Time-domain simulation of ultrasound propagation with fractional Laplacians for lossy-medium biological tissues with complicated geometries

Simulations of ultrasound wave propagation inside biological tissues have a wide range of practical applications. In previous studies, wave propagation equations in lossy biological media are solved either with convolutions, which consume a large amount of memory, or with pseudo-spectral methods, which cannot handle complicated geometries effectively. The approach described in the paper employed a fractional central difference method (FCD), combined with the immersed boundary (IB) method for the finite-difference, time-domain simulation. The FCD method can solve the fractional Laplace terms in Chen and Holm’s lossy-medium equations directly in the physical domain without integral transforms. It also works naturally with the IB method, which enables a simple Cartesian-type grid mesh to be used to solve problems with complicated geometries. The numerical results agree very well with the analytical solutions for frequency power-law attenuation lossy mediaThis research is partly supported by the U.S. Army under a cooperative Agreement No. W911NF-14-2-007

Stabilization of switched neural networks with time-varying delay via bumpless transfer control

This paper investigates the stabilization of switched neural networks with time-varying delay. In order to overcome the drawback that the classical switching state feedback controller may generate the bumps at switching time, a new switching feedback controller which can smooth effectively the bumps is proposed. According to mode-dependent average dwell time, new exponential stabilization results are deduced for switched neural networks under the proposed feedback controller. Based on a simple corollary, the procedures which are used to calculate the feedback control gain matrices are also obtained. Two simple numerical examples are employed to demonstrate the effectiveness of the proposed results.Peer reviewe

Effective Removal of Sulfanilic Acid From Water Using a Low-Pressure Electrochemical RuO2-TiO2@Ti/PVDF Composite Membrane

Removal of sulfanilic acid (SA) from water is an urgent but still challenging task. Herein, we developed a low pressure electrochemical membrane filtration (EMF) system for SA decontamination using RuO2-TiO2@Ti/PVDF composite membrane to serve as not only a filter but also an anode. Results showed that efficient removal of SA was achieved in this EMF system. At a charging voltage of 1.5 V and a electrolyte concentration of 15 mM, flow-through operation with a hydraulic retention time (HRT) of 2 h led to a high SA removal efficiency (80.4%), as expected from the improved contact reaction of this compound with ROS present at the anode surface. Cyclic voltammetry (CV) analysis indicated that the direct anodic oxidation played a minor role in SA degradation. Electron spin resonance (ESR) spectra demonstrated the production of •OH in the EMF system. Compared to the cathodic polarization, anodic generated ROS was more likely responsible for SA removal. Scavenging tests suggested that adsorbed •OH on the anode (>•OH) played a dominant role in SA degradation, while O2•- was an important intermediate oxidant which mediated the production of •OH. The calculated mineralization current efficiency (MCE) of the flow-through operated system 29.3% with this value much higher than that of the flow-by mode (5.1%). As a consequence, flow-through operation contributed to efficient oxidation of SA toward CO2 and nontoxic carboxylic acids accounting for 71.2% of initial C. These results demonstrate the potential of the EMF system to be used as an effective technology for water decontamination

Corrigendum to “Amiodarone Induces Cell Proliferation and Myofibroblast Differentiation via ERK1/2 and p38 MAPK Signaling in Fibroblasts” (Biomedicine & Amp; Pharmacotherapy (2019) 115, (S0753332218378752), (10.1016/j.biopha.2019.108889))

The authors regret the order and address of corresponding authors of the original article were given incorrectly. The correct order of all authors is as follows: Jie Weng1, Mengyun Tu1, Peng Wang, Xiaoming Zhou, Chuanyi Wang, Xinlong Wan, Zhiliang Zhou, Liang Wang, Xiaoqun Zheng, Junjian Li, Chan Chen**, Zhiyi Wang**, Zhibin Wang*. The correct corresponding author at: Institute of Bioscaffold Transplantation and Immunology, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, 325035, China. This reflects the fact that Zhibin Wang was the main contributing corresponding author to the original article. The authors would like to apologise for any inconvenience caused

Amiodarone Induces Cell Proliferation and Myofibroblast Differentiation via ERK1/2 and p38 MAPK Signaling in Fibroblasts

Amiodarone is a potent antidysrhythmic agent that can cause potentially life-threatening pulmonary fibrosis. Accumulating evidence has demonstrated that myofibroblast differentiation is related to the pathogenesis of pulmonary fibrosis. In the present study, we treated human embryonic lung fibroblasts (HELFs) with amiodarone, and investigated the relative molecular mechanism of amiodarone-induced pulmonary fibrosis and pathway determinants PD98059 (extracellular signal-regulated kinase (ERK) inhibitor) and SB203580 (p38 mitogen-activated protein kinase (MAPK) inhibitor). Cell proliferation was assessed by Cell Counting Kit-8 (CCK-8). The secretion of collagen Ⅰ was detected by ELISA. The expressions of α-smooth muscle actin (α-SMA), vimentin, phosphorylated ERK1/2 (p-ERK1/2), ERK1/2, phosphorylated p38 MAPK (p-p38), and p38 MAPK were investigated using Western blot analysis. The levels of α-SMA and vimentin were also determined by immunofluorescence and qRT-PCR. We report that amiodarone promoted cell proliferation and collagen Ⅰ secretion, induced α-SMA and vimentin protein and mRNA expression accompanied by increased phosphorylation of ERK1/2 and p38 MAPK, and furthermore, PD98059 and SB203580 remarkably reduced the proliferative response of HELFs compared with amiodarone group and greatly attenuated α-SMA, vimentin and collagen Ⅰ protein production induced by amiodarone. Taken together, our study suggests that amiodarone regulates cell proliferation and myofibroblast differentiation in HELFs through modulating ERK1/2 and p38 MAPK pathways, and these signal pathways may therefore represent an attractive treatment modality in amiodarone-induced pulmonary fibrosis

Distributed load sharing under false data injection attack in inverter-based microgrid

In microgrids, distributed load sharing plays an important role in maintaining the supply-demand balance of power. Because False Data Injection (FDI) is one of the crucial threats faced by future microgrids, the study of the impact of FDI on distributed load sharing is of both theoretical merit and practical value. In this paper, we consider the distributed load sharing problem of the microgrids operating in autonomous mode under FDI. Each bus is assumed to be equipped with an agent. Under a well developed distributed load sharing protocol based on multiagent systems, we first construct an FDI attack model, where the attacker is capable of injecting false data into the bus agents. Then, a utilization level is introduced for coordinating generators and its variation is evaluated in the presence of FDI attacks with given injection strategies. The stable region of the microgrid is defined and conditions are given to determine stability. Finally, the theoretical results are validated on the Canadian urban distribution system

Engineering of aerogel-based biomaterials for biomedical applications

Biomaterials with porous structure and high surface area attract growing interest in biomedical research and applications. Aerogel-based biomaterials, as highly porous materials that are made from different sources of macromolecules, inorganic materials, and composites, mimic the structures of the biological extracellular matrix (ECM), which is a three-dimensional network of natural macromolecules (e.g., collagen and glycoproteins), and provide structural support and exert biochemical effects to surrounding cells in tissues. In recent years, the higher requirements on biomaterials significantly promote the design and development of aerogel-based biomaterials with high biocompatibility and biological activ-ity. These biomaterials with multilevel hierarchical structures display excellent biological functions by promoting cell adhesion, proliferation, and differentiation, which are critical for biomedical applications. This review highlights and discusses the recent progress in the preparation of aerogel-based biomaterials and their biomedical applications, including wound healing, bone regeneration, and drug delivery. Moreover, the current review provides different strategies for modulating the biological performance of aerogel-based biomaterials and further sheds light on the current status of these materials in biomedical research.</p

State of Health Estimation Based on the Long Short-Term Memory Network Using Incremental Capacity and Transfer Learning

Battery state of health (SOH) estimating is essential for the safety and preservation of electric vehicles. The degradation mechanism of batteries under different aging conditions has attracted considerable attention in SOH prediction. In this article, the discharge voltage curve early in the cycle is considered to be strongly characteristic during cell aging. Therefore, the battery aging state can be quantitatively characterized by an incremental capacity analysis (ICA) of the voltage distribution. Due to the interference of vibration noise of the test platform, the discrete wavelet transform (DWT) methods are accustomed to soften the premier incremental capacity curves in different hierarchical decompositions. By analyzing the battery aging mechanism, the peak of the curve and its corresponding voltage are used in the characterization of capacity decay by grey relation analysis (GRA) and to optimize the input of the deep learning model, and finally, the double-layer long short-term memory network (LSTM) model is used to train the data. The results demonstrate that the proposed model can predict the SOH of a single battery cycle using only small batch data and the relative error is less than 2%. Further, by freezing the LSTM layer for transfer learning, it can be used for battery health estimation in different loading modes. The results of training and verification show that this method has high accuracy and reliability in SOH estimation