Nanoparticles (NPs) are promising drug carriers for targeted therapies, diagnostic imaging and advanced vaccines. However, their clinical translation is limited by complex biological barriers that reduce cellular uptake and efficacy. Specifically, the interaction with the cellular membrane controls nanoparticle adhesion, wrapping or full engulfment, which ultimately govern nanoparticle internalization efficiency. Flexible nanocarriers (e.g. liposomes, polymeric, micelles) are particularly attractive because their deformability could help them enhance the probability of successful cellular entry. To understand the physical mechanisms associated with cellular uptake, we investigate the interaction of semi-flexible nanocarriers with a symmetric lipid bilayer using coarse-grained simulations. We represent a flexible nanoparticle using the previously introduced MetaParticle (MP) model and the membrane using the Cooke-Deserno model. By systematically varying nanoparticle properties, i.e., adhesion strength and topology, we identify distinct interaction regimes ranging from surface adhesion and trapping to complete wrapping and endocytosis. These regimes correlate with nanoparticle shape, size and surface properties, providing quantitative design principles for optimizing cellular uptake. Overall, this frame-work offers predictive insight into how the interplay between nanoparticle properties and membrane interaction governs cellular internalization, informing the rational design of next-generation soft nanocarriers.
The Hidden Power of Normalization: Exponential Capacity Control in Deep Neural Networks
arXiv:2511.00958v1 Announce Type: cross Abstract: Normalization methods are fundamental components of modern deep neural networks (DNNs). Empirically, they are known to stabilize optimization dynamics and