Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : LASER OPTIMAL (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Mario Negri Institute for Pharmacological Research — Pays : Italie — Rôle : Partner
Type d'entité : Recherche
Financement ERC de l'entité : 75000.0 €

Laser Ablation (LA) was extensively investigated for its benefits as minimally invasive thermal therapy for tumor. Despite the LA pros as potential alternative to surgical resection (e.g., use of small fiber optics, echo-endoscope procedures and image-guidance without artifact), the lack of tools for safe and patient-specific treatment restrained its clinical use. LASER OPTIMAL offers a renaissance to LA for the practical management of challenging tumors (e.g., pancreatic cancer): it investigates and develops integrated solutions to achieve an effective and selective LA, that thermally destroys the whole tumor mass, while spearing the normal tissue around. The excellent ambition of LASER OPTIMAL is to achieve and merge: a) biocompatible nanoparticles (BNPs) injected in the tumor, to enhance the selective absorption of laser light; b) patient-specific anatomy of tumor and its surrounding, extracted from clinical images, to retrieve the optimal laser settings; c) accurate, fast and real-time heat-transfer model to simulate laser-tissue-BNPs interaction, predict and visualize the treatment dynamics; d) real-time temperature measurement system to monitor LA effects, account for unpredictable physiological events and tune the settings (closed-loop). The design of ex vivo and in vivo animal tests allows assessing the system performances and driving the possible workflow re-design. Finally, human trials are envisaged to prove the significant impact of the LASER OPTIMAL paradigm. The collaboration of researchers, engineers and clinicians will drive the use of this innovative strategy in clinical routine. The research on the patient-specific system for the mini-invasive tumors removal, and the ground-breaking insights on clinical use of BNPs will strongly impact on EU healthcare system and society, by creating a novel product. This paradigm is also embeddable in existing system of industrial partner, extendable to other procedures, thus able to encourage a dedicated market.

Mots-clés : laser ablation,gold nanoparticles,heat transfer model,laser tissue interaction,hyperthermal treatment of tumor,thermometry,medical imaging
Projet ERC : LASER OPTIMAL (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Institut national de recherche en sciences et technologies du numérique — Pays : France — Rôle : Partner
Acronyme de l'entité : Inria
Type d'entité : Recherche
Financement ERC de l'entité : 69125.0 €

Laser Ablation (LA) was extensively investigated for its benefits as minimally invasive thermal therapy for tumor. Despite the LA pros as potential alternative to surgical resection (e.g., use of small fiber optics, echo-endoscope procedures and image-guidance without artifact), the lack of tools for safe and patient-specific treatment restrained its clinical use. LASER OPTIMAL offers a renaissance to LA for the practical management of challenging tumors (e.g., pancreatic cancer): it investigates and develops integrated solutions to achieve an effective and selective LA, that thermally destroys the whole tumor mass, while spearing the normal tissue around. The excellent ambition of LASER OPTIMAL is to achieve and merge: a) biocompatible nanoparticles (BNPs) injected in the tumor, to enhance the selective absorption of laser light; b) patient-specific anatomy of tumor and its surrounding, extracted from clinical images, to retrieve the optimal laser settings; c) accurate, fast and real-time heat-transfer model to simulate laser-tissue-BNPs interaction, predict and visualize the treatment dynamics; d) real-time temperature measurement system to monitor LA effects, account for unpredictable physiological events and tune the settings (closed-loop). The design of ex vivo and in vivo animal tests allows assessing the system performances and driving the possible workflow re-design. Finally, human trials are envisaged to prove the significant impact of the LASER OPTIMAL paradigm. The collaboration of researchers, engineers and clinicians will drive the use of this innovative strategy in clinical routine. The research on the patient-specific system for the mini-invasive tumors removal, and the ground-breaking insights on clinical use of BNPs will strongly impact on EU healthcare system and society, by creating a novel product. This paradigm is also embeddable in existing system of industrial partner, extendable to other procedures, thus able to encourage a dedicated market.

Mots-clés : laser ablation,gold nanoparticles,heat transfer model,laser tissue interaction,hyperthermal treatment of tumor,thermometry,medical imaging
Projet ERC : LASER OPTIMAL (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Beckman research institute of the city of hope — Pays : Etats-Unis — Rôle : Partner
Type d'entité : Autres
Financement ERC de l'entité : 105462.26 €

Laser Ablation (LA) was extensively investigated for its benefits as minimally invasive thermal therapy for tumor. Despite the LA pros as potential alternative to surgical resection (e.g., use of small fiber optics, echo-endoscope procedures and image-guidance without artifact), the lack of tools for safe and patient-specific treatment restrained its clinical use. LASER OPTIMAL offers a renaissance to LA for the practical management of challenging tumors (e.g., pancreatic cancer): it investigates and develops integrated solutions to achieve an effective and selective LA, that thermally destroys the whole tumor mass, while spearing the normal tissue around. The excellent ambition of LASER OPTIMAL is to achieve and merge: a) biocompatible nanoparticles (BNPs) injected in the tumor, to enhance the selective absorption of laser light; b) patient-specific anatomy of tumor and its surrounding, extracted from clinical images, to retrieve the optimal laser settings; c) accurate, fast and real-time heat-transfer model to simulate laser-tissue-BNPs interaction, predict and visualize the treatment dynamics; d) real-time temperature measurement system to monitor LA effects, account for unpredictable physiological events and tune the settings (closed-loop). The design of ex vivo and in vivo animal tests allows assessing the system performances and driving the possible workflow re-design. Finally, human trials are envisaged to prove the significant impact of the LASER OPTIMAL paradigm. The collaboration of researchers, engineers and clinicians will drive the use of this innovative strategy in clinical routine. The research on the patient-specific system for the mini-invasive tumors removal, and the ground-breaking insights on clinical use of BNPs will strongly impact on EU healthcare system and society, by creating a novel product. This paradigm is also embeddable in existing system of industrial partner, extendable to other procedures, thus able to encourage a dedicated market.

Mots-clés : laser ablation,gold nanoparticles,heat transfer model,laser tissue interaction,hyperthermal treatment of tumor,thermometry,medical imaging
Projet ERC : SALT (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Friedrich Schiller University Jena — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : FSU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2490912.5 €

Lasers, and in particular ultrafast lasers, are an enabling technology for many applications, with the particularity that they can emit high-powers and are tabletop at the same time. These characteristics have made intense laser radiation widely available, which has decisively contributed to the advancement of many fields. However, the spectral coverage of lasers is limited and, thus, there are many applications that can only be addressed with other sources such as synchrotrons. Unfortunately, synchrotrons have two strong disadvantages: they are very large facilities with restricted user access and are extremely expensive. This is seriously hampering the widespread use of this radiation and, with it, the progress and development of many fields. Since a direct (i.e. a laser-based), high-power emission of coherent light with a wavelength coverage comparable to that of a synchrotron is impossible, nonlinear frequency conversion driven by a high-power solid-state laser seems to be the most elegant solution to achieve a high photon flux in important spectral regions such as the mid-infrared, the THz- and the soft-X-ray range. Most remarkably, frequency conversion into these spectral regions would strongly benefit from a longer driving laser wavelength than the standard Titanium:Sapphire or Ytterbium-based near-infrared emission. On top of that, the shift of the emission to longer wavelengths can unleash a hidden performance scaling potential of ultrafast fiber lasers, as nonlinear and thermal limitations scale favorably. The goals of the project SALT are twofold. First, it targets a revolution in the performance level of ultrafast lasers by unlocking the potential of Thulium-doped fiber lasers. Second, it aims at demonstrating new realms of flux in selected wavelength regions by frequency-converting these high-power 2µm sources. This will pave the way for frontier applications allowing for seminal discoveries and breakthroughs.

Mots-clés : high repetition rate femtosecond lasers,power scaling of laser sources,ultrafast fiber lasers,advanced large mode area fibers,nonlinear effects in fibers,thermal issues in solid state lasers,long wavelength lasers,2 m ultrafast lasers,frequency conversion,nonlinear pulse compression,few cycle lasers,high harmonic generation,generation of coherent euv to soft xray radiation,thz generation,parametric downconversion towards the mid infrared,mid infrared laser sources,laser for me
Projet ERC : AXPLAST (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : University of Basel — Pays : Suisse — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 533597.5 €

Learning and memory are the basis of our behaviour and mental well-being. Understanding the mechanisms of structural and cellular plasticity in defined neuronal circuits in vivo will be crucial to elucidate principles of circuit-specific memory formation and their relation to changes in neuronal ensemble dynamics. Structural plasticity studies were technically limited to cortex, excluding deep brain areas like the amygdala, and mainly focussed on the input site (dendritic spines), whilst the plasticity of the axon initial segment (AIS), a neuron’s site of output generation, was so far not studied in vivo. Length and location of the AIS are plastic and strongly affects a neurons spike output. However, it remains unknown if AIS plasticity regulates neuronal activity upon learning in vivo. We will combine viral expression of AIS live markers and genetically-encoded Ca2+-sensors with novel deep brain imaging techniques via gradient index (GRIN) lenses to investigate how AIS location and length are regulated upon associative learning in amygdala circuits in vivo. Two-photon time-lapse imaging of the AIS of amygdala neurons upon fear conditioning will help us to track learning-driven AIS location dynamics. Next, we will combine miniature microscope imaging of neuronal activity in freely moving animals with two-photon imaging to link AIS location, length and plasticity to the intrinsic activity as well as learning-related response plasticity of amygdala neurons during fear learning and extinction in vivo. Finally, we will test if AIS plasticity is a general cellular plasticity mechanisms in brain areas afferent to the amygdala, e.g. thalamus. Using a combination of two-photon and miniature microscopy imaging to map structural dynamics of defined neural circuits in the amygdala and its thalamic input areas will provide fundamental insights into the cellular mechanisms underlying sensory processing upon learning and relate network level plasticity with the cellular level.

Mots-clés : axon initial segment,deep brain imaging,amygdala
Projet ERC : BeeDanceGap (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
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 : 54884.5 €

Learning from others is fundamental to ecological success across the animal kingdom, but a key theme to emerge from recent research is that individuals respond differently to social information. Understanding this diversity is an imposing challenge, because it is hard to replicate the overwhelming complexity of free-living groups within controlled laboratory conditions. Yet here I propose that one of the most complex social models that we know of— the sophisticated eusocial societies of honeybees— offer unrivaled and yet unrecognized potential to study social information flow through a natural group. The honeybee “dance language” is one of the most celebrated communication systems in the animal world, and central to a powerful information network that drives our most high-profile pollinator to food, but bee colonies are uniquely tractable for two reasons. Firstly, next-generation transcriptomics could allow us to delve deep into this complexity at the molecular level, on a scale that is simply not available in vertebrate social systems. I propose to track information flow through a natural group using brain gene expression profiles, to understand how dances elicit learning in the bee brain. Secondly, although bee foraging ranges are vast and diverse, social learning takes place in one centralized location (the hive). The social sciences now offer powerful new tools to analyze social networks, and I will use a cutting-edge network-based modelling approach to understand how the importance of social learning mechanisms shifts with ecology. In the face of global pollinator decline, understanding the contribution of foraging drivers to colony success has never been more pressing, but the importance of the dance language reaches far beyond food security concerns. This research integrates proximate and ultimate perspectives to produce a comprehensive, multi-disciplinary program; a high-risk, high-gain journey into new territory for understanding animal communication.

Mots-clés : social learning,communication,pollinator behaviour
Projet ERC : BeeDanceGap (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
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 : 1320301.59 €

Learning from others is fundamental to ecological success across the animal kingdom, but a key theme to emerge from recent research is that individuals respond differently to social information. Understanding this diversity is an imposing challenge, because it is hard to replicate the overwhelming complexity of free-living groups within controlled laboratory conditions. Yet here I propose that one of the most complex social models that we know of— the sophisticated eusocial societies of honeybees— offer unrivaled and yet unrecognized potential to study social information flow through a natural group. The honeybee “dance language” is one of the most celebrated communication systems in the animal world, and central to a powerful information network that drives our most high-profile pollinator to food, but bee colonies are uniquely tractable for two reasons. Firstly, next-generation transcriptomics could allow us to delve deep into this complexity at the molecular level, on a scale that is simply not available in vertebrate social systems. I propose to track information flow through a natural group using brain gene expression profiles, to understand how dances elicit learning in the bee brain. Secondly, although bee foraging ranges are vast and diverse, social learning takes place in one centralized location (the hive). The social sciences now offer powerful new tools to analyze social networks, and I will use a cutting-edge network-based modelling approach to understand how the importance of social learning mechanisms shifts with ecology. In the face of global pollinator decline, understanding the contribution of foraging drivers to colony success has never been more pressing, but the importance of the dance language reaches far beyond food security concerns. This research integrates proximate and ultimate perspectives to produce a comprehensive, multi-disciplinary program; a high-risk, high-gain journey into new territory for understanding animal communication.

Mots-clés : social learning,communication,pollinator behaviour
Projet ERC : LearnBugs (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Stuttgart — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1458375.0 €

Learning to Find Software Bugs Software has become the cornerstone of modern society, economy, and life. Since software is created by humans, though, every non-trivial program contains various bugs, i.e., programming errors that may have disastrous consequences. Traditional approaches to find bugs include automated bug detection tools. Such tools search for instances of bug patterns that recur across projects and application domains. However, automated bug detection currently cannot unleash its full potential because each bug detector addresses one bug pattern and one programming language, while creating new bug detectors is feasible only for program analysis experts. The objective of this proposal is to radically change the way automated bug detection tools are created. The core idea is to replace manually written program analyses with trained machine learning models. To this end, developers will train a bug detector for a particular bug pattern with examples of buggy and non-buggy code, which the model learns to distinguish. The project will realize this vision by developing a reusable framework that addresses several fundamental challenges at the intersection of software engineering, programming languages, and machine learning, e.g.: (i) How to support developers in creating large amounts of training data of buggy and non-buggy code examples? (ii) How to represent programs in a way suitable for advanced machine learning techniques? The proposed project has the potential to revolutionize how software developers find bugs. To date, no other research has addressed the problem of automatically learning bug detection tools. If successful, the project will "democratize" bug detection by enabling all software developers, instead of a few program analysis experts, to create and share bug detection tools. Ultimately, the project will contribute to increasing the reliability, security, and efficiency of complex software systems used by millions of people.

Mots-clés : program analysis,bug detection
Projet ERC : MechaDynA (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Aix-Marseille Université — Pays : France — Rôle : PI
Acronyme de l'entité : amU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2068959.0 €

Leukocytes, white blood cells, patrol the vascular wall of our vessels in search of sites of inflammation. In the so-called leukocyte adhesion cascade, leukocytes flowing at high velocities (up to mm/s) impact the vessel wall, roll at µm/s, and finally migrate at nm/s to the site of inflammation. They are thus subjected to mechanical forces from sub-msec to several minutes. Complete understanding of the physical processes behind leukocyte adhesion requires an approach over multiple length and time scales, from single protein molecules to the whole cell. This is far from being established due, in part, to the lack of techniques covering the wide range of length and time scales involved. We have recently implemented high-speed atomic force microscopy (HS-AFM) to perform force spectroscopy measurements on biological samples with microsec time resolution. The novel acoustic force spectroscopy (AFS) traps hundreds of particles in parallel allowing hours-long measurements on single molecules. MechaDynA proposes to develop and apply these two novel nanotools to allow force measurements on living cells with the goal of obtaining a complete, multi-scale picture of the physics behind the leukocyte adhesion cascade over the widest dynamic range (µs-min). This will require development of HS-AFM technology and coupling with advanced optical microscopy. We will probe the binding strength of single adhesion complexes, and membrane and cytoskeleton mechanics at physiologically relevant time scales not explored so far. Technologically, it will establish HS-AFM and AFS as force measurement tools for living cells covering the widest temporal range. This will open the door to unexplored physical phenomena in cell biology, biological physics and soft condensed matter. Biomedically, the expected outcomes will provide a mechanistic description of the physical phenomena in leukocyte immune response that may lead to better diagnosis and therapeutics.

Mots-clés : cell mechanics,cell adhesion,membrane mechanics,molecular mechanics,atomic force microscopy,force spectroscopy,high speed atomic force microscopy,leukocytes,single molecules