The prevalence and levels of enteric viruses in untreated groundwater of private wells used for drinking and/or agricultural practices in rural Alberta were studied using the qPCR panel assay, integrated cell culture with qPCR and cell culture in the volume of 500 liters per sample through serial sampling. Seven viruses were assessed including adenovirus, rotavirus, norovirus, astrovirus, sapovirus, reovirus and JC virus. Five viruses were detected with an overall positive detection rate of 6.33 % (45 of 711 samples). The most frequently detected virus was adenovirus (48.9%, 22/45) followed by rotavirus (44.4%, 20/45), reovirus (20%, 9/45), JC virus (6.7%, 3/45) and norovirus (6.7%, 3/45). There was no significant difference in the positive detection rates, ranging from 1.1% to 3.4% by various well settings used for broiler farms, cow/calf farms, feedlots and rural acreages. Effects of well characteristics (aquifer type, well depth, static level of water, well seal) and well completion lithology on potential viral contamination of groundwater of private wells were also analyzed upon available data. The findings demonstrate that occurrence of enteric viruses is low and viral contamination is sporadic in groundwater of private wells in rural Alberta. Conventional fecal bacterial indicators (coliform and/or E. coli) were not a representative marker for viral contamination in groundwater wells in rural Alberta.Phosphorus (P) extraction from human urine is a potential strategy to address global resource shortage, but few approaches are able to obtain high-quality liquid P products. In this study, we introduced an innovative flow-electrode capacitive deionization (FCDI) system, also called ion-capture electrochemical system (ICES), for selectively extracting P and N (i.e., urea) from fresh human urine simply by integrating a liquid membrane chamber (LMC) using a pair of anion exchange membrane (AEM). In the charging process, negatively charged P ions (i.e., HPO42- and H2PO4-) can be captured by acidic extraction solutions (e.g., solutions of HCl, HNO3 and H2SO4) on their way to the anode chamber, leading to the conversion of P ions to uncharged H3PO4, while other undesired ions such as Cl- and SO42- are expelled. Simultaneously, uncharged urea molecules remain in the urine effluent with the removal of salt. Thus, high-purity phosphoric acid and urea solutions can be obtained in the LMC and spacer chambers, respectively. The purification of P in an acidic environment is ascribed largely to the competitive migration and protonation of ions. The latter contributes ~27% for the selective capture of P. Under the optimal operating conditions (i.e., ratio of the urine volume to the HCl volume = 73, initial pH of the extraction solution = 1.43, current density = 20 A/m2 and threshold pH ~ 2.0), satisfactory recovery performance (811 mg/L P with 73.85% purity and 8.3 g/L urea-N with 81.4% extraction efficiency) and desalination efficiency (91.1%) were obtained after 37.5 h of continuous operation. Our results reveal a promising strategy for improving in selective separation and continuous operation via adjustments to the cell configuration, initiating a new research dimension toward selective ion separation and high-quality P recovery.As an important source of arsenic (As) pollution in mine drainage, arsenopyrite undergoes redox and adsorption reactions with dissolved As, which further affects the fate of As in natural waters. This study investigated the interactions between dissolved As(III) and arsenopyrite and the factors influencing the geochemical behavior of As, including initial As(III) concentration, dissolved oxygen and pH. The hydrogen peroxide (H2O2) and hydroxyl radical (OH•) generated from the interaction between Fe(II) on arsenopyrite surface and oxygen were found to facilitate the rapid oxidation of As(III), and the production of As(V) in the reaction system increased with increasing initial As(III) concentration. An increase of pH from 3.0 to 7.0 led to a gradual decrease in the oxidation rate of As(III). At pH 3.0, the presence of As(III) accelerated the oxidation rate of arsenopyrite; while at pH 5.0 and 7.0, As(III) inhibited the oxidative dissolution of arsenopyrite. This work reveals the potential environmental process of the interaction between dissolved As(III) and arsenopyrite, and provides important implications for the prevention and control of As(III) pollution in mine drainage.
  Recent natural language processing (NLP) research is dominated by neural network methods that employ word embeddings as basic building blocks. Pre-training with neural methods that capture local and global distributional properties (e.g., skip-gram, GLoVE) using free text corpora is often used to embed both words and concepts.  https://www.selleckchem.com/products/nf-kb-activator-1.html  Pre-trained embeddings are typically leveraged in downstream tasks using various neural architectures that are designed to optimize task-specific objectives that might further tune such embeddings.
 
  Despite advances in contextualized language model based embeddings, static word embeddings still form an essential starting point in BioNLP research and applications. They are useful in low resource settings and in lexical semantics studies. Our main goal is to build improved biomedical word embeddings and make them publicly available for downstream applications.
 
  We jointly learn word and concept embeddings by first using the skip-gram method and further fine-tuning them with correlatioic use for downstream applications and research endeavors https//github.com/bionlproc/BERT-CRel-Embeddings.
 We repurposed a transformer architecture (typically used to generate dynamic embeddings) to improve static biomedical word embeddings using concept correlations. We provide our code and embeddings for public use for downstream applications and research endeavors https//github.com/bionlproc/BERT-CRel-Embeddings.The analysis of human body composition plays a critical role in health management and disease prevention. However, current medical technologies to accurately assess body composition such as dual energy X-ray absorptiometry, computed tomography, and magnetic resonance imaging have the disadvantages of prohibitive cost or ionizing radiation. Recently, body shape based techniques using body scanners and depth cameras, have brought new opportunities for improving body composition estimation by intelligently analyzing body shape descriptors. In this paper, we present a multi-task deep neural network method utilizing a conditional generative adversarial network to predict the pixel level body composition using only 3D body surfaces. The proposed method can predict 2D subcutaneous and visceral fat maps in a single network with a high accuracy. We further introduce an interpreted patch discriminator which optimizes the texture accuracy of the 2D fat maps. The validity and effectiveness of our new method are demonstrated experimentally on TCIA and LiTS datasets.