Spatial mapping of the provenance of storm dust: Application of data mining and ensemble modelling

Spatial modelling of storm dust provenance is essential to mitigate its on-site and off-site effects in the arid and semi-arid environments of the world. Therefore, the main aim of this study was to apply eight data mining algorithms including random forest (RF), support vector machine (SVM), bayesian additive regression trees (BART), radial basis function (RBF), extreme gradient boosting (XGBoost), regression tree analysis (RTA), Cubist model and boosted regression trees (BRT) and an ensemble modelling (EM) approach for generating spatial maps of dust provenance in the Khuzestan province, a main region with active sources for producing dust in southwestern Iran. This study is the first attempt at predicting storm dust provenance by applying individual data mining models and ensemble modelling. We identified and mapped in a geographic information system (GIS), 12 potential effective factors for dust emissions comprising two for climate (wind speed, precipitation), five soil characteristics (texture, bulk density, Ec, organic matter (OM), available water capacity (AWC)), a normalized difference vegetation index (NDVI), land use, geology, a digital elevation model (DEM) and land type, and used a mean decrease accuracy measure (MDAM) to determine the corresponding importance scores (IS). A multicollinearity test (including the variance inflation factor (VIF) and tolerance coefficient (TC)) was applied to assess relationships between the effective factors, and an existing map of dust provenance was randomly categorized into two groups consisting of training (70%) and validation (30%) data. The individual data mining models were validated using the area under the curve (AUC). Based on the TC and VIF results, no collinearity was detected among the 12 effective factors for dust emissions. The prediction accuracies of the eight data mining models and an EM assessed by the AUC were as follows: EM (with AUC=99.8%) > XGBoost>RBF > Cubist>RF > BART>SVM > BRT > RTA (with AUC=79.1%). Among all models, the EM was found to provide the highest accuracy for predicting storm dust provenance. Using the EM, areas classified as being low, moderate, high and very high susceptibility for storm dust provenance comprised 36, 13, 23 and 28% of the total mapped area, respectively. Based on MDAM results, the highest and lowest IS were obtained for the wind speed (IS=23) and geology (IS=6.5) factors, respectively. Overall, the modelling techniques used in this research are helpful for predicting storm dust provenance and thereby targeting mitigation. Therefore, we recommend applying data mining EM approaches to the spatial mapping of storm dust provenance worldwide

Predicting land susceptibility to atmospheric dust emissions in central Iran by combining integrated data mining and a regional climate model

This study aims to predict land susceptibility (a term that indicates the degree of sensitivity of land to detachment of soil particles by wind) to dust emissions in Yazd province, central Iran, by combining a new integrated data mining (DM) model and the RegCM4 climatic model. The study further determines the relative importance of key factors controlling dust emissions by applying 12 individual DM models. The integrated model is based on the individual models returning Nash Sutcliffe coefficient (NSC) values > 90% for the spatial modelling of land susceptibility to dust emissions and using the area under the curve (AUC) for validation. 13 key factors controlling dust emissions are mapped including soil characteristics, climatic variables, vegetation cover, a Digital Elevation Model (DEM), geology and land use. Based on Spearman clustering analysis and multi-collinearity tests (tolerance coefficient -TC and variance inflation factor -VIF), the effective factors for dust emissions are classified into nine clusters and no multi-collinearity is found among the effective factors. DEM, NDVI (normalized difference vegetation index), geology and calcium carbonate are identified as the most important factors controlling dust emissions. Seven individual models return NSC in the range of 90–98% and are used to generate the integrated model for the final mapping of land susceptibility to dust emissions. Among 851 pixels located in the dust sources, 30% (255 pixels) and 70% (596 pixels) are randomly selected as validation and training datasets, respectively for the new integrated model. Using this model, 9%, 17%, 7% and 67% of the study area correspond to low, moderate, high and very high susceptibility classes, while the validation results in AUC = 99.3%. Simulations with the RegCM4 model reveal high consistency regarding the spatial distribution of the most susceptible areas and dust emissions. Overall, combining DM approaches and physical models is useful in aeolian geomorphology studies

Study of the association between the donors and recipients angiotensin-converting enzyme insertion/deletion gene polymorphism and the acute renal allograft rejection

BACKGROUND: Angiotensin converting enzyme (ACE) is involved in various pathophysiological conditions including renal function. ACE levels are under genetic control. OBJECTIVES: This study was designed to investigate the association between the donors and recipients ACE-I/D gene polymorphism and risk of acute rejection outcome in renal allograft recipients. PATIENTS AND METHODS: ACE-I/D polymorphism was determined in 200 donor-recipient pairs who had been referred to Afzalipour hospital in Kerman. ACE-I/D polymorphism was detected using polymerase chain reaction (PCR). Acute rejection (AR) during at least six months post-transplantation was defined as a 20% increase in creatinine level from the postoperative baseline in the absence of other causes of graft dysfunction which responded to antirejection therapy. RESULTS: The observed allele frequencies were II 9.8%, ID 35.6% and DD 44.4% in donors and II 9.8%, ID 35.1% and DD 52.7% in recipients. There were no significant association between ACE genotypes and AR episodes (ORID=0.96 [0.18-5.00] and ORDD: 1.24 [0.25-6.07] for the donors) and (ORID: 0.29 [0.06-1.45] and ORDD: 0.75 [0.19-2.90] for the recipients). CONCLUSIONS: It seems that donor and recipient ACE-I/D genotype might not be a risk factor for acute renal allograft rejection. However, due to conflicting results from this and other studies, multicenter collaborative studies with more participants and concomitant evaluation of ACE polymorphism with other polymorphisms in renin-angiotensin system (RAS) are suggested to determine whether ACE genotypes are significant predictors of renal allograft rejection

Cancer incidence in Iran in 2014: Results of the Iranian National Population-based Cancer Registry

Background: We aimed to report, for the first time, the results of the Iranian National Population-based Cancer Registry (INPCR) for the year 2014. Methods: Total population of Iran in 2014 was 76,639,000. The INPCR covered 30 out of 31 provinces (98 of total population). It registered only cases diagnosed with malignant new primary tumors. The main sources for data collection included pathology center, hospitals as well as death registries. Quality assessment and analysis of data were performed by CanReg-5 software. Age standardized incidence rates (ASR) (per 100,000) were reported at national and subnational levels. Results: Overall, 112,131 new cancer cases were registered in INPCR in 2014, of which 60,469 (53.9) were male. The diagnosis of cancer was made by microscopic confirmation in 76,568 cases (68.28). The ASRs of all cancers were 177.44 and 141.18 in male and female, respectively. Cancers of the stomach (ASR = 21.24), prostate (18.41) and colorectum (16.57) were the most common cancers in men and the top three cancers in women were malignancies of breast (34.53), colorectum (11.86) and stomach (9.44). The ASR of cervix uteri cancer in women was 1.78. Our findings suggested high incidence of cancers of the esophagus, stomach and lung in North/ North West of Iran. Conclusion: Our results showed that Iran is a medium-risk area for incidence of cancers. We found differences in the most common cancers in Iran comparing to those reported for the World. Our results also suggested geographical diversifies in incidence rates of cancers in different subdivisions of Iran