Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Modelling and dynamic assessment of integrated health and care pathways enhancing response capacity of health systems (DYNAMO)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Health
Destination: Ensuring access to innovative, sustainable and high-quality health-care
Action type: Pre-Commercial Procurement
Countries involved: Germany,Spain,Portugal,Italy,United Kingdom,Poland
Call: Pre-commercial research and innovation procurement (PCP) for building the resilience of health care systems in the context of recovery (HORIZON-HLTH-2022-CARE-08, 2022)
Start date: 2023-01-01
End date: 2026-02-28
Duration (months): 38.0
Total project cost (EUR): 4998750.0
EU funding (EUR): 4998750.0
Participants: 9
Project status: SIGNED

DYNAMO will result in a lean and powerful solution enabling quick, data-driven and platform-independent planning of care pathways for situations where health system functions are threatened. This solution will allow EU health care systems to build resilience and respond to public health threats better than they can today. DYNAMO brings together five procurers from Italy, Portugal, Spain, Greece and the United Kingdom that not only see the need for such a solution but are willing to act on it now. They will set up a collaborative demand-side initiative to create the economies of scale and early adoption cases required to build momentum for DYNAMO. As a strategic planning tool, DYNAMO will guide stakeholders in concept development, data modelling, process design and implementation planning. DYNAMO will be able to reliably inform medium-term investment planning to adapt existing processes and infrastructures (incl. IT systems). More specifically, DYNAMO will be a socio-technical system to 1. Efficiently adapt service delivery processes to shocks and structural changes 2. Share and integrate cross-sectoral data to accurately forecast outcomes and impacts of alternative pathway configurations 3. Facilitate task planning and skills matching in times of crises The Buyers Group includes health and care organisations and authorities representing diverse health care systems and settings, thus reflecting the requirements of different national health care ecosystems that DYNAMO will have to meet. Suppliers will be rigorously evaluated after each procurement phase, comprising: (I) an Open Market Consultation; (II) specification of architecture and system aligned to requirements of health and care professionals, IT planners, decision makers; (III) prototype development and testing with end-users; (IV) evaluation of implementation fidelity and tool performance, output feasibility in terms of validity and applicability of the generated pathways as well as (V) overall cost-effectiveness.

Keywords: multi disciplinary care pathways,digital solutions,health system resilience,rapid response,preparedness
Get track of time for all events from the nano- to the crystal particle level in nanoporous materials (TIME)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Physical and Analytical Chemical Sciences
Panel description: Analytical chemistry, chemical theory, physical chemistry/chemical physics
Countries involved: Belgium
Call: ERC ADVANCED GRANTS (ERC-2024-ADG, 2024)
Start date: 2025-12-01
End date: 2030-11-30
Duration (months): 60.0
Total project cost (EUR): 2498875.0
EU funding (EUR): 2498875.0
Participants: 1
Project status: SIGNED

This proposal aims to get track of time for all events taking place when guest molecules are sent over a crystal particle of a nanoporous material, as is occurring during catalysis, separation, sensing. The observation: When a feed of molecules is sent over a crystalline nanoporous framework following events take place: first the molecules need to enter the crystal, then travel through the material to find interesting sites where they adsorb/desorb or react and eventually products leave the crystal. All these events are characterized by vastly different time- and length scales varying from the picosecond to the second/hours and the nano- to micrometre. Length and time phenomena are entangled - the dynamics is altered by modifications at the length scale - leading to spatiotemporal phenomena. The gap of knowledge: We have insufficient knowledge on characteristic time scales of all these events and consequently can not use the time axis as a control parameter to steer the functionality of nanoporous materials. The hypothesis: By coupling Machine Learning Potentials, reaction path discovery with advanced kinetic theories, it is possible to obtain kinetic and time information for all events taking place at the crystal particle from the nano- to the mesoscale with quantum mechanical accuracy. This contrasts sharply with current practice where only local events at the nanoscale are modelled with interatomic forces having quantum accuracy. The grand challenge: Building consistent kinetics models for events – reaction, diffusion, adsorption – with disparate time scales, thereby reconciling the dynamics of local and non-local events. The mission: Setting up integrated reaction diffusion models based on underlying characteristic times/kinetics obtained with quantum accuracy. The outcome: Spatiotemporal control over nanoporous materials used in catalysis, separation, sorption, sensing. It’s TIME to obtain a time perspective on functional nanoporous materials.

Keywords: heterogeneous catalysis,diffusion,zeolites,machine learning potentials,molecular dynamics,kinetics
De novo design of modulators for protein-nucleic acid interactions (NAMPify)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Molecules of Life: B iological  Mechanisms , Structures  and Functions
Panel description: For all organisms : Molecular biology , biochemistry, structural biology, molecular biophysics , synthetic and chemical biology, drug design, innovative methods and modelling
Countries involved: Switzerland
Call: ERC STARTING GRANTS (ERC-2025-STG, 2025)
Start date: 2026-01-01
End date: 2030-12-31
Duration (months): 60.0
Total project cost (EUR): 1524383.0
EU funding (EUR): 1524383.0
Participants: 1
Project status: SIGNED

Protein–nucleic acid (NA) interactions are fundamental to many cellular processes, and their disruption can lead to diseases such as autoimmune disorders and cancer. However, targeting NA-binding proteins with small molecules is difficult due to the “undruggable” nature of their interfaces. Recent advancements in computational protein design and deep learning have created new opportunities for the development of protein-based therapeutics. However, current methods struggle to design proteins that can effectively modulate or mimic the intricate protein-NA interactions directly. While some NA-aware models have been developed, they often underperform due to our limited understanding of protein-NA interactions and the scarcity of diverse structural training data. To overcome these challenges, I propose to pioneer the design of NA-mimicking proteins (NAMPs) capable of directly modulating protein-NA interactions with high specificity and adaptable functionality. I will develop computational methods that incorporate the structural and chemical features of NAs, and create NAMPs tailored to engage therapeutically relevant NA-binding proteins and regulate their activity. In addition, I will develop approaches to expand the structural data on protein-NA complexes using accelerated structure determination methods to refine NA-aware models. This will enable the design of NA-binding proteins with sequence- and shape-specific recognition. Lastly, to create molecular machines with complex functionalities, I will develop new methodologies for the de novo design of multi-domain proteins capable of adopting multiple, defined conformations—long considered a “holy grail” in protein design. By integrating these approaches, I aim to overcome the current limitations in studying and designing protein-NA interactions, paving the way for the creation of sophisticated molecular tools with vast applications in synthetic biology, biotechnology, and medicine.

Keywords: protein nucleic acid interactions,mimicry,cryoem,deep learning,molecular machines,binder,multi domain protein
Advanced Sensing Platforms for Single-Cell Metabolic Flux Analysis in Heterogeneous Cell Systems (HySENSE)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Italy
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2024-POC, 2024)
Start date: 2025-09-01
End date: 2027-02-28
Duration (months): 18.0
Total project cost (EUR): 167142.86
EU funding (EUR): 150000.0
Participants: 2
Project status: SIGNED

Accurately assessing hypoxia and acidosis in the tumor microenvironment is crucial for understanding tumor behavior and determining treatments effectiveness. These conditions, caused by uncontrolled cancer cell proliferation, are key indicators of aggressiveness and metastasis risk, yet remain difficult to measure precisely. The HySENSE project addresses this gap with an innovative sensor kit capable of real-time, cell-specific measurements of oxygen and pH at high spatio-temporal resolution. This advanced technology surpasses current tools, offering deeper insights into tumor behavior and therapeutic effects. HySENSE will be validated using patient-derived colorectal cancer models, and a business feasibility study will define the path to commercialization. By enabling precise analysis of hypoxia and acidosis, HySENSE aims to transform cancer diagnostics and treatment planning, ultimately improving patient care.
Catalysing scientific innovation into food safety action (CATALYSE)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Food, Bioeconomy Natural Resources, Agriculture and Environment
Theme: Bio-Environment
Destination: Fair, healthy and environmentally-friendly food systems from primary production to consumption
Action type: Coordination and support actions
Countries involved: Italy,Portugal,Belgium,Netherlands,Norway,Spain,Hungary,Finland,Slovakia,France,Ukraine,Switzerland,United Kingdom
Call: Thematic network ensuring food safety by translating research and innovation into practice (HORIZON-CL6-2023-FARM2FORK-01, 2023)
Start date: 2024-01-01
End date: 2026-12-31
Duration (months): 36.0
Total project cost (EUR): 1929041.38
EU funding (EUR): 1886655.67
Participants: 17
Project status: SIGNED

The CATALYSE project will create a network of food safety actors with the aim to support adoption of knowledge and innovative solutions along the value chain. The network will foster collaboration and food safety knowledge sharing in a model that COLLECTS and then TRANSLATES knowledge and practices across our community through active EDUCATION and FACILITATION. The project aims to bridge the gap between end users, innovators, practitioners, trainers, and regulators by facilitating communication among these parties while matching practical needs with innovative solutions. During network activities we will set priorities for future work, provide food safety education and training, and support food business start-ups and SMEs. Data on inventions and practices related to food safety will be made available on an open access platform to support broad communication. Education and facilitation will be cornerstone activities to support innovation and change management to ensure the successful implementation of newly designed solutions and adoption by member states food safety authorities. CATALYSE will establish a community of practice to connect stakeholders from the complete value chain and promote cross-fertilization of ideas to create a more resilient, sustainable, and equitable community.

Keywords: innovative solutions,community of practice,database,traditional and local products,education,sme support
OPTIMIZING THE IMPLEMENTATION AND SCALE-UP OF THE WHO TB TREATMENT DECISION ALGORITHMS FOR CHILDREN WITH PULMONARY TUBERCULOSIS IN SUB-SAHARAN AFRICA (OPTIC-TB)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Public-private partnerships
Destination: Global Health EDCTP3
Action type: Research and Innovation actions
Partnership: EDCTP3 (JU-JTI)
Countries involved: Norway,Tanzania, United Republic of,Congo, The Democratic Republic of the,Uganda
Call: Implementation research/real life assessment of existing interventions in women and children’s health (HORIZON-JU-GH-EDCTP3-2023-01, 2023)
Start date: 2024-04-01
End date: 2028-03-31
Duration (months): 48.0
Total project cost (EUR): 3999998.25
EU funding (EUR): 3999998.25
Participants: 6
Project status: SIGNED

Every year, about a million children under 15 years are diagnosed with Tuberculosis (TB) worldwide, and about a quarter die. However, over 60% of children with TB remain undiagnosed, particularly in sub-Saharan Africa, which accounts for about 20% of the global burden of paediatric TB cases. In 2022, WHO issued an interim general recommendation to use integrated Treatment-Decision Algorithms (TDAs) to diagnose TB in children under 10 years. However, this recommendation was based on very low certainty of evidence. Therefore, we are seeking funding to address four objectives: 1) To compare the effectiveness of TDAs and the Standard of Care in routine clinical settings 2) To identify processes and contextual factors that influence the effectiveness and fidelity in the implementation of TDAs 3) To compare the costs, cost-effectiveness, and the population level impact of the TDA strategies on the burden of TB and 4) To validate the diagnostic performance (sensitivity, specificity, negative and positive predictive values) of the TDAs in various health system settings and clinical context. A four-year pragmatic open-label cluster randomized controlled trial will be conducted in 120 primary health facilities in Tanzania, Uganda, and DR Congo. The primary outcome is the proportion of children detected and initiated on TB treatment. About 60,000 children with presumptive TB will be screened. We anticipate that this project will yield results that address the issue of low detection rates of paediatric TB in sub-Saharan Africa and beyond, build research capacity in conducting implementation research in low-income countries, and foster networking and collaboration between the partner institutions.

Keywords: cluster randomized controlled trial
Compressed Indexes for Regular Languages with Applications to Computational Pan-genomics (REGINDEX)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Computer Science and Informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: Italy
Call: ERC STARTING GRANTS (ERC-2021-STG, 2021)
Start date: 2022-09-01
End date: 2027-08-31
Duration (months): 60.0
Total project cost (EUR): 1385743.75
EU funding (EUR): 1385743.0
Participants: 1
Project status: SIGNED

Sorting is, arguably, the most powerful algorithmic primitive when it comes to indexing data. At the same time, the regularities exposed by sorting are precisely those enabling data compression. In the last two decades, this fascinating duality has led researchers to the design of compressed full-text indexes: data structures supporting fast pattern matching queries over compressed text. In this project, we revisit the natural generalization of the problem to labeled graphs from a new perspective: we interpret graphs as finite-state automata and investigate the connections existing between their propensity to be sorted and the languages they recognize. Our novel language-theoretic approach makes it possible to transfer fundamental results between the mature fields of regular language theory and compressed text indexing. We aim at building this bridge by developing a new theory of compressed regular language indexing. This project finds fundamental applications to the rapidly-expanding field of computational pan-genomics, where the goal is to study the variations contained in the genomes of an entire population. Recent research has shown that representing pan-genomes as labeled graphs is an important step to reduce reference allele bias. Existing approaches, however, can index only restricted classes of graphs, thereby limiting the practical applicability of such powerful pan-genome representations. Our innovative approach, based on sorting regular languages by partial co-lexicographic orders, changes the perspective from which the compressed indexing problem has been tackled in the literature. This project aims at developing a theory of graph indexing and compression based on the natural interplay between sorting and regular language theory. We will apply these findings inside practical tools for aligning arbitrarily-long DNA fragments against compressed pan-genome graphs.

Keywords: compressed data structures,pattern matching algorithms,graph data structures,data compression,regular languages computational pan genomics
Systems and Engineering Science Doctorate (SEED)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Co-funding of regional, national and international programmes
Action type: Marie Skłodowska-Curie actions
Countries involved: Finland,Belgium,Singapore,Italy,Germany,Netherlands
Call: MSCA COFUND 2021 (HORIZON-MSCA-2021-COFUND-01, 2021)
Start date: 2023-09-01
End date: 2028-08-31
Duration (months): 60.0
Total project cost (EUR): 1747200.0
EU funding (EUR): 1747200.0
Participants: 20
Project status: SIGNED

LUT University is developing doctoral education with the MSCA COFUND Systems and Engineering Science Doctorate (SEED) programme for the duration of 60 months with 13 Doctoral Candidates (DC). The programme builds on a versatile scholar programme, a systematic secondment scheme and detailed communication, dissemination and exploitation measures. The systematic secondment scheme builds on each DC having an academic secondment in a European or international university of high standing and excellence and a non-academic secondment in industry in Finland. The fields of study and research are software engineering, chemical engineering, industrial engineering & management (IEM), computational engineering, applied mathematics and technical physics. LUT is the main beneficiary and we have 20 excellent associated partner organisations committed to hosting the DCs during their secondments. During the SEED programme we will develop more systematic and transparent selection processes for DCs, promote equal opportunities, and improve the quality of supervision and management of doctoral education. This all leads to a doctoral training programme of higher quality career guidance and more systematic skill development increasing the career prospects of the DCs in both academic and non-academic environments.

Keywords: engineering,doctoral education,doctoral training,career development
Revealing liver micrometastases in vivo using ultra-high definition MRI (MicroMetSCAN)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Prevention,  Diagnosis and T reatment of Human Diseases
Panel description: Medical technologies  and tools for prevention, diagnosis and treatment of human diseases, therapeutic approaches and interventions , pharmacology, preventative  medicine, epidemiology and public health , digital medicine
Countries involved: United Kingdom
Call: ERC STARTING GRANTS (ERC-2024-STG, 2024)
Start date: 2025-04-01
End date: 2030-03-31
Duration (months): 60.0
Total project cost (EUR): 1998456.0
EU funding (EUR): 1998456.0
Participants: 1
Project status: SIGNED

Over 90% of cancer-related deaths are caused by metastases. A major obstacle for accurate patient stratification, development of new treatments against metastases and thorough biological understanding is our inability to detect early-stage micrometastases (µMets). The liver is one of the first and most common sites for metastases, particularly in gastrointestinal tract tumours. Thus, early detection of liver metastases is an urgent need in cancer diagnosis for many patients. Present image-based diagnostic methods, including state-of-the-art Magnetic Resonance Imaging (MRI), cannot detect µMets due to major limitations in current image contrast and resolution, as well as a lack of understanding of liver tissue changes during the metastatic process. MicroMetSCAN proposes a paradigm-shifting MRI approach based on highly promising preliminary results that leverages recent progress in MRI acquisition, denoising and contrast generation, to reach the breakthrough of exquisite sensitivity and specificity required to detect and characterise μMets in the liver and provide new insights about metastases-related tissue microenvironment dynamics. To achieve its overarching goal, my proposal consists of four objectives: 1) developing new ultra-high resolution MRI methods targeting robust µMets detection, 2) tailoring the specificity of contrasts towards μMet environments, 3) unravelling the transformations from µMets to full metastases in animal models, yielding critical insights into tumour growth patterns, and 4) translating the new MRI approach to the clinic in a prospective clinical study. MicroMetSCAN will significantly advance the state-of-the-art in cancer imaging, to enable precision diagnosis and treatment planning, with wide-reaching applicability, especially benefitting small lesion detection. Furthermore, it will unveil important biological insights on the progression of metastasis, guiding future therapeutic strategies for better prognosis and outcomes.

Keywords: cancer imaging,liver metastases,magnetic resonance imaging,mouse models of liver metastases,tissue microstructure,quantitative mri,colorectal cancer,pancreatic cancer