A correlational study of uranium in groundwater with other physicochemical parameters using GIS mapping in Godda district of Jharkhand, India.

The present research concentrates on the cumulative use of GPS and GIS technologies, which are excellent resources for analyzing and monitoring divergent physicochemical parameters in groundwater, including pH, TDS, EC, ORP, Ca+2, Mg+2, NO3-, F-, SO4-2, Cl- and PO4-3 with explicit regard to uranium. Garmin GPS is used to record the locations of the sampling points in the Godda study area. The research aims to offer a thorough understanding of the relationship between soil and water, its impact on public health and the extent to which water can be used in various ways based on its quality. Utilizing the inverse distance weighted (IDW) technique, it is examined how these groundwater parameters and the Water Quality Index (WQI) can be estimated spatially. Additionally, a correlation analysis of the water quality parameters is computed to estimate the local population's cancer risk living in the study area. Except for calcium and magnesium, which are present in excess concentrations throughout the study area with the highest values of 325 and 406 mg/l, respectively at Amediha and Meherma, the results showed that the maximum concentration parameters are within limits with the standard. The main reason might be the area's predominance of Alfisol soil type. The radioactive element uranium is found to be in a limited  range. Chemo-toxicity and radiological risk assessment of the whole area lie far below the restricted cancer risk limit i.e., 30 ppb with the highest concentration of 14 ppb in the 'Sunderpahari' region, following the results obtained. The WQI for the area ranges from 'good' to 'very poor.' The results were favorable but a few sites such as 'Boarijor' and its surroundings, require additional attention to enhance groundwater quality. Given uranium's low availability in groundwater the region's cancer risk assessment is below average.

Mapping in a radon-prone area in Adamawa region, Cameroon, by measurement of radon activity concentration in soil.

The radon-prone area of the Adamawa region in Cameroon is characterized by high natural radiation background resulting from the high concentrations of radium-226, thorium-232, and indoor radon. To produce a radon-risk map, radon measurements in soil were carried out in the city of Ngaoundere. The radon activity concentration in soil gas ranged from 256 to 166 kBq m-3 with a mean of 80 kBq m-3 and a standard deviation of 38 kBq m-3. The area is mostly classified as high risk (80%) according to the Swedish classification, and 20% as medium risk. A low-risk area was not observed. Granite-like geology sites were characterized by higher radon concentration. A ratio of about 295:1 was obtained for soil radon gas to indoor radon concentrations, with a positive correlation (R = 0.40), and a transfer factor of 3 per mil. These results demonstrate that in situ measurements of radon concentration in soil can provide accurate information on the level of indoor radon concentrations. Geostatistical and deterministic interpolation techniques have been used to obtain a radon map by comparing the suitability of ordinary kriging and inverse-distance-weighted (IDW) interpolation methods. It turned out that there is not much difference in the prediction errors of the two techniques (Root Mean Square Error = 34.4 for ordinary kriging and 34.3 for IDW). It is concluded that both methods give acceptable results. In situ measurements and geostatistical analysis allow assessment of expected indoor radon exposure in a given area at reduced costs and time required. However, for the investigated area, more research is needed to produce reliable radon-risk maps.

Estimated Worldwide Prevalence of the PI*ZZ Alpha-1 Antitrypsin Genotype in Subjects With Chronic Obstructive Pulmonary Disease

Introduction: The prevalence of α1-antitrypsin PI*ZZ genotypes in patients with COPD is only partially known. We aimed to estimate this prevalence worldwide. Method: A systematic review of the literature was conducted for studies investigating the prevalence of COPD and the prevalence of severe alpha-1-antitrypsin deficiency (AATD) PI*ZZ genotype. Results are shown in tables and on a whole world interpolation map. Results: Studies from 48 countries with available data (21 from Europe, 9 from the Americas, 5 from Africa, 11 from Asia and 2 from Australasia) were selected. About 235,000 individuals with PI*ZZ genotypes were accounted: 50% in Europe, 37% in America, 9% in Asia, 3% in Australasia and 1% in Africa. The estimated crude prevalence of COPD in adults older than 40 years was 12.45% in Europe, 13.51% in America, 13.22% in Africa, 11.70% in Asia and 11.86% in Australasia. The highest PI*ZZ weighted average prevalence among COPD subjects (expressed as 1/x [95% confidence intervals]) were found in Northern Europe (395 [252–576]) followed by Western (797 [538–1165]), Southern (944 [600–1475]) and Central Europe (1096 [687–1738]). Outside Europe, high values were found in Australia–New Zealand (1007 [684–1509]), Saudi Arabia (1276 [563–2961]), United States (1298 [1094–1540]), Canada (1482 [1057–2083]) and Thailand (1807 [717–4692]). In the rest of the world, prevalence was significantly lower, especially in vast regions of Asia and Africa where the PI*Z gene is practically non-existent. Conclusions: Severe AATD is associated with a significant number of cases of COPD, especially in Europe, USA, Canada, New Zealand and Australia. (AU)

Changes in per capita wheat production in China in the context of climate change and population growth.

To address challenges associated with climate change, population growth and decline in international trade linked to the COVID-19 pandemic, determining whether national crop production can meet populations' requirements and contribute to socio-economic resilience is crucial. Three crop models and three global climate models were used in conjunction with predicted population changes. Compared with wheat production in 2000-2010, total production and per capita wheat production were significantly (P < 0.05) increase in 2020-2030, 2030-2040 and 2040-2050, respectively, under RCP4.5 and RCP8.5 due to climate change in China. However, when considering population and climate changes, the predicted per capita production values were 125.3 ± 0.3, 127.1 ± 2.3 and 128.8 ± 2.7 kg during the 2020-2030, 2030-2040, 2040-2050 periods under RCP4.5, or 126.2 ± 0.7, 128.7 ± 2.5, and 131.0 ± 4.1 kg, respectively, under RCP8.5. These values do not significantly differ (P > 0.05) from the baseline level (127.9 ± 1.3 kg). The average per capita production in Loess Plateau and Gansu-Xinjiang subregions declined. In contrast, per capita production in the Huanghuai, Southwestern China, and Middle-Lower Yangtze Valleys subregions increased. The results suggest that climate change will increase total wheat production in China, but population change will partly offset the benefits to the grain market. In addition, domestic grain trade will be influenced by both climate and population changes. Wheat supply capacity will decline in the main supply areas. Further research is required to address effects of the changes on more crops and in more countries to obtain deeper understanding of the implications of climate change and population growth for global food production and assist formulation of robust policies to enhance food security. Supplementary Information: The online version contains supplementary material available at 10.1007/s12571-023-01351-x.

Estimated Worldwide Prevalence of the PI*ZZ Alpha-1 Antitrypsin Genotype in Subjects With Chronic Obstructive Pulmonary Disease.

INTRODUCTION: The prevalence of α1-antitrypsin PI*ZZ genotypes in patients with COPD is only partially known. We aimed to estimate this prevalence worldwide. METHOD: A systematic review of the literature was conducted for studies investigating the prevalence of COPD and the prevalence of severe alpha-1-antitrypsin deficiency (AATD) PI*ZZ genotype. Results are shown in tables and on a whole world interpolation map. RESULTS: Studies from 48 countries with available data (21 from Europe, 9 from the Americas, 5 from Africa, 11 from Asia and 2 from Australasia) were selected. About 235,000 individuals with PI*ZZ genotypes were accounted: 50% in Europe, 37% in America, 9% in Asia, 3% in Australasia and 1% in Africa. The estimated crude prevalence of COPD in adults older than 40 years was 12.45% in Europe, 13.51% in America, 13.22% in Africa, 11.70% in Asia and 11.86% in Australasia. The highest PI*ZZ weighted average prevalence among COPD subjects (expressed as 1/x [95% confidence intervals]) were found in Northern Europe (395 [252-576]) followed by Western (797 [538-1165]), Southern (944 [600-1475]) and Central Europe (1096 [687-1738]). Outside Europe, high values were found in Australia-New Zealand (1007 [684-1509]), Saudi Arabia (1276 [563-2961]), United States (1298 [1094-1540]), Canada (1482 [1057-2083]) and Thailand (1807 [717-4692]). In the rest of the world, prevalence was significantly lower, especially in vast regions of Asia and Africa where the PI*Z gene is practically non-existent. CONCLUSIONS: Severe AATD is associated with a significant number of cases of COPD, especially in Europe, USA, Canada, New Zealand and Australia.

Effects of short-term and long-term exposure to ambient air pollution and temperature on long recovery duration in COVID-19 patients.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) has spread rapidly around the world since December 8, 2019. However, the key factors affecting the duration of recovery from COVID-19 remain unclear. OBJECTIVE: To investigate the associations of long recovery duration of COVID-19 patients with ambient air pollution, temperature, and diurnal temperature range (DTR) exposure. METHODS: A total of 427 confirmed cases in Changsha during the first wave of the epidemic in January 2020 were selected. We used inverse distance weighting (IDW) method to estimate personal exposure to seven ambient air pollutants (PM2.5, PM2.5-10, PM10, SO2, NO2, CO, and O3) at each subject's home address. Meteorological conditions included temperature and DTR. Multiple logistic regression model was used to investigate the relationship of air pollution exposure during short-term (past week and past month) and long-term (past three months) with recovery duration among COVID-19 patients. RESULTS: We found that long recovery duration among COVID-19 patients was positively associated with short-term exposure to CO during past week with OR (95% CI) = 1.42 (1.01-2.00) and PM2.5, NO2, and CO during past month with ORs (95% CI) = 2.00 (1.30-3.07) and 1.95 (1.30-2.93), and was negatively related with short-term exposure to O3 during past week and past month with ORs (95% CI) = 0.68 (0.46-0.99) and 0.41 (0.27-0.62), respectively. No association was observed for long-term exposure to air pollution during past three months. Furthermore, increased temperature during past three months elevated risk of long recovery duration in VOCID-19 patients, while DTR exposure during past week and past month decreased the risk. Male and younger patients were more susceptible to the effect of air pollution on long recovery duration, while female and older patients were more affected by exposure to temperature and DTR. CONCLUSION: Our findings suggest that both TRAP exposure and temperature indicators play important roles in prolonged recovery among COVID-19 patients, especially for the sensitive populations, which provide potential strategies for effective reduction and early prevention of long recovery duration of COVID-19.

Estimation of methane emission from an urban wastewater treatment plant applying inverse Gaussian model.

The methane (CH4) emissions from urban sources are increasing, and they depend on the processes and technologies applied in each one. Thus, studying them individually to quantify their emissions and understand their behavior to design CH4 mitigation strategies is meaningful. Although many studies have been carried out in different cities worldwide, the complex methodologies and technologies applied are not readily available in developing countries. The main objective of this work is to apply a simple and inexpensive methodology to collect air samples in urban areas using syringes with a three-way stopcock. Considering the baseline concentration in different urban zones, the WWTP contribution to atmospheric CH4 concentration was assessed. Moreover, it was possible to estimate the CH4 emission rate from the source by applying the inverse Gaussian model. The atmospheric CH4 concentrations inside and around the WWTP varied from 2.04 to 32.78 ppm. Most of the highest concentrations were found inside the WWTP; however, high concentrations were found up to 500 m from its center. The values in the urban zones were between 2.06 and 3.52 ppm, consistently higher in the area with the highest population density. Finally, considering the WWTP as a single source and according to the operational and atmospheric conditions during the studied period, the mean CH4 emission rate from this source was 2.08E + 04 µg s-1. The proposed sampling methodology could be applied to estimate CH4 emission rates from fixed sources in areas with overlapping sources.

Estimated Prevalence and Number of PiMZ Genotypes of Alpha-1 Antitrypsin in Seventy-Four Countries Worldwide.

BACKGROUND: The α-1 antitrypsin (AAT) protease inhibitor PiMZ is a moderately deficient genotype, until recently considered of little or negligible risk. However, a growing number of studies show that MZ carriers have an increased risk of developing lung and liver diseases, if exposed to smoking or other airborne or industrial pollutants, and hepatotoxic substances. METHODS: We used the epidemiological studies performed to determine the frequencies of PiM and PiZ worldwide, based on the following criteria: 1) samples representative of the general population; 2) AAT phenotyping or genotyping characterized by adequate methods, including isoelectric focusing and polymerase chain reaction; and 3) studies with reliable results assessed with a coefficient of variation calculated from the sample size and 95% confidence intervals, to measure the precision of the results in terms of dispersion of the data around the mean. RESULTS: The present review reveals an impressive number of MZs of more than 35 million in 74 countries of the world with available data. Seventy-five percent of them are people of Caucasian European heritage, mostly living in Europe, America, Australia and New Zealand. Twenty percent of the remaining MZs live in Asia, with the highest concentrations in the Middle East, Eastern¸ Southern, and South-eastern regions of the Asian continent. The remaining five percent are Africans residing in Western and Eastern Africa. CONCLUSION: Considering the high rate of smoking, the outdoor and the indoor air pollution from solid fuels used in cooking and heating, and the exposure to industrial dusts and chemicals in many of these countries, these figures are very worrying, and hence the importance of adequately assessing MZ subjects, recommending them rigorous preventive measures based on the adoption of healthy lifestyles, including avoidance of smoking and alcohol.

Environmental effects of prohibiting urban fireworks and firecrackers in Jinan, China.

Eight national air quality monitoring stations were selected to examine the environmental effects of prohibiting fireworks and firecrackers since January 1, 2018, in Jinan, China, by using an air quality index (AQI) on three time scales. In 2014-2018, the average annual AQI decreased year on year, but a downward trend in 2018 was only found by applying a Daniel trend test. The change in monthly data for 2016-2018 followed a "W" pattern. The overall AQI value was lower on New Year's Eve than during Spring Festival, and the 2-day AQI in 2018 was lower than that in 2017. The GIS analysis method was used for spatial visualization. The AQI in the built-up part of Jinan was high in the west and low in the east on New Year's Eve and Spring Festival of 2017, being lowest in the Development Zone. The AQI spatial distribution was high in the city core but low in its periphery; in 2018, the high-AQI center appeared near the Provincial Seed Warehouse on New Year's Eve and Spring Festival. Multiple linear regression was used to analyze the relationship between AQI and pollutants. Six pollutants were found to be positively correlated with the AQI. PM2.5 and PM10 had the strongest correlations on New Year's Eve and Spring Festival, for which the correlations of SO2, CO, and NO2 were significantly weaker in 2018 than in 2017.

Adopting Machine Learning and Spatial Analysis Techniques for Driver Risk Assessment: Insights from a Case Study.

Traffic violations usually caused by aggressive driving behavior are often seen as a primary contributor to traffic crashes. Violations are either caused by an unintentional or deliberate act of drivers that jeopardize the lives of fellow drivers, pedestrians, and property. This study is aimed to investigate different traffic violations (overspeeding, wrong-way driving, illegal parking, non-compliance traffic control devices, etc.) using spatial analysis and different machine learning methods. Georeferenced violation data along two expressways (S308 and S219) for the year 2016 was obtained from the traffic police department, in the city of Luzhou, China. Detailed descriptive analysis of the data showed that wrong-way driving was the most common violation type observed. Inverse Distance Weighted (IDW) interpolation in the ArcMap Geographic Information System (GIS) was used to develop violation hotspots zones to guide on efficient use of limited resources during the treatment of high-risk sites. Lastly, a systematic Machine Learning (ML) framework, such as K Nearest Neighbors (KNN) models (using k = 3, 5, 7, 10, and 12), support vector machine (SVM), and CN2 Rule Inducer, was utilized for classification and prediction of each violation type as a function of several explanatory variables. The predictive performance of proposed ML models was examined using different evaluation metrics, such as Area Under the Curve (AUC), F-score, precision, recall, specificity, and run time. The results also showed that the KNN model with k = 7 using manhattan evaluation had an accuracy of 99% and outperformed the SVM and CN2 Rule Inducer. The outcome of this study could provide the practitioners and decision-makers with essential insights for appropriate engineering and traffic control measures to improve the safety of road-users.

Data on the assessment of Groundwater Quality in Gomti-Ganga alluvial plain of Northern India.

This data article deals with the assessment of groundwater quality based on water quality index (WQI) and irrigation indices. A total of 8 sites have been selected for the qualification of groundwater fitness. The assessment of groundwater quality has been done by selecting 13 physico-chemical parameters such as pH, EC, TDS, Ca2+, Mg2+, Na+, K+, Cl-, SO4-, HCO3-, NO3-, F-, and TH. Inverse distance-weighted (IDW) application was used to prepare the spatial distribution maps of WQI for the pre and post-monsoon period. All the samples were found in the rock dominance zone in Gibbs plot and according to the Piper plot, Ca-HCO3 is the dominant hydrochemical facies in the study area. On the other hand, irrigation water quality was examined by computing irrigation indices such as SAR, RSC, SSP, MHR, KR, %Na, PI, and PS. The outcomes of the irrigation indices suggests that the water quality is of a good and excellent category except for MHR and RSC.

Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin'anjiang River in Huangshan, China.

This study was aimed at assessing the spatial and temporal distribution of surface water quality variables of the Xin'anjiang River (Huangshan). For this purpose, 960 water samples were collected monthly along the Xin'anjiang River from 2008 to 2017. Twenty-four water quality indicators, according to the environmental quality standards for surface water (GB 3838-2002), were detected to evaluate the water quality of the Xin'anjiang River over the past 10 years. Principal component analysis (PCA) was used to comprehensively evaluate the water quality across eight monitoring stations and analyze the sources of water pollution. The results showed that all samples could be analyzed by three main components, which accounted for 87.24% of the total variance. PCA technology identified important water quality parameters and revealed that nutrient pollution and organic pollution are major latent factors which influence the water quality of Xin'anjiang River. It also showed that agricultural activities, erosion, domestic, and industrial discharges are fundamental causes of water pollution in the study area. It is of great significance for water quality safety management and pollution control of the Xin'anjiang River. Meanwhile, the inverse distance weighted (IDW) method was used to interpolate the PCA comprehensive score. Based on this, the temporal and spatial structure and changing characteristics of water quality in the Xin'anjiang River were analyzed. We found that the overall water quality of Xin'anjiang River (Huangshan) was stable from 2008 to 2017, but the pollution of the Pukou sampling point was of great concern. The results of IDW helped us to identify key areas requiring control in the Xin'anjiang River, which pointed the way for further delicacy management of the river. This study proved that the combination of PCA and IDW interpolation is an effective tool for determining surface water quality. It was of great significance for the control of water pollution in Xin'anjiang River and the reduction of eutrophication pressure in Thousand Island Lake.

Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing.

Spatial interpolation method is the basis of soil heavy metal pollution assessment and remediation. The existing evaluation index for interpolation accuracy did not combine with actual situation. The selection of interpolation methods needs to be based on specific research purposes and research object characteristics. In this paper, As pollution in soils of Beijing was taken as an example. The prediction accuracy of ordinary kriging (OK) and inverse distance weighted (IDW) were evaluated based on the cross validation results and spatial distribution characteristics of influencing factors. The results showed that, under the condition of specific spatial correlation, the cross validation results of OK and IDW for every soil point and the prediction accuracy of spatial distribution trend are similar. But the prediction accuracy of OK for the maximum and minimum is less than IDW, while the number of high pollution areas identified by OK are less than IDW. It is difficult to identify the high pollution areas fully by OK, which shows that the smoothing effect of OK is obvious. In addition, with increasing of the spatial correlation of As concentration, the cross validation error of OK and IDW decreases, and the high pollution area identified by OK is approaching the result of IDW, which can identify the high pollution areas more comprehensively. However, because the semivariogram constructed by OK interpolation method is more subjective and requires larger number of soil samples, IDW is more suitable for spatial prediction of heavy metal pollution in soils.

Geo-statistical analysis of Culicoides spp. distribution and abundance in Sicily, Italy.

BACKGROUND: Biting midges belonging to Culicoides imicola, Culicoides obsoletus complex and Culicoides pulicaris complex (Diptera: Ceratopogonidae) are increasingly implicated as vectors of bluetongue virus in Palaearctic regions. Culicoides obsoletus complex includes C. obsoletus (sensu stricto), C. scoticus, C. dewulfi and C. chiopterus. Culicoides pulicaris and C. lupicaris belong to the Culicoides pulicaris complex. The aim of this study was a geo-statistical analysis of the abundance and spatial distribution of Culicoides spp. involved in bluetongue virus transmission. As part of the national bluetongue surveillance plan 7081 catches were collected in 897 Sicilian farms from 2000 to 2013. METHODS: Onderstepoort-type blacklight traps were used for sample collection and each catch was analysed for the presence of Culicoides spp. and for the presence and abundance of Culicoides vector species (C. imicola, C. pulicaris / C. obsoletus complexes). A geo-statistical analysis was carried out monthly via the interpolation of measured values based on the Inverse Distance Weighted method, using a GIS tool. Raster maps were reclassified into seven classes according to the presence and abundance of Culicoides, in order to obtain suitable maps for Map Algebra operations. RESULTS: Sicilian provinces showing a very high abundance of Culicoides vector species were Messina (80% of the whole area), Palermo (20%) and Catania (12%). A total of 5654 farms fell within the very high risk area for bluetongue (21% of the 26,676 farms active in Sicily); of these, 3483 farms were in Messina, 1567 in Palermo and 604 in Catania. Culicoides imicola was prevalent in Palermo, C. pulicaris in Messina and C. obsoletus complex was very abundant over the whole island with the highest abundance value in Messina. CONCLUSIONS: Our study reports the results of a geo-statistical analysis concerning the abundance and spatial distribution of Culicoides spp. in Sicily throughout the fourteen year study. It provides useful decision support in the field of epidemiology, allowing the identification of areas to be monitored as bases for improved surveillance plans. Moreover, this knowledge can become a tool for the evaluation of virus transmission risks, especially if related to vector competence.

Spatial distribution characteristics analysis of chronic Keshan disease in China / 中华地方病学杂志

Objective To explore the spatial distribution clustering and influencing factors of chronic Keshan disease in China,and to provide evidence for prevention and control of Keshan disease.Methods Using non-probability sampling methods,combined with case search and key surveys,data on national detection rate of chronic Keshan disease,on disease influencing factors in 2013-2014 were collected;a spatial database was established,and ArcGIS 9.0 software was used to perform global Moran'sI,local Moran's I,local Getis-Ord Gi and inverse distance weighted interpolation analysis for the detection rate of national chronic Keshan disease.Spatial regression was used to analyze the influencing factors of chronic Keshan disease.Results Global autocorrelation analysis showed that Moran's I =0.03,Z =2.72,P ＜ 0.01,indicating that there was aggregation in the detection rate of Keshan disease.The results of local Moran's Ii showed that there were local high-detection rate clusters in the wards of Keshan disease,and the high-high aggregation areas were mainly concentrated in the wards of Gansu,Inner Mongolia,and Shanxi;the high-low aggregation areas were mainly located in the wards of Heilongjiang,Jilin,Shandong;the low-high aggregation area were mainly located in the wards of Heilongjiang.Getis-Ord Gi autocorrelation results showed that Keshan disease hotspots were mainly located in the wards of Inner Mongolia,Heilongjiang,Gansu,Shandong,Shanxi and Yunnan;the results of reverse distance weighted interpolation showed that the detection rates of the counties in Gansu and Inner Mongolia were higher than that in Heilongjiang,Jilin,Liaoning,Shanxi,Shandong,Shaanxi and Yunnan,the detection rate of wards in other provinces was at a lower level.Spatial regression analysis showed that the spatial distribution of chronic Keshan disease was negatively related to rural per capita net income and annual average temperature in the ward (Z =-2.808,-2.747,P ＜ 0.05).Conclusions Global chronic Keshan disease exists spatial aggregation,the local gathering area is mainly located in the wards of Gansu,Inner Mongolia.The spatial distribution of chronic Keshan disease may be affected by the level of rural per capita net income and annual average temperature in the ward.

Ancestry inference using principal component analysis and spatial analysis: a distance-based analysis to account for population substructure.

BACKGROUND: Accurate inference of genetic ancestry is of fundamental interest to many biomedical, forensic, and anthropological research areas. Genetic ancestry memberships may relate to genetic disease risks. In a genome association study, failing to account for differences in genetic ancestry between cases and controls may also lead to false-positive results. Although a number of strategies for inferring and taking into account the confounding effects of genetic ancestry are available, applying them to large studies (tens thousands samples) is challenging. The goal of this study is to develop an approach for inferring genetic ancestry of samples with unknown ancestry among closely related populations and to provide accurate estimates of ancestry for application to large-scale studies. METHODS: In this study we developed a novel distance-based approach, Ancestry Inference using Principal component analysis and Spatial analysis (AIPS) that incorporates an Inverse Distance Weighted (IDW) interpolation method from spatial analysis to assign individuals to population memberships. RESULTS: We demonstrate the benefits of AIPS in analyzing population substructure, specifically related to the four most commonly used tools EIGENSTRAT, STRUCTURE, fastSTRUCTURE, and ADMIXTURE using genotype data from various intra-European panels and European-Americans. While the aforementioned commonly used tools performed poorly in inferring ancestry from a large number of subpopulations, AIPS accurately distinguished variations between and within subpopulations. CONCLUSIONS: Our results show that AIPS can be applied to large-scale data sets to discriminate the modest variability among intra-continental populations as well as for characterizing inter-continental variation. The method we developed will protect against spurious associations when mapping the genetic basis of a disease. Our approach is more accurate and computationally efficient method for inferring genetic ancestry in the large-scale genetic studies.

Alpha-1 antitrypsin Pi*SZ genotype: estimated prevalence and number of SZ subjects worldwide.

The alpha-1 antitrypsin (AAT) haplotype Pi*S, when inherited along with the Pi*Z haplotype to form a Pi*SZ genotype, can be associated with pulmonary emphysema in regular smokers, and less frequently with liver disease, panniculitis, and systemic vasculitis in a small percentage of people, but this connection is less well established. Since the detection of cases can allow the application of preventive measures in patients and relatives with this congenital disorder, the objective of this study was to update the prevalence of the SZ genotype to achieve accurate estimates of the number of Pi*SZ subjects worldwide, based on studies performed according to the following criteria: 1) samples representative of the general population, 2) AAT phenotyping characterized by adequate methods, and 3) selection of studies with reliable results assessed with a coefficient of variation calculated from the sample size and 95% confidence intervals. Studies fulfilling these criteria were used to develop tables and maps with an inverse distance-weighted (IDW) interpolation method, to provide numerical and geographical information of the Pi*SZ distribution worldwide. A total of 262 cohorts from 71 countries were included in the analysis. With the data provided by these cohorts, a total of 1,490,816 Pi*SZ were estimated: 708,792 in Europe; 582,984 in America and Caribbean; 85,925 in Africa; 77,940 in Asia; and 35,176 in Australia and New Zealand. Remarkably, the IDW interpolation maps predicted the Pi*SZ prevalence throughout the entire world even in areas lacking real data. These results may be useful to plan strategies for future research, diagnosis, and management of affected individuals.

Alpha-1 antitrypsin Pi*Z gene frequency and Pi*ZZ genotype numbers worldwide: an update.

In alpha-1 antitrypsin deficiency (AATD), the Z allele is present in 98% of cases with severe disease, and knowledge of the frequency of this allele is essential from a public health perspective. However, there is a remarkable lack of epidemiological data on AATD worldwide, and many of the data currently used are outdated. Therefore, the objective of this study was to update the knowledge of the frequency of the Z allele to achieve accurate estimates of the prevalence and number of Pi*ZZ genotypes worldwide based on studies performed according to the following criteria: 1) samples representative of the general population, 2) AAT phenotyping characterized by adequate methods, and 3) measurements performed using a coefficient of variation calculated from the sample size and 95% confidence intervals. Studies fulfilling these criteria were used to develop maps with an inverse distance weighted (IDW)-interpolation method, providing numerical and graphical information of Pi*Z distribution worldwide. A total of 224 cohorts from 65 countries were included in the study. With the data provided by these cohorts, a total of 253,404 Pi*ZZ were estimated worldwide: 119,594 in Europe, 91,490 in America and Caribbean, 3,824 in Africa, 32,154 in Asia, 4,126 in Australia, and 2,216 in New Zealand. In addition, the IDW-interpolation maps predicted Pi*Z frequencies throughout the world even in some areas that lack real data. In conclusion, the inclusion of new well-designed studies and the exclusion of the low-quality ones have significantly improved the reliability of results, which may be useful to plan strategies for future research and diagnosis and to rationalize the therapeutic resources available.

Spatial interpolation methods and geostatistics for mapping groundwater contamination in a coastal area.

Mapping groundwater contaminants and identifying the sources are the initial steps in pollution control and mitigation. Due to the availability of different mapping methods and the large number of emerging pollutants, these methods need to be used together in decision making. The present study aims to map the contaminated areas in Richards Bay, South Africa and compare the results of ordinary kriging (OK) and inverse distance weighted (IDW) interpolation techniques. Statistical methods were also used for identifying contamination sources. Na-Cl groundwater type was dominant followed by Ca-Mg-Cl. Data analysis indicate that silicate weathering, ion exchange and fresh water-seawater mixing are the major geochemical processes controlling the presence of major ions in groundwater. Factor analysis also helped to confirm the results. Overlay analysis by OK and IDW gave different results. Areas where groundwater was unsuitable as a drinking source were 419 and 116 km2 for OK and IDW, respectively. Such diverse results make decision making difficult, if only one method was to be used. Three highly contaminated zones within the study area were more accurately identified by OK. If large areas are identified as being contaminated such as by IDW in this study, the mitigation measures will be expensive. If these areas were underestimated, then even though management measures are taken, it will not be effective for a longer time. Use of multiple techniques like this study will help to avoid taking harsh decisions. Overall, the groundwater quality in this area was poor, and it is essential to identify alternate drinking water source or treat the groundwater before ingestion.

Particle transport in the human respiratory tract: formulation of a nodal inverse distance weighted Eulerian-Lagrangian transport and implementation of the Wind-Kessel algorithm for an oral delivery.

This paper is the first in a series wherein efficient computational methods are developed and implemented to accurately quantify the transport, deposition, and clearance of the microsized particles (range of interest: 2 to 10 µm) in the human respiratory tract. In particular, this paper (part I) deals with (i) development of a detailed 3D computational finite volume mesh comprising of the NOPL (nasal, oral, pharyngeal and larynx), trachea and several airway generations; (ii) use of CFD Research Corporation's finite volume Computational Biology (CoBi) flow solver to obtain the flow physics for an oral inhalation simulation; (iii) implement a novel and accurate nodal inverse distance weighted Eulerian-Lagrangian formulation to accurately obtain the deposition, and (iv) development of Wind-Kessel boundary condition algorithm. This new Wind-Kessel boundary condition algorithm allows the 'escaped' particles to reenter the airway through the outlets, thereby to an extent accounting for the drawbacks of having a finite number of lung generations in the computational mesh. The deposition rates in the NOPL, trachea, the first and second bifurcation were computed, and they were in reasonable accord with the Typical Path Length model. The quantitatively validated results indicate that these developments will be useful for (i) obtaining depositions in diseased lungs (because of asthma and COPD), for which there are no empirical models, and (ii) obtaining the secondary clearance (mucociliary clearance) of the deposited particles. Copyright © 2015 John Wiley & Sons, Ltd.