3 research outputs found
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 polyÂ(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