The Long non-coding RNA MALAT1 functions as a competing endogenous RNA to regulate vascular remodeling by sponging miR-145-5p/HK2 in hypertension.

Long non-coding RNAs (LncRNAs) have been found to play a regulatory role in the pathophysiology of vascular remodeling-associated illnesses through the lncRNA-microRNA (miRNA) regulation axis. LncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is thought to be involved in proliferation, migration, apoptosis, and calcification of vascular smooth muscle cells (VSMCs). The purpose of this study was to investigate the regulatory role of MALAT1 on vascular remodeling in hypertension. Our data indicate that the expression of MALAT1 is significantly upregulated in hypertensive aortic smooth muscle. Knockdown of MALAT1 inhibited the proliferation, migration, and phenotypic transition of VSMCs induced by Ang II. Bioinformatics analysis was used to predict the complementary binding of miR-145-5p to the 3'-untranslated region of MALAT1. Besides, the expressions of MALAT1 and miR-145-5p were negatively correlated, while luciferase reporter assays and RNA immunoprecipitation assay validated the interaction between miR-145-5p and MALAT1. The proliferation, migration and phenotypic transformation of VSMCs induced by overexpression of MALAT1 were reversed in the presence of miR-145-5p. Furthermore, we verified that miR-145-5p could directly target and bind to hexokinase 2 (HK2) mRNA, and that HK2 expression was negatively correlated with miR-145-5p in VSMCs. Knockdown of HK2 significantly inhibited the effects of overexpression of MALAT1 on Ang II-induced VSMCs proliferation, migration and phenotypic transformation. Taken together, the MALAT1/miR-145-5p/HK2 axis may play a critical regulatory role in the vascular remodeling of VSMCs in hypertension.

Urban rainstorm and waterlogging scenario simulation based on SWMM under changing environment.

Urban rainstorm and waterlogging occurred more frequently in recent years, causing huge economic losses and serious social harms. Accurate rainstorm and waterlogging simulation is of significant value for disaster prevention and mitigation. This paper proposed a numerical model for urban rainstorm and waterlogging based on the Storm Water Management Model (SWMM) and Geographic Information System (GIS), and the model was applied in Lianhu district of Xi'an city of China. Furthermore, the effects of rainfall characteristics, pipe network implementation level and urbanization level on waterlogging were explored from the perspectives of spatial distribution of waterlogging points, drainage capacity of pipe network and surface runoff generation and confluence. The results show that: (1) with the increase of rainfall recurrence period, the peak of total water accumulating volume, the average decline rate of water accumulating volume and the number of waterlogging nodes increase; (2) optimizing the pipe diameter can shorten the average overload time of the pipe network from the entire pipe network, but for a single pipe, optimizing the pipe diameter may lead to overloading of unoptimized downstream pipeline; (3) the lower the imperviousness, the less the number of waterlogging nodes and average time of water accumulating, and (4) the west, northwest and southwest areas are relatively affected by the imperviousness, only improving the underlying surface conditions has limited influence on waterlogging in the study area. This study can provide reference for urban waterlogging prevention and reduction and pipe network reconstruction.

Assessment of vegetation growth and drought conditions using satellite-based vegetation health indices in Jing-Jin-Ji region of China.

Terrestrial vegetation growth activity plays pivotal roles on regional development, which has attracted wide attention especially in water resources shortage areas. The paper investigated the spatiotemporal change characteristics of vegetation growth activity using satellite-based Vegetation Health Indices (VHIs) including smoothed Normalized Difference Vegetation Index (SMN), smoothed Brightness Temperature (SMT), Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and VHI, based on 7-day composite temporal resolution and 16 km spatial resolution gridded data, and also estimated the drought conditions for the period of 1982-2016 in Jing-Jin-Ji region of China. The Niño 3.4 was used as a substitution of El Niño Southern Oscillation (ENSO) to reveal vegetation sensitivity to ENSO using correlation and wavelet analysis. Results indicated that monthly SMN has increased throughout the year especially during growing season, starts at approximate April and ends at about October. The correlation analysis between SMN and SMT, SMN and precipitation indicated that the vegetation growth was affected by joint effects of temperature and precipitation. The VCI during growing season was positive trends dominated and vice versa for TCI. The relationships between VHIs and drought make it possible to identify and quantify drought intensity, duration and affected area using different ranges of VHIs. Generally, the intensity and affected area of drought had mainly decreased, but the trends varied for different drought intensities, regions and time periods. Large-scale global climate anomalies such as Niño 3.4 exerted obvious impacts on the VHIs. The Niño 3.4 was mainly negatively correlated to VCI and positively correlated to TCI, and the spatial distributions of areas with positive (negative) correlation coefficients were mainly opposite. The linear relationships between Niño 3.4 and VHIs were in accordance with results of nonlinear relationships revealed using wavelet analysis. The results are of great importance to assess the vegetation growth activity, to monitor and quantify drought using satellite-based VHIs in Jing-Jin-Ji region.

Application of edge computing and GIS in ecological water requirement prediction and optimal allocation of water resources in irrigation area.

The purposes are to use water resources efficiently and ensure the sustainable development of social water resources. The edge computing technology and GIS (Geographic Information Science) image data are combined from the perspective of sustainable development. A prediction model for the water resources in the irrigation area is constructed. With the goal of maximizing comprehensive benefits, the optimal allocation of water quality and quantity of water resources is determined. Finally, the actual effect of the model is verified through specific instance data in a province. Results demonstrate that the proposed irrigation area ecological prediction model based on edge computing and GIS images can provide better performance than other state of the art models on water resources prediction. Specifically, the accuracy can remain above 90%. The proposed model for ecological water demand prediction in the irrigation area and optimal allocation of water resources is based on the principle of quality water supply. The optimal allocation of water resources reveals the sustainable development ideas and the requirements of the optimal allocation model, which is very reasonable. The improvement of the system is effective and feasible, and the optimal allocation results are reasonable. This allocation model aims at the water quality and quantity conditions, water conservancy project conditions, and specific water demand requirements in the study area. The calculation results have great practicability and a strong guiding significance for the sustainable utilization and management of the irrigation area.

Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for different period of year ecologists might focus on.

Oxidized high-density lipoprotein induces the proliferation and migration of vascular smooth muscle cells by promoting the production of ROS.

AIM: As the major atheroprotective particle in plasma, high-density lipoprotein(HDL) is oxidized during atherosclerotic processes. Oxidized HDL(ox-HDL) may lose its cardioprotective properties and develop a proinflammatory and proatherogenic phenotype. The proliferation and migration of vascular smooth muscle cells(VSMCs) play a crucial role in atherogenesis. However, the influence of ox-HDL on VSMC proliferation and migration remains poorly understood. METHODS: VSMCs were treated with native HDL(N-HDL) or ox-HDL at varying concentrations for different time intervals and used in several analyses. The degree of cell proliferation was assayed using CCK-8 kits. The level of cell migration was determined using a Transwell chamber and scratch-wound assay. The presence of intracellular reactive oxygen species(ROS) was detected based on ROS-mediated 2',7'-dichlorofluorescein fluorescence. The activation of NADPH oxidase was measured in terms of the Rac1 activity and NADP(＋)/NADPH ratio. RESULTS: Compared to N-HDL, ox-HDL significantly promoted VSMC proliferation and migration in a dose-dependent manner. In addition, ox-HDL remarkably activated NADPH oxidase and enhanced ROS generation in the VSMCs. Diphenyleneiodonium chloride, an inhibitor of NADPH oxidase, and N-acetylcysteine, a ROS scavenger, efficiently inhibited the ROS production triggered by ox-HDL and subsequently blocked the proliferating and migrating effects of ox-HDL in the VSMCs. CONCLUSIONS: Ox-HDL significantly induces VSMC proliferation and migration by promoting NADPH oxidase activation and ROS production. Furthermore, the inhibition of NADPH oxidase and ROS generation blocks the proliferation and migration of VSMCs induced by ox-HDL. These proliferating and migrating effects of ox-HDL are closely related to its proinflammatory and proatherogenic roles.