A transient solution for vesicle electrodeformation and relaxation

A transient analysis for vesicle deformation under DC electric fields is developed. The theory extends from a droplet model, with the additional consideration of a lipid membrane separating two fluids of arbitrary properties. For the latter, both a membrane-charging and a membrane-mechanical model are supplied. The vesicle is assumed to remain spheroidal in shape for all times. The main result is an ODE governing the evolution of the vesicle aspect ratio. The effects of initial membrane tension and pulse length are examined. The model prediction is extensively compared with experimental data, and is shown to accurately capture the system behavior in the regime of no or weak electroporation. More importantly, the comparison reveals that vesicle relaxation obeys a universal behavior regardless of the means of deformation. The process is governed by a single timescale that is a function of the vesicle initial radius, the fluid viscosity, and the initial membrane tension. This universal scaling law can be used to calculate membrane properties from experimental data

Low-Rank Linear Dynamical Systems for Motor Imagery EEG

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from “BCI Competition III Dataset IVa” and “BCI Competition IV Database 2a.” The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP

Low-Rank Linear Dynamical Systems for Motor Imagery EEG

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from "BCI Competition III Dataset IVa" and "BCI Competition IV Database 2a." The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP

Effect of Microstructure on the Compressive Mechanical Properties of Ti-20Zr-6.5Al-4V Alloy

The effect of microstructure on the mechanical properties of Ti-20Zr-6.5Al-4V alloy after solution and aging treatment was investigated by compression tests. The results showed that the microstructure was consisted of a duplex phase αʺ + β structure after solution treatment. With increasing solution treatment temperature, the size of β phase grain increased and the amount of αʺ phase decreased. The ultimate compressive strength and elongation decreased with increasing solution treatment temperature, while the yield strength increased. After aging-treatment at 700 ºC for 1.5 h, the microstructure consisted of a large amount of globular α phase, a little amount of fine acicular α phase and bulk β phase for the samples solution-treated at 850 ºC for 0.5 h. With increasing initial solution treatment temperature, the amount of globular α phase and bulk β phase decreased, however, the amount and size of acicular α phase increased after aging treatment. The ultimate compressive strength and elongation decreased with increasing initial solution treatment temperature, whereas the yield strength firstly increased, and then slightly decreased.</p

Slip-Flow and Heat Transfer of a Non-Newtonian Nanofluid in a Microtube

The slip-flow and heat transfer of a non-Newtonian nanofluid in a microtube is theoretically studied. The power-law rheology is adopted to describe the non-Newtonian characteristics of the flow, in which the fluid consistency coefficient and the flow behavior index depend on the nanoparticle volume fraction. The velocity profile, volumetric flow rate and local Nusselt number are calculated for different values of nanoparticle volume fraction and slip length. The results show that the influence of nanoparticle volume fraction on the flow of the nanofluid depends on the pressure gradient, which is quite different from that of the Newtonian nanofluid. Increase of the nanoparticle volume fraction has the effect to impede the flow at a small pressure gradient, but it changes to facilitate the flow when the pressure gradient is large enough. This remarkable phenomenon is observed when the tube radius shrinks to micrometer scale. On the other hand, we find that increase of the slip length always results in larger flow rate of the nanofluid. Furthermore, the heat transfer rate of the nanofluid in the microtube can be enhanced due to the non-Newtonian rheology and slip boundary effects. The thermally fully developed heat transfer rate under constant wall temperature and constant heat flux boundary conditions is also compared

The Interplay of Rogue and Clustered Ryanodine Receptors Regulates Ca2+ Waves in Cardiac Myocytes

Ca2+ waves in cardiac myocytes can lead to arrhythmias owing to delayed after-depolarisations. Based on Ca2+ regulation from the junctional sarcoplasmic reticulum (JSR), a mathematical model was developed to investigate the interplay of clustered and rogue RyRs on Ca2+ waves. The model successfully reproduces Ca2+ waves in cardiac myocytes, which are in agreement with experimental results. A new wave propagation mode of “spark-diffusion-quark-spark” is put forward. It is found that rogue RyRs greatly increase the initiation of Ca2+ sparks, further contribute to the formation and propagation of Ca2+ waves when the free Ca2+ concentration in JSR lumen ([Ca2+]lumen) is higher than a threshold value of 0.7 mM. Computational results show an exponential increase in the velocity of Ca2+ waves with [Ca2+]lumen. In addition, more CRUs of rogue RyRs and Ca2+ release from rogue RyRs result in higher velocity and amplitude of Ca2+ waves. Distance between CRUs significantly affects the velocity of Ca2+ waves, but not the amplitude. This work could improve understanding the mechanism of Ca2+ waves in cardiac myocytes