Nanotechnology and Plant Extracts as a Future Control Strategy for Meat and Milk Products

Plant extracts, well known for their antibacterial and antioxidant activity, have potential to be widely used preservatives in the food industry as natural alternatives to numerous synthetic additives which have adverse impacts on health and the environment. Most plant compounds and extracts are generally recognized as safe (GRAS). The use of preservatives is of great importance for perishable foods such as meat and milk, which, along with their products, are commonly consumed food items globally. However, the bioavailability of plant compounds could be diminished by their interaction with food components, processing, and storage. Nanoencapsulation of plant extracts, especially essential oils, is an effective method for their application in food model systems. This technique increases the bioactivity of plant compounds by increasing their physical stability and reducing their size, without negative effects on organoleptic properties. Furthermore, a recent study showed that plant extracts act as good bioreductants for biosynthesis of nanoparticles. This so-called green synthesis method using plant extracts is a rapid, relatively inexpensive, safe, and efficient method for synthesis of nanoparticles including silver, gold, iron, lead, copper, cobalt, palladium, platinum, zinc, zinc oxide, titanium oxide, magnetite, and nickel. Some of these nanoparticles have antimicrobial potential which is why they are of great interest to the food industry. In this chapter, the nanoencapsulation of plant extracts and plant extract-mediated synthesis of nanoparticles and their potential application in order to improve the safety and quality and prolong the shelf life of meat and milk products are reviewed and discussed

Search for Nonresonant Pair Production of Highly Energetic Higgs Bosons Decaying to Bottom Quarks

International audienceA search for nonresonant Higgs boson (H) pair production via gluon and vector boson (V) fusion is performed in the four-bottom-quark final state, using proton-proton collision data at 13 TeV corresponding to 138  fb-1 collected by the CMS experiment at the LHC. The analysis targets Lorentz-boosted H pairs identified using a graph neural network. It constrains the strengths relative to the standard model of the H self-coupling and the quartic VVHH couplings, κ2V, excluding κ2V=0 for the first time, with a significance of 6.3 standard deviations when other H couplings are fixed to their standard model values