Improving controllability of complex networks by rewiring links regularly

Network science have constantly been in the focus of research for the last decade, with considerable advances in the controllability of their structural. However, much less effort has been devoted to study that how to improve the controllability of complex networks. In this paper, a new algorithm is proposed to improve the controllability of complex networks by rewiring links regularly which transforms the network structure. Then it is demonstrated that our algorithm is very effective after numerical simulation experiment on typical network models (Erd\"os-R\'enyi and scale-free network). We find that our algorithm is mainly determined by the average degree and positive correlation of in-degree and out-degree of network and it has nothing to do with the network size. Furthermore, we analyze and discuss the correlation between controllability of complex networks and degree distribution index: power-law exponent and heterogeneit

Complexation of Z-ligustilide with hydroxypropyl-β-cyclodextrin to improve stability and oral bioavailability

To improve the stability and oral bioavailability of Z-ligustilide (LIG), the inclusion complex of LIG with hydroxypropyl-β-cyclodextrin (HP-β-CD) was prepared by the kneading method and characterized by UV-Vis spectroscopy, differential thermal analysis (DTA) and Fourier transform infrared (FTIR) spectroscopy. LIG is capable of forming an inclusion complex with HP-β-CD and the stoichiometry of the complex was 1:1. Stability of the inclusion complex against temperature and light was greatly enhanced compared to that of free LIG. Further, oral bioavailability of LIG and the inclusion complex in rats were studied and the plasma drug concentration-time curves fitted well with the non-compartment model to estimate the absolute bioavailability, which was 7.5 and 35.9 %, respectively. In conclusion, these results show that LIG/HP-β-CD complexation can be of great use for increasing the stability and biological efficacy of LIG

Fixation-induced cell blebbing on spread cells inversely correlates with phosphatidylinositol 4,5-bisphosphate level in the plasma membrane

AbstractWhile most attention has been focused on physiologically generated blebs, the molecular mechanisms for fixation-induced cell blebbing are less investigated. We show that protein-fixing (e.g. aldehydes and picric acid) but not lipid-stabilizing (e.g. OsO4 and KMnO4) fixatives induce blebbing on spread cells. We also show that aldehyde fixation may induce the loss or delocalization of phosphatidylinositol 4,5-bisphosphate (PIP2) in the plasma membrane and that the asymmetric distribution of fixation-induced blebs on spread/migrating cells coincides with that of PIP2 on the cells prefixed by lipid-stabilizing fixatives (e.g., OsO4). Moreover, fixation induces blebbing less readily on PIP2-elevated spread cells but more readily on PIP2-lowered or lipid raft-disrupted spread cells. Our data suggest that fixation-induced lowering of PIP2 level at cytoskeleton-attaching membrane sites causes bleb formation via local breakdown of the membrane–cytoskeleton coupling

Analysis and design of transition radiation in layered uniaxial crystals using Tandem neural networks

With the flourishing development of nanophotonics, Cherenkov radiation pattern can be designed to achieve superior performance in particle detection by fine-tuning the properties of metamaterials such as photonic crystals (PCs) surrounding the swift particle. However, the radiation pattern can be sensitive to the geometry and material properties of PCs, such as periodicity, unit thickness, and dielectric fraction, making direct analysis and inverse design difficult. In this article, we propose a systematic method to analyze and design PC-based transition radiation, which is assisted by deep learning neural networks. By matching boundary conditions at the interfaces, Cherenkov-like radiation of multilayered structures can be resolved analytically using the cascading scattering matrix method, despite the optical axes not being aligned with the swift electron trajectory. Once well trained, forward deep learning neural networks can be utilized to predict the radiation pattern without further direct electromagnetic simulations; moreover, Tandem neural networks have been proposed to inversely design the geometry and/or material properties for desired Cherenkov radiation pattern. Our proposal demonstrates a promising strategy for dealing with layered-medium-based Cherenkov radiation detectors, and it can be extended for other emerging metamaterials, such as photonic time crystals