Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Computational ONcology TRaining Alliance (CONTRA)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Training Networks
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Spain,Germany,Spain,Spain,Switzerland,Poland,Poland,Sweden,Sweden,United Kingdom,United Kingdom,United Kingdom,Canada,United Kingdom
Call: Innovative Training Networks (H2020-MSCA-ITN-2017, 2017)
Start date: 2018-01-01
End date: 2022-06-30
Duration (months): 54.0
Total project cost (EUR): 3863978.28
EU funding (EUR): 3863978.2800000003
Participants: 15
Project status: CLOSED

Cancer is a major cause of death and suffering, rendering it a huge concern to the general public. Consequently, it has been targeted by application of the molecular techniques developed during the last 20-25 years, thereby improving diagnosis and treatment. Nevertheless, although current biomarker and treatment concepts often are successful initially, they subsequently frequently fail to achieve durable drug response and long-term survival for cancer patients. The study of somatic evolution in cancer is a very promising approach to rectify this situation. Fortunately, single cell genomic sequencing has recently begun to provide opportunity to unprecedented detailed insights into tumour evolution and new techniques are emerging for assaying the spatial distribution of tumour heterogeneity. Analysing and developing methods for these emerging data sets are under-researched areas that lie at the intersection of medical, evolutionary biology, and computational research. In CONTRA, European researchers with complementary expertise have joined forces in order to collectively facilitate training of future European computational cancer researchers. We will by applying modern recruitment and selection methods in combination with offering excellent training and employments conditions, including social security, facilitate recruitment of 15 excellent ESRs, across genders and geographical areas. We will train these 15 ESRs in the mathematical, computational, and applied skills required to tackle the complex analysis problems posed by somatic evolution in cancer. We will also teach them entrepreneurship and the requirements of pharmaceutical and biotech industry. Using a novel scheme, this substantial academic cancer expertise will be complemented with industrial expertise in software development, especially for biotech and pharmaceutical industry, which will unleash key components of the innovative force of the European commercial sector in the struggle against cancer.

Keywords: somatic evolution in cancer,tumour evolution in cancer,single cell sequencing,cancer metastasis,circulating tumour cells,tumour phylogeny,tumour cell morphology,mutations in cancer
Preclinical concept validation of tumor endothelial cell metabolism for novel anti-angiogenic therapy (TECNEC)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Physiology in Health, Disease and Ageing
Panel description: Organ and tissue physiology, comparative physiology, physiology of ageing, pathophysiology, interorgan and tissue communication, endocrinology, nutrition, metabolism, interaction with the microbiome, non-communicable diseases including cancer (and except disorders of the nervous system and immunity-related diseases)
Countries involved: Belgium
Call: ERC Advanced Grant (ERC-2016-ADG, 2016)
Start date: 2017-11-01
End date: 2022-10-31
Duration (months): 60.0
Total project cost (EUR): 2500000.0
EU funding (EUR): 2500000.0
Participants: 1
Project status: CLOSED

MAIN GOAL: To characterize the metabolic changes, fuelling growth of tumor endothelial cells (TECs) versus normal healthy endothelial cells (NECs), and identify & validate metabolic genes, deregulated in TECs, as targets for anti-angiogenic cancer therapy. BACKGROUND & RATIONALE: Tumors stimulate blood vessel growth (angiogenesis) to nourish cancer cells, but current anti-angiogenic treatments lack sufficient efficacy. NECs are widely used for anti-angiogenesis development, but TECs, the real target cells, have been dismissed, even though they differ fundamentally from NECs. We pioneered NEC metabolism research and demonstrated that inhibition of specific metabolic pathways can block angiogenesis. Nothing is however known on TEC metabolism. I hypothesize that TECs “hyper-activate” their metabolism in a tissue-specific manner, and that targeting TEC metabolism is a novel concept for inhibiting tumor angiogenesis. METHODOLOGY: We will perform a transcriptomic and metabolomic analysis of TECs & NECs isolated from cancer patients, use an in-house developed bioinformatics platform (BIOMEX) to analyze the omics data in order to rank deregulated metabolic genes, and construct TEC-tailored genome scale metabolic network models (GEMs) allowing in silico computational modeling to predict metabolic targets that affect TEC but not NEC growth. The in vitro angiogenic activity of top targets will be biologically validated directly or after a negative selection shRNA-based screen, followed by validation in preclinical tumor models in vivo, using conditional knockout mice. All procedures are operational. Deliverables include in vivo validated metabolic targets, driving tumor angiogenesis. NOVELTY, OVERALL & CLINICAL IMPACT: These pioneering studies on TEC metabolism promise to open up new horizons/opportunities for cancer research. Treatment with inhibitors of the validated targets promises to normalize hyperactive TEC metabolism as a novel anti-angiogenic cancer therapy.

Keywords: angiogenesis,endothelial cells,endothelial cell metabolism,anti angiogenic therapy
Automated Synthesis of Cyber-Physical Systems: A Compositional Approach (AutoCPS)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Systems and communication engineering
Panel description: Electrical, electronic, communication, optical and systems engineering
Countries involved: Germany
Call: ERC Starting Grant (ERC-2018-STG, 2018)
Start date: 2019-02-01
End date: 2024-01-31
Duration (months): 60.0
Total project cost (EUR): 1470800.0
EU funding (EUR): 1470800.0
Participants: 1
Project status: SIGNED

Embedded Control software plays a critical role in many safety-critical applications. For instance, modern vehicles use interacting software and hardware components to control steering and braking. Control software forms the main core of autonomous transportation, power networks, and aerospace. These applications are examples of cyber-physical systems (CPS), where distributed software systems interact tightly with spatially distributed physical systems with complex dynamics. CPS are becoming ubiquitous due to rapid advances in computation, communication, and memory. However, the development of core control software running in these systems is still ad hoc and error-prone and much of the engineering costs today go into ensuring that control software works correctly. In order to reduce the design costs and guaranteeing its correctness, I aim to develop an innovative design process, in which the embedded control software is synthesized from high-level correctness requirements in a push-button and formal manner. Requirements for modern CPS applications go beyond conventional properties in control theory (e.g. stability) and in computer science (e.g. protocol design). Here, I propose a compositional methodology for automated synthesis of control software by combining compositional techniques from computer science (e.g. assume-guarantee rules) with those from control theory (e.g. small-gain theorems). I will leverage decomposition and abstraction as two key tools to tackle the design complexity, by either breaking the design object into semi-independent parts or by aggregating components and eliminating unnecessary details. My project is high-risk because it requires a fundamental re-thinking of design techniques till now studied in separate disciplines. It is high-gain because a successful method for automated synthesis of control software will make it finally possible to develop complex yet reliable CPS applications while considerably reducing the engineering cost.

Keywords: interconnected systems,compositionality,finite abstractions,symbolic control,correct by construction controller synthesis
Implementing the European Photonics21 PPP strategy (EuroPho21)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Theme: Micro- and nanoelectronics and photonics: Key enabling technologies related to micro- and nanoelectronics and to photonics, covering also quantum technologies
Action type: Coordination and support actions
Partnership: Photonics (cPPP)
Countries involved: Austria,Italy,Spain,Spain,Italy,Germany,Switzerland,Finland,Sweden,Poland,Greece,Slovenia,France,Netherlands,United Kingdom
Call: Photonics KET (H2020-ICT-2014-1, 2014)
Start date: 2015-01-01
End date: 2017-12-31
Duration (months): 36.0
Total project cost (EUR): 2986498.25
EU funding (EUR): 2848990.75
Participants: 15
Project status: CLOSED

EuroPho21 provides the decisive support to Photonics21 for strategy development and implementation and paves the way for the re-industrialization of Europe. The networking and coordination among Photonics21 stakeholders and the end user industry in the strategy development and implementation will speed up the innovation process and time to market for new products. This is a critical step to overcome the so called “Valley of Death”. EuroPho21 will build bridges to other parts of the Horizon2020 Programme to create value chains. Furthermore, it will reach out to regions where a strong end user industry is present to increase the deployment of Photonics as a Key Enabling Technology. To this end the project supports building up manufacturing and creating jobs in Europe. The current fragmentation of the European, national and regional activities represents a major barrier to the implementation of one photonics strategy in Europe. EuroPho21 will overcome these barriers and will build bridges between regional, national and European funding in photonics. Increasing the participation of Member States and regions in the Photonics21 Mirror Group enables Horizon2020 photonics projects to be jointly financed by regions, Member States and the European Commission through the ERANET+ scheme. Combining regional funding with Horizon2020 Photonics project funding will be enabled by bringing together the relevant stakeholders. Photonics21 as the central communication platform for Photonics Research and Innovation in Europe will communicate information about Horizon2020 Photonics KET projects, national and regional activities reaching out to more than 600.000 recipients, with special focus on end users. The EuroPho21 proposal is submitted by the Photonics21 secretariat and 12 National Technology Platforms - representing more than 30 Cluster - at the request, with the consent and on behalf of the Photonics21 Board of Stakeholders which will closely steer all activities of the project.

Keywords: european technology platform photonics21,re creation of value chains,networking,coordination,regional,member states
COuNt data TimE SerieS Analysis: significance tests and sequencing data application (CONTESSA)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: United Kingdom
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2014, 2014)
Start date: 2015-08-01
End date: 2017-07-31
Duration (months): 24.0
Total project cost (EUR): 195454.8
EU funding (EUR): 195454.8
Participants: 1
Project status: CLOSED

The aim of this project is to develop methods for analysis of time-series based on count data. For example, detecting significant differences between two count data time series would distinguish between two different models: one in which the two time series are interchangeable, and one in which the second sample is a modification of the first, i.e. the two time series are non-interchangeable. This will broaden the target of my project to general analysis of count time-series data such as clustering, classification, perturbations inference and machine learning over sequential count data. The project will focus on count data sets from ribonucleic acid sequencing (RNA-seq) time course experiments. The method I plan to develop potentially has promising applications in a variety of multidisciplinary fields where event-counting is required, such as economics and biology. In economics, examples include the number of applicants for a job, or the number of labour strikes during a year. In biology, recent examples include high-throughput sequencing, such as RNA-seq and chromatin immunoprecipitation sequencing (ChIP-seq) analyses. These examples are especially relevant to this project because the method I will be developing enables various features of organisms to be compared through tag counts. I am enthusiastic about having the opportunity to be instrumental to a field where once developed this project will have a real impact in finding better treatments for patients with neurodegenerative diseases including amyotrophic lateral sclerosis (ALS), Alzheimer’s and Parkinson’s Disease. Professor Neil Lawrence will act as the supervisor of the fellowship and will take over responsibility for my training and development. My fellowship experience will be enriched further through a six month secondment period at University of Manchester with Professor Magnus Rattray and through the opportunity to collaborate with SITraN’s Professor Winston Hide and Biogen Idec Industry.

Keywords: count data,time series,hypothesis testing,rna sequencing,chip sequencing,negative binomial
Photodynamic therapy triggered by spectral scanner CT: an efficient tool for cancer treatment (SCANnTREAT)

Framework: Horizon 2020 — Excellent Science
Programme: Future and Emerging Technologies (FET)
Theme: EIC Pathfinder
Destination: Novel ideas for radically new technologies
Action type: Research and Innovation actions
Countries involved: Israel,France,France,Netherlands
Call: FET-Open Challenging Current Thinking (H2020-FETOPEN-2018-2019-2020-01, 2019)
Start date: 2020-09-01
End date: 2024-02-29
Duration (months): 42.0
Total project cost (EUR): 2999955.0
EU funding (EUR): 2999955.0
Participants: 6
Project status: SIGNED

According to Eurostat, cancer accounts for the second cause of death in Europe. We are aiming at a breakthrough technology that will contribute to increase drastically patients five years survival and have significant positive impact on their quality of life. Such ambition will be reached thanks to a new future technology that will ensure the efficient treatment of cancer with low side-effects (even if radioresistant), allow to perform accurate diagnostic and give access to in situ monitoring of the treatment. The other ground-breaking aspect of this technology is the fact that everything will take place at the same time, with the same machine which will be much less stressful and disturbing for patients compared to other treatment. SCANnTREAT is based on the association of cutting-edge technologies: spectral photon counting scanner CT (SPCCT) which is a ground-breaking imaging modality and a new X-ray based treatment known as X-rays activated Photodynamic Therapy (X-PDT). The perfect match between these two technologies will be ensured with specifically designed probes acting both as contrast media and therapeutic agents. The SCANnTREAT consortium will gather 3 academic and 1 industrial partners who are international leaders in their respective fields (chemistry, biochemistry, physics, radiobiology, experimental imaging, radiotherapy, clinics).

Keywords: hybrid nanoparticles,x rays,photodynamic therapy,spectral scanner ct,cancer treatment
Single Particle Cryo-EM Reconstruction with Convolutional Neural Networks (EM-PRIOR)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2020-08-01
End date: 2022-07-31
Duration (months): 24.0
Total project cost (EUR): 224933.76
EU funding (EUR): 224933.76
Participants: 1
Project status: CLOSED

Electron cryo-microscopy (cryo-EM) is the fastest growing technique to explore the structure of biological macromolecules. To limit radiation damage, images are recorded under low-dose conditions, which leads to high levels of experimental noise. To reduce the noise, one averages over many images, but this requires alignment and classification algorithms that are robust to the high levels of noise. When signal-to-noise ratios drop, cryo-EM 3D reconstruction algorithms become susceptible to overfitting, ultimately limiting their applicability. The algorithms can be improved by incorporating prior knowledge. The most widely used approaches in the field to date incorporate the prior knowledge that cryo-EM reconstructions are smooth in a Bayesian approach. However, in terms of information content, the smoothness prior reflects poorly compared to the vast amount of prior knowledge that structural biology has gathered in the past 50 years. I aim to develop a computational pipeline that can exploit much more of the existing knowledge about biological structures in the cryo-EM structure determination process. I will express this prior knowledge through convolutional neural networks that have been trained on many reconstructions, and use these networks in novel algorithms that optimise a regularised likelihood function. Similar approaches have excelled in image denoising and reconstruction in related areas. Preliminary results with simulated data suggest that significant improvements beyond the existing methods are possible, both in computational speed and in signal recovery capabilities. The proposed methods will enable faster computations with less user involvement, but most importantly, they will extend the applicability of cryo-EM structure determination to many more samples, alleviating the existing experimental requirements of particle size, ice thickness and sample purity.

Keywords: cryo em,relion,regularization,inverse problems,denoising,adversarial regularizers
Next Generation Earth Modelling Systems (NextGEMS)

Framework: Horizon 2020 — Societal Challenges
Programme: Climate action, environment,  resource efficiency and raw materials
Theme: Fighting and adapting to climate change
Action type: Research and Innovation actions
Countries involved: Germany,Germany,Spain,Spain,Italy,Spain,Germany,Germany,Germany,Switzerland,Switzerland,Norway,Finland,Poland,Sweden,France,France,Netherlands,Denmark,United Kingdom,United Kingdom,Portugal,Senegal
Call: Developing the next generation of Earth System Models (H2020-LC-CLA-2020-2, 2020)
Start date: 2021-09-01
End date: 2025-08-31
Duration (months): 48.0
Total project cost (EUR): 11118307.14
EU funding (EUR): 11000000.0
Participants: 27
Project status: SIGNED

NextGEMS will develop and apply a new generation of global coupled Storm-Resolving Earth System Models (SR-ESMs) to the study of anthropogenic climate change. SR-ESMs are distinguished by their fine, 3 km, grid in the atmosphere and ocean. This allows a more physical representation of atmospheric and oceanic circulation systems, including their coupling to Earth-system processes such as the carbon, nutrients, water and atmospheric particulate (aerosol) cycles. NextGEMS will develop two prototypes SR-ESMs into production systems and produce multi-decadal (30 y) projections of future climate change. Improved resolution is expected to reduce biases and enhance the realism of these simulations. Ensembles of simulations will address scientific puzzles such as the impact of convective organization on climate sensitivity, the magnitude of aerosol forcing, and the changes in extremes associated with tropical air-sea interaction (including the African Monsoon and Atlantic Hurricanes) and land-surface interaction in the mid-latitudes (dry-spells and links between hydrology and carbon). By developing models that are structurally different than existing ones, NextGEMS will reshape perceptions of uncertainty and provide a basis for reassessing the risk global warming poses for society and ecology. By focusing on just two models, NextGEMS builds a European community of scientists and users around a technologically more ambitious modelling enterprise. This concentration is needed if Europe is to maintain its position at the forefront of Earth-system modelling. By representing the scales of motion and driving forces of high impact weather globally, NextGEMS links more directly to applications, thereby shortening the value chain. Knowledge coproduction projects focusing on how circulation influences both solar energy production and marine nutrients will demonstrate how applications and downstream users can thus be directly integrated into the model development enterprise.

Keywords: monsoon,hurricanes,dry spells,carbon cycle,aerosol forcing,climate sensitivity,renewable energy,marine nutrients
Zero-defect manufacturing of composite parts in the aerospace industry (ZAero)

Framework: Horizon 2020 — Industrial Leadership
Programme: Nanotechnologies, Advanced Materials, Advanced Manufacturing and Processing, and Biotechnology
Theme: Technologies for Factories of the Future
Action type: Innovation actions
Countries involved: Germany,Spain,Spain,Spain,Austria,France,United Kingdom
Call: Zero-defect strategies at system level for multi-stage manufacturing in production lines (H2020-FOF-2016, 2016)
Start date: 2016-10-01
End date: 2019-09-30
Duration (months): 36.0
Total project cost (EUR): 4124143.75
EU funding (EUR): 3548206.01
Participants: 9
Project status: CLOSED

In the aerospace industry very high quality standards have to be met. For the manufacturing of carbon fibre parts this is currently solved through extended end-of-line inspection in combination with re-work processes to deal with defective parts. Also, in-situ visual inspection is used for quality control, which is currently causing huge productivity losses (30%-50%) during lay-up and has become a real bottleneck in carbon fibre parts manufacturing. The project will provide a solution by developing inline quality control methods for the key process steps: automatic lay-up (dry fibre placement and automatic dry material placement) and curing. At the system level decision support systems will be developed that assist human decision-making when assessing defects and when planning the part flow through the production line. These will be supported by simulation tools for part verification and logistical planning. The future manufacturing of the A320neo wing covers will be provide the background for the developments. Each such wing cover consists of two parts, that each cost several hundred thousand Euros in manufacturing. Assuming the planned production rates of 60 planes per month from 2025, savings of 150 MEUR in production costs can be obtained per year. The consortium consists of all key players that will play a future role in the manufacturing of such large carbon fibre parts. Airbus with its research centers Airbus Group Innovations and FIDAMC will play a leading role in the consortium as far as the multi-stage manufacturing process is concerned. Machine builders (MTorres, Danobat) and research centers will develop the inline quality control, while Dassault Systémes will provide simulation support.

Keywords: composite parts,aerospace,decision support tools,simulation