Arsenic is an environmental contaminant, its multiple effects on human tend to increase the rate of disease, cancer and other health problems. Some of long non-coding RNAs (lncRNAs) can be induced in major cellular processes such as necrosis, proliferation, and mutation. While the toxicity of arsenic is well established, the association between arsenic exposure and long non-coding RNAs has not been studied enough. This study investigated the association between arsenic and the expression of HOTAIR and LincRNA-p21 in vivo and vitro. In epidemiological studies, the expression of HOTAIR and LincRNA-p21 was increased after long-term arsenic exposure. HOTAIR and LincRNA-p21 expression were positively linked to monomethylarsenic acid (MMA), dimethylarsenic acid (DMA), inorganic arsenic (iAs), total arsenic (tAs), and MMA% and negatively linked to secondary methylation index (SMI). In A549 cells, arsenic exposure resulted in enhanced HOTAIR and LincRNA-p21 expression dose-dependently. The expression of HOTAIR was considerably high in the presence of NaAsO2 and MMA but showed no difference in DMA compared with control group. And LincRNA-p21 expression was increased in the presence of NaAsO2, MMA, and DMA. The expression of HOTAIR and LincRNA-p21 induced by iAs was much higher than that induced by MMA and DMA. Compared with the control group, treatment of A549 cells with NaAsO2/S-adenosylmethionine (SAM) and NaAsO2/glutathione (GSH) combination increased HOTAIR and LincRNA-p21 expression. The expression of LincRNA-p21 in combination of NaAsO2/GSH was significantly decreased compared with NaAsO2 alone. Besides, in the presence of arsenic, both of HOTAIR and LincRNA-p21 were upregulated significantly when P53 was knocked down. We revealed that inorganic arsenic, its methylated metabolites, and arsenic metabolism efficiency affect the expression of HOTAIR and LincRNA-p21.
Environmental Science and Pollution Research - Saline-sodic soil is widely distributed around the world and has induced severe impacts on ecosystems and agriculture. Biomass pyrolysis fluid (BPF),... 相似文献
Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses. 相似文献
Forests are essential common-pool resources. Understanding children's and adolescents’ motivations for conservation is critical to improving conservation education. In 2 experiments, we investigated 1086 school-aged children and adolescents (6–16 years old) from the United States, China, and the Democratic Republic of Congo. Testing participants in groups, we assessed their motivation for conservation based on collective-risk common-pool goods games in which they were threatened with losing their endowment unless the group donation exceeded a threshold needed to maintain the forest. Extrinsic motivations, rather than intrinsic, tended to lead to successful cooperation to maintain a forest. Certainty of losing individual payoffs significantly boosted successful cooperative conservation efforts across cultures (success rates were 90.63% and 74.19% in the 2 risk-extrinsic conditions, and 43.75% in the control condition). In U.S. participants, 2 extrinsic incentives, priming discussions of the value of forests and delay of payoffs as punishment, also increased success of cooperative conservation (success rates were 97.22% and 76.92% in the 2 extrinsic-incentive conditions, and 29.19% and 30.77% in the 2 control conditions). Conservation simulations, like those we used, may allow educators to encourage forest protection by leading groups to experience successful cooperation and the extrinsic incentives needed to motivate forest conservation. 相似文献