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007) and quadratically (P=0.003) as the dose of GLP increased. The mRNA expression of intercellular cell adhesion molecule-1 (ICAM-1) in the 20-mg/kg GLP group was increased significantly (P less then 0.05). There was a significant increase in the mRNA levels of pregnenolone X receptor (PXR), constitutive androstane receptor (CAR), and uridine diphosphate glucuronosyltransferase 1A3 (UGT1A3) (P less then 0.05). Our findings found that kidney nuclear xenobiotic receptors (NXRs) may play an important role in defense against GBHs.Accurate runoff modeling has an important role in water resource management. [https://www.selleckchem.com/products/azd9291.html click here] Attributable to the effects of climate variability and vegetation dynamics, runoff time series is nonstationary, resulting in the difficulty of runoff modeling. Detecting the temporal features of runoff and its potential influencing factors can help to increase the modeling accuracy. Selecting the Yihe watershed in the rocky mountainous area of northern China as a case study, multivariate empirical mode decomposition (MEMD) was adopted to analyze the time scales of the monthly runoff and its influencing factors, i.e., precipitation (P), normalized difference vegetation index (NDVI), temperature (T), relative humidity (RH), and potential evapotranspiration (PE). Using the MEMD technique, the original monthly runoff and its influencing factors were decomposed into six orthogonal and bandlimited functions, i.e., intrinsic mode functions (IMF1-6) and one residue, respectively. Each IMF is a counterpart of the simple harmonic function andlts indicated that MEMD was efficient for improving the accuracy of nonstationary runoff modeling.The authors investigate how artificial intelligence modifies a huge piece of the energy area, the oil and gas industry. This paper attempts to evaluate technical and non-technical factors affecting the adoption of machine learning technologies. The study includes machine learning development platforms, network architecture, and opportunities and challenges of adopting machine learning technologies in the oil and gas industry. The authors elaborate on the three different sectors in this industry namely upstream, midstream, and downstream. Herein, a review is presented to evaluate the applications and scope of machine learning in the oil and gas industry to optimize the upstream operations (including exploration, drilling, reservoir, and production), midstream operations (including transportation using pipelines, ships, and road vehicles), and downstream operations (including production of refinery products like fuels, lubricants, and plastics). Enhanced processing of seismic data is illustrated which provides the industry with a better understanding of machine learning applications. Basing on the investigation of AI implementation prospects and the survey of subsisting implementations, they diagram the latest patterns in creating AI-based instruments and distinguish their impacts on speeding up and de-gambling measures in the business. They examine AI proposition and calculations, just as the job and accessibility of information in the portion. Furthermore, they examine the principal non-specialized difficulties that forestall the concentrated use of man-made brainpower in the oil and gas industry (OGI), identified with information, individuals, and new types of joint effort. They additionally diagram potential situations of how man-made reasoning will create in the OGI and how it might transform it later on (in 5, 10, and 20 years).Industrial revolution markedly increased the environmental contamination by different pollutants, which include the metal lead (Pb). The phytoremediation potential of native species from tropical regions is little known, especially for woody plants. The present study aimed to evaluate the performance of Lonchocarpus cultratus (Fabaceae), a tree species from the Brazilian savanna, grown in soil that was artificially contaminated with increasing Pb concentrations (control and 4 Pb treatments, 56, 120, 180, and 292 mg kg-1) for 6 months. The biomass of L. cultratus was not depressed by exposure to Pb, despite the high accumulation of this metal (up to 7421.23 μg plant-1), indicating a high plant tolerance to this trace metal. Lead was mainly accumulated in roots (from 67 to 99%), suggesting that the low root-to-shoot Pb translocation is a plant strategy to avoid Pb-induced damages in photosynthetic tissues. Accordingly, the content of chlorophylls a and b was maintained at similar levels between Pb-treated and control plants. Moreover, increments in leaf area were noticed in Pb-treated plants in comparison to the control plants (on average, 24.7%). In addition, root length was boosted in plants under Pb exposure (22.6-66.7%). In conclusion, L. cultratus is able to endure the exposure to high Pb concentrations in soil, being a potential plant species to be used for Pb phytostabilization in metal-contaminated soils in tropical regions.Arsenic and the compounds thereof can be carcinogens or therapeutic agents for different cancer types. However, for breast cancer (BC), studies have yielded conflicted results on the role of arsenic. A previous study by the present authors indicated a potential relationship between circDHX34 and sodium arsenite-treated BC cells. As such, the expression, function, and potential mechanism of circDHX34 in sodium arsenite-treated MDA-MB-231 cells were further detected. In the present study, findings were made that sodium arsenite upregulated circDHX34 expression in MDA-MB-231 cells in a dose-dependent manner, and knockdown of circDHX34 could promote cell proliferation and inhibit apoptosis. Further investigations revealed that knockdown of circDHX34 upregulated the expression levels of antiapoptotic genes BCL2 and BCL2L1 and downregulated the expression levels of proapoptotic genes CASP8 and CASP9. To conclude, by regulating apoptotic genes, sodium arsenite-mediated upregulation of circDHX34 promotes apoptosis in hormone-independent breast cancer cells.Over the last few years, global warming and rapid climate change have become major risk factors that pose a serious threat to global security. A key factor behind these risk factors is greenhouse gases, which emit mainly carbon dioxide (CO2). The existing literature seeks to determine the economic and non-economic aspects of CO2 emissions to prevent environmental degradation. However, the effects of economic policy uncertainty and foreign direct investment on CO2 emissions are undeniable. This study examines the impact of economic policy uncertainty and foreign direct investment on CO2 emissions in the panel of 24 developed and developing nations from 2001 to 2019. After verifying cross-sectional dependency and co-integration among parameters, the dynamic seemingly unrelated regression and panel vector error correction model (VECM) Granger causality methods are used for long-run estimates and verify the causal link among variables. Our findings show that economic policy uncertainty, economic growth, trade, and energy consumption adversely impact the environment, while foreign direct investment enhances sample countries' environmental quality. Furthermore, a bidirectional relationship exists between CO2, economic policy uncertainty, economic growth, trade, and energy consumption. In addition, this study observed similar results in a robustness analysis using the dynamic common correlated effects and fixed effect panel quantile regression frameworks. Based on the inclusive outcomes, this study forms significant suggestions for policy implications. Specifically, policymakers should design environmental-friendly trade policies, explore renewable energy options, and implement green investment and financing strategies to improve the environment.Various types of pollutants derived from rapid industrialization and urbanization have largely threaten biodiversity and functioning of freshwater ecosystems globally. Morphological plasticity, especially body size-associated traits, is considered a functional response to water pollution in species, as such changes are often directly related to functioning of freshwater ecosystems through dynamics of food webs. However, detailed dynamics of pollution impacts on morphological plasticity remain largely unknown, particularly in the wild. Here, we used the model planktonic rotifer Brachionus calyciflorus to assess morphological response to chemical pollution in a river reach disturbed by sewage discharges. Multiple analyses showed dynamic morphological response to water pollution in wild B. calyciflorus populations. The distance between anterior lateral spines, lorica length, and egg short diameter were the most sensitive morphological indicators to water pollution, while spine length was stable in varied pollution conditions. Interestingly, body size and egg size were increased with accentuated water pollution, suggesting that wild populations maintain fitness by increasing feeding efficiency and reducing vulnerability to predation and ensure survival by producing large newborns in polluted environments. Total ammonia nitrogen was the leading nitrogen pollutant affecting body size, while total phosphorus and elements of Mn and As were the key factors relating to egg size. The results obtained here provide new sights into biological consequences of environmental pollution in the wild, thus advancing our understanding of pollution impacts on structure and functioning of freshwater ecosystems.Pollution with trace metals (TM) has been shown to affect diversity and/or composition of plant and animal communities. While ecotoxicological studies have estimated the impact of TM contamination on plant and animal communities separately, ecological studies have widely demonstrated that vegetation is an important factor shaping invertebrate communities. It is supposed that changes in invertebrate communities under TM contamination would be explained by both direct impact of TM on invertebrate organisms and indirect effects due to changes in plant communities. However, no study has clearly investigated which would more importantly shape invertebrate communities under TM contamination. Here, we hypothesized that invertebrate communities under TM contamination would be affected more importantly by plant communities which constitute their habitat and/or food than by direct impact of TM. Our analysis showed that diversity and community identity of flying invertebrates were explained only by plant diversity which was not affected by TM contamination. Diversity of ground-dwelling (GD) invertebrates in spring was explained more importantly by plant diversity (27% of variation) than by soil characteristics including TM concentrations (8%), whereas their community identity was evenly explained by plant diversity and soil characteristics (2-7%). In autumn, diversity of GD invertebrates was only explained by plant diversity (12%), and their identity was only explained by soil characteristics (8%). We conclude that vegetation shapes invertebrate communities more importantly than direct effects of TM on invertebrates. Vegetation should be taken into account when addressing the impacts of environmental contamination on animal communities.