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Spanish National Research Council
Country: Spain
Funder (5)
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1,953 Projects, page 1 of 391
  • Funder: EC Project Code: 795272
    Overall Budget: 257,191 EURFunder Contribution: 257,191 EUR

    Despite significant advances in recent years, cancer remains the 2nd cause of death globally. Immunotherapy is an exciting new therapeutic approach that triggers natural immune response against cancer cells. Theranostics is an emerging therapeutic intervention, that uses dual (or multiple) purpose nanomedine, combining diagnostic and therapeutic effects within a single multifaceted formulation. ONCOTHERANOSTICS proposes the use of a novel theranostic agent to target key tumoral genes by siRNA and monitor the impact of the downregulation non-invasively in vivo. Specific aims include understanding the interplay between choline kinase and tumor escaping natural immune surveillance, and to develop new combinatorial therapies in a model of primary and metastatic triple negative breast cancer. We anticipate that successful completion of the project will result in significant advances in personalized tumor treatments, rendering safer strategies to treat cancer with a dual effect; boosting natural immune response and knocking down one of the main mechanisms of malignancy. Dr. Pacheco is an organic chemist with expertise in MRI who will be trained (outgoing) in nanoparticle synthesis, non-MRI imaging techniques and advanced molecular biology/genetic approaches. Training will be transferred to the host European institution (incoming) by the establishment of a new research line in the emerging field of Theranostics. ONCOTHERANOSTICS seeks to improve our understanding of cancer and how to personalize treatments, fostering the development of nanomedicine targeting specific genes in a way that can be assessed non-invasively. ONCOTHERANOSTICS provides a multidisciplinary environment for cancer treatment, involving multifaceted nanomedicine, multimodal molecular imaging and molecular biology/genetic approaches. The proposal directly addresses the cross cutting priority of personalizing health and care established by H2020, reinforcing European competitiveness in cancer research.

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  • Funder: EC Project Code: 101110366
    Funder Contribution: 181,153 EUR

    Global warming is leading to serious climate consequences worrying the world. What’s worse, the global CO2 budget will be used up soon, likely before 2030. Therefore, actions for CO2 mitigation are urgently needed to meet the agreed climate targets. Negative Emissions Technologies (NETs) are possibly the only implementable option to tackle this problem in such short period. This project stands on a novel NETs technology called Chemical Looping Gasification (CLG) and aims at producing the 3rd-Generation BioFuels (G3BioF). CLG is an unmixed gasification using oxygen carrier to transfer oxygen for gasification, thus fuel and air never mix. In light of this unique feature, the gasification product is absent of N2 dilution (thus the CO2 capture is inherent), and in the ash-free air reactor heat-exchanger corrosion is less possible. Microalgae is biomass, a major source for G3BioF, but it has very high content of problematic ash impurities. However, CLG is hypothesized capable to handle this fuel, in addition to its negative CO2 emissions. This project will first select suitable oxygen carrier for microalgae-CLG, through intensive experiments in a batch fluidized-bed reactor and an 1.5 kW continuous pilot. The selected oxygen carrier will be used in a 50 kW dual fluidized-bed pilot for proof of concept and performance optimization. A process model with reaction kinetics details will be established and used to analyze the technology integration with downstream processes. Finally, Life Cycle Assessment (LCA) will be applied to the process chain to evaluate the environmental impacts. This is expected to identify the most promising integration having the best economic and lowest environmental impact. Through the project, the applicant will progress as a scientist and gain at least skills of LCA, complex pilot operation, tar analysis. This project is ambitious for a much greener production of biofuels and the results are important to power a sustainable future.

  • Funder: EC Project Code: 639226
    Overall Budget: 1,499,980 EURFunder Contribution: 1,499,980 EUR

    Recent reports suggest that early microbial colonization has an important role for in promoting health. This may contribute to reduce the risk of chronic diseases such as obesity, allergies and inflammatory conditions. Advances in understanding host-microbe interactions imply that maternal microbiota plays a crucial role on health programming. This process begins in utero and it is modulated by mode of delivery and diet. My research has shown that i) specific shifts in milk microbial composition are associated with lactation time and mode of delivery, ii) milk microbes drive the infant microbiota composition; iii) maternal microbiota dysbiosis may be transferred to the infant. However, factors defining maternal microbiota and its biological role upon infant’s health are not yet fully understood. Hence, this project aims to characterize maternal microbes to be transferred to neonates and determine their function in infant health programming. The specific aims are:(1) understanding how the maternal microbiome is influenced by host and environmental factors;(2) characterizing the microbial core and bioactive compounds transmitted to the offspring mainly via breastfeeding and their key roles in the microbial modulation and host response;(3) understanding the interactions among breast milk bioactive compounds and their role in infant health;(4) shedding light on how maternal microbes influence the infant immune system & (5)development of new dietary strategies and therapies based on microbial replacement and modulation. To achieve these objectives, a systems biology approach by means of state-of-the-art techniques and new methodologies based on subpopulation enrichment by flow cytometer-sorter to study host–microbe interactions will be used. Results obtained will demonstrate the interaction between infant nutrition, microbes and host response in early life and its key role in health programming, enabling new applications in the field of personalized nutrition & medicine.

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  • Funder: EC Project Code: 101024255
    Overall Budget: 160,932 EURFunder Contribution: 160,932 EUR

    In the recent years, the continual improvements of weather forecasting models and the sustained need for reliable weather predictions beyond the weekly timescale resulted in the development of subseasonal to seasonal (S2S) forecast models and an intense research work from the scientific community. Despite the large number of research studies, S2S forecast models still show a limited skill in summer over Europe. In addition, southern Europe, has received much less attention, even though it is highly vulnerable to high-impact summer heatwaves, and very sensitive to climate change. The aim of this project, ISSUL, is to better understand and improve the S2S prediction of heatwave frequency and intensity and their associated weather patterns over southern Europe. To do this, a combination of two machine learning algorithms, an optimisation algorithm, to identify the best set of predictors, and a neural network, to provide non-linear predictions will be used. This approach has never been attempted before for these timescales. It is expected to perform better than standard S2S forecast models in predicting heatwave frequency and intensity and associated weather patterns and to bring larger improvements compared with traditional statistical forecasts that do not identify all the predictors and cannot represent non-linear complex interactions. ISSUL is divided into three parts. The first part aims at identifying the best set of predictors, using the optimisation algorithm, at evaluating it and understanding it is related to heatwaves over southern Europe via a dynamical analysis. The second part aims a predicting the frequency and intensity of heatwaves and associated weather patterns using a neural network. The third part aims at evaluating the performance of this combined machine learning approach compared with standard S2S forecasting model.

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