Abdiwu3429

6-Hydroxy-BDE-47 (6-OH-BDE-47) is an important in vivo metabolite derived from 2,2',4,4'-tetrabromodiphenyl ether (BDE-47), a ubiquitous environmental pollutant. https://www.selleckchem.com/products/bda-366.html The chemical has been widely detected in environmental and biological samples. However, as a potential neurotoxin, whether 6-OH-BDE-47 could promote the development of typical neurodegenerative diseases such as Parkinson's disease (PD) is still unknown. Here, we tested the potential PD-related neurotoxic effect of 6-OH-BDE-47 in rat. The chemical with levels of 0.1, 1 and 10 µg was stereotaxically injected into the right midbrain regions of rat where contain abundant dopaminergic neurons. The resulting deteriorated motor function and decreased levels of striatal dopamine and nigrostriatal tyrosine hydroxylase indicate the dopaminergic neuron loss after the injection. Proteomics study revealed that protein degradation pathways were affected. Western blot analysis confirmed that 6-OH-BDE-47 could inhibit ubiquitination and autophagy, resulting in the increased formation of α-synuclein (α-syn) aggregate, an important pathological hallmark of PD. Overall, our study demonstrated that the 6-OH-BDE-47 administration could induce motor defect by impairing dopaminergic system and promote α-syn aggregation by inhibiting ubiquitination and autophagy, suggesting that the occurrence of 6-OH-BDE-47 in brain could be a risk for developing PD. China is the largest rice producer and consumer in the world. Accurate estimations of paddy rice planting area and rice grain production is important for feeding the increasing population in China. However, Southern China had substantial losses in paddy rice area over the last three decades in those regions where paddy rice has traditionally been produced. Several studies have shown increased paddy rice area in Northeast China. Here we document the annual dynamics of paddy rice area, gross primary production (GPP), and grain production in Northeast China (Heilongjiang, Jilin and Liaoning provinces) during 2000-2017 using agricultural statistical data, satellite images, and model simulations. Annual maps derived from satellite images show that paddy rice area in Northeast China has increased by 3.68 million ha from 2000 to 2017, which is more than the total combined paddy rice area of North Korea, South Korea, and Japan. Approximately 82% of paddy rice pixels had an increase in annual GPP during 2000-2017. The expansion of paddy rice area slowed down substantially since 2015. Annual GPP from those paddy rice fields cultivated continuously over the 18 years were moderately higher than that from other paddy rice fields, which suggested that improved management practices could increase grain production in the region. There was a strong linear relationship between annual GPP and annual rice grain production in Northeast China by province and year, which illustrates the potential of using satellite-based data-driven model to track and assess grain production of paddy rice in the region. Northeast China is clearly an emerging rice production base and plays an increasing role in crop production and food security in China. However, many challenges for the further expansion and sustainable cultivation of paddy rice in Northeast China remain. Morphological species identification is often a difficult, expensive, and time-consuming process which hinders the ability for reliable biomonitoring of aquatic ecosystems. An alternative approach is to automate the whole process, accelerating the identification process. Here, we demonstrate an automatic machine-based identification approach for non-biting midges (Diptera Chironomidae) using Convolutional Neural Networks (CNNs) as a means of increasing taxonomic resolution of biomonitoring data at a minimal cost. Chironomidae were used to build the automatic identifier, as a family of insects that are abundant and ecologically important, yet difficult and time-consuming to accurately identify. The approach was tested with 10 morphologically very similar species from the same genus or subfamilies, comprising 1846 specimens from the South Morava river basin, Serbia. Three CNN models were built utilizing either species, genus, or subfamily data. After training the artificial neural network, images that the network had not seen during the training phase achieved an accuracy of 99.5% for species-level identification, while at the genus and subfamily level all images were correctly assigned (100% accuracy). Gradient-weighted Class Activation Mapping (Grad-CAM) visualized the mentum, ventromental plates, mandibles, submentum, and postoccipital margin to be morphologically important features for CNN classification. Thus, the CNN approach was a highly accurate solution for chironomid identification of aquatic macroinvertebrates opening a new avenue for implementation of artificial intelligence and deep learning methodology in the biomonitoring world. This approach also provides a means to overcome the gap in bioassessment for developing countries where widespread use techniques for routine monitoring are currently limited. The increasing production and use of silver nanoparticles (AgNPs) have attracted more and more attention due to their environmental and health risks. Municipal sewage biological treatment unit has been playing an important role in the removal of AgNPs. This study investigated the mechanism and characteristics of AgNPs and their removal from aqueous solution by activated sludge. Results from Scanning Electron Microscope and Energy Dispersive Spectrometer (SEM/EDS) showed that mixed AgNPs were immobilized by activated sludge. It was shown by X-ray photoelectron spectroscopy (XPS) that the fixed AgNPs had an oxidation state of +1. It was inferred by fourier transform infra-red (FTIR) spectra that AgNPs were adsorbed by activated sludge via binding with its primary amino (R-NH2) radical groups on the surface. These results revealed that the major mechanism for the removal of AgNPs by activated sludge was adsorption. The experiment data were in agreement with the Langmuir and Redlich-Peterson isotherms. The maximum adsorption capacity ranged from 12-32 mg g-1 at temperatures of 10-30 °C.