Salvianolic Acid B Attenuates Rat Hepatic Fibrosis via Downregulating Angiotensin II Signaling

The renin-angiotensin system (RAS) plays an important role in hepatic fibrosis. Salvianolic acid B (Sal B), one of the water-soluble components from Radix Salviae miltiorrhizae, has been used to treat hepatic fibrosis, but it is still not clear whether the effect of Sal B is related to angiotensin II (Ang II) signaling pathway. In the present study, we studied Sal B effect on rat liver fibrosis and Ang-II related signaling mediators in dimethylnitrosamine-(DMN-) induced rat fibrotic model in vivo and Ang-II stimulated hepatic stellate cells (HSCs) in vitro, with perindopril or losartan as control drug, respectively. The results showed that Sal B and perindopril inhibited rat hepatic fibrosis and reduced expression of Ang II receptor type 1 (AT1R) and ERK activation in fibrotic liver. Sal B and losartan also inhibited Ang II-stimulated HSC activation including cell proliferation and expression of type I collagen I (Col-I) and α-smooth muscle actin (α-SMA) production in vitro, reduced the gene expression of transforming growth factor beta (TGF-β), and downregulated AT1R expression and ERK and c-Jun phosphorylation. In conclusion, our results indicate that Sal B may exert an antihepatic fibrosis effect via downregulating Ang II signaling in HSC activation

Data driven discovery of cyber physical systems

Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber- physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance

Hierarchical Clustering-Aligning Framework Based Fast Large-Scale 3D Reconstruction Using Aerial Imagery

With extensive applications of Unmanned Aircraft Vehicle (UAV) in the field of remote sensing, 3D reconstruction using aerial images has been a vibrant area of research. However, fast large-scale 3D reconstruction is a challenging task. For aerial image datasets, large scale means that the number and resolution of images are enormous, which brings significant computational cost to the 3D reconstruction, especially in the process of Structure from Motion (SfM). In this paper, for fast large-scale SfM, we propose a clustering-aligning framework that hierarchically merges partial structures to reconstruct the full scene. Through image clustering, an overlapping relationship between image subsets is established. With the overlapping relationship, we propose a similarity transformation estimation method based on joint camera poses of common images. Finally, we introduce the closed-loop constraint and propose a similarity transformation-based hybrid optimization method to make the merged complete scene seamless. The advantage of the proposed method is a significant efficiency improvement without a marginal loss in accuracy. Experimental results on the Qinling dataset captured over Qinling mountain covering 57 square kilometers demonstrate the efficiency and robustness of the proposed method

Canagliflozin ameliorates hypobaric hypoxia-induced pulmonary arterial hypertension by inhibiting pulmonary arterial smooth muscle cell proliferation

ABSTRACTPulmonary arterial hypertension (PAH) is a disease with a high mortality and few treatment options to prevent the development of pulmonary vessel remodeling, pulmonary vascular resistance, and right ventricular failure. Canagliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor, is originally used in diabetes patients which could assist the glucose excretion and decrease blood glucose. Recently, a few studies have reported the protective effect of SGLT2 inhibitor on monocrotaline-induced PAH. However, the effects of canagliflozin on hypobaric hypoxia-induced PAH as well as its mechanism still unclear. In this study, we used hypobaric hypoxia-induced PAH mice model to demonstrate if canagliflozin could alleviate PAH and prevent pulmonary vessel remodeling. We found that daily canagliflozin administration significantly improved survival in mice with hypobaric hypoxia-induced PAH compared to vehicle control. Canagliflozin treatment significantly reduced right ventricular systolic pressure and increased pulmonary acceleration time determined by hemodynamic assessments. Canagliflozin significantly reduced medial wall thickening and decreased muscularization of pulmonary arterioles compared to vehicle treated mice. In addition, canagliflozin inhibited the proliferation and migration of pulmonary arterial smooth muscle cells through suppressing glycolysis and reactivating AMP-activated protein kinase signaling pathway under hypoxia condition. In summary, our findings suggest that canagliflozin is sufficient to inhibit pulmonary arterial remodeling which is a potential therapeutic strategy for PAH treatment

Dynamical network size estimation from local observations

Here we present a method to estimate the total number of nodes of a network using locally observed response dynamics. The algorithm has the following advantages: (a) it is data-driven. Therefore it does not require any prior knowledge about the model; (b) it does not need to collect measurements from multiple stimulus; and (c) it is distributed as it uses local information only, without any prior information about the global network. Even if only a single node is measured, the exact network size can be correctly estimated using a single trajectory. The proposed algorithm has been applied to both linear and nonlinear networks in simulation, illustrating the applicability to real-world physical networks. © 2020 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft

The Role of Mitochondrial Functional Proteins in ROS Production in Ischemic Heart Diseases

Ischemic heart diseases (IHD) have become the leading cause of death around the world, killing more than 7 million people annually. In IHD, the blockage of coronary vessels will cause irreversible cell injury and even death. As the “powerhouse” and “apoptosis center” in cardiomyocytes, mitochondria play critical roles in IHD. Ischemia insult can reduce myocardial ATP content, resulting in energy stress and overproduction of reactive oxygen species (ROS). Thus, mitochondrial abnormality has been identified as a hallmark of multiple cardiovascular disorders. To date, many studies have suggested that these mitochondrial proteins, such as electron transport chain (ETC) complexes, uncoupling proteins (UCPs), mitochondrial dynamic proteins, translocases of outer membrane (Tom) complex, and mitochondrial permeability transition pore (MPTP), can directly or indirectly influence mitochondria-originated ROS production, consequently determining the degree of mitochondrial dysfunction and myocardial impairment. Here, the focus of this review is to summarize the present understanding of the relationship between some mitochondrial functional proteins and ROS production in IHD