Combined-penalized likelihood estimations with a diverging number of parameters

<div><p>In the economics and biological gene expression study area where a large number of variables will be involved, even when the predictors are independent, as long as the dimension is high, the maximum sample correlation can be large. Variable selection is a fundamental method to deal with such models. The ridge regression performs well when the predictors are highly correlated and some nonconcave penalized thresholding estimators enjoy the nice oracle property. In order to provide a satisfactory solution to the collinearity problem, in this paper we report the combined-penalization (CP) mixed by the nonconcave penalty and ridge, with a diverging number of parameters. It is observed that the CP estimator with a diverging number of parameters can correctly select covariates with nonzero coefficients and can estimate parameters simultaneously in the presence of multicollinearity. Simulation studies and a real data example demonstrate the well performance of the proposed method.</p></div

Systematic Kinetic Analysis on Monolayer Lamellar Crystal Thickening via Chain-Sliding Diffusion of Polymers

Lamellar polymer crystals are metastable due to their limited lamellar thickness. We performed dynamic Monte Carlo simulations of lattice linear polymers to investigate the kinetics of isothermal thickening via chain-sliding diffusion in single lamellar crystals of polyethylene and poly­(ethylene oxide). We sorted out three typical cases for controversial experimental observations. The basic case is a continuous increase of lamellar thickness for heavily folded long chains, with a logarithmic time dependence typical at the lateral growth front. Its kinetics is dominated by the activation energy barrier for sliding diffusion with higher speeds at higher temperatures. For integer-folded short chains, however, the lamellar thickness increases discontinuously, and its kinetics is dominated by a free energy barrier for surface nucleation. The latter can be further split into two cases: the thickening in the melt is mainly driven by the bulk free energy, with lower speeds at higher temperatures due to a temperature-sensitive barrier; while the thickening on a solid substrate is mainly driven by the surface free energy, with higher speeds at higher temperatures due to a temperature-insensitive barrier. The simulations facilitate our systematic understanding to the case-by-case microscopic mechanisms for the thickening of monolayer lamellar crystals via sliding diffusion of polymers

Evolution of Multivalent Nanoparticle Adhesion via Specific Molecular Interactions

The targeted delivery of nanoparticle carriers holds tremendous potential to transform the detection and treatment of diseases. A major attribute of nanoparticles is the ability to form multiple bonds with target cells, which greatly improves the adhesion strength. However, the multivalent binding of nanoparticles is still poorly understood, particularly from a dynamic perspective. In previous experimental work, we studied the kinetics of nanoparticle adhesion and found that the rate of detachment decreased over time. Here, we have applied the adhesive dynamics simulation framework to investigate binding dynamics between an antibody-conjugated, 200-nm-diameter sphere and an ICAM-1-coated surface on the scale of individual bonds. We found that nano adhesive dynamics (NAD) simulations could replicate the time-varying nanoparticle detachment behavior that we observed in experiments. As expected, this behavior correlated with a steady increase in mean bond number with time, but this was attributed to bond accumulation only during the first second that nanoparticles were bound. Longer-term increases in bond number instead were manifested from nanoparticle detachment serving as a selection mechanism to eliminate nanoparticles that had randomly been confined to lower bond valencies. Thus, time-dependent nanoparticle detachment reflects an evolution of the remaining nanoparticle population toward higher overall bond valency. We also found that NAD simulations precisely matched experiments whenever mechanical force loads on bonds were high enough to directly induce rupture. These mechanical forces were in excess of 300 pN and primarily arose from the Brownian motion of the nanoparticle, but we also identified a valency-dependent contribution from bonds pulling on each other. In summary, we have achieved excellent kinetic consistency between NAD simulations and experiments, which has revealed new insights into the dynamics and biophysics of multivalent nanoparticle adhesion. In future work, we will leverage the simulation as a design tool for optimizing targeted nanoparticle agents