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The Drinking Water Quality Index (DWQI) and the Human Health Risk Index (HHRI) are two of the most promising tools for assessing the health impact of water quality on humans. Each of these indices has its own ability to determine a specific level of safety for drinking, and their results may vary. This study aims to develop an aggregated index to identify vulnerable areas in relation to safe drinking water and, subsequently, risk areas for human health, particularly non-cancerous diseases, in the Maku–Bazargan–Poldasht area in NW Iran through the use of a data fusion technique. Nitrate (NO3−) and fluoride (F−) are the predominant contaminants that threaten the local population’s health. The DWQI revealed that the majority of the study sites had poor to improper quality for drinking water class. Health risk assessments showed an excessive potential for non-carcinogenic health risks because of high NO3− and F− exposure through drinking water. Children are at a higher risk for non-carcinogenic changes than adults, according to the total hazard index (THI; NO3− and F−), suggesting that locals have faced a lifetime risk of non-cancer changes as a consequence of their exposure to these pollutants. Using data fusion techniques can assist in developing a comprehensive water resources risk map for decision-making.

Background Although the descending aortic diameter is larger in smokers, data about thoracic aortic growth is missing. Our aim is to present the distribution of thoracic aortic growth in smokers and to compare it with literature of the general population. Methods Current and ex-smokers aged 50–70 years from the longitudinal Danish Lung Cancer Screening Trial, were included. Mean and 95th percentile of annual aortic growth of the ascending aortic (AA) and descending aortic (DA) diameters were calculated with the first and last non-contrast computed tomography scans during follow-up. Determinants of change in aortic diameter over time were investigated with linear mixed models. Results A total of 1987 participants (56% male, mean age 57.4 ± 4.8 years) were included. During a median follow-up of 48 months, mean AA and DA growth rates were comparable between males (AA 0.12 ± 0.31 mm/year and DA 0.10 ± 0.30 mm/year) and females (AA 0.11 ± 0.29 mm/year and DA 0.13 ± 0.27 mm/year). The 95th percentile ranged from 0.42 to 0.47 mm/year, depending on sex and location. Aortic growth was comparable between current and ex-smokers and aortic growth was not associated with pack-years. Our findings are consistent with aortic growth rates of 0.08 to 0.17 mm/years in the general population. Larger aortic growth was associated with lower age, increased height, absence of medication for hypertension or hypercholesterolemia and lower Agatston scores. Conclusions This longitudinal study of smokers in the age range of 50–70 years shows that ascending and descending aortic growth is approximately 0.1 mm/year and is consistent with growth in the general population.

PURPOSE: Accurate segmentation of the pulmonary arteries and aorta is important due to the association of the diameter and the shape of these vessels with several cardiovascular diseases and with the risk of exacerbations and death in patients with Chronic Obstructive Pulmonary Disease (COPD). We propose a fully automatic method based on an optimal surface graph cut algorithm to quantify the full 3D shape and the diameters of the pulmonary arteries and aorta in non-contrast Computed Tomography (CT) scans.METHODS: The proposed algorithm first extracts seed points in the right and left pulmonary arteries, the pulmonary trunk, and the ascending and descending aorta by using multi-atlas registration. Subsequently, the centerlines of the pulmonary arteries and aorta are extracted by a minimum cost path tracking between the extracted seed points, with a cost based on a combination of lumen intensity similarity and multiscale medialness in 3 planes. The centerlines are refined by applying the path tracking algorithm to curved multi-planar reformatted scans and are then smoothed and dilated non-uniformly according to the extracted local vessel radius from the medialness filter. The resulting coarse estimates of the vessels are used as initialization for a graph-cut segmentation. Once the vessels are segmented, the diameters of the pulmonary artery (PA) and the ascending aorta (AA) and the ratio are automatically calculated both in a single axial slice and in a 10 mm volume around the automatically extracted pulmonary artery bifurcation level. The method is evaluated on non-contrast CT scans from the Danish Lung Cancer Screening Trial (DLCST). Segmentation accuracy is determined by comparing with manual annotations on 25 CT scans. Intra-class correlation (ICC) between manual and automatic diameters, both measured in axial slices at the pulmonary artery bifurcation level, is computed on an additional 200 CT scans. Repeatability of the automated 3D volumetric diameter and ratio calculations (perpendicular to the vessel axis) are evaluated on 118 scan-rescan pairs with an average in-between time of 3 months.RESULTS: We obtained a Dice segmentation overlap of 0.94 ± 0.02 for pulmonary arteries and 0.96 ± 0.01 for the aorta, with a mean surface distance of 0.62 ± 0.33 mm and 0.43 ± 0.07 mm, respectively. ICC between manual and automatic in-slice diameter measures was 0.92 for PA, 0.97 for AA, and 0.90 for the ratio, and for automatic diameters in 3D volumes around the pulmonary artery bifurcation level between scan and rescan were 0.89, 0.95, and 0.86, respectively.CONCLUSION: The proposed automatic segmentation method can reliably extract diameters of the large arteries in non-ECG-gated non-contrast CT scans such as are acquired in lung cancer screening. This article is protected by copyright. All rights reserved.

Objectives To develop and evaluate a fully automatic method to measure diameters of the ascending and descending aorta on non-ECG-gated, non-contrast computed tomography (CT) scans. Material and methods The method combines multi-atlas registration to obtain seed points, aorta centerline extraction, and an optimal surface segmentation approach to extract the aorta surface around the centerline. From the extracted 3D aorta segmentation, the diameter of the ascending and descending aorta was calculated at cross-sectional slices perpendicular to the extracted centerline, at the level of the pulmonary artery bifurcation, and at 1-cm intervals up to 3 cm above and below this level. Agreement with manual annotations was evaluated by dice similarity coefficient (DSC) for segmentation overlap, mean surface distance (MSD), and intra-class correlation (ICC) of diameters on 100 CT scans from a lung cancer screening trial. Repeatability of the diameter measurements was evaluated on 617 baseline-one year follow-up CT scan pairs. Results The agreement between manual and automatic segmentations was good with 0.95 ± 0.01 DSC and 0.56 ± 0.08 mm MSD. ICC between the diameters derived from manual and from automatic segmentations was 0.97, with the per-level ICC ranging from 0.87 to 0.94. An ICC of 0.98 for all measurements and per-level ICC ranging from 0.91 to 0.96 were obtained for repeatability. Conclusion This fully automatic method can assess diameters in the thoracic aorta reliably even in non-ECG-gated, non-contrast CT scans. This could be a promising tool to assess aorta dilatation in screening and in clinical practice. Key Points • Fully automatic method to assess thoracic aorta diameters. • High agreement between fully automatic method and manual segmentations. • Method is suitable for non-ECG-gated CT and can therefore be used in screening. Electronic supplementary material The online version of this article (10.1007/s00330-018-5931-z) contains supplementary material, which is available to authorized users.

An investigation is presented to improve on the performances of the Basic DRASTIC Framework (BDF) and its variation by the Fuzzy-Catastrophe Framework (FCF), both of which provide an estimate of intrinsic aquifer vulnerabilities to anthropogenic contamination. BDF prescribes rates and weights for 7 data layers but FCF is an unsupervised learning framework based on a multicriteria decision theory by integrating fuzzy membership function and catastrophe theory. The challenges in the paper include: (i) the study area comprises confined and unconfined aquifers and (ii) Artificial Intelligence (AI) is used to run Multiple Framework (AIMF) in order to map specific vulnerability due to a specific contaminant. Predicted results by AIMF are referred to as Specific Vulnerability Indices, as the learned VIs are referenced to site-specific nitrate-N. The results show that correlation coefficient between BDF or FCF with nitrate-N is lower than 0.7 but the AIMF strategy improves it to greater than 0.95. The results are evidence for the proof-of-concept for transforming frameworks to models capable of further learning.

The aquifer at Varzeqan plain, with multiple confined/unconfined and hard-rock boundaries, is exposed to risks from several contaminants (nitrate-N, fluoride and arsenic) originated by anthropogenic and/or geogenic activities, which are possibly accelerating by anthropogenic activities. The study is a research initiative driven by impacts of poor or non-existent planning/ regulation practices to produce insights despite the sparsity of the available data and the unknown baseline. A methodology is given, which seeks ‘total information management’ by pooling together the following five dimensions: (i) a perceptual model to collect existing knowledge-base; (ii) a conceptual model to analyse a sample of ion-concentrations by a set of existing techniques (e.g. statistical, graphical and multivariate analysis); (iii) risk cells to contextualise each contaminant; (iv) “soft modelling” to firm up information by learning from convergences and/or divergences within the conceptual model; and (v) study the processes within each risk cell through the OSPRC framework (Origins, Sources, Pathways, Receptors and Consequence). The research caters for inherent variabilities in the study area by 15 risk cells delineated within the boundaries of confined, unconfined and hard-rock aquifers as follows: 4 risk cells account for minor ions of nitrate-N pollution of anthropogenic origins; 6 for minor ions of fluoride and 5 for trace ions of geogenic arsenic anomalies. It further identifies the possibility of anthropogenic activities encouraging geogenic anomalies. The findings are presented as a descriptive model but this will be transformed into quantitative models in due course when more data become available.

Driven by contamination risks, mapping Vulnerability Indices (VI) of multiple aquifers (both unconfined and confined) is investigated by integrating the basic DRASTIC framework with multiple models overarched by Artificial Neural Networks (ANN). The DRASTIC framework is a proactive tool to assess VI values using the data from the hydrosphere, lithosphere and anthroposphere. However, a research case arises for the application of multiple models on the ground of poor determination coefficients between the VI values and non-point anthropogenic contaminants. The paper formulates SCFL models, which are derived from the multiple model philosophy of Supervised Committee (SC) machines and Fuzzy Logic (FL) and hence SCFL as their integration. The Fuzzy Logic-based (FL) models include: Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Larsen Fuzzy Logic (LFL) models. The basic DRASTIC framework uses prescribed rating and weighting values based on expert judgment but the four FL-based models (SFL, MFL, LFL and SCFL) derive their values as per internal strategy within these models. The paper reports that FL and multiple models improve considerably on the correlation between the modeled vulnerability indices and observed nitrate-N values and as such it provides evidence that the SCFL multiple models can be an alternative to the basic framework even for multiple aquifers. The study area with multiple aquifers is in Varzeqan plain, East Azerbaijan, northwest Iran.