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https://www.selleckchem.com/products/sgi-110.html Fusarium culmorum and F. proliferatum can grow and produce, respectively, zearalenone (ZEA) and fumonisins (FUM) in different points of the food chain. Application of antifungal chemicals to control these fungi and mycotoxins increases the risk of toxic residues in foods and feeds, and induces fungal resistances. In this study, a new and multidisciplinary approach based on the use of bioactive ethylene-vinyl alcohol copolymer (EVOH) films containing pure components of essential oils (EOCs) and machine learning (ML) methods is evaluated. Bioactive EVOH-EOC films were made incorporating cinnamaldehyde (CINHO), citral (CIT), isoeugenol (IEG) or linalool (LIN). Several ML methods (neural networks, random forests and extreme gradient boosted trees) and multiple linear regression (MLR) were applied and compared for modeling fungal growth and toxin production under different water activity (aw) (0.96 and 0.99) and temperature (20 and 28 °C) regimes. The effective doses to reduce fungal growth rate (GR) by 50, 90 andfilms containing EOCs and environmental conditions have on F. culmorum and F. proliferatum growth and on ZEA and FUM production. The results suggest that these innovative packaging systems in combination with ML methods can be promising tools in the prediction and control of the risks associated with these toxigenic fungi and mycotoxins in food.The introduction of additive manufacturing (AM) technologies has profoundly revolutionized the implant manufacturing industry, with a particularly significant impact on the field of orthopedics. Electron Beam Melting (EBM) and Direct Metal Laser Sintering (DMLS) represents AM fabrication techniques with a pivotal role in the realization of complex and innovative structure starting from virtual 3D model data. In this study, Ti-6Al-4V and Co-Cr-Mo materials, developed by EBM (Ti-POR) and DMLS (Co-POR) techniques, respectively, with hydroxyapatite (Ti-POR + HA; Co-POR + HA) a

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