Cells assemble invadopodia-like structures and invade into matrigel in a matrix metalloprotease dependent manner in the circular invasion assay

The ability of tumor cells to invade is one of the hallmarks of the metastatic phenotype. To elucidate the mechanisms by which tumor cells acquire an invasive phenotype, in vitro assays have been developed that mimic the process of cancer cell invasion through basement membrane or in the stroma. We have extended the characterization of the circular invasion assay and found that it provides a simple and amenable system to study cell invasion in matrix in an environment that closely mimics 3D invasion. Furthermore, it allows detailed microscopic analysis of both live and fixed cells during the invasion process. We find that cells invade in a protease dependent manner in this assay and that they assemble focal adhesions and invadopodia that resemble structures visualized in 3D embedded cells. We propose that this is a useful assay for routine and medium throughput analysis of invasion of cancer cells in vitro and the study of cells migrating in a 3D environment

A Memristor Model with Piecewise Window Function

In this paper, we present a memristor model with piecewise window function, which is continuously differentiable and consists of three nonlinear pieces. By introducing two parameters, the shape of this window function can be flexibly adjusted to model different types of memristors. Using this model, one can easily obtain an expression of memristance depending on charge, from which the numerical value of memristance can be readily calculated for any given charge, and eliminate the error occurring in the simulation of some existing window function models

Toward an understanding of thermal X-ray emission of pulsars

We present a theoretical model for the thermal X-ray emission and cooling of isolated pulsars, assuming that pulsars are solid quark stars. We calculate the heat capacity for such a quark star, and the results show that the residual thermal energy cannot sustain the observed thermal X-ray luminosities seen in typical isolated X-ray pulsars. We conclude that other heating mechanisms must be in operation if the pulsars are in fact solid quark stars. Two possible heating mechanisms are explored. Firstly, for pulsars with little magnetospheric activities, accretion from the interstellar medium or from the material in the associated supernova remnants may power the observed thermal emission. In the propeller regime, a disk-accretion rate ${\dot M}\sim$1% of the Eddington rate with an accretion onto the stellar surface at a rate of $\sim 0.1$ could explain the observed emission luminosities of the dim isolated neutron stars and the central compact objects. Secondly, for pulsars with significant magnetospheric activities, the pulsar spindown luminosities may have been as the sources of the thermal energy via reversing plasma current flows. A phenomenological study between pulsar bolometric X-ray luminosities and the spin energy loss rates presents the probable existence of a 1/2-law or a linear law, i.e. $L_{\rm bol}^{\infty}\propto\dot{E}^{1/2}$ or $L_{\rm bol}^{\infty}\propto\dot{E}$. This result together with the thermal properties of solid quark stars allow us to calculate the thermal evolution of such stars. Thermal evolution curves, or cooling curves, are calculated and compared with the `temperature-age' data obtained from 17 active X-ray pulsars. It is shown that the bolometric X-ray observations of these sources are consistent with the solid quark star pulsar model.Comment: Astroparticle Physics Accepte

Study on Actuator Line Modeling of Two NREL 5-MW Wind Turbine Wakes

The wind turbine wakes impact the efficiency and lifespan of the wind farm. Therefore, to improve the wind plant performance, research on wind plant control is essential. The actuator line model (ALM) is proposed to simulate the wind turbine efficiently. This research investigates the National Renewable Energy Laboratory 5 Million Watts (NREL 5-MW) wind turbine wakes with Open Field Operation and Manipulation (OpenFOAM) using ALM. Firstly, a single NREL 5-MW turbine is simulated. The comparison of the power and thrust with Fatigue, Aerodynamics, Structures, and Turbulence (FAST) shows a good agreement below the rated wind speed. The information relating to wind turbine wakes is given in detail. The top working status is proved at the wind speed of 8 m/s and the downstream distance of more than 5 rotor diameters (5D). Secondly, another case with two NREL 5-MW wind turbines aligned is also carried out, in which 7D is validated as the optimum distance between the two turbines. The result also shows that the upstream wind turbine has an obvious influence on the downstream one

Towards efficient SimRank computation on large networks

SimRank has been a powerful model for assessing the similarity of pairs of vertices in a graph. It is based on the concept that two vertices are similar if they are referenced by similar vertices. Due to its self-referentiality, fast SimRank computation on large graphs poses significant challenges. The state-of-the-art work [17] exploits partial sums memorization for computing SimRank in O(Kmn) time on a graph with n vertices and m edges, where K is the number of iterations. Partial sums memorizing can reduce repeated calculations by caching part of similarity summations for later reuse. However, we observe that computations among different partial sums may have duplicate redundancy. Besides, for a desired accuracy ϵ, the existing SimRank model requires K = [logC ϵ] iterations [17], where C is a damping factor. Nevertheless, such a geometric rate of convergence is slow in practice if a high accuracy is desirable. In this paper, we address these gaps. (1) We propose an adaptive clustering strategy to eliminate partial sums redundancy (i.e., duplicate computations occurring in partial sums), and devise an efficient algorithm for speeding up the computation of SimRank to 0(Kdn2) time, where d is typically much smaller than the average in-degree of a graph. (2) We also present a new notion of SimRank that is based on a differential equation and can be represented as an exponential sum of transition matrices, as opposed to the geometric sum of the conventional counterpart. This leads to a further speedup in the convergence rate of SimRank iterations. (3) Using real and synthetic data, we empirically verify that our approach of partial sums sharing outperforms the best known algorithm by up to one order of magnitude, and that our revised notion of SimRank further achieves a 5X speedup on large graphs while also fairly preserving the relative order of original SimRank scores

Modeling the Flux-Charge Relation of Memristor with Neural Network of Smooth Hinge Functions

The memristor was proposed to characterize the flux-charge relation. We propose the generalized flux-charge relation model of memristor with neural network of smooth hinge functions. There is effective identification algorithm for the neural network of smooth hinge functions. The representation capability of this model is theoretically guaranteed. Any functional flux-charge relation of a memristor can be approximated by the model. We also give application examples to show that the given model can approximate the flux-charge relation of existing piecewise linear memristor model, window function memristor model, and a physical memristor device