Vangsgaardcallesen4127

Gluconeogenesis, as measured by phosphoenolpyruvate carboxykinase (PEPCK) activity, decreased by ~30% 48 h following 2.5% FPW exposure and ~20% 3 weeks after 7.5% FPW exposure. The abundance of pepck mRNA activity followed similar, yet non-significant, trends. Finally, a delayed increase in amino acid catabolism was observed, as glutamate dehydrogenase (GDH) activity was increased 2-fold in 7.5% FPW exposed fish when compared to saline control fish at the 3-week time point. We provide evidence to suggest that although hepatic metabolism is altered following acute FPW exposure, metabolic homeostasis generally returns 3-weeks post-exposure.A multilayered iron oxide/reduced graphene oxide (ION-RGO) nanocomposite electrode is reported for the voltammetric sensing of bisphenol-A (BPA). Structural characterizations reveal the nanocomposite features RGO sheets decorated with nanometric spherical ION in a mixture of maghemite and magnetite phases. ITO substrate modified with the ION-RGO multilayered film exhibits strong electrocatalytic effect toward BPA oxidation, which is made possible by Fe(III) catalysts generated at the ION's surface after scanning the electrode potential from below 0 V (vs Ag/AgCl) and followed by the RGO phase conducting the transferred electrons. Under optimized differential pulse voltammetry conditions, the proposed sensor shows three linear working ranges 0.09-1.17 (r2 = 0.999), 1.17-3.81 (r2 = 0.995) and 3.81-8.20 (r2 = 0.998), with the highest sensitivity equaling 7.76 μA cm-2/μmol L-1 and the lowest limit of detection of 15 nmol L-1. A single electrode can be used for at least twenty consecutive runs loosing less than 15% of sensitivity, whereas electrodes fabricated in different bacthes exhibit almost identical perfomances. Determination of BPA in a thermal paper sample shows no difference (at 95% confidence level) between the proposed sensor and HPLC/UV. The sensor is neither influenced by the matrix composition nor by other emerging contaminants.Arsenic (As) is uptaken more readily by rice over wheat and barley. The exposure of As to humans being in the rice-consuming regions is a serious issue. Thus, an effective practice to reduce the translocation of As from soil to rice grain should be implemented. During a flooding period, the water layer greatly limits the transport of oxygen from atmosphere to soil, which provides favorable conditions for reduction of oxygen. The reduction of Fe in the soil during the flooding condition is closely related to the As mobility, which expedites the release of As to the soil pore solution and increases As uptake by rice plants. Therefore, the performance of oxygen releasing compounds (ORCs) was evaluated to lower the translocation of As from soil to soil solution. Specifically, in the simple system containing ORCs and water, the oxygen releasing capacity of ORCs was scrutinized. In addition, ORCs was applied to sea sand and arsenic bearing ferrihydrite to identify the contribution of ORCs to As and iron mobility. Olaparib inhibitor Especially, ORCs were introduced to the closed (completely mixed system) and open (static) systems to simulate the paddy soil environment. Introducing ORCs increased the DO in the aqueous phase, and CaO2 was more effective in increasing DO than MgO2. In the static system simulating a rice field, the dissolution of ORCs was inhibited. The pH increased due to the formation of hydroxide, but the increase was not significant in the soil due to the buffering capacity of the soil. Finally, the As concentration in the soil solution was lowered to 25-50% of that of the control system by application of ORCs in the static paddy soil system. All experimental findings signify that the application of ORCs can be an effective practice to lower the translocation of As from soil to pore solution.To elucidate the variations in the East Asian monsoon system during seasonal changes and their impacts on continental outflow of polycyclic aromatic hydrocarbons (PAHs), sixteen integrated air samples were collected during a research cruise covering the Yellow Sea (YS) and East China Sea (ECS) in mid-spring of 2017. The concentrations of total suspended particle (TSP), aerosol-phase PAH fractions, ratios of organic to elemental carbon (OC/EC) and gas-particle partitioning of atmospheric PAHs exhibited clear regional differences associated with variations in the monsoon regime. The total concentrations of 16 USEPA priority PAHs (Σ16PAHs) varied from 3.11 to 13.4 ng/m3 throughout the cruise, with medium-to-high molecular weight (MW) PAHs more enriched over the YS and north ECS than the south ECS. Together with the relatively low gaseous PAH fraction over the YS and north ECS (78 ± 4%) relative to the south ECS (95 ± 13%), this result indicates the pattern of regional atmospheric transport. The ratio of organic to elemental carbon varied significantly between the south ECS (lower than 4) and the YS and north ECS (greater than 4), indicating contributions from vehicle emissions and coal combustion or biomass burning, respectively, following different atmospheric input pathways of carbonaceous aerosols, as supported by backward trajectory analysis. Considering the gas-particle partitioning of PAHs, soot adsorption was the main partitioning mechanism in the study region; while high-MW PAHs in the YS and north ECS were influenced by both absorption and adsorption. The Koa absorption model provided better predictions for high-MW PAHs when continental air masses prevailed, despite underestimating the partition coefficients (kp) of low-MW PAHs. Meanwhile, predicted kp for medium MW PAHs was better estimated over the YS and ECS when Ksa was included.Carbon price is the basis of developing a low carbon economy. The accurate carbon price forecast can not only stimulate the actions of enterprises and families, but also encourage the study and development of low carbon technology. However, as the original carbon price series is non-stationary and nonlinear, traditional methods are less robust to predict it. In this study, an innovative nonlinear ensemble paradigm of improved feature extraction and deep learning algorithm is proposed for carbon price forecasting, which includes complete ensemble empirical mode decomposition (CEEMDAN), sample entropy (SE), long short-term memory (LSTM) and random forest (RF). As the core of the proposed model, LSTM enhanced from the recurrent neural network is utilized to establish appropriate prediction models by extracting memory features of the long and short term. Improved feature extraction, as assistant data preprocessing, represents its unique advantage for improving calculating efficiency and accuracy. Removing irrelevant features from original time series through CEEMDAN lets learning easier and it's even better for using SE to recombine similar-complexity modes.