induced by cold exposure, termed thermogenesis. This process has gained significant clinical interest as a strategy against obesity and diabetes because of its potential to expend excess calories as heat. However, our understanding of BAT metabolism on an organismal level is still rudimentary, thus, to advance BAT-targeted therapies, we must first comprehensively define how BAT utilizes its key fuels, such as glucose in vivo. It is widely assumed that the major role of glucose during thermogenesis is to provide energy for the TCA cycle and subsequent uncoupling by the key thermogenic protein in BAT, uncoupling protein 1 (UCP1). However, recent literature reveal that this assumption is oversimplified since glucose has many fates once it enters cells, feeding into additional metabolic pathways beyond the TCA cycle, which are necessary for optimum thermogenesis. In this project, I will use state-of-the-art glucose-tracing technology that utilizes mass spectrometry, combined with classic Cre-Lox and novel CRISPR/Cas9 genome editing strategies to precisely define how thermogenic adipose tissue utilizes glucose in vivo and to delineate the role of solute carrier transporters of the SCL family, including SLC25a1, for glucose utilization and thermogenesis. SLC25a1 delivers the glucose-derived metabolite citrate across the mitochondrial membrane for use in de novo lipid synthesis, a process that are highly regulated in the BAT of cold exposed mice. This project will expand our understanding of how thermogenic adipocytes utilize glucose and will potentially reveal novel therapeutic strategies to enhance thermogenesis. My previous experience with BAT metabolism and mass spectrometry-based isotopic tracing combined with the technological tools of my outgoing host and my host institute ideally poises me to successfully achieve my goals.

Mots clés : brown adipose tissue, glucose metabolism, thermogenesis, solute carrier transporters

Participants :
- University of Massachusetts System (Etats-Unis) — Partner, organization partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
- University of Copenhagen (Danemark) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 286921.92 €
Projet SelectX (Horizon 2020, appel à projets 2015)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : CLOSED

Building artificial neuronal networks (ANN) that mimic their biological prototypes is one of the remaining grand challenges in computing. Despite transistor scaling and improved architectures, modern supercomputers require kWs of power to replicate functionality for which living neural networks take only a few Ws. Currently there is no hardware that can provide a high synaptic density with reasonable energy consumption. Hybrid CMOS chips with stacked crossbars of analog non-volatile memory devices (NVM), like memristors, promise to deliver the required high density and connectivity. However, the crossbar architecture suffers from the sneak path problem, neighbouring devices creating electrical shorts around the selected device. Biological systems do not have this problem due to the inherent nonlinearity of their potentiation and spiking. A high nonlinearity can be reproduced in a crossbar by using a selector for each memory device. The selector commonly used is a transistor which limits the scalability and stackability. This project proposes an alternative selector based on a two-terminal MEMS switch. MEMS switches are heavily researched for RF applications, but their high nonlinearity makes them attractive as selectors for NVM. This project will design, simulate and fabricate a crossbar of integrated MEMS selectors and TiO2 memristor devices. Initially, the selectors and memristors will be optimized separately, then monolithically integrated. Their scalability and stackability will be investigated. Finally, a prototype 3x3 crossbar of integrated memristor/MEMS selectors will be fabricated and used to demonstrate vector matrix multiplication - a foundational element of many complex ANNs like a perceptron. The proposed work is not linked to a particular NVM (ReRAM is an example), being suitable for any dense crossbar system that requires selectors. The findings are relevant to the fields of hardware ANNs and non-volatile memories and to major industry players.

Mots clés : memristor, mems switch, crossbar, non volatile memories, hardware implementation of neural networks

Participants :
- National Institute for Research and Development in Microtechnologies (Roumanie) — Coordinator, beneficiary, Recherche, subvention : 125422.8 €
Projet LaFS (Horizon 2020, appel à projets 2017)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Social Sciences and Humanities
Statut du projet : CLOSED

Building on my previous research on Family Language Policy (FLP), LaFS (‘Language, Families, and Society’) will focus on three types of linguistic minority families—autochthonous, immigrant, and refugee—as a means to elucidating more about how social inequality is perpetuated (or arrested) along linguistic lines, and how policy at the local, national, and international levels can better support linguistic minority families. The project will therefore provide a key means to understanding more about Europe’s three main sociolinguistic challenges: the decline of its many autochthonous minority languages; increased linguistic diversity due to increased mobility among European member states; and the refugee crisis. LaFS will centre on families who speak Irish as a home language (autochthonous); Polish (immigrant); and Arabic (refugee) as a means to understanding the challenges these linguistic minority families face and how these challenges affect their sense of identity, belonging, and overall well-being. This understanding will be broadened by a secondment with the Glasgow Refugee, Asylum, and Migration Network (GRAMNet). LaFS will be hosted by the National University of Ireland, Galway, complementing NUIG’s Centre for Population and Migration Research and UNESCO Child and Family Research Centre. The project will be supervised by Prof. Tadhg Ó hIfearnáin, a leading expert in minority language issues. The secondment will be supervised by Professor Alison Phipps, UNESCO Chair in Refugee Integration through Languages and the Arts. My professional development over the course of the project in terms of innovative research practice; high-impact dissemination and communication skills; and effective project management skills will be invaluable to my long-term goal of embedding sociolinguistics into social justice research.

Mots clés : family language policy, minority languages

Participants :
- National University of Ireland (Irlande) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 175866.0 €
Projet Eco-LWG (Horizon 2020, appel à projets 2014)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : CLOSED

Buildings in Europe are responsible for more than 40% of total energy consumption and 25% of carbon emissions, mainly due to the high number of old housing stock with very poor insulation systems. This research will develop new energy efficient materials by engineering eco-lightweight granules (eco-LWG) to mitigate these problems. The eco-LWG will have high strength and high water absorption, ~100 wt.%, and be made from widely available waste materials: recycled mixed coloured glass, clay and limestone. The eco-LWG will be used to contain phase change materials (PCM), which use chemical bonds to store and release heat, allowing control of temperature fluctuations. The high level of porosity in the eco-LWG will allow them to absorb and retain much higher levels of PCM than has previously been possible using existing lightweight aggregates. Both the eco-LWG and the PCM loaded eco-LWG will be optimised by statistical design methodology and they will be used to develop new energy efficient construction products. Two impregnation methods will be compared to introduce the PCM into the eco-LWG: vacuum sorption to achieve maximum PCM uptake and short term immersion to partially fill the connected porosity. These will produce innovative lightweight and high heat-storage materials. Incorporating PCM loaded granules in mortars and other construction materials will result in reduced energy requirements to heat and cool buildings and improve indoor living conditions. The eco-LWG developed in this research will also be used in green roofs to provide water retention capacity and slow nutrient release. Green roofs can reduce the energy requirements of buildings and urban pollution. This multidisciplinary innovative research will deliver new energy efficient construction materials that can contribute to achieving the 2020 target of nearly zero-energy and highly material efficient buildings, which is an essential target to achieve sustainable growth in the EU.

Mots clés : lightweight material, waste, high water absorption, statistical design methodology, phase change material, energy efficient material, mortars, building insulation, green roofs

Participants :
- Imperial College London (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 183454.8 €
Projet ToughMG (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Physics
Statut du projet : SIGNED

Bulk metallic glasses (BMGs) are promising materials that combine the strength of metal alloys with the elasticity of glassy polymers. Compared to their crystalline counterpart, the lack of dislocations and grain boundaries translates into better energy restitution, excellent wear, and corrosion resistance, making them promising candidates for sports goods to biomedical materials. Unfortunately, BMGs are notorious for exhibiting crack growth, fracture, and, ultimately, catastrophic failure, severely limiting their applications. Recently, experimental observation showed that BMGs could exhibit a mechanical transition revealed by a sharp drop in fracture toughness (ability to resist failure in the presence of a crack) as a function of a protocol (fictive) temperature that controls the glass stability. This transition strongly echoes with the ductile to brittle transition seen in recent numerical and theoretical works and is found to be linked to a sharp decrease in plastic defects (soft spots), which play a role similar to dislocations in crystals. In this action, we propose investigating the toughening transition seen in BMGs through a novel multiscale numerical approach. This action aims to enable the parametrization of continuum models with the insight gained from microscopic simulations. In ToughMG, I will associate my experience in the detection and micromechanics of plastic defects at the microscopic level to the prominent expertise provided by Prof. Barrat and the host institution in modeling plasticity at the mesoscopic and macroscopic scale. The methodology developed will allow me to predict large scale plastic strain observed prior fracture as a function of the material's protocol history. This research plan can substantially advance our understanding of the connection between glassy structure and fracture mechanics of bulk metallic glasses and allows for better material design.

Mots clés : plasticity, fracture, materials science

Participants :
- Université Grenoble Alpes (France) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 196707.84 €
Projet DYNASSEMBLY (Horizon 2020, appel à projets 2016)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Chemistry
Statut du projet : CLOSED

By combining biology's information storage and replication strategies, namely specific hydrogen-bonding between nucleobases, with the potential for hydrogen-bond reorganisation in novel 'refoldable foldamers', we propose to develop a new, artificial but bio-inspired mechanism for the transfer of information in chemical systems. Based on preliminary modelling studies, we will focus on one type of polyurea structures, built from 'Nowick'-style motifs, which spontaneously fold in solution, forming intramolecular polarized hydrogen-bond networks. Upon addition of simple chemical additives forming competitive intermolecular hydrogen bonds with the foldamer, the directionality of these networks could be modified, transforming a conformational mixture to a well-defined unidirectional network. Molecular events such as reversible switching, chemical sensing, and long-distance information communication will be studied. Then, biomimetic additives such as nucleobases will be investigated, and their impact on the folding of the oligomers will be analysed. We will also investigate 3D hydrogen bond networks, which will provide highly functionalised, well-defined macromolecules in a bottom-up approach, and study the change of macromolecular structure and chirality upon stimulus. Finally, structures that give a non-spectroscopic 'readout' of the interaction with the additive will be built, which for example exhibit 'off-on' fluorescence or 'off-on' catalytic activity. The application of these foldamers in the construction of photochemically switchable molecular devices will also be investigated.

Mots clés : hydrogen bond networks, foldamers, systems chemistry

Participants :
- University of Bristol (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 183454.8 €