FREECLIMB - Fruit Crops Resilience to Climate Change in the Mediterranean Basin

SCALA-MEDI Improving sustainability and quality of Sheep and Chicken production by leveraging the Adaptation potential of LocAl breeds in the MEDIterranean area

The SCALA-MEDI project will optimize the sustainable use and conservation of local sheep and poultry genetic resources in the Mediterranean region, focusing on adaptation to climate conditions and consumer preferences.

The experience and data from previous EU projects will be extended to the genetic and epigenetic characterization of local resources and their adaptation to different production environments in three North African countries: Tunisia, Algeria, and Morocco.

Tools and strategies will be developed to improve local breeds for sustainable production. The application of these tools will be demonstrated to farmers in different Mediterranean production systems.

DeepMicroCore: Harnessing the power of artificial intelligence to identify the core microbiome

In DeepMicroCore we will develop DNN models to predict the substrate (species, tissue, food), time (e.g. developmental/physiological stage, experimental timepoint) and place (geographical location) of origin from microbiome data. We refer (and will use) specifically to 16S/18S/ITS rRNA-gene/amplicon sequencing data (metataxonomics). These predictive models will be optimized to maximise the accuracy of predictions in an unbiased way through cross-validation. With these working models, we will then be able to estimate variable importance and retrieve interesting features, e.g. the most relevant ASV/OTU for their impact on the prediction accuracy, or combinations and functions of ASV/OTU, or between-sample distances from embeddings. The features extracted from the DNN predictive models will then help us define and identify the core microbiome. For example, given microbiome data from multiple human/animal tissues (e.g. gut, skin, milk) sampled in different geographical locations, we will build a DNN model able to accurately predict to which tissue and location each sample belongs. Then, by extracting the relevant features from this model we will be able to identify the gut/skin/milk core microbiome. The core microbiome will be composed of those microbial taxa that discriminate between different tissues: some taxa may overlap between different microbiomes, and these will be identified by specific predictive models (e.g. one vs all predictions).

PROB-AI: Identifying novel probiotics from sequence data by means of artificial intelligence algorithms

The objective of the PROB-AI project is to train artificial intellgence (machine learning: ML) on a reduced set of confirmed probiotic species to then be able to identify new potential probiotics from all bacterial sequences available in public databases or research projects. The results of PROB-AI will help scientists to evaluate the specific features of probiotic and non-ptobiotic bacterial species, which will facilitate the discovery of new probiotics to be used in the biomedical and pharmaceutical sectors. Furthermore, PROB-AI will validate experimentally the new probiotics detected in silico by the algortihms, by using in vitro and in vivo validation approaches, and by highlighting the fundamental characteristics that tell apart symbiotic from pathogenic microorganisms in the human digestive tract. The use of ML for the accurate identification of novel probiotics is a highly innovative approach, still underexplored in the field of microbiology.

FARM-INN Farm-level interventions supporting dairy industry innovation

The project FARM-INN aims to provide farm-level interventions supporting dairy industry enhancing safety and quality of milk and cheese and providing the necessary scientific evidence and new insight regarding their functional properties. The proposed actions will be carried out assessing and improving animal welfare and the environmental sustainability. In particular, two aspects will be tackled and studied in the proposed project: i) the development and use of new feed supplements adsorbing mycotoxins and reducing pathogenic and spoilage clostridia in milk ii) the characterization of cheese making and functional properties of A1 and A2 variants of beta-casein in milk. The evaluation of environmental sustainability of the adsorbent supplementations to the cow rations and the potential effect of cheese making using A1 and A2 beta-casein types in milk analyses through a Life Cycle Assessment approach will be performed.

The project will offer opportunities to the dairy farmers to strengthen their competitiveness, in the context of a better control of safety and quality issues, and will help dairy industry in placing on the market high-quality products adapted to the new expectations of consumers.