Hougaardkamper1143

The widespread use of plastic products has led to the widespread presence of plasticizers in the environment. As a common environmental pollutant, research on plasticizer toxicity is insufficient in fish cells. In particular, research on the toxicity of dibutyl phthalate (DBP) in grass carp hepatocyte lines is insufficient. To further explore these mechanisms, we treated grass carp hepatocytes with 300 μM DBP, a common plasticizer, for 24 h, and hepatocytes were also treated with 1 μM taxifolin (TAX), an antioxidant, for 24 h to study its antagonistic effect on DBP. After DBP exposure, oxidative stress levels and inflammation in hepatocytes increased, and the mRNA and protein expression of apoptosis-related markers increased significantly, leading to hepatocyte apoptosis. Moreover, AO/EB staining, Hoechst staining and flow cytometry also showed that the level of apoptotic cells increased after DBP exposure. Notably, both TAX pretreatment and TAX simultaneous treatment alleviated oxidative stress, increased inflammatory factor levels and apoptosis induced by DBP. In comparison, the effect of simultaneous TAX treatment was better than that of TAX pretreatment. Our results showed that TAX alleviates DBP-induced apoptosis in grass carp hepatocytes through oxidative stress and inflammation, and TAX pretreatment and simultaneous treatment exhibited specific effects. Specifically, simultaneous treatment had a better effect. Our study assessed the toxicity of DBP in grass carp hepatocytes and provided a theoretical and research basis for the in vivo study of animal models in the future. The innovation of this study involves the exploration of the interaction between DBP and TAX for the first time. This study may enrich knowledge regarding the theoretical mechanism of DBP toxicity in fish hepatocytes and propose methods address DBP toxicity.Hypersaline environments are found around the world, above and below ground, and many are exposed to hydrocarbons on a continuous or a frequent basis. Some surface hypersaline environments are exposed to hydrocarbons because they have active petroleum seeps while others are exposed because of oil exploration and production, or nearby human activities. Many oil reservoirs overlie highly saline connate water, and some national oil reserves are stored in salt caverns. Surface hypersaline ecosystems contain consortia of halophilic and halotolerant microorganisms that decompose organic compounds including hydrocarbons, and subterranean ones are likely to contain the same. However, the rates and extents of hydrocarbon biodegradation are poorly understood in such ecosystems. Here we describe hypersaline environments potentially or likely to become contaminated with hydrocarbons, including perennial and transient environments above and below ground, and discuss what is known about the microbes degrading hydrocarbons and the extent of their activities. We also discuss what limits the microbial hydrocarbon degradation in hypersaline environments and whether there are opportunities for inhibiting (oil storage) or stimulating (oil spills) such biodegradation as the situation requires.Microbial pollution of beach water can expose swimmers to harmful pathogens. Predictive modeling provides an alternative method for beach management that addresses several limitations associated with traditional culture-based methods of assessing water quality. Widely-used machine learning methods often suffer from high variability in performance from one year or beach to another. Therefore, the best machine learning method varies between beaches and years, making method selection difficult. This study proposes an ensemble machine learning approach referred to as model stacking that has a two-layered learning structure, where the outputs of five widely-used individual machine learning models (multiple linear regression, partial least square, sparse partial least square, random forest, and Bayesian network) are taken as input features for another model that produces the final prediction. Applying this approach to three beaches along eastern Lake Erie, New York, USA, we show that generally the model stacking approach was able to generate reliably good predictions compared to all of the five base models. The accuracy rankings of the stacking model consistently stayed 1st or 2nd every year, with yearly-average accuracy of 78%, 81%, and 82.3% at the three studied beaches, respectively. STS inhibitor order This study highlights the value of the model stacking approach in predicting beach water quality and solving other pressing environmental problems.Measurements of water-soluble total nitrogen (WSTN), water-soluble inorganic nitrogen (WSIN), water-soluble organic nitrogen (WSON) and ẟ15NTN (total N) was carried out on PM2.5 aerosol samples during wintertime to understand the major sources of ambient nitrogenous species at a heavily polluted location of Kanpur in north India. During the nighttime sampling campaign, WSON and NH4+_N contributed dominantly to the WSTN. Ammonium-rich condition persisted during sampling (NH4+/SO42- average equivalent mass ratio = 3.1 ± 0.7), suggesting complete neutralization of SO42- and formation of NH4NO3, which is stable in winter due to low temperature and high relative humidity (RH). Stagnant atmospheric conditions during wintertime enhanced concentrations of ionic species (SO42-, NH4+, and NO3-) at this location. Good correlations between NO3-_N, NH4+_N and biomass burning tracer K+BB (and also between NO3-_N, NH4+_N and SO42-) suggests a strong impact of biomass burning activities. Multi-linear regression (MLR) analysis shows a strong dependence of ẟ15N on NO3-_N, SO42- and WSON in night-1 (1000 pm to 200 am) and on NO3-_N and SO42- in night-2 (200 am to 600 am) depicting different formation and removal mechanism of aerosols during both the time-periods. ẟ15NTN in PM2.5 varied from +8.8 to +15.5‰ (10.8 ± 1.3), similar to the variability observed for many urban locations in India and elsewhere. NH4+_N and WSON control the final ẟ15N value of nitrogenous aerosols. High relative humidity during nighttime enhanced the secondary organic aerosols formation due to aqueous-phase formation and gas to particle-phase partitioning. Isotopic fractionations associated with multi-phase reactions during gas to particle conversion of NH3 would result in an increase in ẟ15N by ~48‰ to 51‰ (at T of 5.4 °C to 15.4 °C) than that of the emission source(s), which indicates the most likely N-emission sources at Kanpur to be from agriculture activities and waste generation.