Effect of ciprofloxacin dosages on the performance of sponge membrane bioreactor treating hospital wastewater

© 2018 Elsevier Ltd This study aimed to evaluate treatment performance and membrane fouling of a lab-scale Sponge-MBR under the added ciprofloxacin (CIP) dosages (20; 50; 100 and 200 µg L−1) treating hospital wastewater. The results showed that Sponge-MBR exhibited effective removal of COD (94–98%) during the operation period despite increment of CIP concentrations from 20 to 200 µg L−1. The applied CIP dosage of 200 µg L−1 caused an inhibition of microorganisms in sponges, i.e. significant reduction of the attached biomass and a decrease in the size of suspended flocs. Moreover, this led to deteriorating the denitrification rate to 3–12% compared to 35% at the other lower CIP dosages. Importantly, Sponge-MBR reinforced the stability of CIP removal at various added CIP dosages (permeate of below 13 µg L−1). Additionally, the fouling rate at CIP dosage of 200 µg L−1 was 30.6 times lower compared to the control condition (no added CIP dosage)

High rate nitrogen removal by ANAMMOX internal circulation reactor (IC) for old landfill leachate treatment

© 2017 Elsevier Ltd This study aimed to evaluate the performance of a high rate nitrogen removal lab-scale ANAMMOX reactor, namely Internal Circulation (IC) reactor, for old landfill leachate treatment. The reactor was operated with pre-treated leachate from a pilot Partial Nitritation Reactor (PNR) using a high nitrogen loading rate ranging from 2 to 10 kg N m−3 d−1. High rate removal of nitrogen (9.52 ± 1.11 kg N m−3 d−1) was observed at an influent nitrogen concentration of 1500 mg N L−1. The specific ANAMMOX activity was found to be 0.598 ± 0.026 gN2-N gVSS−1 d−1. Analysis of ANAMMOX granules suggested that 0.5–1.0 mm size granular sludge was the dominant group. The results of DNA analysis revealed that Candidatus Kueneniastuttgartiensis was the dominant species (37.45%) in the IC reactor, whereas other species like uncultured Bacteroidetes bacterium only constituted 5.37% in the system, but they were still responsible for removing recalcitrant organic matter

A hybrid computational intelligence approach to groundwater spring potential mapping

© 2019 by the authors. This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely "AB-ADTree", for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including singleADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB-ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources

Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas

A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran)

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811)

Physiological Effects of Superoxide Dismutase on Altered Visual Function of Retinal Ganglion Cells in db/db Mice

Background: The C57BLKS/J db/db (db/db) mouse is a widely used type 2 diabetic animal model, and this model develops early inner retinal neuronal dysfunction beginning at 24 weeks. The neural mechanisms that mediate early stage retinal dysfunction in this model are unknown. We evaluated visual response properties of retinal ganglion cells (RGCs) during the early stage of diabetic insult (8, 12, and 20 wk) in db/db mice and determined if increased oxidative stress plays a role in impaired visual functions of RGCs in 20 wk old db/db mice. Methodology/Principal Findings: In vitro extracellular single-unit recordings from RGCs in wholemount retinas were performed. The receptive field size, luminance threshold, and contrast gain of the RGCs were investigated. Although ONand OFF-RGCs showed a different time course of RF size reduction, by 20 wk, the RF of ON- and OFF-RGCs were similarly affected. The LT of ON-RGCs was significantly elevated in 12 and 20 wk db/db mice compared to the LT of OFF-RGCs. The diabetic injury also affected contrast gains of ON- and OFF-RGCs differently. The generation of reactive oxidative species (ROS) in fresh retina was estimated by dihydroethidium. Superoxide dismutase (SOD) (300 unit/ml) was applied in Ames medium to the retina, and visual responses of RGCs were recorded for five hours. ROS generation in the retinas of db/db mice increased at 8wk and continued to progress at 20 wk of ages. In vitro application of SOD improved visual functions in 20 wk db/db mice but the SOD treatment affected ON- and OFF-RGCs differently in db/m retina

In silico pathway reconstruction: Iron-sulfur cluster biogenesis in Saccharomyces cerevisiae

BACKGROUND: Current advances in genomics, proteomics and other areas of molecular biology make the identification and reconstruction of novel pathways an emerging area of great interest. One such class of pathways is involved in the biogenesis of Iron-Sulfur Clusters (ISC). RESULTS: Our goal is the development of a new approach based on the use and combination of mathematical, theoretical and computational methods to identify the topology of a target network. In this approach, mathematical models play a central role for the evaluation of the alternative network structures that arise from literature data-mining, phylogenetic profiling, structural methods, and human curation. As a test case, we reconstruct the topology of the reaction and regulatory network for the mitochondrial ISC biogenesis pathway in S. cerevisiae. Predictions regarding how proteins act in ISC biogenesis are validated by comparison with published experimental results. For example, the predicted role of Arh1 and Yah1 and some of the interactions we predict for Grx5 both matches experimental evidence. A putative role for frataxin in directly regulating mitochondrial iron import is discarded from our analysis, which agrees with also published experimental results. Additionally, we propose a number of experiments for testing other predictions and further improve the identification of the network structure. CONCLUSION: We propose and apply an iterative in silico procedure for predictive reconstruction of the network topology of metabolic pathways. The procedure combines structural bioinformatics tools and mathematical modeling techniques that allow the reconstruction of biochemical networks. Using the Iron Sulfur cluster biogenesis in S. cerevisiae as a test case we indicate how this procedure can be used to analyze and validate the network model against experimental results. Critical evaluation of the obtained results through this procedure allows devising new wet lab experiments to confirm its predictions or provide alternative explanations for further improving the models