Groundwater augmentation through the site selection of floodwater spreading using a data mining approach (case study: Mashhad Plain, Iran)

© 2018 by the authors. It is a well-known fact that sustainable development goals are difficult to achieve without a proper water resources management strategy. This study tries to implement some state-of-the-art statistical and data mining models i.e., weights-of-evidence (WoE), boosted regression trees (BRT), and classification and regression tree (CART) to identify suitable areas for artificial recharge through floodwater spreading (FWS). At first, suitable areas for the FWS project were identified in a basin in north-eastern Iran based on the national guidelines and a literature survey. Using the same methodology, an identical number of FWS unsuitable areas were also determined. Afterward, a set of different FWS conditioning factors were selected for modeling FWS suitability. The models were applied using 70% of the suitable and unsuitable locations and validated with the rest of the input data (i.e., 30%). Finally, a receiver operating characteristics (ROC) curve was plotted to compare the produced FWS suitability maps. The findings depicted acceptable performance of the BRT, CART, and WoE for FWS suitability mapping with an area under the ROC curves of 92, 87.5, and 81.6%, respectively. Among the considered variables, transmissivity, distance from rivers, aquifer thickness, and electrical conductivity were determined as the most important contributors in the modeling. FWS suitability maps produced by the proposed method in this study could be used as a guideline for water resource managers to control flood damage and obtain new sources of groundwater. This methodology could be easily replicated to produce FWS suitability maps in other regions with similar hydrogeological conditions

APG: A novel Python-based ArcGIS toolbox to generate absence-datasets for geospatial studies

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs. To solve this problem, this study develops the Absence Point Generation (APG) toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies. The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude, slope degree, topographic wetness index, and distance from rivers, and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are generated. To test the APG toolbox, we applied two benchmark algorithms of random forest (RF) and boosted regression trees (BRT) in a case study to investigate groundwater potential using three absence datasets i.e., the APG, random, and selection of absence samples (SAS) toolbox. The BRT-APG and RF-APG had the area under receiver operating curve (AUC) values of 0.947 and 0.942, while BRT and RF had weaker performances with the SAS and Random datasets. This effect resulted in AUC improvements for BRT and RF by 7.2, and 9.7% from the Random dataset, and AUC improvements for BRT and RF by 6.1, and 5.4% from the SAS dataset, respectively. The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps, which proves the importance of absence points in environmental binary issues. The proposed APG toolbox could be easily applied in other environmental hazards such as landslides, floods, and gully erosion, and land subsidence

Membrane Partitioning: “Classical” and “Nonclassical” Hydrophobic Effects

The free energy of transfer of nonpolar solutes from water to lipid bilayers is often dominated by a large negative enthalpy rather than the large positive entropy expected from the hydrophobic effect. This common observation has led to the idea that membrane partitioning is driven by the “nonclassical” hydrophobic effect. We examined this phenomenon by characterizing the partitioning of the well-studied peptide melittin using isothermal titration calorimetry (ITC) and circular dichroism (CD). We studied the temperature dependence of the entropic (−TΔS) and enthalpic (ΔH) components of free energy (ΔG) of partitioning of melittin into lipid membranes made of various mixtures of zwitterionic and anionic lipids. We found significant variations of the entropic and enthalpic components with temperature, lipid composition and vesicle size but only small changes in ΔG (entropy–enthalpy compensation). The heat capacity associated with partitioning had a large negative value of about −0.5 kcal mol−1 K−1. This hallmark of the hydrophobic effect was found to be independent of lipid composition. The measured heat capacity values were used to calculate the hydrophobic-effect free energy ΔGhΦ, which we found to dominate melittin partitioning regardless of lipid composition. In the case of anionic membranes, additional free energy comes from coulombic attraction, which is characterized by a small effective peptide charge due to the lack of additivity of hydrophobic and electrostatic interactions in membrane interfaces [Ladokhin and White J Mol Biol 309:543–552, 2001]. Our results suggest that there is no need for a special effect—the nonclassical hydrophobic effect—to describe partitioning into lipid bilayers

Diagnostic accuracy of magnetic resonance enterography and small bowel ultrasound for the extent and activity of newly diagnosed and relapsed Crohn's disease (METRIC): a multicentre trial

Magnetic resonance enterography (MRE) and ultrasound are used to image Crohn's disease, but their comparative accuracy for assessing disease extent and activity is not known with certainty. Therefore, we did a multicentre trial to address this issue. We recruited patients from eight UK hospitals. Eligible patients were 16 years or older, with newly diagnosed Crohn's disease or with established disease and suspected relapse. Consecutive patients had MRE and ultrasound in addition to standard investigations. Discrepancy between MRE and ultrasound for the presence of small bowel disease triggered an additional investigation, if not already available. The primary outcome was difference in per-patient sensitivity for small bowel disease extent (correct identification and segmental localisation) against a construct reference standard (panel diagnosis). This trial is registered with the International Standard Randomised Controlled Trial, number ISRCTN03982913, and has been completed. 284 patients completed the trial (133 in the newly diagnosed group, 151 in the relapse group). Based on the reference standard, 233 (82%) patients had small bowel Crohn's disease. The sensitivity of MRE for small bowel disease extent (80% [95% CI 72-86]) and presence (97% [91-99]) were significantly greater than that of ultrasound (70% [62-78] for disease extent, 92% [84-96] for disease presence); a 10% (95% CI 1-18; p=0·027) difference for extent, and 5% (1-9; p=0·025) difference for presence. The specificity of MRE for small bowel disease extent (95% [85-98]) was significantly greater than that of ultrasound (81% [64-91]); a difference of 14% (1-27; p=0·039). The specificity for small bowel disease presence was 96% (95% CI 86-99) with MRE and 84% (65-94) with ultrasound (difference 12% [0-25]; p=0·054). There were no serious adverse events. Both MRE and ultrasound have high sensitivity for detecting small bowel disease presence and both are valid first-line investigations, and viable alternatives to ileocolonoscopy. However, in a national health service setting, MRE is generally the preferred radiological investigation when available because its sensitivity and specificity exceed ultrasound significantly. National Institute of Health and Research Health Technology Assessment. [Abstract copyright: Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

Diagnostic accuracy for the extent and activity of newly diagnosed and relapsed Crohn’s disease: a multicentre prospective comparison of magnetic resonance enterography and small bowel ultrasound –The METRIC Trial

Background Magnetic resonance enterography (MRE) and ultrasound (US) are used to image Crohn’s disease, but comparative accuracy for disease extent and activity is not known with certainty. We undertook a prospective multicentre cohort trial to address this Methods We recruited from 8 UK hospitals. Eligible patients were 16 years or older, newly diagnosed with Crohn’s disease, or had established disease with suspected relapse. Consecutive patients underwent MRE and US in addition to standard investigations. Discrepancy between MRE and US for small bowel (SB) disease presence triggered an additional investigation, if not already available. The primary outcome was difference in per patient sensitivity for SB disease extent (correct identification and segmental localisation) against a construct reference standard (panel diagnosis). Accuracy for SB and colonic disease presence and activity were secondary outcomes. The trial is completed (ISRCTN03982913). Findings 284 patients completed the trial (133 new diagnosis, 151 relapse). MRE sensitivity (n=233) for SB disease extent (80% [95%CI 72 to 86]) and presence (97% [91 to 99]) were significantly greater than US (70% [62 to 78], 92% [84 to 96]); a 10% (1 to 18; p=0.027), and 5% (1 to 9), difference respectively. MRE specificity for SB disease extent (95% [85 to 98]) was significantly greater than US (81% [64 to 91]). Sensitivity for active SB disease was significantly greater for MRE than US (96% [92 to 99] vs. 90% [82 to 95]), difference 6% (2 to 11). Overall, there were no significant accuracy differences for colonic disease presence. Accuracy in newly diagnosed and relapse patients was similar, although US had significantly greater sensitivity for colonic disease than MRE in newly diagnosed patients (67% [49 to 81) vs. 47% [31 to 64]), difference 20% (1 to 39). There were no serious adverse events. Interpretation MRE has higher diagnostic accuracy for the extent and activity of SB Crohn’s disease than US when tested in a prospective multi centre cohort trial setting

Photodegradation of organic pollutants RhB dye using UV simulated sunlight on ceria based TiO2 nanomaterials for antibacterial applications

To photo-catalytically degrade RhB dye using solar irradiation, CeO2 doped TiO2 nanocomposites were synthesized hydrothermally at 700 °C for 9 hrs. All emission spectra showed a prominent band centered at 442 nm that was attributed to oxygen related defects in the CeO2-TiO2 nanocrystals. Two sharp absorption bands at 1418 cm−1 and 3323 cm−1 were attributed to the deformation and stretching vibration, and bending vibration of the OH group of water physisorbed to TiO2, respectively. The photocatalytic activities of Ce-TiO2 nanocrystals were investigated through the degradation of RhB under UV and UV+ visible light over a period of 8 hrs. After 8 hrs, the most intense absorption peak at 579 nm disappeared under the highest photocatalytic activity and 99.89% of RhB degraded under solar irradiation. Visible light-activated TiO2 could be prepared from metal-ion incorporation, reduction of TiO2, non-metal doping or sensitizing of TiO2 using dyes. Studying the antibacterial activity of Ce-TiO2 nanocrystals against E. coli revealed significant activity when 10 μg was used, suggesting that it can be used as an antibacterial agent. Its effectiveness is likely related to its strong oxidation activity and superhydrophilicity. This study also discusses the mechanism of heterogeneous photocatalysis in the presence of TiO2

Use of multidimensional item response theory methods for dementia prevalence prediction : an example using the Health and Retirement Survey and the Aging, Demographics, and Memory Study

Background Data sparsity is a major limitation to estimating national and global dementia burden. Surveys with full diagnostic evaluations of dementia prevalence are prohibitively resource-intensive in many settings. However, validation samples from nationally representative surveys allow for the development of algorithms for the prediction of dementia prevalence nationally. Methods Using cognitive testing data and data on functional limitations from Wave A (2001-2003) of the ADAMS study (n = 744) and the 2000 wave of the HRS study (n = 6358) we estimated a two-dimensional item response theory model to calculate cognition and function scores for all individuals over 70. Based on diagnostic information from the formal clinical adjudication in ADAMS, we fit a logistic regression model for the classification of dementia status using cognition and function scores and applied this algorithm to the full HRS sample to calculate dementia prevalence by age and sex. Results Our algorithm had a cross-validated predictive accuracy of 88% (86-90), and an area under the curve of 0.97 (0.97-0.98) in ADAMS. Prevalence was higher in females than males and increased over age, with a prevalence of 4% (3-4) in individuals 70-79, 11% (9-12) in individuals 80-89 years old, and 28% (22-35) in those 90 and older. Conclusions Our model had similar or better accuracy as compared to previously reviewed algorithms for the prediction of dementia prevalence in HRS, while utilizing more flexible methods. These methods could be more easily generalized and utilized to estimate dementia prevalence in other national surveys