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Coatings offer a means to control nanoparticle (NP) size, regulate dissolution, and mitigate runoff when added to crops through soil. Simultaneously, coatings can enhance particle binding to plants and provide an additional source of nutrients, making them a valuable component to existing nanoparticle delivery systems. Here, the surface functionalization of metal and metal-oxide nanoparticles to inhibit aggregation and preserve smaller agglomerate sizes for enhanced transport to the rooting zone and improved uptake in plants is reviewed. Coatings are classified by type and by their efficacy to mitigate agglomeration in soils with variable pH, ionic concentration, and natural organic matter profiles. Varying degrees of success have been reported using a range of different polymers, biomolecules, and inorganic surface coatings. Advances in zwitterionic coatings show the best results for maintaining nanoparticle stability in solutions even under high salinity and temperature conditions, whereas coating by the soil component humic acid may show additional benefits such as promoting dissolution and enhancing bioavailability in soils. Pre-tuning of NP surface properties through exposure to select natural organic matter, microbial products, and other biopolymers may yield more cost-effective nonagglomerating metal/metal-oxide NPs for soil applications in agriculture. 

Nanofertilizer application is becoming a sustainable alternative for plants micronutrients supply. Seed nutrient priming before seeding reduces non- target dispersion; although, applying nanofertilizer in correct concentration must be narrowly chosen to prevent germination and development issues. Here, we evaluated corn seedlings development and germination after seed priming with Mn3O4 nanoparticle (NP), Mn3O4 bulk and MnCl2. Sterile seeds were soaked for 8hours in priming solutions of 0, 20, 40, 80 and 160mg L
1 for each Mn sources. The seeds vigor and germination were evaluated after 7 days on germination paper. Root, shoot and total lengths were measured as well as root, shoot and total dry biomass. Compared to the control, the Mn3O4 NP and Mn3O4 bulk promoted beneficial effects. Mn3O4 NP seed-priming exhibited a concentration dependent profile in improving seedling growth, with greatest benefit around 20mg L
1, pro- viding higher germination, vigor, dry biomass and length than control and the other source tested. Particle size plays an important role in the reactiv- ity of Mn3O4 NP. On the other hand, seeds primed with soluble source did not differ from the control. These findings support NP-seed priming as an alternative to delivery micronutrients. 

Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide–MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide–MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide–MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.