Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
First of a kind bio n-butanol industrial facility (RenewChem)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Open to supporting projects in all areas of science, technology,...
Action type: EIC actions
Countries involved: Spain
Call: EIC Accelerator Open 2024 (HORIZON-EIC-2024-ACCELERATOR-02, 2024)
Start date: 2024-09-01
End date: 2026-09-30
Duration (months): 25.0
Total project cost (EUR): 3565178.57
EU funding (EUR): 2495625.0
Participants: 1
Project status: SIGNED

Catalyxx possesses an innovative, patented thermochemical catalytic biorefinery technology that transforms bioethanol into high-value bio-based chemicals such as bio n-butanol, bio n-hexanol, and bio n-octanol, offering sustainable alternatives to fossil-based feedstocks. With over a decade of technology development and 50,000 hours of testing leading to TRL7, Catalyxx has demonstrated success with its Demonstrator Plant operating for over 8,000 hours. The next milestone is the construction of the groundbreaking first-of-a-kind ("FOAK") 15,000 tons per year plant. Positioned uniquely in the market with sustainable production methods, Catalyxx aims to cater to the rising demand for low-carbon chemicals, providing a cost-competitive and environmentally friendly alternative to traditional methods. Independent LCA analysis showcases our environmentally friendly approach with a carbon footprint of -1.0 kg CO2 eq/kg butanol compared to 2.5 kg CO2 eq/kg for oxo butanol. Our intermediate chemicals play a pivotal role in decarbonizing downstream value chains. Support from the EIC is essential to expedite technology scale-up, showcase performance at scale, attract further investment, and facilitate the transition towards sustainable petrochemistry.

Keywords: bio n butanol,renewable chemistry,catalytic process,bioeconomy
The acoustic dimension of human and terrestrial wildlife interactions (WildSOUND)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Postdoctoral Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: Italy
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2025-05-05
End date: 2027-05-04
Duration (months): 24.0
Total project cost (EUR): 188590.08
EU funding (EUR): 188590.08
Participants: 1
Project status: SIGNED

Sound plays a critical role in many species behaviour and their intra and inter specific interactions, thus disruption of animal soundscapes due to anthropogenic noise has led to significant negative impacts on wildlife populations. Although, in terrestrial environments, anthropogenic noise is recognized as a major pollutant, regulations are targeted towards human wellbeing and often not relevant to wild populations. Hence, it is critical to better understand how noise influences and disrupts wildlife ecological niches. This project will take an interdisciplinary approach to investigate the ecoacoustic dimension of human and terrestrial wildlife interactions, focussing on mammals (currently underrepresented in the bioacoustic literature). Specifically, the project will evaluate how anthropogenic noise perturbates wildlife species ecological niches in Alpine ecosystems and what are the mechanism and the consequences of such perturbation on animal behaviour and performance. I will use a large array (>200) of acoustic recorders to measure sound levels variation across anthropic gradients and identify sources of anthropogenic noise (including outdoor sports). First, acoustic data will be integrated with a similar array of camera traps to investigate how noise levels affect the spatio-temporal occurrence of wildlife. Second, I will use biologging data and hormonal stress levels from faecal samples to assess the behavioural and physiological consequences of anthropogenic noise exposure. Finally, I will use a controlled experiment to disentangle the role of auditory and visual cues, and their interaction, in the elicitation of animal behavioural responses to anthropogenic disturbance. I will complement my expertis in bioacoustics with training in camera trapping, biologging and modelling. The knowledge generated in this action will inform and support the sustainable use of terrestrial ecosystems (UN SDG 15, HE Cluster6 and NextGenerationEU).

Keywords: ecoacustics,movement ecology,disturbance
REAL-WORLD IMPLEMENTATION, DEPLOYMENT AND VALIDATION OF EARLY DETECTION TOOLS AND LIFESTYLE ENHANCEMENT (AD-RIDDLE)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Public-private partnerships
Destination: Innovative Health Initiative
Action type: Research and Innovation actions
Partnership: IHI (JU-JTI)
Countries involved: Sweden,Finland,Netherlands,Spain,United Kingdom,Luxembourg,Italy,United States,France,Germany,Belgium
Call: Screening platform and biomarkers for prediction and prevention of diseases of unmet public health need (HORIZON-JU-IHI-2022-03-single-stage, 2022)
Start date: 2024-01-01
End date: 2028-12-31
Duration (months): 60.0
Total project cost (EUR): 29051202.25
EU funding (EUR): 12090250.0
Participants: 28
Project status: SIGNED

Alzheimer's disease (AD), the main cause of dementia, is one of the major global health challenges of our time. This disabling disorder affects 55 million people worldwide, with costs above $1.3 trillion. With 67 million people at risk for AD dementia (prodromal and preclinical AD) in Europe alone, it is a major unmet public health need, with increasing burden for European economies, healthcare and social care systems and a concern for the future. Yet, prevention and early detection strategies have been lacking. Recent advances in disease-modifying therapies and the huge potential benefits of preventive multidomain interventions based on modifiable lifestyle and vascular factors (prevention potential ~ 40%) provide new opportunities. New developments in blood-based biomarkers (BBMs) and digital cognitive tests enable broader, cost-effective testing. The AD-RIDDLE platform is envisioned to revolutionize how AD is detected and diagnosed, prevented and treated across healthcare settings (HCS). AD-RIDDLE centres around a toolbox platform concept, providing a series of validated tools at each key step to enable HCS and practitioners to deploy enhanced AD management across diverse patient populations. The AD-RIDDLE toolbox platform will include a) community outreach tools to increase awareness/activation on AD prevention; b) combination of easily available, clinically validated biomarkers: digital cognitive tools and BBMs, for early detection of AD dementia risk; c) validated algorithms supporting clinical decisions, to identify people who can benefit from specific preventive therapies (pharma and/or non-pharma) using precision prevention approach. The toolbox usability and efficacy with be tested in real-world settings. We expect that the AD-RIDDLE modular and flexible design can facilitate broad uptake from different healthcare systems, and its tools can advance research to develop cost-effective preventive therapies for AD, with scalability for other dementias

Keywords: alzheimer s disease,digital and blood based biomarkers,population screening and clinical decision support toolkit
Revealing Environmental Causes of Preterm Births in a Quasi-Experimental Framework (TinyTrend)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Postdoctoral Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Italy
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2025-01-07
End date: 2027-01-06
Duration (months): 24.0
Total project cost (EUR): 172750.08
EU funding (EUR): 172750.08
Participants: 1
Project status: SIGNED

Preterm birth (<37 weeks' gestation) is the leading cause of death in children under the age of 5 worldwide, accounting for 900k neonatal deaths a year. Preterm birth rates vary widely within and between countries, with environmental causes of yet unclear aetiology suspected to be the main drivers. The main goal of TinyTrend is to reveal the role of environmental risk factors and inform risk-mitigation policies for preterm births by evaluating environmental policy changes in a quasi-experimental setting. This interdisciplinary project will devise a robust and novel methodology through embedding data science and artificial intelligence within an epidemiology framework to: 1) systematically identify policy changes targeting environmental factors, 2) evaluate their role on preterm birth rates in time and space (quasi-experimental framework), 3) draw causal inferences integrating original and previous evidence on the impact of these environmental factors on preterm births. The Experienced Researcher (ER) will advance his skills in environmental science, public health, epidemiology and informatics through high-calibre external and in-house training from the host institution. TinyTrend will leverage high-quality population-wide administrative health data from Lombardy (Italy), including 1M pregnancies for the 2010-2023 period, and generate a new FAIR dataset of policy changes. For the first time, the ER will combine both data sources in an interrupted time series analysis with control series to generate robust evidence on new environmental causes and their critical time windows for preterm births. TinyTrend will present a best practice template for other Italian regions to follow that is transferable to generate new insights into other diseases with a significant environmental component and elusive aetiology.
Understanding Deep Learning (Understanding DL)

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: Israel
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): 1499750.0
EU funding (EUR): 1499750.0
Participants: 1
Project status: SIGNED

While extremely successful, deep learning (DL) still lacks a solid theoretical foundation. In the last 5 years the PI focused almost entirely on DL theory, yielding a strong publication record with 7 papers at NeurIPS (the leading ML conference), including 2 spotlights (top 3% of submitted papers) and one oral (top 1%), 2 papers at ICLR (the leading DL conference), and 1 paper at COLT (the leading ML theory conference). These results are amongst the first that break a 20 years hiatus in NN theory, thereby giving some hope for a solid deep learning theory. This includes 1) the first poly-time learnability result for non-trivial function class by SGD on NN, 2) the first such result with near optimal rate, 3) new methodology to bound the sample complexity of NN, that established the first sample complexity bound that is sublinear in the number of parameters, under norm constraints that are valid in practice, 4) an explanation to the phenomena of adversarial examples. We plan to go far beyond these and other results, and to build a coherent theory for DL, addressing all three pillars of learning theory: Optimization: We plan to investigate the success of SGD in finding a good model, arguably the greatest mystery of modern deep learning. Specifically our goal is to understand what models are learnable by SGD on neural networks. To this end, we plan to come up with a new class of models that can potentially lead to new deep learning algorithms, with a solid theory behind them. Statistical Complexity: We plan to crack the second great mystery of modern deep learning, which is their ability to generalize with fewer examples than parameters. Our plan is to investigate the sample complexity of classes of neural networks that are defined by bounds on the weights’ magnitude. Representation: We plan to investigate functions that can be realized by NN. This includes classical questions such as the benefits of depth, as well as more modern aspects such as adversarial examples.
COnnecting the Dots – embedding PLUG-IN within national innovation support schemes (CoDEPlugin)

Framework: Horizon Europe — Innovative Europe
Programme: European innovation ecosystems
Theme: Interconnected Innovation Ecosystems
Destination: Interconnected Innovation Ecosystems
Action type: Coordination and support actions
Countries involved: Poland,Germany,France,Italy,Portugal
Call: Mutual learning and support scheme for national and regional innovation programmes (HORIZON-EIE-2024-CONNECT-02, 2024)
Start date: 2025-10-01
End date: 2027-09-30
Duration (months): 24.0
Total project cost (EUR): 128696.43
EU funding (EUR): 115437.5
Participants: 8
Project status: SIGNED

The CoDEPlugin project (COnnecting the Dots – Embedding Plug-In within national innovation support schemes) aims to enhance the alignment of national and regional innovation programs with the European Innovation Council's (EIC) Plug-in scheme. This novelty under Horizon Europe, is designed to streamline the process for innovation-driven companies—startups and SMEs—to access support under the EIC Accelerator. However, many countries still face challenges in certifying their programs, resulting in unequal access to this European-level support. CoDEPlugin responds this issue by analyzing the gaps between national support programs and the Plug-in scheme's requirements. The project will conduct an in-depth assessment of the needs of startups and SMEs, the state of certified and uncertified programs, and the barriers hindering wider certification across the EU. The CoDEPlugin develops policy recommendations and practical guidelines to help innovation agencies design programs that align with Plug-in standards. Key activities include knowledge exchange among stakeholders, including Plug-in contact points, policymakers, and innovation agencies, and the creation of a Design Options Paper. It will provide actionable recommendations for aligning national programs with the Plug-in scheme and will be piloted to test its applicability. Fostering collaboration and sharing best practices lead to a more coherent and efficient European innovation support ecosystem. The CoDEPlugin consortium is well-represented by a diverse group of countries: strong innovators like FR, DE, moderate (IT, PT), which have successfully certified their programs. In contrast, Poland (emerging innovator), the largest of widening countries, ‘s not achieved certification. The consortium ensures a comprehensive analysis of the challenges and opportunities across stages of Plug-in implementation, providing a robust response to the needs of both well-established and underperforming innovation systems.

Keywords: innovation system,innovation funding mechanisms,alignement with national and regional funding systems,plugin standards,plug in certification,deep tech smes,widening countries,commercialization
IMAGING FOR PATIENT BENEFIT IN ARTHRITIS (AutoPiX)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Public-private partnerships
Destination: Innovative Health Initiative
Action type: Research and Innovation actions
Partnership: IHI (JU-JTI)
Countries involved: Austria,Denmark,France,Sweden,Belgium,Switzerland,United States,Germany,United Kingdom,Spain
Call: Clinical validation of biomarkers for diagnosis, monitoring disease progression and treatment response (HORIZON-JU-IHI-2024-07-single-stage, 2024)
Start date: 2024-11-01
End date: 2029-10-31
Duration (months): 60.0
Total project cost (EUR): 21167177.0
EU funding (EUR): 10729428.06
Participants: 20
Project status: SIGNED

Patients at risk or diagnosed with arthritis are constantly assessed by innovative imaging techniques to document the onset or progression of their disease. However, despite their impressive abundance and resolution, these images lack the analysis and interpretation tools necessary to deliver unbiased and precise diagnosis, monitoring and prognosis to the patients. Additionally, some key advanced imaging methodologies such as ultrasound are hardly accessible to most of patients, urging improvements of more accessible imaging methods. The AutoPiX project is an ambitious international multi-stakeholder effort led jointly and synergistically by academic and industry partners to achieve breakthroughs in both the applicability and harnessing of imaging technologies for the benefit of patients by creating new powerful analysis and decision tools. We will first generate tools to transform unstructured images into quantitative biomarkers using artificial intelligence (AI) and machine learning (ML) models, and validate them clinically for their diagnosis, monitoring and prognosis power. This will considerably increase the utility of imaging biomarkers for arthritis and bring them to the same level as laboratory biomarkers. In parallel we will develop accessible imaging strategies such as remote monitoring and robotic-powered point-of-care ultrasound exams for patients to mitigate the often-observed shortage of qualified personnel in real world settings. To achieve this, we will improve the precision and interpretability of these methods and further validate them with clinical, molecular and histological analyses. Our consortium is built on multi-disciplinarity and the constant synergistic interaction of all the actors of arthritis care: rheumatologists, radiologists, patients, researchers, regulators, industries and small- and medium sized enterprises (SMEs). On the long term, AutoPiX will create new clinically-validated methods to achieve a/ more precise, accessible and effective diagnosis, b/ shortened and better-tailored treatment paths and c/ improved treatment response assessments and outcome prediction for patients with arthritis.

Keywords: imaging biomarkers,artificial intelligence,validation
Validation and Comparitive Multi-Omics Benchmarking of Fluid-Derived Volatilomics Biomarkers for the Prevention and Early detection of Schizophrenia (VOLABIOS)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Health
Destination: Tackling diseases and reducing disease burden
Action type: Research and Innovation actions
Countries involved: Israel,Greece,Spain,Belgium,Germany,United Kingdom,Austria,Lithuania,France,Portugal,Czechia
Call: Validation of fluid-derived biomarkers for the prediction and prevention of brain disorders (HORIZON-HLTH-2024-DISEASE-03-two-stage, 2024)
Start date: 2025-01-01
End date: 2029-06-30
Duration (months): 54.0
Total project cost (EUR): 9244738.31
EU funding (EUR): 7988082.73
Participants: 22
Project status: SIGNED

Schizophrenia (SZ) impacts around 1% of the global population, posing diagnostic challenges that lead to a misdiagnosis rate of 23-40%. The need for reliable biomarkers is crucial to improve early detection and treatment outcomes. VOLABIOS aims to revolutionize SZ management by harnessing innovative real-time and portable spectrograph technology to validate volatile organic compounds (VOCs) as non-invasive and cost-effective biomarkers detectable during the early stages of the disease. This entails extensive data analysis, prospective clinical studies involving 3,692 subjects across six centres, and a blind validation study with 1,000 subjects, focusing on longitudinal monitoring of at-risk individuals to identify early SZ transition. To further substantiate the efficacy of VOCs and gain profound insights, VOLABIOS will integrate VOC analysis with genomics, proteomics, and transcriptomics. This integration will be supported by a biobank comprising approximately 4,000 samples and a dataset consisting of 9 million records. By considering external and personal risk factors as well as biological responses, VOLABIOS aims to unravel intricate correlations that contribute to SZ development. This comprehensive approach will enhance prediction accuracy, facilitate early detection, and improve management strategies. Furthermore, VOLABIOS plans to explore the real-time effects of validated VOCs through controlled in-vivo experiments using animal models, providing valuable information about associated cellular and molecular pathways. VOLABIOS's endeavours to validate and establish EU-wide SZ diagnosis protocols are set to significantly enhance patient care and healthcare efficiency, marking a substantial step forward in addressing the complexities surrounding SZ.

Keywords: schizophrenia sz,biomarkers,volatilomics,genomics,proteomics,ai artificial intelligence,clinical decision support system
PerfusiX-Imaging, a breakthrough surgical device that prevents gastrointestinal anastomotic leakage through plug-and-play laser speckle contrast imaging technology (PerfusiX)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Other destinations
Action type: EIC actions
Countries involved: Netherlands
Call: EIC Accelerator 2024 - Short application (HORIZON-EIC-2024-ACCELERATOR-01, 2024)
Duration (months): 12.0
Total project cost (EUR): 1890000.0
EU funding (EUR): 1890000.0
Participants: 1
Project status: SIGNED

LIMIS Development B.V. develops PerfusiX-Imaging, a breakthrough surgical device which uses laser speckle contrast imaging to reduce the chance of anastomotic leakage (AL) after colorectal resection by 50-60%. PerfusiX-Imaging is a dye-free solution which easily integrates with existing laparoscopic equipment for instant, continuous viewing of perfusion in order to guide clinical decision making. This prevents AL and accelerates patient recovery, critically reducing morbidity, mortality, and healthcare costs. During the EICA project, we will focus on product finalization and achieving CE-marking and 510k approval. The equity phase will contain efficacy clinical trials and end-user pilot studies to gain market traction and make PerfusiX-Imaging ready for initial distribution deals with regional distributors and a global distribution partnership with a multinational medtech company in 2029. LIMIS will generate revenues from direct sales to distributors. Revenues will be used to accelerate the expansion of PerfusiX-Imaging’s application to other indications beyond gastrointestinal surgery as well as compatibility with open image guided surgery and AI integration.

Keywords: anastomotic leakage gastrointestinal surgery minimally invasive surgery perfusion imaging technology laser speckle contrast imaging