A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012

PubMed Central

Rubikowska, Barbara; Bratkowski, Jakub; Ustrnul, Zbigniew; Vanwambeke, Sophie O.

2018-01-01

During 1999–2012, 77% of the cases of tick-borne encephalitis (TBE) were recorded in two out of 16 Polish provinces. However, historical data, mostly from national serosurveys, suggest that the disease could be undetected in many areas. The aim of this study was to identify which routinely-measured meteorological, environmental, and socio-economic factors are associated to TBE human risk across Poland, with a particular focus on areas reporting few cases, but where serosurveys suggest higher incidence. We fitted a zero-inflated Poisson model using data on TBE incidence recorded in 108 NUTS-5 administrative units in high-risk areas over the period 1999–2012. Subsequently we applied the best fitting model to all Polish municipalities. Keeping the remaining variables constant, the predicted rate increased with the increase of air temperature over the previous 10–20 days, precipitation over the previous 20–30 days, in forestation, forest edge density, forest road density, and unemployment. The predicted rate decreased with increasing distance from forests. The map of predicted rates was consistent with the established risk areas. It predicted, however, high rates in provinces considered TBE-free. We recommend raising awareness among physicians working in the predicted high-risk areas and considering routine use of household animal surveys for risk mapping. PMID:29617333

A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999-2012.

PubMed

Stefanoff, Pawel; Rubikowska, Barbara; Bratkowski, Jakub; Ustrnul, Zbigniew; Vanwambeke, Sophie O; Rosinska, Magdalena

2018-04-04

During 1999–2012, 77% of the cases of tick-borne encephalitis (TBE) were recorded in two out of 16 Polish provinces. However, historical data, mostly from national serosurveys, suggest that the disease could be undetected in many areas. The aim of this study was to identify which routinely-measured meteorological, environmental, and socio-economic factors are associated to TBE human risk across Poland, with a particular focus on areas reporting few cases, but where serosurveys suggest higher incidence. We fitted a zero-inflated Poisson model using data on TBE incidence recorded in 108 NUTS-5 administrative units in high-risk areas over the period 1999–2012. Subsequently we applied the best fitting model to all Polish municipalities. Keeping the remaining variables constant, the predicted rate increased with the increase of air temperature over the previous 10–20 days, precipitation over the previous 20–30 days, in forestation, forest edge density, forest road density, and unemployment. The predicted rate decreased with increasing distance from forests. The map of predicted rates was consistent with the established risk areas. It predicted, however, high rates in provinces considered TBE-free. We recommend raising awareness among physicians working in the predicted high-risk areas and considering routine use of household animal surveys for risk mapping.

The Miller Assessment for Preschoolers: A Longitudinal and Predictive Study. Final Report.

ERIC Educational Resources Information Center

Foundation for Knowledge in Development, Littleton, CO.

The study reported here sought to establish the predictive validity of the Miller Assessment for Preschoolers (MAP), an instrument designed to identify preschool children at risk for school-related problems in the primary years. Children (N=338) in 11 states who were originally tested in 1980 as part of the MAP standardization project were given a…

Proarrhythmia risk prediction using human induced pluripotent stem cell-derived cardiomyocytes.

PubMed

Yamazaki, Daiju; Kitaguchi, Takashi; Ishimura, Masakazu; Taniguchi, Tomohiko; Yamanishi, Atsuhiro; Saji, Daisuke; Takahashi, Etsushi; Oguchi, Masao; Moriyama, Yuta; Maeda, Sanae; Miyamoto, Kaori; Morimura, Kaoru; Ohnaka, Hiroki; Tashibu, Hiroyuki; Sekino, Yuko; Miyamoto, Norimasa; Kanda, Yasunari

2018-04-01

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are expected to become a useful tool for proarrhythmia risk prediction in the non-clinical drug development phase. Several features including electrophysiological properties, ion channel expression profile and drug responses were investigated using commercially available hiPSC-CMs, such as iCell-CMs and Cor.4U-CMs. Although drug-induced arrhythmia has been extensively examined by microelectrode array (MEA) assays in iCell-CMs, it has not been fully understood an availability of Cor.4U-CMs for proarrhythmia risk. Here, we evaluated the predictivity of proarrhythmia risk using Cor.4U-CMs. MEA assay revealed linear regression between inter-spike interval and field potential duration (FPD). The hERG inhibitor E-4031 induced reverse-use dependent FPD prolongation. We next evaluated the proarrhythmia risk prediction by a two-dimensional map, which we have previously proposed. We determined the relative torsade de pointes risk score, based on the extent of FPD with Fridericia's correction (FPDcF) change and early afterdepolarization occurrence, and calculated the margins normalized to free effective therapeutic plasma concentrations. The drugs were classified into three risk groups using the two-dimensional map. This risk-categorization system showed high concordance with the torsadogenic information obtained by a public database CredibleMeds. Taken together, these results indicate that Cor.4U-CMs can be used for drug-induced proarrhythmia risk prediction. Copyright © 2018 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Validation of a mapping and prediction model for human fasciolosis transmission in Andean very high altitude endemic areas using remote sensing data.

PubMed

Fuentes, M V; Malone, J B; Mas-Coma, S

2001-04-27

The present paper aims to validate the usefulness of the Normalized Difference Vegetation Index (NDVI) obtained by satellite remote sensing for the development of local maps of risk and for prediction of human fasciolosis in the Northern Bolivian Altiplano. The endemic area, which is located at very high altitudes (3800-4100 m) between Lake Titicaca and the valley of the city of La Paz, presents the highest prevalences and intensities of fasciolosis known in humans. NDVI images of 1.1 km resolution from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board the National Oceanic and Atmospheric Administration (NOAA) series of environmental satellites appear to provide adequate information for a study area such as that of the Northern Bolivian Altiplano. The predictive value of the remotely sensed map based on NDVI data appears to be better than that from forecast indices based only on climatic data. A close correspondence was observed between real ranges of human fasciolosis prevalence at 13 localities of known prevalence rates and the predicted ranges of fasciolosis prevalence using NDVI maps. However, results based on NDVI map data predicted zones as risk areas where, in fact, field studies have demonstrated the absence of lymnaeid populations during snail surveys, corroborated by the absence of the parasite in humans and livestock. NDVI data maps represent a useful data component in long-term efforts to develop a comprehensive geographical information system control program model that accurately fits real epidemiological and transmission situations of human fasciolosis in high altitude endemic areas in Andean countries.

A dominance-based approach to map risks of ecological invasions in the presence of severe uncertainty

Treesearch

Denys Yemshanov; Frank H. Koch; D. Barry Lyons; Mark Ducey; Klaus Koehler

2012-01-01

Aim Uncertainty has been widely recognized as one of the most critical issues in predicting the expansion of ecological invasions. The uncertainty associated with the introduction and spread of invasive organisms influences how pest management decision makers respond to expanding incursions. We present a model-based approach to map risk of ecological invasions that...

Risk mapping of dengue in Selangor and Kuala Lumpur, Malaysia.

PubMed

Hassan, Hafiz; Shohaimi, Shamarina; Hashim, Nor R

2012-11-01

Dengue fever is a recurring public health problem afflicting thousands of Malaysians annually. In this paper, the risk map for dengue fever in the peninsular Malaysian states of Selangor and Kuala Lumpur was modelled based on co-kriging and geographical information systems. Using population density and rainfall as the model's only input factors, the area with the highest risk for dengue infection was given as Gombak and Petaling, two districts located on opposite sides of Kuala Lumpur city that was also included in the risk assessment. Comparison of the modelled risk map with the dengue case dataset of 2010, obtained from the Ministry of Health of Malaysia, confirmed that the highest number of cases had been found in an area centred on Kuala Lumpur as predicted our risk profiling.

Predicting disease risk, identifying stakeholders, and informing control strategies: A case study of anthrax in Montana

PubMed Central

Morris, Lillian R.; Blackburn, Jason K.

2018-01-01

Infectious diseases that affect wildlife and livestock are challenging to manage, and can lead to large scale die offs, economic losses, and threats to human health. The management of infectious diseases in wildlife and livestock is made easier with knowledge of disease risk across space and identifying stakeholders associated with high risk landscapes. This study focuses on anthrax, caused by the bacterium Bacillus anthracis, risk to wildlife and livestock in Montana. There is a history of anthrax in Montana, but the spatial extent of disease risk and subsequent wildlife species at risk are not known. Our objective was to predict the potential geographic distribution of anthrax risk across Montana, identify wildlife species at risk and their distributions, and define stakeholders. We used an ecological niche model to predict the potential distribution of anthrax risk. We overlaid susceptible wildlife species distributions and land ownership delineations on our risk map. We found that there was an extensive region across Montana predicted as potential anthrax risk. These potentially risky landscapes overlapped the ranges of all 6 ungulate species considered in the analysis and livestock grazing allotments, and this overlap was on public and private land for all species. Our findings suggest that there is the potential for a multi species anthrax outbreak on multiple landscapes across Montana. Our potential anthrax risk map can be used to prioritize landscapes for surveillance and for implementing livestock vaccination programs. PMID:27169560

Predicting Disease Risk, Identifying Stakeholders, and Informing Control Strategies: A Case Study of Anthrax in Montana.

PubMed

Morris, Lillian R; Blackburn, Jason K

2016-06-01

Infectious diseases that affect wildlife and livestock are challenging to manage and can lead to large-scale die-offs, economic losses, and threats to human health. The management of infectious diseases in wildlife and livestock is made easier with knowledge of disease risk across space and identifying stakeholders associated with high-risk landscapes. This study focuses on anthrax, caused by the bacterium Bacillus anthracis, risk to wildlife and livestock in Montana. There is a history of anthrax in Montana, but the spatial extent of disease risk and subsequent wildlife species at risk are not known. Our objective was to predict the potential geographic distribution of anthrax risk across Montana, identify wildlife species at risk and their distributions, and define stakeholders. We used an ecological niche model to predict the potential distribution of anthrax risk. We overlaid susceptible wildlife species distributions and land ownership delineations on our risk map. We found that there was an extensive region across Montana predicted as potential anthrax risk. These potentially risky landscapes overlapped the ranges of all 6 ungulate species considered in the analysis and livestock grazing allotments, and this overlap was on public and private land for all species. Our findings suggest that there is the potential for a multi-species anthrax outbreak on multiple landscapes across Montana. Our potential anthrax risk map can be used to prioritize landscapes for surveillance and for implementing livestock vaccination programs.

Arrhythmic hazard map for a 3D whole-ventricles model under multiple ion channel block.

PubMed

Okada, Jun-Ichi; Yoshinaga, Takashi; Kurokawa, Junko; Washio, Takumi; Furukawa, Tetsushi; Sawada, Kohei; Sugiura, Seiryo; Hisada, Toshiaki

2018-05-10

To date, proposed in silico models for preclinical cardiac safety testing are limited in their predictability and usability. We previously reported a multi-scale heart simulation that accurately predicts arrhythmogenic risk for benchmark drugs. We extend this approach and report the first comprehensive hazard map of drug-induced arrhythmia based on the exhaustive in silico electrocardiogram (ECG) database of drug effects, developed using a petaflop computer. A total of 9075 electrocardiograms constitute the five-dimensional hazard map, with coordinates representing the extent of the block of each of the five ionic currents (rapid delayed rectifier potassium current (IKr), fast (INa) and late (INa,L) components of the sodium current, L-type calcium current (ICa,L) and slow delayed rectifier current (IKs)), involved in arrhythmogenesis. Results of the evaluation of arrhythmogenic risk based on this hazard map agreed well with the risk assessments reported in three references. ECG database also suggested that the interval between the J-point and the T-wave peak is a superior index of arrhythmogenicity compared to other ECG biomarkers including the QT interval. Because concentration-dependent effects on electrocardiograms of any drug can be traced on this map based on in vitro current assay data, its arrhythmogenic risk can be evaluated without performing costly and potentially risky human electrophysiological assays. Hence, the map serves as a novel tool for use in pharmaceutical research and development. This article is protected by copyright. All rights reserved.

Mapping the Transmission Risk of Zika Virus using Machine Learning Models.

PubMed

Jiang, Dong; Hao, Mengmeng; Ding, Fangyu; Fu, Jingying; Li, Meng

2018-06-19

Zika virus, which has been linked to severe congenital abnormalities, is exacerbating global public health problems with its rapid transnational expansion fueled by increased global travel and trade. Suitability mapping of the transmission risk of Zika virus is essential for drafting public health plans and disease control strategies, which are especially important in areas where medical resources are relatively scarce. Predicting the risk of Zika virus outbreak has been studied in recent years, but the published literature rarely includes multiple model comparisons or predictive uncertainty analysis. Here, three relatively popular machine learning models including backward propagation neural network (BPNN), gradient boosting machine (GBM) and random forest (RF) were adopted to map the probability of Zika epidemic outbreak at the global level, pairing high-dimensional multidisciplinary covariate layers with comprehensive location data on recorded Zika virus infection in humans. The results show that the predicted high-risk areas for Zika transmission are concentrated in four regions: Southeastern North America, Eastern South America, Central Africa and Eastern Asia. To evaluate the performance of machine learning models, the 50 modeling processes were conducted based on a training dataset. The BPNN model obtained the highest predictive accuracy with a 10-fold cross-validation area under the curve (AUC) of 0.966 [95% confidence interval (CI) 0.965-0.967], followed by the GBM model (10-fold cross-validation AUC = 0.964[0.963-0.965]) and the RF model (10-fold cross-validation AUC = 0.963[0.962-0.964]). Based on training samples, compared with the BPNN-based model, we find that significant differences (p = 0.0258* and p = 0.0001***, respectively) are observed for prediction accuracies achieved by the GBM and RF models. Importantly, the prediction uncertainty introduced by the selection of absence data was quantified and could provide more accurate fundamental and scientific information for further study on disease transmission prediction and risk assessment. Copyright © 2018. Published by Elsevier B.V.

Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy

NASA Astrophysics Data System (ADS)

Acosta, Oscar; Dowling, Jason; Cazoulat, Guillaume; Simon, Antoine; Salvado, Olivier; de Crevoisier, Renaud; Haigron, Pascal

The prediction of toxicity is crucial to managing prostate cancer radiotherapy (RT). This prediction is classically organ wise and based on the dose volume histograms (DVH) computed during the planning step, and using for example the mathematical Lyman Normal Tissue Complication Probability (NTCP) model. However, these models lack spatial accuracy, do not take into account deformations and may be inappropiate to explain toxicity events related with the distribution of the delivered dose. Producing voxel wise statistical models of toxicity might help to explain the risks linked to the dose spatial distribution but is challenging due to the difficulties lying on the mapping of organs and dose in a common template. In this paper we investigate the use of atlas based methods to perform the non-rigid mapping and segmentation of the individuals' organs at risk (OAR) from CT scans. To build a labeled atlas, 19 CT scans were selected from a population of patients treated for prostate cancer by radiotherapy. The prostate and the OAR (Rectum, Bladder, Bones) were then manually delineated by an expert and constituted the training data. After a number of affine and non rigid registration iterations, an average image (template) representing the whole population was obtained. The amount of consensus between labels was used to generate probabilistic maps for each organ. We validated the accuracy of the approach by segmenting the organs using the training data in a leave one out scheme. The agreement between the volumes after deformable registration and the manually segmented organs was on average above 60% for the organs at risk. The proposed methodology provides a way to map the organs from a whole population on a single template and sets the stage to perform further voxel wise analysis. With this method new and accurate predictive models of toxicity will be built.

Fine-Scale Mapping by Spatial Risk Distribution Modeling for Regional Malaria Endemicity and Its Implications under the Low-to-Moderate Transmission Setting in Western Cambodia

PubMed Central

Okami, Suguru; Kohtake, Naohiko

2016-01-01

The disease burden of malaria has decreased as malaria elimination efforts progress. The mapping approach that uses spatial risk distribution modeling needs some adjustment and reinvestigation in accordance with situational changes. Here we applied a mathematical modeling approach for standardized morbidity ratio (SMR) calculated by annual parasite incidence using routinely aggregated surveillance reports, environmental data such as remote sensing data, and non-environmental anthropogenic data to create fine-scale spatial risk distribution maps of western Cambodia. Furthermore, we incorporated a combination of containment status indicators into the model to demonstrate spatial heterogeneities of the relationship between containment status and risks. The explanatory model was fitted to estimate the SMR of each area (adjusted Pearson correlation coefficient R2 = 0.774; Akaike information criterion AIC = 149.423). A Bayesian modeling framework was applied to estimate the uncertainty of the model and cross-scale predictions. Fine-scale maps were created by the spatial interpolation of estimated SMRs at each village. Compared with geocoded case data, corresponding predicted values showed conformity [Spearman’s rank correlation r = 0.662 in the inverse distance weighed interpolation and 0.645 in ordinal kriging (95% confidence intervals of 0.414–0.827 and 0.368–0.813, respectively), Welch’s t-test; Not significant]. The proposed approach successfully explained regional malaria risks and fine-scale risk maps were created under low-to-moderate malaria transmission settings where reinvestigations of existing risk modeling approaches were needed. Moreover, different representations of simulated outcomes of containment status indicators for respective areas provided useful insights for tailored interventional planning, considering regional malaria endemicity. PMID:27415623

New developments in cerebral blood flow autoregulation analysis in preterm infants: a mechanistic approach.

PubMed

Riera, Joan; Cabañas, Fernando; Serrano, José Javier; Madero, Rosario; Pellicer, Adelina

2016-03-01

Impaired autoregulation capacity implies that changes in cerebral perfusion follow changes in blood pressure; however, no analytical method has explored such a signal causality relationship in infants. We sought to develop a method to assess cerebral autoregulation from a mechanistic point of view and explored the predictive capacity of the method to classify infants at risk for adverse outcomes. The partial directed coherence (PDC) method, which considers synchronicity and directionality of signal dependence across frequencies, was used to analyze the relationship between spontaneous changes in mean arterial pressure (MAP) and the cerebral tissue oxygenation index (TOI). PDCMAP>TOI indicated that changes in TOI were induced by MAP changes, and PDCTOI>MAP indicated the opposite. The PDCMAP>TOI and PDCTOI>MAP values differed. PDCMAP>TOI adjusted by gestational age predicted low superior vena cava flow (≤41 ml/kg per min), with an area under the receiver operating characteristic curve of 0.72 (95% CI: 0.63-0.81; P < 0.001), whereas PDCTOI>MAP did not. The adjusted pPDCMAP>TOI (the average value per patient) predicted severe intracranial hemorrhage and mortality. PDCMAP>TOI allows for a noninvasive physiological interpretation of the pressure autoregulation process in neonates. PDCMAP>TOI is a good classifier for infants at risk of brain hypoperfusion and adverse outcomes.

Ecological Niche Modeling for Filoviruses: A Risk Map for Ebola and Marburg Virus Disease Outbreaks in Uganda.

PubMed

Nyakarahuka, Luke; Ayebare, Samuel; Mosomtai, Gladys; Kankya, Clovice; Lutwama, Julius; Mwiine, Frank Norbert; Skjerve, Eystein

2017-09-05

Uganda has reported eight outbreaks caused by filoviruses between 2000 to 2016, more than any other country in the world. We used species distribution modeling to predict where filovirus outbreaks are likely to occur in Uganda to help in epidemic preparedness and surveillance. The MaxEnt software, a machine learning modeling approach that uses presence-only data was used to establish filovirus - environmental relationships. Presence-only data for filovirus outbreaks were collected from the field and online sources. Environmental covariates from Africlim that have been downscaled to a nominal resolution of 1km x 1km were used. The final model gave the relative probability of the presence of filoviruses in the study area obtained from an average of 100 bootstrap runs. Model evaluation was carried out using Receiver Operating Characteristic (ROC) plots. Maps were created using ArcGIS 10.3 mapping software. We showed that bats as potential reservoirs of filoviruses are distributed all over Uganda. Potential outbreak areas for Ebola and Marburg virus disease were predicted in West, Southwest and Central parts of Uganda, which corresponds to bat distribution and previous filovirus outbreaks areas. Additionally, the models predicted the Eastern Uganda region and other areas that have not reported outbreaks before to be potential outbreak hotspots. Rainfall variables were the most important in influencing model prediction compared to temperature variables. Despite the limitations in the prediction model due to lack of adequate sample records for outbreaks, especially for the Marburg cases, the models provided risk maps to the Uganda surveillance system on filovirus outbreaks. The risk maps will aid in identifying areas to focus the filovirus surveillance for early detection and responses hence curtailing a pandemic. The results from this study also confirm previous findings that suggest that filoviruses are mainly limited by the amount of rainfall received in an area.

Ecological Niche Modeling for Filoviruses: A Risk Map for Ebola and Marburg Virus Disease Outbreaks in Uganda

PubMed Central

Nyakarahuka, Luke; Ayebare, Samuel; Mosomtai, Gladys; Kankya, Clovice; Lutwama, Julius; Mwiine, Frank Norbert; Skjerve, Eystein

2017-01-01

Introduction: Uganda has reported eight outbreaks caused by filoviruses between 2000 to 2016, more than any other country in the world. We used species distribution modeling to predict where filovirus outbreaks are likely to occur in Uganda to help in epidemic preparedness and surveillance. Methods: The MaxEnt software, a machine learning modeling approach that uses presence-only data was used to establish filovirus – environmental relationships. Presence-only data for filovirus outbreaks were collected from the field and online sources. Environmental covariates from Africlim that have been downscaled to a nominal resolution of 1km x 1km were used. The final model gave the relative probability of the presence of filoviruses in the study area obtained from an average of 100 bootstrap runs. Model evaluation was carried out using Receiver Operating Characteristic (ROC) plots. Maps were created using ArcGIS 10.3 mapping software. Results: We showed that bats as potential reservoirs of filoviruses are distributed all over Uganda. Potential outbreak areas for Ebola and Marburg virus disease were predicted in West, Southwest and Central parts of Uganda, which corresponds to bat distribution and previous filovirus outbreaks areas. Additionally, the models predicted the Eastern Uganda region and other areas that have not reported outbreaks before to be potential outbreak hotspots. Rainfall variables were the most important in influencing model prediction compared to temperature variables. Conclusions: Despite the limitations in the prediction model due to lack of adequate sample records for outbreaks, especially for the Marburg cases, the models provided risk maps to the Uganda surveillance system on filovirus outbreaks. The risk maps will aid in identifying areas to focus the filovirus surveillance for early detection and responses hence curtailing a pandemic. The results from this study also confirm previous findings that suggest that filoviruses are mainly limited by the amount of rainfall received in an area. PMID:29034123

Quantitative assessment of a spatial multicriteria model for highly pathogenic avian influenza H5N1 in Thailand, and application in Cambodia

PubMed Central

Paul, Mathilde C.; Goutard, Flavie L.; Roulleau, Floriane; Holl, Davun; Thanapongtharm, Weerapong; Roger, François L.; Tran, Annelise

2016-01-01

The Highly Pathogenic Avian Influenza H5N1 (HPAI) virus is now considered endemic in several Asian countries. In Cambodia, the virus has been circulating in the poultry population since 2004, with a dramatic effect on farmers’ livelihoods and public health. In Thailand, surveillance and control are still important to prevent any new H5N1 incursion. Risk mapping can contribute effectively to disease surveillance and control systems, but is a very challenging task in the absence of reliable disease data. In this work, we used spatial multicriteria decision analysis (MCDA) to produce risk maps for HPAI H5N1 in poultry. We aimed to i) evaluate the performance of the MCDA approach to predict areas suitable for H5N1 based on a dataset from Thailand, comparing the predictive capacities of two sources of a priori knowledge (literature and experts), and ii) apply the best method to produce a risk map for H5N1 in poultry in Cambodia. Our results showed that the expert-based model had a very high predictive capacity in Thailand (AUC = 0.97). Applied in Cambodia, MCDA mapping made it possible to identify hotspots suitable for HPAI H5N1 in the Tonlé Sap watershed, around the cities of Battambang and Kampong Cham, and along the Vietnamese border. PMID:27489997

Family history of diabetes modifies the effect of blood pressure for incident diabetes in Middle Eastern women: Tehran Lipid and Glucose Study.

PubMed

Hatami, M; Hadaegh, F; Khalili, D; Sheikholeslami, F; Azizi, F

2012-02-01

Elevated blood pressure (BP) may lead to incident diabetes. However, data about the effect of different BP components on incident diabetes in Middle Eastern women is lacking. We evaluated systolic BP (SBP), diastolic BP (DBP), pulse pressure (PP) and mean arterial pressure (MAP) as independent predictors of diabetes in Iranian women. We performed a population-based prospective study among 3028 non-diabetic women, aged ≥20 years. Odds ratios (ORs) of diabetes were calculated for every 1 s.d. increase in SBP, DBP, PP and MAP. During ≈6 years of follow-up, 220 women developed diabetes. There were significant interactions between family history of diabetes and SBP, PP and MAP (P≤0.01) in predicting incident diabetes. In women without a family history of diabetes, all BP components were significantly associated with diabetes in the age-adjusted model; the risk factor-adjusted ORs were significant (P<0.05) for SBP, PP and MAP (1.30, 1.34 and 1.27, respectively) with similar predictive ability (area under the receiver operating characteristic curve ≈83%). In women with family history of diabetes, in the age-adjusted model, SBP, DBP and MAP were associated with diabetes; in multivariable model, they were not independent predictors of diabetes. In conclusion, in women without family history of diabetes, SBP, PP and MAP, were independent predictors of diabetes with almost similar predictive ability; hence, in the evaluation of the risk of BP components for prediction of diabetes, the presence of family history of diabetes should be considered.

Making predictions of mangrove deforestation: a comparison of two methods in Kenya.

PubMed

Rideout, Alasdair J R; Joshi, Neha P; Viergever, Karin M; Huxham, Mark; Briers, Robert A

2013-11-01

Deforestation of mangroves is of global concern given their importance for carbon storage, biogeochemical cycling and the provision of other ecosystem services, but the links between rates of loss and potential drivers or risk factors are rarely evaluated. Here, we identified key drivers of mangrove loss in Kenya and compared two different approaches to predicting risk. Risk factors tested included various possible predictors of anthropogenic deforestation, related to population, suitability for land use change and accessibility. Two approaches were taken to modelling risk; a quantitative statistical approach and a qualitative categorical ranking approach. A quantitative model linking rates of loss to risk factors was constructed based on generalized least squares regression and using mangrove loss data from 1992 to 2000. Population density, soil type and proximity to roads were the most important predictors. In order to validate this model it was used to generate a map of losses of Kenyan mangroves predicted to have occurred between 2000 and 2010. The qualitative categorical model was constructed using data from the same selection of variables, with the coincidence of different risk factors in particular mangrove areas used in an additive manner to create a relative risk index which was then mapped. Quantitative predictions of loss were significantly correlated with the actual loss of mangroves between 2000 and 2010 and the categorical risk index values were also highly correlated with the quantitative predictions. Hence, in this case the relatively simple categorical modelling approach was of similar predictive value to the more complex quantitative model of mangrove deforestation. The advantages and disadvantages of each approach are discussed, and the implications for mangroves are outlined. © 2013 Blackwell Publishing Ltd.

A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013–2014

PubMed Central

Dong, Wen; Yang, Kun; Xu, Quan-Li; Yang, Yu-Lian

2015-01-01

This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to describe the spatial-temporal autocorrelation of H7N9, an improved model was developed by introducing a spatial-temporal factor in this paper. Logistic regression analyses were utilized to investigate the risk factors associated with their distribution, and nine risk factors were significantly associated with the occurrence of A(H7N9) human infections: the spatial-temporal factor φ (OR = 2546669.382, p < 0.001), migration route (OR = 0.993, p < 0.01), river (OR = 0.861, p < 0.001), lake(OR = 0.992, p < 0.001), road (OR = 0.906, p < 0.001), railway (OR = 0.980, p < 0.001), temperature (OR = 1.170, p < 0.01), precipitation (OR = 0.615, p < 0.001) and relative humidity (OR = 1.337, p < 0.001). The improved model obtained a better prediction performance and a higher fitting accuracy than the traditional model: in the improved model 90.1% (91/101) of the cases during February 2014 occurred in the high risk areas (the predictive risk > 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk > 0.70) for the traditional model, and the fitting accuracy of the improved model was 91.6% which was superior to the traditional model (86.1%). The predictive risk map generated based on the improved model revealed that the east and southeast of China were the high risk areas of A(H7N9) human infections in February 2014. These results provided baseline data for the control and prevention of future human infections. PMID:26633446

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PubMed

Kropat, Georg; Bochud, Francois; Jaboyedoff, Michel; Laedermann, Jean-Pascal; Murith, Christophe; Palacios Gruson, Martha; Baechler, Sébastien

2015-09-01

According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information. Copyright © 2015 Elsevier Ltd. All rights reserved.

Human risk of infection with Borrelia burgdorferi, the Lyme disease agent, in eastern United States.

PubMed

Diuk-Wasser, Maria A; Hoen, Anne Gatewood; Cislo, Paul; Brinkerhoff, Robert; Hamer, Sarah A; Rowland, Michelle; Cortinas, Roberto; Vourc'h, Gwenaël; Melton, Forrest; Hickling, Graham J; Tsao, Jean I; Bunikis, Jonas; Barbour, Alan G; Kitron, Uriel; Piesman, Joseph; Fish, Durland

2012-02-01

The geographic pattern of human risk for infection with Borrelia burgdorferi sensu stricto, the tick-borne pathogen that causes Lyme disease, was mapped for the eastern United States. The map is based on standardized field sampling in 304 sites of the density of Ixodes scapularis host-seeking nymphs infected with B. burgdorferi, which is closely associated with human infection risk. Risk factors for the presence and density of infected nymphs were used to model a continuous 8 km×8 km resolution predictive surface of human risk, including confidence intervals for each pixel. Discontinuous Lyme disease risk foci were identified in the Northeast and upper Midwest, with a transitional zone including sites with uninfected I. scapularis populations. Given frequent under- and over-diagnoses of Lyme disease, this map could act as a tool to guide surveillance, control, and prevention efforts and act as a baseline for studies tracking the spread of infection.

Mapping intra-urban malaria risk using high resolution satellite imagery: a case study of Dar es Salaam.

PubMed

Kabaria, Caroline W; Molteni, Fabrizio; Mandike, Renata; Chacky, Frank; Noor, Abdisalan M; Snow, Robert W; Linard, Catherine

2016-07-30

With more than half of Africa's population expected to live in urban settlements by 2030, the burden of malaria among urban populations in Africa continues to rise with an increasing number of people at risk of infection. However, malaria intervention across Africa remains focused on rural, highly endemic communities with far fewer strategic policy directions for the control of malaria in rapidly growing African urban settlements. The complex and heterogeneous nature of urban malaria requires a better understanding of the spatial and temporal patterns of urban malaria risk in order to design effective urban malaria control programs. In this study, we use remotely sensed variables and other environmental covariates to examine the predictability of intra-urban variations of malaria infection risk across the rapidly growing city of Dar es Salaam, Tanzania between 2006 and 2014. High resolution SPOT satellite imagery was used to identify urban environmental factors associated malaria prevalence in Dar es Salaam. Supervised classification with a random forest classifier was used to develop high resolution land cover classes that were combined with malaria parasite prevalence data to identify environmental factors that influence localized heterogeneity of malaria transmission and develop a high resolution predictive malaria risk map of Dar es Salaam. Results indicate that the risk of malaria infection varied across the city. The risk of infection increased away from the city centre with lower parasite prevalence predicted in administrative units in the city centre compared to administrative units in the peri-urban suburbs. The variation in malaria risk within Dar es Salaam was shown to be influenced by varying environmental factors. Higher malaria risks were associated with proximity to dense vegetation, inland water and wet/swampy areas while lower risk of infection was predicted in densely built-up areas. The predictive maps produced can serve as valuable resources for municipal councils aiming to shrink the extents of malaria across cities, target resources for vector control or intensify mosquito and disease surveillance. The semi-automated modelling process developed can be replicated in other urban areas to identify factors that influence heterogeneity in malaria risk patterns and detect vulnerable zones. There is a definite need to expand research into the unique epidemiology of malaria transmission in urban areas for focal elimination and sustained control agendas.

OS087. Maternal characteristics, mean arterial pressure and PLGF in early prediction of preeclampsia.

PubMed

Kuc, S; Koster, M P; Franx, A; Schielen, P C; Visser, G H

2012-07-01

In a previous study, we described the predictive value of first-trimester pregnancy-associated plasma protein-A (PAPP-A), free beta-subunit of human chorionic gonadotrophin (fb-hCG), Placental Growth Factor (PlGF) and A Desintegrin And Metalloproteinase 12 (ADAM12) for early onset preeclampsia (delivery <34 weeks) [1]. The objective of the current study was to obtain the predictive value of these serum makers, for both early onset PE (EOPE) and late onset PE (LOPE), combined with maternal characteristics and first-trimester maternal mean arterial blood pressure (MAP). This was a nested case-control study, using stored first-trimester maternal serum from 167 women who subsequently developed PE, and 500 uncomplicated singleton pregnancies which resulted in a live birth =>37 weeks. Maternal characteristics (i.e. medical records, parity, weight, length) MAP and pregnancy outcome (i.e. gestational age at delivery, birthweight, fetal sex) were collected for each individual and used to calculate prior risks for PE in a multiple logistic regression model. MAP values and marker levels of PAPP-A, fb-hCG, PlGF and ADAM12 were expressed as multiples of the gestation-specific normal median (MoMs). Subsequently, MoMs were log-transformed and compared between PE and controls using Student's t-tests. Posterior risks were calculated using different combinations of variables;(1) maternal characteristics, serum markers, and MAP separately (2) maternal characteristics combined with serum markers or MAP (3) maternal characteristics combined with serum markers and MAP. The model-predicted detection rates (DR) for fixed 10% false-positive rates were obtained for EOPE and LOPE with or without intra-uterine growth restriction (IUGR,birth weight <10th centile). The maternal characteristics: maternal age, weight, length, smoking status and nulliparity were discriminative between PE and control groups and therefore incorporated in the multiple logistic regression model. MoM MAP was significantly elevated (1.10 p<0.001; 1.07 p<0.001) and MoM PlGF was significantly reduced (0.95 p=0.016; 0.90 p=0.029) in the EOPE and LOPE group, respectively. The differences in markers for IUGR groups were larger. The estimated DRs of the three different models are presented in the table. This study demonstrates that first-trimester MAP and PlGF combined with maternal characteristics are promising markers in risk assessment for PE. Combination of markers proved especially useful for risk assessment for term PE. Detection rates were higher in the presence of IUGR. Copyright © 2012. Published by Elsevier B.V.

Lymphatic filariasis transmission risk map of India, based on a geo-environmental risk model.

PubMed

Sabesan, Shanmugavelu; Raju, Konuganti Hari Kishan; Subramanian, Swaminathan; Srivastava, Pradeep Kumar; Jambulingam, Purushothaman

2013-09-01

The strategy adopted by a global program to interrupt transmission of lymphatic filariasis (LF) is mass drug administration (MDA) using chemotherapy. India also followed this strategy by introducing MDA in the historically known endemic areas. All other areas, which remained unsurveyed, were presumed to be nonendemic and left without any intervention. Therefore, identification of LF transmission risk areas in the entire country has become essential so that they can be targeted for intervention. A geo-environmental risk model (GERM) developed earlier was used to create a filariasis transmission risk map for India. In this model, a Standardized Filariasis Transmission Risk Index (SFTRI, based on geo-environmental risk variables) was used as a predictor of transmission risk. The relationship between SFTRI and endemicity (historically known) of an area was quantified by logistic regression analysis. The quantified relationship was validated by assessing the filarial antigenemia status of children living in the unsurveyed areas through a ground truth study. A significant positive relationship was observed between SFTRI and the endemicity of an area. Overall, the model prediction of filarial endemic status of districts was found to be correct in 92.8% of the total observations. Thus, among the 190 districts hitherto unsurveyed, as many as 113 districts were predicted to be at risk, and the remaining at no risk. The GERM developed on geographic information system (GIS) platform is useful for LF spatial delimitation on a macrogeographic/regional scale. Furthermore, the risk map developed will be useful for the national LF elimination program by identifying areas at risk for intervention and for undertaking surveillance in no-risk areas.

Robustness of risk maps and survey networks to knowledge gaps about a new invasive pest.

PubMed

Yemshanov, Denys; Koch, Frank H; Ben-Haim, Yakov; Smith, William D

2010-02-01

In pest risk assessment it is frequently necessary to make management decisions regarding emerging threats under severe uncertainty. Although risk maps provide useful decision support for invasive alien species, they rarely address knowledge gaps associated with the underlying risk model or how they may change the risk estimates. Failure to recognize uncertainty leads to risk-ignorant decisions and miscalculation of expected impacts as well as the costs required to minimize these impacts. Here we use the information gap concept to evaluate the robustness of risk maps to uncertainties in key assumptions about an invading organism. We generate risk maps with a spatial model of invasion that simulates potential entries of an invasive pest via international marine shipments, their spread through a landscape, and establishment on a susceptible host. In particular, we focus on the question of how much uncertainty in risk model assumptions can be tolerated before the risk map loses its value. We outline this approach with an example of a forest pest recently detected in North America, Sirex noctilio Fabricius. The results provide a spatial representation of the robustness of predictions of S. noctilio invasion risk to uncertainty and show major geographic hotspots where the consideration of uncertainty in model parameters may change management decisions about a new invasive pest. We then illustrate how the dependency between the extent of uncertainties and the degree of robustness of a risk map can be used to select a surveillance network design that is most robust to knowledge gaps about the pest.

Using remote sensing, ecological niche modeling, and Geographic Information Systems for Rift Valley fever risk assessment in the United States

NASA Astrophysics Data System (ADS)

Tedrow, Christine Atkins

The primary goal in this study was to explore remote sensing, ecological niche modeling, and Geographic Information Systems (GIS) as aids in predicting candidate Rift Valley fever (RVF) competent vector abundance and distribution in Virginia, and as means of estimating where risk of establishment in mosquitoes and risk of transmission to human populations would be greatest in Virginia. A second goal in this study was to determine whether the remotely-sensed Normalized Difference Vegetation Index (NDVI) can be used as a proxy variable of local conditions for the development of mosquitoes to predict mosquito species distribution and abundance in Virginia. As part of this study, a mosquito surveillance database was compiled to archive the historical patterns of mosquito species abundance in Virginia. In addition, linkages between mosquito density and local environmental and climatic patterns were spatially and temporally examined. The present study affirms the potential role of remote sensing imagery for species distribution prediction, and it demonstrates that ecological niche modeling is a valuable predictive tool to analyze the distributions of populations. The MaxEnt ecological niche modeling program was used to model predicted ranges for potential RVF competent vectors in Virginia. The MaxEnt model was shown to be robust, and the candidate RVF competent vector predicted distribution map is presented. The Normalized Difference Vegetation Index (NDVI) was found to be the most useful environmental-climatic variable to predict mosquito species distribution and abundance in Virginia. However, these results indicate that a more robust prediction is obtained by including other environmental-climatic factors correlated to mosquito densities (e.g., temperature, precipitation, elevation) with NDVI. The present study demonstrates that remote sensing and GIS can be used with ecological niche and risk modeling methods to estimate risk of virus establishment in mosquitoes and transmission to humans. Maps delineating the geographic areas in Virginia with highest risk for RVF establishment in mosquito populations and RVF disease transmission to human populations were generated in a GIS using human, domestic animal, and white-tailed deer population estimates and the MaxEnt potential RVF competent vector species distribution prediction. The candidate RVF competent vector predicted distribution and RVF risk maps presented in this study can help vector control agencies and public health officials focus Rift Valley fever surveillance efforts in geographic areas with large co-located populations of potential RVF competent vectors and human, domestic animal, and wildlife hosts. Keywords. Rift Valley fever, risk assessment, Ecological Niche Modeling, MaxEnt, Geographic Information System, remote sensing, Pearson's Product-Moment Correlation Coefficient, vectors, mosquito distribution, mosquito density, mosquito surveillance, United States, Virginia, domestic animals, white-tailed deer, ArcGIS

Rapid mapping of schistosomiasis and other neglected tropical diseases in the context of integrated control programmes in Africa

PubMed Central

BROOKER, S.; KABATEREINE, N. B.; GYAPONG, J. O.; STOTHARD, J. R.; UTZINGER, J.

2009-01-01

SUMMARY There is growing interest and commitment to the control of schistosomiasis and other so-called neglected tropical diseases (NTDs). Resources for control are inevitably limited, necessitating assessment methods that can rapidly and accurately identify and map high-risk communities so that interventions can be targeted in a spatially-explicit and cost-effective manner. Here, we review progress made with (i) mapping schistosomiasis across Africa using available epidemiological data and more recently, climate-based risk prediction; (ii) the development and use of morbidity questionnaires for rapid identification of high-risk communities of urinary schistosomiasis; and (iii) innovative sampling-based approaches for intestinal schistosomiasis, using the lot quality assurance sampling technique. Experiences are also presented for the rapid mapping of other NTDs, including onchocerciasis, loiasis and lymphatic filariasis. Future directions for an integrated rapid mapping approach targeting multiple NTDs simultaneously are outlined, including potential challenges in developing an integrated survey tool. The lessons from the mapping of human helminth infections may also be relevant for the rapid mapping of malaria as its control efforts are intensified. PMID:19450373

An experimental system for flood risk forecasting at global scale

NASA Astrophysics Data System (ADS)

Alfieri, L.; Dottori, F.; Kalas, M.; Lorini, V.; Bianchi, A.; Hirpa, F. A.; Feyen, L.; Salamon, P.

2016-12-01

Global flood forecasting and monitoring systems are nowadays a reality and are being applied by an increasing range of users and practitioners in disaster risk management. Furthermore, there is an increasing demand from users to integrate flood early warning systems with risk based forecasts, combining streamflow estimations with expected inundated areas and flood impacts. To this end, we have developed an experimental procedure for near-real time flood mapping and impact assessment based on the daily forecasts issued by the Global Flood Awareness System (GloFAS). The methodology translates GloFAS streamflow forecasts into event-based flood hazard maps based on the predicted flow magnitude and the forecast lead time and a database of flood hazard maps with global coverage. Flood hazard maps are then combined with exposure and vulnerability information to derive flood risk. Impacts of the forecasted flood events are evaluated in terms of flood prone areas, potential economic damage, and affected population, infrastructures and cities. To further increase the reliability of the proposed methodology we integrated model-based estimations with an innovative methodology for social media monitoring, which allows for real-time verification of impact forecasts. The preliminary tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management. In particular, the link with social media is crucial for improving the accuracy of impact predictions.

Modelling the spatial distribution of Fasciola hepatica in bovines using decision tree, logistic regression and GIS query approaches for Brazil.

PubMed

Bennema, S C; Molento, M B; Scholte, R G; Carvalho, O S; Pritsch, I

2017-11-01

Fascioliasis is a condition caused by the trematode Fasciola hepatica. In this paper, the spatial distribution of F. hepatica in bovines in Brazil was modelled using a decision tree approach and a logistic regression, combined with a geographic information system (GIS) query. In the decision tree and the logistic model, isothermality had the strongest influence on disease prevalence. Also, the 50-year average precipitation in the warmest quarter of the year was included as a risk factor, having a negative influence on the parasite prevalence. The risk maps developed using both techniques, showed a predicted higher prevalence mainly in the South of Brazil. The prediction performance seemed to be high, but both techniques failed to reach a high accuracy in predicting the medium and high prevalence classes to the entire country. The GIS query map, based on the range of isothermality, minimum temperature of coldest month, precipitation of warmest quarter of the year, altitude and the average dailyland surface temperature, showed a possibility of presence of F. hepatica in a very large area. The risk maps produced using these methods can be used to focus activities of animal and public health programmes, even on non-evaluated F. hepatica areas.

From pest data to abundance-based risk maps combining eco-physiological knowledge, weather, and habitat variability.

PubMed

Lacasella, Federica; Marta, Silvio; Singh, Aditya; Stack Whitney, Kaitlin; Hamilton, Krista; Townsend, Phil; Kucharik, Christopher J; Meehan, Timothy D; Gratton, Claudio

2017-03-01

Noxious species, i.e., crop pest or invasive alien species, are major threats to both natural and managed ecosystems. Invasive pests are of special importance, and knowledge about their distribution and abundance is fundamental to minimize economic losses and prioritize management activities. Occurrence models are a common tool used to identify suitable zones and map priority areas (i.e., risk maps) for noxious species management, although they provide a simplified description of species dynamics (i.e., no indication on species density). An alternative is to use abundance models, but translating abundance data into risk maps is often challenging. Here, we describe a general framework for generating abundance-based risk maps using multi-year pest data. We used an extensive data set of 3968 records collected between 2003 and 2013 in Wisconsin during annual surveys of soybean aphid (SBA), an exotic invasive pest in this region. By using an integrative approach, we modelled SBA responses to weather, seasonal, and habitat variability using generalized additive models (GAMs). Our models showed good to excellent performance in predicting SBA occurrence and abundance (TSS = 0.70, AUC = 0.92; R 2  = 0.63). We found that temperature, precipitation, and growing degree days were the main drivers of SBA trends. In addition, a significant positive relationship between SBA abundance and the availability of overwintering habitats was observed. Our models showed aphid populations were also sensitive to thresholds associated with high and low temperatures, likely related to physiological tolerances of the insects. Finally, the resulting aphid predictions were integrated using a spatial prioritization algorithm ("Zonation") to produce an abundance-based risk map for the state of Wisconsin that emphasized the spatiotemporal consistency and magnitude of past infestation patterns. This abundance-based risk map can provide information on potential foci of pest outbreaks where scouting efforts and prophylactic measures should be concentrated. The approach we took is general, relatively simple, and can be applied to other species, habitats and geographical areas for which species abundance data and biotic and abiotic data are available. © 2016 by the Ecological Society of America.

Mapping human health risks from exposure to trace metal contamination of drinking water sources in Pakistan.

PubMed

Bhowmik, Avit Kumar; Alamdar, Ambreen; Katsoyiannis, Ioannis; Shen, Heqing; Ali, Nadeem; Ali, Syeda Maria; Bokhari, Habib; Schäfer, Ralf B; Eqani, Syed Ali Musstjab Akber Shah

2015-12-15

The consumption of contaminated drinking water is one of the major causes of mortality and many severe diseases in developing countries. The principal drinking water sources in Pakistan, i.e. ground and surface water, are subject to geogenic and anthropogenic trace metal contamination. However, water quality monitoring activities have been limited to a few administrative areas and a nationwide human health risk assessment from trace metal exposure is lacking. Using geographically weighted regression (GWR) and eight relevant spatial predictors, we calculated nationwide human health risk maps by predicting the concentration of 10 trace metals in the drinking water sources of Pakistan and comparing them to guideline values. GWR incorporated local variations of trace metal concentrations into prediction models and hence mitigated effects of large distances between sampled districts due to data scarcity. Predicted concentrations mostly exhibited high accuracy and low uncertainty, and were in good agreement with observed concentrations. Concentrations for Central Pakistan were predicted with higher accuracy than for the North and South. A maximum 150-200 fold exceedance of guideline values was observed for predicted cadmium concentrations in ground water and arsenic concentrations in surface water. In more than 53% (4 and 100% for the lower and upper boundaries of 95% confidence interval (CI)) of the total area of Pakistan, the drinking water was predicted to be at risk of contamination from arsenic, chromium, iron, nickel and lead. The area with elevated risks is inhabited by more than 74 million (8 and 172 million for the lower and upper boundaries of 95% CI) people. Although these predictions require further validation by field monitoring, the results can inform disease mitigation and water resources management regarding potential hot spots. Copyright © 2015 Elsevier B.V. All rights reserved.

Plasmodium vivax Malaria Endemicity in Indonesia in 2010

PubMed Central

Elyazar, Iqbal R. F.; Gething, Peter W.; Patil, Anand P.; Rogayah, Hanifah; Sariwati, Elvieda; Palupi, Niken W.; Tarmizi, Siti N.; Kusriastuti, Rita; Baird, J. Kevin; Hay, Simon I.

2012-01-01

Background Plasmodium vivax imposes substantial morbidity and mortality burdens in endemic zones. Detailed understanding of the contemporary spatial distribution of this parasite is needed to combat it. We used model based geostatistics (MBG) techniques to generate a contemporary map of risk of Plasmodium vivax malaria in Indonesia in 2010. Methods Plasmodium vivax Annual Parasite Incidence data (2006–2008) and temperature masks were used to map P. vivax transmission limits. A total of 4,658 community surveys of P. vivax parasite rate (PvPR) were identified (1985–2010) for mapping quantitative estimates of contemporary endemicity within those limits. After error-checking a total of 4,457 points were included into a national database of age-standardized 1–99 year old PvPR data. A Bayesian MBG procedure created a predicted PvPR1–99 endemicity surface with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population surface. Results We estimated 129.6 million people in Indonesia lived at risk of P. vivax transmission in 2010. Among these, 79.3% inhabited unstable transmission areas and 20.7% resided in stable transmission areas. In western Indonesia, the predicted P. vivax prevalence was uniformly low. Over 70% of the population at risk in this region lived on Java and Bali islands, where little malaria transmission occurs. High predicted prevalence areas were observed in the Lesser Sundas, Maluku and Papua. In general, prediction uncertainty was relatively low in the west and high in the east. Conclusion Most Indonesians living with endemic P. vivax experience relatively low risk of infection. However, blood surveys for this parasite are likely relatively insensitive and certainly do not detect the dormant liver stage reservoir of infection. The prospects for P. vivax elimination would be improved with deeper understanding of glucose-6-phosphate dehydrogenase deficiency (G6PDd) distribution, anti-relapse therapy practices and manageability of P. vivax importation risk, especially in Java and Bali. PMID:22615978

How well are malaria maps used to design and finance malaria control in Africa?

PubMed

Omumbo, Judy A; Noor, Abdisalan M; Fall, Ibrahima S; Snow, Robert W

2013-01-01

Rational decision making on malaria control depends on an understanding of the epidemiological risks and control measures. National Malaria Control Programmes across Africa have access to a range of state-of-the-art malaria risk mapping products that might serve their decision-making needs. The use of cartography in planning malaria control has never been methodically reviewed. An audit of the risk maps used by NMCPs in 47 malaria endemic countries in Africa was undertaken by examining the most recent national malaria strategies, monitoring and evaluation plans, malaria programme reviews and applications submitted to the Global Fund. The types of maps presented and how they have been used to define priorities for investment and control was investigated. 91% of endemic countries in Africa have defined malaria risk at sub-national levels using at least one risk map. The range of risk maps varies from maps based on suitability of climate for transmission; predicted malaria seasons and temperature/altitude limitations, to representations of clinical data and modelled parasite prevalence. The choice of maps is influenced by the source of the information. Maps developed using national data through in-country research partnerships have greater utility than more readily accessible web-based options developed without inputs from national control programmes. Although almost all countries have stratification maps, only a few use them to guide decisions on the selection of interventions allocation of resources for malaria control. The way information on the epidemiology of malaria is presented and used needs to be addressed to ensure evidence-based added value in planning control. The science on modelled impact of interventions must be integrated into new mapping products to allow a translation of risk into rational decision making for malaria control. As overseas and domestic funding diminishes, strategic planning will be necessary to guide appropriate financing for malaria control.

Updates to the zoonotic niche map of Ebola virus disease in Africa

PubMed Central

Pigott, David M; Millear, Anoushka I; Earl, Lucas; Morozoff, Chloe; Han, Barbara A; Shearer, Freya M; Weiss, Daniel J; Brady, Oliver J; Kraemer, Moritz UG; Moyes, Catherine L; Bhatt, Samir; Gething, Peter W; Golding, Nick; Hay, Simon I

2016-01-01

As the outbreak of Ebola virus disease (EVD) in West Africa is now contained, attention is turning from control to future outbreak prediction and prevention. Building on a previously published zoonotic niche map (Pigott et al., 2014), this study incorporates new human and animal occurrence data and expands upon the way in which potential bat EVD reservoir species are incorporated. This update demonstrates the potential for incorporating and updating data used to generate the predicted suitability map. A new data portal for sharing such maps is discussed. This output represents the most up-to-date estimate of the extent of EVD zoonotic risk in Africa. These maps can assist in strengthening surveillance and response capacity to contain viral haemorrhagic fevers. DOI: http://dx.doi.org/10.7554/eLife.16412.001 PMID:27414263

Beyond the single species climate envelope: A multifaceted approach to mapping climate change vulnerability

Treesearch

Christopher S. Balzotti; Stanley G. Kitchen; Clinton McCarthy

2016-01-01

Federal land management agencies and conservation organizations have begun incorporating climate change vulnerability assessments (CCVAs) as an important component in the management and conservation of landscapes. It is often a challenge to translate that knowledge into management plans and actions, even when research infers species risk. Predictive maps can...

IDENTIFYING AREAS WITH A HIGH RISK OF HUMAN INFECTION WITH THE AVIAN INFLUENZA A (H7N9) VIRUS IN EAST ASIA

PubMed Central

Fuller, Trevon; Havers, Fiona; Xu, Cuiling; Fang, Li-Qun; Cao, Wu-Chun; Shu, Yuelong; Widdowson, Marc-Alain; Smith, Thomas B.

2014-01-01

Summary Objectives The rapid emergence, spread, and disease severity of avian influenza A(H7N9) in China has prompted concerns about a possible pandemic and regional spread in the coming months. The objective of this study was to predict the risk of future human infections with H7N9 in China and neighboring countries by assessing the association between H7N9 cases at sentinel hospitals and putative agricultural, climatic, and demographic risk factors. Methods This cross-sectional study used the locations of H7N9 cases and negative cases from China’s influenza-like illness surveillance network. After identifying H7N9 risk factors with logistic regression, we used Geographic Information Systems (GIS) to construct predictive maps of H7N9 risk across Asia. Results Live bird market density was associated with human H7N9 infections reported in China from March-May 2013. Based on these cases, our model accurately predicted the virus’ spread into Guangxi autonomous region in February 2014. Outside China, we find there is a high risk that the virus will spread to northern Vietnam, due to the import of poultry from China. Conclusions Our risk map can focus efforts to improve surveillance in poultry and humans, which may facilitate early identification and treatment of human cases. PMID:24642206

Human Risk of Infection with Borrelia burgdorferi, the Lyme Disease Agent, in Eastern United States

PubMed Central

Diuk-Wasser, Maria A.; Hoen, Anne Gatewood; Cislo, Paul; Brinkerhoff, Robert; Hamer, Sarah A.; Rowland, Michelle; Cortinas, Roberto; Vourc'h, Gwenaël; Melton, Forrest; Hickling, Graham J.; Tsao, Jean I.; Bunikis, Jonas; Barbour, Alan G.; Kitron, Uriel; Piesman, Joseph; Fish, Durland

2012-01-01

The geographic pattern of human risk for infection with Borrelia burgdorferi sensu stricto, the tick-borne pathogen that causes Lyme disease, was mapped for the eastern United States. The map is based on standardized field sampling in 304 sites of the density of Ixodes scapularis host-seeking nymphs infected with B. burgdorferi, which is closely associated with human infection risk. Risk factors for the presence and density of infected nymphs were used to model a continuous 8 km×8 km resolution predictive surface of human risk, including confidence intervals for each pixel. Discontinuous Lyme disease risk foci were identified in the Northeast and upper Midwest, with a transitional zone including sites with uninfected I. scapularis populations. Given frequent under- and over-diagnoses of Lyme disease, this map could act as a tool to guide surveillance, control, and prevention efforts and act as a baseline for studies tracking the spread of infection. PMID:22302869

Mapping of risk and susceptibility of shallow-landslide in the city of São Paulo, Brazil

NASA Astrophysics Data System (ADS)

Listo, Fabrizio de Luiz Rosito; Carvalho Vieira, Bianca

2012-10-01

In the city of São Paulo, where about 11 million people live, landslides and flooding occur frequently, especially during the summer. These landslides cause the destruction of houses and urban equipment, economic damage, and the loss of lives. The number of areas threatened by landslides has been increasing each year. The objective of this article is to analyze the probability of risk and susceptibility to shallow landslides in the Limoeiro River basin, which is located at the head of the Aricanduva River basin, one of the main hydrographic basins in the city of São Paulo. To map areas of risk, we created a cadastral survey form to evaluate landslide risk in the field. Risk was categorized into four levels based on natural and anthropogenic factors: R1 (low risk), R2 (average risk), R3 (high risk), and R4 (very high risk). To analyze susceptibility to shallow landslides, we used the SHALSTAB (Shallow Landsliding Stability) mathematical model and calculated the Distribution Frequency (DF) of the susceptibility classes for the entire basin. Finally, we performed a joint analysis of the average Risk Concentration (RC) and Risk Potential (RP). We mapped 14 risk sectors containing approximately 685 at-risk homes, more than half of which presented a high (R3) or very high (R4) probability of risk to the population. In the susceptibility map, 41% of the area was classified as stable and 20% as unconditionally unstable. Although the latter category accounted a smaller proportion of the total area, it contained a concentration (RC) of 41% of the mapped risk areas with a risk potential (RP) of 12%. We found that the locations of areas predicted to be unstable by the model coincided with the risk areas mapped in the field. This combination of methods can be applied to evaluate the risk of shallow landslides in densely populated areas and can assist public managers in defining areas that are unstable and inappropriate for occupation.

Apparent diffusion coefficient mapping in medulloblastoma predicts non-infiltrative surgical planes.

PubMed

Marupudi, Neena I; Altinok, Deniz; Goncalves, Luis; Ham, Steven D; Sood, Sandeep

2016-11-01

An appropriate surgical approach for posterior fossa lesions is to start tumor removal from areas with a defined plane to where tumor is infiltrating the brainstem or peduncles. This surgical approach minimizes risk of damage to eloquent areas. Although magnetic resonance imaging (MRI) is the current standard preoperative imaging obtained for diagnosis and surgical planning of pediatric posterior fossa tumors, it offers limited information on the infiltrative planes between tumor and normal structures in patients with medulloblastomas. Because medulloblastomas demonstrate diffusion restriction on apparent diffusion coefficient map (ADC map) sequences, we investigated the role of ADC map in predicting infiltrative and non-infiltrative planes along the brain stem and/or cerebellar peduncles by medulloblastomas prior to surgery. Thirty-four pediatric patients with pathologically confirmed medulloblastomas underwent surgical resection at our facility from 2004 to 2012. An experienced pediatric neuroradiologist reviewed the brain MRIs/ADC map, assessing the planes between the tumor and cerebellar peduncles/brain stem. An independent evaluator documented surgical findings from operative reports for comparison to the radiographic findings. The radiographic findings were statistically compared to the documented intraoperative findings to determine predictive value of the test in identifying tumor infiltration of the brain stem cerebellar peduncles. Twenty-six patients had preoperative ADC mapping completed and thereby, met inclusion criteria. Mean age at time of surgery was 8.3 ± 4.6 years. Positive predictive value of ADC maps to predict tumor invasion of the brain stem and cerebellar peduncles ranged from 69 to 88 %; negative predictive values ranged from 70 to 89 %. Sensitivity approached 93 % while specificity approached 78 %. ADC maps are valuable in predicting the infiltrative and non-infiltrative planes along the tumor and brain stem interface in medulloblastomas. Inclusion and evaluation of ADC maps in preoperative evaluation can assist in surgical resection planning in patients with medulloblastoma.

Risk analysis for dengue suitability in Africa using the ArcGIS predictive analysis tools (PA tools).

PubMed

Attaway, David F; Jacobsen, Kathryn H; Falconer, Allan; Manca, Germana; Waters, Nigel M

2016-06-01

Risk maps identifying suitable locations for infection transmission are important for public health planning. Data on dengue infection rates are not readily available in most places where the disease is known to occur. A newly available add-in to Esri's ArcGIS software package, the ArcGIS Predictive Analysis Toolset (PA Tools), was used to identify locations within Africa with environmental characteristics likely to be suitable for transmission of dengue virus. A more accurate, robust, and localized (1 km × 1 km) dengue risk map for Africa was created based on bioclimatic layers, elevation data, high-resolution population data, and other environmental factors that a search of the peer-reviewed literature showed to be associated with dengue risk. Variables related to temperature, precipitation, elevation, and population density were identified as good predictors of dengue suitability. Areas of high dengue suitability occur primarily within West Africa and parts of Central Africa and East Africa, but even in these regions the suitability is not homogenous. This risk mapping technique for an infection transmitted by Aedes mosquitoes draws on entomological, epidemiological, and geographic data. The method could be applied to other infectious diseases (such as Zika) in order to provide new insights for public health officials and others making decisions about where to increase disease surveillance activities and implement infection prevention and control efforts. The ability to map threats to human and animal health is important for tracking vectorborne and other emerging infectious diseases and modeling the likely impacts of climate change. Copyright © 2016 Elsevier B.V. All rights reserved.

Mapping as a tool for predicting the risk of anthrax outbreaks in Northern Region of Ghana.

PubMed

Nsoh, Ayamdooh Evans; Kenu, Ernest; Forson, Eric Kofi; Afari, Edwin; Sackey, Samuel; Nyarko, Kofi Mensah; Yebuah, Nathaniel

2016-01-01

Anthrax is a febrile soil-born infectious disease that can affect all warm-blooded animals including man. Outbreaks of anthrax have been reported in northern region of Ghana but no concerted effort has been made to implement risk-based surveillance systems to document outbreaks so as to implement policies to address the disease. We generated predictive maps using soil pH, temperature and rainfall as predictor variables to identify hotspot areas for the outbreaks. A 10-year secondary data records on soil pH, temperature and rainfall were used to create climate-based risk maps using ArcGIS 10.2. The monthly mean values of rainfall and temperature for ten years were calculated and anthrax related evidence based constant raster values were created as weights for the three factors. All maps were generated using the Kriging interpolation method. There were 43 confirmed outbreaks. The deaths involved were 131 cattle, 44 sheep, 15 goats, 562 pigs with 6 human deaths and 22 developed cutaneous anthrax. We found three strata of well delineated distribution pattern indicating levels of risk due to suitability of area for anthrax spore survival. The likelihood of outbreaks occurrence and reoccurrence was higher in Strata I, Strata II and strata III respectively in descending order, due to the suitability of soil pH, temperature and rainfall for the survival and dispersal of B. anthracis spore. The eastern corridor of Northern region is a Hots spot area. Policy makers can develop risk based surveillance system and focus on this area to mitigate anthrax outbreaks and reoccurrence.

Coupling of Bayesian Networks with GIS for wildfire risk assessment on natural and agricultural areas of the Mediterranean

NASA Astrophysics Data System (ADS)

Scherb, Anke; Papakosta, Panagiota; Straub, Daniel

2014-05-01

Wildfires cause severe damages to ecosystems, socio-economic assets, and human lives in the Mediterranean. To facilitate coping with wildfire risks, an understanding of the factors influencing wildfire occurrence and behavior (e.g. human activity, weather conditions, topography, fuel loads) and their interaction is of importance, as is the implementation of this knowledge in improved wildfire hazard and risk prediction systems. In this project, a probabilistic wildfire risk prediction model is developed, with integrated fire occurrence and fire propagation probability and potential impact prediction on natural and cultivated areas. Bayesian Networks (BNs) are used to facilitate the probabilistic modeling. The final BN model is a spatial-temporal prediction system at the meso scale (1 km2 spatial and 1 day temporal resolution). The modeled consequences account for potential restoration costs and production losses referred to forests, agriculture, and (semi-) natural areas. BNs and a geographic information system (GIS) are coupled within this project to support a semi-automated BN model parameter learning and the spatial-temporal risk prediction. The coupling also enables the visualization of prediction results by means of daily maps. The BN parameters are learnt for Cyprus with data from 2006-2009. Data from 2010 is used as validation data set. A special focus is put on the performance evaluation of the BN for fire occurrence, which is modeled as binary classifier and thus, could be validated by means of Receiver Operator Characteristic (ROC) curves. With the final best models, AUC values of more than 70% for validation could be achieved, which indicates potential for reliable prediction performance via BN. Maps of selected days in 2010 are shown to illustrate final prediction results. The resulting system can be easily expanded to predict additional expected damages in the mesoscale (e.g. building and infrastructure damages). The system can support planning of preventive measures (e.g. state resources allocation for wildfire prevention and preparedness) and assist recuperation plans of damaged areas.

Preoperative Cerebral Oxygen Extraction Fraction Imaging Generated from 7T MR Quantitative Susceptibility Mapping Predicts Development of Cerebral Hyperperfusion following Carotid Endarterectomy.

PubMed

Nomura, J-I; Uwano, I; Sasaki, M; Kudo, K; Yamashita, F; Ito, K; Fujiwara, S; Kobayashi, M; Ogasawara, K

2017-12-01

Preoperative hemodynamic impairment in the affected cerebral hemisphere is associated with the development of cerebral hyperperfusion following carotid endarterectomy. Cerebral oxygen extraction fraction images generated from 7T MR quantitative susceptibility mapping correlate with oxygen extraction fraction images on positron-emission tomography. The present study aimed to determine whether preoperative oxygen extraction fraction imaging generated from 7T MR quantitative susceptibility mapping could identify patients at risk for cerebral hyperperfusion following carotid endarterectomy. Seventy-seven patients with unilateral internal carotid artery stenosis (≥70%) underwent preoperative 3D T2*-weighted imaging using a multiple dipole-inversion algorithm with a 7T MR imager. Quantitative susceptibility mapping images were then obtained, and oxygen extraction fraction maps were generated. Quantitative brain perfusion single-photon emission CT was also performed before and immediately after carotid endarterectomy. ROIs were automatically placed in the bilateral middle cerebral artery territories in all images using a 3D stereotactic ROI template, and affected-to-contralateral ratios in the ROIs were calculated on quantitative susceptibility mapping-oxygen extraction fraction images. Ten patients (13%) showed post-carotid endarterectomy hyperperfusion (cerebral blood flow increases of ≥100% compared with preoperative values in the ROIs on brain perfusion SPECT). Multivariate analysis showed that a high quantitative susceptibility mapping-oxygen extraction fraction ratio was significantly associated with the development of post-carotid endarterectomy hyperperfusion (95% confidence interval, 33.5-249.7; P = .002). Sensitivity, specificity, and positive- and negative-predictive values of the quantitative susceptibility mapping-oxygen extraction fraction ratio for the prediction of the development of post-carotid endarterectomy hyperperfusion were 90%, 84%, 45%, and 98%, respectively. Preoperative oxygen extraction fraction imaging generated from 7T MR quantitative susceptibility mapping identifies patients at risk for cerebral hyperperfusion following carotid endarterectomy. © 2017 by American Journal of Neuroradiology.

Bulk tank milk prevalence and production losses, spatial analysis, and predictive risk mapping of Ostertagia ostertagi infections in Mexican cattle herds.

PubMed

Villa-Mancera, Abel; Pastelín-Rojas, César; Olivares-Pérez, Jaime; Córdova-Izquierdo, Alejandro; Reynoso-Palomar, Alejandro

2018-05-01

This study investigated the prevalence, production losses, spatial clustering, and predictive risk mapping in different climate zones in five states of Mexico. The bulk tank milk samples obtained between January and April 2015 were analyzed for antibodies against Ostertagia ostertagi using the Svanovir ELISA. A total of 1204 farm owners or managers answered the questionnaire. The overall herd prevalence and mean optical density ratio (ODR) of parasite were 61.96% and 0.55, respectively. Overall, the production loss was approximately 0.542 kg of milk per parasited cow per day (mean ODR = 0.92, 142 farms, 11.79%). The spatial disease cluster analysis using SatScan software indicated that two high-risk clusters were observed. In the multivariable analysis, three models were tested for potential association with the ELISA results supported by climatic, environmental, and management factors. The final logistic regression model based on both climatic/environmental and management variables included the factors rainfall, elevation, land surface temperature (LST) day, and parasite control program that were significantly associated with an increased risk of infection. Geostatistical kriging was applied to generate a risk map for the presence of parasite in dairy cattle herds in Mexico. The results indicate that climatic and meteorological factors had a higher potential impact on the spatial distribution of O. ostertagi than the management factors.

Early Prediction of Cancer Progression by Depth-Resolved Nanoscale Mapping of Nuclear Architecture from Unstained Tissue Specimens.

PubMed

Uttam, Shikhar; Pham, Hoa V; LaFace, Justin; Leibowitz, Brian; Yu, Jian; Brand, Randall E; Hartman, Douglas J; Liu, Yang

2015-11-15

Early cancer detection currently relies on screening the entire at-risk population, as with colonoscopy and mammography. Therefore, frequent, invasive surveillance of patients at risk for developing cancer carries financial, physical, and emotional burdens because clinicians lack tools to accurately predict which patients will actually progress into malignancy. Here, we present a new method to predict cancer progression risk via nanoscale nuclear architecture mapping (nanoNAM) of unstained tissue sections based on the intrinsic density alteration of nuclear structure rather than the amount of stain uptake. We demonstrate that nanoNAM detects a gradual increase in the density alteration of nuclear architecture during malignant transformation in animal models of colon carcinogenesis and in human patients with ulcerative colitis, even in tissue that appears histologically normal according to pathologists. We evaluated the ability of nanoNAM to predict "future" cancer progression in patients with ulcerative colitis who did and did not develop colon cancer up to 13 years after their initial colonoscopy. NanoNAM of the initial biopsies correctly classified 12 of 15 patients who eventually developed colon cancer and 15 of 18 who did not, with an overall accuracy of 85%. Taken together, our findings demonstrate great potential for nanoNAM in predicting cancer progression risk and suggest that further validation in a multicenter study with larger cohorts may eventually advance this method to become a routine clinical test. ©2015 American Association for Cancer Research.

Development of risk maps to minimize uranium exposures in the Navajo Churchrock mining district

PubMed Central

2009-01-01

Background Decades of improper disposal of uranium-mining wastes on the Navajo Nation has resulted in adverse human and ecological health impacts as well as socio-cultural problems. As the Navajo people become increasingly aware of the contamination problems, there is a need to develop a risk-communication strategy to properly inform tribal members of the extent and severity of the health risks. To be most effective, this strategy needs to blend accepted risk-communication techniques with Navajo perspectives such that the strategy can be used at the community level to inform culturally- and toxicologically-relevant decisions about land and water use as well as mine-waste remediation. Objective The objective of this study was to develop GIS-based thematic maps as communication tools to clearly identify high risk exposure areas and offer alternatives to minimize public and ecological health impacts. Methods Thematic maps were produced that incorporated data derived from environmental sampling and public health surveys. The maps show the location and quality of unregulated water resources and identify regulated water sources that could be used as alternatives. In addition, the maps show the location of contaminated soil and sediment areas in which disturbance of surface deposits should be avoided. Preliminary feedback was collected from an informal Navajo working group to assess the clarity and efficacy of this proposed communication method. Results The working group found the maps to be both clear and effective, and made suggestions for improvements, such as the addition of more map features. The working group predicted that once the maps are presented to the public, water hauling and soil use behaviors will change, and dialogue with chapter officials will be initiated to accelerate further risk reduction efforts. Implications Because risk communication is complicated by language barriers, lack of infrastructure, and historical mistrust of non-Navajo researchers, mapping provides an easily interpretable medium that can be objectively viewed by community members and decision makers to evaluate activities that affect toxicant exposures. PMID:19589163

Development of risk maps to minimize uranium exposures in the Navajo Churchrock mining district.

PubMed

deLemos, Jamie L; Brugge, Doug; Cajero, Miranda; Downs, Mallery; Durant, John L; George, Christine M; Henio-Adeky, Sarah; Nez, Teddy; Manning, Thomas; Rock, Tommy; Seschillie, Bess; Shuey, Chris; Lewis, Johnnye

2009-07-09

Decades of improper disposal of uranium-mining wastes on the Navajo Nation has resulted in adverse human and ecological health impacts as well as socio-cultural problems. As the Navajo people become increasingly aware of the contamination problems, there is a need to develop a risk-communication strategy to properly inform tribal members of the extent and severity of the health risks. To be most effective, this strategy needs to blend accepted risk-communication techniques with Navajo perspectives such that the strategy can be used at the community level to inform culturally- and toxicologically-relevant decisions about land and water use as well as mine-waste remediation. The objective of this study was to develop GIS-based thematic maps as communication tools to clearly identify high risk exposure areas and offer alternatives to minimize public and ecological health impacts. Thematic maps were produced that incorporated data derived from environmental sampling and public health surveys. The maps show the location and quality of unregulated water resources and identify regulated water sources that could be used as alternatives. In addition, the maps show the location of contaminated soil and sediment areas in which disturbance of surface deposits should be avoided. Preliminary feedback was collected from an informal Navajo working group to assess the clarity and efficacy of this proposed communication method. The working group found the maps to be both clear and effective, and made suggestions for improvements, such as the addition of more map features. The working group predicted that once the maps are presented to the public, water hauling and soil use behaviors will change, and dialogue with chapter officials will be initiated to accelerate further risk reduction efforts. Because risk communication is complicated by language barriers, lack of infrastructure, and historical mistrust of non-Navajo researchers, mapping provides an easily interpretable medium that can be objectively viewed by community members and decision makers to evaluate activities that affect toxicant exposures.

Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India.

PubMed

Sowmya, S V; Somashekar, R K

2010-11-01

Fire is the most spectacular natural disturbance that affects the forest ecosystem composition and diversity. Fire has a devastating effect on the landscape and its impact is felt at every level of the ecosystem and it is possible to map forest fire risk zone and thereby minimize the frequency of fire. There is a need for supranational approaches that analyze wide scenarios of factors involved and global fire effects. Fires can be monitored and analyzed over large areas in a timely and cost effective manner by using satellite imagery. Also Geographical Information System (GIS) can be used effectively to demarcate the fire risk zone map. Bhadra wildlife Sanctuary located in Kamataka, India was selected for this study. Vegetation, slope, distance from roads, settlements parameters were derived for a study area using topographic maps and field information. The Remote Sensing (RS) and Geographical Information System (GIS)-based forest fire risk model of the study area appeared to be highly compatible with the actual fire-affected sites. The temporal satellite data from 1989 to2006 have been analyzed to map the burnt areas. These classes were weighted according to their influence on forest fire. Four categories of fire risk regions such as Low, Moderate, High and Very high fire intensity zones were identified. It is predicted that around 10.31% of the area falls undermoderate risk zone.

An emission-weighted proximity model for air pollution exposure assessment.

PubMed

Zou, Bin; Wilson, J Gaines; Zhan, F Benjamin; Zeng, Yongnian

2009-08-15

Among the most common spatial models for estimating personal exposure are Traditional Proximity Models (TPMs). Though TPMs are straightforward to configure and interpret, they are prone to extensive errors in exposure estimates and do not provide prospective estimates. To resolve these inherent problems with TPMs, we introduce here a novel Emission Weighted Proximity Model (EWPM) to improve the TPM, which takes into consideration the emissions from all sources potentially influencing the receptors. EWPM performance was evaluated by comparing the normalized exposure risk values of sulfur dioxide (SO(2)) calculated by EWPM with those calculated by TPM and monitored observations over a one-year period in two large Texas counties. In order to investigate whether the limitations of TPM in potential exposure risk prediction without recorded incidence can be overcome, we also introduce a hybrid framework, a 'Geo-statistical EWPM'. Geo-statistical EWPM is a synthesis of Ordinary Kriging Geo-statistical interpolation and EWPM. The prediction results are presented as two potential exposure risk prediction maps. The performance of these two exposure maps in predicting individual SO(2) exposure risk was validated with 10 virtual cases in prospective exposure scenarios. Risk values for EWPM were clearly more agreeable with the observed concentrations than those from TPM. Over the entire study area, the mean SO(2) exposure risk from EWPM was higher relative to TPM (1.00 vs. 0.91). The mean bias of the exposure risk values of 10 virtual cases between EWPM and 'Geo-statistical EWPM' are much smaller than those between TPM and 'Geo-statistical TPM' (5.12 vs. 24.63). EWPM appears to more accurately portray individual exposure relative to TPM. The 'Geo-statistical EWPM' effectively augments the role of the standard proximity model and makes it possible to predict individual risk in future exposure scenarios resulting in adverse health effects from environmental pollution.

Mapping Soil Erosion Factors and Potential Erosion Risk for the National Park "Central Balkan"

NASA Astrophysics Data System (ADS)

Ilieva, Diliana; Malinov, Ilia

2014-05-01

Soil erosion is widely recognised environmental problem. The report aims at presenting the main results from assessment and mapping of the factors of sheet water erosion and the potential erosion risk on the territory of National Park "Central Balkan". For this purpose, the Universal Soil Loss Equation (USLE) was used for predicting soil loss from erosion. The influence of topography (LS-factor) and soil erodibility (K-factor) was assessed using small-scale topographic and soil maps. Rainfall erosivity (R-factor) was calculated from data of rainfalls with amounts exceeding 9.5 mm from 14 hydro-meteorological stations. The values of the erosion factors (R, K and LS) were presented for the areas of forest, sub-alpine and alpine zones. Using the methods of GIS, maps were plotted presenting the area distribution among the classes of the soil erosion factors and the potential risk in the respective zones. The results can be used for making accurate decisions for soil conservation and sustainable land management in the park.

Assessment of macroseismic intensity in the Nile basin, Egypt

NASA Astrophysics Data System (ADS)

Fergany, Elsayed

2018-01-01

This work intends to assess deterministic seismic hazard and risk analysis in terms of the maximum expected intensity map of the Egyptian Nile basin sector. Seismic source zone model of Egypt was delineated based on updated compatible earthquake catalog in 2015, focal mechanisms, and the common tectonic elements. Four effective seismic source zones were identified along the Nile basin. The observed macroseismic intensity data along the basin was used to develop intensity prediction equation defined in terms of moment magnitude. Expected maximum intensity map was proven based on the developed intensity prediction equation, identified effective seismic source zones, and maximum expected magnitude for each zone along the basin. The earthquake hazard and risk analysis was discussed and analyzed in view of the maximum expected moment magnitude and the maximum expected intensity values for each effective source zone. Moderate expected magnitudes are expected to put high risk at Cairo and Aswan regions. The results of this study could be a recommendation for the planners in charge to mitigate the seismic risk at these strategic zones of Egypt.

Uncertainty in surface water flood risk modelling

NASA Astrophysics Data System (ADS)

Butler, J. B.; Martin, D. N.; Roberts, E.; Domuah, R.

2009-04-01

Two thirds of the flooding that occurred in the UK during summer 2007 was as a result of surface water (otherwise known as ‘pluvial') rather than river or coastal flooding. In response, the Environment Agency and Interim Pitt Reviews have highlighted the need for surface water risk mapping and warning tools to identify, and prepare for, flooding induced by heavy rainfall events. This need is compounded by the likely increase in rainfall intensities due to climate change. The Association of British Insurers has called for the Environment Agency to commission nationwide flood risk maps showing the relative risk of flooding from all sources. At the wider European scale, the recently-published EC Directive on the assessment and management of flood risks will require Member States to evaluate, map and model flood risk from a variety of sources. As such, there is now a clear and immediate requirement for the development of techniques for assessing and managing surface water flood risk across large areas. This paper describes an approach for integrating rainfall, drainage network and high-resolution topographic data using Flowroute™, a high-resolution flood mapping and modelling platform, to produce deterministic surface water flood risk maps. Information is provided from UK case studies to enable assessment and validation of modelled results using historical flood information and insurance claims data. Flowroute was co-developed with flood scientists at Cambridge University specifically to simulate river dynamics and floodplain inundation in complex, congested urban areas in a highly computationally efficient manner. It utilises high-resolution topographic information to route flows around individual buildings so as to enable the prediction of flood depths, extents, durations and velocities. As such, the model forms an ideal platform for the development of surface water flood risk modelling and mapping capabilities. The 2-dimensional component of Flowroute employs uniform flow formulae (Manning's Equation) to direct flow over the model domain, sourcing water from the channel or sea so as to provide a detailed representation of river and coastal flood risk. The initial development step was to include spatially-distributed rainfall as a new source term within the model domain. This required optimisation to improve computational efficiency, given the ubiquity of ‘wet' cells early on in the simulation. Collaboration with UK water companies has provided detailed drainage information, and from this a simplified representation of the drainage system has been included in the model via the inclusion of sinks and sources of water from the drainage network. This approach has clear advantages relative to a fully coupled method both in terms of reduced input data requirements and computational overhead. Further, given the difficulties associated with obtaining drainage information over large areas, tests were conducted to evaluate uncertainties associated with excluding drainage information and the impact that this has upon flood model predictions. This information can be used, for example, to inform insurance underwriting strategies and loss estimation as well as for emergency response and planning purposes. The Flowroute surface-water flood risk platform enables efficient mapping of areas sensitive to flooding from high-intensity rainfall events due to topography and drainage infrastructure. As such, the technology has widespread potential for use as a risk mapping tool by the UK Environment Agency, European Member States, water authorities, local governments and the insurance industry. Keywords: Surface water flooding, Model Uncertainty, Insurance Underwriting, Flood inundation modelling, Risk mapping.

To the Greatest Lengths: Al Qaeda, Proximity and Recruitment Risk

DTIC Science & Technology

2010-12-01

activity (Boba, 2005, pp. 218–219). On the complex end of this spectrum, density mapping uses mathematical formulas to determine degrees of criminal...area. These calculations "combines actuarial risk prediction with environmental criminology to assign risk values to places according to their...translated records, and the compilation of distance variables are correct. 46 2. Model Mathematically , the formula for this test is

Predicting impacts of climate change on Fasciola hepatica risk.

PubMed

Fox, Naomi J; White, Piran C L; McClean, Colin J; Marion, Glenn; Evans, Andy; Hutchings, Michael R

2011-01-10

Fasciola hepatica (liver fluke) is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

An environmental assessment and risk map of Ascaris lumbricoides and Necator americanus distributions in Manufahi District, Timor-Leste

PubMed Central

Wardell, Rebecca; Clements, Archie C. A.; Lal, Aparna; Summers, David; Llewellyn, Stacey; Campbell, Suzy J.; McCarthy, James; Gray, Darren J.; V. Nery, Susana

2017-01-01

Background In Timor-Leste there have been intermittent and ineffective soil-transmitted helminth (STH) deworming programs since 2004. In a resource-constrained setting, having information on the geographic distribution of STH can aid in prioritising high risk communities for intervention. This study aimed to quantify the environmental risk factors for STH infection and to produce a risk map of STH in Manufahi district, Timor-Leste. Methodology/Principal findings Georeferenced cross-sectional data and stool samples were obtained from 2,194 participants in 606 households in 24 villages in the Manufahi District as part of cross sectional surveys done in the context of the “WASH for Worms” randomised controlled trial. Infection status was determined for Ascaris lumbricoides and Necator americanus using real-time quantitative polymerase chain reaction. Baseline infection data were linked to environmental data obtained for each household. Univariable and multivariable multilevel mixed-effects logistic regression analysis with random effects at the village and household level were conducted, with all models adjusted for age and sex. For A. lumbricoides, being a school-aged child increased the odds of infection, whilst higher temperatures in the coolest quarter of the year, alkaline soils, clay loam/loam soils and woody savannas around households were associated with decreased infection odds. For N. americanus, greater precipitation in the driest month, higher average enhanced vegetation index, age and sandy loam soils increased infection odds, whereas being female and living at higher elevations decreased the odds of infection. Predictive risk maps generated for Manufahi based upon these final models highlight the high predicted risk of N. americanus infection across the district and the more focal nature of A. lumbricoides infection. The predicted risk of any STH infection is high across the entire district. Conclusions/Significance The widespread predicted risk of any STH infection in 6 to 18 year olds provides strong evidence to support strategies for control across the entire geographical area. As few studies include soil texture and pH in their analysis, this study adds to a growing body of evidence suggesting these factors influence STH infection distribution. This study also further supports that A. lumbricoides prefers acidic soils, highlighting a potential relatively unexplored avenue for control. Trial registration ClinicalTrials.gov ACTRN12614000680662. PMID:28489889

Multi-dimensional perspectives of flood risk - using a participatory framework to develop new approaches to flood risk communication

NASA Astrophysics Data System (ADS)

Rollason, Edward; Bracken, Louise; Hardy, Richard; Large, Andy

2017-04-01

Flooding is a major hazard across Europe which, since, 1998 has caused over €52 million in damages and displaced over half a million people. Climate change is predicted to increase the risks posed by flooding in the future. The 2007 EU Flood Directive cemented the use of flood risk maps as a central tool in understanding and communicating flood risk. Following recent flooding in England, an urgent need to integrate people living at risk from flooding into flood management approaches, encouraging flood resilience and the up-take of resilient activities has been acknowledged. The effective communication of flood risk information plays a major role in allowing those at risk to make effective decisions about flood risk and increase their resilience, however, there are emerging concerns over the effectiveness of current approaches. The research presented explores current approaches to flood risk communication in England and the effectiveness of these methods in encouraging resilient actions before and during flooding events. The research also investigates how flood risk communications could be undertaken more effectively, using a novel participatory framework to integrate the perspectives of those living at risk. The research uses co-production between local communities and researchers in the environmental sciences, using a participatory framework to bring together local knowledge of flood risk and flood communications. Using a local competency group, the research explores what those living at risk from flooding want from flood communications in order to develop new approaches to help those at risk understand and respond to floods. Suggestions for practice are refined by the communities to co-produce recommendations. The research finds that current approaches to real-time flood risk communication fail to forecast the significance of predicted floods, whilst flood maps lack detailed information about how floods occur, or use scientific terminology which people at risk find confusing or lacking in realistic grounding. This means users do not have information they find useful to make informed decisions about how to prepare for and respond to floods. Working together with at-risk participants, the research has developed new approaches for communicating flood risk. These approaches focus on understanding flood mechanisms and dynamics, to help participants imagine their flood risk and link potential scenarios to reality, and provide forecasts of predicted flooding at a variety of scales, allowing participants to assess the significance of predicted flooding and make more informed judgments on what action to take in response. The findings presented have significant implications for the way in which flood risk is communicated, changing the focus of mapping from probabilistic future scenarios to understanding flood dynamics and mechanisms. Such ways of communicating flood risk embrace how people would like to see risk communicated, and help those at risk grow their resilience. Communicating in such a way has wider implications for flood modelling and data collection. However, these represent potential opportunities to build more effective local partnerships for assessing and managing flood risks.

Sustainable and Smart City Planning Using Spatial Data in Wallonia

NASA Astrophysics Data System (ADS)

Stephenne, N.; Beaumont, B.; Hallot, E.; Wolff, E.; Poelmans, L.; Baltus, C.

2016-09-01

Simulating population distribution and land use changes in space and time offer opportunities for smart city planning. It provides a holistic and dynamic vision of fast changing urban environment to policy makers. Impacts, such as environmental and health risks or mobility issues, of policies can be assessed and adapted consequently. In this paper, we suppose that "Smart" city developments should be sustainable, dynamic and participative. This paper addresses these three smart objectives in the context of urban risk assessment in Wallonia, Belgium. The sustainable, dynamic and participative solution includes (i) land cover and land use mapping using remote sensing and GIS, (ii) population density mapping using dasymetric mapping, (iii) predictive modelling of land use changes and population dynamics and (iv) risk assessment. The comprehensive and long-term vision of the territory should help to draw sustainable spatial planning policies, to adapt remote sensing acquisition, to update GIS data and to refine risk assessment from regional to city scale.

Using integrated modeling for generating watershed-scale dynamic flood maps for Hurricane Harvey

NASA Astrophysics Data System (ADS)

Saksena, S.; Dey, S.; Merwade, V.; Singhofen, P. J.

2017-12-01

Hurricane Harvey, which was categorized as a 1000-year return period event, produced unprecedented rainfall and flooding in Houston. Although the expected rainfall was forecasted much before the event, there was no way to identify which regions were at higher risk of flooding, the magnitude of flooding, and when the impacts of rainfall would be highest. The inability to predict the location, duration, and depth of flooding created uncertainty over evacuation planning and preparation. This catastrophic event highlighted that the conventional approach to managing flood risk using 100-year static flood inundation maps is inadequate because of its inability to predict flood duration and extents for 500-year or 1000-year return period events in real-time. The purpose of this study is to create models that can dynamically predict the impacts of rainfall and subsequent flooding, so that necessary evacuation and rescue efforts can be planned in advance. This study uses a 2D integrated surface water-groundwater model called ICPR (Interconnected Channel and Pond Routing) to simulate both the hydrology and hydrodynamics for Hurricane Harvey. The methodology involves using the NHD stream network to create a 2D model that incorporates rainfall, land use, vadose zone properties and topography to estimate streamflow and generate dynamic flood depths and extents. The results show that dynamic flood mapping captures the flood hydrodynamics more accurately and is able to predict the magnitude, extent and time of occurrence for extreme events such as Hurricane Harvey. Therefore, integrated modeling has the potential to identify regions that are more susceptible to flooding, which is especially useful for large-scale planning and allocation of resources for protection against future flood risk.

An integrated approach to flood hazard assessment on alluvial fans using numerical modeling, field mapping, and remote sensing

USGS Publications Warehouse

Pelletier, J.D.; Mayer, L.; Pearthree, P.A.; House, P.K.; Demsey, K.A.; Klawon, J.K.; Vincent, K.R.

2005-01-01

Millions of people in the western United States live near the dynamic, distributary channel networks of alluvial fans where flood behavior is complex and poorly constrained. Here we test a new comprehensive approach to alluvial-fan flood hazard assessment that uses four complementary methods: two-dimensional raster-based hydraulic modeling, satellite-image change detection, fieldbased mapping of recent flood inundation, and surficial geologic mapping. Each of these methods provides spatial detail lacking in the standard method and each provides critical information for a comprehensive assessment. Our numerical model simultaneously solves the continuity equation and Manning's equation (Chow, 1959) using an implicit numerical method. It provides a robust numerical tool for predicting flood flows using the large, high-resolution Digital Elevation Models (DEMs) necessary to resolve the numerous small channels on the typical alluvial fan. Inundation extents and flow depths of historic floods can be reconstructed with the numerical model and validated against field- and satellite-based flood maps. A probabilistic flood hazard map can also be constructed by modeling multiple flood events with a range of specified discharges. This map can be used in conjunction with a surficial geologic map to further refine floodplain delineation on fans. To test the accuracy of the numerical model, we compared model predictions of flood inundation and flow depths against field- and satellite-based flood maps for two recent extreme events on the southern Tortolita and Harquahala piedmonts in Arizona. Model predictions match the field- and satellite-based maps closely. Probabilistic flood hazard maps based on the 10 yr, 100 yr, and maximum floods were also constructed for the study areas using stream gage records and paleoflood deposits. The resulting maps predict spatially complex flood hazards that strongly reflect small-scale topography and are consistent with surficial geology. In contrast, FEMA Flood Insurance Rate Maps (FIRMs) based on the FAN model predict uniformly high flood risk across the study areas without regard for small-scale topography and surficial geology. ?? 2005 Geological Society of America.

An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Wang, Yunzhi; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Liu, Hong; Zheng, Bin

2016-03-01

In order to establish a new personalized breast cancer screening paradigm, it is critically important to accurately predict the short-term risk of a woman having image-detectable cancer after a negative mammographic screening. In this study, we developed and tested a novel short-term risk assessment model based on deep learning method. During the experiment, a number of 270 "prior" negative screening cases was assembled. In the next sequential ("current") screening mammography, 135 cases were positive and 135 cases remained negative. These cases were randomly divided into a training set with 200 cases and a testing set with 70 cases. A deep learning based computer-aided diagnosis (CAD) scheme was then developed for the risk assessment, which consists of two modules: adaptive feature identification module and risk prediction module. The adaptive feature identification module is composed of three pairs of convolution-max-pooling layers, which contains 20, 10, and 5 feature maps respectively. The risk prediction module is implemented by a multiple layer perception (MLP) classifier, which produces a risk score to predict the likelihood of the woman developing short-term mammography-detectable cancer. The result shows that the new CAD-based risk model yielded a positive predictive value of 69.2% and a negative predictive value of 74.2%, with a total prediction accuracy of 71.4%. This study demonstrated that applying a new deep learning technology may have significant potential to develop a new short-term risk predicting scheme with improved performance in detecting early abnormal symptom from the negative mammograms.

Improving Flood Predictions in Data-Scarce Basins

NASA Astrophysics Data System (ADS)

Vimal, Solomon; Zanardo, Stefano; Rafique, Farhat; Hilberts, Arno

2017-04-01

Flood modeling methodology at Risk Management Solutions Ltd. has evolved over several years with the development of continental scale flood risk models spanning most of Europe, the United States and Japan. Pluvial (rain fed) and fluvial (river fed) flood maps represent the basis for the assessment of regional flood risk. These maps are derived by solving the 1D energy balance equation for river routing and 2D shallow water equation (SWE) for overland flow. The models are run with high performance computing and GPU based solvers as the time taken for simulation is large in such continental scale modeling. These results are validated with data from authorities and business partners, and have been used in the insurance industry for many years. While this methodology has been proven extremely effective in regions where the quality and availability of data are high, its application is very challenging in other regions where data are scarce. This is generally the case for low and middle income countries, where simpler approaches are needed for flood risk modeling and assessment. In this study we explore new methods to make use of modeling results obtained in data-rich contexts to improve predictive ability in data-scarce contexts. As an example, based on our modeled flood maps in data-rich countries, we identify statistical relationships between flood characteristics and topographic and climatic indicators, and test their generalization across physical domains. Moreover, we apply the Height Above Nearest Drainage (HAND)approach to estimate "probable" saturated areas for different return period flood events as functions of basin characteristics. This work falls into the well-established research field of Predictions in Ungauged Basins.

Modelling Soil Erosion in the Densu River Basin Using RUSLE and GIS Tools.

PubMed

Ashiagbori, G; Forkuo, E K; Laari, P; Aabeyir, R

2014-07-01

Soil erosion involves detachment and transport of soil particles from top soil layers, degrading soil quality and reducing the productivity of affected lands. Soil eroded from the upland catchment causes depletion of fertile agricultural land and the resulting sediment deposited at the river networks creates river morphological change and reservoir sedimentation problems. However, land managers and policy makers are more interested in the spatial distribution of soil erosion risk than in absolute values of soil erosion loss. The aim of this paper is to model the spatial distribution of soil erosion in Densu River Basin of Ghana using RUSLE and GIS tools and to use the model to explore the relationship between erosion susceptibility, slope and land use/land cover (LULC) in the Basin. The rainfall map, digital elevation model, soil type map, and land cover map, were input data in the soil erosion model developed. This model was then categorized into four different erosion risk classes. The developed soil erosion map was then overlaid with the slope and LULC maps of the study area to explore their effects on erosion susceptibility of the soil in the Densu River Basin. The Model, predicted 88% of the basin as low erosion risk and 6% as moderate erosion risk, 3% as high erosion risk and 3% as severe risk. The high and severe erosion areas were distributed mainly within the areas of high slope gradient and also sections of the moderate forest LULC class. Also, the areas within the moderate forest LULC class found to have high erosion risk, had an intersecting high erodibility soil group.

Development of an In Silico Metabolic Simulator and Searchable Metabolism Database for Chemical Risk Assessments

EPA Science Inventory

The US EPA is faced with long lists of chemicals that need to be assessed for hazard, and a gap in evaluating chemical risk is accounting for metabolic activation resulting in increased toxicity. The goals of this project are to develop a capability to predict metabolic maps of x...

The need for sustained and integrated high-resolution mapping of dynamic coastal environments

USGS Publications Warehouse

Stockdon, Hilary F.; Lillycrop, Jeff W.; Howd, Peter A.; Wozencraft, Jennifer M.

2007-01-01

The evolution of the United States' coastal zone response to both human activities and natural processes is dynamic. Coastal resource and population protection requires understanding, in detail, the processes needed for change as well as the physical setting. Sustained coastal area mapping allows change to be documented and baseline conditions to be established, as well as future behavior to be predicted in conjunction with physical process models. Hyperspectral imagers and airborne lidars, as well as other recent mapping technology advances, allow rapid national scale land use information and high-resolution elevation data collection. Coastal hazard risk evaluation has critical dependence on these rich data sets. A fundamental storm surge model parameter in predicting flooding location, for example, is coastal elevation data, and a foundation in identifying the most vulnerable populations and resources is land use maps. A wealth of information for physical change process study, coastal resource and community management and protection, and coastal area hazard vulnerability determination, is available in a comprehensive national coastal mapping plan designed to take advantage of recent mapping technology progress and data distribution, management, and collection.

Using the information value method in a geographic information system and remote sensing for malaria mapping: a case study from India.

PubMed

Rai, Praveen Kumar; Nathawat, Mahendra Singh; Rai, Shalini

2013-01-01

This paper explores the scope of malaria-susceptibility modelling to predict malaria occurrence in an area. An attempt has been made in Varanasi district, India, to evaluate the status of malaria disease and to develop a model by which malaria-prone zones could be predicted using five classes of relative malaria susceptibility, i.e.very low, low, moderate, high and very high categories. The information value (Info Val) method was used to assess malaria occurrence and various time-were used as the independent variables. A geographical information system (GIS) is employed to investigate associations between such variables and distribution of different mosquitoes responsible for malaria transmission. Accurate prediction of risk depends on a number of variables, such as land use, NDVI, climatic factors, population, distance to health centres, ponds, streams and roads etc., all of which have an influence on malaria transmission or reporting. Climatic factors, particularly rainfall, temperature and relative humidity, are known to have a major influence on the biology of mosquitoes. To produce a malaria-susceptibility map using this method, weightings are calculated for various classes in each group. The groups are then superimposed to prepare a Malaria Susceptibility Index (MSI) map. We found that 3.87% of the malaria cases were found in areas with a low malaria-susceptibility level predicted from the model, whereas 39.86% and 26.29% of malaria cases were found in predicted high and very high susceptibility level areas, respectively. Malaria susceptibility modelled using a GIS may have a role in predicting the risks of malaria and enable public health interventions to be better targeted.

A review of geographic information system and remote sensing with applications to the epidemiology and control of schistosomiasis in China.

PubMed

Yang, Guo-Jing; Vounatsou, Penelope; Zhou, Xiao-Nong; Utzinger, Jürg; Tanner, Marcel

2005-01-01

Geographic information system (GIS) and remote sensing (RS) technologies offer new opportunities for rapid assessment of endemic areas, provision of reliable estimates of populations at risk, prediction of disease distributions in areas that lack baseline data and are difficult to access, and guidance of intervention strategies, so that scarce resources can be allocated in a cost-effective manner. Here, we focus on the epidemiology and control of schistosomiasis in China and review GIS and RS applications to date. These include mapping prevalence and intensity data of Schistosoma japonicum at a large scale, and identifying and predicting suitable habitats for Oncomelania hupensis, the intermediate host snail of S. japonicum, at a small scale. Other prominent applications have been the prediction of infection risk due to ecological transformations, particularly those induced by floods and water resource developments, and the potential impact of climate change. We also discuss the limitations of the previous work, and outline potential new applications of GIS and RS techniques, namely quantitative GIS, WebGIS, and utilization of emerging satellite information, as they hold promise to further enhance infection risk mapping and disease prediction. Finally, we stress current research needs to overcome some of the remaining challenges of GIS and RS applications for schistosomiasis, so that further and sustained progress can be made to control this disease in China and elsewhere.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions.

PubMed

Truong, Tuyet T A; Hardy, Giles E St J; Andrew, Margaret E

2017-01-01

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam's lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions

PubMed Central

Truong, Tuyet T. A.; Hardy, Giles E. St. J.; Andrew, Margaret E.

2017-01-01

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam’s lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species. PMID:28555147

First-trimester mean arterial blood pressure and the risk of preeclampsia: The Great Obstetrical Syndromes (GOS) study.

PubMed

Gasse, Cédric; Boutin, Amélie; Coté, Maxime; Chaillet, Nils; Bujold, Emmanuel; Demers, Suzanne

2018-04-01

To estimate the predictive value of first-trimester mean arterial pressure (MAP) for the hypertensive disorders of pregnancy (HDP). We performed a prospective cohort study of nulliparous women recruited at 11 0/7 -13 6/7  weeks. MAP was calculated from blood pressure measured on both arms simultaneously using an automated device taking a series of recordings until blood pressure stability was reached. MAP was reported as multiples of the median adjusted for gestational age. Participants were followed for development of gestational hypertension (GH), preeclampsia (PE), preterm PE (<37 weeks) and early-onset (EO) PE (<34 weeks). Receiver operating characteristic curves and the area under the curve (AUC) were used to estimate the predictive values of MAP. Multivariate logistic regressions were used to develop predictive models combining MAP and maternal characteristics. We obtained complete follow-up in 4700 (99%) out of 4749 eligible participants. GH without PE was observed in 250 (5.3%) participants, and PE in 241 (5.1%), including 33 (0.7%) preterm PE and 10 (0.2%) EO-PE. First-trimester MAP was associated with GH (AUC: 0.77; 95%CI: 0.74-0.80); term PE (0.73; 95%CI: 0.70-0.76), preterm PE (0.80; 95%CI: 0.73-0.87) and EO-PE (0.79; 95%CI: 0.62-0.96). At a 10% false-positive rate, first-trimester MAP could have predicted 39% of GH, 34% of term PE, 48% of preterm PE and 60% of EO-PE. The addition of maternal characteristics improved the predictive values (to 40%, 37%, 55% and 70%, respectively). First-trimester MAP is a strong predictor of GH and PE in nulliparous women. Copyright © 2017 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. All rights reserved.

Role of malnutrition and parasite infections in the spatial variation in children's anaemia risk in northern Angola.

PubMed

Soares Magalhães, Ricardo J; Langa, Antonio; Pedro, João Mário; Sousa-Figueiredo, José Carlos; Clements, Archie C A; Vaz Nery, Susana

2013-05-01

Anaemia is known to have an impact on child development and mortality and is a severe public health problem in most countries in sub-Saharan Africa. We investigated the consistency between ecological and individual-level approaches to anaemia mapping by building spatial anaemia models for children aged ≤15 years using different modelling approaches. We aimed to (i) quantify the role of malnutrition, malaria, Schistosoma haematobium and soil-transmitted helminths (STHs) in anaemia endemicity; and (ii) develop a high resolution predictive risk map of anaemia for the municipality of Dande in northern Angola. We used parasitological survey data for children aged ≤15 years to build Bayesian geostatistical models of malaria (PfPR≤15), S. haematobium, Ascaris lumbricoides and Trichuris trichiura and predict small-scale spatial variations in these infections. Malnutrition, PfPR≤15, and S. haematobium infections were significantly associated with anaemia risk. An estimated 12.5%, 15.6% and 9.8% of anaemia cases could be averted by treating malnutrition, malaria and S. haematobium, respectively. Spatial clusters of high risk of anaemia (>86%) were identified. Using an individual-level approach to anaemia mapping at a small spatial scale, we found that anaemia in children aged ≤15 years is highly heterogeneous and that malnutrition and parasitic infections are important contributors to the spatial variation in anaemia risk. The results presented in this study can help inform the integration of the current provincial malaria control programme with ancillary micronutrient supplementation and control of neglected tropical diseases such as urogenital schistosomiasis and STH infections.

Benchmarking an operational procedure for rapid flood mapping and risk assessment in Europe

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Salamon, Peter; Kalas, Milan; Bianchi, Alessandra; Feyen, Luc

2016-04-01

The development of real-time methods for rapid flood mapping and risk assessment is crucial to improve emergency response and mitigate flood impacts. This work describes the benchmarking of an operational procedure for rapid flood risk assessment based on the flood predictions issued by the European Flood Awareness System (EFAS). The daily forecasts produced for the major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations, based on the hydro-meteorological dataset of EFAS. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in near real-time in terms of flood prone areas, potential economic damage, affected population, infrastructures and cities. An extensive testing of the operational procedure is carried out using the catastrophic floods of May 2014 in Bosnia-Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-derived flood footprints, while ground-based estimations of economic damage and affected population is compared against modelled estimates. We evaluated the skill of flood hazard and risk estimations derived from EFAS flood forecasts with different lead times and combinations. The assessment includes a comparison of several alternative approaches to produce and present the information content, in order to meet the requests of EFAS users. The tests provided good results and showed the potential of the developed real-time operational procedure in helping emergency response and management.

An integrated approach of analytical network process and fuzzy based spatial decision making systems applied to landslide risk mapping

NASA Astrophysics Data System (ADS)

Abedi Gheshlaghi, Hassan; Feizizadeh, Bakhtiar

2017-09-01

Landslides in mountainous areas render major damages to residential areas, roads, and farmlands. Hence, one of the basic measures to reduce the possible damage is by identifying landslide-prone areas through landslide mapping by different models and methods. The purpose of conducting this study is to evaluate the efficacy of a combination of two models of the analytical network process (ANP) and fuzzy logic in landslide risk mapping in the Azarshahr Chay basin in northwest Iran. After field investigations and a review of research literature, factors affecting the occurrence of landslides including slope, slope aspect, altitude, lithology, land use, vegetation density, rainfall, distance to fault, distance to roads, distance to rivers, along with a map of the distribution of occurred landslides were prepared in GIS environment. Then, fuzzy logic was used for weighting sub-criteria, and the ANP was applied to weight the criteria. Next, they were integrated based on GIS spatial analysis methods and the landslide risk map was produced. Evaluating the results of this study by using receiver operating characteristic curves shows that the hybrid model designed by areas under the curve 0.815 has good accuracy. Also, according to the prepared map, a total of 23.22% of the area, amounting to 105.38 km2, is in the high and very high-risk class. Results of this research are great of importance for regional planning tasks and the landslide prediction map can be used for spatial planning tasks and for the mitigation of future hazards in the study area.

Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models.

PubMed

Scholte, Ronaldo G C; Schur, Nadine; Bavia, Maria E; Carvalho, Edgar M; Chammartin, Frédérique; Utzinger, Jürg; Vounatsou, Penelope

2013-11-01

Soil-transmitted helminths (Ascaris lumbricoides, Trichuris trichiura and hookworm) negatively impact the health and wellbeing of hundreds of millions of people, particularly in tropical and subtropical countries, including Brazil. Reliable maps of the spatial distribution and estimates of the number of infected people are required for the control and eventual elimination of soil-transmitted helminthiasis. We used advanced Bayesian geostatistical modelling, coupled with geographical information systems and remote sensing to visualize the distribution of the three soil-transmitted helminth species in Brazil. Remotely sensed climatic and environmental data, along with socioeconomic variables from readily available databases were employed as predictors. Our models provided mean prevalence estimates for A. lumbricoides, T. trichiura and hookworm of 15.6%, 10.1% and 2.5%, respectively. By considering infection risk and population numbers at the unit of the municipality, we estimate that 29.7 million Brazilians are infected with A. lumbricoides, 19.2 million with T. trichiura and 4.7 million with hookworm. Our model-based maps identified important risk factors related to the transmission of soiltransmitted helminths and confirm that environmental variables are closely associated with indices of poverty. Our smoothed risk maps, including uncertainty, highlight areas where soil-transmitted helminthiasis control interventions are most urgently required, namely in the North and along most of the coastal areas of Brazil. We believe that our predictive risk maps are useful for disease control managers for prioritising control interventions and for providing a tool for more efficient surveillance-response mechanisms.

Landslide risk mapping and modeling in China

NASA Astrophysics Data System (ADS)

Li, W.; Hong, Y.

2015-12-01

Under circumstances of global climate change, tectonic stress and human effect, landslides are among the most frequent and severely widespread natural hazards on Earth, as demonstrated in the World Atlas of Natural Hazards (McGuire et al., 2004). Every year, landslide activities cause serious economic loss as well as casualties (Róbert et al., 2005). How landslides can be monitored and predicted is an urgent research topic of the international landslide research community. Particularly, there is a lack of high quality and updated landslide risk maps and guidelines that can be employed to better mitigate and prevent landslide disasters in many emerging regions, including China (Hong, 2007). Since the 1950s, landslide events have been recorded in the statistical yearbooks, newspapers, and monographs in China. As disasters have been increasingly concerned by the government and the public, information about landslide events is becoming available from online news reports (Liu et al., 2012).This study presents multi-scale landslide risk mapping and modeling in China. At the national scale, based on historical data and practical experiences, we carry out landslide susceptibility and risk mapping by adopting a statistical approach and pattern recognition methods to construct empirical models. Over the identified landslide hot-spot areas, we further evaluate the slope-stability for each individual site (Sidle and Hirotaka, 2006), with the ultimate goal to set up a space-time multi-scale coupling system of Landslide risk mapping and modeling for landslide hazard monitoring and early warning.

Paleontological baselines for evaluating extinction risk in the modern oceans

NASA Astrophysics Data System (ADS)

Finnegan, Seth; Anderson, Sean C.; Harnik, Paul G.; Simpson, Carl; Tittensor, Derek P.; Byrnes, Jarrett E.; Finkel, Zoe V.; Lindberg, David R.; Liow, Lee Hsiang; Lockwood, Rowan; Lotze, Heike K.; McClain, Craig R.; McGuire, Jenny L.; O'Dea, Aaron; Pandolfi, John M.

2015-05-01

Marine taxa are threatened by anthropogenic impacts, but knowledge of their extinction vulnerabilities is limited. The fossil record provides rich information on past extinctions that can help predict biotic responses. We show that over 23 million years, taxonomic membership and geographic range size consistently explain a large proportion of extinction risk variation in six major taxonomic groups. We assess intrinsic risk—extinction risk predicted by paleontologically calibrated models—for modern genera in these groups. Mapping the geographic distribution of these genera identifies coastal biogeographic provinces where fauna with high intrinsic risk are strongly affected by human activity or climate change. Such regions are disproportionately in the tropics, raising the possibility that these ecosystems may be particularly vulnerable to future extinctions. Intrinsic risk provides a prehuman baseline for considering current threats to marine biodiversity.

A new world malaria map: Plasmodium falciparum endemicity in 2010.

PubMed

Gething, Peter W; Patil, Anand P; Smith, David L; Guerra, Carlos A; Elyazar, Iqbal R F; Johnston, Geoffrey L; Tatem, Andrew J; Hay, Simon I

2011-12-20

Transmission intensity affects almost all aspects of malaria epidemiology and the impact of malaria on human populations. Maps of transmission intensity are necessary to identify populations at different levels of risk and to evaluate objectively options for disease control. To remain relevant operationally, such maps must be updated frequently. Following the first global effort to map Plasmodium falciparum malaria endemicity in 2007, this paper describes the generation of a new world map for the year 2010. This analysis is extended to provide the first global estimates of two other metrics of transmission intensity for P. falciparum that underpin contemporary questions in malaria control: the entomological inoculation rate (PfEIR) and the basic reproductive number (PfR). Annual parasite incidence data for 13,449 administrative units in 43 endemic countries were sourced to define the spatial limits of P. falciparum transmission in 2010 and 22,212 P. falciparum parasite rate (PfPR) surveys were used in a model-based geostatistical (MBG) prediction to create a continuous contemporary surface of malaria endemicity within these limits. A suite of transmission models were developed that link PfPR to PfEIR and PfR and these were fitted to field data. These models were combined with the PfPR map to create new global predictions of PfEIR and PfR. All output maps included measured uncertainty. An estimated 1.13 and 1.44 billion people worldwide were at risk of unstable and stable P. falciparum malaria, respectively. The majority of the endemic world was predicted with a median PfEIR of less than one and a median PfRc of less than two. Values of either metric exceeding 10 were almost exclusive to Africa. The uncertainty described in both PfEIR and PfR was substantial in regions of intense transmission. The year 2010 has a particular significance as an evaluation milestone for malaria global health policy. The maps presented here contribute to a rational basis for control and elimination decisions and can serve as a baseline assessment as the global health community looks ahead to the next series of milestones targeted at 2015.

A remote sensing-assisted risk rating study to predict oak decline and recovery in the Missouri Ozark Highlands, USA

Treesearch

Cuizhen Wang; Hong S. He; John M. Kabrick

2008-01-01

Forests in the Ozark Highlands underwent widespread oak decline affected by severe droughts in 1999-2000. In this study, the differential normalized difference water index was calculated to detect crown dieback. A multi-factor risk rating system was built to map risk levels of stands. As a quick response to drought, decline in 2000 mostly occurred in stands at low to...

PREDICTING RELATIVE RISK OF INVASION BY SALTCEDAR AND MUD SNAILS IN RIVER NETWORKS UNDER DIFFERENT SCENARIOS OF CLIMATE CHANGE AND DAM OPERATIONS IN THE WESTERN UNITED STATES

EPA Science Inventory

This synthetic, multi-scale approach will generate a sequence of spatially explicit maps that will provide science guidance to support strategic decision-making regarding the spatially-distributed risk of, and possible adaptation to, the spread of invasive species at local to ...

A risk management model for familial breast cancer: A new application using Fuzzy Cognitive Map method.

PubMed

Papageorgiou, Elpiniki I; Jayashree Subramanian; Karmegam, Akila; Papandrianos, Nikolaos

2015-11-01

Breast cancer is the most deadly disease affecting women and thus it is natural for women aged 40-49 years (who have a family history of breast cancer or other related cancers) to assess their personal risk for developing familial breast cancer (FBC). Besides, as each individual woman possesses different levels of risk of developing breast cancer depending on their family history, genetic predispositions and personal medical history, individualized care setting mechanism needs to be identified so that appropriate risk assessment, counseling, screening, and prevention options can be determined by the health care professionals. The presented work aims at developing a soft computing based medical decision support system using Fuzzy Cognitive Map (FCM) that assists health care professionals in deciding the individualized care setting mechanisms based on the FBC risk level of the given women. The FCM based FBC risk management system uses NHL to learn causal weights from 40 patient records and achieves a 95% diagnostic accuracy. The results obtained from the proposed model are in concurrence with the comprehensive risk evaluation tool based on Tyrer-Cuzick model for 38/40 patient cases (95%). Besides, the proposed model identifies high risk women by calculating higher accuracy of prediction than the standard Gail and NSAPB models. The testing accuracy of the proposed model using 10-fold cross validation technique outperforms other standard machine learning based inference engines as well as previous FCM-based risk prediction methods for BC. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Mapping eastern equine encephalitis virus risk for white-tailed deer in Michigan

PubMed Central

Downs, Joni A.; Hyzer, Garrett; Marion, Eric; Smith, Zachary J.; Kelen, Patrick Vander; Unnasch, Thomas R.

2015-01-01

Eastern equine encephalitis (EEE) is a mosquito-borne viral disease that is often fatal to humans and horses. Some species including white-tailed deer and passerine birds can survive infection with the EEE virus (EEEV) and develop antibodies that can be detected using laboratory techniques. In this way, collected serum samples from free ranging white-tailed deer can be used to monitor the presence of the virus in ecosystems. This study developed and tested a risk index model designed to predict EEEV activity in white-tailed deer in a three-county area of Michigan. The model evaluates EEEV risk on a continuous scale from 0.0 (no measurable risk) to 1.0 (highest possible risk). High risk habitats are identified as those preferred by white-tailed deer that are also located in close proximity to an abundance of wetlands and lowland forests, which support disease vectors and hosts. The model was developed based on relevant literature and was tested with known locations of infected deer that showed neurological symptoms. The risk index model accurately predicted the known locations, with the mean value for those sites equal to the 94th percentile of values in the study area. The risk map produced by the model could be used refine future EEEV monitoring efforts that use serum samples from free-ranging white-tailed deer to monitor viral activity. Alternatively, it could be used focus educational efforts targeted toward deer hunters that may have elevated risks of infection. PMID:26494931

Impacts of Climate Change on Native Landcover: Seeking Future Climatic Refuges.

PubMed

Zanin, Marina; Mangabeira Albernaz, Ana Luisa

2016-01-01

Climate change is a driver for diverse impacts on global biodiversity. We investigated its impacts on native landcover distribution in South America, seeking to predict its effect as a new force driving habitat loss and population isolation. Moreover, we mapped potential future climatic refuges, which are likely to be key areas for biodiversity conservation under climate change scenarios. Climatically similar native landcovers were aggregated using a decision tree, generating a reclassified landcover map, from which 25% of the map's coverage was randomly selected to fuel distribution models. We selected the best geographical distribution models among twelve techniques, validating the predicted distribution for current climate with the landcover map and used the best technique to predict the future distribution. All landcover categories showed changes in area and displacement of the latitudinal/longitudinal centroid. Closed vegetation was the only landcover type predicted to expand its distributional range. The range contractions predicted for other categories were intense, even suggesting extirpation of the sparse vegetation category. The landcover refuges under future climate change represent a small proportion of the South American area and they are disproportionately represented and unevenly distributed, predominantly occupying five of 26 South American countries. The predicted changes, regardless of their direction and intensity, can put biodiversity at risk because they are expected to occur in the near future in terms of the temporal scales of ecological and evolutionary processes. Recognition of the threat of climate change allows more efficient conservation actions.

Effective Mitigation and Adaptation Strategies for Public Health Impacts of Heatwaves for Brookline, MA

NASA Astrophysics Data System (ADS)

Jalalzadeh Fard, B.; Hassanzadeh, H.; Bhatia, U.; Ganguly, A. R.

2016-12-01

Studies on urban areas show a significant increase in frequency and intensity of heatwaves over the past decades, and predict the same trend for future. Since heatwaves have been responsible for a large number of life losses, urgent adaptation and mitigation strategies are required in the policy and decision making level for a sustainable urban planning. The Sustainability and Data Sciences Laboratory at Northeastern University, under the aegis of Thriving Earth Exchange of AGU, is working with the town of Brookline to understand the potential public health impacts of anticipated heatwaves. We consider the most important social and physical factors to obtain vulnerability and exposure parameters for each census block group of the town. Utilizing remote sensing data, we locate Urban Heat Islands (UHIs) during a recent heatwave event, as the hazard parameter. We then create priority risk map using the risk framework. Our analyses show spatial correlations between the UHIs and social factors such as poverty, and physical factors such as land cover variations. Furthermore, we investigate the future heatwave frequency and intensity increases by analyzing the climate models predictions. For future changes of UHIs, land cover changes are investigated using available predictive data. Also, socioeconomic predictions are carried out to complete the futuristic models of heatwave risks. Considering plausible scenarios for Brookline, we develop different risk maps based on the vulnerability, exposure and hazard parameters. Eventually, we suggest guidelines for Heatwave Action Plans for prioritizing effective mitigation and adaptation strategies in urban planning for the town of Brookline.

Targeting trachoma control through risk mapping: the example of Southern Sudan.

PubMed

Clements, Archie C A; Kur, Lucia W; Gatpan, Gideon; Ngondi, Jeremiah M; Emerson, Paul M; Lado, Mounir; Sabasio, Anthony; Kolaczinski, Jan H

2010-08-17

Trachoma is a major cause of blindness in Southern Sudan. Its distribution has only been partially established and many communities in need of intervention have therefore not been identified or targeted. The present study aimed to develop a tool to improve targeting of survey and control activities. A national trachoma risk map was developed using Bayesian geostatistics models, incorporating trachoma prevalence data from 112 geo-referenced communities surveyed between 2001 and 2009. Logistic regression models were developed using active trachoma (trachomatous inflammation follicular and/or trachomatous inflammation intense) in 6345 children aged 1-9 years as the outcome, and incorporating fixed effects for age, long-term average rainfall (interpolated from weather station data) and land cover (i.e. vegetation type, derived from satellite remote sensing), as well as geostatistical random effects describing spatial clustering of trachoma. The model predicted the west of the country to be at no or low trachoma risk. Trachoma clusters in the central, northern and eastern areas had a radius of 8 km after accounting for the fixed effects. In Southern Sudan, large-scale spatial variation in the risk of active trachoma infection is associated with aridity. Spatial prediction has identified likely high-risk areas to be prioritized for more data collection, potentially to be followed by intervention.

Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan

PubMed Central

Clements, Archie C. A.; Kur, Lucia W.; Gatpan, Gideon; Ngondi, Jeremiah M.; Emerson, Paul M.; Lado, Mounir; Sabasio, Anthony; Kolaczinski, Jan H.

2010-01-01

Background Trachoma is a major cause of blindness in Southern Sudan. Its distribution has only been partially established and many communities in need of intervention have therefore not been identified or targeted. The present study aimed to develop a tool to improve targeting of survey and control activities. Methods/Principal Findings A national trachoma risk map was developed using Bayesian geostatistics models, incorporating trachoma prevalence data from 112 geo-referenced communities surveyed between 2001 and 2009. Logistic regression models were developed using active trachoma (trachomatous inflammation follicular and/or trachomatous inflammation intense) in 6345 children aged 1–9 years as the outcome, and incorporating fixed effects for age, long-term average rainfall (interpolated from weather station data) and land cover (i.e. vegetation type, derived from satellite remote sensing), as well as geostatistical random effects describing spatial clustering of trachoma. The model predicted the west of the country to be at no or low trachoma risk. Trachoma clusters in the central, northern and eastern areas had a radius of 8 km after accounting for the fixed effects. Conclusion In Southern Sudan, large-scale spatial variation in the risk of active trachoma infection is associated with aridity. Spatial prediction has identified likely high-risk areas to be prioritized for more data collection, potentially to be followed by intervention. PMID:20808910

FIGO stage IIIC endometrial cancer identification among patients with complex atypical hyperplasia, grade 1 and 2 endometrioid endometrial cancer: laparoscopic indocyanine green sentinel lymph node mapping versus frozen section of the uterus, why get around the problem?

PubMed

Papadia, Andrea; Gasparri, Maria Luisa; Siegenthaler, Franziska; Imboden, Sara; Mohr, Stefan; Mueller, Michael D

2017-03-01

To compare two surgical strategies used to identify lymph node metastases in patients with preoperative diagnosis of complex atypical hyperplasia (CAH), grade 1 and 2 endometrial cancer (EC). Data on patients with preoperative diagnosis of CAH, grade 1 and 2 EC undergoing laparoscopic indocyanine green (ICG) sentinel lymph node (SLN) mapping followed by frozen section of the uterus were collected. When risk factors were identified at frozen section, patients were subjected to a systematic lymphadenectomy. False negative (FN) rates, negative predictive values (NPV), positive predictive values (PPV) and correlation with stage IIIC EC were calculated for the systematic lymphadenectomy based on frozen section of the uterus and for the SLN mapping. Six (9.5%) out of 63 patients had lymph nodal metastases. Based on frozen section of the uterus, 22 (34.9%) and 15 (22.2%) patients underwent a pelvic and a pelvic and paraaortic lymphadenectomy, respectively. Five patients with stage IIIC disease were identified with a FN rate of 16.7% and a NPV and PPV of 97.6 and 27.3%, respectively. Overall and bilateral detection rates of ICG SLN mapping were 100 and 97.6%, respectively; no FN were recorded. The identification of patients with stage IIIC disease with ICG SLN mapping showed a NPV and PPV of 100%. Correlation between indication to lymphadenectomy and stage IIIC disease was poor (κ = 0.244) when based on frozen section of the uterus and excellent (κ = 1) when based on SLN mapping. ICG SLN mapping reduces the number of unnecessary systematic lymphadenectomies and the risk of underdiagnosing patients with metastatic lymph nodes.

Towards a Global Land Subsidence Map

NASA Astrophysics Data System (ADS)

Erkens, G.; Kooi, H.; Sutanudjaja, E.

2017-12-01

Land subsidence is a global problem, but a global land subsidence map is not available yet. Such map is crucial to raise global awareness of land subsidence, as land subsidence causes extensive damage (probably in the order of billions of dollars annually). Insights in the rates of subsidence are particularly relevant for low lying deltas and coastal zones, for which any further loss in elevation is unwanted. With the global land subsidence map relative sea level rise predictions may be improved, contributing to global flood risk calculations. In this contribution, we discuss the approach and progress we have made so far in making a global land subsidence map. The first results will be presented and discussed, and we give an outlook on the work needed to derive a global land subsidence map.

Large-scale drivers of malaria and priority areas for prevention and control in the Brazilian Amazon region using a novel multi-pathogen geospatial model.

PubMed

Valle, Denis; Lima, Joanna M Tucker

2014-11-20

Most of the malaria burden in the Americas is concentrated in the Brazilian Amazon but a detailed spatial characterization of malaria risk has yet to be undertaken. Utilizing 2004-2008 malaria incidence data collected from six Brazilian Amazon states, large-scale spatial patterns of malaria risk were characterized with a novel Bayesian multi-pathogen geospatial model. Data included 2.4 million malaria cases spread across 3.6 million sq km. Remotely sensed variables (deforestation rate, forest cover, rainfall, dry season length, and proximity to large water bodies), socio-economic variables (rural population size, income, and literacy rate, mortality rate for children age under five, and migration patterns), and GIS variables (proximity to roads, hydro-electric dams and gold mining operations) were incorporated as covariates. Borrowing information across pathogens allowed for better spatial predictions of malaria caused by Plasmodium falciparum, as evidenced by a ten-fold cross-validation. Malaria incidence for both Plasmodium vivax and P. falciparum tended to be higher in areas with greater forest cover. Proximity to gold mining operations was another important risk factor, corroborated by a positive association between migration rates and malaria incidence. Finally, areas with a longer dry season and areas with higher average rural income tended to have higher malaria risk. Risk maps reveal striking spatial heterogeneity in malaria risk across the region, yet these mean disease risk surface maps can be misleading if uncertainty is ignored. By combining mean spatial predictions with their associated uncertainty, several sites were consistently classified as hotspots, suggesting their importance as priority areas for malaria prevention and control. This article provides several contributions. From a methodological perspective, the benefits of jointly modelling multiple pathogens for spatial predictions were illustrated. In addition, maps of mean disease risk were contrasted with that of statistically significant disease clusters, highlighting the critical importance of uncertainty in determining disease hotspots. From an epidemiological perspective, forest cover and proximity to gold mining operations were important large-scale drivers of disease risk in the region. Finally, the hotspot in Western Acre was identified as the area that should receive highest priority from the Brazilian national malaria prevention and control programme.

Real time forest fire warning and forest fire risk zoning: a Vietnamese case study

NASA Astrophysics Data System (ADS)

Chu, T.; Pham, D.; Phung, T.; Ha, A.; Paschke, M.

2016-12-01

Forest fire occurs seriously in Vietnam and has been considered as one of the major causes of forest lost and degradation. Several studies of forest fire risk warning were conducted using Modified Nesterov Index (MNI) but remaining shortcomings and inaccurate predictions that needs to be urgently improved. In our study, several important topographic and social factors such as aspect, slope, elevation, distance to residential areas and road system were considered as "permanent" factors while meteorological data were updated hourly using near-real-time (NRT) remotely sensed data (i.e. MODIS Terra/Aqua and TRMM) for the prediction and warning of fire. Due to the limited number of weather stations in Vietnam, data from all active stations (i.e. 178) were used with the satellite data to calibrate and upscale meteorological variables. These data with finer resolution were then used to generate MNI. The only significant "permanent" factors were selected as input variables based on the correlation coefficients that computed from multi-variable regression among true fire-burning (collected from 1/2007) and its spatial characteristics. These coefficients also used to suggest appropriate weight for computing forest fire risk (FR) model. Forest fire risk model was calculated from the MNI and the selected factors using fuzzy regression models (FRMs) and GIS based multi-criteria analysis. By this approach, the FR was slightly modified from MNI by the integrated use of various factors in our fire warning and prediction model. Multifactor-based maps of forest fire risk zone were generated from classifying FR into three potential danger levels. Fire risk maps were displayed using webgis technology that is easy for managing data and extracting reports. Reported fire-burnings thereafter have been used as true values for validating the forest fire risk. Fire probability has strong relationship with potential danger levels (varied from 5.3% to 53.8%) indicating that the higher potential risk, the more chance of fire happen. By adding spatial factors to continuous daily updated remote sensing based meteo-data, results are valuable for both mapping forest fire risk zones in short and long-term and real time fire warning in Vietnam. Key words: Near-real-time, forest fire warning, fuzzy regression model, remote sensing.

Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models.

PubMed

Raso, Giovanna; Schur, Nadine; Utzinger, Jürg; Koudou, Benjamin G; Tchicaya, Emile S; Rohner, Fabian; N'goran, Eliézer K; Silué, Kigbafori D; Matthys, Barbara; Assi, Serge; Tanner, Marcel; Vounatsou, Penelope

2012-05-09

In Côte d'Ivoire, an estimated 767,000 disability-adjusted life years are due to malaria, placing the country at position number 14 with regard to the global burden of malaria. Risk maps are important to guide control interventions, and hence, the aim of this study was to predict the geographical distribution of malaria infection risk in children aged <16 years in Côte d'Ivoire at high spatial resolution. Using different data sources, a systematic review was carried out to compile and geo-reference survey data on Plasmodium spp. infection prevalence in Côte d'Ivoire, focusing on children aged <16 years. The period from 1988 to 2007 was covered. A suite of Bayesian geo-statistical logistic regression models was fitted to analyse malaria risk. Non-spatial models with and without exchangeable random effect parameters were compared to stationary and non-stationary spatial models. Non-stationarity was modelled assuming that the underlying spatial process is a mixture of separate stationary processes in each ecological zone. The best fitting model based on the deviance information criterion was used to predict Plasmodium spp. infection risk for entire Côte d'Ivoire, including uncertainty. Overall, 235 data points at 170 unique survey locations with malaria prevalence data for individuals aged <16 years were extracted. Most data points (n = 182, 77.4%) were collected between 2000 and 2007. A Bayesian non-stationary regression model showed the best fit with annualized rainfall and maximum land surface temperature identified as significant environmental covariates. This model was used to predict malaria infection risk at non-sampled locations. High-risk areas were mainly found in the north-central and western area, while relatively low-risk areas were located in the north at the country border, in the north-east, in the south-east around Abidjan, and in the central-west between two high prevalence areas. The malaria risk map at high spatial resolution gives an important overview of the geographical distribution of the disease in Côte d'Ivoire. It is a useful tool for the national malaria control programme and can be utilized for spatial targeting of control interventions and rational resource allocation.

Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models

PubMed Central

2012-01-01

Background In Côte d’Ivoire, an estimated 767,000 disability-adjusted life years are due to malaria, placing the country at position number 14 with regard to the global burden of malaria. Risk maps are important to guide control interventions, and hence, the aim of this study was to predict the geographical distribution of malaria infection risk in children aged <16 years in Côte d’Ivoire at high spatial resolution. Methods Using different data sources, a systematic review was carried out to compile and geo-reference survey data on Plasmodium spp. infection prevalence in Côte d’Ivoire, focusing on children aged <16 years. The period from 1988 to 2007 was covered. A suite of Bayesian geo-statistical logistic regression models was fitted to analyse malaria risk. Non-spatial models with and without exchangeable random effect parameters were compared to stationary and non-stationary spatial models. Non-stationarity was modelled assuming that the underlying spatial process is a mixture of separate stationary processes in each ecological zone. The best fitting model based on the deviance information criterion was used to predict Plasmodium spp. infection risk for entire Côte d’Ivoire, including uncertainty. Results Overall, 235 data points at 170 unique survey locations with malaria prevalence data for individuals aged <16 years were extracted. Most data points (n = 182, 77.4%) were collected between 2000 and 2007. A Bayesian non-stationary regression model showed the best fit with annualized rainfall and maximum land surface temperature identified as significant environmental covariates. This model was used to predict malaria infection risk at non-sampled locations. High-risk areas were mainly found in the north-central and western area, while relatively low-risk areas were located in the north at the country border, in the north-east, in the south-east around Abidjan, and in the central-west between two high prevalence areas. Conclusion The malaria risk map at high spatial resolution gives an important overview of the geographical distribution of the disease in Côte d’Ivoire. It is a useful tool for the national malaria control programme and can be utilized for spatial targeting of control interventions and rational resource allocation. PMID:22571469

Mapping polycyclic aromatic hydrocarbon and total toxicity equivalent soil concentrations by visible and near-infrared spectroscopy.

PubMed

Okparanma, Reuben N; Coulon, Frederic; Mayr, Thomas; Mouazen, Abdul M

2014-09-01

In this study, we used data from spectroscopic models based on visible and near-infrared (vis-NIR; 350-2500 nm) diffuse reflectance spectroscopy to develop soil maps of polycyclic aromatic hydrocarbons (PAHs) and total toxicity equivalent concentrations (TTEC) of the PAH mixture. The TTEC maps were then used for hazard assessment of three petroleum release sites in the Niger Delta province of Nigeria (5.317°N, 6.467°E). As the paired t-test revealed, there were non-significant (p > 0.05) differences between soil maps of PAH and TTEC developed with chemically measured and vis-NIR-predicted data. Comparison maps of PAH showed a slight to moderate agreement between measured and predicted data (Kappa coefficient = 0.19-0.56). Using proposed generic assessment criteria, hazard assessment showed that the degree of action for site-specific risk assessment and/or remediation is similar for both measurement methods. This demonstrates that the vis-NIR method may be useful for monitoring hydrocarbon contamination in a petroleum release site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Risk mapping of West Nile virus circulation in Spain, 2015.

PubMed

Sánchez-Gómez, Amaya; Amela, Carmen; Fernández-Carrión, Eduardo; Martínez-Avilés, Marta; Sánchez-Vizcaíno, José Manuel; Sierra-Moros, María José

2017-05-01

West Nile fever is an emergent disease in Europe. The objective of this study was to conduct a predictive risk mapping of West Nile Virus (WNV) circulation in Spain based on historical data of WNV circulation. Areas of Spain with evidence of WNV circulation were mapped based on data from notifications to the surveillance systems and a literature review. A logistic regression-based spatial model was used to assess the probability of WNV circulation. Data were analyzed at municipality level. Mean temperatures of the period from June to October, presence of wetlands and presence of Special Protection Areas for birds were considered as potential predictors. Two predictors of WNV circulation were identified: higher temperature [adjusted odds ratio (AOR) 2.07, 95% CI 1.82-2.35, p<0.01] and presence of wetlands (3.37, 95% CI 1.89-5.99, p<0.01). Model validations indicated good predictions: area under the ROC curve was 0.895 (95% CI 0.870-0.919) for internal validation and 0.895 (95% CI 0.840-0.951) for external validation. This model could support improvements of WNV risk- based surveillance in Spain. The importance of a comprehensive surveillance for WNF, including human, animal and potential vectors is highlighted, which could additionally result in model refinements. Copyright © 2017 Elsevier B.V. All rights reserved.

REAL-TIME high-resolution urban surface water flood mapping to support flood emergency management

NASA Astrophysics Data System (ADS)

Guan, M.; Yu, D.; Wilby, R.

2016-12-01

Strong evidence has shown that urban flood risks will substantially increase because of urbanisation, economic growth, and more frequent weather extremes. To effectively manage these risks require not only traditional grey engineering solutions, but also a green management solution. Surface water flood risk maps based on return period are useful for planning purposes, but are limited for application in flood emergencies, because of the spatiotemporal heterogeneity of rainfall and complex urban topography. Therefore, a REAL-TIME urban surface water mapping system is highly beneficial to increasing urban resilience to surface water flooding. This study integrated numerical weather forecast and high-resolution urban surface water modelling into a real-time multi-level surface water mapping system for Leicester City in the UK. For rainfall forecast, the 1km composite rain radar from the Met Office was used, and we used the advanced rainfall-runoff model - FloodMap to predict urban surface water at both city-level (10m-20m) and street-level (2m-5m). The system is capable of projecting 3-hour urban surface water flood, driven by rainfall derived from UK Met Office radar. Moreover, this system includes real-time accessibility mapping to assist the decision-making of emergency responders. This will allow accessibility (e.g. time to travel) from individual emergency service stations (e.g. Fire & Rescue; Ambulance) to vulnerable places to be evaluated. The mapping results will support contingency planning by emergency responders ahead of potential flood events.

G6PD Deficiency Prevalence and Estimates of Affected Populations in Malaria Endemic Countries: A Geostatistical Model-Based Map

PubMed Central

Howes, Rosalind E.; Piel, Frédéric B.; Patil, Anand P.; Nyangiri, Oscar A.; Gething, Peter W.; Dewi, Mewahyu; Hogg, Mariana M.; Battle, Katherine E.; Padilla, Carmencita D.; Baird, J. Kevin; Hay, Simon I.

2012-01-01

Background Primaquine is a key drug for malaria elimination. In addition to being the only drug active against the dormant relapsing forms of Plasmodium vivax, primaquine is the sole effective treatment of infectious P. falciparum gametocytes, and may interrupt transmission and help contain the spread of artemisinin resistance. However, primaquine can trigger haemolysis in patients with a deficiency in glucose-6-phosphate dehydrogenase (G6PDd). Poor information is available about the distribution of individuals at risk of primaquine-induced haemolysis. We present a continuous evidence-based prevalence map of G6PDd and estimates of affected populations, together with a national index of relative haemolytic risk. Methods and Findings Representative community surveys of phenotypic G6PDd prevalence were identified for 1,734 spatially unique sites. These surveys formed the evidence-base for a Bayesian geostatistical model adapted to the gene's X-linked inheritance, which predicted a G6PDd allele frequency map across malaria endemic countries (MECs) and generated population-weighted estimates of affected populations. Highest median prevalence (peaking at 32.5%) was predicted across sub-Saharan Africa and the Arabian Peninsula. Although G6PDd prevalence was generally lower across central and southeast Asia, rarely exceeding 20%, the majority of G6PDd individuals (67.5% median estimate) were from Asian countries. We estimated a G6PDd allele frequency of 8.0% (interquartile range: 7.4–8.8) across MECs, and 5.3% (4.4–6.7) within malaria-eliminating countries. The reliability of the map is contingent on the underlying data informing the model; population heterogeneity can only be represented by the available surveys, and important weaknesses exist in the map across data-sparse regions. Uncertainty metrics are used to quantify some aspects of these limitations in the map. Finally, we assembled a database of G6PDd variant occurrences to inform a national-level index of relative G6PDd haemolytic risk. Asian countries, where variants were most severe, had the highest relative risks from G6PDd. Conclusions G6PDd is widespread and spatially heterogeneous across most MECs where primaquine would be valuable for malaria control and elimination. The maps and population estimates presented here reflect potential risk of primaquine-associated harm. In the absence of non-toxic alternatives to primaquine, these results represent additional evidence to help inform safe use of this valuable, yet dangerous, component of the malaria-elimination toolkit. Please see later in the article for the Editors' Summary PMID:23152723

G6PD deficiency prevalence and estimates of affected populations in malaria endemic countries: a geostatistical model-based map.

PubMed

Howes, Rosalind E; Piel, Frédéric B; Patil, Anand P; Nyangiri, Oscar A; Gething, Peter W; Dewi, Mewahyu; Hogg, Mariana M; Battle, Katherine E; Padilla, Carmencita D; Baird, J Kevin; Hay, Simon I

2012-01-01

Primaquine is a key drug for malaria elimination. In addition to being the only drug active against the dormant relapsing forms of Plasmodium vivax, primaquine is the sole effective treatment of infectious P. falciparum gametocytes, and may interrupt transmission and help contain the spread of artemisinin resistance. However, primaquine can trigger haemolysis in patients with a deficiency in glucose-6-phosphate dehydrogenase (G6PDd). Poor information is available about the distribution of individuals at risk of primaquine-induced haemolysis. We present a continuous evidence-based prevalence map of G6PDd and estimates of affected populations, together with a national index of relative haemolytic risk. Representative community surveys of phenotypic G6PDd prevalence were identified for 1,734 spatially unique sites. These surveys formed the evidence-base for a Bayesian geostatistical model adapted to the gene's X-linked inheritance, which predicted a G6PDd allele frequency map across malaria endemic countries (MECs) and generated population-weighted estimates of affected populations. Highest median prevalence (peaking at 32.5%) was predicted across sub-Saharan Africa and the Arabian Peninsula. Although G6PDd prevalence was generally lower across central and southeast Asia, rarely exceeding 20%, the majority of G6PDd individuals (67.5% median estimate) were from Asian countries. We estimated a G6PDd allele frequency of 8.0% (interquartile range: 7.4-8.8) across MECs, and 5.3% (4.4-6.7) within malaria-eliminating countries. The reliability of the map is contingent on the underlying data informing the model; population heterogeneity can only be represented by the available surveys, and important weaknesses exist in the map across data-sparse regions. Uncertainty metrics are used to quantify some aspects of these limitations in the map. Finally, we assembled a database of G6PDd variant occurrences to inform a national-level index of relative G6PDd haemolytic risk. Asian countries, where variants were most severe, had the highest relative risks from G6PDd. G6PDd is widespread and spatially heterogeneous across most MECs where primaquine would be valuable for malaria control and elimination. The maps and population estimates presented here reflect potential risk of primaquine-associated harm. In the absence of non-toxic alternatives to primaquine, these results represent additional evidence to help inform safe use of this valuable, yet dangerous, component of the malaria-elimination toolkit. Please see later in the article for the Editors' Summary.

Habitat analysis of North American sand flies near veterans returning from leishmania-endemic war zones.

PubMed

Claborn, David; Masuoka, Penny; Morrow, Meredith; Keep, Lisa

2008-12-18

Nearly 1300 cases of leishmaniasis have been identified in American military personnel deployed to Iraq and Afghanistan. The symptoms of this disease can range from a mild, self-limiting cutaneous infection to a deadly visceral infection and are not prevented by chemoprophylaxis or immunization. Effective treatments, however, are available. The disease-causing parasite is spread through the bite of the female sand fly. Although the disease occurs in both the Old World and the New World, the parasite species differ between the hemispheres. The large number of cases in military veterans has caused some concern that Old World, temperate-adapted parasite species could be introduced into the native sand fly populations of American military facilities where veterans of the current conflicts return following their deployments. This paper reports part of a larger study to analyze the risk of such an accidental importation. Four potential habitats on two large Army facilities in the Southeast United States were surveyed to determine relative sand fly density. The National Land Cover Map was used to provide sand fly density prediction maps by habitat. Sand fly density was significantly higher in deciduous forest and even higher at the interface between forest and open grassland. The evergreen forest and agricultural fields supported very low densities. On Fort Campbell, KY, the percentage of land covered by suitable habitat was very high. A sand fly density prediction map identified large tracts of land where infected individuals would be at higher risk of exposure to sand fly bites, resulting in an increased risk of introducing the parasite to a native insect population. On Fort Bragg, NC, however, commercial farming of long leaf pine reduced the percentage of the land covered in vegetation suitable for the support of sand flies. The risk of introducing an exotic Leishmania spp. on Fort Bragg, therefore, is considered to be much lower than on Fort Campbell. A readily available land cover product can be used at the regional level to identify areas of sand fly habitat where human populations may be at higher risk of exposure. The sand fly density prediction maps can be used to direct further surveillance, insect control, or additional patient monitoring of potentially infected soldiers.

Extinctions. Paleontological baselines for evaluating extinction risk in the modern oceans.

PubMed

Finnegan, Seth; Anderson, Sean C; Harnik, Paul G; Simpson, Carl; Tittensor, Derek P; Byrnes, Jarrett E; Finkel, Zoe V; Lindberg, David R; Liow, Lee Hsiang; Lockwood, Rowan; Lotze, Heike K; McClain, Craig R; McGuire, Jenny L; O'Dea, Aaron; Pandolfi, John M

2015-05-01

Marine taxa are threatened by anthropogenic impacts, but knowledge of their extinction vulnerabilities is limited. The fossil record provides rich information on past extinctions that can help predict biotic responses. We show that over 23 million years, taxonomic membership and geographic range size consistently explain a large proportion of extinction risk variation in six major taxonomic groups. We assess intrinsic risk-extinction risk predicted by paleontologically calibrated models-for modern genera in these groups. Mapping the geographic distribution of these genera identifies coastal biogeographic provinces where fauna with high intrinsic risk are strongly affected by human activity or climate change. Such regions are disproportionately in the tropics, raising the possibility that these ecosystems may be particularly vulnerable to future extinctions. Intrinsic risk provides a prehuman baseline for considering current threats to marine biodiversity. Copyright © 2015, American Association for the Advancement of Science.

An operational procedure for rapid flood risk assessment in Europe

NASA Astrophysics Data System (ADS)

Dottori, Francesco; Kalas, Milan; Salamon, Peter; Bianchi, Alessandra; Alfieri, Lorenzo; Feyen, Luc

2017-07-01

The development of methods for rapid flood mapping and risk assessment is a key step to increase the usefulness of flood early warning systems and is crucial for effective emergency response and flood impact mitigation. Currently, flood early warning systems rarely include real-time components to assess potential impacts generated by forecasted flood events. To overcome this limitation, this study describes the benchmarking of an operational procedure for rapid flood risk assessment based on predictions issued by the European Flood Awareness System (EFAS). Daily streamflow forecasts produced for major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in terms of flood-prone areas, economic damage and affected population, infrastructures and cities.An extensive testing of the operational procedure has been carried out by analysing the catastrophic floods of May 2014 in Bosnia-Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-based and report-based flood extent data, while modelled estimates of economic damage and affected population are compared against ground-based estimations. Finally, we evaluate the skill of risk estimates derived from EFAS flood forecasts with different lead times and combinations of probabilistic forecasts. Results highlight the potential of the real-time operational procedure in helping emergency response and management.

Arsenic concentrations, related environmental factors, and the predicted probability of elevated arsenic in groundwater in Pennsylvania

USGS Publications Warehouse

Gross, Eliza L.; Low, Dennis J.

2013-01-01

Logistic regression models were created to predict and map the probability of elevated arsenic concentrations in groundwater statewide in Pennsylvania and in three intrastate regions to further improve predictions for those three regions (glacial aquifer system, Gettysburg Basin, Newark Basin). Although the Pennsylvania and regional predictive models retained some different variables, they have common characteristics that can be grouped by (1) geologic and soils variables describing arsenic sources and mobilizers, (2) geochemical variables describing the geochemical environment of the groundwater, and (3) locally specific variables that are unique to each of the three regions studied and not applicable to statewide analysis. Maps of Pennsylvania and the three intrastate regions were produced that illustrate that areas most at risk are those with geology and soils capable of functioning as an arsenic source or mobilizer and geochemical groundwater conditions able to facilitate redox reactions. The models have limitations because they may not characterize areas that have localized controls on arsenic mobility. The probability maps associated with this report are intended for regional-scale use and may not be accurate for use at the field scale or when considering individual wells.

Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata.

PubMed

Galdino, Tarcísio Visintin da Silva; Kumar, Sunil; Oliveira, Leonardo S S; Alfenas, Acelino C; Neven, Lisa G; Al-Sadi, Abdullah M; Picanço, Marcelo C

2016-01-01

The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs.

Mapping Global Potential Risk of Mango Sudden Decline Disease Caused by Ceratocystis fimbriata

PubMed Central

Oliveira, Leonardo S. S.; Alfenas, Acelino C.; Neven, Lisa G.; Al-Sadi, Abdullah M.

2016-01-01

The Mango Sudden Decline (MSD), also referred to as Mango Wilt, is an important disease of mango in Brazil, Oman and Pakistan. This fungus is mainly disseminated by the mango bark beetle, Hypocryphalus mangiferae (Stebbing), by infected plant material, and the infested soils where it is able to survive for long periods. The best way to avoid losses due to MSD is to prevent its establishment in mango production areas. Our objectives in this study were to: (1) predict the global potential distribution of MSD, (2) identify the mango growing areas that are under potential risk of MSD establishment, and (3) identify climatic factors associated with MSD distribution. Occurrence records were collected from Brazil, Oman and Pakistan where the disease is currently known to occur in mango. We used the correlative maximum entropy based model (MaxEnt) algorithm to assess the global potential distribution of MSD. The MaxEnt model predicted suitable areas in countries where the disease does not already occur in mango, but where mango is grown. Among these areas are the largest mango producers in the world including India, China, Thailand, Indonesia, and Mexico. The mean annual temperature, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest month variables contributed most to the potential distribution of MSD disease. The mango bark beetle vector is known to occur beyond the locations where MSD currently exists and where the model predicted suitable areas, thus showing a high likelihood for disease establishment in areas predicted by our model. Our study is the first to map the potential risk of MSD establishment on a global scale. This information can be used in designing strategies to prevent introduction and establishment of MSD disease, and in preparation of efficient pest risk assessments and monitoring programs. PMID:27415625

Improvements on mapping soil liquefaction at a regional scale

NASA Astrophysics Data System (ADS)

Zhu, Jing

Earthquake induced soil liquefaction is an important secondary hazard during earthquakes and can lead to significant damage to infrastructure. Mapping liquefaction hazard is important in both planning for earthquake events and guiding relief efforts by positioning resources once the events have occurred. This dissertation addresses two aspects of liquefaction hazard mapping at a regional scale including 1) predictive liquefaction hazard mapping and 2) post-liquefaction cataloging. First, current predictive hazard liquefaction mapping relies on detailed geologic maps and geotechnical data, which are not always available in at-risk regions. This dissertation improves the predictive liquefaction hazard mapping by the development and validation of geospatial liquefaction models (Chapter 2 and 3) that predict liquefaction extent and are appropriate for global application. The geospatial liquefaction models are developed using logistic regression from a liquefaction database consisting of the data from 27 earthquake events from six countries. The model that performs best over the entire dataset includes peak ground velocity (PGV), VS30, distance to river, distance to coast, and precipitation. The model that performs best over the noncoastal dataset includes PGV, VS30, water table depth, distance to water body, and precipitation. Second, post-earthquake liquefaction cataloging historically relies on field investigation that is often limited by time and expense, and therefore results in limited and incomplete liquefaction inventories. This dissertation improves the post-earthquake cataloging by the development and validation of a remote sensing-based method that can be quickly applied over a broad region after an earthquake and provide a detailed map of liquefaction surface effects (Chapter 4). Our method uses the optical satellite images before and after an earthquake event from the WorldView-2 satellite with 2 m spatial resolution and eight spectral bands. Our method uses the changes of spectral variables that are sensitive to surface moisture and soil characteristics paired with a supervised classification.

The First Prediction of a Rift Valley Fever Outbreak

NASA Technical Reports Server (NTRS)

Anyamba, Assaf; Chretien, Jean-Paul; Small, Jennifer; Tucker, Compton J.; Formenty, Pierre; Richardson, Jason H.; Britch, Seth C.; Schnabel, David C.; Erickson, Ralph L.; Linthicum, Kenneth J.

2009-01-01

El Nino/Southern Oscillation (ENSO) related anomalies were analyzed using a combination of satellite measurements of elevated sea surface temperatures, and subsequent elevated rainfall and satellite derived normalized difference vegetation index data. A Rift Valley fever risk mapping model using these climate data predicted areas where outbreaks of Rift Valley fever in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. This is the first prospective prediction of a Rift Valley fever outbreak.

Prediction of a Rift Valley fever outbreak

PubMed Central

Anyamba, Assaf; Chretien, Jean-Paul; Small, Jennifer; Tucker, Compton J.; Formenty, Pierre B.; Richardson, Jason H.; Britch, Seth C.; Schnabel, David C.; Erickson, Ralph L.; Linthicum, Kenneth J.

2009-01-01

El Niño/Southern Oscillation related climate anomalies were analyzed by using a combination of satellite measurements of elevated sea-surface temperatures and subsequent elevated rainfall and satellite-derived normalized difference vegetation index data. A Rift Valley fever (RVF) risk mapping model using these climate data predicted areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. To our knowledge, this is the first prospective prediction of a RVF outbreak. PMID:19144928

Assessing the methods needed for improved dengue mapping: a SWOT analysis

PubMed Central

Attaway, David Frost; Jacobsen, Kathryn H; Falconer, Allan; Manca, Germana; Waters, Nigel M

2014-01-01

Introduction Dengue fever, a mosquito-borne viral infection, is a growing threat to human health in tropical and subtropical areas worldwide. There is a demand from public officials for maps that capture the current distribution of dengue and maps that analyze risk factors to predict the future burden of disease. Methods To identify relevant articles, we searched Google Scholar, PubMed, BioMed Central, and WHOLIS (World Health Organization Library Database) for published articles with a specific set of dengue criteria between January 2002 and July 2013. Results After evaluating the currently available dengue models, we identified four key barriers to the creation of high-quality dengue maps: (1) data limitations related to the expense of diagnosing and reporting dengue cases in places where health information systems are underdeveloped; (2) issues related to the use of socioeconomic proxies in places with limited dengue incidence data; (3) mosquito ranges which may be changing as a result of climate changes; and (4) the challenges of mapping dengue events at a variety of scales. Conclusion An ideal dengue map will present endemic and epidemic dengue information from both rural and urban areas. Overcoming the current barriers requires expanded collaboration and data sharing by geographers, epidemiologists, and entomologists. Enhanced mapping techniques would allow for improved visualizations of dengue rates and risks. PMID:25328585

Assessing the methods needed for improved dengue mapping: a SWOT analysis.

PubMed

Attaway, David Frost; Jacobsen, Kathryn H; Falconer, Allan; Manca, Germana; Waters, Nigel M

2014-01-01

Dengue fever, a mosquito-borne viral infection, is a growing threat to human health in tropical and subtropical areas worldwide. There is a demand from public officials for maps that capture the current distribution of dengue and maps that analyze risk factors to predict the future burden of disease. To identify relevant articles, we searched Google Scholar, PubMed, BioMed Central, and WHOLIS (World Health Organization Library Database) for published articles with a specific set of dengue criteria between January 2002 and July 2013. After evaluating the currently available dengue models, we identified four key barriers to the creation of high-quality dengue maps: (1) data limitations related to the expense of diagnosing and reporting dengue cases in places where health information systems are underdeveloped; (2) issues related to the use of socioeconomic proxies in places with limited dengue incidence data; (3) mosquito ranges which may be changing as a result of climate changes; and (4) the challenges of mapping dengue events at a variety of scales. An ideal dengue map will present endemic and epidemic dengue information from both rural and urban areas. Overcoming the current barriers requires expanded collaboration and data sharing by geographers, epidemiologists, and entomologists. Enhanced mapping techniques would allow for improved visualizations of dengue rates and risks.

Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Akhavan, P.; Karimi, M.; Pahlavani, P.

2014-10-01

Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

Predicting risk of trace element pollution from municipal roads using site-specific soil samples and remotely sensed data.

PubMed

Reeves, Mari Kathryn; Perdue, Margaret; Munk, Lee Ann; Hagedorn, Birgit

2018-07-15

Studies of environmental processes exhibit spatial variation within data sets. The ability to derive predictions of risk from field data is a critical path forward in understanding the data and applying the information to land and resource management. Thanks to recent advances in predictive modeling, open source software, and computing, the power to do this is within grasp. This article provides an example of how we predicted relative trace element pollution risk from roads across a region by combining site specific trace element data in soils with regional land cover and planning information in a predictive model framework. In the Kenai Peninsula of Alaska, we sampled 36 sites (191 soil samples) adjacent to roads for trace elements. We then combined this site specific data with freely-available land cover and urban planning data to derive a predictive model of landscape scale environmental risk. We used six different model algorithms to analyze the dataset, comparing these in terms of their predictive abilities and the variables identified as important. Based on comparable predictive abilities (mean R 2 from 30 to 35% and mean root mean square error from 65 to 68%), we averaged all six model outputs to predict relative levels of trace element deposition in soils-given the road surface, traffic volume, sample distance from the road, land cover category, and impervious surface percentage. Mapped predictions of environmental risk from toxic trace element pollution can show land managers and transportation planners where to prioritize road renewal or maintenance by each road segment's relative environmental and human health risk. Published by Elsevier B.V.

Spatial modeling of malaria incidence rates in Sistan and Baluchistan province, Islamic Republic of Iran.

PubMed

Salehi, Masoud; Mohammad, Kazem; Farahani, Mahmud M; Zeraati, Hojjat; Nourijelyani, Keramat; Zayeri, Farid

2008-12-01

To identify the effect of environmental factors on malaria risk, and to visualize spatial map of malaria standard incidence rates in Sistan and Baluchistan province, Islamic Republic of Iran. In this cross-sectional study, the data from 42,162 registered new malaria cases from 21 March 2001 (Iranian new year) to 21 of March 2006 were studied. To describe the statistical association between environmental factors and malaria risk, a generalized linear mixed model approach was utilized. In addition, we used the second ordered stationary Kriging, and a variogram to determine the appropriate spatial correlation structure among the malaria standard incidence rates, and provide a proper malaria risk map in the area under study. The obtained results from the spatial modeling revealed that humidity (p=0.0004), temperature (p<0.0001), and elevation (p<0.0001) were positively, and precipitation (p=0.0029) was inversely correlated with the malaria risk. Moreover, the malaria risk map based on the predicted values showed that the south part of this province (Baluchistan), has a higher risk of malaria, compared to the northern area (Sistan). Since the effective environmental factors on malaria risk are out of human's control, the health policy makers in this province should pay more attention to the areas with high temperature, elevation, and humidity, as well as, low rainfall districts.

Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction.

PubMed

Antoniou, Antonis C; Beesley, Jonathan; McGuffog, Lesley; Sinilnikova, Olga M; Healey, Sue; Neuhausen, Susan L; Ding, Yuan Chun; Rebbeck, Timothy R; Weitzel, Jeffrey N; Lynch, Henry T; Isaacs, Claudine; Ganz, Patricia A; Tomlinson, Gail; Olopade, Olufunmilayo I; Couch, Fergus J; Wang, Xianshu; Lindor, Noralane M; Pankratz, Vernon S; Radice, Paolo; Manoukian, Siranoush; Peissel, Bernard; Zaffaroni, Daniela; Barile, Monica; Viel, Alessandra; Allavena, Anna; Dall'Olio, Valentina; Peterlongo, Paolo; Szabo, Csilla I; Zikan, Michal; Claes, Kathleen; Poppe, Bruce; Foretova, Lenka; Mai, Phuong L; Greene, Mark H; Rennert, Gad; Lejbkowicz, Flavio; Glendon, Gord; Ozcelik, Hilmi; Andrulis, Irene L; Thomassen, Mads; Gerdes, Anne-Marie; Sunde, Lone; Cruger, Dorthe; Birk Jensen, Uffe; Caligo, Maria; Friedman, Eitan; Kaufman, Bella; Laitman, Yael; Milgrom, Roni; Dubrovsky, Maya; Cohen, Shimrit; Borg, Ake; Jernström, Helena; Lindblom, Annika; Rantala, Johanna; Stenmark-Askmalm, Marie; Melin, Beatrice; Nathanson, Kate; Domchek, Susan; Jakubowska, Ania; Lubinski, Jan; Huzarski, Tomasz; Osorio, Ana; Lasa, Adriana; Durán, Mercedes; Tejada, Maria-Isabel; Godino, Javier; Benitez, Javier; Hamann, Ute; Kriege, Mieke; Hoogerbrugge, Nicoline; van der Luijt, Rob B; van Asperen, Christi J; Devilee, Peter; Meijers-Heijboer, E J; Blok, Marinus J; Aalfs, Cora M; Hogervorst, Frans; Rookus, Matti; Cook, Margaret; Oliver, Clare; Frost, Debra; Conroy, Don; Evans, D Gareth; Lalloo, Fiona; Pichert, Gabriella; Davidson, Rosemarie; Cole, Trevor; Cook, Jackie; Paterson, Joan; Hodgson, Shirley; Morrison, Patrick J; Porteous, Mary E; Walker, Lisa; Kennedy, M John; Dorkins, Huw; Peock, Susan; Godwin, Andrew K; Stoppa-Lyonnet, Dominique; de Pauw, Antoine; Mazoyer, Sylvie; Bonadona, Valérie; Lasset, Christine; Dreyfus, Hélène; Leroux, Dominique; Hardouin, Agnès; Berthet, Pascaline; Faivre, Laurence; Loustalot, Catherine; Noguchi, Tetsuro; Sobol, Hagay; Rouleau, Etienne; Nogues, Catherine; Frénay, Marc; Vénat-Bouvet, Laurence; Hopper, John L; Daly, Mary B; Terry, Mary B; John, Esther M; Buys, Saundra S; Yassin, Yosuf; Miron, Alexander; Goldgar, David; Singer, Christian F; Dressler, Anne Catharina; Gschwantler-Kaulich, Daphne; Pfeiler, Georg; Hansen, Thomas V O; Jønson, Lars; Agnarsson, Bjarni A; Kirchhoff, Tomas; Offit, Kenneth; Devlin, Vincent; Dutra-Clarke, Ana; Piedmonte, Marion; Rodriguez, Gustavo C; Wakeley, Katie; Boggess, John F; Basil, Jack; Schwartz, Peter E; Blank, Stephanie V; Toland, Amanda Ewart; Montagna, Marco; Casella, Cinzia; Imyanitov, Evgeny; Tihomirova, Laima; Blanco, Ignacio; Lazaro, Conxi; Ramus, Susan J; Sucheston, Lara; Karlan, Beth Y; Gross, Jenny; Schmutzler, Rita; Wappenschmidt, Barbara; Engel, Christoph; Meindl, Alfons; Lochmann, Magdalena; Arnold, Norbert; Heidemann, Simone; Varon-Mateeva, Raymonda; Niederacher, Dieter; Sutter, Christian; Deissler, Helmut; Gadzicki, Dorothea; Preisler-Adams, Sabine; Kast, Karin; Schönbuchner, Ines; Caldes, Trinidad; de la Hoya, Miguel; Aittomäki, Kristiina; Nevanlinna, Heli; Simard, Jacques; Spurdle, Amanda B; Holland, Helene; Chen, Xiaoqing; Platte, Radka; Chenevix-Trench, Georgia; Easton, Douglas F

2010-12-01

The known breast cancer susceptibility polymorphisms in FGFR2, TNRC9/TOX3, MAP3K1, LSP1, and 2q35 confer increased risks of breast cancer for BRCA1 or BRCA2 mutation carriers. We evaluated the associations of 3 additional single nucleotide polymorphisms (SNPs), rs4973768 in SLC4A7/NEK10, rs6504950 in STXBP4/COX11, and rs10941679 at 5p12, and reanalyzed the previous associations using additional carriers in a sample of 12,525 BRCA1 and 7,409 BRCA2 carriers. Additionally, we investigated potential interactions between SNPs and assessed the implications for risk prediction. The minor alleles of rs4973768 and rs10941679 were associated with increased breast cancer risk for BRCA2 carriers (per-allele HR = 1.10, 95% CI: 1.03-1.18, P = 0.006 and HR = 1.09, 95% CI: 1.01-1.19, P = 0.03, respectively). Neither SNP was associated with breast cancer risk for BRCA1 carriers, and rs6504950 was not associated with breast cancer for either BRCA1 or BRCA2 carriers. Of the 9 polymorphisms investigated, 7 were associated with breast cancer for BRCA2 carriers (FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, P = 7 × 10(-11) - 0.03), but only TOX3 and 2q35 were associated with the risk for BRCA1 carriers (P = 0.0049, 0.03, respectively). All risk-associated polymorphisms appear to interact multiplicatively on breast cancer risk for mutation carriers. Based on the joint genotype distribution of the 7 risk-associated SNPs in BRCA2 mutation carriers, the 5% of BRCA2 carriers at highest risk (i.e., between 95th and 100th percentiles) were predicted to have a probability between 80% and 96% of developing breast cancer by age 80, compared with 42% to 50% for the 5% of carriers at lowest risk. Our findings indicated that these risk differences might be sufficient to influence the clinical management of mutation carriers.

Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction

PubMed Central

Antoniou, Antonis C; Beesley, Jonathan; McGuffog, Lesley; Sinilnikova, Olga M.; Healey, Sue; Neuhausen, Susan L.; Ding, Yuan Chun; Rebbeck, Timothy R.; Weitzel, Jeffrey N.; Lynch, Henry T.; Isaacs, Claudine; Ganz, Patricia A.; Tomlinson, Gail; Olopade, Olufunmilayo I.; Couch, Fergus J.; Wang, Xianshu; Lindor, Noralane M.; Pankratz, Vernon S.; Radice, Paolo; Manoukian, Siranoush; Peissel, Bernard; Zaffaroni, Daniela; Barile, Monica; Viel, Alessandra; Allavena, Anna; Dall’Olio, Valentina; Peterlongo, Paolo; Szabo, Csilla I.; Zikan, Michal; Claes, Kathleen; Poppe, Bruce; Foretova, Lenka; Mai, Phuong L.; Greene, Mark H.; Rennert, Gad; Lejbkowicz, Flavio; Glendon, Gord; Ozcelik, Hilmi; Andrulis, Irene L.; Thomassen, Mads; Gerdes, Anne-Marie; Sunde, Lone; Cruger, Dorthe; Jensen, Uffe Birk; Caligo, Maria; Friedman, Eitan; Kaufman, Bella; Laitman, Yael; Milgrom, Roni; Dubrovsky, Maya; Cohen, Shimrit; Borg, Ake; Jernström, Helena; Lindblom, Annika; Rantala, Johanna; Stenmark-Askmalm, Marie; Melin, Beatrice; Nathanson, Kate; Domchek, Susan; Jakubowska, Ania; Lubinski, Jan; Huzarski, Tomasz; Osorio, Ana; Lasa, Adriana; Durán, Mercedes; Tejada, Maria-Isabel; Godino, Javier; Benitez, Javier; Hamann, Ute; Kriege, Mieke; Hoogerbrugge, Nicoline; van der Luijt, Rob B; van Asperen, Christi J; Devilee, Peter; Meijers-Heijboer, E.J.; Blok, Marinus J; Aalfs, Cora M.; Hogervorst, Frans; Rookus, Matti; Cook, Margaret; Oliver, Clare; Frost, Debra; Conroy, Don; Evans, D. Gareth; Lalloo, Fiona; Pichert, Gabriella; Davidson, Rosemarie; Cole, Trevor; Cook, Jackie; Paterson, Joan; Hodgson, Shirley; Morrison, Patrick J.; Porteous, Mary E.; Walker, Lisa; Kennedy, M. John; Dorkins, Huw; Peock, Susan; Godwin, Andrew K.; Stoppa-Lyonnet, Dominique; de Pauw, Antoine; Mazoyer, Sylvie; Bonadona, Valérie; Lasset, Christine; Dreyfus, Hélène; Leroux, Dominique; Hardouin, Agnès; Berthet, Pascaline; Faivre, Laurence; Loustalot, Catherine; Noguchi, Tetsuro; Sobol, Hagay; Rouleau, Etienne; Nogues, Catherine; Frénay, Marc; Vénat-Bouvet, Laurence; Hopper, John L.; Daly, Mary B.; Terry, Mary B.; John, Esther M.; Buys, Saundra S.; Yassin, Yosuf; Miron, Alex; Goldgar, David; Singer, Christian F.; Dressler, Anne Catharina; Gschwantler-Kaulich, Daphne; Pfeiler, Georg; Hansen, Thomas V. O.; Jønson, Lars; Agnarsson, Bjarni A.; Kirchhoff, Tomas; Offit, Kenneth; Devlin, Vincent; Dutra-Clarke, Ana; Piedmonte, Marion; Rodriguez, Gustavo C.; Wakeley, Katie; Boggess, John F.; Basil, Jack; Schwartz, Peter E.; Blank, Stephanie V.; Toland, Amanda Ewart; Montagna, Marco; Casella, Cinzia; Imyanitov, Evgeny; Tihomirova, Laima; Blanco, Ignacio; Lazaro, Conxi; Ramus, Susan J.; Sucheston, Lara; Karlan, Beth Y.; Gross, Jenny; Schmutzler, Rita; Wappenschmidt, Barbara; Engel, Christoph; Meindl, Alfons; Lochmann, Magdalena; Arnold, Norbert; Heidemann, Simone; Varon-Mateeva, Raymonda; Niederacher, Dieter; Sutter, Christian; Deissler, Helmut; Gadzicki, Dorothea; Preisler-Adams, Sabine; Kast, Karin; Schönbuchner, Ines; Caldes, Trinidad; de la Hoya, Miguel; Aittomäki, Kristiina; Nevanlinna, Heli; Simard, Jacques; Spurdle, Amanda B.; Holland, Helene; Chen, Xiaoqing; Platte, Radka; Chenevix-Trench, Georgia; Easton, Douglas F.

2010-01-01

The known breast cancer (BC) susceptibility polymorphisms in FGFR2, TNRC9/TOX3, MAP3K1,LSP1 and 2q35 confer increased risks of BC for BRCA1 or BRCA2 mutation carriers. We evaluated the associations of three additional SNPs, rs4973768 in SLC4A7/NEK10, rs6504950 in STXBP4/COX11 and rs10941679 at 5p12 and reanalyzed the previous associations using additional carriers in a sample of 12,525 BRCA1 and 7,409 BRCA2 carriers. Additionally, we investigated potential interactions between SNPs and assessed the implications for risk prediction. The minor alleles of rs4973768 and rs10941679 were associated with increased BC risk for BRCA2 carriers (per-allele Hazard Ratio (HR)=1.10, 95%CI:1.03-1.18, p=0.006 and HR=1.09, 95%CI:1.01-1.19, p=0.03, respectively). Neither SNP was associated with BC risk for BRCA1 carriers and rs6504950 was not associated with BC for either BRCA1 or BRCA2 carriers. Of the nine polymorphisms investigated, seven were associated with BC for BRCA2 carriers (FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, p-values:7×10−11-0.03), but only TOX3 and 2q35 were associated with the risk for BRCA1 carriers (p=0.0049, 0.03 respectively). All risk associated polymorphisms appear to interact multiplicatively on BC risk for mutation carriers. Based on the joint genotype distribution of the seven risk associated SNPs in BRCA2 mutation carriers, the 5% of BRCA2 carriers at highest risk (i.e. between 95th and 100th percentiles) were predicted to have a probability between 80% and 96% of developing BC by age 80, compared with 42-50% for the 5% of carriers at lowest risk. Our findings indicated that these risk differences may be sufficient to influence the clinical management of mutation carriers. PMID:21118973

Unravelling the structure of species extinction risk for predictive conservation science.

PubMed

Lee, Tien Ming; Jetz, Walter

2011-05-07

Extinction risk varies across species and space owing to the combined and interactive effects of ecology/life history and geography. For predictive conservation science to be effective, large datasets and integrative models that quantify the relative importance of potential factors and separate rapidly changing from relatively static threat drivers are urgently required. Here, we integrate and map in space the relative and joint effects of key correlates of The International Union for Conservation of Nature-assessed extinction risk for 8700 living birds. Extinction risk varies significantly with species' broad-scale environmental niche, geographical range size, and life-history and ecological traits such as body size, developmental mode, primary diet and foraging height. Even at this broad scale, simple quantifications of past human encroachment across species' ranges emerge as key in predicting extinction risk, supporting the use of land-cover change projections for estimating future threat in an integrative setting. A final joint model explains much of the interspecific variation in extinction risk and provides a remarkably strong prediction of its observed global geography. Our approach unravels the species-level structure underlying geographical gradients in extinction risk and offers a means of disentangling static from changing components of current and future threat. This reconciliation of intrinsic and extrinsic, and of past and future extinction risk factors may offer a critical step towards a more continuous, forward-looking assessment of species' threat status based on geographically explicit environmental change projections, potentially advancing global predictive conservation science.

Mapping Seabird Sensitivity to Offshore Wind Farms

PubMed Central

Bradbury, Gareth; Trinder, Mark; Furness, Bob; Banks, Alex N.; Caldow, Richard W. G.; Hume, Duncan

2014-01-01

We present a Geographic Information System (GIS) tool, SeaMaST (Seabird Mapping and Sensitivity Tool), to provide evidence on the use of sea areas by seabirds and inshore waterbirds in English territorial waters, mapping their relative sensitivity to offshore wind farms. SeaMaST is a freely available evidence source for use by all connected to the offshore wind industry and will assist statutory agencies in assessing potential risks to seabird populations from planned developments. Data were compiled from offshore boat and aerial observer surveys spanning the period 1979–2012. The data were analysed using distance analysis and Density Surface Modelling to produce predicted bird densities across a grid covering English territorial waters at a resolution of 3 km×3 km. Coefficients of Variation were estimated for each grid cell density, as an indication of confidence in predictions. Offshore wind farm sensitivity scores were compiled for seabird species using English territorial waters. The comparative risks to each species of collision with turbines and displacement from operational turbines were reviewed and scored separately, and the scores were multiplied by the bird density estimates to produce relative sensitivity maps. The sensitivity maps reflected well the amassed distributions of the most sensitive species. SeaMaST is an important new tool for assessing potential impacts on seabird populations from offshore development at a time when multiple large areas of development are proposed which overlap with many seabird species’ ranges. It will inform marine spatial planning as well as identifying priority areas of sea usage by marine birds. Example SeaMaST outputs are presented. PMID:25210739

Mapping seabird sensitivity to offshore wind farms.

PubMed

Bradbury, Gareth; Trinder, Mark; Furness, Bob; Banks, Alex N; Caldow, Richard W G; Hume, Duncan

2014-01-01

We present a Geographic Information System (GIS) tool, SeaMaST (Seabird Mapping and Sensitivity Tool), to provide evidence on the use of sea areas by seabirds and inshore waterbirds in English territorial waters, mapping their relative sensitivity to offshore wind farms. SeaMaST is a freely available evidence source for use by all connected to the offshore wind industry and will assist statutory agencies in assessing potential risks to seabird populations from planned developments. Data were compiled from offshore boat and aerial observer surveys spanning the period 1979-2012. The data were analysed using distance analysis and Density Surface Modelling to produce predicted bird densities across a grid covering English territorial waters at a resolution of 3 km×3 km. Coefficients of Variation were estimated for each grid cell density, as an indication of confidence in predictions. Offshore wind farm sensitivity scores were compiled for seabird species using English territorial waters. The comparative risks to each species of collision with turbines and displacement from operational turbines were reviewed and scored separately, and the scores were multiplied by the bird density estimates to produce relative sensitivity maps. The sensitivity maps reflected well the amassed distributions of the most sensitive species. SeaMaST is an important new tool for assessing potential impacts on seabird populations from offshore development at a time when multiple large areas of development are proposed which overlap with many seabird species' ranges. It will inform marine spatial planning as well as identifying priority areas of sea usage by marine birds. Example SeaMaST outputs are presented.

A landslide susceptibility map of Africa

NASA Astrophysics Data System (ADS)

Broeckx, Jente; Vanmaercke, Matthias; Duchateau, Rica; Poesen, Jean

2017-04-01

Studies on landslide risks and fatalities indicate that landslides are a global threat to humans, infrastructure and the environment, certainly in Africa. Nonetheless our understanding of the spatial patterns of landslides and rockfalls on this continent is very limited. Also in global landslide susceptibility maps, Africa is mostly underrepresented in the inventories used to construct these maps. As a result, predicted landslide susceptibilities remain subject to very large uncertainties. This research aims to produce a first continent-wide landslide susceptibility map for Africa, calibrated with a well-distributed landslide dataset. As a first step, we compiled all available landslide inventories for Africa. This data was supplemented by additional landslide mapping with Google Earth in underrepresented regions. This way, we compiled 60 landslide inventories from the literature (ca. 11000 landslides) and an additional 6500 landslides through mapping in Google Earth (including 1500 rockfalls). Various environmental variables such as slope, lithology, soil characteristics, land use, precipitation and seismic activity, were investigated for their significance in explaining the observed spatial patterns of landslides. To account for potential mapping biases in our dataset, we used Monte Carlo simulations that selected different subsets of mapped landslides, tested the significance of the considered environmental variables and evaluated the performance of the fitted multiple logistic regression model against another subset of mapped landslides. Based on these analyses, we constructed two landslide susceptibility maps for Africa: one for all landslide types and one excluding rockfalls. In both maps, topography, lithology and seismic activity were the most significant variables. The latter factor may be surprising, given the overall limited degree of seismicity in Africa. However, its significance indicates that frequent seismic events may serve as in important preparatory factor for landslides. This finding concurs with several other recent studies. Rainfall explains a significant, but limited part of the observed landslide pattern and becomes insignificant when also rockfalls are considered. This may be explained by the fact that a significant fraction of the mapped rockfalls occurred in the Sahara desert. Overall, both maps perform well in predicting intra-continental patterns of mass movements in Africa and explain about 80% of the observed variance in landslide occurrence. As a result, these maps may be a valuable tool for planning and risk reduction strategies.

Evaluating Predictive Models of Software Quality

NASA Astrophysics Data System (ADS)

Ciaschini, V.; Canaparo, M.; Ronchieri, E.; Salomoni, D.

2014-06-01

Applications from High Energy Physics scientific community are constantly growing and implemented by a large number of developers. This implies a strong churn on the code and an associated risk of faults, which is unavoidable as long as the software undergoes active evolution. However, the necessities of production systems run counter to this. Stability and predictability are of paramount importance; in addition, a short turn-around time for the defect discovery-correction-deployment cycle is required. A way to reconcile these opposite foci is to use a software quality model to obtain an approximation of the risk before releasing a program to only deliver software with a risk lower than an agreed threshold. In this article we evaluated two quality predictive models to identify the operational risk and the quality of some software products. We applied these models to the development history of several EMI packages with intent to discover the risk factor of each product and compare it with its real history. We attempted to determine if the models reasonably maps reality for the applications under evaluation, and finally we concluded suggesting directions for further studies.

Estimating cross-validatory predictive p-values with integrated importance sampling for disease mapping models.

PubMed

Li, Longhai; Feng, Cindy X; Qiu, Shi

2017-06-30

An important statistical task in disease mapping problems is to identify divergent regions with unusually high or low risk of disease. Leave-one-out cross-validatory (LOOCV) model assessment is the gold standard for estimating predictive p-values that can flag such divergent regions. However, actual LOOCV is time-consuming because one needs to rerun a Markov chain Monte Carlo analysis for each posterior distribution in which an observation is held out as a test case. This paper introduces a new method, called integrated importance sampling (iIS), for estimating LOOCV predictive p-values with only Markov chain samples drawn from the posterior based on a full data set. The key step in iIS is that we integrate away the latent variables associated the test observation with respect to their conditional distribution without reference to the actual observation. By following the general theory for importance sampling, the formula used by iIS can be proved to be equivalent to the LOOCV predictive p-value. We compare iIS and other three existing methods in the literature with two disease mapping datasets. Our empirical results show that the predictive p-values estimated with iIS are almost identical to the predictive p-values estimated with actual LOOCV and outperform those given by the existing three methods, namely, the posterior predictive checking, the ordinary importance sampling, and the ghosting method by Marshall and Spiegelhalter (2003). Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Wyoming Basin Rapid Ecoregional Assessment

USGS Publications Warehouse

Carr, Natasha B.; Means, Robert E.

2013-01-01

The overall goal of the Wyoming Basin Rapid Ecoregional Assessment (REA) is to provide information that supports regional planning and analysis for the management of ecological resources. The REA provides an assessment of baseline ecological conditions, an evaluation of current risks from drivers of ecosystem change (including energy development, fire, and invasive species), and a predictive capacity for evaluating future risks (including climate change). Additionally, the REA may be used for identifying priority areas for conservation or restoration and for assessing cumulative effects of multiple land uses. The Wyoming Basin REA will address Management Questions developed by the Bureau of Land Management and other agency partners for 8 major biomes and 19 species or species assemblages. The maps developed for addressing Management Questions will be integrated into overall maps of landscape-level ecological values and risks. The maps can be used to address the goals of the REA at a number of levels: for individual species, species assemblages, aquatic and terrestrial systems, and for the entire ecoregion. This allows flexibility in how the products of the REA are compiled to inform planning and management actions across a broad range of spatial scales.

Application of Poisson kriging to the mapping of cholera and dysentery incidence in an endemic area of Bangladesh.

PubMed

Ali, Mohammad; Goovaerts, Pierre; Nazia, Nushrat; Haq, M Zahirul; Yunus, Mohammad; Emch, Michael

2006-10-13

Disease maps can serve to display incidence rates geographically, to inform on public health provision about the success or failure of interventions, and to make hypothesis or to provide evidences concerning disease etiology. Poisson kriging was recently introduced to filter the noise attached to rates recorded over sparsely populated administrative units. Its benefit over simple population-weighted averages and empirical Bayesian smoothers was demonstrated by simulation studies using county-level cancer mortality rates. This paper presents the first application of Poisson kriging to the spatial interpolation of local disease rates, resulting in continuous maps of disease rate estimates and the associated prediction variance. The methodology is illustrated using cholera and dysentery data collected in a cholera endemic area (Matlab) of Bangladesh. The spatial analysis was confined to patrilineally-related clusters of households, known as baris, located within 9 kilometers from the Matlab hospital to avoid underestimating the risk of disease incidence, since patients far away from the medical facilities are less likely to travel. Semivariogram models reveal a range of autocorrelation of 1.1 km for dysentery and 0.37 km for cholera. This result translates into a cholera risk map that is patchier than the dysentery map that shows a large zone of high incidence in the south-central part of the study area, which is quasi-urban. On both maps, lower risk values are found in the Northern part of the study area, which is also the most distant from the Matlab hospital. The weaker spatial continuity of cholera versus dysentery incidence rates resulted in larger kriging variance across the study area. The approach presented in this paper enables researchers to incorporate the pattern of spatial dependence of incidence rates into the mapping of risk values and the quantification of the associated uncertainty. Differences in spatial patterns, in particular the range of spatial autocorrelation, reflect differences in the mode of transmission of cholera and dysentery. Our risk maps for cholera and dysentery incidences should help identifying putative factors of increased disease incidence, leading to more effective prevention and remedial actions in endemic areas.

Application of Poisson kriging to the mapping of cholera and dysentery incidence in an endemic area of Bangladesh

PubMed Central

Ali, Mohammad; Goovaerts, Pierre; Nazia, Nushrat; Haq, M Zahirul; Yunus, Mohammad; Emch, Michael

2006-01-01

Background Disease maps can serve to display incidence rates geographically, to inform on public health provision about the success or failure of interventions, and to make hypothesis or to provide evidences concerning disease etiology. Poisson kriging was recently introduced to filter the noise attached to rates recorded over sparsely populated administrative units. Its benefit over simple population-weighted averages and empirical Bayesian smoothers was demonstrated by simulation studies using county-level cancer mortality rates. This paper presents the first application of Poisson kriging to the spatial interpolation of local disease rates, resulting in continuous maps of disease rate estimates and the associated prediction variance. The methodology is illustrated using cholera and dysentery data collected in a cholera endemic area (Matlab) of Bangladesh. Results The spatial analysis was confined to patrilineally-related clusters of households, known as baris, located within 9 kilometers from the Matlab hospital to avoid underestimating the risk of disease incidence, since patients far away from the medical facilities are less likely to travel. Semivariogram models reveal a range of autocorrelation of 1.1 km for dysentery and 0.37 km for cholera. This result translates into a cholera risk map that is patchier than the dysentery map that shows a large zone of high incidence in the south-central part of the study area, which is quasi-urban. On both maps, lower risk values are found in the Northern part of the study area, which is also the most distant from the Matlab hospital. The weaker spatial continuity of cholera versus dysentery incidence rates resulted in larger kriging variance across the study area. Conclusion The approach presented in this paper enables researchers to incorporate the pattern of spatial dependence of incidence rates into the mapping of risk values and the quantification of the associated uncertainty. Differences in spatial patterns, in particular the range of spatial autocorrelation, reflect differences in the mode of transmission of cholera and dysentery. Our risk maps for cholera and dysentery incidences should help identifying putative factors of increased disease incidence, leading to more effective prevention and remedial actions in endemic areas. PMID:17038192

Using Public Input to Create a Better Online Flood Mapping Framework

NASA Astrophysics Data System (ADS)

Eubanks, K. E.; Jackson, C.; Carlberg, B.; Cohen, S.

2017-12-01

One topic of consistent relevance in flooding research is how best to provide information and communicate risk from scientists and researchers to the general public. Additionally, communicators face challenges on how to fully convey the dangers flooding poses in a manner that the public comprehends and will apply to reactions to flooding. Many of the inundation and hazard maps currently in use are highly technical, making it difficult for the average person, without formal education in flooding, to glean valuable information and insight from the intended tools. Working with the public, a set of three surveys were administered via social media to gain insight into public understanding of floods and flooding risk. The surveys indicated that the general population does not have a firm understanding of basic flooding terms or how to navigate current, technical flood inundation maps. The surveys also suggested that those surveyed desire a simpler interface for flood maps that also relates a sense of varying risk. Using the feedback from each survey, a conceptual framework was produced for a set of inundation maps, including more relatable terms and educational components within a user-friendly web interface. Goals for the website, shaped by survey feedback, included simple, readable map layers that convey a sense of uncertainty, a clear and detailed legend, the ability show or hide components of the map, and the option to learn more about flood terminology on the site or via links to outside resources. The public indicated that the final map interface was more concise and simplified than the current inundation map platforms they navigated as part of the first survey, and that the proposed interface was overall more likely to be used. Using public input is one way to bridge the gap between scientific data and predictions to the general public, who need this information. It is vital to provide accurate data in a form that is relatable, and therefore helpful, to the members of the community trying to make educated decisions. The findings on gearing inundation map web interfaces to the public are being used to create tools that are more usable, therefore hopefully saving lives by better informing those in danger of their risk.

Mapping for prevention: GIS models for directing childhood lead poisoning prevention programs.

PubMed Central

Miranda, Marie Lynn; Dolinoy, Dana C; Overstreet, M Alicia

2002-01-01

Environmental threats to children's health--especially low-level lead exposure--are complex and multifaceted; consequently, mitigation of these threats has proven costly and insufficient and has produced economic and racial disparities in exposure among populations. Policy makers, public health officials, child advocates, and others currently lack the appropriate infrastructure to evaluate children's risk and exposure potential across a broad range of risks. Unable to identify where the highest risk of exposure occurs, children's environmental health programs remain mitigative instead of preventive. In this article we use geographic information system spatial analysis of data from blood lead screening, county tax assessors, and the U.S. Census to predict statistically based lead exposure risk levels mapped at the individual tax parcel unit in six counties in North Carolina. The resulting model uses weighted risk factors to spatially locate modeled exposure zones, thus highlighting critical areas for targeted intervention. The methods presented here hold promise for application and extension to the other 94 North Carolina counties and nationally, as well as to other environmental health risks. PMID:12204831

Predicting potential distribution of poorly known species with small database: the case of four-horned antelope Tetracerus quadricornis on the Indian subcontinent.

PubMed

Pokharel, Krishna Prasad; Ludwig, Tobias; Storch, Ilse

2016-04-01

Information gaps on the distribution of data deficient and rare species such as four-horned antelope (FHA) in Nepal may impair their conservation. We aimed to empirically predict the distribution of FHA in Nepal with the help of data from the Indian subcontinent. Additionally, we wanted to identify core areas and gaps within the reported range limits and to assess the degree of isolation of known Nepalese populations from the main distribution areas in India. The tropical part of the Indian subcontinent (65°-90° eastern longitude, 5°-30° northern latitude), that is, the areas south of the Himalayan Mountains. Using MaxEnt and accounting for sampling bias, we developed predictive distribution models from environmental and topographical variables, and known presence locations of the study species in India and Nepal. We address and discuss the use of target group vs. random background. The prediction map reveals a disjunct distribution of FHA with core areas in the tropical parts of central to southern-western India. At the scale of the Indian subcontinent, suitable FHA habitat area in Nepal was small. The Indo-Gangetic Plain isolates Nepalese from the Indian FHA populations, but the distribution area extends further south than proposed by the current IUCN map. A low to intermediate temperature seasonality as well as low precipitation during the dry and warm season contributed most to the prediction of FHA distribution. The predicted distribution maps confirm other FHA range maps but also indicate that suitable areas exist south of the known range. Results further highlight that small populations in the Nepalese Terai Arc are isolated from the Indian core distribution and therefore might be under high extinction risk.

A World Malaria Map: Plasmodium falciparum Endemicity in 2007

PubMed Central

Hay, Simon I; Guerra, Carlos A; Gething, Peter W; Patil, Anand P; Tatem, Andrew J; Noor, Abdisalan M; Kabaria, Caroline W; Manh, Bui H; Elyazar, Iqbal R. F; Brooker, Simon; Smith, David L; Moyeed, Rana A; Snow, Robert W

2009-01-01

Background Efficient allocation of resources to intervene against malaria requires a detailed understanding of the contemporary spatial distribution of malaria risk. It is exactly 40 y since the last global map of malaria endemicity was published. This paper describes the generation of a new world map of Plasmodium falciparum malaria endemicity for the year 2007. Methods and Findings A total of 8,938 P. falciparum parasite rate (PfPR) surveys were identified using a variety of exhaustive search strategies. Of these, 7,953 passed strict data fidelity tests for inclusion into a global database of PfPR data, age-standardized to 2–10 y for endemicity mapping. A model-based geostatistical procedure was used to create a continuous surface of malaria endemicity within previously defined stable spatial limits of P. falciparum transmission. These procedures were implemented within a Bayesian statistical framework so that the uncertainty of these predictions could be evaluated robustly. The uncertainty was expressed as the probability of predicting correctly one of three endemicity classes; previously stratified to be an informative guide for malaria control. Population at risk estimates, adjusted for the transmission modifying effects of urbanization in Africa, were then derived with reference to human population surfaces in 2007. Of the 1.38 billion people at risk of stable P. falciparum malaria, 0.69 billion were found in Central and South East Asia (CSE Asia), 0.66 billion in Africa, Yemen, and Saudi Arabia (Africa+), and 0.04 billion in the Americas. All those exposed to stable risk in the Americas were in the lowest endemicity class (PfPR2−10 ≤ 5%). The vast majority (88%) of those living under stable risk in CSE Asia were also in this low endemicity class; a small remainder (11%) were in the intermediate endemicity class (PfPR2−10 > 5 to < 40%); and the remaining fraction (1%) in high endemicity (PfPR2−10 ≥ 40%) areas. High endemicity was widespread in the Africa+ region, where 0.35 billion people are at this level of risk. Most of the rest live at intermediate risk (0.20 billion), with a smaller number (0.11 billion) at low stable risk. Conclusions High levels of P. falciparum malaria endemicity are common in Africa. Uniformly low endemic levels are found in the Americas. Low endemicity is also widespread in CSE Asia, but pockets of intermediate and very rarely high transmission remain. There are therefore significant opportunities for malaria control in Africa and for malaria elimination elsewhere. This 2007 global P. falciparum malaria endemicity map is the first of a series with which it will be possible to monitor and evaluate the progress of this intervention process. PMID:19323591

Application of Species Distribution Modeling for Avian Influenza surveillance in the United States considering the North America Migratory Flyways

NASA Astrophysics Data System (ADS)

Belkhiria, Jaber; Alkhamis, Moh A.; Martínez-López, Beatriz

2016-09-01

Highly Pathogenic Avian Influenza (HPAI) has recently (2014-2015) re-emerged in the United States (US) causing the largest outbreak in US history with 232 outbreaks and an estimated economic impact of $950 million. This study proposes to use suitability maps for Low Pathogenic Avian Influenza (LPAI) to identify areas at high risk for HPAI outbreaks. LPAI suitability maps were based on wild bird demographics, LPAI surveillance, and poultry density in combination with environmental, climatic, and socio-economic risk factors. Species distribution modeling was used to produce high-resolution (cell size: 500m x 500m) maps for Avian Influenza (AI) suitability in each of the four North American migratory flyways (NAMF). Results reveal that AI suitability is heterogeneously distributed throughout the US with higher suitability in specific zones of the Midwest and coastal areas. The resultant suitability maps adequately predicted most of the HPAI outbreak areas during the 2014-2015 epidemic in the US (i.e. 89% of HPAI outbreaks were located in areas identified as highly suitable for LPAI). Results are potentially useful for poultry producers and stakeholders in designing risk-based surveillance, outreach and intervention strategies to better prevent and control future HPAI outbreaks in the US.

Risk of nitrate in groundwaters of the United States - A national perspective

USGS Publications Warehouse

Nolan, B.T.; Ruddy, B.C.; Hitt, K.J.; Helsel, D.R.

1997-01-01

Nitrate contamination of groundwater occurs in predictable patterns, based on findings of the U.S. Geological Survey's (USGS) National Water Quality Assessment (NAWQA) Program. The NAWQA Program was begun in 1991 to describe the quality of the Nation's water resources, using nationally consistent methods. Variables affecting nitrate concentration in groundwater were grouped as 'input' factors (population density end the amount of nitrogen contributed by fertilizer, manure, and atmospheric sources) and 'aquifer vulnerability' factors (soil drainage characteristic and the ratio of woodland acres to cropland acres in agricultural areas) and compiled in a national map that shows patterns of risk for nitrate contamination of groundwater. Areas with high nitrogen input, well-drained soils, and low woodland to cropland ratio have the highest potential for contamination of shallow groundwater by nitrate. Groundwater nitrate data collected through 1992 from wells less than 100 ft deep generally verified the risk patterns shown on the national map. Median nitrate concentration was 0.2 mg/L in wells representing the low-risk group, and the maximum contaminant level (MCL) was exceeded in 3% of the wells. In contrast, median nitrate concentration was 4.8 mg/L in wells representing the high-risk group, and the MCL was exceeded in 25% of the wells.Nitrate contamination of groundwater occurs in predictable patterns, based on findings of the U.S. Geological Survey's (USGS) National Water Quality Assessment (NAWQA) Program. The NAWQA Program was begun in 1991 to describe the quality of the Nation's water resources, using nationally consistent methods. Variables affecting nitrate concentration in groundwater were grouped as `input' factors (population density and the amount of nitrogen contributed by fertilizer, manure, and atmospheric sources) and `aquifer vulnerability' factors (soil drainage characteristic and the ratio of woodland acres to cropland acres in agricultural areas) and compiled in a national map that shows patterns of risk for nitrate contamination of groundwater. Areas with high nitrogen input, well-drained soils, and low woodland to cropland ratio have the highest potential for contamination of shallow groundwater by nitrate. Groundwater nitrate data collected through 1992 from wells less than 100 ft deep generally verified the risk patterns shown on the national map. Median nitrate concentration was 0.2 mg/L in wells representing the low-risk group, and the maximum contaminant level (MCL) was exceeded in 3% of the wells. In contrast, median nitrate concentration was 4.8 mg/L in wells representing the high-risk group, and the MCL was exceeded in 25% of the wells.

Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

NASA Astrophysics Data System (ADS)

Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo

2017-11-01

The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.

Spatial tools for managing hemlock woolly adelgid in the southern Appalachians

NASA Astrophysics Data System (ADS)

Koch, Frank Henry, Jr.

The hemlock woolly adelgid (Adelges tsugae) has recently spread into the southern Appalachians. This insect attacks both native hemlock species (Tsuga canadensis and T. caroliniana ), has no natural enemies, and can kill hemlocks within four years. Biological control displays promise for combating the pest, but counter-measures are impeded because adelgid and hemlock distribution patterns have been detailed poorly. We developed a spatial management system to better target control efforts, with two components: (1) a protocol for mapping hemlock stands, and (2) a technique to map areas at risk of imminent infestation. To construct a hemlock classifier, we used topographically normalized satellite images from Great Smoky Mountains National Park. Employing a decision tree approach that supplemented image spectral data with several environmental variables, we generated rules distinguishing hemlock areas from other forest types. We then implemented these rules in a geographic information system and generated hemlock distribution maps. Assessment yielded an overall thematic accuracy of 90% for one study area, and 75% accuracy in capturing hemlocks in a second study area. To map areas at risk, we combined first-year infestation locations from Great Smoky Mountains National Park and the Blue Ridge Parkway with points from uninfested hemlock stands, recording a suite of environmental variables for each point. We applied four different multivariate classification techniques to generate models from this sample predicting locations with high infestation risk, and used the resulting models to generate risk maps for the study region. All techniques performed well, accurately capturing 70--90% of training and validation samples, with the logistic regression model best balancing accuracy and regional applicability. Areas close to trails, roads, and streams appear to have the highest initial risk, perhaps due to bird- or human-mediated dispersal. Both components of our management system are general enough for use throughout the southern Appalachians. Overlay of derived maps will allow forest managers to reduce the area where they must focus their control efforts and thus allocate resources more efficiently.

Early Warning System for West Nile Virus Risk Areas, California, USA

PubMed Central

Ahearn, Sean C.; McConchie, Alan; Glaser, Carol; Jean, Cynthia; Barker, Chris; Park, Bborie; Padgett, Kerry; Parker, Erin; Aquino, Ervic; Kramer, Vicki

2011-01-01

The Dynamic Continuous-Area Space-Time (DYCAST) system is a biologically based spatiotemporal model that uses public reports of dead birds to identify areas at high risk for West Nile virus (WNV) transmission to humans. In 2005, during a statewide epidemic of WNV (880 cases), the California Department of Public Health prospectively implemented DYCAST over 32,517 km2 in California. Daily risk maps were made available online and used by local agencies to target public education campaigns, surveillance, and mosquito control. DYCAST had 80.8% sensitivity and 90.6% specificity for predicting human cases, and κ analysis indicated moderate strength of chance-adjusted agreement for >4 weeks. High-risk grid cells (populations) were identified an average of 37.2 days before onset of human illness; relative risk for disease was >39× higher than for low-risk cells. Although prediction rates declined in subsequent years, results indicate DYCAST was a timely and effective early warning system during the severe 2005 epidemic. PMID:21801622

Zika virus: Endemic and epidemic ranges of Aedes mosquito transmission.

PubMed

Attaway, David F; Waters, Nigel M; Geraghty, Estella M; Jacobsen, Kathryn H

As evidence linking Zika virus with serious health complications strengthens, public health officials and clinicians worldwide need to know which locations are likely to be at risk for autochthonous Zika infections. We created risk maps for epidemic and endemic Aedes-borne Zika virus infections globally using a predictive analysis method that draws on temperature, precipitation, elevation, land cover, and population density variables to identify locations suitable for mosquito activity seasonally or year-round. Aedes mosquitoes capable of transmitting Zika and other viruses are likely to live year-round across many tropical areas in the Americas, Africa, and Asia. Our map provides an enhanced global projection of where vector control initiatives may be most valuable for reducing the risk of Zika virus and other Aedes-borne infections. Copyright © 2016 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

Schistosomiasis: Geospatial Surveillance and Response Systems in Southeast Asia

NASA Astrophysics Data System (ADS)

Malone, John; Bergquist, Robert; Rinaldi, Laura; Xiao-nong, Zhou

2016-10-01

Geographic information system (GIS) and remote sensing (RS) from Earth-observing satellites offer opportunities for rapid assessment of areas endemic for vector-borne diseases including estimates of populations at risk and guidance to intervention strategies. This presentation deals with GIS and RS applications for the control of schistosomiasis in China and the Philippines. It includes large-scale risk mapping including identification of suitable habitats for Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum. Predictions of infection risk are discussed with reference to ecological transformations and the potential impact of climate change and the potential for long-term temperature increases in the North as well as the impact on rivers, lakes and water resource developments. Potential integration of geospatial mapping and modeling in schistosomiasis surveillance and response systems in Asia within Global Earth Observation System of Systems (GEOSS) guidelines in the health societal benefit area is discussed.

Risk Map of Cholera Infection for Vaccine Deployment: The Eastern Kolkata Case

PubMed Central

You, Young Ae; Ali, Mohammad; Kanungo, Suman; Sah, Binod; Manna, Byomkesh; Puri, Mahesh; Nair, G. Balakrish; Bhattacharya, Sujit Kumar; Convertino, Matteo; Deen, Jacqueline L.; Lopez, Anna Lena; Wierzba, Thomas F.; Clemens, John; Sur, Dipika

2013-01-01

Background Despite advancement of our knowledge, cholera remains a public health concern. During March-April 2010, a large cholera outbreak afflicted the eastern part of Kolkata, India. The quantification of importance of socio-environmental factors in the risk of cholera, and the calculation of the risk is fundamental for deploying vaccination strategies. Here we investigate socio-environmental characteristics between high and low risk areas as well as the potential impact of vaccination on the spatial occurrence of the disease. Methods and Findings The study area comprised three wards of Kolkata Municipal Corporation. A mass cholera vaccination campaign was conducted in mid-2006 as the part of a clinical trial. Cholera cases and data of the trial to identify high risk areas for cholera were analyzed. We used a generalized additive model (GAM) to detect risk areas, and to evaluate the importance of socio-environmental characteristics between high and low risk areas. During the one-year pre-vaccination and two-year post-vaccination periods, 95 and 183 cholera cases were detected in 111,882 and 121,827 study participants, respectively. The GAM model predicts that high risk areas in the west part of the study area where the outbreak largely occurred. High risk areas in both periods were characterized by poor people, use of unsafe water, and proximity to canals used as the main drainage for rain and waste water. Cholera vaccine uptake was significantly lower in the high risk areas compared to low risk areas. Conclusion The study shows that even a parsimonious model like GAM predicts high risk areas where cholera outbreaks largely occurred. This is useful for indicating where interventions would be effective in controlling the disease risk. Data showed that vaccination decreased the risk of infection. Overall, the GAM-based risk map is useful for policymakers, especially those from countries where cholera remains to be endemic with periodic outbreaks. PMID:23936491

Retrospective validation of the laparoscopic ICG SLN mapping in patients with grade 3 endometrial cancer.

PubMed

Papadia, Andrea; Gasparri, Maria Luisa; Radan, Anda P; Stämpfli, Chantal A L; Rau, Tilman T; Mueller, Michael D

2018-04-24

To evaluate the sensitivity, negative predictive value (NPV) and false-negative (FN) rate of the near infrared (NIR) indocyanine green (ICG) sentinel lymph node (SLN) mapping in patients with poorly differentiated endometrial cancer who have undergone a full pelvic and para-aortic lymphadenectomy after SLN mapping. We performed a retrospective analysis of patients with endometrial cancer undergoing a laparoscopic NIR-ICG SLN mapping followed by a systematic pelvic and para-aortic lymphadenectomy. Inclusion criteria were a grade 3 endometrial cancer or a high-risk histology (papillary serous, clear cell carcinoma, carcinosarcoma, and neuroendocrine carcinoma) and a completion pelvic and para-aortic lymphadenectomy to the renal vessels after SLN mapping. Overall and bilateral detection rates, sensitivity, NPV, and FN rates were calculated. From December 2012 until January 2017, 42 patients fulfilled inclusion criteria. Overall and bilateral detection rates were 100 and 90.5%, respectively. Overall, 23.8% of the patients had lymph node metastases. In one patient, despite negative bilateral pelvic SLNs, a metastatic non-SLN-isolated para-aortic metastasis was detected. This NSLN was clinically suspicious and sent to frozen section analysis during the surgery. FN rate, sensitivity, and NPV were 10, 90, and 97.1%, respectively. For the SLN mapping algorithm, FN rate, sensitivity, and NPV were 0, 100, and 100%, respectively. Laparoscopic NIR-ICG SLN mapping in high-risk endometrial cancer patients has acceptable sensitivity, FN rate, and NPV.

Climate change induced lanslide hazard mapping over Greece- A case study in Pelion Mountain (SE Thessaly, Central Greece)

NASA Astrophysics Data System (ADS)

Angelitsa, Varvara; Loupasakis, Constantinos; Anagnwstopoulou, Christina

2015-04-01

Landslides, as a major type of geological hazard, represent one of the natural events that occur most frequently worldwide after hydro-meteorological events. Landslides occur when the stability of a slope changes due to a number of factors, such as the steep terrain and prolonged precipitation. Identification of landslides and compilation of landslide susceptibility, hazard and risk maps are very important issues for the public authorities providing substantial information regarding, the strategic planning and management of the land-use. Although landslides cannot be predicted accurately, many attempts have been made to compile these maps. Important factors for the the compilation of reliable maps are the quality and the amount of available data and the selection of the best method for the analysis. Numerous studies and publications providing landslide susceptibility,hazard and risk maps, for different regions of Greece, have completed up to now. Their common characteristic is that they are static, taking into account parameters like geology, mean annual precipitaion, slope, aspect, distance from roads, faults and drainage network, soil capability, land use etc., without introducing the dimension of time. The current study focuses on the Pelion Mountain, which is located at the southeastern part of Thessaly in Central Greece; aiming to compile "dynamic" susceptibility and hazard maps depending on climate changes. For this purpose, past and future precipipation data from regional climate models (RCMs) datasets are introduced as input parameters for the compilation of "dynamic" landslide hazard maps. Moreover, land motion mapping data produced by Persistent Scatterer Interferometry (PSI) are used for the validation of the landslide occurrence during the period from June 1992 to December 2003 and as a result for the calibration of the mapping procedure. The PSI data can be applied at a regional scale as support for land motion mapping and at local scale for the monitoring of single well-known ground motion event. The PSI data were produced within the framework of the Terrafirma project. Terrafirma is a pan- European ground motion information service focused on seismic risk, flood defense and costal lowland subsidence,inactive mines and hydrogeological risks. The produced maps provided substantial information for the land use planning and the civil protection of an area presenting excelent natural beauty and numerous preservable trtaditional villages. Keywords: landslide, psi technique, regional climate models, lanslide susceptibility maps, Greece

Effects of Humidity Variation on the Hantavirus Infection and Hemorrhagic Fever with Renal Syndrome Occurrence in Subtropical China

PubMed Central

Xiao, Hong; Huang, Ru; Gao, Li-Dong; Huang, Cun-Rui; Lin, Xiao-Ling; Li, Na; Liu, Hai-Ning; Tong, Shi-Lu; Tian, Huai-Yu

2016-01-01

Infection rates of rodents have a significant influence on the transmission of hemorrhagic fever with renal syndrome (HFRS). In this study, four cities and two counties with high HFRS incidence in eastern Hunan Province in China were studied, and surveillance data of rodents, as well as HFRS cases and related environmental variables from 2007 to 2010, were collected. Results indicate that the distribution and infection rates of rodents are closely associated with environmental conditions. Hantavirus infections in rodents were positively correlated with temperature vegetation dryness index and negatively correlated with elevation. The predictive risk maps based on multivariate regression model revealed that the annual variation of infection risks is small, whereas monthly variation is large and corresponded well to the seasonal variation of human HFRS incidence. The identification of risk factors and risk prediction provides decision support for rodent surveillance and the prevention and control of HFRS. PMID:26711521

Impacts of Climate Change on Native Landcover: Seeking Future Climatic Refuges

PubMed Central

Mangabeira Albernaz, Ana Luisa

2016-01-01

Climate change is a driver for diverse impacts on global biodiversity. We investigated its impacts on native landcover distribution in South America, seeking to predict its effect as a new force driving habitat loss and population isolation. Moreover, we mapped potential future climatic refuges, which are likely to be key areas for biodiversity conservation under climate change scenarios. Climatically similar native landcovers were aggregated using a decision tree, generating a reclassified landcover map, from which 25% of the map’s coverage was randomly selected to fuel distribution models. We selected the best geographical distribution models among twelve techniques, validating the predicted distribution for current climate with the landcover map and used the best technique to predict the future distribution. All landcover categories showed changes in area and displacement of the latitudinal/longitudinal centroid. Closed vegetation was the only landcover type predicted to expand its distributional range. The range contractions predicted for other categories were intense, even suggesting extirpation of the sparse vegetation category. The landcover refuges under future climate change represent a small proportion of the South American area and they are disproportionately represented and unevenly distributed, predominantly occupying five of 26 South American countries. The predicted changes, regardless of their direction and intensity, can put biodiversity at risk because they are expected to occur in the near future in terms of the temporal scales of ecological and evolutionary processes. Recognition of the threat of climate change allows more efficient conservation actions. PMID:27618445

Spatiotemporal floodplain mapping and prediction using HEC-RAS - GIS tools: Case of the Mejerda river, Tunisia

NASA Astrophysics Data System (ADS)

Ben Khalfallah, C.; Saidi, S.

2018-06-01

The floods have become a scourge in recent years (Floods of, 2003, 2006, 2009, 2011, and 2012), increasingly frequent and devastating. Tunisia does not escape flooding problems, the flood management requires basically a better knowledge of the phenomenon (flood), and the use of predictive methods. In order to limit this risk, we became interested in hydrodynamics modeling of Medjerda basin. To reach this aim, rainfall distribution is studied and mapped using GIS tools. In addition, flood and return period estimation of rainfall are calculated using Hyfran. Also, Simulations of recent floods are calculated and mapped using HEC-RAS and HEC-GeoRAS for the most recent flood occurred in February-March 2015 in Medjerda basin. The analysis of the results shows a good correlation between simulated parameters and those measured. There is a flood of the river exceeding 240 m3/s (DGRE, 2015) and more flowing sections are observed in the future simulations; for return periods of 10yr, 20yr and 50yr.

Rift Valley Fever Prediction and Risk Mapping: 2014-2015 Season

NASA Technical Reports Server (NTRS)

Anyamba, Assaf

2015-01-01

Extremes in either direction (+-) of precipitation temperature have significant implications for disease vectors and pathogen emergence and spread Magnitude of ENSO influence on precipitation temperature cannot be currently predicted rely on average history and patterns. Timing of event and emergence disease can be exploited (GAP) in to undertake vector control and preparedness measures. Currently - no risk for ecologically-coupled RVFV activity however we need to be vigilant during the coming fall season due the ongoing buildup of energy in the central Pacific Ocean. Potential for the dual-use of the RVF Monitor system for other VBDs Need to invest in early ground surveillance and the use of rapid field diagnostic capabilities for vector identification and virus isolation.

The global distribution of Crimean-Congo hemorrhagic fever

PubMed Central

Messina, Jane P.; Pigott, David M.; Golding, Nick; Duda, Kirsten A.; Brownstein, John S.; Weiss, Daniel J.; Gibson, Harry; Robinson, Timothy P.; Gilbert, Marius; William Wint, G. R.; Nuttall, Patricia A.; Gething, Peter W.; Myers, Monica F.; George, Dylan B.; Hay, Simon I.

2015-01-01

Background Crimean-Congo hemorrhagic fever (CCHF) is a tick-borne infection caused by a virus (CCHFV) from the Bunyaviridae family. Domestic and wild vertebrates are asymptomatic reservoirs for the virus, putting animal handlers, slaughter-house workers and agricultural labourers at highest risk in endemic areas, with secondary transmission possible through contact with infected blood and other bodily fluids. Human infection is characterized by severe symptoms that often result in death. While it is known that CCHFV transmission is limited to Africa, Asia and Europe, definitive global extents and risk patterns within these limits have not been well described. Methods We used an exhaustive database of human CCHF occurrence records and a niche modeling framework to map the global distribution of risk for human CCHF occurrence. Results A greater proportion of shrub or grass land cover was the most important contributor to our model, which predicts highest levels of risk around the Black Sea, Turkey, and some parts of central Asia. Sub-Saharan Africa shows more focalized areas of risk throughout the Sahel and the Cape region. Conclusions These new risk maps provide a valuable starting point for understanding the zoonotic niche of CCHF, its extent and the risk it poses to humans. PMID:26142451

Mapping Developmental Precursors of Cyber-Aggression: Trajectories of Risk Predict Perpetration and Victimization

ERIC Educational Resources Information Center

Modecki, Kathryn L.; Barber, Bonnie L.; Vernon, Lynnette

2013-01-01

Technologically mediated contexts are social arenas in which adolescents can be both perpetrators and victims of aggression. Yet, there remains little understanding of the developmental etiology of cyber aggression, itself, as experienced by either perpetrators or victims. The current study examines 3-year latent within-person trajectories of…

Flood prediction, its risk and mitigation for the Babura River with GIS

NASA Astrophysics Data System (ADS)

Tarigan, A. P. M.; Hanie, M. Z.; Khair, H.; Iskandar, R.

2018-03-01

This paper describes the flood prediction along the Babura River, the catchment of which is within the comparatively larger watershed of the Deli River which crosses the centre part of Medan City. The flood plain and ensuing inundation area were simulated using HECRAS based on the available data of rainfall, catchment, and river cross-sections. The results were shown in a GIS format in which the city map of Medan and other infrastructure layers were stacked for spatial analysis. From the resulting GIS, it can be seen that 13 sub-districts were likely affected by the flood, and then the risk calculation of the flood damage could be estimated. In the spirit of flood mitigation thoughts, 6 locations of evacuation centres were identified and 15 evacuation routes were recommended to reach the centres. It is hoped that the flood prediction and its risk estimation in this study will inspire the preparedness of the stakeholders for the probable threat of flood disaster.

Assessment of the Appalachian Basin Geothermal Field: Combining Risk Factors to Inform Development of Low Temperature Projects

NASA Astrophysics Data System (ADS)

Smith, J. D.; Whealton, C.; Camp, E. R.; Horowitz, F.; Frone, Z. S.; Jordan, T. E.; Stedinger, J. R.

2015-12-01

Exploration methods for deep geothermal energy projects must primarily consider whether or not a location has favorable thermal resources. Even where the thermal field is favorable, other factors may impede project development and success. A combined analysis of these factors and their uncertainty is a strategy for moving geothermal energy proposals forward from the exploration phase at the scale of a basin to the scale of a project, and further to design of geothermal systems. For a Department of Energy Geothermal Play Fairway Analysis we assessed quality metrics, which we call risk factors, in the Appalachian Basin of New York, Pennsylvania, and West Virginia. These included 1) thermal field variability, 2) productivity of natural reservoirs from which to extract heat, 3) potential for induced seismicity, and 4) presence of thermal utilization centers. The thermal field was determined using a 1D heat flow model for 13,400 bottomhole temperatures (BHT) from oil and gas wells. Steps included the development of i) a set of corrections to BHT data and ii) depth models of conductivity stratigraphy at each borehole based on generalized stratigraphy that was verified for a select set of wells. Wells are control points in a spatial statistical analysis that resulted in maps of the predicted mean thermal field properties and of the standard error of the predicted mean. Seismic risk was analyzed by comparing earthquakes and stress orientations in the basin to gravity and magnetic potential field edges at depth. Major edges in the potential fields served as interpolation boundaries for the thermal maps (Figure 1). Natural reservoirs were identified from published studies, and productivity was determined based on the expected permeability and dimensions of each reservoir. Visualizing the natural reservoirs and population centers on a map of the thermal field communicates options for viable pilot sites and project designs (Figure 1). Furthermore, combining the four risk factors at favorable sites enables an evaluation of project feasibility across sites based on tradeoffs in the risk factors. Uncertainties in each risk factor can also be considered to determine if the tradeoffs in risk factors between sites are meaningful.

Association mapping of genetic risk factors for chronic wasting disease in wild deer

USGS Publications Warehouse

Tomomi Matsumoto,; Samuel, Michael D.; Trent Bollinger,; Margo Pybus,; David W. Coltman,

2013-01-01

Chronic wasting disease (CWD) is a fatal transmissible spongiform encephalopathy affecting North American cervids. We assessed the feasibility of association mapping CWD genetic risk factors in wild white-tailed deer (Odocoileus virginianus) and mule deer (Odocoileus hemionus) using a panel of bovine microsatellite markers from three homologous deer linkage groups predicted to contain candidate genes. These markers had a low cross-species amplification rate (27.9%) and showed weak linkage disequilibrium (<1 cM). Markers near the prion protein and the neurofibromin 1 (NF1) genes were suggestively associated with CWD status in white-tailed deer (P = 0.006) and mule deer (P = 0.02), respectively. This is the first time an association between the NF1 region and CWD has been reported.

Use of USLE/GIS methodology for predicting soil loss in a semiarid agricultural watershed.

PubMed

Erdogan, Emrah H; Erpul, Günay; Bayramin, Ilhami

2007-08-01

The Universal Soil Loss Equation (USLE) is an erosion model to estimate average soil loss that would generally result from splash, sheet, and rill erosion from agricultural plots. Recently, use of USLE has been extended as a useful tool predicting soil losses and planning control practices in agricultural watersheds by the effective integration of the GIS-based procedures to estimate the factor values in a grid cell basis. This study was performed in the Kazan Watershed located in the central Anatolia, Turkey, to predict soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Rain erosivity (R), soil erodibility (K), and cover management factor (C) values of the model were calculated from erosivity map, soil map, and land use map of Turkey, respectively. R values were site-specifically corrected using DEM and climatic data. The topographical and hydrological effects on the soil loss were characterized by LS factor evaluated by the flow accumulation tool using DEM and watershed delineation techniques. From resulting soil loss map of the watershed, the magnitude of the soil erosion was estimated in terms of the different soil units and land uses and the most erosion-prone areas where irreversible soil losses occurred were reasonably located in the Kazan watershed. This could be very useful for deciding restoration practices to control the soil erosion of the sites to be severely influenced.

Spread of the Tiger: Global Risk of Invasion by the Mosquito Aedes albopictus

PubMed Central

BENEDICT, MARK Q.; LEVINE, REBECCA S.; HAWLEY, WILLIAM A.; LOUNIBOS, L. PHILIP

2008-01-01

Aedes albopictus, commonly known as the Asian tiger mosquito, is currently the most invasive mosquito in the world. It is of medical importance due to its aggressive daytime human-biting behavior and ability to vector many viruses, including dengue, LaCrosse, and West Nile. Invasions into new areas of its potential range are often initiated through the transportation of eggs via the international trade in used tires. We use a genetic algorithm, Genetic Algorithm for Rule Set Production (GARP), to determine the ecological niche of Ae. albopictus and predict a global ecological risk map for the continued spread of the species. We combine this analysis with risk due to importation of tires from infested countries and their proximity to countries that have already been invaded to develop a list of countries most at risk for future introductions and establishments. Methods used here have potential for predicting risks of future invasions of vectors or pathogens. PMID:17417960

Characteristics of genomic signatures derived using univariate methods and mechanistically anchored functional descriptors for predicting drug- and xenobiotic-induced nephrotoxicity.

PubMed

Shi, Weiwei; Bugrim, Andrej; Nikolsky, Yuri; Nikolskya, Tatiana; Brennan, Richard J

2008-01-01

ABSTRACT The ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers ("signatures") have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps-sets of genes coordinately involved in key biological processes-with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map "ephrins signaling" performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of predictive genomic investigations.

Climate Change Could Increase the Geographic Extent of Hendra Virus Spillover Risk.

PubMed

Martin, Gerardo; Yanez-Arenas, Carlos; Chen, Carla; Plowright, Raina K; Webb, Rebecca J; Skerratt, Lee F

2018-03-19

Disease risk mapping is important for predicting and mitigating impacts of bat-borne viruses, including Hendra virus (Paramyxoviridae:Henipavirus), that can spillover to domestic animals and thence to humans. We produced two models to estimate areas at potential risk of HeV spillover explained by the climatic suitability for its flying fox reservoir hosts, Pteropus alecto and P. conspicillatus. We included additional climatic variables that might affect spillover risk through other biological processes (such as bat or horse behaviour, plant phenology and bat foraging habitat). Models were fit with a Poisson point process model and a log-Gaussian Cox process. In response to climate change, risk expanded southwards due to an expansion of P. alecto suitable habitat, which increased the number of horses at risk by 175-260% (110,000-165,000). In the northern limits of the current distribution, spillover risk was highly uncertain because of model extrapolation to novel climatic conditions. The extent of areas at risk of spillover from P. conspicillatus was predicted shrink. Due to a likely expansion of P. alecto into these areas, it could replace P. conspicillatus as the main HeV reservoir. We recommend: (1) HeV monitoring in bats, (2) enhancing HeV prevention in horses in areas predicted to be at risk, (3) investigate and develop mitigation strategies for areas that could experience reservoir host replacements.

Development and Sensitivity Analysis of a Frost Risk model based primarily on freely distributed Earth Observation data

NASA Astrophysics Data System (ADS)

Louka, Panagiota; Petropoulos, George; Papanikolaou, Ioannis

2015-04-01

The ability to map the spatiotemporal distribution of extreme climatic conditions, such as frost, is a significant tool in successful agricultural management and decision making. Nowadays, with the development of Earth Observation (EO) technology, it is possible to obtain accurately, timely and in a cost-effective way information on the spatiotemporal distribution of frost conditions, particularly over large and otherwise inaccessible areas. The present study aimed at developing and evaluating a frost risk prediction model, exploiting primarily EO data from MODIS and ASTER sensors and ancillary ground observation data. For the evaluation of our model, a region in north-western Greece was selected as test site and a detailed sensitivity analysis was implemented. The agreement between the model predictions and the observed (remotely sensed) frost frequency obtained by MODIS sensor was evaluated thoroughly. Also, detailed comparisons of the model predictions were performed against reference frost ground observations acquired from the Greek Agricultural Insurance Organization (ELGA) over a period of 10-years (2000-2010). Overall, results evidenced the ability of the model to produce reasonably well the frost conditions, following largely explainable patterns in respect to the study site and local weather conditions characteristics. Implementation of our proposed frost risk model is based primarily on satellite imagery analysis provided nowadays globally at no cost. It is also straightforward and computationally inexpensive, requiring much less effort in comparison for example to field surveying. Finally, the method is adjustable to be potentially integrated with other high resolution data available from both commercial and non-commercial vendors. Keywords: Sensitivity analysis, frost risk mapping, GIS, remote sensing, MODIS, Greece

Geological Prediction Ahead of Tunnel Face in the Limestone Formation Tunnel using Multi-Modal Geophysical Surveys

NASA Astrophysics Data System (ADS)

Zaki, N. F. M.; Ismail, M. A. M.; Hazreek Zainal Abidin, Mohd; Madun, Aziman

2018-04-01

Tunnel construction in typical karst topography face the risk which unknown geological condition such as abundant rainwater, ground water and cavities. Construction of tunnel in karst limestone frequently lead to potentially over-break of rock formation and cause failure to affected area. Physical character of limestone which consists large cavity prone to sudden failure and become worsen due to misinterpretation of rock quality by engineer and geologists during analysis stage and improper method adopted in construction stage. Consideration for execution of laboratory and field testing in rock limestone should be well planned and arranged in tunnel construction project. Several tests including Ground Penetration Radar (GPR) and geological face mapping were studied in this research to investigate the performances of limestone rock in tunnel construction, measured in term of rock mass quality that used for risk assessment. The objective of this study is to focus on the prediction of geological condition ahead of tunnel face using short range method (GPR) and verified by geological face mapping method to determine the consistency of actual geological condition on site. Q-Value as the main indicator for rock mass classification was obtained from geological face mapping method. The scope of this study is covering for tunnelling construction along 756 meters in karst limestone area which located at Timah Tasoh Tunnel, Bukit Tebing Tinggi, Perlis. For this case study, 15% of GPR results was identified as inaccurate for rock mass classification in which certain chainage along this tunnel with 34 out of 224 data from GPR was identified as incompatible with actual face mapping.

Language mapping with navigated transcranial magnetic stimulation in pediatric and adult patients undergoing epilepsy surgery: Comparison with extraoperative direct cortical stimulation.

PubMed

Lehtinen, Henri; Mäkelä, Jyrki P; Mäkelä, Teemu; Lioumis, Pantelis; Metsähonkala, Liisa; Hokkanen, Laura; Wilenius, Juha; Gaily, Eija

2018-06-01

Navigated transcranial magnetic stimulation (nTMS) is becoming increasingly popular in noninvasive preoperative language mapping, as its results correlate well enough with those obtained by direct cortical stimulation (DCS) during awake surgery in adult patients with tumor. Reports in the context of epilepsy surgery or extraoperative DCS in adults are, however, sparse, and validation of nTMS with DCS in children is lacking. Furthermore, little is known about the risk of inducing epileptic seizures with nTMS in pediatric epilepsy patients. We provide the largest validation study to date in an epilepsy surgery population. We compared language mapping with nTMS and extraoperative DCS in 20 epilepsy surgery patients (age range 9-32 years; 14 children and adolescents). In comparison with DCS, sensitivity of nTMS was 68%, specificity 76%, positive predictive value 27%, and negative predictive value 95%. Age, location of ictal-onset zone near or within DCS-mapped language areas or severity of cognitive deficits had no significant effect on these values. None of our patients had seizures during nTMS. Our study suggests that nTMS language mapping is clinically useful and safe in epilepsy surgery patients, including school-aged children and patients with extensive cognitive dysfunction. Similar to in tumor surgery, mapping results in the frontal region are most reliable. False negative findings may be slightly more likely in epilepsy than in tumor surgery patients. Mapping results should always be verified by other methods in individual patients.

Mapping the zoonotic niche of Lassa fever in Africa

PubMed Central

Mylne, Adrian Q. N.; Pigott, David M.; Longbottom, Joshua; Shearer, Freya; Duda, Kirsten A.; Messina, Jane P.; Weiss, Daniel J.; Moyes, Catherine L.; Golding, Nick; Hay, Simon I.

2015-01-01

Background Lassa fever is a viral haemorrhagic illness responsible for disease outbreaks across West Africa. It is a zoonosis, with the primary reservoir species identified as the Natal multimammate mouse, Mastomys natalensis. The host is distributed across sub-Saharan Africa while the virus' range appears to be restricted to West Africa. The majority of infections result from interactions between the animal reservoir and human populations, although secondary transmission between humans can occur, particularly in hospital settings. Methods Using a species distribution model, the locations of confirmed human and animal infections with Lassa virus (LASV) were used to generate a probabilistic surface of zoonotic transmission potential across sub-Saharan Africa. Results Our results predict that 37.7 million people in 14 countries, across much of West Africa, live in areas where conditions are suitable for zoonotic transmission of LASV. Four of these countries, where at-risk populations are predicted, have yet to report any cases of Lassa fever. Conclusions These maps act as a spatial guide for future surveillance activities to better characterise the geographical distribution of the disease and understand the anthropological, virological and zoological interactions necessary for viral transmission. Combining this zoonotic niche map with detailed patient travel histories can aid differential diagnoses of febrile illnesses, enabling a more rapid response in providing care and reducing the risk of onward transmission. PMID:26085474

Mapping the zoonotic niche of Lassa fever in Africa.

PubMed

Mylne, Adrian Q N; Pigott, David M; Longbottom, Joshua; Shearer, Freya; Duda, Kirsten A; Messina, Jane P; Weiss, Daniel J; Moyes, Catherine L; Golding, Nick; Hay, Simon I

2015-08-01

Lassa fever is a viral haemorrhagic illness responsible for disease outbreaks across West Africa. It is a zoonosis, with the primary reservoir species identified as the Natal multimammate mouse, Mastomys natalensis. The host is distributed across sub-Saharan Africa while the virus' range appears to be restricted to West Africa. The majority of infections result from interactions between the animal reservoir and human populations, although secondary transmission between humans can occur, particularly in hospital settings. Using a species distribution model, the locations of confirmed human and animal infections with Lassa virus (LASV) were used to generate a probabilistic surface of zoonotic transmission potential across sub-Saharan Africa. Our results predict that 37.7 million people in 14 countries, across much of West Africa, live in areas where conditions are suitable for zoonotic transmission of LASV. Four of these countries, where at-risk populations are predicted, have yet to report any cases of Lassa fever. These maps act as a spatial guide for future surveillance activities to better characterise the geographical distribution of the disease and understand the anthropological, virological and zoological interactions necessary for viral transmission. Combining this zoonotic niche map with detailed patient travel histories can aid differential diagnoses of febrile illnesses, enabling a more rapid response in providing care and reducing the risk of onward transmission. © The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene.

Using remote sensing to map larval and adult populations of Anopheles hyrcanus (Diptera: Culicidae) a potential malaria vector in Southern France

PubMed Central

Tran, Annelise; Ponçon, Nicolas; Toty, Céline; Linard, Catherine; Guis, Hélène; Ferré, Jean-Baptiste; Lo Seen, Danny; Roger, François; de la Rocque, Stéphane; Fontenille, Didier; Baldet, Thierry

2008-01-01

Background Although malaria disappeared from southern France more than 60 years ago, suspicions of recent autochthonous transmission in the French Mediterranean coast support the idea that the area could still be subject to malaria transmission. The main potential vector of malaria in the Camargue area, the largest river delta in southern France, is the mosquito Anopheles hyrcanus (Diptera: Culicidae). In the context of recent climatic and landscape changes, the evaluation of the risk of emergence or re-emergence of such a major disease is of great importance in Europe. When assessing the risk of emergence of vector-borne diseases, it is crucial to be able to characterize the arthropod vector's spatial distribution. Given that remote sensing techniques can describe some of the environmental parameters which drive this distribution, satellite imagery or aerial photographs could be used for vector mapping. Results In this study, we propose a method to map larval and adult populations of An. hyrcanus based on environmental indices derived from high spatial resolution imagery. The analysis of the link between entomological field data on An. hyrcanus larvae and environmental indices (biotopes, distance to the nearest main productive breeding sites of this species i.e., rice fields) led to the definition of a larval index, defined as the probability of observing An. hyrcanus larvae in a given site at least once over a year. Independent accuracy assessments showed a good agreement between observed and predicted values (sensitivity and specificity of the logistic regression model being 0.76 and 0.78, respectively). An adult index was derived from the larval index by averaging the larval index within a buffer around the trap location. This index was highly correlated with observed adult abundance values (Pearson r = 0.97, p < 0.05). This allowed us to generate predictive maps of An. hyrcanus larval and adult populations from the landscape indices. Conclusion This work shows that it is possible to use high resolution satellite imagery to map malaria vector spatial distribution. It also confirms the potential of remote sensing to help target risk areas, and constitutes a first essential step in assessing the risk of re-emergence of malaria in southern France. PMID:18302749

Using Remote Sensing, Weather, and Demographic Data to Create Risk Maps for Zika, Dengue, and Chikungunya in Brazil

NASA Astrophysics Data System (ADS)

Manore, C.; Conrad, J.; Del Valle, S.; Ziemann, A.; Fairchild, G.; Generous, E. N.

2017-12-01

Mosquito-borne diseases such as Zika, dengue, and chikungunya viruses have dynamics coupled to weather, ecology, human infrastructure, socio-economic demographics, and behavior. We use time-varying remote sensing and weather data, along with demographics and ecozones to predict risk through time for Zika, dengue, and chikungunya outbreaks in Brazil. We use distributed lag methods to quantify the lag between outbreaks and weather. Our statistical model indicates that the relationships between the variables are complex, but that quantifying risk is possible with the right data at appropriate spatio-temporal scales.

Predictable bacterial composition and hydrocarbon degradation in Arctic soils following diesel and nutrient disturbance.

PubMed

Bell, Terrence H; Yergeau, Etienne; Maynard, Christine; Juck, David; Whyte, Lyle G; Greer, Charles W

2013-06-01

Increased exploration and exploitation of resources in the Arctic is leading to a higher risk of petroleum contamination. A number of Arctic microorganisms can use petroleum for growth-supporting carbon and energy, but traditional approaches for stimulating these microorganisms (for example, nutrient addition) have varied in effectiveness between sites. Consistent environmental controls on microbial community response to disturbance from petroleum contaminants and nutrient amendments across Arctic soils have not been identified, nor is it known whether specific taxa are universally associated with efficient bioremediation. In this study, we contaminated 18 Arctic soils with diesel and treated subsamples of each with monoammonium phosphate (MAP), which has successfully stimulated degradation in some contaminated Arctic soils. Bacterial community composition of uncontaminated, diesel-contaminated and diesel+MAP soils was assessed through multiplexed 16S (ribosomal RNA) rRNA gene sequencing on an Ion Torrent Personal Genome Machine, while hydrocarbon degradation was measured by gas chromatography analysis. Diversity of 16S rRNA gene sequences was reduced by diesel, and more so by the combination of diesel and MAP. Actinobacteria dominated uncontaminated soils with <10% organic matter, while Proteobacteria dominated higher-organic matter soils, and this pattern was exaggerated following disturbance. Degradation with and without MAP was predictable by initial bacterial diversity and the abundance of specific assemblages of Betaproteobacteria, respectively. High Betaproteobacteria abundance was positively correlated with high diesel degradation in MAP-treated soils, suggesting this may be an important group to stimulate. The predictability with which bacterial communities respond to these disturbances suggests that costly and time-consuming contaminated site assessments may not be necessary in the future.

Mapping the risk of sudden oak death in Oregon: prioritizing locations for early detection and eradication

Treesearch

V& aacute; clavík Tom& aacute; & scaron; ; Alan Kanaskie; Ellen Goheen; Janet Ohmann; Everett Hansen; Ross Meentemeyer

2010-01-01

Phytophthora ramorum was first discovered in forests of southwestern Oregon in 2001. Despite intense eradication efforts, disease continues to spread from initially infested sites because of the late discovery of disease outbreaks and incomplete detection. Here we present two GIS predictive models of sudden oak death (SOD) establishment and spread...

Susceptibility mapping of visceral leishmaniasis based on fuzzy modelling and group decision-making methods.

PubMed

Rajabi, Mohamadreza; Mansourian, Ali; Bazmani, Ahad

2012-11-01

Visceral leishmaniasis (VL) is a vector-borne disease, highly influenced by environmental factors, which is an increasing public health problem in Iran, especially in the north-western part of the country. A geographical information system was used to extract data and map environmental variables for all villages in the districts of Kalaybar and Ahar in the province of East Azerbaijan. An attempt to predict VL prevalence based on an analytical hierarchy process (AHP) module combined with ordered weighted averaging (OWA) with fuzzy quantifiers indicated that the south-eastern part of Ahar is particularly prone to high VL prevalence. With the main objective to locate the villages most at risk, the opinions of experts and specialists were generalised into a group decision-making process by means of fuzzy weighting methods and induced OWA. The prediction model was applied throughout the entire study area (even where the disease is prevalent and where data already exist). The predicted data were compared with registered VL incidence records in each area. The results suggest that linguistic fuzzy quantifiers, guided by an AHP-OWA model, are capable of predicting susceptive locations for VL prevalence with an accuracy exceeding 80%. The group decision-making process demonstrated that people in 15 villages live under particularly high risk for VL contagion, i.e. villages where the disease is highly prevalent. The findings of this study are relevant for the planning of effective control strategies for VL in northwest Iran.

A catchment-scale model to predict spatial and temporal burden of E. coli on pasture from grazing livestock.

PubMed

Oliver, David M; Bartie, Phil J; Louise Heathwaite, A; Reaney, Sim M; Parnell, Jared A Q; Quilliam, Richard S

2018-03-01

Effective management of diffuse microbial water pollution from agriculture requires a fundamental understanding of how spatial patterns of microbial pollutants, e.g. E. coli, vary over time at the landscape scale. The aim of this study was to apply the Visualising Pathogen &Environmental Risk (ViPER) model, developed to predict E. coli burden on agricultural land, in a spatially distributed manner to two contrasting catchments in order to map and understand changes in E. coli burden contributed to land from grazing livestock. The model was applied to the River Ayr and Lunan Water catchments, with significant correlations observed between area of improved grassland and the maximum total E. coli per 1km 2 grid cell (Ayr: r=0.57; p<0.001, Lunan: r=0.32; p<0.001). There was a significant difference in the predicted maximum E. coli burden between seasons in both catchments, with summer and autumn predicted to accrue higher E. coli contributions relative to spring and winter (P<0.001), driven largely by livestock presence. The ViPER model thus describes, at the landscape scale, spatial nuances in the vulnerability of E. coli loading to land as driven by stocking density and livestock grazing regimes. Resulting risk maps therefore provide the underpinning evidence to inform spatially-targeted decision-making with respect to managing sources of E. coli in agricultural environments. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Multi-hazard risk analysis using the FP7 RASOR Platform

NASA Astrophysics Data System (ADS)

Koudogbo, Fifamè N.; Duro, Javier; Rossi, Lauro; Rudari, Roberto; Eddy, Andrew

2014-10-01

Climate change challenges our understanding of risk by modifying hazards and their interactions. Sudden increases in population and rapid urbanization are changing exposure to risk around the globe, making impacts harder to predict. Despite the availability of operational mapping products, there is no single tool to integrate diverse data and products across hazards, update exposure data quickly and make scenario-based predictions to support both short and long-term risk-related decisions. RASOR (Rapid Analysis and Spatialization Of Risk) will develop a platform to perform multi-hazard risk analysis for the full cycle of disaster management, including targeted support to critical infrastructure monitoring and climate change impact assessment. A scenario-driven query system simulates future scenarios based on existing or assumed conditions and compares them with historical scenarios. RASOR will thus offer a single work environment that generates new risk information across hazards, across data types (satellite EO, in-situ), across user communities (global, local, climate, civil protection, insurance, etc.) and across the world. Five case study areas are considered within the project, located in Haiti, Indonesia, Netherlands, Italy and Greece. Initially available over those demonstration areas, RASOR will ultimately offer global services to support in-depth risk assessment and full-cycle risk management.

Genetic-based prediction of disease traits: prediction is very difficult, especially about the future†

PubMed Central

Schrodi, Steven J.; Mukherjee, Shubhabrata; Shan, Ying; Tromp, Gerard; Sninsky, John J.; Callear, Amy P.; Carter, Tonia C.; Ye, Zhan; Haines, Jonathan L.; Brilliant, Murray H.; Crane, Paul K.; Smelser, Diane T.; Elston, Robert C.; Weeks, Daniel E.

2014-01-01

Translation of results from genetic findings to inform medical practice is a highly anticipated goal of human genetics. The aim of this paper is to review and discuss the role of genetics in medically-relevant prediction. Germline genetics presages disease onset and therefore can contribute prognostic signals that augment laboratory tests and clinical features. As such, the impact of genetic-based predictive models on clinical decisions and therapy choice could be profound. However, given that (i) medical traits result from a complex interplay between genetic and environmental factors, (ii) the underlying genetic architectures for susceptibility to common diseases are not well-understood, and (iii) replicable susceptibility alleles, in combination, account for only a moderate amount of disease heritability, there are substantial challenges to constructing and implementing genetic risk prediction models with high utility. In spite of these challenges, concerted progress has continued in this area with an ongoing accumulation of studies that identify disease predisposing genotypes. Several statistical approaches with the aim of predicting disease have been published. Here we summarize the current state of disease susceptibility mapping and pharmacogenetics efforts for risk prediction, describe methods used to construct and evaluate genetic-based predictive models, and discuss applications. PMID:24917882

Geostatistical analysis of disease data: accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging

PubMed Central

Goovaerts, Pierre

2006-01-01

Background Geostatistical techniques that account for spatially varying population sizes and spatial patterns in the filtering of choropleth maps of cancer mortality were recently developed. Their implementation was facilitated by the initial assumption that all geographical units are the same size and shape, which allowed the use of geographic centroids in semivariogram estimation and kriging. Another implicit assumption was that the population at risk is uniformly distributed within each unit. This paper presents a generalization of Poisson kriging whereby the size and shape of administrative units, as well as the population density, is incorporated into the filtering of noisy mortality rates and the creation of isopleth risk maps. An innovative procedure to infer the point-support semivariogram of the risk from aggregated rates (i.e. areal data) is also proposed. Results The novel methodology is applied to age-adjusted lung and cervix cancer mortality rates recorded for white females in two contrasted county geographies: 1) state of Indiana that consists of 92 counties of fairly similar size and shape, and 2) four states in the Western US (Arizona, California, Nevada and Utah) forming a set of 118 counties that are vastly different geographical units. Area-to-point (ATP) Poisson kriging produces risk surfaces that are less smooth than the maps created by a naïve point kriging of empirical Bayesian smoothed rates. The coherence constraint of ATP kriging also ensures that the population-weighted average of risk estimates within each geographical unit equals the areal data for this unit. Simulation studies showed that the new approach yields more accurate predictions and confidence intervals than point kriging of areal data where all counties are simply collapsed into their respective polygon centroids. Its benefit over point kriging increases as the county geography becomes more heterogeneous. Conclusion A major limitation of choropleth maps is the common biased visual perception that larger rural and sparsely populated areas are of greater importance. The approach presented in this paper allows the continuous mapping of mortality risk, while accounting locally for population density and areal data through the coherence constraint. This form of Poisson kriging will facilitate the analysis of relationships between health data and putative covariates that are typically measured over different spatial supports. PMID:17137504

Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study.

PubMed

De Roeck, Els; Van Coillie, Frieke; De Wulf, Robert; Soenen, Karen; Charlier, Johannes; Vercruysse, Jozef; Hantson, Wouter; Ducheyne, Els; Hendrickx, Guy

2014-12-01

The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke, as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m2 and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations.

Mapping Sources of Food Safety Information for U.S. Consumers: Findings From a National Survey.

PubMed

Nan, Xiaoli; Verrill, Linda; Kim, Jarim

2017-03-01

This research examines the sources from which U.S. consumers obtain their food safety information. It seeks to determine differences in the types of information sources used by U.S. consumers of different sociodemographic background, as well as the relationships between the types of information sources used and food safety risk perceptions. Analyzing the 2010 Food Safety Survey (N = 4,568) conducted by the U.S. Food and Drug Administration, we found that age, gender, education, and race predicted the use of different sources for food safety information. Additionally, use of several information sources predicted perceived susceptibility to foodborne illnesses and severity of food contamination. Implications of the findings for food safety risk communication are discussed.

Effect of genetic architecture on the prediction accuracy of quantitative traits in samples of unrelated individuals.

PubMed

Morgante, Fabio; Huang, Wen; Maltecca, Christian; Mackay, Trudy F C

2018-06-01

Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.

Non-linear feature extraction from HRV signal for mortality prediction of ICU cardiovascular patient.

PubMed

Karimi Moridani, Mohammad; Setarehdan, Seyed Kamaledin; Motie Nasrabadi, Ali; Hajinasrollah, Esmaeil

2016-01-01

Intensive care unit (ICU) patients are at risk of in-ICU morbidities and mortality, making specific systems for identifying at-risk patients a necessity for improving clinical care. This study presents a new method for predicting in-hospital mortality using heart rate variability (HRV) collected from the times of a patient's ICU stay. In this paper, a HRV time series processing based method is proposed for mortality prediction of ICU cardiovascular patients. HRV signals were obtained measuring R-R time intervals. A novel method, named return map, is then developed that reveals useful information from the HRV time series. This study also proposed several features that can be extracted from the return map, including the angle between two vectors, the area of triangles formed by successive points, shortest distance to 45° line and their various combinations. Finally, a thresholding technique is proposed to extract the risk period and to predict mortality. The data used to evaluate the proposed algorithm obtained from 80 cardiovascular ICU patients, from the first 48 h of the first ICU stay of 40 males and 40 females. This study showed that the angle feature has on average a sensitivity of 87.5% (with 12 false alarms), the area feature has on average a sensitivity of 89.58% (with 10 false alarms), the shortest distance feature has on average a sensitivity of 85.42% (with 14 false alarms) and, finally, the combined feature has on average a sensitivity of 92.71% (with seven false alarms). The results showed that the last half an hour before the patient's death is very informative for diagnosing the patient's condition and to save his/her life. These results confirm that it is possible to predict mortality based on the features introduced in this paper, relying on the variations of the HRV dynamic characteristics.

Mapping H5N1 highly pathogenic avian influenza risk in Southeast Asia

PubMed Central

Gilbert, Marius; Xiao, Xiangming; Pfeiffer, Dirk U.; Epprecht, M.; Boles, Stephen; Czarnecki, Christina; Chaitaweesub, Prasit; Kalpravidh, Wantanee; Minh, Phan Q.; Otte, M. J.; Martin, Vincent; Slingenbergh, Jan

2008-01-01

The highly pathogenic avian influenza (HPAI) H5N1 virus that emerged in southern China in the mid-1990s has in recent years evolved into the first HPAI panzootic. In many countries where the virus was detected, the virus was successfully controlled, whereas other countries face periodic reoccurrence despite significant control efforts. A central question is to understand the factors favoring the continuing reoccurrence of the virus. The abundance of domestic ducks, in particular free-grazing ducks feeding in intensive rice cropping areas, has been identified as one such risk factor based on separate studies carried out in Thailand and Vietnam. In addition, recent extensive progress was made in the spatial prediction of rice cropping intensity obtained through satellite imagery processing. This article analyses the statistical association between the recorded HPAI H5N1 virus presence and a set of five key environmental variables comprising elevation, human population, chicken numbers, duck numbers, and rice cropping intensity for three synchronous epidemic waves in Thailand and Vietnam. A consistent pattern emerges suggesting risk to be associated with duck abundance, human population, and rice cropping intensity in contrast to a relatively low association with chicken numbers. A statistical risk model based on the second epidemic wave data in Thailand is found to maintain its predictive power when extrapolated to Vietnam, which supports its application to other countries with similar agro-ecological conditions such as Laos or Cambodia. The model's potential application to mapping HPAI H5N1 disease risk in Indonesia is discussed. PMID:18362346

Directions of the US Geological Survey Landslide Hazards Reduction Program

USGS Publications Warehouse

Wieczorek, G.F.

1993-01-01

The US Geological Survey (USGS) Landslide Hazards Reduction Program includes studies of landslide process and prediction, landslide susceptibility and risk mapping, landslide recurrence and slope evolution, and research application and technology transfer. Studies of landslide processes have been recently conducted in Virginia, Utah, California, Alaska, and Hawaii, Landslide susceptibility maps provide a very important tool for landslide hazard reduction. The effects of engineering-geologic characteristics of rocks, seismic activity, short and long-term climatic change on landslide recurrence are under study. Detailed measurement of movement and deformation has begun on some active landslides. -from Author

Improving Flood Risk Maps as a Capacity Building Activity: Fostering Public Participation and Raising Flood Risk Awareness in the German Mulde Region (project RISK MAP)

NASA Astrophysics Data System (ADS)

Luther, J.; Meyer, V.; Kuhlicke, C.; Scheuer, S.; Unnerstall, H.

2012-04-01

The EU Floods Directive requires the establishment of flood risk maps for high risk areas in all EU Member States by 2013. However, if existing at all, the current practice of risk mapping still shows some deficits: Risk maps are often seen as an information tool rather than a communication tool. This means that e.g. important local knowledge is not incorporated and forms a contrast to the understanding of capacity building which calls for engaging individuals in the process of learning and adapting to change and for the establishment of a more interactive public administration that learns equally from its actions and from the feedback it receives. Furthermore, the contents of risk maps often do not match the requirements of the end users, so that risk maps are often designed and visualised in a way which cannot be easily understood by laypersons and/or which is not suitable for the respective needs of public authorities in risk and flood event management. The project RISK MAP aimed at improving flood risk maps as a means to foster public participation and raising flood risk awareness. For achieving this aim, RISK MAP (1) developed rules for appropriate stakeholder participation enabling the incorporation of local knowledge and preferences; (2) improved the content of risk maps by considering different risk criteria through the use of a deliberative multicriteria risk mapping tool; and (3) improved the visualisation of risk maps in order to produce user-friendly risk maps by applying the experimental graphic semiology (EGS) method that uses the eye tracking approach. The research was carried out in five European case studies where the status quo of risk mapping and the legal framework was analysed, several stakeholder interviews and workshops were conducted, the visual perception of risk maps was tested and - based on this empirical work - exemplary improved risk maps were produced. The presentation and paper will outline the main findings of the project which ended in September 2011, focussing on the participatory aspects in one of the German case studies (the Mulde River in Saxony). In short, different map users such as strategic planners, emergency managers or the (affected) public require different maps, with varying information density and complexity. The purpose of participation may therefore have a substantive rationale (i.e. improving the content, including local knowledge) or a more instrumental rationale (i.e. building trust, raising awareness, increasing legitimacy). The degree to which both rationales are accommodated depends on the project objectives and determines the participants and process type. In the Mulde case study, both the process of collaborating with each other and considering the (local) knowledge and different experiences as well as the results were highly appreciated. Hazard and risk maps are thus not an end-product that could be complemented e.g. by emergency management information on existing or planned defences, evacuation routes, assembly points, but they should be embedded into a participatory maintenance/updating framework. Map visualisation could be enhanced by using more common and/or self-explanatory symbols, text and a limited number of colour grades for hazard and risk information. Keywords: Flood mapping, hazard and risk maps, participation, risk communication, flood risk awareness, emergency management

Plasmodium falciparum Malaria Endemicity in Indonesia in 2010

PubMed Central

Elyazar, Iqbal R. F.; Gething, Peter W.; Patil, Anand P.; Rogayah, Hanifah; Kusriastuti, Rita; Wismarini, Desak M.; Tarmizi, Siti N.; Baird, J. Kevin; Hay, Simon I.

2011-01-01

Background Malaria control programs require a detailed understanding of the contemporary spatial distribution of infection risk to efficiently allocate resources. We used model based geostatistics (MBG) techniques to generate a contemporary map of Plasmodium falciparum malaria risk in Indonesia in 2010. Methods Plasmodium falciparum Annual Parasite Incidence (PfAPI) data (2006–2008) were used to map limits of P. falciparum transmission. A total of 2,581 community blood surveys of P. falciparum parasite rate (PfPR) were identified (1985–2009). After quality control, 2,516 were included into a national database of age-standardized 2–10 year old PfPR data (PfPR2–10) for endemicity mapping. A Bayesian MBG procedure was used to create a predicted surface of PfPR2–10 endemicity with uncertainty estimates. Population at risk estimates were derived with reference to a 2010 human population count surface. Results We estimate 132.8 million people in Indonesia, lived at risk of P. falciparum transmission in 2010. Of these, 70.3% inhabited areas of unstable transmission and 29.7% in stable transmission. Among those exposed to stable risk, the vast majority were at low risk (93.39%) with the reminder at intermediate (6.6%) and high risk (0.01%). More people in western Indonesia lived in unstable rather than stable transmission zones. In contrast, fewer people in eastern Indonesia lived in unstable versus stable transmission areas. Conclusion While further feasibility assessments will be required, the immediate prospects for sustained control are good across much of the archipelago and medium term plans to transition to the pre-elimination phase are not unrealistic for P. falciparum. Endemicity in areas of Papua will clearly present the greatest challenge. This P. falciparum endemicity map allows malaria control agencies and their partners to comprehensively assess the region-specific prospects for reaching pre-elimination, monitor and evaluate the effectiveness of future strategies against this 2010 baseline and ultimately improve their evidence-based malaria control strategies. PMID:21738634

Predictive vs. Empiric Assessment of Schistosomiasis: Implications for Treatment Projections in Ghana

PubMed Central

Kabore, Achille; Biritwum, Nana-Kwadwo; Downs, Philip W.; Soares Magalhaes, Ricardo J.; Zhang, Yaobi; Ottesen, Eric A.

2013-01-01

Background Mapping the distribution of schistosomiasis is essential to determine where control programs should operate, but because it is impractical to assess infection prevalence in every potentially endemic community, model-based geostatistics (MBG) is increasingly being used to predict prevalence and determine intervention strategies. Methodology/Principal Findings To assess the accuracy of MBG predictions for Schistosoma haematobium infection in Ghana, school surveys were evaluated at 79 sites to yield empiric prevalence values that could be compared with values derived from recently published MBG predictions. Based on these findings schools were categorized according to WHO guidelines so that practical implications of any differences could be determined. Using the mean predicted values alone, 21 of the 25 empirically determined ‘high-risk’ schools requiring yearly praziquantel would have been undertreated and almost 20% of the remaining schools would have been treated despite empirically-determined absence of infection – translating into 28% of the children in the 79 schools being undertreated and 12% receiving treatment in the absence of any demonstrated need. Conclusions/Significance Using the current predictive map for Ghana as a spatial decision support tool by aggregating prevalence estimates to the district level was clearly not adequate for guiding the national program, but the alternative of assessing each school in potentially endemic areas of Ghana or elsewhere is not at all feasible; modelling must be a tool complementary to empiric assessments. Thus for practical usefulness, predictive risk mapping should not be thought of as a one-time exercise but must, as in the current study, be an iterative process that incorporates empiric testing and model refining to create updated versions that meet the needs of disease control operational managers. PMID:23505584

Spatial analysis of plague in California: niche modeling predictions of the current distribution and potential response to climate change

PubMed Central

Holt, Ashley C; Salkeld, Daniel J; Fritz, Curtis L; Tucker, James R; Gong, Peng

2009-01-01

Background Plague, caused by the bacterium Yersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plague foci. Maxent models were constructed using geocoded seroprevalence data from surveillance of California ground squirrels (Spermophilus beecheyi) as case points and Worldclim bioclimatic data as predictor variables, and compared and validated using area under the receiver operating curve (AUC) statistics. Additionally, model results were compared to locations of positive and negative coyote (Canis latrans) samples, in order to determine the correlation between Maxent model predictions and areas of plague risk as determined via wild carnivore surveillance. Results Models of plague activity in California ground squirrels, based on recent climate conditions, accurately identified case locations (AUC of 0.913 to 0.948) and were significantly correlated with coyote samples. The final models were used to identify potential plague risk areas based on an ensemble of six future climate scenarios. These models suggest that by 2050, climate conditions may reduce plague risk in the southern parts of California and increase risk along the northern coast and Sierras. Conclusion Because different modeling approaches can yield substantially different results, care should be taken when interpreting future model predictions. Nonetheless, niche modeling can be a useful tool for exploring and mapping the potential response of plague activity to climate change. The final models in this study were used to identify potential plague risk areas based on an ensemble of six future climate scenarios, which can help public managers decide where to allocate surveillance resources. In addition, Maxent model results were significantly correlated with coyote samples, indicating that carnivore surveillance programs will continue to be important for tracking the response of plague to future climate conditions. PMID:19558717

Mapping the spatio-temporal risk of lead exposure in apex species for more effective mitigation

PubMed Central

Mateo-Tomás, Patricia; Olea, Pedro P.; Jiménez-Moreno, María; Camarero, Pablo R.; Sánchez-Barbudo, Inés S.; Rodríguez Martín-Doimeadios, Rosa C.; Mateo, Rafael

2016-01-01

Effective mitigation of the risks posed by environmental contaminants for ecosystem integrity and human health requires knowing their sources and spatio-temporal distribution. We analysed the exposure to lead (Pb) in griffon vulture Gyps fulvus—an apex species valuable as biomonitoring sentinel. We determined vultures' lead exposure and its main sources by combining isotope signatures and modelling analyses of 691 bird blood samples collected over 5 years. We made yearlong spatially explicit predictions of the species risk of lead exposure. Our results highlight elevated lead exposure of griffon vultures (i.e. 44.9% of the studied population, approximately 15% of the European, showed lead blood levels more than 200 ng ml−1) partly owing to environmental lead (e.g. geological sources). These exposures to environmental lead of geological sources increased in those vultures exposed to point sources (e.g. lead-based ammunition). These spatial models and pollutant risk maps are powerful tools that identify areas of wildlife exposure to potentially harmful sources of lead that could affect ecosystem and human health. PMID:27466455

KULTURisk regional risk assessment methodology for water-related natural hazards - Part 2: Application to the Zurich case study

NASA Astrophysics Data System (ADS)

Ronco, P.; Bullo, M.; Torresan, S.; Critto, A.; Olschewski, R.; Zappa, M.; Marcomini, A.

2015-03-01

The aim of this paper is the application of the KULTURisk regional risk assessment (KR-RRA) methodology, presented in the companion paper (Part 1, Ronco et al., 2014), to the Sihl River basin, in northern Switzerland. Flood-related risks have been assessed for different receptors lying on the Sihl River valley including Zurich, which represents a typical case of river flooding in an urban area, by calibrating the methodology to the site-specific context and features. Risk maps and statistics have been developed using a 300-year return period scenario for six relevant targets exposed to flood risk: people; economic activities: buildings, infrastructure and agriculture; natural and semi-natural systems; and cultural heritage. Finally, the total risk index map has been produced to visualize the spatial pattern of flood risk within the target area and, therefore, to identify and rank areas and hotspots at risk by means of multi-criteria decision analysis (MCDA) tools. Through a tailored participatory approach, risk maps supplement the consideration of technical experts with the (essential) point of view of relevant stakeholders for the appraisal of the specific scores weighting for the different receptor-relative risks. The total risk maps obtained for the Sihl River case study are associated with the lower classes of risk. In general, higher (relative) risk scores are spatially concentrated in the deeply urbanized city centre and areas that lie just above to river course. Here, predicted injuries and potential fatalities are mainly due to high population density and to the presence of vulnerable people; flooded buildings are mainly classified as continuous and discontinuous urban fabric; flooded roads, pathways and railways, most of them in regards to the Zurich central station (Hauptbahnhof) are at high risk of inundation, causing severe indirect damage. Moreover, the risk pattern for agriculture, natural and semi-natural systems and cultural heritage is relatively less important mainly because the scattered presence of these assets. Finally, the application of the KR-RRA methodology to the Sihl River case study, as well as to several other sites across Europe (not presented here), has demonstrated its flexibility and the possible adaptation of it to different geographical and socioeconomic contexts, depending on data availability and particulars of the sites, and for other (hazard) scenarios.

A Study on the Priority Selection of Sediment-related Desaster Evacuation Using Debris Flow Combination Degree of Risk

NASA Astrophysics Data System (ADS)

Woo, C.; Kang, M.; Seo, J.; Kim, D.; Lee, C.

2017-12-01

As the mountainous urbanization has increased the concern about landslides in the living area, it is essential to develop the technology to minimize the damage through quick identification and sharing of the disaster occurrence information. In this study, to establish an effective system of alert evacuation that has influence on the residents, we used the debris flow combination degree of risk to predict the risk of the disaster and the level of damage and to select evacuation priorities. Based on the GIS information, the physical strength and social vulnerability were determined by following the debris flow combination of the risk formula. The results classify the physical strength hazard rating of the debris flow combination of the through the normalization process. Debris flow the estimated residential population included in the damage range of the damage prediction map is based on the area and the unit size data. Prediction of occupant formula was calculated by applying different weighting to the resident population and users, and the result was classified into 5 classes as the debris flow physical strength. The debris flow occurrence physical strength and social and psychological vulnerability were classified into the classifications to be reflected in the debris flow integrated risk map using the matrix technique. In addition, to supplement the risk of incorporation of debris flow, we added weight to disaster vulnerable facilities that require a lot of time and manpower to evacuate. The basic model of welfare facilities was supplemented by using basic data, population density, employment density and GDP. First, evacuate areas with high integrated degree of risk level, and evacuate with consideration of physical class differences if classification difficult because of the same or similar grade among the management areas. When the physical hazard class difference is similar, the population difference of the area including the welfare facility is considered first, and the priority is decided in order of age distribution, population density by period, and class difference of residential facility. The results of this study are expected be used as basic data for establishing a safety net for landslide by evacuation systems for disasters. Keyword: Landslide, Debris flow, Early warning system, evacuation

Genetic studies of Crohn's disease: Past, present and future

PubMed Central

Liu, Jimmy Z.; Anderson, Carl A.

2014-01-01

The exact aetiology of Crohn's disease is unknown, though it is clear from early epidemiological studies that a combination of genetic and environmental risk factors contributes to an individual's disease susceptibility. Here, we review the history of gene-mapping studies of Crohn's disease, from the linkage-based studies that first implicated the NOD2 locus, through to modern-day genome-wide association studies that have discovered over 140 loci associated with Crohn's disease and yielded novel insights into the biological pathways underlying pathogenesis. We describe on-going and future gene-mapping studies that utilise next generation sequencing technology to pinpoint causal variants and identify rare genetic variation underlying Crohn's disease risk. We comment on the utility of genetic markers for predicting an individual's disease risk and discuss their potential for identifying novel drug targets and influencing disease management. Finally, we describe how these studies have shaped and continue to shape our understanding of the genetic architecture of Crohn's disease. PMID:24913378

Coherence of animal health, welfare and carcass quality in pork production chains.

PubMed

Klauke, Thorsten N; Piñeiro, Matilde; Schulze-Geisthövel, Sophia; Plattes, Susanne; Selhorst, Thomas; Petersen, Brigitte

2013-11-01

Aim of the study was to measure the potential impact of animal health and welfare on the carcass quality. 99 pigs under equal housing and feeding conditions were involved in the study. Effects of the immune system on carcass composition, meat quality and performance data of slaughter pigs became measureable by quantification of acute phase proteins (APP), haptoglobin (Hp) and pig major acute phase protein (Pig-MAP). The results were not significantly affected by gender or breed. The calculated correlations between chosen animal health indicators and carcass quality parameters prove an influence of health and welfare on performance, carcass composition and meat quality traits. The acute phase proteins could also be valuable as a predictive indicator for risk assessment in meat inspection, as increased Hp concentrations in slaughter blood indicate a 16 times higher risk for organ abnormalities and Pig-MAP concentrations above 0.7mg/ml a 10 times higher risk. Copyright © 2013 Elsevier Ltd. All rights reserved.

Gender differences in the accuracy of time-dependent blood pressure indices for predicting coronary heart disease: A random-effects modeling approach.

PubMed

Brant, Larry J; Ferrucci, Luigi; Sheng, Shan L; Concin, Hans; Zonderman, Alan B; Kelleher, Cecily C; Longo, Dan L; Ulmer, Hanno; Strasak, Alexander M

2010-12-01

Previous studies on blood pressure (BP) indices as a predictor of coronary heart disease (CHD) have provided equivocal results and generally relied on Cox proportional hazards regression methodology, with age and sex accounting for most of the predictive capability of the model. The aim of the present study was to use serially collected BP measurements to examine age-and gender-related differences in BP indices for predicting CHD. The predictive accuracy of time-dependent BP indices for CHD was investigated using a method of risk prediction based on posterior probabilities calculated from mixed-effects regression to utilize intraindividual differences in serial BP measurements according to age changes within gender groups. Data were collected prospectively from 2 community-dwelling cohort studies in the United States (Baltimore Longitudinal Study of Aging [BLSA]) and Europe (Vorarlberg Health Monitoring and Promotion Program [VHM&PP]). The study comprised 152,633 participants (aged 30-74 years) and 610,061 BP measurements. During mean follow-up of 7.5 years, 2457 nonfatal and fatal CHD events were observed. In both study populations, pulse pressure (PP) and systolic blood pressure (SBP) performed best as individual predictors of CHD in women (area under the receiver operating characteristic curve [AUC(ROC)] was between 0.83 and 0.85 for PP, and between 0.77 and 0.81 for SBP). Mean arterial pressure (MAP) and diastolic blood pressure (DBP) performed better for men (AUC(ROC) = 0.67 and 0.65 for MAP and DBP, respectively, in the BLSA; AUC(ROC) = 0.77 and 0.75 in the VHM&PP) than for women (AUC(ROC) = 0.60 for both MAP and DBP in the BLSA; AUC(ROC) = 0.75 and 0.52, respectively, in the VHM&PP). The degree of discrimination in both populations was overall greater but more varied for all BP indices for women (AUC(ROC) estimates between 0.85 [PP in the VHM&PP] and 0.52 [DBP in the VHM&PP]) than for men (AUC(ROC) estimates between 0.78 [MAP + PP in the VHM&PP] and 0.63 [PP in the BLSA]). Our findings indicate differences in discrimination between women and men in the accuracy of longitudinally collected BP measurements for predicting CHD, implicating the usefulness of gender-specific BP indices to assess individual CHD risk. Copyright © 2010. Published by EM Inc USA.

Escherichia coli O157:H7 reduction in hamburgers with regard to premature browning of minced beef, colour score and method for determining doneness.

PubMed

Boqvist, Sofia; Fernström, Lise-Lotte; Alsanius, Beatrix W; Lindqvist, Roland

2015-12-23

This study investigated the effect of premature browning (PMB) on the survival of Escherichia coli O157:H7 in beef hamburgers after cooking with respect to interior colour of the hamburger and recommendations to cook hamburgers to a core temperature of 71 °C. Assessment of doneness by visual inspection or measurement of internal temperature was compared in terms of survival and the increased relative risk of illness due to PMB was estimated. At the last consume-by-day, hamburgers made from minced meat packaged in 80/20 O2/CO2 (MAP hamburger) and from meat minced at retail packaged in atmospheric condition (control hamburger) were inoculated with a gfp-tagged strain of E. coli O157:H7 (E. coli O157:H7gfp+). Hamburgers were cooked for different times during assessment of the core temperature every 30s and cut in halves after cooking. Doneness was evaluated based on visual judgement of the internal colour using a score chart (C-score) from 'uncooked' (score 1) to 'tan with no evidence of pink' (score 5). An alternative five point score chart (TCC-score) including texture of the meat, clarity of meat juice and internal colour was also developed. Enumeration of viable E. coli O157:H7gfp+ in cooked hamburgers was based on fluorescent colonies recovered from plates. Results showed that MAP hamburgers developed PMB when compared with controls (P=0.0003) and that the shortest cooking time for the highest C-score was 6 and 11 min for MAP and control hamburgers, respectively. The mean temperature in the MAP hamburger was then 60.3 °C. The TCC-score reduced the difference between MAP and control hamburgers. It was also shown that the survival of E. coli O157:H7gfp+ was highest in MAP hamburgers. The predicted absolute risks for illness were highest for MAP hamburgers for all C-scores and the relative risk associated with PMB increased with doneness. For a C-score of 4 (slightly pink) the predicted relative risk for illness was 300 times higher for MAP hamburger than for controls. A variable pathogen reduction was observed when cooking hamburgers to temperatures of 70-76 °C (the 5th and 95th percentile range was around 3.3 log CFU). The lower reductions, at the 5th percentile, may, depending on initial contamination levels, not be enough to ensure sufficient and safe inactivation of E. coli O157:H7. Efforts to inform consumers about PMB in minced meat packaged in high oxygen packages (≥60% O2) are needed with the aim to make consumers use thermometers correctly or at least not determine doneness based only on meat colour. Copyright © 2015. Published by Elsevier B.V.

Using NASA Satellite Observations to Map Wildfire Risk in the United States for Allocation of Fire Management Resources

NASA Astrophysics Data System (ADS)

Farahmand, A.; Reager, J. T., II; Behrangi, A.; Stavros, E. N.; Randerson, J. T.

2017-12-01

Fires are a key disturbance globally acting as a catalyst for terrestrial ecosystem change and contributing significantly to both carbon emissions and changes in surface albedo. The socioeconomic impacts of wildfire activities are also significant with wildfire activity results in billions of dollars of losses every year. Fire size, area burned and frequency are increasing, thus the likelihood of fire danger, defined by United States National Interagency Fire Center (NFIC) as the demand of fire management resources as a function of how flammable fuels (a function of ignitability, consumability and availability) are from normal, is an important step toward reducing costs associated with wildfires. Numerous studies have aimed to predict the likelihood of fire danger, but few studies use remote sensing data to map fire danger at scales commensurate with regional management decisions (e.g., deployment of resources nationally throughout fire season with seasonal and monthly prediction). Here, we use NASA Gravity Recovery And Climate Experiment (GRACE) assimilated surface soil moisture, NASA Atmospheric Infrared Sounder (AIRS) vapor pressure deficit, NASA Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index products and landcover products, along with US Forest Service historical fire activity data to generate probabilistic monthly fire potential maps in the United States. These maps can be useful in not only government operational allocation of fire management resources, but also improving understanding of the Earth System and how it is changing in order to refine predictions of fire extremes.

Prediction of Rainfall-Induced Landslides in Tegucigalpa, Honduras, Using a Hydro-Geotechnical Model

NASA Astrophysics Data System (ADS)

Garcia Urquia, Elias; Axelsson, K.

2010-05-01

Central America is constantly being affected by natural hazards. Among these events are hurricanes and earthquakes, capable of triggering landslides that can alter the natural landscape, destroy infrastructure and cause the death of people in the most important settlements of the region. Hurricane Mitch in October of 1998 was of particular interest for the region, since it provoked hundreds of rainfall-induced landslides, mainly in 4 different countries. Studies carried out after Hurricane Mitch have allowed researchers to identify the factors that contribute to slope instability in many vulnerable areas. As Tegucigalpa, Honduras was partially destroyed due to the various landslide and flooding events triggered by this devastating hurricane, various research teams have deepened in their investigations and have proposed measures to mitigate the effects of similar future incidents. A model coupling an infinite-slope analysis and a simple groundwater flow approach can serve as a basis to predict the occurrence of landslides in Tegucigalpa, Honduras as a function of topographic, hydrological and soil variables. A safety map showing the rainfall-triggered landslide risk zones for Tegucigalpa, Honduras is to be created. As opposed to previous safety maps in which only steady-state conditions are studied, this analysis is extended and different steady-state and quasi-dynamic scenarios are considered for comparison. For the purpose of the latter settings, a hydrological analysis that determines the rainfall extreme values and their return periods in Tegucigalpa will account for the influence of rainfall on the groundwater flow and strength of soils. It is known that the spatial distribution of various factors that contribute to the risk of landslides (i.e. soil thickness, conductivity and strength properties; rainfall intensity and duration; root strength; subsurface flow orientation) is hard to determine. However, an effort is done to derive correlations for these parameters based on the existing information (i.e. rainfall data, soil testing data, land-use data). In addition, the spatial data management and manipulation is done by means of a Geographic Information System (GIS). For such purpose, maps of land-use, topography and geology provided by JICA have bee manually digitized and converted into GIS raster maps. The resulting safety map is then validated by comparing it with existing slope-failure-maps that have been created to show the affected areas during Hurricane Mitch. This safety map represents a useful tool in the prevention of landslide-related disasters, as it would be able to point out which segments of the population are at risk as a consequence of the rainfall-slope interaction in Tegucigalpa.

Isolation and characterization of new highly polymorphic DNA markers from the Huntington disease region

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weber, B.; Hedrick, A.; Andrew, S.

1992-02-01

The defect causing Huntington disease (HD) has been mapped to 4p16.3, distal to the DNA marker D4S10. Subsequently, additional polymorphic markers closer to the HD gene have been isolated, which has led to the establishment of predictive testing programs for individuals at risk for HD. Approximately 17% of persons presenting to the Canadian collaborative study for predictive testing for HD have not received any modification of risk, in part because of limited informativeness of currently available DNA markers. Therefore, more highly polymorphic DNA markers are needed, which well further increase the accuracy and availability of predictive testing, specifically for familiesmore » with complex or incomplete pedigree structures. In addition, new markers are urgently needed in order to refine the breakpoints in the few known recombinant HD chromosomes, which could allow a more accurate localization of the HD gene within 4p16.3 and, therefore, accelerate the cloning of the disease gene. In this study, the authors present the identification and characterization of nine new polymorphic DNA markers, including three markers which detect highly informative multiallelic VNTR-like polymorphisms with PIC values of up to .84. These markers have been isolated from a cloned region of DNA which has been previously mapped approximately 1,000 kb from the 4p telomere.« less

Mapping the potential distribution of the invasive Red Shiner, Cyprinella lutrensis (Teleostei: Cyprinidae) across waterways of the conterminous United States

USGS Publications Warehouse

Poulos, Helen M.; Chernoff, Barry; Fuller, Pam L.; Butman, David

2012-01-01

Predicting the future spread of non-native aquatic species continues to be a high priority for natural resource managers striving to maintain biodiversity and ecosystem function. Modeling the potential distributions of alien aquatic species through spatially explicit mapping is an increasingly important tool for risk assessment and prediction. Habitat modeling also facilitates the identification of key environmental variables influencing species distributions. We modeled the potential distribution of an aggressive invasive minnow, the red shiner (Cyprinella lutrensis), in waterways of the conterminous United States using maximum entropy (Maxent). We used inventory records from the USGS Nonindigenous Aquatic Species Database, native records for C. lutrensis from museum collections, and a geographic information system of 20 raster climatic and environmental variables to produce a map of potential red shiner habitat. Summer climatic variables were the most important environmental predictors of C. lutrensis distribution, which was consistent with the high temperature tolerance of this species. Results from this study provide insights into the locations and environmental conditions in the US that are susceptible to red shiner invasion.

Mapping the Human Toxome by Systems Toxicology

PubMed Central

Bouhifd, Mounir; Hogberg, Helena T.; Kleensang, Andre; Maertens, Alexandra; Zhao, Liang; Hartung, Thomas

2014-01-01

Toxicity testing typically involves studying adverse health outcomes in animals subjected to high doses of toxicants with subsequent extrapolation to expected human responses at lower doses. The low-throughput of current toxicity testing approaches (which are largely the same for industrial chemicals, pesticides and drugs) has led to a backlog of more than 80,000 chemicals to which human beings are potentially exposed whose potential toxicity remains largely unknown. Employing new testing strategies that employ the use of predictive, high-throughput cell-based assays (of human origin) to evaluate perturbations in key pathways, referred as pathways of toxicity, and to conduct targeted testing against those pathways, we can begin to greatly accelerate our ability to test the vast “storehouses” of chemical compounds using a rational, risk-based approach to chemical prioritization, and provide test results that are more predictive of human toxicity than current methods. The NIH Transformative Research Grant project Mapping the Human Toxome by Systems Toxicology aims at developing the tools for pathway mapping, annotation and validation as well as the respective knowledge base to share this information. PMID:24443875

USGS: Building on leadership in mapping oceans and coasts

USGS Publications Warehouse

Myers, M.D.

2008-01-01

The US Geological Survey (USGS) offers continuously improving technologies for mapping oceans and coasts providing unique opportunity for characterizing the marine environment and to expand the understanding of coastal and ocean processes, resources, and hazards. USGS, which has been designated as a leader for mapping the Exclusive Economic Zone, has made an advanced strategic plan, Facing Tomorrow's Challenges- US Geological Survey Science in the Decade 2007 to 2017. This plan focuses on innovative and transformational themes that serve key clients and customers, expand partnerships, and have long-term national impact. The plan includes several key science directions, including Understanding Ecosystems and Predicting Ecosystem Change, Energy and Minerals for America's Future, and A National Hazards, Risk, and Resilience Assessment Program. USGS has also collaborated with diverse partners to incorporate mapping and monitoring within interdisciplinary research programs, addressing the system-scale response of coastal and marine ecosystems.

Time-dependent landslide probability mapping

USGS Publications Warehouse

Campbell, Russell H.; Bernknopf, Richard L.; ,

1993-01-01

Case studies where time of failure is known for rainfall-triggered debris flows can be used to estimate the parameters of a hazard model in which the probability of failure is a function of time. As an example, a time-dependent function for the conditional probability of a soil slip is estimated from independent variables representing hillside morphology, approximations of material properties, and the duration and rate of rainfall. If probabilities are calculated in a GIS (geomorphic information system ) environment, the spatial distribution of the result for any given hour can be displayed on a map. Although the probability levels in this example are uncalibrated, the method offers a potential for evaluating different physical models and different earth-science variables by comparing the map distribution of predicted probabilities with inventory maps for different areas and different storms. If linked with spatial and temporal socio-economic variables, this method could be used for short-term risk assessment.

Zoning the territory of the Republic of Kazakhstan as to the risk of rabies among various categories of animals.

PubMed

Abdrakhmanov, Sarsenbay K; Sultanov, Akhmetzhan A; Beisembayev, Kanatzhan K; Korennoy, Fedor I; Кushubaev, Dosym B; Каdyrov, Ablaikhan S

2016-05-31

This paper presents the zoning of the territory of the Republic of Kazakhstan with respect to the risk of rabies outbreaks in domestic and wild animals considering environmental and climatic conditions. The national database of rabies outbreaks in Kazakhstan in the period 2003-2014 has been accessed in order to find which zones are consistently most exposed to the risk of rabies in animals. The database contains information on the cases in demes of farm livestock, domestic animals and wild animals. To identify the areas with the highest risk of outbreaks, we applied the maximum entropy modelling method. Designated outbreaks were used as input presence data, while the bioclim set of ecological and climatic variables, together with some geographic factors, were used as explanatory variables. The model demonstrated a high predictive ability. The area under the curve for farm livestock was 0.782, for domestic animals -0.859 and for wild animals - 0.809. Based on the model, the map of integral risk was designed by following four categories: negligible risk (disease-free or favourable zone), low risk (surveillance zone), medium risk (vaccination zone), and high risk (unfavourable zone). The map was produced to allow developing a set of preventive measures and is expected to contribute to a better distribution of supervisory efforts from the veterinary service of the country.

Joint Utility of Event-Dependent and Environmental Crime Analysis Techniques for Violent Crime Forecasting

ERIC Educational Resources Information Center

Caplan, Joel M.; Kennedy, Leslie W.; Piza, Eric L.

2013-01-01

Violent crime incidents occurring in Irvington, New Jersey, in 2007 and 2008 are used to assess the joint analytical capabilities of point pattern analysis, hotspot mapping, near-repeat analysis, and risk terrain modeling. One approach to crime analysis suggests that the best way to predict future crime occurrence is to use past behavior, such as…

Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods

Treesearch

Kenneth B. Pierce; Janet L. Ohmann; Michael C. Wimberly; Matthew J. Gregory; Jeremy S. Fried

2009-01-01

Land managers need consistent information about the geographic distribution of wildland fuels and forest structure over large areas to evaluate fire risk and plan fuel treatments. We compared spatial predictions for 12 fuel and forest structure variables across three regions in the western United States using gradient nearest neighbor (GNN) imputation, linear models (...

Maximum entropy modeling risk of anthrax in the Republic of Kazakhstan.

PubMed

Abdrakhmanov, S K; Mukhanbetkaliyev, Y Y; Korennoy, F I; Sultanov, A A; Kadyrov, A S; Kushubaev, D B; Bakishev, T G

2017-09-01

The objective of this study was to zone the territory of the Republic of Kazakhstan (RK) into risk categories according to the probability of anthrax emergence in farm animals as stipulated by the re-activation of preserved natural foci. We used historical data on anthrax morbidity in farm animals during the period 1933 - 2014, collected by the veterinary service of the RK. The database covers the entire territory of the RK and contains 4058 anthrax outbreaks tied to 1798 unique locations. Considering the strongly pronounced natural focality of anthrax, we employed environmental niche modeling (Maxent) to reveal patterns in the outbreaks' linkages to specific combinations of environmental factors. The set of bioclimatic factors BIOCLIM, derived from remote sensing data, the altitude above sea level, the land cover type, the maximum green vegetation fraction (MGVF) and the soil type were examined as explanatory variables. The model demonstrated good predictive ability, while the MGVF, the bioclimatic variables reflecting precipitation level and humidity, and the soil type were found to contribute most significantly to the model. A continuous probability surface was obtained that reflects the suitability of the study area for the emergence of anthrax outbreaks. The surface was turned into a categorical risk map by averaging the probabilities within the administrative divisions at the 2nd level and putting them into four categories of risk, namely: low, medium, high and very high risk zones, where very high risk refers to more than 50% suitability to the disease re-emergence and low risk refers to less than 10% suitability. The map indicated increased risk of anthrax re-emergence in the districts along the northern, eastern and south-eastern borders of the country. It was recommended that the national veterinary service uses the risk map for the development of contra-epizootic measures aimed at the prevention of anthrax re-emergence in historically affected regions of the RK. The map can also be considered when developing large-scale construction projects in the areas comprising preserved soil foci of anthrax. Copyright © 2017 Elsevier B.V. All rights reserved.

Electricity Consumption Risk Map - The use of Urban Climate Mapping for smarter analysis: Case study for Birmingham, UK.

NASA Astrophysics Data System (ADS)

Antunes Azevedo, Juliana; Burghardt, René; Chapman, Lee; Katzchner, Lutz; Muller, Catherine L.

2015-04-01

Climate is a key driving factor in energy consumption. However, income, vegetation, building mass structure, topography also impact on the amount of energy consumption. In a changing climate, increased temperatures are likely to lead to increased electricity consumption, affecting demand, distribution and generation. Furthermore, as the world population becomes more urbanized, increasing numbers of people will need to deal with not only increased temperatures from climate change, but also from the unintentional modification of the urban climate in the form of urban heat islands. Hence, climate and climate change needs to be taken into account for future urban planning aspects to increase the climate and energy resilience of the community and decrease the future social and economic costs. Geographical Information Systems provide a means to create urban climate maps as part of the urban planning process. Geostatistical analyses linking these maps with demographic and social data, enables a geo-statistical analysis to identify linkages to high-risk groups of the community and vulnerable areas of town and cities. Presently, the climatope classification is oriented towards thermal aspects and the ventilation quality (roughness) of the urban areas but can also be adapted to take into account other structural "environmental factors". This study aims to use the climatope approach to predict areas of potential high electricity consumption in Birmingham, UK. Several datasets were used to produce an average surface temperature map, vegetation map, land use map, topography map, building height map, built-up area roughness calculations, an average air temperature map and a domestic electricity consumption map. From the correlations obtained between the layers it is possible to average the importance of each factor and create a map for domestic electricity consumption to understand the influence of environmental aspects on spatial energy consumption. Based on these results city planners and local authorities can guide their directives and policies towards electricity consumption, demand, generation and distribution.

Protection of agriculture against drought in Slovenia based on vulnerability and risk assessment

NASA Astrophysics Data System (ADS)

Dovžak, M.; Stanič, S.; Bergant, K.; Gregorič, G.

2012-04-01

Past and recent extreme events, like earthquakes, extreme droughts, heat waves, flash floods and volcanic eruptions continuously remind us that natural hazards are an integral component of the global environment. Despite rapid improvement of detection techniques many of these events evade long-term or even mid-term prediction and can thus have disastrous impacts on affected communities and environment. Effective mitigation and preparedness strategies will be possible to develop only after gaining the understanding on how and where such hazards may occur, what causes them, what circumstances increase their severity, and what their impacts may be and their study has the recent years emerged as under the common title of natural hazard management. The first step in natural risk management is risk identification, which includes hazard analysis and monitoring, vulnerability analysis and determination of the risk level. The presented research focuses on drought, which is at the present already the most widespread as well as still unpredictable natural hazard. Its primary aim was to assess the frequency and the consequences of droughts in Slovenia based on drought events in the past, to develop methodology for drought vulnerability and risk assessment that can be applied in Slovenia and wider in South-Eastern Europe, to prepare maps of drought risk and crop vulnerability and to guidelines to reduce the vulnerability of the crops. Using the amounts of plant available water in the soil, slope inclination, solar radiation, land use and irrigation infrastructure data sets as inputs, we obtained vulnerability maps for Slovenia using GIS-based multi-criteria decision analysis with a weighted linear combination of the input parameters. The weight configuration was optimized by comparing the modelled crop damage to the assessed actual damage, which was available for the extensive drought case in 2006. Drought risk was obtained quantitatively as a function of hazard and vulnerability and presented in the same way as the vulnerability, as a GIS-based map. Risk maps show geographic regions in Slovenia where droughts pose a major threat to the agriculture and together with the vulnerability maps provide the basis for drought management, in particular for the appropriate mitigation and response actions in specific regions. The developed methodology is expected to be applied to the entire region of South-Eastern Europe within the initiative of the Drought Management Centre for Southeastern Europe.

Development and Assessment of a Geographic Knowledge-Based Model for Mapping Suitable Areas for Rift Valley Fever Transmission in Eastern Africa

PubMed Central

Tran, Annelise; Trevennec, Carlène; Lutwama, Julius; Sserugga, Joseph; Gély, Marie; Pittiglio, Claudia; Pinto, Julio; Chevalier, Véronique

2016-01-01

Rift Valley fever (RVF), a mosquito-borne disease affecting ruminants and humans, is one of the most important viral zoonoses in Africa. The objective of the present study was to develop a geographic knowledge-based method to map the areas suitable for RVF amplification and RVF spread in four East African countries, namely, Kenya, Tanzania, Uganda and Ethiopia, and to assess the predictive accuracy of the model using livestock outbreak data from Kenya and Tanzania. Risk factors and their relative importance regarding RVF amplification and spread were identified from a literature review. A numerical weight was calculated for each risk factor using an analytical hierarchy process. The corresponding geographic data were collected, standardized and combined based on a weighted linear combination to produce maps of the suitability for RVF transmission. The accuracy of the resulting maps was assessed using RVF outbreak locations in livestock reported in Kenya and Tanzania between 1998 and 2012 and the ROC curve analysis. Our results confirmed the capacity of the geographic information system-based multi-criteria evaluation method to synthesize available scientific knowledge and to accurately map (AUC = 0.786; 95% CI [0.730–0.842]) the spatial heterogeneity of RVF suitability in East Africa. This approach provides users with a straightforward and easy update of the maps according to data availability or the further development of scientific knowledge. PMID:27631374

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

PubMed Central

Craig, Marlies H; Sharp, Brian L; Mabaso, Musawenkosi LH; Kleinschmidt, Immo

2007-01-01

Background Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana. Results Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country. Conclusion We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software. PMID:17892584

Modeling of natural risks in GIS, decision support in the Civil Protection and Emergency Planning

NASA Astrophysics Data System (ADS)

Santos, M.; Martins, L.; Moreira, S.; Costa, A.; Matos, F.; Teixeira, M.; Bateira, C.

2012-04-01

The assessment of natural hazards in Civil Protection is essential in the prevention and mitigation of emergency situations. This paper presents the results of the development of mapping susceptibility to landslides, floods, forest fires and soil erosion, using GIS (Geographic Information System) tools in two municipalities - Santo Tirso and Trofa - in the district of Oporto, in the northwest of Portugal. The mapping of natural hazards fits in the legislative plan of the Municipal Civil Protection (Law No. 65/2007 of 12 November) and it provides the key elements to planning and preparing an appropriate response in case some of the processes / phenomena occur, thus optimizing the procedures for protection and relief provided by the Municipal Civil Protection Service. Susceptibility mapping to landslides, floods, forest fires and soil erosion was performed with GIS tools resources. The methodology used to compile the mapping of landslides, forest fires and soil erosion was based on the modeling of different conditioning factors and validated with field work and event log. The mapping of susceptibility to floods and flooding was developed through mathematical parameters (statistical, hydrologic and hydraulic), supported by field work and the recognition of individual characteristics of each sector analysis and subsequently analyzed in a GIS environment The mapping proposal was made in 1:5000 scale which allows not only the identification of large sets affected by the spatial dynamics of the processes / phenomena, but also a more detailed analysis, especially when combined with geographic information systems (GIS) thus allowing to study more specific situations that require a quick response. The maps developed in this study are fundamental to the understanding, prediction and prevention of susceptibility and risks present in the municipalities, being a valuable tool in the process of Emergency Planning, since it identifies priority areas of intervention for farther detail analysis, promote and safeguard mechanisms to prevent injury and it anticipates the possibility of potential interventions that can minimize the risk.

Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms.

PubMed

Barzegar, Rahim; Moghaddam, Asghar Asghari; Deo, Ravinesh; Fijani, Elham; Tziritis, Evangelos

2018-04-15

Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO 3 concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO 3 concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model. Copyright © 2017 Elsevier B.V. All rights reserved.

Predictable bacterial composition and hydrocarbon degradation in Arctic soils following diesel and nutrient disturbance

PubMed Central

Bell, Terrence H; Yergeau, Etienne; Maynard, Christine; Juck, David; Whyte, Lyle G; Greer, Charles W

2013-01-01

Increased exploration and exploitation of resources in the Arctic is leading to a higher risk of petroleum contamination. A number of Arctic microorganisms can use petroleum for growth-supporting carbon and energy, but traditional approaches for stimulating these microorganisms (for example, nutrient addition) have varied in effectiveness between sites. Consistent environmental controls on microbial community response to disturbance from petroleum contaminants and nutrient amendments across Arctic soils have not been identified, nor is it known whether specific taxa are universally associated with efficient bioremediation. In this study, we contaminated 18 Arctic soils with diesel and treated subsamples of each with monoammonium phosphate (MAP), which has successfully stimulated degradation in some contaminated Arctic soils. Bacterial community composition of uncontaminated, diesel-contaminated and diesel+MAP soils was assessed through multiplexed 16S (ribosomal RNA) rRNA gene sequencing on an Ion Torrent Personal Genome Machine, while hydrocarbon degradation was measured by gas chromatography analysis. Diversity of 16S rRNA gene sequences was reduced by diesel, and more so by the combination of diesel and MAP. Actinobacteria dominated uncontaminated soils with <10% organic matter, while Proteobacteria dominated higher-organic matter soils, and this pattern was exaggerated following disturbance. Degradation with and without MAP was predictable by initial bacterial diversity and the abundance of specific assemblages of Betaproteobacteria, respectively. High Betaproteobacteria abundance was positively correlated with high diesel degradation in MAP-treated soils, suggesting this may be an important group to stimulate. The predictability with which bacterial communities respond to these disturbances suggests that costly and time-consuming contaminated site assessments may not be necessary in the future. PMID:23389106

Modelling the distribution of chickens, ducks, and geese in China

USGS Publications Warehouse

Prosser, Diann J.; Wu, Junxi; Ellis, Erie C.; Gale, Fred; Van Boeckel, Thomas P.; Wint, William; Robinson, Tim; Xiao, Xiangming; Gilbert, Marius

2011-01-01

Global concerns over the emergence of zoonotic pandemics emphasize the need for high-resolution population distribution mapping and spatial modelling. Ongoing efforts to model disease risk in China have been hindered by a lack of available species level distribution maps for poultry. The goal of this study was to develop 1 km resolution population density models for China's chickens, ducks, and geese. We used an information theoretic approach to predict poultry densities based on statistical relationships between poultry census data and high-resolution agro-ecological predictor variables. Model predictions were validated by comparing goodness of fit measures (root mean square error and correlation coefficient) for observed and predicted values for 1/4 of the sample data which were not used for model training. Final output included mean and coefficient of variation maps for each species. We tested the quality of models produced using three predictor datasets and 4 regional stratification methods. For predictor variables, a combination of traditional predictors for livestock mapping and land use predictors produced the best goodness of fit scores. Comparison of regional stratifications indicated that for chickens and ducks, a stratification based on livestock production systems produced the best results; for geese, an agro-ecological stratification produced best results. However, for all species, each method of regional stratification produced significantly better goodness of fit scores than the global model. Here we provide descriptive methods, analytical comparisons, and model output for China's first high resolution, species level poultry distribution maps. Output will be made available to the scientific and public community for use in a wide range of applications from epidemiological studies to livestock policy and management initiatives.

Modelling the distribution of chickens, ducks, and geese in China

PubMed Central

Prosser, Diann J.; Wu, Junxi; Ellis, Erle C.; Gale, Fred; Van Boeckel, Thomas P.; Wint, William; Robinson, Tim; Xiao, Xiangming; Gilbert, Marius

2011-01-01

Global concerns over the emergence of zoonotic pandemics emphasize the need for high-resolution population distribution mapping and spatial modelling. Ongoing efforts to model disease risk in China have been hindered by a lack of available species level distribution maps for poultry. The goal of this study was to develop 1 km resolution population density models for China’s chickens, ducks, and geese. We used an information theoretic approach to predict poultry densities based on statistical relationships between poultry census data and high-resolution agro-ecological predictor variables. Model predictions were validated by comparing goodness of fit measures (root mean square error and correlation coefficient) for observed and predicted values for ¼ of the sample data which was not used for model training. Final output included mean and coefficient of variation maps for each species. We tested the quality of models produced using three predictor datasets and 4 regional stratification methods. For predictor variables, a combination of traditional predictors for livestock mapping and land use predictors produced the best goodness of fit scores. Comparison of regional stratifications indicated that for chickens and ducks, a stratification based on livestock production systems produced the best results; for geese, an agro-ecological stratification produced best results. However, for all species, each method of regional stratification produced significantly better goodness of fit scores than the global model. Here we provide descriptive methods, analytical comparisons, and model output for China’s first high resolution, species level poultry distribution maps. Output will be made available to the scientific and public community for use in a wide range of applications from epidemiological studies to livestock policy and management initiatives. PMID:21765567

Quantifying Uncertainty in Flood Inundation Mapping Using Streamflow Ensembles and Multiple Hydraulic Modeling Techniques

NASA Astrophysics Data System (ADS)

Hosseiny, S. M. H.; Zarzar, C.; Gomez, M.; Siddique, R.; Smith, V.; Mejia, A.; Demir, I.

2016-12-01

The National Water Model (NWM) provides a platform for operationalize nationwide flood inundation forecasting and mapping. The ability to model flood inundation on a national scale will provide invaluable information to decision makers and local emergency officials. Often, forecast products use deterministic model output to provide a visual representation of a single inundation scenario, which is subject to uncertainty from various sources. While this provides a straightforward representation of the potential inundation, the inherent uncertainty associated with the model output should be considered to optimize this tool for decision making support. The goal of this study is to produce ensembles of future flood inundation conditions (i.e. extent, depth, and velocity) to spatially quantify and visually assess uncertainties associated with the predicted flood inundation maps. The setting for this study is located in a highly urbanized watershed along the Darby Creek in Pennsylvania. A forecasting framework coupling the NWM with multiple hydraulic models was developed to produce a suite ensembles of future flood inundation predictions. Time lagged ensembles from the NWM short range forecasts were used to account for uncertainty associated with the hydrologic forecasts. The forecasts from the NWM were input to iRIC and HEC-RAS two-dimensional software packages, from which water extent, depth, and flow velocity were output. Quantifying the agreement between output ensembles for each forecast grid provided the uncertainty metrics for predicted flood water inundation extent, depth, and flow velocity. For visualization, a series of flood maps that display flood extent, water depth, and flow velocity along with the underlying uncertainty associated with each of the forecasted variables were produced. The results from this study demonstrate the potential to incorporate and visualize model uncertainties in flood inundation maps in order to identify the high flood risk zones.

Predicting diffuse microbial pollution risk across catchments: The performance of SCIMAP and recommendations for future development.

PubMed

Porter, Kenneth D H; Reaney, Sim M; Quilliam, Richard S; Burgess, Chris; Oliver, David M

2017-12-31

Microbial pollution of surface waters in agricultural catchments can be a consequence of poor farm management practices, such as excessive stocking of livestock on vulnerable land or inappropriate handling of manures and slurries. Catchment interventions such as fencing of watercourses, streamside buffer strips and constructed wetlands have the potential to reduce faecal pollution of watercourses. However these interventions are expensive and occupy valuable productive land. There is, therefore, a requirement for tools to assist in the spatial targeting of such interventions to areas where they will have the biggest impact on water quality improvements whist occupying the minimal amount of productive land. SCIMAP is a risk-based model that has been developed for this purpose but with a focus on diffuse sediment and nutrient pollution. In this study we investigated the performance of SCIMAP in predicting microbial pollution of watercourses and assessed modelled outputs of E. coli, a common faecal indicator organism (FIO), against observed water quality information. SCIMAP was applied to two river catchments in the UK. SCIMAP uses land cover risk weightings, which are routed through the landscape based on hydrological connectivity to generate catchment scale maps of relative in-stream pollution risk. Assessment of the model's performance and derivation of optimum land cover risk weightings was achieved using a Monte-Carlo sampling approach. Performance of the SCIMAP framework for informing on FIO risk was variable with better performance in the Yealm catchment (r s =0.88; p<0.01) than the Wyre (r s =-0.36; p>0.05). Across both catchments much uncertainty was associated with the application of optimum risk weightings attributed to different land use classes. Overall, SCIMAP showed potential as a useful tool in the spatial targeting of FIO diffuse pollution management strategies; however, improvements are required to transition the existing SCIMAP framework to a robust FIO risk-mapping tool. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Heterogeneous Data Fusion Methods for Disaster Risk Assessment using Grid Infrastructure

NASA Astrophysics Data System (ADS)

Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii

2014-05-01

In recent years, a risk-oriented approach to manage disasters has been adopted. Risk is a function of two arguments: hazard probability and vulnerability [1]. In order to assess flood risk, for example, aggregation of heterogeneous data acquired from multiple sources is required. Outputs from hydrological and hydraulic models make it possible to predict floods; in situ observations such as river level and flows are used for early warning and models calibration. Remote sensing observations can be effectively used for rapid mapping in case of emergencies, and can be assimilated into models. One point that is mutual for all datasets is their geospatial nature. In order to enable operational assessment of disaster risk, appropriate technology is necessary. In this paper we discuss different strategies to heterogeneous data fusion and show their application in the domain of disaster monitoring and risk assessment. In particular, two case-studies are presented. The first one focuses on the use of time-series of satellite imagery to flood hazard mapping and flood risk assessment. Flooded areas are extracted from satellite images to generate a maximum flood extent image for each flood event. These maps are fused to determine relative frequency of inundation (RFI) [2]. The RFI values are compared to relative water depth generated from the LISFLOOD-FP model. The model is calibrated against the satellite-derived flood extent. The model with different combinations of Manning's parameters was run in the Grid environment at Space Research Institute NASU-SSAU [3], and the optimal set of parameters was found. It is shown that RFI and water depth exhibit the same probabilistic distribution which is confirmed by Kolmogorov-Smirnov test. Therefore, it justifies the use of RFI values for risk assessment. The second case-study deals with quantitative estimation of drought risk in Ukraine based on satellite data. Drought hazard mapping is performed based on the use of vegetation health index (VHI) derived from NOAA satellites, and the extreme value theory techniques. Drought vulnerability is assessed by estimating the crop areas and crop yield to quantify potential impact of a drought on crop production. Finally, drought hazard and vulnerability maps are fused to derive a drought risk map. [1] N.N. Kussul, B.V. Sokolov, Y.I. Zyelyk, V.A. Zelentsov, S.V. Skakun, and A.Yu. Shelestov, "Disaster Risk Assessment Based on Heterogeneous Geospatial Information," J. of Autom. and Inf. Sci., 42(12), pp. 32-45, 2010. [2] S. Skakun, N. Kussul, A. Shelestov, and O. Kussul, "Flood Hazard and Flood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia," Risk Analysis, 2013, doi: 10.1111/risa.12156. [3] L. Hluchy, N. Kussul, A. Shelestov, S. Skakun, O. Kravchenko, Y. Gripich, P. Kopp, E. Lupian, "The Data Fusion Grid Infrastructure: Project Objectives and Achievements," Computing and Informatics, vol. 29, no. 2, pp. 319-334, 2010.

External Validation of European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) for Risk Prioritization in an Iranian Population

PubMed Central

Atashi, Alireza; Amini, Shahram; Tashnizi, Mohammad Abbasi; Moeinipour, Ali Asghar; Aazami, Mathias Hossain; Tohidnezhad, Fariba; Ghasemi, Erfan; Eslami, Saeid

2018-01-01

Introduction The European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) is a prediction model which maps 18 predictors to a 30-day post-operative risk of death concentrating on accurate stratification of candidate patients for cardiac surgery. Objective The objective of this study was to determine the performance of the EuroSCORE II risk-analysis predictions among patients who underwent heart surgeries in one area of Iran. Methods A retrospective cohort study was conducted to collect the required variables for all consecutive patients who underwent heart surgeries at Emam Reza hospital, Northeast Iran between 2014 and 2015. Univariate and multivariate analysis were performed to identify covariates which significantly contribute to higher EuroSCORE II in our population. External validation was performed by comparing the real and expected mortality using area under the receiver operating characteristic curve (AUC) for discrimination assessment. Also, Brier Score and Hosmer-Lemeshow goodness-of-fit test were used to show the overall performance and calibration level, respectively. Results Two thousand five hundred eight one (59.6% males) were included. The observed mortality rate was 3.3%, but EuroSCORE II had a prediction of 4.7%. Although the overall performance was acceptable (Brier score=0.047), the model showed poor discriminatory power by AUC=0.667 (sensitivity=61.90, and specificity=66.24) and calibration (Hosmer-Lemeshow test, P<0.01). Conclusion Our study showed that the EuroSCORE II discrimination power is less than optimal for outcome prediction and less accurate for resource allocation programs. It highlights the need for recalibration of this risk stratification tool aiming to improve post cardiac surgery outcome predictions in Iran. PMID:29617500

Progress in national-scale landslide susceptibility mapping in Romania using a combined statistical-heuristical approach

NASA Astrophysics Data System (ADS)

Bălteanu, Dan; Micu, Mihai; Malet, Jean-Philippe; Jurchescu, Marta; Sima, Mihaela; Kucsicsa, Gheorghe; Dumitrică, Cristina; Petrea, Dănuţ; Mărgărint, Ciprian; Bilaşco, Ştefan; Văcăreanu, Radu; Georgescu, Sever; Senzaconi, Francisc

2017-04-01

Landslide processes represent a very widespread geohazard in Romania, affecting mainly the hilly and plateau regions as well as the mountain sectors developed on flysch formations. Two main projects provided the framework for improving the existing national landslide susceptibility map (Bălteanu et al. 2010): the ELSUS (Pan-European and nation-wide landslide susceptibility assessment, EC-CERG) and the RO-RISK (Disaster Risk Evaluation at National Level, ESF-POCA) projects. The latter one, a flagship project aiming at strengthening risk prevention and management in Romania, focused on a national-level evaluation of the main risks in the country including landslides. The strategy for modeling landslide susceptibility was designed based on the experience gained from continental and national level assessments conducted in the frame of the International Programme on Landslides (IPL) project IPL-162, the European Landslides Expert Group - JRC and the ELSUS project. The newly proposed landslide susceptibility model used as input a reduced set of landslide conditioning factor maps available at scales of 1:100,000 - 1:200,000 and consisting of lithology, slope angle and land cover. The input data was further differentiated for specific natural environments, defined here as morpho-structural units in order to incorporate differences induced by elevation (vertical climatic zonation), morpho-structure as well as neotectonic features. In order to best discern the specific landslide conditioning elements, the analysis has been carried out for one single process category, namely slides. The existence of a landslide inventory covering the whole country's territory ( 30,000 records, Micu et al. 2014), although affected by incompleteness and lack of homogeneity, allowed for the application of a semi-quantitative, mixed statistical-heuristical approach having the advantage of combining the objectivity of statistics with expert-knowledge in calibrating class and factor weights. The maps obtained for the different units were subjected to evaluation and validation using both expert judgment and two additional landslide inventories with national coverage. Expert evaluations were provided for several parts of the country, where possible also using available regional zonations, and derived knowledge was subsequently used for map improvements. The external landslide datasets allowed for validation of the maps through prediction-rate curves (PRC). An improved national landslide susceptibility map of Romania (100 m resolution) resulted from merging the various unit maps and classifying them according to the PRC-thresholds. The final map reveals good performance for most areas. Finally, improvements compared to the previous version of the national map as well as model limitations and possible enhancement requirements are discussed. This study is part of the RO-RISK project (2016) coordinated by the Romanian General Inspectorate for Emergency Situations (IGSU) and supported by the European Social Fund through the Operational Programme for Administrative Capacity (POCA).

Estimating drought risk across Europe from reported drought impacts, drought indices, and vulnerability factors

NASA Astrophysics Data System (ADS)

Blauhut, Veit; Stahl, Kerstin; Stagge, James Howard; Tallaksen, Lena M.; De Stefano, Lucia; Vogt, Jürgen

2016-07-01

Drought is one of the most costly natural hazards in Europe. Due to its complexity, drought risk, meant as the combination of the natural hazard and societal vulnerability, is difficult to define and challenging to detect and predict, as the impacts of drought are very diverse, covering the breadth of socioeconomic and environmental systems. Pan-European maps of drought risk could inform the elaboration of guidelines and policies to address its documented severity and impact across borders. This work tests the capability of commonly applied drought indices and vulnerability factors to predict annual drought impact occurrence for different sectors and macro regions in Europe and combines information on past drought impacts, drought indices, and vulnerability factors into estimates of drought risk at the pan-European scale. This hybrid approach bridges the gap between traditional vulnerability assessment and probabilistic impact prediction in a statistical modelling framework. Multivariable logistic regression was applied to predict the likelihood of impact occurrence on an annual basis for particular impact categories and European macro regions. The results indicate sector- and macro-region-specific sensitivities of drought indices, with the Standardized Precipitation Evapotranspiration Index (SPEI) for a 12-month accumulation period as the overall best hazard predictor. Vulnerability factors have only limited ability to predict drought impacts as single predictors, with information about land use and water resources being the best vulnerability-based predictors. The application of the hybrid approach revealed strong regional and sector-specific differences in drought risk across Europe. The majority of the best predictor combinations rely on a combination of SPEI for shorter and longer accumulation periods, and a combination of information on land use and water resources. The added value of integrating regional vulnerability information with drought risk prediction could be proven. Thus, the study contributes to the overall understanding of drivers of drought impacts, appropriateness of drought indices selection for specific applications, and drought risk assessment.

Landslide hazard assessment: recent trends and techniques.

PubMed

Pardeshi, Sudhakar D; Autade, Sumant E; Pardeshi, Suchitra S

2013-01-01

Landslide hazard assessment is an important step towards landslide hazard and risk management. There are several methods of Landslide Hazard Zonation (LHZ) viz. heuristic, semi quantitative, quantitative, probabilistic and multi-criteria decision making process. However, no one method is accepted universally for effective assessment of landslide hazards. In recent years, several attempts have been made to apply different methods of LHZ and to compare results in order to find the best suited model. This paper presents the review of researches on landslide hazard mapping published in recent years. The advanced multivariate techniques are proved to be effective in spatial prediction of landslides with high degree of accuracy. Physical process based models also perform well in LHZ mapping even in the areas with poor database. Multi-criteria decision making approach also play significant role in determining relative importance of landslide causative factors in slope instability process. Remote Sensing and Geographical Information System (GIS) are powerful tools to assess landslide hazards and are being used extensively in landslide researches since last decade. Aerial photographs and high resolution satellite data are useful in detection, mapping and monitoring landslide processes. GIS based LHZ models helps not only to map and monitor landslides but also to predict future slope failures. The advancements in Geo-spatial technologies have opened the doors for detailed and accurate assessment of landslide hazards.

Use of portable X-ray fluorescence spectroscopy and geostatistics for health risk assessment.

PubMed

Yang, Meng; Wang, Cheng; Yang, Zhao-Ping; Yan, Nan; Li, Feng-Ying; Diao, Yi-Wei; Chen, Min-Dong; Li, Hui-Ming; Wang, Jin-Hua; Qian, Xin

2018-05-30

Laboratory analysis of trace metals using inductively coupled plasma (ICP) spectroscopy is not cost effective, and the complex spatial distribution of soil trace metals makes their spatial analysis and prediction problematic. Thus, for the health risk assessment of exposure to trace metals in soils, portable X-ray fluorescence (PXRF) spectroscopy was used to replace ICP spectroscopy for metal analysis, and robust geostatistical methods were used to identify spatial outliers in trace metal concentrations and to map trace metal distributions. A case study was carried out around an industrial area in Nanjing, China. The results showed that PXRF spectroscopy provided results for trace metal (Cu, Ni, Pb and Zn) levels comparable to ICP spectroscopy. The results of the health risk assessment showed that Ni posed a higher non-carcinogenic risk than Cu, Pb and Zn, indicating a higher priority of concern than the other elements. Sampling locations associated with adverse health effects were identified as 'hotspots', and high-risk areas were delineated from risk maps. These 'hotspots' and high-risk areas were in close proximity to and downwind from petrochemical plants, indicating the dominant role of industrial activities as the major sources of trace metals in soils. The approach used in this study could be adopted as a cost-effective methodology for screening 'hotspots' and priority areas of concern for cost-efficient health risk management. Copyright © 2018 Elsevier Inc. All rights reserved.

ShakeMap Atlas 2.0: an improved suite of recent historical earthquake ShakeMaps for global hazard analyses and loss model calibration

USGS Publications Warehouse

Garcia, D.; Mah, R.T.; Johnson, K.L.; Hearne, M.G.; Marano, K.D.; Lin, K.-W.; Wald, D.J.

2012-01-01

We introduce the second version of the U.S. Geological Survey ShakeMap Atlas, which is an openly-available compilation of nearly 8,000 ShakeMaps of the most significant global earthquakes between 1973 and 2011. This revision of the Atlas includes: (1) a new version of the ShakeMap software that improves data usage and uncertainty estimations; (2) an updated earthquake source catalogue that includes regional locations and finite fault models; (3) a refined strategy to select prediction and conversion equations based on a new seismotectonic regionalization scheme; and (4) vastly more macroseismic intensity and ground-motion data from regional agencies All these changes make the new Atlas a self-consistent, calibrated ShakeMap catalogue that constitutes an invaluable resource for investigating near-source strong ground-motion, as well as for seismic hazard, scenario, risk, and loss-model development. To this end, the Atlas will provide a hazard base layer for PAGER loss calibration and for the Earthquake Consequences Database within the Global Earthquake Model initiative.

MosquitoMap and the Mal-area calculator: new web tools to relate mosquito species distribution with vector borne disease

PubMed Central

2010-01-01

Background Mosquitoes are important vectors of diseases but, in spite of various mosquito faunistic surveys globally, there is a need for a spatial online database of mosquito collection data and distribution summaries. Such a resource could provide entomologists with the results of previous mosquito surveys, and vector disease control workers, preventative medicine practitioners, and health planners with information relating mosquito distribution to vector-borne disease risk. Results A web application called MosquitoMap was constructed comprising mosquito collection point data stored in an ArcGIS 9.3 Server/SQL geodatabase that includes administrative area and vector species x country lookup tables. In addition to the layer containing mosquito collection points, other map layers were made available including environmental, and vector and pathogen/disease distribution layers. An application within MosquitoMap called the Mal-area calculator (MAC) was constructed to quantify the area of overlap, for any area of interest, of vector, human, and disease distribution models. Data standards for mosquito records were developed for MosquitoMap. Conclusion MosquitoMap is a public domain web resource that maps and compares georeferenced mosquito collection points to other spatial information, in a geographical information system setting. The MAC quantifies the Mal-area, i.e. the area where it is theoretically possible for vector-borne disease transmission to occur, thus providing a useful decision tool where other disease information is limited. The Mal-area approach emphasizes the independent but cumulative contribution to disease risk of the vector species predicted present. MosquitoMap adds value to, and makes accessible, the results of past collecting efforts, as well as providing a template for other arthropod spatial databases. PMID:20167090

MosquitoMap and the Mal-area calculator: new web tools to relate mosquito species distribution with vector borne disease.

PubMed

Foley, Desmond H; Wilkerson, Richard C; Birney, Ian; Harrison, Stanley; Christensen, Jamie; Rueda, Leopoldo M

2010-02-18

Mosquitoes are important vectors of diseases but, in spite of various mosquito faunistic surveys globally, there is a need for a spatial online database of mosquito collection data and distribution summaries. Such a resource could provide entomologists with the results of previous mosquito surveys, and vector disease control workers, preventative medicine practitioners, and health planners with information relating mosquito distribution to vector-borne disease risk. A web application called MosquitoMap was constructed comprising mosquito collection point data stored in an ArcGIS 9.3 Server/SQL geodatabase that includes administrative area and vector species x country lookup tables. In addition to the layer containing mosquito collection points, other map layers were made available including environmental, and vector and pathogen/disease distribution layers. An application within MosquitoMap called the Mal-area calculator (MAC) was constructed to quantify the area of overlap, for any area of interest, of vector, human, and disease distribution models. Data standards for mosquito records were developed for MosquitoMap. MosquitoMap is a public domain web resource that maps and compares georeferenced mosquito collection points to other spatial information, in a geographical information system setting. The MAC quantifies the Mal-area, i.e. the area where it is theoretically possible for vector-borne disease transmission to occur, thus providing a useful decision tool where other disease information is limited. The Mal-area approach emphasizes the independent but cumulative contribution to disease risk of the vector species predicted present. MosquitoMap adds value to, and makes accessible, the results of past collecting efforts, as well as providing a template for other arthropod spatial databases.

Influence of Subjectivity in Geological Mapping on the Net Penetration Rate Prediction for a Hard Rock TBM

NASA Astrophysics Data System (ADS)

Seo, Yongbeom; Macias, Francisco Javier; Jakobsen, Pål Drevland; Bruland, Amund

2018-05-01

The net penetration rate of hard rock tunnel boring machines (TBM) is influenced by rock mass degree of fracturing. This influence is taken into account in the NTNU prediction model by the rock mass fracturing factor ( k s). k s is evaluated by geological mapping, the measurement of the orientation of fractures and the spacing of fractures and fracture type. Geological mapping is a subjective procedure. Mapping results can therefore contain considerable uncertainty. The mapping data of a tunnel mapped by three researchers were compared, and the influence of the variation in geological mapping was estimated to assess the influence of subjectivity in geological mapping. This study compares predicted net penetration rates and actual net penetration rates for TBM tunneling (from field data) and suggests mapping methods that can reduce the error related to subjectivity. The main findings of this paper are as follows: (1) variation of mapping data between individuals; (2) effect of observed variation on uncertainty in predicted net penetration rates; (3) influence of mapping methods on the difference between predicted and actual net penetration rate.

Mapping ecological risks with a portfolio-based technique: incorporating uncertainty and decision-making preferences

Treesearch

Denys Yemshanov; Frank H. Koch; Mark Ducey; Klaus Koehler

2013-01-01

Geographic mapping of risks is a useful analytical step in ecological risk assessments and in particular, in analyses aimed to estimate risks associated with introductions of invasive organisms. In this paper, we approach invasive species risk mapping as a portfolio allocation problem and apply techniques from decision theory to build an invasion risk map that combines...

Procedures for adjusting regional regression models of urban-runoff quality using local data

USGS Publications Warehouse

Hoos, A.B.; Sisolak, J.K.

1993-01-01

Statistical operations termed model-adjustment procedures (MAP?s) can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting `adjusted? regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAP?s examined in this study were: single-factor regression against the regional model prediction, P, (termed MAP-lF-P), regression against P,, (termed MAP-R-P), regression against P, and additional local variables (termed MAP-R-P+nV), and a weighted combination of P, and a local-regression prediction (termed MAP-W). The procedures were tested by means of split-sample analysis, using data from three cities included in the Nationwide Urban Runoff Program: Denver, Colorado; Bellevue, Washington; and Knoxville, Tennessee. The MAP that provided the greatest predictive accuracy for the verification data set differed among the three test data bases and among model types (MAP-W for Denver and Knoxville, MAP-lF-P and MAP-R-P for Bellevue load models, and MAP-R-P+nV for Bellevue concentration models) and, in many cases, was not clearly indicated by the values of standard error of estimate for the calibration data set. A scheme to guide MAP selection, based on exploratory data analysis of the calibration data set, is presented and tested. The MAP?s were tested for sensitivity to the size of a calibration data set. As expected, predictive accuracy of all MAP?s for the verification data set decreased as the calibration data-set size decreased, but predictive accuracy was not as sensitive for the MAP?s as it was for the local regression models.

Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico

NASA Technical Reports Server (NTRS)

Beck, L. R.; Rodriguez, M. H.; Dister, S. W.; Rodriguez, A. D.; Washino, R. K.; Roberts, D. R.; Spanner, M. A.

1997-01-01

A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

Using Participatory Risk Mapping (PRM) to Identify and Understand People's Perceptions of Crop Loss to Animals in Uganda

PubMed Central

Webber, Amanda D.; Hill, Catherine M.

2014-01-01

Considering how people perceive risks to their livelihoods from local wildlife is central to (i) understanding the impact of crop damage by animals on local people and (ii) recognising how this influences their interactions with, and attitudes towards, wildlife. Participatory risk mapping (PRM) is a simple, analytical tool that can be used to identify and classify risk within communities. Here we use it to explore local people's perceptions of crop damage by wildlife and the animal species involved. Interviews (n = 93, n = 76) and seven focus groups were conducted in four villages around Budongo Forest Reserve, Uganda during 2004 and 2005. Farms (N = 129) were simultaneously monitored for crop loss. Farmers identified damage by wildlife as the most significant risk to their crops; risk maps highlighted its anomalous status compared to other anticipated challenges to agricultural production. PRM was further used to explore farmers' perceptions of animal species causing crop damage and the results of this analysis compared with measured crop losses. Baboons (Papio anubis) were considered the most problematic species locally but measurements of loss indicate this perceived severity was disproportionately high. In contrast goats (Capra hircus) were considered only a moderate risk, yet risk of damage by this species was significant. Surprisingly, for wild pigs (Potamochoerus sp), perceptions of severity were not as high as damage incurred might have predicted, although perceived incidence was greater than recorded frequency of damage events. PRM can assist researchers and practitioners to identify and explore perceptions of the risk of crop damage by wildlife. As this study highlights, simply quantifying crop loss does not determine issues that are important to local people nor the complex relationships between perceived risk factors. Furthermore, as PRM is easily transferable it may contribute to the identification and development of standardised approaches of mitigation across sites of negative human-wildlife interaction. PMID:25076415

Ensemble Learning of QTL Models Improves Prediction of Complex Traits

PubMed Central

Bian, Yang; Holland, James B.

2015-01-01

Quantitative trait locus (QTL) models can provide useful insights into trait genetic architecture because of their straightforward interpretability but are less useful for genetic prediction because of the difficulty in including the effects of numerous small effect loci without overfitting. Tight linkage between markers introduces near collinearity among marker genotypes, complicating the detection of QTL and estimation of QTL effects in linkage mapping, and this problem is exacerbated by very high density linkage maps. Here we developed a thinning and aggregating (TAGGING) method as a new ensemble learning approach to QTL mapping. TAGGING reduces collinearity problems by thinning dense linkage maps, maintains aspects of marker selection that characterize standard QTL mapping, and by ensembling, incorporates information from many more markers-trait associations than traditional QTL mapping. The objective of TAGGING was to improve prediction power compared with QTL mapping while also providing more specific insights into genetic architecture than genome-wide prediction models. TAGGING was compared with standard QTL mapping using cross validation of empirical data from the maize (Zea mays L.) nested association mapping population. TAGGING-assisted QTL mapping substantially improved prediction ability for both biparental and multifamily populations by reducing both the variance and bias in prediction. Furthermore, an ensemble model combining predictions from TAGGING-assisted QTL and infinitesimal models improved prediction abilities over the component models, indicating some complementarity between model assumptions and suggesting that some trait genetic architectures involve a mixture of a few major QTL and polygenic effects. PMID:26276383

Study protocol for a prospective cohort study examining the predictive potential of dynamic symptom networks for the onset and progression of psychosis: the Mapping Individual Routes of Risk and Resilience (Mirorr) study.

PubMed

Booij, Sanne H; Wichers, Marieke; de Jonge, Peter; Sytema, Sjoerd; van Os, Jim; Wunderink, Lex; Wigman, Johanna T W

2018-01-21

Our current ability to predict the course and outcome of early psychotic symptoms is limited, hampering timely treatment. To improve our understanding of the development of psychosis, a different approach to psychopathology may be productive. We propose to reconceptualise psychopathology from a network perspective, according to which symptoms act as a dynamic, interconnected system, impacting on each other over time and across diagnostic boundaries to form symptom networks. Adopting this network approach, the Mapping Individual Routes of Risk and Resilience study aims to determine whether characteristics of symptom networks can predict illness course and outcome of early psychotic symptoms. The sample consists of n=100 participants aged 18-35 years, divided into four subgroups (n=4×25) with increasing levels of severity of psychopathology, representing successive stages of clinical progression. Individuals representing the initial stage have a relatively low expression of psychotic experiences (general population), whereas individuals representing the end stage are help seeking and display a psychometric expression of psychosis, putting them at ultra-high risk for transition to psychotic disorder. At baseline and 1-year follow-up, participants report their symptoms, affective states and experiences for three consecutive months in short, daily questionnaires on their smartphone, which will be used to map individual networks. Network parameters, including the strength and directionality of symptom connections and centrality indices, will be estimated and associated to individual differences in and within-individual progression through stages of clinical severity and functioning over the next 3 years. The study has been approved by the local medical ethical committee (ABR no. NL52974.042.15). The results of the study will be published in (inter)national peer-reviewed journals, presented at research, clinical and general public conferences. The results will assist in improving and fine-tuning dynamic models of psychopathology, stimulating both clinical and scientific progress. NTR6205 ; Pre-results. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

DOE PAGES

Preciat Gonzalez, German A.; El Assal, Lemmer R. P.; Noronha, Alberto; ...

2017-06-14

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, manymore » algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

DOE Office of Scientific and Technical Information (OSTI.GOV)

Preciat Gonzalez, German A.; El Assal, Lemmer R. P.; Noronha, Alberto

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, manymore » algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D.

PubMed

Preciat Gonzalez, German A; El Assal, Lemmer R P; Noronha, Alberto; Thiele, Ines; Haraldsdóttir, Hulda S; Fleming, Ronan M T

2017-06-14

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.

Predicting protein contact map using evolutionary and physical constraints by integer programming.

PubMed

Wang, Zhiyong; Xu, Jinbo

2013-07-01

Protein contact map describes the pairwise spatial and functional relationship of residues in a protein and contains key information for protein 3D structure prediction. Although studied extensively, it remains challenging to predict contact map using only sequence information. Most existing methods predict the contact map matrix element-by-element, ignoring correlation among contacts and physical feasibility of the whole-contact map. A couple of recent methods predict contact map by using mutual information, taking into consideration contact correlation and enforcing a sparsity restraint, but these methods demand for a very large number of sequence homologs for the protein under consideration and the resultant contact map may be still physically infeasible. This article presents a novel method PhyCMAP for contact map prediction, integrating both evolutionary and physical restraints by machine learning and integer linear programming. The evolutionary restraints are much more informative than mutual information, and the physical restraints specify more concrete relationship among contacts than the sparsity restraint. As such, our method greatly reduces the solution space of the contact map matrix and, thus, significantly improves prediction accuracy. Experimental results confirm that PhyCMAP outperforms currently popular methods no matter how many sequence homologs are available for the protein under consideration. http://raptorx.uchicago.edu.

Environmental Drivers and Predicted Risk of Bacillary Dysentery in Southwest China.

PubMed

Zhang, Han; Si, Yali; Wang, Xiaofeng; Gong, Peng

2017-07-14

Bacillary dysentery has long been a considerable health problem in southwest China, however, the quantitative relationship between anthropogenic and physical environmental factors and the disease is not fully understand. It is also not clear where exactly the bacillary dysentery risk is potentially high. Based on the result of hotspot analysis, we generated training samples to build a spatial distribution model. Univariate analyses, autocorrelation and multi-collinearity examinations and stepwise selection were then applied to screen the potential causative factors. Multiple logistic regressions were finally applied to quantify the effects of key factors. A bootstrapping strategy was adopted while fitting models. The model was evaluated by area under the receiver operating characteristic curve (AUC), Kappa and independent validation samples. Hotspot counties were mainly mountainous lands in southwest China. Higher risk of bacillary dysentery was found associated with underdeveloped socio-economy, proximity to farmland or water bodies, higher environmental temperature, medium relative humidity and the distribution of the Tibeto-Burman ethnicity. A predictive risk map with high accuracy (88.19%) was generated. The high-risk areas are mainly located in the mountainous lands where the Tibeto-Burman people live, especially in the basins, river valleys or other flat places in the mountains with relatively lower elevation and a warmer climate. In the high-risk areas predicted by this study, improving the economic development, investment in health care and the construction of infrastructures for safe water supply, waste treatment and sewage disposal, and improving health related education could reduce the disease risk.

Environmental Drivers and Predicted Risk of Bacillary Dysentery in Southwest China

PubMed Central

Si, Yali; Gong, Peng

2017-01-01

Bacillary dysentery has long been a considerable health problem in southwest China, however, the quantitative relationship between anthropogenic and physical environmental factors and the disease is not fully understand. It is also not clear where exactly the bacillary dysentery risk is potentially high. Based on the result of hotspot analysis, we generated training samples to build a spatial distribution model. Univariate analyses, autocorrelation and multi-collinearity examinations and stepwise selection were then applied to screen the potential causative factors. Multiple logistic regressions were finally applied to quantify the effects of key factors. A bootstrapping strategy was adopted while fitting models. The model was evaluated by area under the receiver operating characteristic curve (AUC), Kappa and independent validation samples. Hotspot counties were mainly mountainous lands in southwest China. Higher risk of bacillary dysentery was found associated with underdeveloped socio-economy, proximity to farmland or water bodies, higher environmental temperature, medium relative humidity and the distribution of the Tibeto-Burman ethnicity. A predictive risk map with high accuracy (88.19%) was generated. The high-risk areas are mainly located in the mountainous lands where the Tibeto-Burman people live, especially in the basins, river valleys or other flat places in the mountains with relatively lower elevation and a warmer climate. In the high-risk areas predicted by this study, improving the economic development, investment in health care and the construction of infrastructures for safe water supply, waste treatment and sewage disposal, and improving health related education could reduce the disease risk. PMID:28708077

Potential effects of climate change on the risk of accidents with poisonous species of the genus Tityus (Scorpiones, Buthidae) in Argentina.

PubMed

Martinez, Pablo Ariel; Andrade, Mayane Alves; Bidau, Claudio Juan

2018-06-01

The temporal pattern of co-occurrence of human beings and venomous species (scorpions, spiders, snakes) is changing. Thus, the temporal pattern of areas with risk of accidents with such species tends to become dynamic in time. We analyze the areas of occurrence of species of Tityus in Argentina and assess the impact of global climate change on their area of distribution by the construction of risk maps. Using data of occurrence of the species and climatic variables, we constructed models of species distribution (SMDs) under current and future climatic conditions. We also created maps that allow the detection of temporal shifts in the distribution patterns of each Tityus species. Finally, we developed risk maps for the analyzed species. Our results predict that climate change will have an impact on the distribution of Tityus species which will clearly expand to more southern latitudes, with the exception of T. argentinus. T. bahiensis, widely distributed in Brazil, showed a considerable increase of its potential area (ca. 37%) with future climate change. The species T. confluens and T. trivittatus that cause the highest number of accidents in Argentina are expected to show significant changes of their distributions in future scenarios. The former fact is worrying because Buenos Aires province is the more densely populated district in Argentina thus iable to become the most affected by T. trivittatus. These alterations of distributional patterns can lead to amplify the accident risk zones of venomous species, becoming an important subject of concern for public health policies. Copyright © 2018 Elsevier Ltd. All rights reserved.

Mapping the Risk of Anaemia in Preschool-Age Children: The Contribution of Malnutrition, Malaria, and Helminth Infections in West Africa

PubMed Central

Soares Magalhães, Ricardo J.; Clements, Archie C. A.

2011-01-01

Background Childhood anaemia is considered a severe public health problem in most countries of sub-Saharan Africa. We investigated the geographical distribution of prevalence of anaemia and mean haemoglobin concentration (Hb) in children aged 1–4 y (preschool children) in West Africa. The aim was to estimate the geographical risk profile of anaemia accounting for malnutrition, malaria, and helminth infections, the risk of anaemia attributable to these factors, and the number of anaemia cases in preschool children for 2011. Methods and Findings National cross-sectional household-based demographic health surveys were conducted in 7,147 children aged 1–4 y in Burkina Faso, Ghana, and Mali in 2003–2006. Bayesian geostatistical models were developed to predict the geographical distribution of mean Hb and anaemia risk, adjusting for the nutritional status of preschool children, the location of their residence, predicted Plasmodium falciparum parasite rate in the 2- to 10-y age group (Pf PR2–10), and predicted prevalence of Schistosoma haematobium and hookworm infections. In the four countries, prevalence of mild, moderate, and severe anaemia was 21%, 66%, and 13% in Burkina Faso; 28%, 65%, and 7% in Ghana, and 26%, 62%, and 12% in Mali. The mean Hb was lowest in Burkina Faso (89 g/l), in males (93 g/l), and for children 1–2 y (88 g/l). In West Africa, severe malnutrition, Pf PR2–10, and biological synergisms between S. haematobium and hookworm infections were significantly associated with anaemia risk; an estimated 36.8%, 14.9%, 3.7%, 4.2%, and 0.9% of anaemia cases could be averted by treating malnutrition, malaria, S. haematobium infections, hookworm infections, and S. haematobium/hookworm coinfections, respectively. A large spatial cluster of low mean Hb (<80 g/l) and maximal risk of anaemia (>95%) was predicted for an area shared by Burkina Faso and Mali. We estimate that in 2011, approximately 6.7 million children aged 1–4 y are anaemic in the three study countries. Conclusions By mapping the distribution of anaemia risk in preschool children adjusted for malnutrition and parasitic infections, we provide a means to identify the geographical limits of anaemia burden and the contribution that malnutrition and parasites make to anaemia. Spatial targeting of ancillary micronutrient supplementation and control of other anaemia causes, such as malaria and helminth infection, can contribute to efficiently reducing the burden of anaemia in preschool children in Africa. Please see later in the article for the Editors' Summary PMID:21687688

Pre-drill predictions versus post-drill results: use of sequence stratigraphic methods in reduction of exploration risk, Sarawak Deep-water Blocks, Malaysia

NASA Astrophysics Data System (ADS)

Mansor, Md Yazid; Snedden, J. W.; Sarg, J. F.; Smith, B. S.; Kolich, T.; Carter, M.

1999-04-01

Limited well control, great distances from age-equivalent producing fields, and a largely unknown stratigraphy necessitated use of sequence stratigraphic methods to assess exploration risk associated with reservoir, source and seal distribution in the Mobil-operated Deep-water Blocks of Sarawak, Malaysia. These methods allowed predictions to be made and reservoir risks to be halved in each of the locations drilled in 1995. Predictions regarding reservoir and stratigraphy proved correct, as the Mulu-1 and Bako-1 wells penetrated numerous high-quality, thick sandstone reservoirs in the Middle to Lower Miocene section. Shallow marine sandstones dominate the vertical succession in both wells, with characteristic aggradational, upward-coarsening log motifs. Cores display classic wave-generated stratification and hummocky cross-bedding. Evidence, such as marginal-marine to neritic microfauna in cuttings of both wells, supports these interpretations. Lack of hydrocarbon charge in the two wells may be due to their position relative to coaly hydrocarbon source beds. These prospects have high trap and seal integrity, being well defined on seismics as high relief horst blocks covered by a very thick shale-prone section. The Mulu-1 well, for example, is located at least 20-30 km down stratigraphic dip from mapped coeval lower coastal-plain deposits. Amplitude anomalies on the flank of the Mulu horst are probably derived from transported organics buried in deep Plio-Pleistocene kitchens in the northwest portion of the Mobil blocks. Remaining potential of mapped prospects is high and efforts continue at characterizing the petroleum system of the Deep-water Blocks. Seismic attribute and interval velocity analyses provide new clues to the location of probable coaly source rocks, especially when viewed in their regional and sequence stratigraphic context. Future work is planned and will serve to reduce risk to acceptable levels and support further drilling in this prospective hydrocarbon province.

Combined spatial prediction of schistosomiasis and soil-transmitted helminthiasis in Sierra Leone: a tool for integrated disease control.

PubMed

Hodges, Mary H; Soares Magalhães, Ricardo J; Paye, Jusufu; Koroma, Joseph B; Sonnie, Mustapha; Clements, Archie; Zhang, Yaobi

2012-01-01

A national mapping of Schistosoma haematobium was conducted in Sierra Leone before the mass drug administration (MDA) with praziquantel. Together with the separate mapping of S. mansoni and soil-transmitted helminths, the national control programme was able to plan the MDA strategies according to the World Health Organization guidelines for preventive chemotherapy for these diseases. A total of 52 sites/schools were selected according to prior knowledge of S. haematobium endemicity taking into account a good spatial coverage within each district, and a total of 2293 children aged 9-14 years were examined. Spatial analysis showed that S. haematobium is heterogeneously distributed in the country with significant spatial clustering in the central and eastern regions of the country, most prevalent in Bo (24.6% and 8.79 eggs/10 ml), Koinadugu (20.4% and 3.53 eggs/10 ml) and Kono (25.3% and 7.91 eggs/10 ml) districts. By combining this map with the previously reported maps on intestinal schistosomiasis using a simple probabilistic model, the combined schistosomiasis prevalence map highlights the presence of high-risk communities in an extensive area in the northeastern half of the country. By further combining the hookworm prevalence map, the at-risk population of school-age children requiring integrated schistosomiasis/soil-transmitted helminth treatment regimens according to the coendemicity was estimated. The first comprehensive national mapping of urogenital schistosomiasis in Sierra Leone was conducted. Using a new method for calculating the combined prevalence of schistosomiasis using estimates from two separate surveys, we provided a robust coendemicity mapping for overall urogenital and intestinal schistosomiasis. We also produced a coendemicity map of schistosomiasis and hookworm. These coendemicity maps can be used to guide the decision making for MDA strategies in combination with the local knowledge and programme needs.

Quantify landslide exposure in areas with limited hazard information

NASA Astrophysics Data System (ADS)

Pellicani, R.; Spilotro, G.; Van Westen, C. J.

2012-04-01

In Daunia region, located in the North-western part of Apulia (Southern Italy), landslides are the main source of damage to properties in the urban centers of the area, involving especially transportation system and the foundation stability of buildings. In the last 50 years, the growing demand for physical development of these unstable minor hillside and mountain centers has produced a very rapid expansion of built-up areas, often with poor planning of urban and territorial infrastructures, and invasion of the agricultural soil. Because of the expansion of the built-up towards not safe areas, human activities such as deforestation or excavation of slopes for road cuts and building sites, etc., have become important triggers for landslide occurrence. In the study area, the probability of occurrence of landslides is very difficult to predict, as well as the expected magnitude of events, due to the limited data availability on past landslide activity. Because the main limitations concern the availability of temporal data on landslides and triggering events (frequency), run-out distance and landslide magnitude, it was not possible to produce a reliable landslide hazard map and, consequently, a risk map. Given these limitations in data availability and details, a qualitative exposure map has been produced and combined with a landslide susceptibility map, both generated using a spatial multi-criteria evaluation (SMCE) procedure in a GIS system, for obtaining the qualitative landslide risk map. The qualitative analysis has been provided the spatial distribution of the exposure level in the study area; this information could be used in a preliminary stage of regional planning. In order to have a better definition of the risk level in the Daunia territory, the quantification of the economic losses at municipal level was carried out. For transforming these information on economic consequences into landslide risk quantification, it was necessary to assume the temporal probability of landslides, on the basis of the expert knowledge on the landslide phenomena. For each of twenty-five municipalities included in the study area, the expected losses (or consequences), in monetary terms, due to different hazard scenarios have been evaluated. After calculating the economic losses, the total risk at municipal level was evaluated, by generating the risk curves and calculating the area under the curves. The analysis of the risk curves related to the 25 municipalities has showed that the total risk values, expressed in monetary terms, is higher for the bigger municipal areas located in the southern part of the study area where the elevation is lower, as are more numerous the elements at risk distributed on the municipal territory. Finally, this quantitative risk assessment procedure, which calculates the exposure in monetary terms of elements at risk, allows to establish the changes in risk in future with urban development and monetary inflation.

Mapping developmental precursors of cyber-aggression: trajectories of risk predict perpetration and victimization.

PubMed

Modecki, Kathryn L; Barber, Bonnie L; Vernon, Lynette; Vernon, Lynnette

2013-05-01

Technologically mediated contexts are social arenas in which adolescents can be both perpetrators and victims of aggression. Yet, there remains little understanding of the developmental etiology of cyber aggression, itself, as experienced by either perpetrators or victims. The current study examines 3-year latent within-person trajectories of known correlates of cyber-aggression: problem behavior, (low) self-esteem, and depressed mood, in a large and diverse sample of youth (N = 1,364; 54.6% female; 12-14 years old at T1). Findings demonstrate that developmental increases in problem behavior across grades 8-10 predict both cyber-perpetration and victimization in grade 11. Developmental decreases in self-esteem also predicted both grade 11 perpetration and victimization. Finally, early depressed mood predicted both perpetration and victimization later on, regardless of developmental change in depressed mood in the interim. Our results reveal a clear link between risky developmental trajectories across the early high school years and later cyber-aggression and imply that mitigating trajectories of risk early on may lead to decreases in cyber-aggression at a later date.

Making Air Pollution Visible: A Tool for Promoting Environmental Health Literacy.

PubMed

Cleary, Ekaterina Galkina; Patton, Allison P; Wu, Hsin-Ching; Xie, Alan; Stubblefield, Joseph; Mass, William; Grinstein, Georges; Koch-Weser, Susan; Brugge, Doug; Wong, Carolyn

2017-04-12

Digital maps are instrumental in conveying information about environmental hazards geographically. For laypersons, computer-based maps can serve as tools to promote environmental health literacy about invisible traffic-related air pollution and ultrafine particles. Concentrations of these pollutants are higher near major roadways and increasingly linked to adverse health effects. Interactive computer maps provide visualizations that can allow users to build mental models of the spatial distribution of ultrafine particles in a community and learn about the risk of exposure in a geographic context. The objective of this work was to develop a new software tool appropriate for educating members of the Boston Chinatown community (Boston, MA, USA) about the nature and potential health risks of traffic-related air pollution. The tool, the Interactive Map of Chinatown Traffic Pollution ("Air Pollution Map" hereafter), is a prototype that can be adapted for the purpose of educating community members across a range of socioeconomic contexts. We built the educational visualization tool on the open source Weave software platform. We designed the tool as the centerpiece of a multimodal and intergenerational educational intervention about the health risk of traffic-related air pollution. We used a previously published fine resolution (20 m) hourly land-use regression model of ultrafine particles as the algorithm for predicting pollution levels and applied it to one neighborhood, Boston Chinatown. In designing the map, we consulted community experts to help customize the user interface to communication styles prevalent in the target community. The product is a map that displays ultrafine particulate concentrations averaged across census blocks using a color gradation from white to dark red. The interactive features allow users to explore and learn how changing meteorological conditions and traffic volume influence ultrafine particle concentrations. Users can also select from multiple map layers, such as a street map or satellite view. The map legends and labels are available in both Chinese and English, and are thus accessible to immigrants and residents with proficiency in either language. The map can be either Web or desktop based. The Air Pollution Map incorporates relevant language and landmarks to make complex scientific information about ultrafine particles accessible to members of the Boston Chinatown community. In future work, we will test the map in an educational intervention that features intergenerational colearning and the use of supplementary multimedia presentations. ©Ekaterina Galkina Cleary, Allison P Patton, Hsin-Ching Wu, Alan Xie, Joseph Stubblefield, William Mass, Georges Grinstein, Susan Koch-Weser, Doug Brugge, Carolyn Wong. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 12.04.2017.

APPLICATION OF BAYESIAN AND GEOSTATISTICAL MODELING TO THE ENVIRONMENTAL MONITORING OF CS-137 AT THE IDAHO NATIONAL LABORATORY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kara G. Eby

2010-08-01

At the Idaho National Laboratory (INL) Cs-137 concentrations above the U.S. Environmental Protection Agency risk-based threshold of 0.23 pCi/g may increase the risk of human mortality due to cancer. As a leader in nuclear research, the INL has been conducting nuclear activities for decades. Elevated anthropogenic radionuclide levels including Cs-137 are a result of atmospheric weapons testing, the Chernobyl accident, and nuclear activities occurring at the INL site. Therefore environmental monitoring and long-term surveillance of Cs-137 is required to evaluate risk. However, due to the large land area involved, frequent and comprehensive monitoring is limited. Developing a spatial model thatmore » predicts Cs-137 concentrations at unsampled locations will enhance the spatial characterization of Cs-137 in surface soils, provide guidance for an efficient monitoring program, and pinpoint areas requiring mitigation strategies. The predictive model presented herein is based on applied geostatistics using a Bayesian analysis of environmental characteristics across the INL site, which provides kriging spatial maps of both Cs-137 estimates and prediction errors. Comparisons are presented of two different kriging methods, showing that the use of secondary information (i.e., environmental characteristics) can provide improved prediction performance in some areas of the INL site.« less

Global predictability of temperature extremes

NASA Astrophysics Data System (ADS)

Coughlan de Perez, Erin; van Aalst, Maarten; Bischiniotis, Konstantinos; Mason, Simon; Nissan, Hannah; Pappenberger, Florian; Stephens, Elisabeth; Zsoter, Ervin; van den Hurk, Bart

2018-05-01

Extreme temperatures are one of the leading causes of death and disease in both developed and developing countries, and heat extremes are projected to rise in many regions. To reduce risk, heatwave plans and cold weather plans have been effectively implemented around the world. However, much of the world’s population is not yet protected by such systems, including many data-scarce but also highly vulnerable regions. In this study, we assess at a global level where such systems have the potential to be effective at reducing risk from temperature extremes, characterizing (1) long-term average occurrence of heatwaves and coldwaves, (2) seasonality of these extremes, and (3) short-term predictability of these extreme events three to ten days in advance. Using both the NOAA and ECMWF weather forecast models, we develop global maps indicating a first approximation of the locations that are likely to benefit from the development of seasonal preparedness plans and/or short-term early warning systems for extreme temperature. The extratropics generally show both short-term skill as well as strong seasonality; in the tropics, most locations do also demonstrate one or both. In fact, almost 5 billion people live in regions that have seasonality and predictability of heatwaves and/or coldwaves. Climate adaptation investments in these regions can take advantage of seasonality and predictability to reduce risks to vulnerable populations.

US Vulnerability to Natural Disasters

NASA Astrophysics Data System (ADS)

van der Vink, G.; Apgar, S.; Batchelor, A.; Carter, C.; Gail, D.; Jarrett, A.; Levine, N.; Morgan, W.; Orlikowski, M.; Pray, T.; Raymar, M.; Siebert, A.; Shawa, T. W.; Wallace, C.

2002-05-01

Natural disasters result from the coincidence of natural events with the built environment. Our nation's infrastructure is growing at an exponential rate in many areas of high risk, and the Federal government's liability is increasing proportionally. By superimposing population density with predicted ground motion from earthquakes, historical hurricane tracks, historical tornado locations, and areas within the flood plain, we are able to identify locations of high vulnerability within the United States. We present a comprehensive map of disaster risk for the United States that is being produced for the Senate Natural Hazards Caucus. The map allows for the geographic comparison of natural disaster risk with past disaster declarations, the expenditure of Federal dollars for disaster relief, population increase, and variations of GDP. Every state is vulnerable to natural disasters. Although their frequency varies considerably, the annualized losses for disaster relief from hurricanes, earthquakes, and floods are approximately equivalent. While fast-growing states such as California and Florida remain highly vulnerable, changes in the occurrence of natural events combined with population increases are making areas such as Texas, North Carolina, and the East Coast increasingly vulnerable.

Improving risk estimates of runoff producing areas: formulating variable source areas as a bivariate process.

PubMed

Cheng, Xiaoya; Shaw, Stephen B; Marjerison, Rebecca D; Yearick, Christopher D; DeGloria, Stephen D; Walter, M Todd

2014-05-01

Predicting runoff producing areas and their corresponding risks of generating storm runoff is important for developing watershed management strategies to mitigate non-point source pollution. However, few methods for making these predictions have been proposed, especially operational approaches that would be useful in areas where variable source area (VSA) hydrology dominates storm runoff. The objective of this study is to develop a simple approach to estimate spatially-distributed risks of runoff production. By considering the development of overland flow as a bivariate process, we incorporated both rainfall and antecedent soil moisture conditions into a method for predicting VSAs based on the Natural Resource Conservation Service-Curve Number equation. We used base-flow immediately preceding storm events as an index of antecedent soil wetness status. Using nine sub-basins of the Upper Susquehanna River Basin, we demonstrated that our estimated runoff volumes and extent of VSAs agreed with observations. We further demonstrated a method for mapping these areas in a Geographic Information System using a Soil Topographic Index. The proposed methodology provides a new tool for watershed planners for quantifying runoff risks across watersheds, which can be used to target water quality protection strategies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predicting Potential Risk Areas of Human Plague for the Western Usambara Mountains, Lushoto District, Tanzania

PubMed Central

Neerinckx, Simon; Peterson, A. Townsend; Gulinck, Hubert; Deckers, Jozef; Kimaro, Didas; Leirs, Herwig

2010-01-01

A natural focus of plague exists in the Western Usambara Mountains of Tanzania. Despite intense research, questions remain as to why and how plague emerges repeatedly in the same suite of villages. We used human plague incidence data for 1986–2003 in an ecological-niche modeling framework to explore the geographic distribution and ecology of human plague. Our analyses indicate that plague occurrence is related directly to landscape-scale environmental features, yielding a predictive understanding of one set of environmental factors affecting plague transmission in East Africa. Although many environmental variables contribute significantly to these models, the most important are elevation and Enhanced Vegetation Index derivatives. Projections of these models across broader regions predict only 15.5% (under a majority-rule threshold) or 31,997 km2 of East Africa as suitable for plague transmission, but they successfully anticipate most known foci in the region, making possible the development of a risk map of plague. PMID:20207880

Debris flow risk mapping on medium scale and estimation of prospective economic losses

NASA Astrophysics Data System (ADS)

Blahut, Jan; Sterlacchini, Simone

2010-05-01

Delimitation of potential zones affected by debris flow hazard, mapping of areas at risk, and estimation of future economic damage provides important information for spatial planners and local administrators in all countries endangered by this type of phenomena. This study presents a medium scale (1:25 000 - 1: 50 000) analysis applied in the Consortium of Mountain Municipalities of Valtellina di Tirano (Italian Alps, Lombardy Region). In this area a debris flow hazard map was coupled with the information about the elements at risk to obtain monetary values of prospective damage. Two available hazard maps were obtained from GIS medium scale modelling. Probability estimations of debris flow occurrence were calculated using existing susceptibility maps and two sets of aerial images. Value to the elements at risk was assigned according to the official information on housing costs and land value from the Territorial Agency of Lombardy Region. In the first risk map vulnerability values were assumed to be 1. The second risk map uses three classes of vulnerability values qualitatively estimated according to the debris flow possible propagation. Risk curves summarizing the possible economic losses were calculated. Finally these maps of economic risk were compared to maps derived from qualitative evaluation of the values of the elements at risk.

Mapping the spatial patterns of field traffic and traffic intensity to predict soil compaction risks at the field scale

NASA Astrophysics Data System (ADS)

Duttmann, Rainer; Kuhwald, Michael; Nolde, Michael

2015-04-01

Soil compaction is one of the main threats to cropland soils in present days. In contrast to easily visible phenomena of soil degradation, soil compaction, however, is obscured by other signals such as reduced crop yield, delayed crop growth, and the ponding of water, which makes it difficult to recognize and locate areas impacted by soil compaction directly. Although it is known that trafficking intensity is a key factor for soil compaction, until today only modest work has been concerned with the mapping of the spatially distributed patterns of field traffic and with the visual representation of the loads and pressures applied by farm traffic within single fields. A promising method for for spatial detection and mapping of soil compaction risks of individual fields is to process dGPS data, collected from vehicle-mounted GPS receivers and to compare the soil stress induced by farm machinery to the load bearing capacity derived from given soil map data. The application of position-based machinery data enables the mapping of vehicle movements over time as well as the assessment of trafficking intensity. It also facilitates the calculation of the trafficked area and the modeling of the loads and pressures applied to soil by individual vehicles. This paper focuses on the modeling and mapping of the spatial patterns of traffic intensity in silage maize fields during harvest, considering the spatio-temporal changes in wheel load and ground contact pressure along the loading sections. In addition to scenarios calculated for varying mechanical soil strengths, an example for visualizing the three-dimensional stress propagation inside the soil will be given, using the Visualization Toolkit (VTK) to construct 2D or 3D maps supporting to decision making due to sustainable field traffic management.

Structural fire risk of Portugal

NASA Astrophysics Data System (ADS)

Parente, Joana; Pereira, Mário

2017-04-01

Portugal is on the top of the European countries most affected by vegetation fires which underlines the importance of the existence of an updated and coherent fire risk map. This map represent a valuable supporting tool for forest and fire management decisions, focus prevention activities, improve the efficiency of fire detection systems, manage resources and actions of fire fighting with greater effectiveness. Therefore this study proposed a structural fire risk map of the vegetated area of Portugal using a deterministic approach based on the concept of fire risk currently accepted by the scientific community which consists in the combination of the fire hazard and the potential economic damage. The existing fire susceptibility map for Portugal based on the slope, land cover and fire probability, was adopted and updated by the use of a higher resolution digital terrain model, longer burnt area perimeter dataset (1975 - 2013) and the entire set of Corine land cover inventories. Five susceptibility classes were mapped to be in accordance with the Portuguese law and the results confirms the good performance of this model not only in terms of the favourability scores but also in the predictive values. Considering three different scenarios of (maximum, mean, and minimum annual) burnt area, fire hazard were estimate. The vulnerability scores and monetary values of species defined in the literature and by law were used to calculate the potential economic damage. The result was a fire risk map that identifies the areas more prone to be affected by fires in the future and provides an estimate of the economic damage of the fire which will be a valuable tool for forest and fire managers and to minimize the economic and environmental consequences of vegetation fires in Portugal. Acknowledgements: This work was supported by: (i) the project Interact - Integrative Research in Environment,Agro-Chain and Technology, NORTE-01-0145-FEDER-000017, research line BEST, cofinanced by FEDER/NORTE 2020; (ii) the FIREXTR project, PTDC/ATP¬GEO/0462/2014; and, (iii) European Investment Funds by FEDER/COMPETE/POCI-Operacional Competitiveness and Internacionalization Programme, under Project POCI-01-0145-FEDER-006958 and National Funds by FCT - Portuguese Foundation for Science and Technology, under the project UID/AGR/04033. We are especially grateful to ICNF and ISA for providing the fire data.

Evaluation of Seismic Risk of Siberia Territory

NASA Astrophysics Data System (ADS)

Seleznev, V. S.; Soloviev, V. M.; Emanov, A. F.

The outcomes of modern geophysical researches of the Geophysical Survey SB RAS, directed on study of geodynamic situation in large industrial and civil centers on the territory of Siberia with the purpose of an evaluation of seismic risk of territories and prediction of origin of extreme situations of natural and man-caused character, are pre- sented in the paper. First of all it concerns the testing and updating of a geoinformation system developed by Russian Emergency Ministry designed for calculations regarding the seismic hazard and response to distructive earthquakes. The GIS database contains the catalogues of earthquakes and faults, seismic zonation maps, vectorized city maps, information on industrial and housing fund, data on character of building and popula- tion in inhabited places etc. The geoinformation system allows to solve on a basis of probabilistic approaches the following problems: - estimating the earthquake impact, required forces, facilities and supplies for life-support of injured population; - deter- mining the consequences of failures on chemical and explosion-dangerous objects; - optimization problems on assurance technology of conduct of salvage operations. Using this computer program, the maps of earthquake risk have been constructed for several seismically dangerous regions of Siberia. These maps display the data on the probable amount of injured people and relative economic damage from an earthquake, which can occur in various sites of the territory according to the map of seismic zona- tion. The obtained maps have allowed determining places where the detailed seismo- logical observations should be arranged. Along with it on the territory of Siberia the wide-ranging investigations with use of new methods of evaluation of physical state of industrial and civil establishments (buildings and structures, hydroelectric power stations, bridges, dams, etc.), high-performance detailed electromagnetic researches of ground conditions of city territories, roads, runways, etc., studying of seismic con- dition in large industrial and civil centers and others.

Mapping environmental dimensions of dengue fever transmission risk in the Aburrá Valley, Colombia.

PubMed

Arboleda, Sair; Jaramillo-O, Nicolas; Peterson, A Townsend

2009-12-01

Dengue fever (DF) is endemic in Medellín, the second largest Colombian city, and surrounding municipalities. We used DF case and satellite environmental data to investigate conditions associated with suitable areas for DF occurrence in 2008 in three municipalities (Bello, Medellín and Itagüí). We develop spatially stratified tests of ecological niche models, and found generally good predictive ability, with all model tests yielding results significantly better than random expectations. We concluded that Bello and Medellín present ecological conditions somewhat different from, and more suitable for DF than, those of Itagüí. We suggest that areas predicted by our models as suitable for DF could be considered as at-risk, and could be used to guide campaigns for DF prevention in these municipalities.

Mapping Environmental Dimensions of Dengue Fever Transmission Risk in the Aburrá Valley, Colombia

PubMed Central

Arboleda, Sair; Nicolas, Jaramillo-O.; Peterson, A. Townsend

2009-01-01

Dengue fever (DF) is endemic in Medellín, the second largest Colombian city, and surrounding municipalities. We used DF case and satellite environmental data to investigate conditions associated with suitable areas for DF occurrence in 2008 in three municipalities (Bello, Medellín and Itagüí). We develop spatially stratified tests of ecological niche models, and found generally good predictive ability, with all model tests yielding results significantly better than random expectations. We concluded that Bello and Medellín present ecological conditions somewhat different from, and more suitable for DF than, those of Itagüí. We suggest that areas predicted by our models as suitable for DF could be considered as at-risk, and could be used to guide campaigns for DF prevention in these municipalities. PMID:20049244

The influence of uncertain map features on risk beliefs and perceived ambiguity for maps of modeled cancer risk from air pollution

PubMed Central

Myers, Jeffrey D.

2012-01-01

Maps are often used to convey information generated by models, for example, modeled cancer risk from air pollution. The concrete nature of images, such as maps, may convey more certainty than warranted for modeled information. Three map features were selected to communicate the uncertainty of modeled cancer risk: (a) map contours appeared in or out of focus, (b) one or three colors were used, and (c) a verbal-relative or numeric risk expression was used in the legend. Study aims were to assess how these features influenced risk beliefs and the ambiguity of risk beliefs at four assigned map locations that varied by risk level. We applied an integrated conceptual framework to conduct this full factorial experiment with 32 maps that varied by the three dichotomous features and four risk levels; 826 university students participated. Data was analyzed using structural equation modeling. Unfocused contours and the verbal-relative risk expression generated more ambiguity than their counterparts. Focused contours generated stronger risk beliefs for higher risk levels and weaker beliefs for lower risk levels. Number of colors had minimal influence. The magnitude of risk level, conveyed using incrementally darker shading, had a substantial dose-response influence on the strength of risk beliefs. Personal characteristics of prior beliefs and numeracy also had substantial influences. Bottom-up and top-down information processing suggest why iconic visual features of incremental shading and contour focus had the strongest visual influences on risk beliefs and ambiguity. Variations in contour focus and risk expression show promise for fostering appropriate levels of ambiguity. PMID:22985196

Validation of a short-term shoreline evolution model and coastal risk management implications. The case of the NW Portuguese coast (Ovar-Marinha Grande)

NASA Astrophysics Data System (ADS)

Cenci, Luca; Giuseppina Persichillo, Maria; Disperati, Leonardo; Oliveira, Eduardo R.; de Fátima Lopes Alves, Maria; Boni, Giorgio; Pulvirenti, Luca; Phillips, Mike

2015-04-01

Coastal zones are fragile and dynamic environments where environmental, economic and social aspects are interconnected. While these areas are often highly urbanised, they are especially vulnerable to natural hazards (e.g. storms, floods, erosion, storm surges). Hence, high risk affects people and goods in several coastal zones throughout the world. The recent storms that hit the European coasts (Hercules, Christian and Stephanie, among others) showed the high vulnerability of these territories. Integrated Coastal Management (ICM) deals with the sustainable development of coastal zones by taking into account the different aspects that affect them, including risks adaptation and mitigation. Accurate mapping of shoreline position through time and models to predict shoreline evolution play a fundamental role for coastal zone risk management. In this context, spaceborne remote sensing is fundamental because it provides synoptic and multitemporal information that allow the extraction of shorelines' proxies. These are stable coastal features (e.g. the vegetation lines, the foredune toe, etc.) that can be mapped instead of the proper shoreline, which is an extremely dynamic boundary. The use of different proxies may provide different evolutionary patterns for the same study area; therefore it is important to assess which is the most suitable, given the environmental characteristics of a specific area. In Portugal, the coastal stretch between Ovar and Marinha Grande is one of the greatest national challenges in terms of integrated management of resources and risks. This area is characterised by intense erosive processes that largely exceed the shoreline's retreat predictions made in the first Coastal Zone Management Plan, developed in 2000. The aim of this work was to assess the accuracy of a new model of shoreline evolution implemented in 2013 in order to check its robustness for short-term predictions. The method exploited the potentialities of the Landsat archive; selected images, ranging from 1984 to 2011, were processed in order to extract two different vegetation-related proxies (i.e. the Stable Dune Vegetation Line and the Seaward Dune Vegetation Line) and to quantify their uncertainty. The proxies' rates of advance/retreat were calculated by exploiting the Digital Shoreline Analysis System (DSAS), an ESRI ArcGIS software application. Subsequently, it was used a recent Landsat 8 image to extract the 2014 observed shoreline proxies' positions. The latter were compared with the ones predicted for the same year adopting the rates previously obtained from DSAS. Statistical analyses based on the differences between predicted and observed values were calculated in order to i) study the coastal evolution of the study area, ii) predict short-term scenarios (3 years), iii) assess the predictions accuracy and iv) identify the more reliable proxy for the study area. Finally, results were interpreted in terms of coastal planning and management perspectives. This was achieved by taking into account the official coastal risk management framework implemented in 2012 to promote a flexible, integrated and adaptive approach. This new generation of Coastal Zone Master Plans had inspired this research because it reinforced the need for mechanisms of risks prevention and environmental safeguarding.

An assessment of discriminatory power of office blood pressure measurements in predicting optimal ambulatory blood pressure control in people with type 2 diabetes.

PubMed

Kengne, Andre Pascal; Libend, Christelle Nong; Dzudie, Anastase; Menanga, Alain; Dehayem, Mesmin Yefou; Kingue, Samuel; Sobngwi, Eugene

2014-01-01

Ambulatory blood pressure (BP) measurements (ABPM) predict health outcomes better than office BP, and are recommended for assessing BP control, particularly in high-risk patients. We assessed the performance of office BP in predicting optimal ambulatory BP control in sub-Saharan Africans with type 2 diabetes (T2DM). Participants were a random sample of 51 T2DM patients (25 men) drug-treated for hypertension, receiving care in a referral diabetes clinic in Yaounde, Cameroon. A quality control group included 46 non-diabetic individuals with hypertension. Targets for BP control were systolic (and diastolic) BP. Mean age of diabetic participants was 60 years (standard deviation: 10) and median duration of diabetes was 6 years (min-max: 0-29). Correlation coefficients between each office-based variable and the 24-h ABPM equivalent (diabetic vs. non-diabetic participants) were 0.571 and 0.601 for systolic (SBP), 0.520 and 0.539 for diastolic (DBP), 0.631 and 0.549 for pulse pressure (PP), and 0.522 and 0.583 for mean arterial pressure (MAP). The c-statistic for the prediction of optimal ambulatory control from office-BP in diabetic participants was 0.717 for SBP, 0.494 for DBP, 0.712 for PP, 0.582 for MAP, and 0.721 for either SBP + DBP or PP + MAP. Equivalents in diabetes-free participants were 0.805, 0.763, 0.695, 0.801 and 0.813. Office DBP was ineffective in discriminating optimal ambulatory BP control in diabetic patients, and did not improve predictions based on office SBP alone. Targeting ABPM to those T2DM patients who are already at optimal office-based SBP would likely be more cost effective in this setting.

Estimating drought risk across Europe from reported drought impacts, hazard indicators and vulnerability factors

NASA Astrophysics Data System (ADS)

Blauhut, V.; Stahl, K.; Stagge, J. H.; Tallaksen, L. M.; De Stefano, L.; Vogt, J.

2015-12-01

Drought is one of the most costly natural hazards in Europe. Due to its complexity, drought risk, the combination of the natural hazard and societal vulnerability, is difficult to define and challenging to detect and predict, as the impacts of drought are very diverse, covering the breadth of socioeconomic and environmental systems. Pan-European maps of drought risk could inform the elaboration of guidelines and policies to address its documented severity and impact across borders. This work (1) tests the capability of commonly applied hazard indicators and vulnerability factors to predict annual drought impact occurrence for different sectors and macro regions in Europe and (2) combines information on past drought impacts, drought hazard indicators, and vulnerability factors into estimates of drought risk at the pan-European scale. This "hybrid approach" bridges the gap between traditional vulnerability assessment and probabilistic impact forecast in a statistical modelling framework. Multivariable logistic regression was applied to predict the likelihood of impact occurrence on an annual basis for particular impact categories and European macro regions. The results indicate sector- and macro region specific sensitivities of hazard indicators, with the Standardised Precipitation Evapotranspiration Index for a twelve month aggregation period (SPEI-12) as the overall best hazard predictor. Vulnerability factors have only limited ability to predict drought impacts as single predictor, with information about landuse and water resources as best vulnerability-based predictors. (3) The application of the "hybrid approach" revealed strong regional (NUTS combo level) and sector specific differences in drought risk across Europe. The majority of best predictor combinations rely on a combination of SPEI for shorter and longer aggregation periods, and a combination of information on landuse and water resources. The added value of integrating regional vulnerability information with drought risk prediction could be proven. Thus, the study contributes to the overall understanding of drivers of drought impacts, current practice of drought indicators selection for specific application, and drought risk assessment.

A simple methodology to produce flood risk maps consistent with FEMA's base flood elevation maps: Implementation and validation over the entire contiguous United States

NASA Astrophysics Data System (ADS)

Goteti, G.; Kaheil, Y. H.; Katz, B. G.; Li, S.; Lohmann, D.

2011-12-01

In the United States, government agencies as well as the National Flood Insurance Program (NFIP) use flood inundation maps associated with the 100-year return period (base flood elevation, BFE), produced by the Federal Emergency Management Agency (FEMA), as the basis for flood insurance. A credibility check of the flood risk hydraulic models, often employed by insurance companies, is their ability to reasonably reproduce FEMA's BFE maps. We present results from the implementation of a flood modeling methodology aimed towards reproducing FEMA's BFE maps at a very fine spatial resolution using a computationally parsimonious, yet robust, hydraulic model. The hydraulic model used in this study has two components: one for simulating flooding of the river channel and adjacent floodplain, and the other for simulating flooding in the remainder of the catchment. The first component is based on a 1-D wave propagation model, while the second component is based on a 2-D diffusive wave model. The 1-D component captures the flooding from large-scale river transport (including upstream effects), while the 2-D component captures the flooding from local rainfall. The study domain consists of the contiguous United States, hydrologically subdivided into catchments averaging about 500 km2 in area, at a spatial resolution of 30 meters. Using historical daily precipitation data from the Climate Prediction Center (CPC), the precipitation associated with the 100-year return period event was computed for each catchment and was input to the hydraulic model. Flood extent from the FEMA BFE maps is reasonably replicated by the 1-D component of the model (riverine flooding). FEMA's BFE maps only represent the riverine flooding component and are unavailable for many regions of the USA. However, this modeling methodology (1-D and 2-D components together) covers the entire contiguous USA. This study is part of a larger modeling effort from Risk Management Solutions° (RMS) to estimate flood risk associated with extreme precipitation events in the USA. Towards this greater objective, state-of-the-art models of flood hazard and stochastic precipitation are being implemented over the contiguous United States. Results from the successful implementation of the modeling methodology will be presented.

Mapping Physiological Suitability Limits for Malaria in Africa Under Climate Change.

PubMed

Ryan, Sadie J; McNally, Amy; Johnson, Leah R; Mordecai, Erin A; Ben-Horin, Tal; Paaijmans, Krijn; Lafferty, Kevin D

2015-12-01

We mapped current and future temperature suitability for malaria transmission in Africa using a published model that incorporates nonlinear physiological responses to temperature of the mosquito vector Anopheles gambiae and the malaria parasite Plasmodium falciparum. We found that a larger area of Africa currently experiences the ideal temperature for transmission than previously supposed. Under future climate projections, we predicted a modest increase in the overall area suitable for malaria transmission, but a net decrease in the most suitable area. Combined with human population density projections, our maps suggest that areas with temperatures suitable for year-round, highest-risk transmission will shift from coastal West Africa to the Albertine Rift between the Democratic Republic of Congo and Uganda, whereas areas with seasonal transmission suitability will shift toward sub-Saharan coastal areas. Mapping temperature suitability places important bounds on malaria transmissibility and, along with local level demographic, socioeconomic, and ecological factors, can indicate where resources may be best spent on malaria control.

Integrating Socioeconomic and Earth Science Data Using Geobrowsers and Web Services: A Demonstration

NASA Astrophysics Data System (ADS)

Schumacher, J. A.; Yetman, G. G.

2007-12-01

The societal benefit areas identified as the focus for the Global Earth Observing System of Systems (GEOSS) 10- year implementation plan are an indicator of the importance of integrating socioeconomic data with earth science data to support decision makers. To aid this integration, CIESIN is delivering its global and U.S. demographic data to commercial and open source Geobrowsers and providing open standards based services for data access. Currently, data on population distribution, poverty, and detailed census data for the U.S. are available for visualization and access in Google Earth, NASA World Wind, and a browser-based 2-dimensional mapping client. The mapping client allows for the creation of web map documents that pull together layers from distributed servers and can be saved and shared. Visualization tools with Geobrowsers, user-driven map creation and sharing via browser-based clients, and a prototype for characterizing populations at risk to predicted precipitation deficits will be demonstrated.

Mapping physiological suitability limits for malaria in Africa under climate change

USGS Publications Warehouse

Ryan, Sadie J.; McNally, Amy; Johnson, Leah R.; Mordecai, Erin A.; Ben-Horin, Tal; Paaijmans, Krijn P.; Lafferty, Kevin D.

2015-01-01

We mapped current and future temperature suitability for malaria transmission in Africa using a published model that incorporates nonlinear physiological responses to temperature of the mosquito vector Anopheles gambiae and the malaria parasite Plasmodium falciparum. We found that a larger area of Africa currently experiences the ideal temperature for transmission than previously supposed. Under future climate projections, we predicted a modest increase in the overall area suitable for malaria transmission, but a net decrease in the most suitable area. Combined with human population density projections, our maps suggest that areas with temperatures suitable for year-round, highest-risk transmission will shift from coastal West Africa to the Albertine Rift between the Democratic Republic of Congo and Uganda, whereas areas with seasonal transmission suitability will shift toward sub-Saharan coastal areas. Mapping temperature suitability places important bounds on malaria transmissibility and, along with local level demographic, socioeconomic, and ecological factors, can indicate where resources may be best spent on malaria control.

An interoperable standard system for the automatic generation and publication of the fire risk maps based on Fire Weather Index (FWI)

NASA Astrophysics Data System (ADS)

Julià Selvas, Núria; Ninyerola Casals, Miquel

2015-04-01

It has been implemented an automatic system to predict the fire risk in the Principality of Andorra, a small country located in the eastern Pyrenees mountain range, bordered by Catalonia and France, due to its location, his landscape is a set of a rugged mountains with an average elevation around 2000 meters. The system is based on the Fire Weather Index (FWI) that consists on different components, each one, measuring a different aspect of the fire danger calculated by the values of the weather variables at midday. CENMA (Centre d'Estudis de la Neu i de la Muntanya d'Andorra) has a network around 10 automatic meteorological stations, located in different places, peeks and valleys, that measure weather data like relative humidity, wind direction and speed, surface temperature, rainfall and snow cover every ten minutes; this data is sent daily and automatically to the system implemented that will be processed in the way to filter incorrect measurements and to homogenizer measurement units. Then this data is used to calculate all components of the FWI at midday and for the level of each station, creating a database with the values of the homogeneous measurements and the FWI components for each weather station. In order to extend and model this data to all Andorran territory and to obtain a continuous map, an interpolation method based on a multiple regression with spline residual interpolation has been implemented. This interpolation considerer the FWI data as well as other relevant predictors such as latitude, altitude, global solar radiation and sea distance. The obtained values (maps) are validated using a cross-validation leave-one-out method. The discrete and continuous maps are rendered in tiled raster maps and published in a web portal conform to Web Map Service (WMS) Open Geospatial Consortium (OGC) standard. Metadata and other reference maps (fuel maps, topographic maps, etc) are also available from this geoportal.

Quantifying soil burn severity for hydrologic modeling to assess post-fire effects on sediment delivery

NASA Astrophysics Data System (ADS)

Dobre, Mariana; Brooks, Erin; Lew, Roger; Kolden, Crystal; Quinn, Dylan; Elliot, William; Robichaud, Pete

2017-04-01

Soil erosion is a secondary fire effect with great implications for many ecosystem resources. Depending on the burn severity, topography, and the weather immediately after the fire, soil erosion can impact municipal water supplies, degrade water quality, and reduce reservoirs' storage capacity. Scientists and managers use field and remotely sensed data to quickly assess post-fire burn severity in ecologically-sensitive areas. From these assessments, mitigation activities are implemented to minimize post-fire flood and soil erosion and to facilitate post-fire vegetation recovery. Alternatively, land managers can use fire behavior and spread models (e.g. FlamMap, FARSITE, FOFEM, or CONSUME) to identify sensitive areas a priori, and apply strategies such as fuel reduction treatments to proactively minimize the risk of wildfire spread and increased burn severity. There is a growing interest in linking fire behavior and spread models with hydrology-based soil erosion models to provide site-specific assessment of mitigation treatments on post-fire runoff and erosion. The challenge remains, however, that many burn severity mapping and modeling products quantify vegetation loss rather than measuring soil burn severity. Wildfire burn severity is spatially heterogeneous and depends on the pre-fire vegetation cover, fuel load, topography, and weather. Severities also differ depending on the variable of interest (e.g. soil, vegetation). In the United States, Burned Area Reflectance Classification (BARC) maps, derived from Landsat satellite images, are used as an initial burn severity assessment. BARC maps are classified from either a Normalized Burn Ratio (NBR) or differenced Normalized Burned Ratio (dNBR) scene into four classes (Unburned, Low, Moderate, and High severity). The development of soil burn severity maps requires further manual field validation efforts to transform the BARC maps into a product more applicable for post-fire soil rehabilitation activities. Alternative spectral indices and modeled output approaches may prove better predictors of soil burn severity and hydrologic effects, but these have not yet been assessed in a model framework. In this project we compare field-verified soil burn severity maps to satellite-derived and modeled burn severity maps. We quantify the extent to which there are systematic differences in these mapping products. We then use the Water Erosion Prediction Project (WEPP) hydrologic soil erosion model to assess sediment delivery from these fires using the predicted and observed soil burn severity maps. Finally, we discuss differences in observed and predicted soil burn severity maps and application to watersheds in the Pacific Northwest to estimate post-fire sediment delivery.

HSP60, oxidative stress parameters and cardiometabolic risk markers in hypertensive and normotensive Slovak females.

PubMed

Kuka, P; Bucova, M; Penz, P; Paulovicova, E; Blazicek, P; Atalay, M; Lietava, J

2010-01-01

The aim of our study was to analyse the relationships between hypertension, HSP60, oxidative stress, lipid profile and cardiometabolic risk in 126 females with arterial hypertension (AHW) and 39 normotensive females (AH-). Females with AH+ were significantly older and more frequently suffered from ischemic heart disease, angina pectoris, prior MI, abdominal obesity, obesity, metabolic syndrome and diabetes mellitus. On the other hand, normotensive females smoked significantly more often. Plasma levels of HSP60 were similar in both AH+ and AH- groups. However, hypertensive females exhibited almost two times lower values of oxidative glutation and lower levels of carbonyl protein, but significantly higher levels of homocysteine. In normotensive females, the total glutathione was the only parameter predicting females with the plasma level of HSP60 = 60 ng/ml. The independent predictors in hypertensive females were angina pectoris, triglycerides and the mean arterial pressure (MAP). MAP had also a borderline significance in normotensive females suggesting an association between HSP60 and blood pressure. MAP formed a J shaped curve with HSP60. Results suggest the association of blood pressure and heart shock protein 60 Kda in form of the J curve (Tab. 11, Fig. 3, Ref. 29).

Climate Risk Modelling of Balsam Woolly Adelgid Damage Severity in Subalpine Fir Stands of Western North America.

PubMed

Hrinkevich, Kathryn H; Progar, Robert A; Shaw, David C

2016-01-01

The balsam woolly adelgid (Adelges piceae (Ratzeburg) (Homoptera: Adelgidae)) (BWA) is a nonnative, invasive insect that threatens Abies species throughout North America. It is well established in the Pacific Northwest, but continues to move eastward through Idaho and into Montana and potentially threatens subalpine fir to the south in the central and southern Rocky Mountains. We developed a climatic risk model and map that predicts BWA impacts to subalpine fir using a two-step process. Using 30-year monthly climate normals from sites with quantitatively derived BWA damage severity index values, we built a regression model that significantly explained insect damage. The sites were grouped into two distinct damage categories (high damage and mortality versus little or no mortality and low damage) and the model estimates for each group were used to designate distinct value ranges for four climatic risk categories: minimal, low, moderate, and high. We then calculated model estimates for each cell of a 4-kilometer resolution climate raster and mapped the risk categories over the entire range of subalpine fir in the western United States. The spatial variation of risk classes indicates a gradient of climatic susceptibility generally decreasing from the Olympic Peninsula in Washington and the Cascade Range in Oregon and Washington moving eastward, with the exception of some high risk areas in northern Idaho and western Montana. There is also a pattern of decreasing climatic susceptibility from north to south in the Rocky Mountains. Our study provides an initial step for modeling the relationship between climate and BWA damage severity across the range of subalpine fir. We showed that September minimum temperature and a metric calculated as the maximum May temperature divided by total May precipitation were the best climatic predictors of BWA severity. Although winter cold temperatures and summer heat have been shown to influence BWA impacts in other locations, these variables were not as predictive as spring and fall conditions in the Pacific Northwest.

A multicriteria framework for producing local, regional, and national insect and disease risk maps

Treesearch

Frank J. Jr. Krist; Frank J. Sapio

2010-01-01

The construction of the 2006 National Insect and Disease Risk Map, compiled by the USDA Forest Service, State and Private Forestry Area, Forest Health Protection Unit, resulted in the development of a GIS-based, multicriteria approach for insect and disease risk mapping that can account for regional variations in forest health concerns and threats. This risk mapping...

Risk maps for targeting exotic plant pest detection programs in the United States

Treesearch

R.D. Magarey; D.M. Borchert; J.S. Engle; M Garcia-Colunga; Frank H. Koch; et al

2011-01-01

In the United States, pest risk maps are used by the Cooperative Agricultural Pest Survey for spatial and temporal targeting of exotic plant pest detection programs. Methods are described to create standardized host distribution, climate and pathway risk maps for the top nationally ranked exotic pest targets. Two examples are provided to illustrate the risk mapping...

VTE Risk assessment - a prognostic Model: BATER Cohort Study of young women.

PubMed

Heinemann, Lothar Aj; Dominh, Thai; Assmann, Anita; Schramm, Wolfgang; Schürmann, Rolf; Hilpert, Jan; Spannagl, Michael

2005-04-18

BACKGROUND: Community-based cohort studies are not available that evaluated the predictive power of both clinical and genetic risk factors for venous thromboembolism (VTE). There is, however, clinical need to forecast the likelihood of future occurrence of VTE, at least qualitatively, to support decisions about intensity of diagnostic or preventive measures. MATERIALS AND METHODS: A 10-year observation period of the Bavarian Thromboembolic Risk (BATER) study, a cohort study of 4337 women (18-55 years), was used to develop a predictive model of VTE based on clinical and genetic variables at baseline (1993). The objective was to prepare a probabilistic scheme that discriminates women with virtually no VTE risk from those at higher levels of absolute VTE risk in the foreseeable future. A multivariate analysis determined which variables at baseline were the best predictors of a future VTE event, provided a ranking according to the predictive power, and permitted to design a simple graphic scheme to assess the individual VTE risk using five predictor variables. RESULTS: Thirty-four new confirmed VTEs occurred during the observation period of over 32,000 women-years (WYs). A model was developed mainly based on clinical information (personal history of previous VTE and family history of VTE, age, BMI) and one composite genetic risk markers (combining Factor V Leiden and Prothrombin G20210A Mutation). Four levels of increasing VTE risk were arbitrarily defined to map the prevalence in the study population: No/low risk of VTE (61.3%), moderate risk (21.1%), high risk (6.0%), very high risk of future VTE (0.9%). In 10.6% of the population the risk assessment was not possible due to lacking VTE cases. The average incidence rates for VTE in these four levels were: 4.1, 12.3, 47.2, and 170.5 per 104 WYs for no, moderate, high, and very high risk, respectively. CONCLUSION: Our prognostic tool - containing clinical information (and if available also genetic data) - seems to be worthwhile testing in medical practice in order to confirm or refute the positive findings of this study. Our cohort study will be continued to include more VTE cases and to increase predictive value of the model.

Malaria Disease Mapping in Malaysia based on Besag-York-Mollie (BYM) Model

NASA Astrophysics Data System (ADS)

Azah Samat, Nor; Mey, Liew Wan

2017-09-01

Disease mapping is the visual representation of the geographical distribution which give an overview info about the incidence of disease within a population through spatial epidemiology data. Based on the result of map, it helps in monitoring and planning resource needs at all levels of health care and designing appropriate interventions, tailored towards areas that deserve closer scrutiny or communities that lead to further investigations to identify important risk factors. Therefore, the choice of statistical model used for relative risk estimation is important because production of disease risk map relies on the model used. This paper proposes Besag-York-Mollie (BYM) model to estimate the relative risk for Malaria in Malaysia. The analysis involved using the number of Malaria cases that obtained from the Ministry of Health Malaysia. The outcomes of analysis are displayed through graph and map, including Malaria disease risk map that constructed according to the estimation of relative risk. The distribution of high and low risk areas of Malaria disease occurrences for all states in Malaysia can be identified in the risk map.

Comparison between urine albumin-to-creatinine ratio and urine protein dipstick testing for prevalence and ability to predict the risk for chronic kidney disease in the general population (Iwate-KENCO study): a prospective community-based cohort study.

PubMed

Koeda, Yorihiko; Tanaka, Fumitaka; Segawa, Toshie; Ohta, Mutsuko; Ohsawa, Masaki; Tanno, Kozo; Makita, Shinji; Ishibashi, Yasuhiro; Itai, Kazuyoshi; Omama, Shin-Ichi; Onoda, Toshiyuki; Sakata, Kiyomi; Ogasawara, Kuniaki; Okayama, Akira; Nakamura, Motoyuki

2016-05-12

This study compared the combination of estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) vs. eGFR and urine protein reagent strip testing to determine chronic kidney disease (CKD) prevalence, and each method's ability to predict the risk for cardiovascular events in the general Japanese population. Baseline data including eGFR, UACR, and urine dipstick tests were obtained from the general population (n = 22 975). Dipstick test results (negative, trace, positive) were allocated to three levels of UACR (<30, 30-300, >300), respectively. In accordance with Kidney Disease Improving Global Outcomes CKD prognosis heat mapping, the cohort was classified into four risk grades (green: grade 1; yellow: grade 2; orange: grade 3, red: grade 4) based on baseline eGFR and UACR levels or dipstick tests. During the mean follow-up period of 5.6 years, 708 new onset cardiovascular events were recorded. For CKD identified by eGFR and dipstick testing (dipstick test ≥ trace and eGFR <60 mL/min/1.73 m(2)), the incidence of CKD was found to be 9 % in the general population. In comparison to non-CKD (grade 1), although cardiovascular risk was significantly higher in risk grades ≥3 (relative risk (RR) = 1.70; 95 % CI: 1.28-2.26), risk predictive ability was not significant in risk grade 2 (RR = 1.20; 95 % CI: 0.95-1.52). When CKD was defined by eGFR and UACR (UACR ≥30 mg/g Cr and eGFR <60 mL/min/1.73 m(2)), prevalence was found to be 29 %. Predictive ability in risk grade 2 (RR = 1.41; 95 % CI: 1.19-1.66) and risk grade ≥3 (RR = 1.76; 95 % CI: 1.37-2.28) were both significantly greater than for non-CKD. Reclassification analysis showed a significant improvement in risk predictive abilities when CKD risk grading was based on UACR rather than on dipstick testing in this population (p < 0.001). Although prevalence of CKD was higher when detected by UACR rather than urine dipstick testing, the predictive ability for cardiovascular events from UACR-based risk grading was superior to that of dipstick-based risk grading in the general population.

MODIS imagery improves pest risk assessment: A case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA

USGS Publications Warehouse

Lestina, Jordan; Cook, Maxwell; Kumar, Sunil; Morisette, Jeffrey T.; Ode, Paul J.; Peirs, Frank

2016-01-01

Wheat stem sawfly (Cephus cinctus Norton, Hymenoptera: Cephidae) has long been a significant insect pest of spring, and more recently, winter wheat in the northern Great Plains. Wheat stem sawfly was first observed infesting winter wheat in Colorado in 2010 and, subsequently, has spread rapidly throughout wheat production regions of the state. Here, we used maximum entropy modeling (MaxEnt) to generate habitat suitability maps in order to predict the risk of crop damage as this species spreads throughout the winter wheat-growing regions of Colorado. We identified environmental variables that influence the current distribution of wheat stem sawfly in the state and evaluated whether remotely sensed variables improved model performance. We used presence localities of C. cinctus and climatic, topographic, soils, and normalized difference vegetation index and enhanced vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery as environmental variables. All models had high performance in that they were successful in predicting suitable habitat for C. cinctus in its current distribution in eastern Colorado. The enhanced vegetation index for the month of April improved model performance and was identified as a top contributor to MaxEnt model. Soil clay percent at 0–5 cm, temperature seasonality, and precipitation seasonality were also associated with C. cinctus distribution in Colorado. The improved model performance resulting from integrating vegetation indices in our study demonstrates the ability of remote sensing technologies to enhance species distribution modeling. These risk maps generated can assist managers in planning control measures for current infestations and assess the future risk of C. cinctus establishment in currently uninfested regions.

Predicting and mapping potential Whooping Crane stopover habitat to guide site selection for wind energy projects.

PubMed

Belaire, J Amy; Kreakie, Betty J; Keitt, Timothy; Minor, Emily

2014-04-01

Migratory stopover habitats are often not part of planning for conservation or new development projects. We identified potential stopover habitats within an avian migratory flyway and demonstrated how this information can guide the site-selection process for new development. We used the random forests modeling approach to map the distribution of predicted stopover habitat for the Whooping Crane (Grus americana), an endangered species whose migratory flyway overlaps with an area where wind energy development is expected to become increasingly important. We then used this information to identify areas for potential wind power development in a U.S. state within the flyway (Nebraska) that minimize conflicts between Whooping Crane stopover habitat and the development of clean, renewable energy sources. Up to 54% of our study area was predicted to be unsuitable as Whooping Crane stopover habitat and could be considered relatively low risk for conflicts between Whooping Cranes and wind energy development. We suggest that this type of analysis be incorporated into the habitat conservation planning process in areas where incidental take permits are being considered for Whooping Cranes or other species of concern. Field surveys should always be conducted prior to construction to verify model predictions and understand baseline conditions. © 2013 Society for Conservation Biology.

Fundamental niche prediction of the pathogenic yeasts Cryptococcus neoformans and Cryptococcus gattii in Europe.

PubMed

Cogliati, Massimo; Puccianti, Erika; Montagna, Maria T; De Donno, Antonella; Susever, Serdar; Ergin, Cagri; Velegraki, Aristea; Ellabib, Mohamed S; Nardoni, Simona; Macci, Cristina; Trovato, Laura; Dipineto, Ludovico; Rickerts, Volker; Akcaglar, Sevim; Mlinaric-Missoni, Emilija; Bertout, Sebastien; Vencà, Ana C F; Sampaio, Ana C; Criseo, Giuseppe; Ranque, Stéphane; Çerikçioğlu, Nilgün; Marchese, Anna; Vezzulli, Luigi; Ilkit, Macit; Desnos-Ollivier, Marie; Pasquale, Vincenzo; Polacheck, Itzhack; Scopa, Antonio; Meyer, Wieland; Ferreira-Paim, Kennio; Hagen, Ferry; Boekhout, Teun; Dromer, Françoise; Varma, Ashok; Kwon-Chung, Kyung J; Inácio, Joäo; Colom, Maria F

2017-10-01

Fundamental niche prediction of Cryptococcus neoformans and Cryptococcus gattii in Europe is an important tool to understand where these pathogenic yeasts have a high probability to survive in the environment and therefore to identify the areas with high risk of infection. In this study, occurrence data for C. neoformans and C. gattii were compared by MaxEnt software with several bioclimatic conditions as well as with soil characteristics and land use. The results showed that C. gattii distribution can be predicted with high probability along the Mediterranean coast. The analysis of variables showed that its distribution is limited by low temperatures during the coldest season, and by heavy precipitations in the driest season. C. neoformans var. grubii is able to colonize the same areas of C. gattii but is more tolerant to cold winter temperatures and summer precipitations. In contrast, the C. neoformans var. neoformans map was completely different. The best conditions for its survival were displayed in sub-continental areas and not along the Mediterranean coasts. In conclusion, we produced for the first time detailed prediction maps of the species and varieties of the C. neoformans and C. gattii species complex in Europe and Mediterranean area. © 2017 Society for Applied Microbiology and John Wiley & Sons Ltd.

Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales

PubMed Central

Tack, Jason D.; Fedy, Bradley C.

2015-01-01

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development. PMID:26262876

Landscapes for energy and wildlife: conservation prioritization for golden eagles across large spatial scales

USGS Publications Warehouse

Tack, Jason D.; Fedy, Bradley C.

2015-01-01

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.

Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

NASA Astrophysics Data System (ADS)

Madhu, B.; Ashok, N. C.; Balasubramanian, S.

2014-11-01

Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

Use of Preoperative Functional MRI to Predict Verbal Memory Decline After Temporal Lobe Epilepsy Surgery

PubMed Central

Binder, Jeffrey R.; Sabsevitz, David S.; Swanson, Sara J.; Hammeke, Thomas A.; Raghavan, Manoj; Mueller, Wade M.

2010-01-01

Purpose Verbal memory decline is a frequent complication of left anterior temporal lobectomy (L-ATL). The goal of this study was to determine whether preoperative language mapping using functional magnetic resonance imaging (fMRI) is useful for predicting which patients are likely to experience verbal memory decline after L-ATL. Methods Sixty L-ATL patients underwent preoperative language mapping with fMRI, preoperative intracarotid amobarbital (Wada) testing for language and memory lateralization, and pre- and postoperative neuropsychological testing. Demographic, historical, neuropsychological, and imaging variables were examined for their ability to predict pre- to postoperative memory change. Results Verbal memory decline occurred in over 30% of patients. Good preoperative performance, late age at onset of epilepsy, left dominance on fMRI, and left dominance on the Wada test were each predictive of memory decline. Preoperative performance and age at onset together accounted for roughly 50% of the variance in memory outcome (p < .001), and fMRI explained an additional 10% of this variance (p ≤ .003). Neither Wada memory asymmetry nor Wada language asymmetry added additional predictive power beyond these noninvasive measures. Discussion Preoperative fMRI is useful for identifying patients at high risk for verbal memory decline prior to L-ATL surgery. Lateralization of language is correlated with lateralization of verbal memory, whereas Wada memory testing is either insufficiently reliable or insufficiently material-specific to accurately localize verbal memory processes. PMID:18435753

Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales.

PubMed

Tack, Jason D; Fedy, Bradley C

2015-01-01

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.

Estimation of the Forest Fire Risk in Indonesia based on Satellite Remote Sensing

NASA Astrophysics Data System (ADS)

Suzuki, H.; Takahashi, Y.; Hashimoto, A.; Akita, M.; Hasegawa, Y.; Ogino, Y.; Naruse, N.; Takahashi, Y.

2016-12-01

To minimize forest fires in tropical area is extremely important, because the fire has a large impact on global warming, biodiversity, and human society. In the previous study, Shimada and Ishibashi monitored the ground-water lever from the value of Normalized Difference Vegetation Index (NDVI) obtained in Kalimantan Island to predict where the forest fires will happen. We have developed a method to map the forest fire risk by calculating the value of Modified Soil Adjusted Vegetation Index 2 (MSAVI2). Moreover, we investigated the relation between the distance from a road as an artificial factor and the occurrence of the fire.First, calculating the MSAVI2 from Landsat 7 and 8 images of August, 2015 around Martapura in South Sumatra, Indonesia, we mapped the area where the plants were stressed. Next, we checked the degrees of matching between the area of low MSAVI2 and the forest fire points.As a result, half of the fires happened in the area having the MSAVI2 values of 0.20 to 0.35. When we focused on only the area which is over 5 kilometers far from a road, the degrees of matching became higher; it rose up to 62 percent.Those results indicate that the fire risks relate to the dry area calculated as low MSAVI2 in the case with less human activities. We need to consider an effect of artificial factors to estimate the whole risk of forest fire.In conclusion, the map of forest fire risk by calculating the value of MSAVI2 is applicable to an area with less artificial factor, while we have to take the effect of artificial fire factor into the consideration.

Mapping microbial ecosystems and spoilage-gene flow in breweries highlights patterns of contamination and resistance

PubMed Central

Bokulich, Nicholas A; Bergsveinson, Jordyn; Ziola, Barry; Mills, David A

2015-01-01

Distinct microbial ecosystems have evolved to meet the challenges of indoor environments, shaping the microbial communities that interact most with modern human activities. Microbial transmission in food-processing facilities has an enormous impact on the qualities and healthfulness of foods, beneficially or detrimentally interacting with food products. To explore modes of microbial transmission and spoilage-gene frequency in a commercial food-production scenario, we profiled hop-resistance gene frequencies and bacterial and fungal communities in a brewery. We employed a Bayesian approach for predicting routes of contamination, revealing critical control points for microbial management. Physically mapping microbial populations over time illustrates patterns of dispersal and identifies potential contaminant reservoirs within this environment. Habitual exposure to beer is associated with increased abundance of spoilage genes, predicting greater contamination risk. Elucidating the genetic landscapes of indoor environments poses important practical implications for food-production systems and these concepts are translatable to other built environments. DOI: http://dx.doi.org/10.7554/eLife.04634.001 PMID:25756611

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

PubMed Central

Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. PMID:28114334

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PubMed

Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

Using predictive analytics and big data to optimize pharmaceutical outcomes.

PubMed

Hernandez, Inmaculada; Zhang, Yuting

2017-09-15

The steps involved, the resources needed, and the challenges associated with applying predictive analytics in healthcare are described, with a review of successful applications of predictive analytics in implementing population health management interventions that target medication-related patient outcomes. In healthcare, the term big data typically refers to large quantities of electronic health record, administrative claims, and clinical trial data as well as data collected from smartphone applications, wearable devices, social media, and personal genomics services; predictive analytics refers to innovative methods of analysis developed to overcome challenges associated with big data, including a variety of statistical techniques ranging from predictive modeling to machine learning to data mining. Predictive analytics using big data have been applied successfully in several areas of medication management, such as in the identification of complex patients or those at highest risk for medication noncompliance or adverse effects. Because predictive analytics can be used in predicting different outcomes, they can provide pharmacists with a better understanding of the risks for specific medication-related problems that each patient faces. This information will enable pharmacists to deliver interventions tailored to patients' needs. In order to take full advantage of these benefits, however, clinicians will have to understand the basics of big data and predictive analytics. Predictive analytics that leverage big data will become an indispensable tool for clinicians in mapping interventions and improving patient outcomes. Copyright © 2017 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Hemodynamic variables and progression of acute kidney injury in critically ill patients with severe sepsis: data from the prospective observational FINNAKI study

PubMed Central

2013-01-01

Introduction Knowledge of the association of hemodynamics with progression of septic acute kidney injury (AKI) is limited. However, some recent data suggest that mean arterial pressure (MAP) exceeding current guidelines (60–65 mmHg) may be needed to prevent AKI. We hypothesized that higher MAP during the first 24 hours in the intensive care unit (ICU), would be associated with a lower risk of progression of AKI in patients with severe sepsis. Methods We identified 423 patients with severe sepsis and electronically recorded continuous hemodynamic data in the prospective observational FINNAKI study. The primary endpoint was progression of AKI within the first 5 days of ICU admission defined as new onset or worsening of AKI by the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. We evaluated the association of hemodynamic variables with this endpoint. We included 53724 10-minute medians of MAP in the analysis. We analysed the ability of time-adjusted MAP to predict progression of AKI by receiver operating characteristic (ROC) analysis. Results Of 423 patients, 153 (36.2%) had progression of AKI. Patients with progression of AKI had significantly lower time-adjusted MAP, 74.4 mmHg [68.3-80.8], than those without progression, 78.6 mmHg [72.9-85.4], P < 0.001. A cut-off value of 73 mmHg for time-adjusted MAP best predicted the progression of AKI. Chronic kidney disease, higher lactate, higher dose of furosemide, use of dobutamine and time-adjusted MAP below 73 mmHg were independent predictors of progression of AKI. Conclusions The findings of this large prospective multicenter observational study suggest that hypotensive episodes (MAP under 73 mmHg) are associated with progression of AKI in critically ill patients with severe sepsis. PMID:24330815

Wild Fire Risk Map in the Eastern Steppe of Mongolia Using Spatial Multi-Criteria Analysis

NASA Astrophysics Data System (ADS)

Nasanbat, Elbegjargal; Lkhamjav, Ochirkhuyag

2016-06-01

Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.

Progress in diode-pumped alexandrite lasers as a new resource for future space lidar missions

NASA Astrophysics Data System (ADS)

Damzen, M. J.; Thomas, G. M.; Teppitaksak, A.; Minassian, A.

2017-11-01

Satellite-based remote sensing using laser-based lidar techniques provides a powerful tool for global 3-D mapping of atmospheric species (e.g. CO2, ozone, clouds, aerosols), physical attributes of the atmosphere (e.g. temperature, wind speed), and spectral indicators of Earth features (e.g. vegetation, water). Such information provides a valuable source for weather prediction, understanding of climate change, atmospheric science and health of the Earth eco-system. Similarly, laser-based altimetry can provide high precision ground topography mapping and more complex 3-D mapping (e.g. canopy height profiling). The lidar technique requires use of cutting-edge laser technologies and engineered designs that are capable of enduring the space environment over the mission lifetime. The laser must operate with suitably high electrical-to-optical efficiency and risk reduction strategy adopted to mitigate against laser failure or excessive operational degradation of laser performance.

Earthquake hazard assessment in the Zagros Orogenic Belt of Iran using a fuzzy rule-based model

NASA Astrophysics Data System (ADS)

Farahi Ghasre Aboonasr, Sedigheh; Zamani, Ahmad; Razavipour, Fatemeh; Boostani, Reza

2017-08-01

Producing accurate seismic hazard map and predicting hazardous areas is necessary for risk mitigation strategies. In this paper, a fuzzy logic inference system is utilized to estimate the earthquake potential and seismic zoning of Zagros Orogenic Belt. In addition to the interpretability, fuzzy predictors can capture both nonlinearity and chaotic behavior of data, where the number of data is limited. In this paper, earthquake pattern in the Zagros has been assessed for the intervals of 10 and 50 years using fuzzy rule-based model. The Molchan statistical procedure has been used to show that our forecasting model is reliable. The earthquake hazard maps for this area reveal some remarkable features that cannot be observed on the conventional maps. Regarding our achievements, some areas in the southern (Bandar Abbas), southwestern (Bandar Kangan) and western (Kermanshah) parts of Iran display high earthquake severity even though they are geographically far apart.

A quantitative evaluation of a qualitative risk assessment framework: Examining the assumptions and predictions of the Productivity Susceptibility Analysis (PSA)

PubMed Central

2018-01-01

Qualitative risk assessment frameworks, such as the Productivity Susceptibility Analysis (PSA), have been developed to rapidly evaluate the risks of fishing to marine populations and prioritize management and research among species. Despite being applied to over 1,000 fish populations, and an ongoing debate about the most appropriate method to convert biological and fishery characteristics into an overall measure of risk, the assumptions and predictive capacity of these approaches have not been evaluated. Several interpretations of the PSA were mapped to a conventional age-structured fisheries dynamics model to evaluate the performance of the approach under a range of assumptions regarding exploitation rates and measures of biological risk. The results demonstrate that the underlying assumptions of these qualitative risk-based approaches are inappropriate, and the expected performance is poor for a wide range of conditions. The information required to score a fishery using a PSA-type approach is comparable to that required to populate an operating model and evaluating the population dynamics within a simulation framework. In addition to providing a more credible characterization of complex system dynamics, the operating model approach is transparent, reproducible and can evaluate alternative management strategies over a range of plausible hypotheses for the system. PMID:29856869

Rover Slip Validation and Prediction Algorithm

NASA Technical Reports Server (NTRS)

Yen, Jeng

2009-01-01

A physical-based simulation has been developed for the Mars Exploration Rover (MER) mission that applies a slope-induced wheel-slippage to the rover location estimator. Using the digital elevation map from the stereo images, the computational method resolves the quasi-dynamic equations of motion that incorporate the actual wheel-terrain speed to estimate the gross velocity of the vehicle. Based on the empirical slippage measured by the Visual Odometry software of the rover, this algorithm computes two factors for the slip model by minimizing the distance of the predicted and actual vehicle location, and then uses the model to predict the next drives. This technique, which has been deployed to operate the MER rovers in the extended mission periods, can accurately predict the rover position and attitude, mitigating the risk and uncertainties in the path planning on high-slope areas.

A novel registration-based methodology for prediction of trabecular bone fabric from clinical QCT: A comprehensive analysis

PubMed Central

Reyes, Mauricio; Zysset, Philippe

2017-01-01

Osteoporosis leads to hip fractures in aging populations and is diagnosed by modern medical imaging techniques such as quantitative computed tomography (QCT). Hip fracture sites involve trabecular bone, whose strength is determined by volume fraction and orientation, known as fabric. However, bone fabric cannot be reliably assessed in clinical QCT images of proximal femur. Accordingly, we propose a novel registration-based estimation of bone fabric designed to preserve tensor properties of bone fabric and to map bone fabric by a global and local decomposition of the gradient of a non-rigid image registration transformation. Furthermore, no comprehensive analysis on the critical components of this methodology has been previously conducted. Hence, the aim of this work was to identify the best registration-based strategy to assign bone fabric to the QCT image of a patient’s proximal femur. The normalized correlation coefficient and curvature-based regularization were used for image-based registration and the Frobenius norm of the stretch tensor of the local gradient was selected to quantify the distance among the proximal femora in the population. Based on this distance, closest, farthest and mean femora with a distinction of sex were chosen as alternative atlases to evaluate their influence on bone fabric prediction. Second, we analyzed different tensor mapping schemes for bone fabric prediction: identity, rotation-only, rotation and stretch tensor. Third, we investigated the use of a population average fabric atlas. A leave one out (LOO) evaluation study was performed with a dual QCT and HR-pQCT database of 36 pairs of human femora. The quality of the fabric prediction was assessed with three metrics, the tensor norm (TN) error, the degree of anisotropy (DA) error and the angular deviation of the principal tensor direction (PTD). The closest femur atlas (CTP) with a full rotation (CR) for fabric mapping delivered the best results with a TN error of 7.3 ± 0.9%, a DA error of 6.6 ± 1.3% and a PTD error of 25 ± 2°. The closest to the population mean femur atlas (MTP) using the same mapping scheme yielded only slightly higher errors than CTP for substantially less computing efforts. The population average fabric atlas yielded substantially higher errors than the MTP with the CR mapping scheme. Accounting for sex did not bring any significant improvements. The identified fabric mapping methodology will be exploited in patient-specific QCT-based finite element analysis of the proximal femur to improve the prediction of hip fracture risk. PMID:29176881

Dog ownership, abundance and potential for bat-borne rabies spillover in Chile.

PubMed

Astorga, F; Escobar, L E; Poo-Muñoz, D A; Medina-Vogel, G

2015-03-01

Rabies is a viral infectious disease that affects all mammals, including humans. Factors associated with the incidence of rabies include the presence and density of susceptible hosts and potential reservoirs. Currently, Chile is declared free of canine-related rabies, but there is an overpopulation of dogs within the country and an emergence of rabies in bats. Our objectives are to determine potential areas for bat-borne rabies spillover into dog populations expressed as a risk map, and to explore some key features of dog ownership, abundance, and management in Chile. For the risk map, our variables included a dog density surface (dog/km(2)) and a distribution model of bat-borne rabies presence. From literature review, we obtained dog data from 112 municipalities, which represent 33% of the total municipalities (339). At country level, based on previous studies the median human per dog ratio was 4.8, with 64% of houses containing at least one dog, and a median of 0.9 dog per house. We estimate a national median of 5.3 dog/km(2), and a median of 3680 dogs by municipality, from which we estimate a total population of 3.5×10(6) owned dogs. The antirabies vaccination presented a median of 21% of dogs by municipality, and 29% are unrestricted to some degree. Human per dog ratio have a significant (but weak) negative association with human density. Unrestricted dogs have a negative association with human density and income, and a positive association with the number of dogs per house. Considering dog density by municipality, and areas of potential bat-borne rabies occurrence, we found that 163 (∼48%) of Chilean municipalities are at risk of rabies spillover from bats to dogs. Risk areas are concentrated in urban settlements, including Santiago, Chile's capital. To validate the risk map, we included cases of rabies in dogs from the last 27 years; all fell within high-risk areas of our map, confirming the assertive risk prediction. Our results suggest that the use of dog population parameters may be informative to determine risk areas for bat-rabies spillover events. In addition, we confirm that dog abundance is a neglected and emerging public health concern in Chile, particularly within urban areas, which deserves prompt intervention. Copyright © 2015. Published by Elsevier B.V.

An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa

USGS Publications Warehouse

Grover-Kopec, Emily; Kawano, Mika; Klaver, Robert W.; Blumenthal, Benno; Ceccato, Pietro; Connor, Stephen J.

2005-01-01

Periodic epidemics of malaria are a major public health problem for many sub-Saharan African countries. Populations in epidemic prone areas have a poorly developed immunity to malaria and the disease remains life threatening to all age groups. The impact of epidemics could be minimized by prediction and improved prevention through timely vector control and deployment of appropriate drugs. Malaria Early Warning Systems are advocated as a means of improving the opportunity for preparedness and timely response.Rainfall is one of the major factors triggering epidemics in warm semi-arid and desert-fringe areas. Explosive epidemics often occur in these regions after excessive rains and, where these follow periods of drought and poor food security, can be especially severe. Consequently, rainfall monitoring forms one of the essential elements for the development of integrated Malaria Early Warning Systems for sub-Saharan Africa, as outlined by the World Health Organization.The Roll Back Malaria Technical Resource Network on Prevention and Control of Epidemics recommended that a simple indicator of changes in epidemic risk in regions of marginal transmission, consisting primarily of rainfall anomaly maps, could provide immediate benefit to early warning efforts. In response to these recommendations, the Famine Early Warning Systems Network produced maps that combine information about dekadal rainfall anomalies, and epidemic malaria risk, available via their Africa Data Dissemination Service. These maps were later made available in a format that is directly compatible with HealthMapper, the mapping and surveillance software developed by the WHO's Communicable Disease Surveillance and Response Department. A new monitoring interface has recently been developed at the International Research Institute for Climate Prediction (IRI) that enables the user to gain a more contextual perspective of the current rainfall estimates by comparing them to previous seasons and climatological averages. These resources are available at no cost to the user and are updated on a routine basis.

Development of predictive mapping techniques for soil survey and salinity mapping

NASA Astrophysics Data System (ADS)

Elnaggar, Abdelhamid A.

Conventional soil maps represent a valuable source of information about soil characteristics, however they are subjective, very expensive, and time-consuming to prepare. Also, they do not include explicit information about the conceptual mental model used in developing them nor information about their accuracy, in addition to the error associated with them. Decision tree analysis (DTA) was successfully used in retrieving the expert knowledge embedded in old soil survey data. This knowledge was efficiently used in developing predictive soil maps for the study areas in Benton and Malheur Counties, Oregon and accessing their consistency. A retrieved soil-landscape model from a reference area in Harney County was extrapolated to develop a preliminary soil map for the neighboring unmapped part of Malheur County. The developed map had a low prediction accuracy and only a few soil map units (SMUs) were predicted with significant accuracy, mostly those shallow SMUs that have either a lithic contact with the bedrock or developed on a duripan. On the other hand, the developed soil map based on field data was predicted with very high accuracy (overall was about 97%). Salt-affected areas of the Malheur County study area are indicated by their high spectral reflectance and they are easily discriminated from the remote sensing data. However, remote sensing data fails to distinguish between the different classes of soil salinity. Using the DTA method, five classes of soil salinity were successfully predicted with an overall accuracy of about 99%. Moreover, the calculated area of salt-affected soil was overestimated when mapped using remote sensing data compared to that predicted by using DTA. Hence, DTA could be a very helpful approach in developing soil survey and soil salinity maps in more objective, effective, less-expensive and quicker ways based on field data.

Utilization of GIS/GPS-Based Information Technology in Commercial Crop Decision Making in California, Washington, Oregon, Idaho, and Arizona

PubMed Central

Thomas, C. S.; Skinner, P. W.; Fox, A. D.; Greer, C. A.; Gubler, W. D.

2002-01-01

Ground-based weather, plant-stage measurements, and remote imagery were geo-referenced in geographic information system (GIS) software using an integrated approach to determine insect and disease risk and crop cultural requirements. Weather forecasts and disease weather forecasts for agricultural areas were constructed with elevation, weather, and satellite data. Models for 6 insect pests and 12 diseases of various crops were calculated and presented daily in georeferenced maps for agricultural areas in northern California and Washington. Grape harvest dates and yields also were predicted with high accuracy. The data generated from the GIS global positioning system (GPS) analyses were used to make management decisions over a large number of acres in California, Washington, Oregon, Idaho, and Arizona. Information was distributed daily over the Internet as regional weather, insect, and disease risk maps as industry-sponsored or subscription-based products. Use of GIS/GPS technology for semi-automated data analysis is discussed. PMID:19265934

Ground-water vulnerability to nitrate contamination in the mid-atlantic region

USGS Publications Warehouse

Greene, Earl A.; LaMotte, Andrew E.; Cullinan, Kerri-Ann; Smith, Elizabeth R.

2005-01-01

The U.S. Environmental Protection Agency?s (USEPA) Regional Vulnerability Assessment (ReVA) Program has developed a set of statistical tools to support regional-scale, integrated ecological risk-assessment studies. One of these tools, developed by the U.S. Geological Survey (USGS), is used with available water-quality data obtained from USGS National Water-Quality Assessment (NAWQA) and other studies in association with land cover, geology, soils, and other geographic data to develop logistic-regression equations that predict the vulnerability of ground water to nitrate concentrations exceeding specified thresholds in the Mid-Atlantic Region. The models were developed and applied to produce spatial probability maps showing the likelihood of elevated concentrations of nitrate in the region. These maps can be used to identify areas that currently are at risk and help identify areas where ground water has been affected by human activities. This information can be used by regional and local water managers to protect water supplies and identify land-use planning solutions and monitoring programs in these vulnerable areas.

Importance of Calibration Method in Central Blood Pressure for Cardiac Structural Abnormalities.

PubMed

Negishi, Kazuaki; Yang, Hong; Wang, Ying; Nolan, Mark T; Negishi, Tomoko; Pathan, Faraz; Marwick, Thomas H; Sharman, James E

2016-09-01

Central blood pressure (CBP) independently predicts cardiovascular risk, but calibration methods may affect accuracy of central systolic blood pressure (CSBP). Standard central systolic blood pressure (Stan-CSBP) from peripheral waveforms is usually derived with calibration using brachial SBP and diastolic BP (DBP). However, calibration using oscillometric mean arterial pressure (MAP) and DBP (MAP-CSBP) is purported to provide more accurate representation of true invasive CSBP. This study sought to determine which derived CSBP could more accurately discriminate cardiac structural abnormalities. A total of 349 community-based patients with risk factors (71±5years, 161 males) had CSBP measured by brachial oscillometry (Mobil-O-Graph, IEM GmbH, Stolberg, Germany) using 2 calibration methods: MAP-CSBP and Stan-CSBP. Left ventricular hypertrophy (LVH) and left atrial dilatation (LAD) were measured based on standard guidelines. MAP-CSBP was higher than Stan-CSBP (149±20 vs. 128±15mm Hg, P < 0.0001). Although they were modestly correlated (rho = 0.74, P < 0.001), the Bland-Altman plot demonstrated a large bias (21mm Hg) and limits of agreement (24mm Hg). In receiver operating characteristic (ROC) curve analyses, MAP-CSBP significantly better discriminated LVH compared with Stan-CSBP (area under the curve (AUC) 0.66 vs. 0.59, P = 0.0063) and brachial SBP (0.62, P = 0.027). Continuous net reclassification improvement (NRI) (P < 0.001) and integrated discrimination improvement (IDI) (P < 0.001) corroborated superior discrimination of LVH by MAP-CSBP. Similarly, MAP-CSBP better distinguished LAD than Stan-CSBP (AUC 0.63 vs. 0.56, P = 0.005) and conventional brachial SBP (0.58, P = 0.006), whereas Stan-CSBP provided no better discrimination than conventional brachial BP (P = 0.09). CSBP is calibration dependent and when oscillometric MAP and DBP are used, the derived CSBP is a better discriminator for cardiac structural abnormalities. © American Journal of Hypertension, Ltd 2016. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Spatial distribution of early red lesions is a risk factor for development of vision-threatening diabetic retinopathy.

PubMed

Ometto, Giovanni; Assheton, Phil; Calivá, Francesco; Chudzik, Piotr; Al-Diri, Bashir; Hunter, Andrew; Bek, Toke

2017-12-01

Diabetic retinopathy is characterised by morphological lesions related to disturbances in retinal blood flow. It has previously been shown that the early development of retinal lesions temporal to the fovea may predict the development of treatment-requiring diabetic maculopathy. The aim of this study was to map accurately the area where lesions could predict progression to vision-threatening retinopathy. The predictive value of the location of the earliest red lesions representing haemorrhages and/or microaneurysms was studied by comparing their occurrence in a group of individuals later developing vision-threatening diabetic retinopathy with that in a group matched with respect to diabetes type, age, sex and age of onset of diabetes mellitus who did not develop vision-threatening diabetic retinopathy during a similar observation period. The probability of progression to vision-threatening diabetic retinopathy was higher in a circular area temporal to the fovea, and the occurrence of the first lesions in this area was predictive of the development of vision-threatening diabetic retinopathy. The calculated peak value showed that the risk of progression was 39.5% higher than the average. There was no significant difference in the early distribution of lesions in participants later developing diabetic maculopathy or proliferative diabetic retinopathy. The location of early red lesions in diabetic retinopathy is predictive of whether or not individuals will later develop vision-threatening diabetic retinopathy. This evidence should be incorporated into risk models used to recommend control intervals in screening programmes for diabetic retinopathy.

Interpolation Approaches for Characterizing Spatial Variability of Soil Properties in Tuz Lake Basin of Turkey

NASA Astrophysics Data System (ADS)

Gorji, Taha; Sertel, Elif; Tanik, Aysegul

2017-12-01

Soil management is an essential concern in protecting soil properties, in enhancing appropriate soil quality for plant growth and agricultural productivity, and in preventing soil erosion. Soil scientists and decision makers require accurate and well-distributed spatially continuous soil data across a region for risk assessment and for effectively monitoring and managing soils. Recently, spatial interpolation approaches have been utilized in various disciplines including soil sciences for analysing, predicting and mapping distribution and surface modelling of environmental factors such as soil properties. The study area selected in this research is Tuz Lake Basin in Turkey bearing ecological and economic importance. Fertile soil plays a significant role in agricultural activities, which is one of the main industries having great impact on economy of the region. Loss of trees and bushes due to intense agricultural activities in some parts of the basin lead to soil erosion. Besides, soil salinization due to both human-induced activities and natural factors has exacerbated its condition regarding agricultural land development. This study aims to compare capability of Local Polynomial Interpolation (LPI) and Radial Basis Functions (RBF) as two interpolation methods for mapping spatial pattern of soil properties including organic matter, phosphorus, lime and boron. Both LPI and RBF methods demonstrated promising results for predicting lime, organic matter, phosphorous and boron. Soil samples collected in the field were used for interpolation analysis in which approximately 80% of data was used for interpolation modelling whereas the remaining for validation of the predicted results. Relationship between validation points and their corresponding estimated values in the same location is examined by conducting linear regression analysis. Eight prediction maps generated from two different interpolation methods for soil organic matter, phosphorus, lime and boron parameters were examined based on R2 and RMSE values. The outcomes indicate that RBF performance in predicting lime, organic matter and boron put forth better results than LPI. However, LPI shows better results for predicting phosphorus.

Recommendations for the user-specific enhancement of flood maps

NASA Astrophysics Data System (ADS)

Meyer, V.; Kuhlicke, C.; Luther, J.; Fuchs, S.; Priest, S.; Dorner, W.; Serrhini, K.; Pardoe, J.; McCarthy, S.; Seidel, J.; Palka, G.; Unnerstall, H.; Viavattene, C.; Scheuer, S.

2012-05-01

The European Union Floods Directive requires the establishment of flood maps for high risk areas in all European member states by 2013. However, the current practice of flood mapping in Europe still shows some deficits. Firstly, flood maps are frequently seen as an information tool rather than a communication tool. This means that, for example, local stocks of knowledge are not incorporated. Secondly, the contents of flood maps often do not match the requirements of the end-users. Finally, flood maps are often designed and visualised in a way that cannot be easily understood by residents at risk and/or that is not suitable for the respective needs of public authorities in risk and event management. The RISK MAP project examined how end-user participation in the mapping process may be used to overcome these barriers and enhance the communicative power of flood maps, fundamentally increasing their effectiveness. Based on empirical findings from a participatory approach that incorporated interviews, workshops and eye-tracking tests, conducted in five European case studies, this paper outlines recommendations for user-specific enhancements of flood maps. More specific, recommendations are given with regard to (1) appropriate stakeholder participation processes, which allow incorporating local knowledge and preferences, (2) the improvement of the contents of flood maps by considering user-specific needs and (3) the improvement of the visualisation of risk maps in order to produce user-friendly and understandable risk maps for the user groups concerned. Furthermore, "idealised" maps for different user groups are presented: for strategic planning, emergency management and the public.

Dynamical Mapping of Anopheles darlingi Densities in a Residual Malaria Transmission Area of French Guiana by Using Remote Sensing and Meteorological Data

PubMed Central

Adde, Antoine; Roux, Emmanuel; Mangeas, Morgan; Dessay, Nadine; Nacher, Mathieu; Dusfour, Isabelle; Girod, Romain; Briolant, Sébastien

2016-01-01

Local variation in the density of Anopheles mosquitoes and the risk of exposure to bites are essential to explain the spatial and temporal heterogeneities in the transmission of malaria. Vector distribution is driven by environmental factors. Based on variables derived from satellite imagery and meteorological observations, this study aimed to dynamically model and map the densities of Anopheles darlingi in the municipality of Saint-Georges de l’Oyapock (French Guiana). Longitudinal sampling sessions of An. darlingi densities were conducted between September 2012 and October 2014. Landscape and meteorological data were collected and processed to extract a panel of variables that were potentially related to An. darlingi ecology. Based on these data, a robust methodology was formed to estimate a statistical predictive model of the spatial-temporal variations in the densities of An. darlingi in Saint-Georges de l’Oyapock. The final cross-validated model integrated two landscape variables—dense forest surface and built surface—together with four meteorological variables related to rainfall, evapotranspiration, and the minimal and maximal temperatures. Extrapolation of the model allowed the generation of predictive weekly maps of An. darlingi densities at a resolution of 10-m. Our results supported the use of satellite imagery and meteorological data to predict malaria vector densities. Such fine-scale modeling approach might be a useful tool for health authorities to plan control strategies and social communication in a cost-effective, targeted, and timely manner. PMID:27749938

Dynamical Mapping of Anopheles darlingi Densities in a Residual Malaria Transmission Area of French Guiana by Using Remote Sensing and Meteorological Data.

PubMed

Adde, Antoine; Roux, Emmanuel; Mangeas, Morgan; Dessay, Nadine; Nacher, Mathieu; Dusfour, Isabelle; Girod, Romain; Briolant, Sébastien

2016-01-01

Local variation in the density of Anopheles mosquitoes and the risk of exposure to bites are essential to explain the spatial and temporal heterogeneities in the transmission of malaria. Vector distribution is driven by environmental factors. Based on variables derived from satellite imagery and meteorological observations, this study aimed to dynamically model and map the densities of Anopheles darlingi in the municipality of Saint-Georges de l'Oyapock (French Guiana). Longitudinal sampling sessions of An. darlingi densities were conducted between September 2012 and October 2014. Landscape and meteorological data were collected and processed to extract a panel of variables that were potentially related to An. darlingi ecology. Based on these data, a robust methodology was formed to estimate a statistical predictive model of the spatial-temporal variations in the densities of An. darlingi in Saint-Georges de l'Oyapock. The final cross-validated model integrated two landscape variables-dense forest surface and built surface-together with four meteorological variables related to rainfall, evapotranspiration, and the minimal and maximal temperatures. Extrapolation of the model allowed the generation of predictive weekly maps of An. darlingi densities at a resolution of 10-m. Our results supported the use of satellite imagery and meteorological data to predict malaria vector densities. Such fine-scale modeling approach might be a useful tool for health authorities to plan control strategies and social communication in a cost-effective, targeted, and timely manner.

New Tsunami Inundation Maps for California

NASA Astrophysics Data System (ADS)

Barberopoulou, Aggeliki; Borrero, Jose; Uslu, Burak; Kanoglu, Utku; Synolakis, Costas

2010-05-01

California is the first US State to complete its tsunami inundation mapping. A new generation of tsunami inundation maps is now available for 17 coastal counties.. The new maps offer improved coverage for many areas, they are based on the most recent descriptions of potential tsunami farfield and nearfield sources and use the best available bathymetric and topographic data for modelling. The need for new tsunami maps for California became clear since Synolakis et al (1998) described how inundation projections derived with inundation models that fully calculate the wave evolution over dry land can be as high as twice the values predicted with earlier threshold models, for tsunamis originating from tectonic source. Since the 1998 Papua New Guinea tsunami when the hazard from offshore submarine landslides was better understood (Bardet et al, 2003), the State of California funded the development of the first generation of maps, based on local tectonic and landslide sources. Most of the hazard was dominated by offshore landslides, whose return period remains unknown but is believed to be higher than 1000 years for any given locale, at least in Southern California. The new generation of maps incorporates local and distant scenarios. The partnership between the Tsunami Research Center at USC, the California Emergency Management Agency and the California Seismic Safety Commission let the State to be the first among all US States to complete the maps. (Exceptions include the offshore islands and Newport Beach, where higher resolution maps are under way). The maps were produced with the lowest cost per mile of coastline, per resident or per map than all other States, because of the seamless integration of the USC and NOAA databases and the use of the MOST model. They are a significant improvement over earlier map generations. As part of a continuous improvement in response, mitigation and planning and community education, the California inundation maps can contribute in reducing tsunami risk. References -Bardet, JP et al (2003), Landslide tsunamis: Recent findings and research directions, Pure and Applied Geophysics, 160, (10-11), 1793-1809. -Eisner, R., Borrero, C., Synolakis, C.E. (2001) Inundation Maps for the State of California, International Tsunami Symposium, ITS 2001 Proceedings, NHTMP Review Paper #4, 67-81. -Synolakis, C.E., D. McCarthy, V.V. Titov, J.C. Borrero, (1998) Evaluating the Tsunami Risk in California, CALIFORNIA AND THE WORLD OCEAN '97, 1225-1236, Proceedings ASCE, ISBN: 0-7844-0297-3.

A Stochastic Simulation Framework for the Prediction of Strategic Noise Mapping and Occupational Noise Exposure Using the Random Walk Approach

PubMed Central

Haron, Zaiton; Bakar, Suhaimi Abu; Dimon, Mohamad Ngasri

2015-01-01

Strategic noise mapping provides important information for noise impact assessment and noise abatement. However, producing reliable strategic noise mapping in a dynamic, complex working environment is difficult. This study proposes the implementation of the random walk approach as a new stochastic technique to simulate noise mapping and to predict the noise exposure level in a workplace. A stochastic simulation framework and software, namely RW-eNMS, were developed to facilitate the random walk approach in noise mapping prediction. This framework considers the randomness and complexity of machinery operation and noise emission levels. Also, it assesses the impact of noise on the workers and the surrounding environment. For data validation, three case studies were conducted to check the accuracy of the prediction data and to determine the efficiency and effectiveness of this approach. The results showed high accuracy of prediction results together with a majority of absolute differences of less than 2 dBA; also, the predicted noise doses were mostly in the range of measurement. Therefore, the random walk approach was effective in dealing with environmental noises. It could predict strategic noise mapping to facilitate noise monitoring and noise control in the workplaces. PMID:25875019

The Cellular Automata for modelling of spreading of lava flow on the earth surface

NASA Astrophysics Data System (ADS)

Jarna, A.

2012-12-01

Volcanic risk assessment is a very important scientific, political and economic issue in densely populated areas close to active volcanoes. Development of effective tools for early prediction of a potential volcanic hazard and management of crises are paramount. However, to this date volcanic hazard maps represent the most appropriate way to illustrate the geographical area that can potentially be affected by a volcanic event. Volcanic hazard maps are usually produced by mapping out old volcanic deposits, however dynamic lava flow simulation gaining popularity and can give crucial information to corroborate other methodologies. The methodology which is used here for the generation of volcanic hazard maps is based on numerical simulation of eruptive processes by the principle of Cellular Automata (CA). The python script is integrated into ArcToolbox in ArcMap (ESRI) and the user can select several input and output parameters which influence surface morphology, size and shape of the flow, flow thickness, flow velocity and length of lava flows. Once the input parameters are selected, the software computes and generates hazard maps on the fly. The results can be exported to Google Maps (.klm format) to visualize the results of the computation. For validation of the simulation code are used data from a real lava flow. Comparison of the simulation results with real lava flows mapped out from satellite images will be presented.

Topography- and nightlight-based national flood risk assessment in Canada

NASA Astrophysics Data System (ADS)

Elshorbagy, Amin; Bharath, Raja; Lakhanpal, Anchit; Ceola, Serena; Montanari, Alberto; Lindenschmidt, Karl-Erich

2017-04-01

In Canada, flood analysis and water resource management, in general, are tasks conducted at the provincial level; therefore, unified national-scale approaches to water-related problems are uncommon. In this study, a national-scale flood risk assessment approach is proposed and developed. The study focuses on using global and national datasets available with various resolutions to create flood risk maps. First, a flood hazard map of Canada is developed using topography-based parameters derived from digital elevation models, namely, elevation above nearest drainage (EAND) and distance from nearest drainage (DFND). This flood hazard mapping method is tested on a smaller area around the city of Calgary, Alberta, against a flood inundation map produced by the city using hydraulic modelling. Second, a flood exposure map of Canada is developed using a land-use map and the satellite-based nightlight luminosity data as two exposure parameters. Third, an economic flood risk map is produced, and subsequently overlaid with population density information to produce a socioeconomic flood risk map for Canada. All three maps of hazard, exposure, and risk are classified into five classes, ranging from very low to severe. A simple way to include flood protection measures in hazard estimation is also demonstrated using the example of the city of Winnipeg, Manitoba. This could be done for the entire country if information on flood protection across Canada were available. The evaluation of the flood hazard map shows that the topography-based method adopted in this study is both practical and reliable for large-scale analysis. Sensitivity analysis regarding the resolution of the digital elevation model is needed to identify the resolution that is fine enough for reliable hazard mapping, but coarse enough for computational tractability. The nightlight data are found to be useful for exposure and risk mapping in Canada; however, uncertainty analysis should be conducted to investigate the effect of the overglow phenomenon on flood risk mapping.

Estimating hantavirus risk in southern Argentina: a GIS-based approach combining human cases and host distribution.

PubMed

Andreo, Veronica; Neteler, Markus; Rocchini, Duccio; Provensal, Cecilia; Levis, Silvana; Porcasi, Ximena; Rizzoli, Annapaola; Lanfri, Mario; Scavuzzo, Marcelo; Pini, Noemi; Enria, Delia; Polop, Jaime

2014-01-14

We use a Species Distribution Modeling (SDM) approach along with Geographic Information Systems (GIS) techniques to examine the potential distribution of hantavirus pulmonary syndrome (HPS) caused by Andes virus (ANDV) in southern Argentina and, more precisely, define and estimate the area with the highest infection probability for humans, through the combination with the distribution map for the competent rodent host (Oligoryzomys longicaudatus). Sites with confirmed cases of HPS in the period 1995-2009 were mostly concentrated in a narrow strip (~90 km × 900 km) along the Andes range from northern Neuquén to central Chubut province. This area is characterized by high mean annual precipitation (~1,000 mm on average), but dry summers (less than 100 mm), very low percentages of bare soil (~10% on average) and low temperatures in the coldest month (minimum average temperature -1.5 °C), as compared to the HPS-free areas, features that coincide with sub-Antarctic forests and shrublands (especially those dominated by the invasive plant Rosa rubiginosa), where rodent host abundances and ANDV prevalences are known to be the highest. Through the combination of predictive distribution maps of the reservoir host and disease cases, we found that the area with the highest probability for HPS to occur overlaps only 28% with the most suitable habitat for O. longicaudatus. With this approach, we made a step forward in the understanding of the risk factors that need to be considered in the forecasting and mapping of risk at the regional/national scale. We propose the implementation and use of thematic maps, such as the one built here, as a basic tool allowing public health authorities to focus surveillance efforts and normally scarce resources for prevention and control actions in vast areas like southern Argentina.

Analysis of tsunami disaster map by Geographic Information System (GIS): Aceh Singkil-Indonesia

NASA Astrophysics Data System (ADS)

Farhan, A.; Akhyar, H.

2017-02-01

Tsunami risk map is used by stakeholder as a base to decide evacuation plan and evaluates from disaster. Aceh Singkil district of Aceh- Indonesia’s disaster maps have been developed and analyzed by using GIS tool. Overlay methods through algorithms are used to produce hazard map, vulnerability, capacity and finally created disaster risk map. Spatial maps are used topographic maps, administrative map, SRTM. The parameters are social, economic, physical environmental vulnerability, a level of exposed people, parameters of houses, public building, critical facilities, productive land, population density, sex ratio, poor ratio, disability ratio, age group ratio, the protected forest, natural forest, and mangrove forest. The results show high-risk tsunami disaster at nine villages; moderate levels are seventeen villages, and other villages are shown in the low level of tsunami risk disaster.

A study of the breast cancer dynamics in North Carolina.

PubMed

Christakos, G; Lai, J J

1997-11-01

This work is concerned with the study of breast cancer incidence in the State of North Carolina. Methodologically, the current analysis illustrates the importance of spatiotemporal random field modelling and introduces a mode of reasoning that is based on a combination of inductive and deductive processes. The composite space/time analysis utilizes the variability characteristics of incidence and the mathematical features of the random field model to fit it to the data. The analysis is significantly general and can efficiently represent non-homogeneous and non-stationary characteristics of breast cancer variation. Incidence predictions are produced using data at the same time period as well as data from other time periods and disease registries. The random field provides a rigorous and systematic method for generating detailed maps, which offer a quantitative description of the incidence variation from place to place and from time to time, together with a measure of the accuracy of the incidence maps. Spatiotemporal mapping accounts for the geographical locations and the time instants of the incidence observations, which is not usually the case with most empirical Bayes methods. It is also more accurate than purely spatial statistics methods, and can offer valuable information about the breast cancer risk and dynamics in North Carolina. Field studies could be initialized in high-rate areas identified by the maps in an effort to uncover environmental or life-style factors that might be responsible for the high risk rates. Also, the incidence maps can help elucidate causal mechanisms, explain disease occurrences at a certain scale, and offer guidance in health management and administration.

Intelligent seismic risk mitigation system on structure building

NASA Astrophysics Data System (ADS)

Suryanita, R.; Maizir, H.; Yuniorto, E.; Jingga, H.

2018-01-01

Indonesia located on the Pacific Ring of Fire, is one of the highest-risk seismic zone in the world. The strong ground motion might cause catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to properly design the structural response of building against seismic hazard. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. However, the structural analysis could be very difficult and time consuming. This study aims to predict the structural response includes displacement, velocity, and acceleration of multi-storey building with the fixed floor plan using Artificial Neural Network (ANN) method based on the 2010 Indonesian seismic hazard map. By varying the building height, soil condition, and seismic location in 47 cities in Indonesia, 6345 data sets were obtained and fed into the ANN model for the learning process. The trained ANN can predict the displacement, velocity, and acceleration responses with up to 96% of predicted rate. The trained ANN architecture and weight factors were later used to build a simple tool in Visual Basic program which possesses the features for prediction of structural response as mentioned previously.

Predictability of Malaria Transmission Intensity in the Mpumalanga Province, South Africa, Using Land Surface Climatology and Autoregressive Analysis

NASA Technical Reports Server (NTRS)

Grass, David; Jasinski, Michael F.; Govere, John

2003-01-01

There has been increasing effort in recent years to employ satellite remotely sensed data to identify and map vector habitat and malaria transmission risk in data sparse environments. In the current investigation, available satellite and other land surface climatology data products are employed in short-term forecasting of infection rates in the Mpumalanga Province of South Africa, using a multivariate autoregressive approach. The climatology variables include precipitation, air temperature and other land surface states computed by the Off-line Land-Surface Global Assimilation System (OLGA) including soil moisture and surface evaporation. Satellite data products include the Normalized Difference Vegetation Index (NDVI) and other forcing data used in the Goddard Earth Observing System (GEOS-1) model. Predictions are compared to long- term monthly records of clinical and microscopic diagnoses. The approach addresses the high degree of short-term autocorrelation in the disease and weather time series. The resulting model is able to predict 11 of the 13 months that were classified as high risk during the validation period, indicating the utility of applying antecedent climatic variables to the prediction of malaria incidence for the Mpumalanga Province.

Effects of habitat map generalization in biodiversity assessment

NASA Technical Reports Server (NTRS)

Stoms, David M.

1992-01-01

Species richness is being mapped as part of an inventory of biological diversity in California (i.e., gap analysis). Species distributions are modeled with a GIS on the basis of maps of each species' preferred habitats. Species richness is then tallied in equal-area sampling units. A GIS sensitivity analysis examined the effects of the level of generalization of the habitat map on the predicted distribution of species richness in the southern Sierra Nevada. As the habitat map was generalized, the number of habitat types mapped within grid cells tended to decrease with a corresponding decline in numbers of species predicted. Further, the ranking of grid cells in order of predicted numbers of species changed dramatically between levels of generalization. Areas predicted to be of greatest conservation value on the basis of species richness may therefore be sensitive to GIS data resolution.

Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees.

PubMed

Cheong, Yoon Ling; Leitão, Pedro J; Lakes, Tobia

2014-07-01

The transmission of dengue disease is influenced by complex interactions among vector, host and virus. Land use such as water bodies or certain agricultural practices have been identified as likely risk factors for dengue because of the provision of suitable habitats for the vector. Many studies have focused on the land use factors of dengue vector abundance in small areas but have not yet studied the relationship between land use factors and dengue cases for large regions. This study aims to clarify if land use factors other than human settlements, e.g. different types of agricultural land use, water bodies and forest are associated with reported dengue cases from 2008 to 2010 in the state of Selangor, Malaysia. From the correlative relationship, we aim to generate a prediction risk map. We used Boosted Regression Trees (BRT) to account for nonlinearities and interactions between the factors with high predictive accuracies. Our model with a cross-validated performance score (Area Under the Receiver Operator Characteristic Curve, ROC AUC) of 0.81 showed that the most important land use factors are human settlements (model importance of 39.2%), followed by water bodies (16.1%), mixed horticulture (8.7%), open land (7.5%) and neglected grassland (6.7%). A risk map after 100 model runs with a cross-validated ROC AUC mean of 0.81 (±0.001 s.d.) is presented. Our findings may be an important asset for improving surveillance and control interventions for dengue. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Connectivity map identifies HDAC inhibition as a treatment option of high-risk hepatoblastoma.

PubMed

Beck, Alexander; Eberherr, Corinna; Hagemann, Michaela; Cairo, Stefano; Häberle, Beate; Vokuhl, Christian; von Schweinitz, Dietrich; Kappler, Roland

2016-11-01

Hepatoblastoma (HB) is the most common liver tumor of childhood, usually occurring in children under the age of 3 y. The prognosis of patients presenting with distant metastasis, vascular invasion and advanced tumor stages remains poor and children that do survive often face severe late effects from the aggressive chemotherapy regimen. To identify potential new therapeutics for high risk HB we used a 1,000-gene expression signature as input for a Connectivity Map (CMap) analysis, which predicted histone deacetylase (HDAC) inhibitors as a promising therapy option. Subsequent expression analysis of primary HB and HB cell lines revealed a general overexpression of HDAC1 and HDAC2, which has been suggested to be predictive for the efficacy of HDAC inhibition. Accordingly, treatment of HB cells with the HDAC inhibitors SAHA and MC1568 resulted in a potent reduction of cell viability, induction of apoptosis, reactivation of epigenetically suppressed tumor suppressor genes, and the reversion of the 16-gene HB classifier toward the more favorable expression signature. Most importantly, the combination of HDAC inhibitors and cisplatin - a major chemotherapeutic agent of HB treatment - revealed a strong synergistic effect, even at significantly reduced doses of cisplatin. Our findings suggest that HDAC inhibitors skew HB cells toward a more favorable prognostic phenotype through changes in gene expression, thus indicating a targeted molecular mechanism that seems to enhance the anti-proliferative effects of conventional chemotherapy. Thus, adding HDAC inhibitors to the treatment regimen of high risk HB could potentially improve outcomes and reduce severe late effects.

Prediction of postural risk of fall initiation based on a two-variable description of body dynamics: position and velocity of center of mass.

PubMed

Honarvar, Mohammad Hadi; Nakashima, Motomu

2013-10-01

This research addresses the question: what is the risk of fall initiation at a certain human posture? There are postures from which no one is able to keep their balance and a fall will surely initiate (risk=1), and others from which everyone may regain their stability (risk=0). In other postures, only a portion of people can control their stability. One may interpret risk to chance of a fall to be initiated, and based on the portion of fallers assign a risk value to a given human posture (postural risk). Human posture can be mapped to a point in a 2-dimensional space: the x-v plane, the axes of which are horizontal components of the position and velocity of the center of mass of the body. For every pair of (x, v), the outcome of the balance recovery problem defines whether a person with a given strength level is able to regain their stability when released from a posture corresponding to that point. Using strength distribution data, we estimated the portion of the population who will initiate a fall if starting at a certain posture. A fast calculation approach is also introduced to replace the time-consuming method of solving the recovery problem many times. Postural risk of fall initiation for situations expressed by (x, v) pairs for the entire x-v plane is calculated and shown in a color-map. Copyright © 2013 Elsevier B.V. All rights reserved.

Coarse-resolution Ecology of Etiological Agent, Vector, and Reservoirs of Zoonotic Cutaneous Leishmaniasis in Libya.

PubMed

Samy, Abdallah M; Annajar, Badereddin B; Dokhan, Mostafa Ramadhan; Boussaa, Samia; Peterson, A Townsend

2016-02-01

Cutaneous leishmaniasis ranks among the tropical diseases least known and most neglected in Libya. World Health Organization reports recognized associations of Phlebotomus papatasi, Psammomys obesus, and Meriones spp., with transmission of zoonotic cutaneous leishmaniasis (ZCL; caused by Leishmania major) across Libya. Here, we map risk of ZCL infection based on occurrence records of L. major, P. papatasi, and four potential animal reservoirs (Meriones libycus, Meriones shawi, Psammomys obesus, and Gerbillus gerbillus). Ecological niche models identified limited risk areas for ZCL across the northern coast of the country; most species associated with ZCL transmission were confined to this same region, but some had ranges extending to central Libya. All ENM predictions were significant based on partial ROC tests. As a further evaluation of L. major ENM predictions, we compared predictions with 98 additional independent records provided by the Libyan National Centre for Disease Control (NCDC); all of these records fell inside the belt predicted as suitable for ZCL. We tested ecological niche similarity among vector, parasite, and reservoir species and could not reject any null hypotheses of niche similarity. Finally, we tested among possible combinations of vector and reservoir that could predict all recent human ZCL cases reported by NCDC; only three combinations could anticipate the distribution of human cases across the country.

Coarse-resolution Ecology of Etiological Agent, Vector, and Reservoirs of Zoonotic Cutaneous Leishmaniasis in Libya

PubMed Central

Samy, Abdallah M.; Annajar, Badereddin B.; Dokhan, Mostafa Ramadhan; Boussaa, Samia; Peterson, A. Townsend

2016-01-01

Abstract Cutaneous leishmaniasis ranks among the tropical diseases least known and most neglected in Libya. World Health Organization reports recognized associations of Phlebotomus papatasi, Psammomys obesus, and Meriones spp., with transmission of zoonotic cutaneous leishmaniasis (ZCL; caused by Leishmania major) across Libya. Here, we map risk of ZCL infection based on occurrence records of L. major, P. papatasi, and four potential animal reservoirs (Meriones libycus, Meriones shawi, Psammomys obesus, and Gerbillus gerbillus). Ecological niche models identified limited risk areas for ZCL across the northern coast of the country; most species associated with ZCL transmission were confined to this same region, but some had ranges extending to central Libya. All ENM predictions were significant based on partial ROC tests. As a further evaluation of L. major ENM predictions, we compared predictions with 98 additional independent records provided by the Libyan National Centre for Disease Control (NCDC); all of these records fell inside the belt predicted as suitable for ZCL. We tested ecological niche similarity among vector, parasite, and reservoir species and could not reject any null hypotheses of niche similarity. Finally, we tested among possible combinations of vector and reservoir that could predict all recent human ZCL cases reported by NCDC; only three combinations could anticipate the distribution of human cases across the country. PMID:26863317

Replication and predictive value of SNPs associated with melanoma and pigmentation traits in a Southern European case-control study.

PubMed

Stefanaki, Irene; Panagiotou, Orestis A; Kodela, Elisavet; Gogas, Helen; Kypreou, Katerina P; Chatzinasiou, Foteini; Nikolaou, Vasiliki; Plaka, Michaela; Kalfa, Iro; Antoniou, Christina; Ioannidis, John P A; Evangelou, Evangelos; Stratigos, Alexander J

2013-01-01

Genetic association studies have revealed numerous polymorphisms conferring susceptibility to melanoma. We aimed to replicate previously discovered melanoma-associated single-nucleotide polymorphisms (SNPs) in a Greek case-control population, and examine their predictive value. Based on a field synopsis of genetic variants of melanoma (MelGene), we genotyped 284 patients and 284 controls at 34 melanoma-associated SNPs of which 19 derived from GWAS. We tested each one of the 33 SNPs passing quality control for association with melanoma both with and without accounting for the presence of well-established phenotypic risk factors. We compared the risk allele frequencies between the Greek population and the HapMap CEU sample. Finally, we evaluated the predictive ability of the replicated SNPs. Risk allele frequencies were significantly lower compared to the HapMap CEU for eight SNPs (rs16891982--SLC45A2, rs12203592--IRF4, rs258322--CDK10, rs1805007--MC1R, rs1805008--MC1R, rs910873--PIGU, rs17305573--PIGU, and rs1885120--MTAP) and higher for one SNP (rs6001027--PLA2G6) indicating a different profile of genetic susceptibility in the studied population. Previously identified effect estimates modestly correlated with those found in our population (r = 0.72, P<0.0001). The strongest associations were observed for rs401681-T in CLPTM1L (odds ratio [OR] 1.60, 95% CI 1.22-2.10; P = 0.001), rs16891982-C in SCL45A2 (OR 0.51, 95% CI 0.34-0.76; P = 0.001), and rs1805007-T in MC1R (OR 4.38, 95% CI 2.03-9.43; P = 2×10⁻⁵). Nominally statistically significant associations were seen also for another 5 variants (rs258322-T in CDK10, rs1805005-T in MC1R, rs1885120-C in MYH7B, rs2218220-T in MTAP and rs4911442-G in the ASIP region). The addition of all SNPs with nominal significance to a clinical non-genetic model did not substantially improve melanoma risk prediction (AUC for clinical model 83.3% versus 83.9%, p = 0.66). Overall, our study has validated genetic variants that are likely to contribute to melanoma susceptibility in the Greek population.

Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients.

PubMed

Yue, Yong; Osipov, Arsen; Fraass, Benedick; Sandler, Howard; Zhang, Xiao; Nissen, Nicholas; Hendifar, Andrew; Tuli, Richard

2017-02-01

To stratify risks of pancreatic adenocarcinoma (PA) patients using pre- and post-radiotherapy (RT) PET/CT images, and to assess the prognostic value of texture variations in predicting therapy response of patients. Twenty-six PA patients treated with RT from 2011-2013 with pre- and post-treatment 18F-FDG-PET/CT scans were identified. Tumor locoregional texture was calculated using 3D kernel-based approach, and texture variations were identified by fitting discrepancies of texture maps of pre- and post-treatment images. A total of 48 texture and clinical variables were identified and evaluated for association with overall survival (OS). The prognostic heterogeneity features were selected using lasso/elastic net regression, and further were evaluated by multivariate Cox analysis. Median age was 69 y (range, 46-86 y). The texture map and temporal variations between pre- and post-treatment were well characterized by histograms and statistical fitting. The lasso analysis identified seven predictors (age, node stage, post-RT SUVmax, variations of homogeneity, variance, sum mean, and cluster tendency). The multivariate Cox analysis identified five significant variables: age, node stage, variations of homogeneity, variance, and cluster tendency (with P=0.020, 0.040, 0.065, 0.078, and 0.081, respectively). The patients were stratified into two groups based on the risk score of multivariate analysis with log-rank P=0.001: a low risk group (n=11) with a longer mean OS (29.3 months) and higher texture variation (>30%), and a high risk group (n=15) with a shorter mean OS (17.7 months) and lower texture variation (<15%). Locoregional metabolic texture response provides a feasible approach for evaluating and predicting clinical outcomes following treatment of PA with RT. The proposed method can be used to stratify patient risk and help select appropriate treatment strategies for individual patients toward implementing response-driven adaptive RT.

Extinction risks of Amazonian plant species.

PubMed

Feeley, Kenneth J; Silman, Miles R

2009-07-28

Estimates of the number, and preferably the identity, of species that will be threatened by land-use change and habitat loss are an invaluable tool for setting conservation priorities. Here, we use collections data and ecoregion maps to generate spatially explicit distributions for more than 40,000 vascular plant species from the Amazon basin (representing more than 80% of the estimated Amazonian plant diversity). Using the distribution maps, we then estimate the rates of habitat loss and associated extinction probabilities due to land-use changes as modeled under 2 disturbance scenarios. We predict that by 2050, human land-use practices will have reduced the habitat available to Amazonian plant species by approximately 12-24%, resulting in 5-9% of species becoming "committed to extinction," significantly fewer than other recent estimates. Contrary to previous studies, we find that the primary determinant of habitat loss and extinction risk is not the size of a species' range, but rather its location. The resulting extinction risk estimates are a valuable conservation tool because they indicate not only the total percentage of Amazonian plant species threatened with extinction but also the degree to which individual species and habitats will be affected by current and future land-use changes.

Template‐based field map prediction for rapid whole brain B0 shimming

PubMed Central

Shi, Yuhang; Vannesjo, S. Johanna; Miller, Karla L.

2017-01-01

Purpose In typical MRI protocols, time is spent acquiring a field map to calculate the shim settings for best image quality. We propose a fast template‐based field map prediction method that yields near‐optimal shims without measuring the field. Methods The template‐based prediction method uses prior knowledge of the B0 distribution in the human brain, based on a large database of field maps acquired from different subjects, together with subject‐specific structural information from a quick localizer scan. The shimming performance of using the template‐based prediction is evaluated in comparison to a range of potential fast shimming methods. Results Static B0 shimming based on predicted field maps performed almost as well as shimming based on individually measured field maps. In experimental evaluations at 7 T, the proposed approach yielded a residual field standard deviation in the brain of on average 59 Hz, compared with 50 Hz using measured field maps and 176 Hz using no subject‐specific shim. Conclusions This work demonstrates that shimming based on predicted field maps is feasible. The field map prediction accuracy could potentially be further improved by generating the template from a subset of subjects, based on parameters such as head rotation and body mass index. Magn Reson Med 80:171–180, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMID:29193340

Flood Risk Assessment and Forecasting for the Ganges-Brahmaputra-Meghna River Basins

NASA Astrophysics Data System (ADS)

Hopson, T. M.; Priya, S.; Young, W.; Avasthi, A.; Clayton, T. D.; Brakenridge, G. R.; Birkett, C. M.; Riddle, E. E.; Broman, D.; Boehnert, J.; Sampson, K. M.; Kettner, A.; Singh, D.

2017-12-01

During the 2017 South Asia monsoon, torrential rains and catastrophic floods affected more than 45 million people, including 16 million children, across the Ganges-Brahmaputra-Meghna (GBM) basins. The basin is recognized as one of the world's most disaster-prone regions, with severe floods occurring almost annually causing extreme loss of life and property. In light of this vulnerability, the World Bank and collaborators have contributed toward reducing future flood impacts through recent developments to improve operational preparedness for such events, as well as efforts in more general preparedness and resilience building through planning based on detailed risk assessments. With respect to improved event-specific flood preparedness through operational warnings, we discuss a new forecasting system that provides probability-based flood forecasts developed for more than 85 GBM locations. Forecasts are available online, along with near-real-time data maps of rainfall (predicted and actual) and river levels. The new system uses multiple data sets and multiple models to enhance forecasting skill, and provides improved forecasts up to 16 days in advance of the arrival of high waters. These longer lead times provide the opportunity to save both lives and livelihoods. With sufficient advance notice, for example, farmers can harvest a threatened rice crop or move vulnerable livestock to higher ground. Importantly, the forecasts not only predict future water levels but indicate the level of confidence in each forecast. Knowing whether the probability of a danger-level flood is 10 percent or 90 percent helps people to decide what, if any, action to take. With respect to efforts in general preparedness and resilience building, we also present a recent flood risk assessment, and how it provides, for the first time, a numbers-based view of the impacts of different size floods across the Ganges basin. The findings help identify priority areas for tackling flood risks (for example, relocating levees, improving flood warning systems, or boosting overall economic resilience). The assessment includes the locations and numbers of people at risk, as well as the locations and value of buildings, roads and railways, and crops at risk. An accompanying atlas includes easy-to-use risk maps and tables for the Ganges basins.

Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models.

PubMed

Adams, Matthew D; Kanaroglou, Pavlos S

2016-03-01

Air pollution poses health concerns at the global scale. The challenge of managing air pollution is significant because of the many air pollutants, insufficient funds for monitoring and abatement programs, and political and social challenges in defining policy to limit emissions. Some governments provide citizens with air pollution health risk information to allow them to limit their exposure. However, many regions still have insufficient air pollution monitoring networks to provide real-time mapping. Where available, these risk mapping systems either provide absolute concentration data or the concentrations are used to derive an Air Quality Index, which provides the air pollution risk for a mix of air pollutants with a single value. When risk information is presented as a single value for an entire region it does not inform on the spatial variation within the region. Without an understanding of the local variation residents can only make a partially informed decision when choosing daily activities. The single value is typically provided because of a limited number of active monitoring units in the area. In our work, we overcome this issue by leveraging mobile air pollution monitoring techniques, meteorological information and land use information to map real-time air pollution health risks. We propose an approach that can provide improved health risk information to the public by applying neural network models within a framework that is inspired by land use regression. Mobile air pollution monitoring campaigns were conducted across Hamilton from 2005 to 2013. These mobile air pollution data were modelled with a number of predictor variables that included information on the surrounding land use characteristics, the meteorological conditions, air pollution concentrations from fixed location monitors, and traffic information during the time of collection. Fine particulate matter and nitrogen dioxide were both modelled. During the model fitting process we reserved twenty percent of the data to validate the predictions. The models' performances were measured with a coefficient of determination at 0.78 and 0.34 for PM2.5 and NO2, respectively. We apply a relative importance measure to identify the importance of each variable in the neural network to partially overcome the black box issues of neural network models. Copyright © 2015 Elsevier Ltd. All rights reserved.

Tuberculosis disease mapping in Kedah using standardized morbidity ratio

NASA Astrophysics Data System (ADS)

Diah, Ijlal Mohd; Aziz, Nazrina; Kasim, Maznah Mat

2017-10-01

This paper presents the results of relative risk estimation that applied to TB data in Kedah using the most common approach, Standardized Morbidity Ratio (SMR). Disease mapping has been recognized as one of the methods that can be used by government and public health in order to control diseases since it can give a clear picture of the risk areas. To get good disease mapping, relative risk estimation is an important issue that need to be considered. TB risk areas will be recognized through the map. From the result, Kulim shows the lowest risk areas of contracting TB while Kota Setar has the highest risk area.

Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan.

PubMed

Dou, Jie; Tien Bui, Dieu; Yunus, Ali P; Jia, Kun; Song, Xuan; Revhaug, Inge; Xia, Huan; Zhu, Zhongfan

2015-01-01

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.

Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan

PubMed Central

Dou, Jie; Tien Bui, Dieu; P. Yunus, Ali; Jia, Kun; Song, Xuan; Revhaug, Inge; Xia, Huan; Zhu, Zhongfan

2015-01-01

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner. PMID:26214691

Conscious worst case definition for risk assessment, part I: a knowledge mapping approach for defining most critical risk factors in integrative risk management of chemicals and nanomaterials.

PubMed

Sørensen, Peter B; Thomsen, Marianne; Assmuth, Timo; Grieger, Khara D; Baun, Anders

2010-08-15

This paper helps bridge the gap between scientists and other stakeholders in the areas of human and environmental risk management of chemicals and engineered nanomaterials. This connection is needed due to the evolution of stakeholder awareness and scientific progress related to human and environmental health which involves complex methodological demands on risk management. At the same time, the available scientific knowledge is also becoming more scattered across multiple scientific disciplines. Hence, the understanding of potentially risky situations is increasingly multifaceted, which again challenges risk assessors in terms of giving the 'right' relative priority to the multitude of contributing risk factors. A critical issue is therefore to develop procedures that can identify and evaluate worst case risk conditions which may be input to risk level predictions. Therefore, this paper suggests a conceptual modelling procedure that is able to define appropriate worst case conditions in complex risk management. The result of the analysis is an assembly of system models, denoted the Worst Case Definition (WCD) model, to set up and evaluate the conditions of multi-dimensional risk identification and risk quantification. The model can help optimize risk assessment planning by initial screening level analyses and guiding quantitative assessment in relation to knowledge needs for better decision support concerning environmental and human health protection or risk reduction. The WCD model facilitates the evaluation of fundamental uncertainty using knowledge mapping principles and techniques in a way that can improve a complete uncertainty analysis. Ultimately, the WCD is applicable for describing risk contributing factors in relation to many different types of risk management problems since it transparently and effectively handles assumptions and definitions and allows the integration of different forms of knowledge, thereby supporting the inclusion of multifaceted risk components in cumulative risk management. Copyright 2009 Elsevier B.V. All rights reserved.

Climate Prediction Center - Expert Assessments Index

Science.gov Websites

Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Web resources and services. HOME > Monitoring and Data > Global Climate Data & Maps > ; Global Regional Climate Maps Regional Climate Maps Banner The Monthly regional analyses products are

Risk analysis reveals global hotspots for marine debris ingestion by sea turtles.

PubMed

Schuyler, Qamar A; Wilcox, Chris; Townsend, Kathy A; Wedemeyer-Strombel, Kathryn R; Balazs, George; van Sebille, Erik; Hardesty, Britta Denise

2016-02-01

Plastic marine debris pollution is rapidly becoming one of the critical environmental concerns facing wildlife in the 21st century. Here we present a risk analysis for plastic ingestion by sea turtles on a global scale. We combined global marine plastic distributions based on ocean drifter data with sea turtle habitat maps to predict exposure levels to plastic pollution. Empirical data from necropsies of deceased animals were then utilised to assess the consequence of exposure to plastics. We modelled the risk (probability of debris ingestion) by incorporating exposure to debris and consequence of exposure, and included life history stage, species of sea turtle and date of stranding observation as possible additional explanatory factors. Life history stage is the best predictor of debris ingestion, but the best-fit model also incorporates encounter rates within a limited distance from stranding location, marine debris predictions specific to the date of the stranding study and turtle species. There is no difference in ingestion rates between stranded turtles vs. those caught as bycatch from fishing activity, suggesting that stranded animals are not a biased representation of debris ingestion rates in the background population. Oceanic life-stage sea turtles are at the highest risk of debris ingestion, and olive ridley turtles are the most at-risk species. The regions of highest risk to global sea turtle populations are off of the east coasts of the USA, Australia and South Africa; the east Indian Ocean, and Southeast Asia. Model results can be used to predict the number of sea turtles globally at risk of debris ingestion. Based on currently available data, initial calculations indicate that up to 52% of sea turtles may have ingested debris. © 2015 John Wiley & Sons Ltd.

Risk Analysis Reveals Global Hotspots for Marine Debris Ingestion by Sea Turtles

NASA Astrophysics Data System (ADS)

Schuyler, Q. A.; Wilcox, C.; Townsend, K.; Wedemeyer-Strombel, K.; Balazs, G.; van Sebille, E.; Hardesty, B. D.

2016-02-01

Plastic marine debris pollution is rapidly becoming one of the critical environmental concerns facing wildlife in the 21st century. Here we present a risk analysis for plastic ingestion by sea turtles on a global scale. We combined global marine plastic distributions based on ocean drifter data with sea turtle habitat maps to predict exposure levels to plastic pollution. Empirical data from necropsies of deceased animals were then utilised to assess the consequence of exposure to plastics. We modelled the risk (probability of debris ingestion) by incorporating exposure to debris and consequence of exposure, and included life history stage, species of sea turtle, and date of stranding observation as possible additional explanatory factors. Life history stage is the best predictor of debris ingestion, but the best-fit model also incorporates encounter rates within a limited distance from stranding location, marine debris predictions specific to the date of the stranding study, and turtle species. There was no difference in ingestion rates between stranded turtles vs. those caught as bycatch from fishing activity, suggesting that stranded animals are not a biased representation of debris ingestion rates in the background population. Oceanic life-stage sea turtles are at the highest risk of debris ingestion, and olive ridley turtles are the most at-risk species. The regions of highest risk to global sea turtle populations are off of the east coasts of the USA, Australia, and South Africa; the east Indian Ocean, and Southeast Asia. Model results can be used to predict the number of sea turtles globally at risk of debris ingestion. Based on currently available data, initial calculations indicate that up to 52% of sea turtles may have ingested debris.

Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia.

PubMed

Gilbert, Marius; Golding, Nick; Zhou, Hang; Wint, G R William; Robinson, Timothy P; Tatem, Andrew J; Lai, Shengjie; Zhou, Sheng; Jiang, Hui; Guo, Danhuai; Huang, Zhi; Messina, Jane P; Xiao, Xiangming; Linard, Catherine; Van Boeckel, Thomas P; Martin, Vincent; Bhatt, Samir; Gething, Peter W; Farrar, Jeremy J; Hay, Simon I; Yu, Hongjie

2014-06-17

Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.

Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia

PubMed Central

Gilbert, Marius; Golding, Nick; Zhou, Hang; Wint, G. R. William; Robinson, Timothy P.; Tatem, Andrew J.; Lai, Shengjie; Zhou, Sheng; Jiang, Hui; Guo, Danhuai; Huang, Zhi; Messina, Jane P.; Xiao, Xiangming; Linard, Catherine; Van Boeckel, Thomas P.; Martin, Vincent; Bhatt, Samir; Gething, Peter W.; Farrar, Jeremy J.; Hay, Simon I.; Yu, Hongjie

2014-01-01

Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease. PMID:24937647

Developing a smartphone software package for predicting atmospheric pollutant concentrations at mobile locations.

PubMed

Larkin, Andrew; Williams, David E; Kile, Molly L; Baird, William M

2015-06-01

There is considerable evidence that exposure to air pollution is harmful to health. In the U.S., ambient air quality is monitored by Federal and State agencies for regulatory purposes. There are limited options, however, for people to access this data in real-time which hinders an individual's ability to manage their own risks. This paper describes a new software package that models environmental concentrations of fine particulate matter (PM 2.5 ), coarse particulate matter (PM 10 ), and ozone concentrations for the state of Oregon and calculates personal health risks at the smartphone's current location. Predicted air pollution risk levels can be displayed on mobile devices as interactive maps and graphs color-coded to coincide with EPA air quality index (AQI) categories. Users have the option of setting air quality warning levels via color-coded bars and were notified whenever warning levels were exceeded by predicted levels within 10 km. We validated the software using data from participants as well as from simulations which showed that the application was capable of identifying spatial and temporal air quality trends. This unique application provides a potential low-cost technology for reducing personal exposure to air pollution which can improve quality of life particularly for people with health conditions, such as asthma, that make them more susceptible to these hazards.

Bayesian geostatistics in health cartography: the perspective of malaria.

PubMed

Patil, Anand P; Gething, Peter W; Piel, Frédéric B; Hay, Simon I

2011-06-01

Maps of parasite prevalences and other aspects of infectious diseases that vary in space are widely used in parasitology. However, spatial parasitological datasets rarely, if ever, have sufficient coverage to allow exact determination of such maps. Bayesian geostatistics (BG) is a method for finding a large sample of maps that can explain a dataset, in which maps that do a better job of explaining the data are more likely to be represented. This sample represents the knowledge that the analyst has gained from the data about the unknown true map. BG provides a conceptually simple way to convert these samples to predictions of features of the unknown map, for example regional averages. These predictions account for each map in the sample, yielding an appropriate level of predictive precision.

Bayesian geostatistics in health cartography: the perspective of malaria

PubMed Central

Patil, Anand P.; Gething, Peter W.; Piel, Frédéric B.; Hay, Simon I.

2011-01-01

Maps of parasite prevalences and other aspects of infectious diseases that vary in space are widely used in parasitology. However, spatial parasitological datasets rarely, if ever, have sufficient coverage to allow exact determination of such maps. Bayesian geostatistics (BG) is a method for finding a large sample of maps that can explain a dataset, in which maps that do a better job of explaining the data are more likely to be represented. This sample represents the knowledge that the analyst has gained from the data about the unknown true map. BG provides a conceptually simple way to convert these samples to predictions of features of the unknown map, for example regional averages. These predictions account for each map in the sample, yielding an appropriate level of predictive precision. PMID:21420361

Assessment of Three Flood Hazard Mapping Methods: A Case Study of Perlis

NASA Astrophysics Data System (ADS)

Azizat, Nazirah; Omar, Wan Mohd Sabki Wan

2018-03-01

Flood is a common natural disaster and also affect the all state in Malaysia. Regarding to Drainage and Irrigation Department (DID) in 2007, about 29, 270 km2 or 9 percent of region of the country is prone to flooding. Flood can be such devastating catastrophic which can effected to people, economy and environment. Flood hazard mapping can be used is an important part in flood assessment to define those high risk area prone to flooding. The purposes of this study are to prepare a flood hazard mapping in Perlis and to evaluate flood hazard using frequency ratio, statistical index and Poisson method. The six factors affecting the occurrence of flood including elevation, distance from the drainage network, rainfall, soil texture, geology and erosion were created using ArcGIS 10.1 software. Flood location map in this study has been generated based on flooded area in year 2010 from DID. These parameters and flood location map were analysed to prepare flood hazard mapping in representing the probability of flood area. The results of the analysis were verified using flood location data in year 2013, 2014, 2015. The comparison result showed statistical index method is better in prediction of flood area rather than frequency ratio and Poisson method.

Forest fire risk zonation mapping using remote sensing technology

NASA Astrophysics Data System (ADS)

Chandra, Sunil; Arora, M. K.

2006-12-01

Forest fires cause major losses to forest cover and disturb the ecological balance in our region. Rise in temperature during summer season causing increased dryness, increased activity of human beings in the forest areas, and the type of forest cover in the Garhwal Himalayas are some of the reasons that lead to forest fires. Therefore, generation of forest fire risk maps becomes necessary so that preventive measures can be taken at appropriate time. These risk maps shall indicate the zonation of the areas which are in very high, high, medium and low risk zones with regard to forest fire in the region. In this paper, an attempt has been made to generate the forest fire risk maps based on remote sensing data and other geographical variables responsible for the occurrence of fire. These include altitude, temperature and soil variations. Key thematic data layers pertaining to these variables have been generated using various techniques. A rule-based approach has been used and implemented in GIS environment to estimate fuel load and fuel index leading to the derivation of fire risk zonation index and subsequently to fire risk zonation maps. The fire risk maps thus generated have been validated on the ground for forest types as well as for forest fire risk areas. These maps would help the state forest departments in prioritizing their strategy for combating forest fires particularly during the fire seasons.

Assessment of volcanic hazards, vulnerability, risk and uncertainty (Invited)

NASA Astrophysics Data System (ADS)

Sparks, R. S.

2009-12-01

A volcanic hazard is any phenomenon that threatens communities . These hazards include volcanic events like pyroclastic flows, explosions, ash fall and lavas, and secondary effects such as lahars and landslides. Volcanic hazards are described by the physical characteristics of the phenomena, by the assessment of the areas that they are likely to affect and by the magnitude-dependent return period of events. Volcanic hazard maps are generated by mapping past volcanic events and by modelling the hazardous processes. Both these methods have their strengths and limitations and a robust map should use both approaches in combination. Past records, studied through stratigraphy, the distribution of deposits and age dating, are typically incomplete and may be biased. Very significant volcanic hazards, such as surge clouds and volcanic blasts, are not well-preserved in the geological record for example. Models of volcanic processes are very useful to help identify hazardous areas that do not have any geological evidence. They are, however, limited by simplifications and incomplete understanding of the physics. Many practical volcanic hazards mapping tools are also very empirical. Hazards maps are typically abstracted into hazards zones maps, which are some times called threat or risk maps. Their aim is to identify areas at high levels of threat and the boundaries between zones may take account of other factors such as roads, escape routes during evacuation, infrastructure. These boundaries may change with time due to new knowledge on the hazards or changes in volcanic activity levels. Alternatively they may remain static but implications of the zones may change as volcanic activity changes. Zone maps are used for planning purposes and for management of volcanic crises. Volcanic hazards maps are depictions of the likelihood of future volcanic phenomena affecting places and people. Volcanic phenomena are naturally variable, often complex and not fully understood. There are many sources of uncertainty in forecasting the areas that volcanic activity will effect and the severity of the effects. Uncertainties arise from: natural variability, inadequate data, biased data, incomplete data, lack of understanding of the processes, limitations to predictive models, ambiguity, and unknown unknowns. The description of volcanic hazards is thus necessarily probabilistic and requires assessment of the attendant uncertainties. Several issues arise from the probabilistic nature of volcanic hazards and the intrinsic uncertainties. Although zonation maps require well-defined boundaries for administrative pragmatism, such boundaries cannot divide areas that are completely safe from those that are unsafe. Levels of danger or safety need to be defined to decide on and justify boundaries through the concepts of vulnerability and risk. More data, better observations, improved models may reduce uncertainties, but can increase uncertainties and may lead to re-appraisal of zone boundaries. Probabilities inferred by statistical techniques are hard to communicate. Expert elicitation is an emerging methodology for risk assessment and uncertainty evaluation. The method has been applied at one major volcanic crisis (Soufrière Hills Volcano, Montserrat), and is being applied in planning for volcanic crises at Vesuvius.

Provision of a wildfire risk map: informing residents in the wildland urban interface.

PubMed

Mozumder, Pallab; Helton, Ryan; Berrens, Robert P

2009-11-01

Wildfires in the wildland urban interface (WUI) are an increasing concern throughout the western United States and elsewhere. WUI communities continue to grow and thus increase the wildfire risk to human lives and property. Information such as a wildfire risk map can inform WUI residents of potential risks and may help to efficiently sort mitigation efforts. This study uses the survey-based contingent valuation (CV) method to examine annual household willingness to pay (WTP) for the provision of a wildfire risk map. Data were collected through a mail survey of the East Mountain WUI area in the State of New Mexico (USA). The integrated empirical approach includes a system of equations that involves joint estimation of WTP values, along with measures of a respondent's risk perception and risk mitigation behavior. The median estimated WTP is around U.S. $12 for the annual wildfire risk map, which covers at least the costs of producing and distributing available risk information. Further, providing a wildfire risk map can help address policy goals emphasizing information gathering and sharing among stakeholders to mitigate the effects of wildfires.

Dengue: recent past and future threats

PubMed Central

Rogers, David J.

2015-01-01

This article explores four key questions about statistical models developed to describe the recent past and future of vector-borne diseases, with special emphasis on dengue: (1) How many variables should be used to make predictions about the future of vector-borne diseases?(2) Is the spatial resolution of a climate dataset an important determinant of model accuracy?(3) Does inclusion of the future distributions of vectors affect predictions of the futures of the diseases they transmit?(4) Which are the key predictor variables involved in determining the distributions of vector-borne diseases in the present and future?Examples are given of dengue models using one, five or 10 meteorological variables and at spatial resolutions of from one-sixth to two degrees. Model accuracy is improved with a greater number of descriptor variables, but is surprisingly unaffected by the spatial resolution of the data. Dengue models with a reduced set of climate variables derived from the HadCM3 global circulation model predictions for the 1980s are improved when risk maps for dengue's two main vectors (Aedes aegypti and Aedes albopictus) are also included as predictor variables; disease and vector models are projected into the future using the global circulation model predictions for the 2020s, 2040s and 2080s. The Garthwaite–Koch corr-max transformation is presented as a novel way of showing the relative contribution of each of the input predictor variables to the map predictions. PMID:25688021

TU-G-BRA-03: Predicting Radiation Therapy Induced Ventilation Changes Using 4DCT Jacobian Calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patton, T; Du, K; Bayouth, J

2015-06-15

Purpose: Longitudinal changes in lung ventilation following radiation therapy can be mapped using four-dimensional computed tomography(4DCT) and image registration. This study aimed to predict ventilation changes caused by radiation therapy(RT) as a function of pre-RT ventilation and delivered dose. Methods: 4DCT images were acquired before and 3 months after radiation therapy for 13 subjects. Jacobian ventilation maps were calculated from the 4DCT images, warped to a common coordinate system, and a Jacobian ratio map was computed voxel-by-voxel as the ratio of post-RT to pre-RT Jacobian calculations. A leave-one-out method was used to build a response model for each subject: post-RTmore » to pre-RT Jacobian ratio data and dose distributions of 12 subjects were applied to the subject’s pre-RT Jacobian map to predict the post-RT Jacobian. The predicted Jacobian map was compared to the actual post-RT Jacobian map to evaluate efficacy. Within this cohort, 8 subjects had repeat pre-RT scans that were compared as a reference for no ventilation change. Maps were compared using gamma pass rate criteria of 2mm distance-to-agreement and 6% ventilation difference. Gamma pass rates were compared using paired t-tests to determine significant differences. Further analysis masked non-radiation induced changes by excluding voxels below specified dose thresholds. Results: Visual inspection demonstrates the predicted post-RT ventilation map is similar to the actual map in magnitude and distribution. Quantitatively, the percentage of voxels in agreement when excluding voxels receiving below specified doses are: 74%/20Gy, 73%/10Gy, 73%/5Gy, and 71%/0Gy. By comparison, repeat scans produced 73% of voxels within the 6%/2mm criteria. The agreement of the actual post-RT maps with the predicted maps was significantly better than agreement with pre-RT maps (p<0.02). Conclusion: This work validates that significant changes to ventilation post-RT can be predicted. The differences between the predicted and actual outcome are similar to differences between repeat scans with equivalent ventilation. This work was supported by NIH grant CA166703 and a Pilot Grant from University of Iowa Carver College of Medicine.« less

Can Mapping Algorithms Based on Raw Scores Overestimate QALYs Gained by Treatment? A Comparison of Mappings Between the Roland-Morris Disability Questionnaire and the EQ-5D-3L Based on Raw and Differenced Score Data.

PubMed

Madan, Jason; Khan, Kamran A; Petrou, Stavros; Lamb, Sarah E

2017-05-01

Mapping algorithms are increasingly being used to predict health-utility values based on responses or scores from non-preference-based measures, thereby informing economic evaluations. We explored whether predictions in the EuroQol 5-dimension 3-level instrument (EQ-5D-3L) health-utility gains from mapping algorithms might differ if estimated using differenced versus raw scores, using the Roland-Morris Disability Questionnaire (RMQ), a widely used health status measure for low back pain, as an example. We estimated algorithms mapping within-person changes in RMQ scores to changes in EQ-5D-3L health utilities using data from two clinical trials with repeated observations. We also used logistic regression models to estimate response mapping algorithms from these data to predict within-person changes in responses to each EQ-5D-3L dimension from changes in RMQ scores. Predicted health-utility gains from these mappings were compared with predictions based on raw RMQ data. Using differenced scores reduced the predicted health-utility gain from a unit decrease in RMQ score from 0.037 (standard error [SE] 0.001) to 0.020 (SE 0.002). Analysis of response mapping data suggests that the use of differenced data reduces the predicted impact of reducing RMQ scores across EQ-5D-3L dimensions and that patients can experience health-utility gains on the EQ-5D-3L 'usual activity' dimension independent from improvements captured by the RMQ. Mappings based on raw RMQ data overestimate the EQ-5D-3L health utility gains from interventions that reduce RMQ scores. Where possible, mapping algorithms should reflect within-person changes in health outcome and be estimated from datasets containing repeated observations if they are to be used to estimate incremental health-utility gains.

Mapping the EORTC QLQ-C30 onto the EQ-5D-3L: assessing the external validity of existing mapping algorithms.

PubMed

Doble, Brett; Lorgelly, Paula

2016-04-01

To determine the external validity of existing mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses and to establish their generalizability in different types of cancer. A main analysis (pooled) sample of 3560 observations (1727 patients) and two disease severity patient samples (496 and 93 patients) with repeated observations over time from Cancer 2015 were used to validate the existing algorithms. Errors were calculated between observed and predicted EQ-5D-3L utility values using a single pooled sample and ten pooled tumour type-specific samples. Predictive accuracy was assessed using mean absolute error (MAE) and standardized root-mean-squared error (RMSE). The association between observed and predicted EQ-5D utility values and other covariates across the distribution was tested using quantile regression. Quality-adjusted life years (QALYs) were calculated using observed and predicted values to test responsiveness. Ten 'preferred' mapping algorithms were identified. Two algorithms estimated via response mapping and ordinary least-squares regression using dummy variables performed well on number of validation criteria, including accurate prediction of the best and worst QLQ-C30 health states, predicted values within the EQ-5D tariff range, relatively small MAEs and RMSEs, and minimal differences between estimated QALYs. Comparison of predictive accuracy across ten tumour type-specific samples highlighted that algorithms are relatively insensitive to grouping by tumour type and affected more by differences in disease severity. Two of the 'preferred' mapping algorithms suggest more accurate predictions, but limitations exist. We recommend extensive scenario analyses if mapped utilities are used in cost-utility analyses.

Using Geographic Information System-based Ecologic Niche Models to Forecast the Risk of Hantavirus Infection in Shandong Province, China

PubMed Central

Wei, Lan; Qian, Quan; Wang, Zhi-Qiang; Glass, Gregory E.; Song, Shao-Xia; Zhang, Wen-Yi; Li, Xiu-Jun; Yang, Hong; Wang, Xian-Jun; Fang, Li-Qun; Cao, Wu-Chun

2011-01-01

Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in Shandong Province, China. In this study, we combined ecologic niche modeling with geographic information systems (GIS) and remote sensing techniques to identify the risk factors and affected areas of hantavirus infections in rodent hosts. Land cover and elevation were found to be closely associated with the presence of hantavirus-infected rodent hosts. The averaged area under the receiver operating characteristic curve was 0.864, implying good performance. The predicted risk maps based on the model were validated both by the hantavirus-infected rodents' distribution and HFRS human case localities with a good fit. These findings have the applications for targeting control and prevention efforts. PMID:21363991

Predicting the Vegetation Condition of the 2015/16 Drought across the Great Horn of Africa (GHA): Model Evaluation and Preliminary Result

NASA Astrophysics Data System (ADS)

Tadesse, T.; Bayissa, Y. A.; Demisse, G. B.; Wardlow, B.

2017-12-01

The National Drought Mitigation Center (NDMC) funded by NASA has developed a new tool for predicting the general vegetation condition called: the "Vegetation outlook for the Greater Africa (VegOut-GHA)." In this study, the 2015/16 drought across the GHA that has been considered one of the worst in decades across the region was assessed and evaluated using the VegOut-GHA models and products. The VegOut-GHA maps (hindsight prediction maps) for the growing season (June - September) were generated to predict a standardized seasonal greenness (SSG) that is based on seasonally integrated normalized difference vegetation index (a measure that represents a general indicator of relative vegetation health within a growing season). The vegetation condition outlooks were made for 10-day, 1-month, 2-month, and 3-month in hindsight and compared to the observed values of the SSG. The VegOut-GHA model was evaluated and compared to crop yield and other satellite-derived data (e.g., standardized seasonal precipitation based on "Enhancing National Climate Services (ENACTS)" datasets for GHA). Thus, the VegOut-GHA model and its evaluation results will be discussed based on the 2015/2016 drought season in the region. This preliminary results suggest an opportunity to improve management of drought risk in agriculture and food security.

Predictive modelling for startup and investor relationship based on crowdfunding platform data

NASA Astrophysics Data System (ADS)

Alamsyah, Andry; Buono Asto Nugroho, Tri

2018-03-01

Crowdfunding platform is a place where startup shows off publicly their idea for the purpose to get their project funded. Crowdfunding platform such as Kickstarter are becoming popular today, it provides the efficient way for startup to get funded without liabilities, it also provides variety project category that can be participated. There is an available safety procedure to ensure achievable low-risk environment. The startup promoted project must accomplish their funded goal target. If they fail to reach the target, then there is no investment activity take place. It motivates startup to be more active to promote or disseminate their project idea and it also protect investor from losing money. The study objective is to predict the successfulness of proposed project and mapping investor trend using data mining framework. To achieve the objective, we proposed 3 models. First model is to predict whether a project is going to be successful or failed using K-Nearest Neighbour (KNN). Second model is to predict the number of successful project using Artificial Neural Network (ANN). Third model is to map the trend of investor in investing the project using K-Means clustering algorithm. KNN gives 99.04% model accuracy, while ANN best configuration gives 16-14-1 neuron layers and 0.2 learning rate, and K-Means gives 6 best separation clusters. The results of those models can help startup or investor to make decision regarding startup investment.

Climate Prediction Center - Outlooks: Current UV Index Forecast Map

Science.gov Websites

Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News Service NOAA Center for Weather and Climate Prediction Climate Prediction Center 5830 University Research Court College Park, Maryland 20740 Page Author: Climate Prediction Center Internet Team Disclaimer

A combined field/remote sensing approach for characterizing landslide risk in coastal areas

NASA Astrophysics Data System (ADS)

Francioni, Mirko; Coggan, John; Eyre, Matthew; Stead, Doug

2018-05-01

Understanding the key factors controlling slope failure mechanisms in coastal areas is the first and most important step for analyzing, reconstructing and predicting the scale, location and extent of future instability in rocky coastlines. Different failure mechanisms may be possible depending on the influence of the engineering properties of the rock mass (including the fracture network), the persistence and type of discontinuity and the relative aspect or orientation of the coastline. Using a section of the North Coast of Cornwall, UK, as an example we present a multi-disciplinary approach for characterizing landslide risk associated with coastal instabilities in a blocky rock mass. Remotely captured terrestrial and aerial LiDAR and photogrammetric data were interrogated using Geographic Information System (GIS) techniques to provide a framework for subsequent analysis, interpretation and validation. The remote sensing mapping data was used to define the rock mass discontinuity network of the area and to differentiate between major and minor geological structures controlling the evolution of the North Coast of Cornwall. Kinematic instability maps generated from aerial LiDAR data using GIS techniques and results from structural and engineering geological surveys are presented. With this method, it was possible to highlight the types of kinematic failure mechanism that may generate coastal landslides and highlight areas that are more susceptible to instability or increased risk of future instability. Multi-temporal aerial LiDAR data and orthophotos were also studied using GIS techniques to locate recent landslide failures, validate the results obtained from the kinematic instability maps through site observations and provide improved understanding of the factors controlling the coastal geomorphology. The approach adopted is not only useful for academic research, but also for local authorities and consultancy's when assessing the likely risks of coastal instability.

Predicting Anthropogenic Noise Contributions to US Waters.

PubMed

Gedamke, Jason; Ferguson, Megan; Harrison, Jolie; Hatch, Leila; Henderson, Laurel; Porter, Michael B; Southall, Brandon L; Van Parijs, Sofie

2016-01-01

To increase understanding of the potential effects of chronic underwater noise in US waters, the National Oceanic and Atmospheric Administration (NOAA) organized two working groups in 2011, collectively called "CetSound," to develop tools to map the density and distribution of cetaceans (CetMap) and predict the contribution of human activities to underwater noise (SoundMap). The SoundMap effort utilized data on density, distribution, acoustic signatures of dominant noise sources, and environmental descriptors to map estimated temporal, spatial, and spectral contributions to background noise. These predicted soundscapes are an initial step toward assessing chronic anthropogenic noise impacts on the ocean's varied acoustic habitats and the animals utilizing them.

Towards reliable mapping of biosecurity risk: incorporating uncertainty and decision-makers’ risk aversion

Treesearch

Denys Yemshanov; Frank H. Koch; Mark Ducey; Robert A. Haack

2015-01-01

Pest risk maps are an important source of decision support when devising strategies to minimize introductions of invasive organisms and mitigate their impacts. When possible management responses to an invader include costly or socially sensitive activities, decision makers tend to follow a more certain (i.e. risk-averse) course of action. We present a new mapping...

Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker’s perspective

Treesearch

Denys Yemshanov; Frank H. Koch; Mark J. Ducey; Robert A. Haack; Marty Siltanen; Kirsty Wilson

2013-01-01

Pest risk maps are important decision support tools when devising strategies to minimize introductions of invasive organisms and mitigate their impacts. When possible management responses to an invader include costly or socially sensitive activities, decision-makers tend to follow a more certain (i.e., risk-averse) course of action. We presented a new mapping technique...

SU-F-J-219: Predicting Ventilation Change Due to Radiation Therapy: Dependency On Pre-RT Ventilation and Effort Correction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patton, T; Du, K; Bayouth, J

Purpose: Ventilation change caused by radiation therapy (RT) can be predicted using four-dimensional computed tomography (4DCT) and image registration. This study tested the dependency of predicted post-RT ventilation on effort correction and pre-RT lung function. Methods: Pre-RT and 3 month post-RT 4DCT images were obtained for 13 patients. The 4DCT images were used to create ventilation maps using a deformable image registration based Jacobian expansion calculation. The post-RT ventilation maps were predicted in four different ways using the dose delivered, pre-RT ventilation, and effort correction. The pre-RT ventilation and effort correction were toggled to determine dependency. The four different predictedmore » ventilation maps were compared to the post-RT ventilation map calculated from image registration to establish the best prediction method. Gamma pass rates were used to compare the different maps with the criteria of 2mm distance-to-agreement and 6% ventilation difference. Paired t-tests of gamma pass rates were used to determine significant differences between the maps. Additional gamma pass rates were calculated using only voxels receiving over 20 Gy. Results: The predicted post-RT ventilation maps were in agreement with the actual post-RT maps in the following percentage of voxels averaged over all subjects: 71% with pre-RT ventilation and effort correction, 69% with no pre-RT ventilation and effort correction, 60% with pre-RT ventilation and no effort correction, and 58% with no pre-RT ventilation and no effort correction. When analyzing only voxels receiving over 20 Gy, the gamma pass rates were respectively 74%, 69%, 65%, and 55%. The prediction including both pre- RT ventilation and effort correction was the only prediction with significant improvement over using no prediction (p<0.02). Conclusion: Post-RT ventilation is best predicted using both pre-RT ventilation and effort correction. This is the only prediction that provided a significant improvement on agreement. Research support from NIH grants CA166119 and CA166703, a gift from Roger Koch, and a Pilot Grant from University of Iowa Carver College of Medicine.« less

Landscape-scale accessibility of livestock to tigers: implications of spatial grain for modeling predation risk to mitigate human-carnivore conflict.

PubMed

Miller, Jennifer R B; Jhala, Yadvendradev V; Jena, Jyotirmay; Schmitz, Oswald J

2015-03-01

Innovative conservation tools are greatly needed to reduce livelihood losses and wildlife declines resulting from human-carnivore conflict. Spatial risk modeling is an emerging method for assessing the spatial patterns of predator-prey interactions, with applications for mitigating carnivore attacks on livestock. Large carnivores that ambush prey attack and kill over small areas, requiring models at fine spatial grains to predict livestock depredation hot spots. To detect the best resolution for predicting where carnivores access livestock, we examined the spatial attributes associated with livestock killed by tigers in Kanha Tiger Reserve, India, using risk models generated at 20, 100, and 200-m spatial grains. We analyzed land-use, human presence, and vegetation structure variables at 138 kill sites and 439 random sites to identify key landscape attributes where livestock were vulnerable to tigers. Land-use and human presence variables contributed strongly to predation risk models, with most variables showing high relative importance (≥0.85) at all spatial grains. The risk of a tiger killing livestock increased near dense forests and near the boundary of the park core zone where human presence is restricted. Risk was nonlinearly related to human infrastructure and open vegetation, with the greatest risk occurring 1.2 km from roads, 1.1 km from villages, and 8.0 km from scrubland. Kill sites were characterized by denser, patchier, and more complex vegetation with lower visibility than random sites. Risk maps revealed high-risk hot spots inside of the core zone boundary and in several patches in the human-dominated buffer zone. Validation against known kills revealed predictive accuracy for only the 20 m model, the resolution best representing the kill stage of hunting for large carnivores that ambush prey, like the tiger. Results demonstrate that risk models developed at fine spatial grains can offer accurate guidance on landscape attributes livestock should avoid to minimize human-carnivore conflict.

Diurnal blood pressure variations are associated with changes in distal-proximal skin temperature gradient.

PubMed

Kräuchi, Kurt; Gompper, Britta; Hauenstein, Daniela; Flammer, Josef; Pflüger, Marlon; Studerus, Erich; Schötzau, Andy; Orgül, Selim

2012-11-01

It is generally assumed that skin vascular resistance contributes only to a small extent to total peripheral resistance and hence to blood pressure (BP). However, little is known about the impact of skin blood flow (SBF) changes on the diurnal variations of BP under ambulatory conditions. The main aim of the study was to determine whether diurnal patterns of distal SBF are related to mean arterial BP (MAP). Twenty-four-hour ambulatory measurements of BP, heart rate (HR) and distal (mean of hands and feet) as well as proximal (mean of sternum and infraclavicular region) skin temperatures were carried out in 51 patients (men/women = 18/33) during a 2-d eye hospital investigation. The standardized ambulatory protocol allowed measurements with minimal interference from uncontrolled parameters and, hence, some conclusive interpretations. The distal minus proximal skin temperature gradient (DPG) provided a measure for distal SBF. Individual cross-correlation analyses revealed that the diurnal pattern of MAP was nearly a mirror image of DPG and hence of distal SBF. Scheduled lunch and dinner induced an increase in DPG and a decline in MAP, while HR increased. Low daytime DPG (i.e. low distal SBF) levels significantly predicted sleep-induced BP dipping (r = -.436, p = .0014). Preliminary path analysis suggested that outdoor air temperature and atmospheric pressure may act on MAP via changed distal SBF. Changes in distal SBF may contribute to diurnal variation in MAP, including sleep-induced BP dipping and changes related to food intake. This finding might have an impact on individual cardiovascular risk prediction with respect to diurnal, seasonal and weather variations; however, the underlying mechanisms remain to be discovered.

Cartographic Design in Flood Risk Mapping - A Challenge for Communication and Stakeholder Involvement

NASA Astrophysics Data System (ADS)

Fuchs, S.; Serrhini, K.; Dorner, W.

2009-12-01

In order to mitigate flood hazards and to minimise associated losses, technical protection measures have been additionally and increasingly supplemented by non-technical mitigation, i.e. land-use planning activities. This is commonly done by creating maps which indicate such areas by different cartographic symbols, such as colour, size, shape, and typography. Hazard and risk mapping is the accepted procedure when communicating potential threats to stakeholders, and is therefore required in the European Member States in order to meet the demands of the European Flood Risk Directive. However, available information is sparse concerning the impact of such maps on different stakeholders, i.e., specialists in flood risk management, politicians, and affected citizens. The lack of information stems from a traditional approach to map production which does not take into account specific end-user needs. In order to overcome this information shortage the current study used a circular approach such that feed-back mechanisms originating from different perception patterns of the end user would be considered. Different sets of small-scale as well as large-scale risk maps were presented to different groups of test persons in order to (1) study reading behaviour as well as understanding and (2) deduce the most attractive components that are essential for target-oriented communication of cartographic information. Therefore, the method of eye tracking was applied using a video-oculography technique. This resulted in a suggestion for a map template which fulfils the requirement to serve as an efficient communication tool for specialists and practitioners in hazard and risk mapping as well as for laypersons. Taking the results of this study will enable public authorities who are responsible for flood mitigation to (1) improve their flood risk maps, (2) enhance flood risk awareness, and therefore (3) create more disaster-resilient communities.

The Sardinian Way to Type 1 Diabetes

PubMed Central

Songini, Marco; Lombardo, Cira

2010-01-01

Sardinia and Finland are the “hottest” areas for type 1 diabetes mellitus (T1DM) worldwide. Its genetic and epidemiological background make Sardinia an ideal region for investigating environmental, immunological, and genetic factors related to the etiopathogenesis of T1DM. Consequently, in 1990, the Insulin-Dependent Diabetes Mellitus Sardinia Project was launched in order to map the geographical distribution of T1DM in the island and to investigate preclinical phases of T1DM in a large cohort of people genetically at risk. The final goal would be to design models of prediction and to formulate safe preventive measures, especially addressed to the general population living in areas at high risk. PMID:20920447

An application of quantile random forests for predictive mapping of forest attributes

Treesearch

E.A. Freeman; G.G. Moisen

2015-01-01

Increasingly, random forest models are used in predictive mapping of forest attributes. Traditional random forests output the mean prediction from the random trees. Quantile regression forests (QRF) is an extension of random forests developed by Nicolai Meinshausen that provides non-parametric estimates of the median predicted value as well as prediction quantiles. It...

Developing a Global Database of Historic Flood Events to Support Machine Learning Flood Prediction in Google Earth Engine

NASA Astrophysics Data System (ADS)

Tellman, B.; Sullivan, J.; Kettner, A.; Brakenridge, G. R.; Slayback, D. A.; Kuhn, C.; Doyle, C.

2016-12-01

There is an increasing need to understand flood vulnerability as the societal and economic effects of flooding increases. Risk models from insurance companies and flood models from hydrologists must be calibrated based on flood observations in order to make future predictions that can improve planning and help societies reduce future disasters. Specifically, to improve these models both traditional methods of flood prediction from physically based models as well as data-driven techniques, such as machine learning, require spatial flood observation to validate model outputs and quantify uncertainty. A key dataset that is missing for flood model validation is a global historical geo-database of flood event extents. Currently, the most advanced database of historical flood extent is hosted and maintained at the Dartmouth Flood Observatory (DFO) that has catalogued 4320 floods (1985-2015) but has only mapped 5% of these floods. We are addressing this data gap by mapping the inventory of floods in the DFO database to create a first-of- its-kind, comprehensive, global and historical geospatial database of flood events. To do so, we combine water detection algorithms on MODIS and Landsat 5,7 and 8 imagery in Google Earth Engine to map discrete flood events. The created database will be available in the Earth Engine Catalogue for download by country, region, or time period. This dataset can be leveraged for new data-driven hydrologic modeling using machine learning algorithms in Earth Engine's highly parallelized computing environment, and we will show examples for New York and Senegal.

Role of post-mapping computed tomography in virtual-assisted lung mapping.

PubMed

Sato, Masaaki; Nagayama, Kazuhiro; Kuwano, Hideki; Nitadori, Jun-Ichi; Anraku, Masaki; Nakajima, Jun

2017-02-01

Background Virtual-assisted lung mapping is a novel bronchoscopic preoperative lung marking technique in which virtual bronchoscopy is used to predict the locations of multiple dye markings. Post-mapping computed tomography is performed to confirm the locations of the actual markings. This study aimed to examine the accuracy of marking locations predicted by virtual bronchoscopy and elucidate the role of post-mapping computed tomography. Methods Automated and manual virtual bronchoscopy was used to predict marking locations. After bronchoscopic dye marking under local anesthesia, computed tomography was performed to confirm the actual marking locations before surgery. Discrepancies between marking locations predicted by the different methods and the actual markings were examined on computed tomography images. Forty-three markings in 11 patients were analyzed. Results The average difference between the predicted and actual marking locations was 30 mm. There was no significant difference between the latest version of the automated virtual bronchoscopy system (30.7 ± 17.2 mm) and manual virtual bronchoscopy (29.8 ± 19.1 mm). The difference was significantly greater in the upper vs. lower lobes (37.1 ± 20.1 vs. 23.0 ± 6.8 mm, for automated virtual bronchoscopy; p < 0.01). Despite this discrepancy, all targeted lesions were successfully resected using 3-dimensional image guidance based on post-mapping computed tomography reflecting the actual marking locations. Conclusions Markings predicted by virtual bronchoscopy were dislocated from the actual markings by an average of 3 cm. However, surgery was accurately performed using post-mapping computed tomography guidance, demonstrating the indispensable role of post-mapping computed tomography in virtual-assisted lung mapping.

Temporal, Diagnostic, and Tissue-Specific Regulation of NRG3 Isoform Expression in Human Brain Development and Affective Disorders.

PubMed

Paterson, Clare; Wang, Yanhong; Hyde, Thomas M; Weinberger, Daniel R; Kleinman, Joel E; Law, Amanda J

2017-03-01

Genes implicated in schizophrenia are enriched in networks differentially regulated during human CNS development. Neuregulin 3 (NRG3), a brain-enriched neurotrophin, undergoes alternative splicing and is implicated in several neurological disorders with developmental origins. Isoform-specific increases in NRG3 are observed in schizophrenia and associated with rs10748842, a NRG3 risk polymorphism, suggesting NRG3 transcriptional dysregulation as a molecular mechanism of risk. The authors quantitatively mapped the temporal trajectories of NRG3 isoforms (classes I-IV) in the neocortex throughout the human lifespan, examined whether tissue-specific regulation of NRG3 occurs in humans, and determined if abnormalities in NRG3 transcriptomics occur in mood disorders and are genetically determined. NRG3 isoform classes I-IV were quantified using quantitative real-time polymerase chain reaction in human postmortem dorsolateral prefrontal cortex from 286 nonpsychiatric control individuals, from gestational week 14 to 85 years old, and individuals diagnosed with either bipolar disorder (N=34) or major depressive disorder (N=69). Tissue-specific mapping was investigated in several human tissues. rs10748842 was genotyped in individuals with mood disorders, and association with NRG3 isoform expression examined. NRG3 classes displayed individually specific expression trajectories across human neocortical development and aging; classes I, II, and IV were significantly associated with developmental stage. NRG3 class I was increased in bipolar and major depressive disorder, consistent with observations in schizophrenia. NRG3 class II was increased in bipolar disorder, and class III was increased in major depression. The rs10748842 risk genotype predicted elevated class II and III expression, consistent with previous reports in the brain, with tissue-specific analyses suggesting that classes II and III are brain-specific isoforms of NRG3. Mapping the temporal expression of genes during human brain development provides vital insight into gene function and identifies critical sensitive periods whereby genetic factors may influence risk for psychiatric disease. Here the authors provide comprehensive insight into the transcriptional landscape of the psychiatric risk gene, NRG3, in human neocortical development and expand on previous findings in schizophrenia to identify increased expression of developmentally and genetically regulated isoforms in the brain of patients with mood disorders. Principally, the finding that NRG3 classes II and III are brain-specific isoforms predicted by rs10748842 risk genotype and are increased in mood disorders further implicates a molecular mechanism of psychiatric risk at the NRG3 locus and identifies a potential developmental role for NRG3 in bipolar disorder and major depression. These observations encourage investigation of the neurobiology of NRG3 isoforms and highlight inhibition of NRG3 signaling as a potential target for psychiatric treatment development.

Temporal, Diagnostic, and Tissue-Specific Regulation of NRG3 Isoform Expression in Human Brain Development and Affective Disorders

PubMed Central

Paterson, Clare; Wang, Yanhong; Hyde, Thomas M.; Weinberger, Daniel R.; Kleinman, Joel E.; Law, Amanda J.

2018-01-01

Objective Genes implicated in schizophrenia are enriched in networks differentially regulated during human CNS development. Neuregulin 3 (NRG3), a brain-enriched neurotrophin, undergoes alternative splicing and is implicated in several neurological disorders with developmental origins. Isoform-specific increases in NRG3 are observed in schizophrenia and associated with rs10748842, a NRG3 risk polymorphism, suggesting NRG3 transcriptional dysregulation as a molecular mechanism of risk. The authors quantitatively mapped the temporal trajectories of NRG3 isoforms (classes I–IV) in the neocortex throughout the human lifespan, examined whether tissue-specific regulation of NRG3 occurs in humans, and determined if abnormalities in NRG3 transcriptomics occur in mood disorders and are genetically determined. Method NRG3 isoform classes I–IV were quantified using quantitative real-time polymerase chain reaction in human postmortem dorsolateral prefrontal cortex from 286 nonpsychiatric control individuals, from gestational week 14 to 85 years old, and individuals diagnosed with either bipolar disorder (N=34) or major depressive disorder (N=69). Tissue-specific mapping was investigated in several human tissues. rs10748842 was genotyped in individuals with mood disorders, and association with NRG3 isoform expression examined. Results NRG3 classes displayed individually specific expression trajectories across human neocortical development and aging; classes I, II, and IV were significantly associated with developmental stage. NRG3 class I was increased in bipolar and major depressive disorder, consistent with observations in schizophrenia. NRG3 class II was increased in bipolar disorder, and class III was increased in major depression. The rs10748842 risk genotype predicted elevated class II and III expression, consistent with previous reports in the brain, with tissue-specific analyses suggesting that classes II and III are brain-specific isoforms of NRG3. Conclusions Mapping the temporal expression of genes during human brain development provides vital insight into gene function and identifies critical sensitive periods whereby genetic factors may influence risk for psychiatric disease. Here the authors provide comprehensive insight into the transcriptional landscape of the psychiatric risk gene, NRG3, in human neocortical development and expand on previous findings in schizophrenia to identify increased expression of developmentally and genetically regulated isoforms in the brain of patients with mood disorders. Principally, the finding that NRG3 classes II and III are brain-specific isoforms predicted by rs10748842 risk genotype and are increased in mood disorders further implicates a molecular mechanism of psychiatric risk at the NRG3 locus and identifies a potential developmental role for NRG3 in bipolar disorder and major depression. These observations encourage investigation of the neurobiology of NRG3 isoforms and highlight inhibition of NRG3 signaling as a potential target for psychiatric treatment development. PMID:27771971

A Cartographic Tool for Managing African Swine Fever in Eurasia: Mapping Wild Boar Distribution Based on the Quality of Available Habitats.

PubMed

Bosch, J; Iglesias, I; Muñoz, M J; de la Torre, A

2017-12-01

The current African swine fever (ASF) epidemic in Eurasia represents a risk for the swine industry with devastating socio-economic and political consequences. Wild boar appears to be a key factor in maintaining the disease in endemic areas (mainly the Russian Federation) and spreading the disease across borders, including within the European Union. To help predict and interpret the dynamics of ASF infection, we developed a standardized distribution map based on global land cover vegetation (GLOBCOVER) that quantifies the quality of available habitats (QAH) for wild boar across Eurasia as an indirect index for quantifying numbers of wild boar. QAHs were estimated using a seven-level scale based on expert opinion and found to correlate closely with georeferenced presence of wild boar (n = 22 362): the highest wild boar densities (74.47%) were found in areas at the two highest QAH levels, while the lowest densities (5.66%) were found in areas at the lowest QAH levels. Mapping notifications from 2007 to 2016 onto the QAH map showed that in endemic areas, 60% of ASF notifications occurred in domestic pigs, mostly in agricultural landscapes (QAHs 1.75 and 1) containing low-biosecurity domestic pig farms. In the EU, in contrast, 95% of ASF notifications occurred in wild boar, within natural landscapes (QAH 2). These results suggest that the QAH map can be a useful epi-tool for defining risk scenarios and identifying potential travel corridors for ASF. This tool will help inform resource allocation decisions and improve prevention, control and surveillance of ASF and potentially of other diseases affecting swine and wild boar in Eurasia. © 2016 Blackwell Verlag GmbH.

Landscape structure and management alter the outcome of a pesticide ERA: Evaluating impacts of endocrine disruption using the ALMaSS European Brown Hare model.

PubMed

Topping, Chris J; Dalby, Lars; Skov, Flemming

2016-01-15

There is a gradual change towards explicitly considering landscapes in regulatory risk assessment. To realise the objective of developing representative scenarios for risk assessment it is necessary to know how detailed a landscape representation is needed to generate a realistic risk assessment, and indeed how to generate such landscapes. This paper evaluates the contribution of landscape and farming components to a model based risk assessment of a fictitious endocrine disruptor on hares. In addition, we present methods and code examples for generation of landscape structures and farming simulation from data collected primarily for EU agricultural subsidy support and GIS map data. Ten different Danish landscapes were generated and the ERA carried out for each landscape using two different assumed toxicities. The results showed negative impacts in all cases, but the extent and form in terms of impacts on abundance or occupancy differed greatly between landscapes. A meta-model was created, predicting impact from landscape and farming characteristics. Scenarios based on all combinations of farming and landscape for five landscapes representing extreme and middle impacts were created. The meta-models developed from the 10 real landscapes failed to predict impacts for these 25 scenarios. Landscape, farming, and the emergent density of hares all influenced the results of the risk assessment considerably. The study indicates that prediction of a reasonable worst case scenario is difficult from structural, farming or population metrics; rather the emergent properties generated from interactions between landscape, management and ecology are needed. Meta-modelling may also fail to predict impacts, even when restricting inputs to combinations of those used to create the model. Future ERA may therefore need to make use of multiple scenarios representing a wide range of conditions to avoid locally unacceptable risks. This approach could now be feasible Europe wide given the landscape generation methods presented.

Impact of climate change on risk of incursion of Crimean-Congo haemorrhagic fever virus in livestock in Europe through migratory birds.

PubMed

Gale, P; Stephenson, B; Brouwer, A; Martinez, M; de la Torre, A; Bosch, J; Foley-Fisher, M; Bonilauri, P; Lindström, A; Ulrich, R G; de Vos, C J; Scremin, M; Liu, Z; Kelly, L; Muñoz, M J

2012-02-01

To predict the risk of incursion of Crimean-Congo haemorrhagic fever virus (CCHFV) in livestock in Europe introduced through immature Hyalomma marginatum ticks on migratory birds under current conditions and in the decade 2075-2084 under a climate-change scenario. A spatial risk map of Europe comprising 14 282 grid cells (25 × 25 km) was constructed using three data sources: (i) ranges and abundances of four species of bird which migrate from sub-Saharan Africa to Europe each spring, namely Willow warbler (Phylloscopus trochilus), Northern wheatear (Oenanthe oenanthe), Tree pipit (Anthus trivialis) and Common quail (Coturnix coturnix); (ii) UK Met Office HadRM3 spring temperatures for prediction of moulting success of immature H. marginatum ticks and (iii) livestock densities. On average, the number of grid cells in Europe predicted to have at least one CCHFV incursion in livestock in spring was 1·04 per year for the decade 2005-2014 and 1·03 per year for the decade 2075-2084. In general with the assumed climate-change scenario, the risk increased in northern Europe but decreased in central and southern Europe, although there is considerable local variation in the trends. The absolute risk of incursion of CCHFV in livestock through ticks introduced by four abundant species of migratory bird (totalling 120 million individual birds) is very low. Climate change has opposing effects, increasing the success of the moult of the nymphal ticks into adults but decreasing the projected abundance of birds by 34% in this model. For Europe, climate change is not predicted to increase the overall risk of incursion of CCHFV in livestock through infected ticks introduced by these four migratory bird species. © 2011 Crown Copyright, AHVLA. Journal of Applied Microbiology © 2011 The Society for Applied Microbiology.

Application of laboratory reflectance spectroscopy to target and map expansive soils: example of the western Loiret, France

NASA Astrophysics Data System (ADS)

Hohmann, Audrey; Dufréchou, Grégory; Grandjean, Gilles; Bourguignon, Anne

2014-05-01

Swelling soils contain clay minerals that change volume with water content and cause extensive and expensive damage on infrastructures. Based on spatial distribution of infrastructure damages and existing geological maps, the Bureau de Recherches Géologiques et Minières (BRGM, i.e. the French Geological Survey) published in 2010 a 1:50 000 swelling hazard map of France, indexing the territory to low, moderate, or high swelling risk. This study aims to use SWIR (1100-2500 nm) reflectance spectra of soils acquired under laboratory controlled conditions to estimate the swelling potential of soils and improve the swelling risk map of France. 332 samples were collected at the W of Orléans (France) in various geological formations and swelling risk areas. Comparisons of swelling potential of soil samples and swelling risk areas of the map show several inconsistent associations that confirm the necessity to redraw the actual swelling risk map of France. New swelling risk maps of the sampling area were produce from soil samples using three interpolation methods. Maps produce using kriging and Natural neighbour interpolation methods did not permit to show discrete lithological units, introduced unsupported swelling risk zones, and did not appear useful to refine swelling risk map of France. Voronoi polygon was also used to produce map where swelling potential estimated from each samples were extrapolated to a polygon and all polygons were thus supported by field information. From methods tested here, Voronoi polygon appears thus the most adapted method to produce expansive soils maps. However, size of polygon is highly dependent of the samples spacing and samples may not be representative of the entire polygon. More samples are thus needed to provide reliable map at the scale of the sampling area. Soils were also sampled along two sections with a sampling interval of ca. 260 m and ca. 50 m. Sample interval of 50 m appears more adapted for mapping of smallest lithological units. The presence of several samples close to themselves indicating the same swelling potential is a good indication of the presence of a zone with constant swelling potential. Combination of Voronoi method and sampling interval of ca. 50 m appear adapted to produce local swelling potential maps in areas where doubt remain or where infrastructure damages attributed to expansive soils are knew.

Creating and validating cis-regulatory maps of tissue-specific gene expression regulation

PubMed Central

O'Connor, Timothy R.; Bailey, Timothy L.

2014-01-01

Predicting which genomic regions control the transcription of a given gene is a challenge. We present a novel computational approach for creating and validating maps that associate genomic regions (cis-regulatory modules–CRMs) with genes. The method infers regulatory relationships that explain gene expression observed in a test tissue using widely available genomic data for ‘other’ tissues. To predict the regulatory targets of a CRM, we use cross-tissue correlation between histone modifications present at the CRM and expression at genes within 1 Mbp of it. To validate cis-regulatory maps, we show that they yield more accurate models of gene expression than carefully constructed control maps. These gene expression models predict observed gene expression from transcription factor binding in the CRMs linked to that gene. We show that our maps are able to identify long-range regulatory interactions and improve substantially over maps linking genes and CRMs based on either the control maps or a ‘nearest neighbor’ heuristic. Our results also show that it is essential to include CRMs predicted in multiple tissues during map-building, that H3K27ac is the most informative histone modification, and that CAGE is the most informative measure of gene expression for creating cis-regulatory maps. PMID:25200088

Analysis of spatial distribution of land cover maps accuracy

NASA Astrophysics Data System (ADS)

Khatami, R.; Mountrakis, G.; Stehman, S. V.

2017-12-01

Land cover maps have become one of the most important products of remote sensing science. However, classification errors will exist in any classified map and affect the reliability of subsequent map usage. Moreover, classification accuracy often varies over different regions of a classified map. These variations of accuracy will affect the reliability of subsequent analyses of different regions based on the classified maps. The traditional approach of map accuracy assessment based on an error matrix does not capture the spatial variation in classification accuracy. Here, per-pixel accuracy prediction methods are proposed based on interpolating accuracy values from a test sample to produce wall-to-wall accuracy maps. Different accuracy prediction methods were developed based on four factors: predictive domain (spatial versus spectral), interpolation function (constant, linear, Gaussian, and logistic), incorporation of class information (interpolating each class separately versus grouping them together), and sample size. Incorporation of spectral domain as explanatory feature spaces of classification accuracy interpolation was done for the first time in this research. Performance of the prediction methods was evaluated using 26 test blocks, with 10 km × 10 km dimensions, dispersed throughout the United States. The performance of the predictions was evaluated using the area under the curve (AUC) of the receiver operating characteristic. Relative to existing accuracy prediction methods, our proposed methods resulted in improvements of AUC of 0.15 or greater. Evaluation of the four factors comprising the accuracy prediction methods demonstrated that: i) interpolations should be done separately for each class instead of grouping all classes together; ii) if an all-classes approach is used, the spectral domain will result in substantially greater AUC than the spatial domain; iii) for the smaller sample size and per-class predictions, the spectral and spatial domain yielded similar AUC; iv) for the larger sample size (i.e., very dense spatial sample) and per-class predictions, the spatial domain yielded larger AUC; v) increasing the sample size improved accuracy predictions with a greater benefit accruing to the spatial domain; and vi) the function used for interpolation had the smallest effect on AUC.

Objective rapid delineation of areas at risk from block-and-ash pyroclastic flows and surges

USGS Publications Warehouse

Widiwijayanti, C.; Voight, B.; Hidayat, D.; Schilling, S.P.

2009-01-01

Assessments of pyroclastic flow (PF) hazards are commonly based on mapping of PF and surge deposits and estimations of inundation limits, and/or computer models of varying degrees of sophistication. In volcanic crises a PF hazard map may be sorely needed, but limited time, exposures, or safety aspects may preclude fieldwork, and insufficient time or baseline data may be available for reliable dynamic simulations. We have developed a statistically constrained simulation model for block-and-ash type PFs to estimate potential areas of inundation by adapting methodology from Iverson et al. (Geol Soc America Bull 110:972-984, (1998) for lahars. The predictive equations for block-and-ash PFs are calibrated with data from several volcanoes and given by A = (0.05 to 0.1) V2/3, B = (35 to 40) V2/3, where A is cross-sectional area of inundation, B is planimetric area and V is deposit volume. The proportionality coefficients were obtained from regression analyses and comparison of simulations to mapped deposits. The method embeds the predictive equations in a GIS program coupled with DEM topography, using the LAHARZ program of Schilling (1998). Although the method is objective and reproducible, any PF hazard zone so computed should be considered as an approximate guide only, due to uncertainties on the coefficients applicable to individual PFs, the authenticity of DEM details, and the volume of future collapses. The statistical uncertainty of the predictive equations, which imply a factor of two or more in predicting A or B for a specified V, is superposed on the uncertainty of forecasting V for the next PF to descend a particular valley. Multiple inundation zones, produced by simulations using a selected range of volumes, partly accommodate these uncertainties. The resulting maps show graphically that PF inundation potentials are highest nearest volcano sources and along valley thalwegs, and diminish with distance from source and lateral distance from thalweg. The model does not explicitly consider dynamic behavior, which can be important. Ash-cloud surge impact limits must be extended beyond PF hazard zones and we provide several approaches to do this. The method has been used to supply PF and surge hazard maps in two crises: Merapi 2006; and Montserrat 2006-2007. ?? Springer-Verlag 2008.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Susandi, Armi, E-mail: armi@meteo.itb.ac.id; Tamamadin, Mamad, E-mail: mamadtama@meteo.itb.ac.id; Djamal, Erizal, E-mail: erizal-jamal@yahoo.com

This paper describes information system of rice planting calendar to help farmers in determining the time for rice planting. The information includes rainfall prediction in ten days (dasarian) scale overlaid to map of rice field to produce map of rice planting in village level. The rainfall prediction was produced by stochastic modeling using Fast Fourier Transform (FFT) and Non-Linier Least Squares methods to fit the curve of function to the rainfall data. In this research, the Fourier series has been modified become non-linear function to follow the recent characteristics of rainfall that is non stationary. The results have been alsomore » validated in 4 steps, including R-Square, RMSE, R-Skill, and comparison with field data. The development of information system (cyber extension) provides information such as rainfall prediction, prediction of the planting time, and interactive space for farmers to respond to the information submitted. Interfaces for interactive response will be critical to the improvement of prediction accuracy of information, both rainfall and planting time. The method used to get this information system includes mapping on rice planting prediction, converting the format file, developing database system, developing website, and posting website. Because of this map was overlaid with the Google map, the map files must be converted to the .kml file format.« less

Potential Risk Areas of Aedes albopictus in South-Eastern Iran: A Vector of Dengue Fever, Zika, and Chikungunya

PubMed Central

Nejati, Jalil; Bueno-Marí, Rubén; Collantes, Francisco; Hanafi-Bojd, Ahmad A.; Vatandoost, Hassan; Charrahy, Zabihollah; Tabatabaei, Seyed M.; Yaghoobi-Ershadi, Mohammad R.; Hasanzehi, Abdolghafar; Shirzadi, Mohammad R.; Moosa-Kazemi, Seyed H.; Sedaghat, Mohammad M.

2017-01-01

The possibility of the rapid and global spread of Zika, chikungunya, yellow fever, and dengue fever by Aedes albopictus is well documented and may be facilitated by changes in climate. To avert and manage health risks, climatic and topographic information can be used to model and forecast which areas may be most prone to the establishment of Ae. albopictus. We aimed to weigh and prioritize the predictive value of various meteorological and climatic variables on distributions of Ae. albopictus in south-eastern Iran using the Analytical Hierarchy Process. Out of eight factors used to predict the presence of Ae. albopictus, the highest weighted were land use, followed by temperature, altitude, and precipitation. The inconsistency of this analysis was 0.03 with no missing judgments. The areas predicted to be most at risk of Ae. albopictus-borne diseases were mapped using Geographic Information Systems and remote sensing data. Five-year (2011–2015) meteorological data was collected from 11 meteorological stations and other data was acquired from Landsat and Terra satellite images. Southernmost regions were at greatest risk of Ae. albopictus colonization as well as more urban sites connected by provincial roads. This is the first study in Iran to determine the regional probability of Ae. albopictus establishment. Monitoring and collection of Ae. albopictus from the environment confirmed our projections, though on-going field work is necessary to track the spread of this vector of life-threatening disease. PMID:28928720

Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci.

PubMed

Schumacher, Fredrick R; Al Olama, Ali Amin; Berndt, Sonja I; Benlloch, Sara; Ahmed, Mahbubl; Saunders, Edward J; Dadaev, Tokhir; Leongamornlert, Daniel; Anokian, Ezequiel; Cieza-Borrella, Clara; Goh, Chee; Brook, Mark N; Sheng, Xin; Fachal, Laura; Dennis, Joe; Tyrer, Jonathan; Muir, Kenneth; Lophatananon, Artitaya; Stevens, Victoria L; Gapstur, Susan M; Carter, Brian D; Tangen, Catherine M; Goodman, Phyllis J; Thompson, Ian M; Batra, Jyotsna; Chambers, Suzanne; Moya, Leire; Clements, Judith; Horvath, Lisa; Tilley, Wayne; Risbridger, Gail P; Gronberg, Henrik; Aly, Markus; Nordström, Tobias; Pharoah, Paul; Pashayan, Nora; Schleutker, Johanna; Tammela, Teuvo L J; Sipeky, Csilla; Auvinen, Anssi; Albanes, Demetrius; Weinstein, Stephanie; Wolk, Alicja; Håkansson, Niclas; West, Catharine M L; Dunning, Alison M; Burnet, Neil; Mucci, Lorelei A; Giovannucci, Edward; Andriole, Gerald L; Cussenot, Olivier; Cancel-Tassin, Géraldine; Koutros, Stella; Beane Freeman, Laura E; Sorensen, Karina Dalsgaard; Orntoft, Torben Falck; Borre, Michael; Maehle, Lovise; Grindedal, Eli Marie; Neal, David E; Donovan, Jenny L; Hamdy, Freddie C; Martin, Richard M; Travis, Ruth C; Key, Tim J; Hamilton, Robert J; Fleshner, Neil E; Finelli, Antonio; Ingles, Sue Ann; Stern, Mariana C; Rosenstein, Barry S; Kerns, Sarah L; Ostrer, Harry; Lu, Yong-Jie; Zhang, Hong-Wei; Feng, Ninghan; Mao, Xueying; Guo, Xin; Wang, Guomin; Sun, Zan; Giles, Graham G; Southey, Melissa C; MacInnis, Robert J; FitzGerald, Liesel M; Kibel, Adam S; Drake, Bettina F; Vega, Ana; Gómez-Caamaño, Antonio; Szulkin, Robert; Eklund, Martin; Kogevinas, Manolis; Llorca, Javier; Castaño-Vinyals, Gemma; Penney, Kathryn L; Stampfer, Meir; Park, Jong Y; Sellers, Thomas A; Lin, Hui-Yi; Stanford, Janet L; Cybulski, Cezary; Wokolorczyk, Dominika; Lubinski, Jan; Ostrander, Elaine A; Geybels, Milan S; Nordestgaard, Børge G; Nielsen, Sune F; Weischer, Maren; Bisbjerg, Rasmus; Røder, Martin Andreas; Iversen, Peter; Brenner, Hermann; Cuk, Katarina; Holleczek, Bernd; Maier, Christiane; Luedeke, Manuel; Schnoeller, Thomas; Kim, Jeri; Logothetis, Christopher J; John, Esther M; Teixeira, Manuel R; Paulo, Paula; Cardoso, Marta; Neuhausen, Susan L; Steele, Linda; Ding, Yuan Chun; De Ruyck, Kim; De Meerleer, Gert; Ost, Piet; Razack, Azad; Lim, Jasmine; Teo, Soo-Hwang; Lin, Daniel W; Newcomb, Lisa F; Lessel, Davor; Gamulin, Marija; Kulis, Tomislav; Kaneva, Radka; Usmani, Nawaid; Singhal, Sandeep; Slavov, Chavdar; Mitev, Vanio; Parliament, Matthew; Claessens, Frank; Joniau, Steven; Van den Broeck, Thomas; Larkin, Samantha; Townsend, Paul A; Aukim-Hastie, Claire; Dominguez, Manuela Gago; Castelao, Jose Esteban; Martinez, Maria Elena; Roobol, Monique J; Jenster, Guido; van Schaik, Ron H N; Menegaux, Florence; Truong, Thérèse; Koudou, Yves Akoli; Xu, Jianfeng; Khaw, Kay-Tee; Cannon-Albright, Lisa; Pandha, Hardev; Michael, Agnieszka; Thibodeau, Stephen N; McDonnell, Shannon K; Schaid, Daniel J; Lindstrom, Sara; Turman, Constance; Ma, Jing; Hunter, David J; Riboli, Elio; Siddiq, Afshan; Canzian, Federico; Kolonel, Laurence N; Le Marchand, Loic; Hoover, Robert N; Machiela, Mitchell J; Cui, Zuxi; Kraft, Peter; Amos, Christopher I; Conti, David V; Easton, Douglas F; Wiklund, Fredrik; Chanock, Stephen J; Henderson, Brian E; Kote-Jarai, Zsofia; Haiman, Christopher A; Eeles, Rosalind A

2018-06-11

Genome-wide association studies (GWAS) and fine-mapping efforts to date have identified more than 100 prostate cancer (PrCa)-susceptibility loci. We meta-analyzed genotype data from a custom high-density array of 46,939 PrCa cases and 27,910 controls of European ancestry with previously genotyped data of 32,255 PrCa cases and 33,202 controls of European ancestry. Our analysis identified 62 novel loci associated (P < 5.0 × 10 -8 ) with PrCa and one locus significantly associated with early-onset PrCa (≤55 years). Our findings include missense variants rs1800057 (odds ratio (OR) = 1.16; P = 8.2 × 10 -9 ; G>C, p.Pro1054Arg) in ATM and rs2066827 (OR = 1.06; P = 2.3 × 10 -9 ; T>G, p.Val109Gly) in CDKN1B. The combination of all loci captured 28.4% of the PrCa familial relative risk, and a polygenic risk score conferred an elevated PrCa risk for men in the ninetieth to ninety-ninth percentiles (relative risk = 2.69; 95% confidence interval (CI): 2.55-2.82) and first percentile (relative risk = 5.71; 95% CI: 5.04-6.48) risk stratum compared with the population average. These findings improve risk prediction, enhance fine-mapping, and provide insight into the underlying biology of PrCa 1 .

Assessing groundwater vulnerability in the Kinshasa region, DR Congo, using a calibrated DRASTIC model

NASA Astrophysics Data System (ADS)

Mfumu Kihumba, Antoine; Vanclooster, Marnik; Ndembo Longo, Jean

2017-02-01

This study assessed the vulnerability of groundwater against pollution in the Kinshasa region, DR Congo, as a support of a groundwater protection program. The parametric vulnerability model (DRASTIC) was modified and calibrated to predict the intrinsic vulnerability as well as the groundwater pollution risk. The method uses groundwater body specific parameters for the calibration of the factor ratings and weightings of the original DRASTIC model. These groundwater specific parameters are inferred from the statistical relation between the original DRASTIC model and observed nitrate pollution for a specific period. In addition, site-specific land use parameters are integrated into the method. The method is fully embedded in a Geographic Information System (GIS). Following these modifications, the correlation coefficient between groundwater pollution risk and observed nitrate concentrations for the 2013-2014 survey improved from r = 0.42, for the original DRASTIC model, to r = 0.61 for the calibrated model. As a way to validate this pollution risk map, observed nitrate concentrations from another survey (2008) are compared to pollution risk indices showing a good degree of coincidence with r = 0.51. The study shows that a calibration of a vulnerability model is recommended when vulnerability maps are used for groundwater resource management and land use planning at the regional scale and that it is adapted to a specific area.

Enhancing the prediction of protein pairings between interacting families using orthology information

PubMed Central

Izarzugaza, Jose MG; Juan, David; Pons, Carles; Pazos, Florencio; Valencia, Alfonso

2008-01-01

Background It has repeatedly been shown that interacting protein families tend to have similar phylogenetic trees. These similarities can be used to predicting the mapping between two families of interacting proteins (i.e. which proteins from one family interact with which members of the other). The correct mapping will be that which maximizes the similarity between the trees. The two families may eventually comprise orthologs and paralogs, if members of the two families are present in more than one organism. This fact can be exploited to restrict the possible mappings, simply by impeding links between proteins of different organisms. We present here an algorithm to predict the mapping between families of interacting proteins which is able to incorporate information regarding orthologues, or any other assignment of proteins to "classes" that may restrict possible mappings. Results For the first time in methods for predicting mappings, we have tested this new approach on a large number of interacting protein domains in order to statistically assess its performance. The method accurately predicts around 80% in the most favourable cases. We also analysed in detail the results of the method for a well defined case of interacting families, the sensor and kinase components of the Ntr-type two-component system, for which up to 98% of the pairings predicted by the method were correct. Conclusion Based on the well established relationship between tree similarity and interactions we developed a method for predicting the mapping between two interacting families using genomic information alone. The program is available through a web interface. PMID:18215279

Modeling Research Project Risks with Fuzzy Maps

ERIC Educational Resources Information Center

Bodea, Constanta Nicoleta; Dascalu, Mariana Iuliana

2009-01-01

The authors propose a risks evaluation model for research projects. The model is based on fuzzy inference. The knowledge base for fuzzy process is built with a causal and cognitive map of risks. The map was especially developed for research projects, taken into account their typical lifecycle. The model was applied to an e-testing research…

Conceptual Model for Basement and Surface Structure Relationships in an Oblique Collision, Sawtooth Range, MT

NASA Astrophysics Data System (ADS)

Palu, J. M.; Burberry, C. M.

2014-12-01

The reactivation potential of pre-existing basement structures affects the geometry of subsequent deformation structures. A conceptual model depicting the results of these interactions can be applied to multiple fold-thrust systems and lead to valuable deformation predictions. These predictions include the potential for hydrocarbon traps or seismic risk in an actively deforming area. The Sawtooth Range, Montana, has been used as a study area. A model for the development of structures close to the Augusta Syncline in the Sawtooth Range is being developed using: 1) an ArcGIS map of the basement structures of the belt based on analysis of geophysical data indicating gravity anomalies and aeromagnetic lineations, seismic data indicating deformation structures, and well logs for establishing lithologies, previously collected by others and 2) an ArcGIS map of the surface deformation structures of the belt based on interpretation of remote sensing images and verification through the collection of surface field data indicating stress directions and age relationships, resulting in a conceptual model based on the understanding of the interaction of the two previous maps including statistical correlations of data and development of balanced cross-sections using Midland Valley's 2D/3D Move software. An analysis of the model will then indicate viable deformation paths where prominent basement structures influenced subsequently developed deformation structures and reactivated faults. Preliminary results indicate that the change in orientation of thrust faults observed in the Sawtooth Range, from a NNW-SSE orientation near the Gibson Reservoir to a WNW-ESE trend near Haystack Butte correlates with pre-existing deformation structures lying within the Great Falls Tectonic Zone. The Scapegoat-Bannatyne trend appears to be responsible for this orientation change and rather than being a single feature, may be composed of up to 4 NE-SW oriented basement strike-slip faults. This indicates that the pre-existing basement features have a profound effect on the geometry of the later deformation. This conceptual model can also be applied to other deformed belts to provide a prediction for the potential hydrocarbon trap locations of the belt as well as their seismic risk.

Spatial epidemiological techniques in cholera mapping and analysis towards a local scale predictive modelling

NASA Astrophysics Data System (ADS)

Rasam, A. R. A.; Ghazali, R.; Noor, A. M. M.; Mohd, W. M. N. W.; Hamid, J. R. A.; Bazlan, M. J.; Ahmad, N.

2014-02-01

Cholera spatial epidemiology is the study of the spread and control of the disease spatial pattern and epidemics. Previous studies have shown that multi-factorial causation such as human behaviour, ecology and other infectious risk factors influence the disease outbreaks. Thus, understanding spatial pattern and possible interrelationship factors of the outbreaks are crucial to be explored an in-depth study. This study focuses on the integration of geographical information system (GIS) and epidemiological techniques in exploratory analyzing the cholera spatial pattern and distribution in the selected district of Sabah. Spatial Statistic and Pattern tools in ArcGIS and Microsoft Excel software were utilized to map and analyze the reported cholera cases and other data used. Meanwhile, cohort study in epidemiological technique was applied to investigate multiple outcomes of the disease exposure. The general spatial pattern of cholera was highly clustered showed the disease spread easily at a place or person to others especially 1500 meters from the infected person and locations. Although the cholera outbreaks in the districts are not critical, it could be endemic at the crowded areas, unhygienic environment, and close to contaminated water. It was also strongly believed that the coastal water of the study areas has possible relationship with the cholera transmission and phytoplankton bloom since the areas recorded higher cases. GIS demonstrates a vital spatial epidemiological technique in determining the distribution pattern and elucidating the hypotheses generating of the disease. The next research would be applying some advanced geo-analysis methods and other disease risk factors for producing a significant a local scale predictive risk model of the disease in Malaysia.

Risk maps for navigation in liver surgery

NASA Astrophysics Data System (ADS)

Hansen, C.; Zidowitz, S.; Schenk, A.; Oldhafer, K.-J.; Lang, H.; Peitgen, H.-O.

2010-02-01

The optimal transfer of preoperative planning data and risk evaluations to the operative site is challenging. A common practice is to use preoperative 3D planning models as a printout or as a presentation on a display. One important aspect is that these models were not developed to provide information in complex workspaces like the operating room. Our aim is to reduce the visual complexity of 3D planning models by mapping surgically relevant information onto a risk map. Therefore, we present methods for the identification and classification of critical anatomical structures in the proximity of a preoperatively planned resection surface. Shadow-like distance indicators are introduced to encode the distance from the resection surface to these critical structures on the risk map. In addition, contour lines are used to accentuate shape and spatial depth. The resulting visualization is clear and intuitive, allowing for a fast mental mapping of the current resection surface to the risk map. Preliminary evaluations by liver surgeons indicate that damage to risk structures may be prevented and patient safety may be enhanced using the proposed methods.

Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields

NASA Astrophysics Data System (ADS)

Xu, Yiming; Smith, Scot E.; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P.

2017-01-01

Soil prediction models based on spectral indices from some multispectral images are too coarse to characterize spatial pattern of soil properties in small and heterogeneous agricultural lands. Image pan-sharpening has seldom been utilized in Digital Soil Mapping research before. This research aimed to analyze the effects of pan-sharpened (PAN) remote sensing spectral indices on soil prediction models in smallholder farm settings. This research fused the panchromatic band and multispectral (MS) bands of WorldView-2, GeoEye-1, and Landsat 8 images in a village in Southern India by Brovey, Gram-Schmidt and Intensity-Hue-Saturation methods. Random Forest was utilized to develop soil total nitrogen (TN) and soil exchangeable potassium (Kex) prediction models by incorporating multiple spectral indices from the PAN and MS images. Overall, our results showed that PAN remote sensing spectral indices have similar spectral characteristics with soil TN and Kex as MS remote sensing spectral indices. There is no soil prediction model incorporating the specific type of pan-sharpened spectral indices always had the strongest prediction capability of soil TN and Kex. The incorporation of pan-sharpened remote sensing spectral data not only increased the spatial resolution of the soil prediction maps, but also enhanced the prediction accuracy of soil prediction models. Small farms with limited footprint, fragmented ownership and diverse crop cycle should benefit greatly from the pan-sharpened high spatial resolution imagery for soil property mapping. Our results show that multiple high and medium resolution images can be used to map soil properties suggesting the possibility of an improvement in the maps' update frequency. Additionally, the results should benefit the large agricultural community through the reduction of routine soil sampling cost and improved prediction accuracy.

Malaria Risk Mapping for Control in the Republic of Sudan

PubMed Central

Noor, Abdisalan M.; ElMardi, Khalid A.; Abdelgader, Tarig M.; Patil, Anand P.; Amine, Ahmed A. A.; Bakhiet, Sahar; Mukhtar, Maowia M.; Snow, Robert W.

2012-01-01

Evidence shows that malaria risk maps are rarely tailored to address national control program ambitions. Here, we generate a malaria risk map adapted for malaria control in Sudan. Community Plasmodium falciparum parasite rate (PfPR) data from 2000 to 2010 were assembled and were standardized to 2–10 years of age (PfPR2–10). Space-time Bayesian geostatistical methods were used to generate a map of malaria risk for 2010. Surfaces of aridity, urbanization, irrigation schemes, and refugee camps were combined with the PfPR2–10 map to tailor the epidemiological stratification for appropriate intervention design. In 2010, a majority of the geographical area of the Sudan had risk of < 1% PfPR2–10. Areas of meso- and hyperendemic risk were located in the south. About 80% of Sudan's population in 2011 was in the areas in the desert, urban centers, or where risk was < 1% PfPR2–10. Aggregated data suggest reducing risks in some high transmission areas since the 1960s. PMID:23033400

Type 2 Diabetes Risk Alleles Demonstrate Extreme Directional Differentiation among Human Populations, Compared to Other Diseases

PubMed Central

Chen, Rong; Corona, Erik; Sikora, Martin; Dudley, Joel T.; Morgan, Alex A.; Moreno-Estrada, Andres; Nilsen, Geoffrey B.; Ruau, David; Lincoln, Stephen E.; Bustamante, Carlos D.; Butte, Atul J.

2012-01-01

Many disease-susceptible SNPs exhibit significant disparity in ancestral and derived allele frequencies across worldwide populations. While previous studies have examined population differentiation of alleles at specific SNPs, global ethnic patterns of ensembles of disease risk alleles across human diseases are unexamined. To examine these patterns, we manually curated ethnic disease association data from 5,065 papers on human genetic studies representing 1,495 diseases, recording the precise risk alleles and their measured population frequencies and estimated effect sizes. We systematically compared the population frequencies of cross-ethnic risk alleles for each disease across 1,397 individuals from 11 HapMap populations, 1,064 individuals from 53 HGDP populations, and 49 individuals with whole-genome sequences from 10 populations. Type 2 diabetes (T2D) demonstrated extreme directional differentiation of risk allele frequencies across human populations, compared with null distributions of European-frequency matched control genomic alleles and risk alleles for other diseases. Most T2D risk alleles share a consistent pattern of decreasing frequencies along human migration into East Asia. Furthermore, we show that these patterns contribute to disparities in predicted genetic risk across 1,397 HapMap individuals, T2D genetic risk being consistently higher for individuals in the African populations and lower in the Asian populations, irrespective of the ethnicity considered in the initial discovery of risk alleles. We observed a similar pattern in the distribution of T2D Genetic Risk Scores, which are associated with an increased risk of developing diabetes in the Diabetes Prevention Program cohort, for the same individuals. This disparity may be attributable to the promotion of energy storage and usage appropriate to environments and inconsistent energy intake. Our results indicate that the differential frequencies of T2D risk alleles may contribute to the observed disparity in T2D incidence rates across ethnic populations. PMID:22511877

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

PubMed Central

Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

2016-01-01

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides. PMID:27187430

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China.

PubMed

Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

2016-05-11

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%-19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

Asia-Pacific Region Global Earthquake and Volcanic Eruption Risk Management (G-EVER) project and a next-generation real-time volcano hazard assessment system

NASA Astrophysics Data System (ADS)

Takarada, S.

2012-12-01

The first Workshop of Asia-Pacific Region Global Earthquake and Volcanic Eruption Risk Management (G-EVER1) was held in Tsukuba, Ibaraki Prefecture, Japan from February 23 to 24, 2012. The workshop focused on the formulation of strategies to reduce the risks of disasters worldwide caused by the occurrence of earthquakes, tsunamis, and volcanic eruptions. More than 150 participants attended the workshop. During the workshop, the G-EVER1 accord was approved by the participants. The Accord consists of 10 recommendations like enhancing collaboration, sharing of resources, and making information about the risks of earthquakes and volcanic eruptions freely available and understandable. The G-EVER Hub website (http://g-ever.org) was established to promote the exchange of information and knowledge among the Asia-Pacific countries. Several G-EVER Working Groups and Task Forces were proposed. One of the working groups was tasked to make the next-generation real-time volcano hazard assessment system. The next-generation volcano hazard assessment system is useful for volcanic eruption prediction, risk assessment, and evacuation at various eruption stages. The assessment system is planned to be developed based on volcanic eruption scenario datasets, volcanic eruption database, and numerical simulations. Defining volcanic eruption scenarios based on precursor phenomena leading up to major eruptions of active volcanoes is quite important for the future prediction of volcanic eruptions. Compiling volcanic eruption scenarios after a major eruption is also important. A high quality volcanic eruption database, which contains compilations of eruption dates, volumes, and styles, is important for the next-generation volcano hazard assessment system. The volcanic eruption database is developed based on past eruption results, which only represent a subset of possible future scenarios. Hence, different distributions from the previous deposits are mainly observed due to the differences in vent position, volume, eruption rate, wind directions and topography. Therefore, numerical simulations with controlled parameters are needed for more precise volcanic eruption predictions. The use of the next-generation system should enable the visualization of past volcanic eruptions datasets such as distributions, eruption volumes and eruption rates, on maps and diagrams using timeline and GIS technology. Similar volcanic eruptions scenarios should be easily searchable from the eruption database. Using the volcano hazard assessment system, prediction of the time and area that would be affected by volcanic eruptions at any locations near the volcano should be possible, using numerical simulations. The system should estimate volcanic hazard risks by overlaying the distributions of volcanic deposits on major roads, houses and evacuation areas using a GIS enabled systems. Probabilistic volcanic hazards maps in active volcano sites should be made based on numerous numerical simulations. The next-generation real-time hazard assessment system would be implemented with user-friendly interface, making the risk assessment system easily usable and accessible online.

Risk mapping of Rinderpest sero-prevalence in Central and Southern Somalia based on spatial and network risk factors.

PubMed

Ortiz-Pelaez, Angel; Pfeiffer, Dirk U; Tempia, Stefano; Otieno, F Tom; Aden, Hussein H; Costagli, Riccardo

2010-04-28

In contrast to most pastoral systems, the Somali livestock production system is oriented towards domestic trade and export with seasonal movement patterns of herds/flocks in search of water and pasture and towards export points. Data from a rinderpest survey and other data sources have been integrated to explore the topology of a contact network of cattle herds based on a spatial proximity criterion and other attributes related to cattle herd dynamics. The objective of the study is to integrate spatial mobility and other attributes with GIS and network approaches in order to develop a predictive spatial model of presence of rinderpest. A spatial logistic regression model was fitted using data for 562 point locations. It includes three statistically significant continuous-scale variables that increase the risk of rinderpest: home range radius, herd density and clustering coefficient of the node of the network whose link was established if the sum of the home ranges of every pair of nodes was equal or greater than the shortest distance between the points. The sensitivity of the model is 85.1% and the specificity 84.6%, correctly classifying 84.7% of the observations. The spatial autocorrelation not accounted for by the model is negligible and visual assessment of a semivariogram of the residuals indicated that there was no undue amount of spatial autocorrelation. The predictive model was applied to a set of 6176 point locations covering the study area. Areas at high risk of having serological evidence of rinderpest are located mainly in the coastal districts of Lower and Middle Juba, the coastal area of Lower Shabele and in the regions of Middle Shabele and Bay. There are also isolated spots of high risk along the border with Kenya and the southern area of the border with Ethiopia. The identification of point locations and areas with high risk of presence of rinderpest and their spatial visualization as a risk map will be useful for informing the prioritization of disease surveillance and control activities for rinderpest in Somalia. The methodology applied here, involving spatial and network parameters, could also be applied to other diseases and/or species as part of a standardized approach for the design of risk-based surveillance activities in nomadic pastoral settings.

How well should probabilistic seismic hazard maps work?

NASA Astrophysics Data System (ADS)

Vanneste, K.; Stein, S.; Camelbeeck, T.; Vleminckx, B.

2016-12-01

Recent large earthquakes that gave rise to shaking much stronger than shown in earthquake hazard maps have stimulated discussion about how well these maps forecast future shaking. These discussions have brought home the fact that although the maps are designed to achieve certain goals, we know little about how well they actually perform. As for any other forecast, this question involves verification and validation. Verification involves assessing how well the algorithm used to produce hazard maps implements the conceptual PSHA model ("have we built the model right?"). Validation asks how well the model forecasts the shaking that actually occurs ("have we built the right model?"). We explore the verification issue by simulating the shaking history of an area with assumed distribution of earthquakes, frequency-magnitude relation, temporal occurrence model, and ground-motion prediction equation. We compare the "observed" shaking at many sites over time to that predicted by a hazard map generated for the same set of parameters. PSHA predicts that the fraction of sites at which shaking will exceed that mapped is p = 1 - exp(t/T), where t is the duration of observations and T is the map's return period. This implies that shaking in large earthquakes is typically greater than shown on hazard maps, as has occurred in a number of cases. A large number of simulated earthquake histories yield distributions of shaking consistent with this forecast, with a scatter about this value that decreases as t/T increases. The median results are somewhat lower than predicted for small values of t/T and approach the predicted value for larger values of t/T. Hence, the algorithm appears to be internally consistent and can be regarded as verified for this set of simulations. Validation is more complicated because a real observed earthquake history can yield a fractional exceedance significantly higher or lower than that predicted while still being consistent with the hazard map in question. As a result, given that in the real world we have only a single sample, it is hard to assess whether a misfit between a map and observations arises by chance or reflects a biased map.

Rift Valley Fever Risk Map Model and Seroprevalence in Selected Wild Ungulates and Camels from Kenya

PubMed Central

Ruder, Mark G.; Linthicum, Kenneth J.; Anyamba, Assaf; Small, Jennifer L.; Tucker, Compton J.; Ateya, Leonard O.; Oriko, Abuu A.; Gacheru, Stephen; Wilson, William C.

2013-01-01

Since the first isolation of Rift Valley fever virus (RVFV) in the 1930s, there have been multiple epizootics and epidemics in animals and humans in sub-Saharan Africa. Prospective climate-based models have recently been developed that flag areas at risk of RVFV transmission in endemic regions based on key environmental indicators that precede Rift Valley fever (RVF) epizootics and epidemics. Although the timing and locations of human case data from the 2006–2007 RVF outbreak in Kenya have been compared to risk zones flagged by the model, seroprevalence of RVF antibodies in wildlife has not yet been analyzed in light of temporal and spatial predictions of RVF activity. Primarily wild ungulate serum samples from periods before, during, and after the 2006–2007 RVF epizootic were analyzed for the presence of RVFV IgM and/or IgG antibody. Results show an increase in RVF seropositivity from samples collected in 2007 (31.8%), compared to antibody prevalence observed from 2000–2006 (3.3%). After the epizootic, average RVF seropositivity diminished to 5% in samples collected from 2008–2009. Overlaying maps of modeled RVF risk assessments with sampling locations indicated positive RVF serology in several species of wild ungulate in or near areas flagged as being at risk for RVF. Our results establish the need to continue and expand sero-surveillance of wildlife species Kenya and elsewhere in the Horn of Africa to further calibrate and improve the RVF risk model, and better understand the dynamics of RVFV transmission. PMID:23840512

Rift Valley Fever Risk Map Model and Seroprevalence in Selected Wild Ungulates and Camels from Kenya

NASA Technical Reports Server (NTRS)

Britch, Seth C.; Binepal, Yatinder S.; Ruder, Mark G.; Kariithi, Henry M.; Linthicum, Kenneth J.; Anyamba, Assaf; Small, Jennifer L.; Tucker, Compton J.; Ateya, Leonard O.; Oriko, Abuu A.;

Gender, smoking, body size, and aneurysm geometry influence the biomechanical rupture risk of abdominal aortic aneurysms as estimated by finite element analysis.

PubMed

Lindquist Liljeqvist, Moritz; Hultgren, Rebecka; Siika, Antti; Gasser, T Christian; Roy, Joy

2017-04-01

Finite element analysis (FEA) has been suggested to be superior to maximal diameter measurements in predicting rupture of abdominal aortic aneurysms (AAAs). Our objective was to investigate to what extent previously described rupture risk factors were associated with FEA-estimated rupture risk. One hundred forty-six patients with an asymptomatic AAA of a 40- to 60-mm diameter were retrospectively identified and consecutively included. The patients' computed tomography angiograms were analyzed by FEA without (neutral) and with (specific) input of patient-specific mean arterial pressure (MAP), gender, family history, and age. The maximal wall stress/wall strength ratio was described as a rupture risk equivalent diameter (RRED), which translated this ratio into an average aneurysm diameter of corresponding rupture risk. In multivariate linear regression, RRED neutral increased with female gender (3.7 mm; 95% confidence interval [CI], 0.13-7.3) and correlated with patient height (0.27 mm/cm; 95% CI, 0.11-0.43) and body surface area (BSA, 16 mm/m 2 ; 95% CI, 8.3-24) and inversely with body mass index (BMI, -0.40 mm/kg m -2 ; 95% CI, -0.75 to -0.054) in a wall stress-dependent manner. Wall stress-adjusted RRED neutral was raised if the patient was currently smoking (1.1 mm; 95% CI, 0.21-1.9). Age, MAP, family history, and patient weight were unrelated to RRED neutral . In specific FEA, RRED specific increased with female gender, MAP, family history positive for AAA, height, and BSA, whereas it was inversely related to BMI. All results were independent of aneurysm diameter. Peak wall stress and RRED correlated with aneurysm diameter and lumen volume. Female gender, current smoking, increased patient height and BSA, and low BMI were found to increase the mechanical rupture risk of AAAs. Previously described rupture risk factors may in part be explained by patient characteristic-dependent variations in aneurysm biomechanics. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Risky business: The impact of climate and climate variability on human population dynamics in Western Europe during the Last Glacial Maximum

NASA Astrophysics Data System (ADS)

Burke, Ariane; Kageyama, Masa; Latombe, Guilllaume; Fasel, Marc; Vrac, Mathieu; Ramstein, Gilles; James, Patrick M. A.

2017-05-01

The extent to which climate change has affected the course of human evolution is an enduring question. The ability to maintain spatially extensive social networks and a fluid social structure allows human foragers to ;map onto; the landscape, mitigating the impact of ecological risk and conferring resilience. But what are the limits of resilience and to which environmental variables are foraging populations sensitive? We address this question by testing the impact of a suite of environmental variables, including climate variability, on the distribution of human populations in Western Europe during the Last Glacial Maximum (LGM). Climate variability affects the distribution of plant and animal resources unpredictably, creating an element of risk for foragers for whom mobility comes at a cost. We produce a model of habitat suitability that allows us to generate predictions about the probable distribution of human populations and discuss the implications of these predictions for the structure of human populations and their social and cultural evolution during the LGM.

Action Tweets Linked to Reduced County-Level HIV Prevalence in the United States: Online Messages and Structural Determinants

PubMed Central

Ireland, Molly E.; Chen, Qijia; Schwartz, H. Andrew; Ungar, Lyle H.; Albarracin, Dolores

2016-01-01

HIV is uncommon in most US counties but travels quickly through vulnerable communities when it strikes. Tracking behavior through social media may provide an unobtrusive, naturalistic means of predicting HIV outbreaks and understanding the behavioral and psychological factors that increase communities'; risk. General action goals, or the motivation to engage in cognitive and motor activity, may support protective health behavior (e.g., using condoms) or encourage activity indiscriminately (e.g., risky sex), resulting in mixed health effects. We explored these opposing hypotheses by regressing county-level HIV prevalence on action language (e.g., work, plan) in over 150 million tweets mapped to US counties. Controlling for demographic and structural predictors of HIV, more active language was associated with lower HIV rates. By leveraging language used on social media to improve existing predictive models of geographic variation in HIV, future targeted HIV-prevention interventions may have a better chance of reaching high-risk communities before outbreaks occur. PMID:26650382

Mapping Malaria Transmission Risk in Northern Morocco Using Entomological and Environmental Data

PubMed Central

Adlaoui, E.; Faraj, C.; El Bouhmi, M.; El Aboudi, A.; Ouahabi, S.; Tran, A.; Fontenille, D.; El Aouad, R.

2011-01-01

Malaria resurgence risk in Morocco depends, among other factors, on environmental changes as well as the introduction of parasite carriers. The aim of this paper is to analyze the receptivity of the Loukkos area, large wetlands in Northern Morocco, to quantify and to map malaria transmission risk in this region using biological and environmental data. This risk was assessed on entomological risk basis and was mapped using environmental markers derived from satellite imagery. Maps showing spatial and temporal variations of entomological risk for Plasmodium vivax and P. falciparum were produced. Results showed this risk to be highly seasonal and much higher in rice fields than in swamps. This risk is lower for Afrotropical P. falciparum strains because of the low infectivity of Anopheles labranchiae, principal malaria vector in Morocco. However, it is very high for P. vivax mainly during summer corresponding to the rice cultivation period. Although the entomological risk is high in Loukkos region, malaria resurgence risk remains very low, because of the low vulnerability of the area. PMID:22312566

Assessment and application of national environmental databases and mapping tools at the local level to two community case studies.

PubMed

Hammond, Davyda; Conlon, Kathryn; Barzyk, Timothy; Chahine, Teresa; Zartarian, Valerie; Schultz, Brad

2011-03-01

Communities are concerned over pollution levels and seek methods to systematically identify and prioritize the environmental stressors in their communities. Geographic information system (GIS) maps of environmental information can be useful tools for communities in their assessment of environmental-pollution-related risks. Databases and mapping tools that supply community-level estimates of ambient concentrations of hazardous pollutants, risk, and potential health impacts can provide relevant information for communities to understand, identify, and prioritize potential exposures and risk from multiple sources. An assessment of existing databases and mapping tools was conducted as part of this study to explore the utility of publicly available databases, and three of these databases were selected for use in a community-level GIS mapping application. Queried data from the U.S. EPA's National-Scale Air Toxics Assessment, Air Quality System, and National Emissions Inventory were mapped at the appropriate spatial and temporal resolutions for identifying risks of exposure to air pollutants in two communities. The maps combine monitored and model-simulated pollutant and health risk estimates, along with local survey results, to assist communities with the identification of potential exposure sources and pollution hot spots. Findings from this case study analysis will provide information to advance the development of new tools to assist communities with environmental risk assessments and hazard prioritization. © 2010 Society for Risk Analysis.

Regional mapping of soil parent material by machine learning based on point data

NASA Astrophysics Data System (ADS)

Lacoste, Marine; Lemercier, Blandine; Walter, Christian

2011-10-01

A machine learning system (MART) has been used to predict soil parent material (SPM) at the regional scale with a 50-m resolution. The use of point-specific soil observations as training data was tested as a replacement for the soil maps introduced in previous studies, with the aim of generating a more even distribution of training data over the study area and reducing information uncertainty. The 27,020-km 2 study area (Brittany, northwestern France) contains mainly metamorphic, igneous and sedimentary substrates. However, superficial deposits (aeolian loam, colluvial and alluvial deposits) very often represent the actual SPM and are typically under-represented in existing geological maps. In order to calibrate the predictive model, a total of 4920 point soil descriptions were used as training data along with 17 environmental predictors (terrain attributes derived from a 50-m DEM, as well as emissions of K, Th and U obtained by means of airborne gamma-ray spectrometry, geological variables at the 1:250,000 scale and land use maps obtained by remote sensing). Model predictions were then compared: i) during SPM model creation to point data not used in model calibration (internal validation), ii) to the entire point dataset (point validation), and iii) to existing detailed soil maps (external validation). The internal, point and external validation accuracy rates were 56%, 81% and 54%, respectively. Aeolian loam was one of the three most closely predicted substrates. Poor prediction results were associated with uncommon materials and areas with high geological complexity, i.e. areas where existing maps used for external validation were also imprecise. The resultant predictive map turned out to be more accurate than existing geological maps and moreover indicated surface deposits whose spatial coverage is consistent with actual knowledge of the area. This method proves quite useful in predicting SPM within areas where conventional mapping techniques might be too costly or lengthy or where soil maps are insufficient for use as training data. In addition, this method allows producing repeatable and interpretable results, whose accuracy can be assessed objectively.

Flood maps in Europe - methods, availability and use

NASA Astrophysics Data System (ADS)

de Moel, H.; van Alphen, J.; Aerts, J. C. J. H.

2009-03-01

To support the transition from traditional flood defence strategies to a flood risk management approach at the basin scale in Europe, the EU has adopted a new Directive (2007/60/EC) at the end of 2007. One of the major tasks which member states must carry out in order to comply with this Directive is to map flood hazards and risks in their territory, which will form the basis of future flood risk management plans. This paper gives an overview of existing flood mapping practices in 29 countries in Europe and shows what maps are already available and how such maps are used. Roughly half of the countries considered have maps covering as good as their entire territory, and another third have maps covering significant parts of their territory. Only five countries have very limited or no flood maps available yet. Of the different flood maps distinguished, it appears that flood extent maps are the most commonly produced floods maps (in 23 countries), but flood depth maps are also regularly created (in seven countries). Very few countries have developed flood risk maps that include information on the consequences of flooding. The available flood maps are mostly developed by governmental organizations and primarily used for emergency planning, spatial planning, and awareness raising. In spatial planning, flood zones delimited on flood maps mainly serve as guidelines and are not binding. Even in the few countries (e.g. France, Poland) where there is a legal basis to regulate floodplain developments using flood zones, practical problems are often faced which reduce the mitigating effect of such binding legislation. Flood maps, also mainly extent maps, are also created by the insurance industry in Europe and used to determine insurability, differentiate premiums, or to assess long-term financial solvency. Finally, flood maps are also produced by international river commissions. With respect to the EU Flood Directive, many countries already have a good starting point to map their flood hazards. A flood risk based map that includes consequences, however, has yet to be developed by most countries.

The Application of a WEPP Technology to a Complex Watershed Analysis

NASA Astrophysics Data System (ADS)

Elliot, William; Miller, Ina Sue; Dobre, Mariana

2017-04-01

Forest restoration activities are essential in many forest stands, where previous management and fire suppression has resulted in stands with high density, diseased trees and excessive fuel loads. Trying to balance the watershed impacts of restoration activities such as thinning, selective harvesting, and prescribed fire against the significant impact of wildfire is challenging. The process is further aggravated by the necessity of a road network if management activities include timber removal. We propose to present an approach to a watershed analysis for a 3400-ha of fuel reduction project within an 18,0000-ha sensitive watershed in the Nez Perce National Forest in Northern Idaho, USA. The FlamMap fire spread model was first used to predict the distribution of potential fire severity on the landscape for the current fuel load, and for a landscape that had been treated by thinning and/or prescribed fire. FlamMap predicts the flame length by 30-m pixel as a function of fuel load and water content, wind speed, and slope steepness and aspect. The flame length distribution was then classified so that the distribution of burn severity (unburned, low, moderate and high severity) was similar to the distributions observed on recent wildfires in the Forest. The flame length classes determined for the current fuel loads were also used for the treated condition flame lengths, where predominantly unburned or low severity fire severities were predicted. The burn severity maps were uploaded to a web site that was developed to provide soil and management files reflecting burn severity and soil texture, formatted for the Geospatial interface to the Water Erosion Prediction Project (GeoWEPP). The study area was divided into 40 sub watersheds under 2.5 km2 each for GeoWEPP analysis. GeoWEPP was run for an undisturbed forest; for the burn severity following wildfire for the current and treated fuel loads; for prescribed fire, either broadcast or jack pot burn; and for thinning either by tractor or by skyline logging. The GeoWEPP erosion estimates by hillslope polygon were merged with the proposed treatment polygons to produce maps of erosion for each condition for each treatment polygon. Road network erosion was estimated using a new online GIS tool to estimate road segment length and steepness, and linking those topographic values to the WEPP model for erosion prediction by road segment. The results were summarized and compared to earlier estimates of sediment delivery using a locally-developed cumulative watershed effects analysis. The results were similar from both tools, in spite of using very different erosion estimation methods, and similar to regional observations of forest watershed sediment delivery ( 12.5 Mg/sq km). The study found that the erosion risk from wildfire was 5 times greater than sediment generated by forest management, justifying the proposed restoration activities to reduce fire risk. Sediment generated from the road network, however, was unacceptably high suggesting that methods improve road erosion prediction and/or to reduce road erosion are warranted.

Molecular epidemiology, cancer-related symptoms, and cytokines pathway

PubMed Central

Reyes-Gibby, Cielito C; Wu, Xifeng; Spitz, Margaret; Kurzrock, Razelle; Fisch, Michael; Bruera, Eduardo; Shete, Sanjay

2012-01-01

The Human Genome Project and HapMap have led to a better appreciation of the importance of common genetic variation in determining cancer risk, created potential for predicting response to therapy, and made possible the development of targeted prevention and therapeutic interventions. Advances in molecular epidemiology can be used to explore the role of genetic variation in modulating the risk for severe and persistent symptoms, such as pain, depression, and fatigue, in patients with cancer. The same genes that are implicated in cancer risk might also be involved in the modulation of therapeutic outcomes. For example, polymorphisms in several cytokine genes are potential markers for genetic susceptibility both for cancer risk and for cancer-related symptoms. These genetic polymorphisms are stable markers and easily and reliably assayed to explore the extent to which genetic variation might prove useful in identifying patients with cancer at high-risk of symptom development. Likewise, they could identify subgroups who might benefit most from symptom intervention, and contribute to developing personalised and more effective therapies for persistent symptoms. PMID:18672213

Landslide Susceptibility Mapping of Tegucigalpa, Honduras Using Artificial Neural Network, Bayesian Network and Decision Trees

NASA Astrophysics Data System (ADS)

Garcia Urquia, E. L.; Braun, A.; Yamagishi, H.

2016-12-01

Tegucigalpa, the capital city of Honduras, experiences rainfall-induced landslides on a yearly basis. The high precipitation regime and the rugged topography the city has been built in couple with the lack of a proper urban expansion plan to contribute to the occurrence of landslides during the rainy season. Thousands of inhabitants live at risk of losing their belongings due to the construction of precarious shelters in landslide-prone areas on mountainous terrains and next to the riverbanks. Therefore, the city is in the need for landslide susceptibility and hazard maps to aid in the regulation of future development. Major challenges in the context of highly dynamic urbanizing areas are the overlap of natural and anthropogenic slope destabilizing factors, as well as the availability and accuracy of data. Data-driven multivariate techniques have proven to be powerful in discovering interrelations between factors, identifying important factors in large datasets, capturing non-linear problems and coping with noisy and incomplete data. This analysis focuses on the creation of a landslide susceptibility map using different methods from the field of data mining, Artificial Neural Networks (ANN), Bayesian Networks (BN) and Decision Trees (DT). The input dataset of the study contains geomorphological and hydrological factors derived from a digital elevation model with a 10 m resolution, lithological factors derived from a geological map, and anthropogenic factors, such as information on the development stage of the neighborhoods in Tegucigalpa and road density. Moreover, a landslide inventory map that was developed in 2014 through aerial photo interpretation was used as target variable in the analysis. The analysis covers an area of roughly 100 km2, while 8.95 km2 are occupied by landslides. In a first step, the dataset was explored by assessing and improving the data quality, identifying unimportant variables and finding interrelations. Then, based on a training partition of the dataset, the ANN, BN and DT were optimized for the prediction of landslides. The predictive power and ability to generalize of the resulting models were assessed in a test partition and evaluated using success rate curves, skill scores and by ensuring the spatial plausibility of the prediction.

Exploring local risk managers' use of flood hazard maps for risk communication purposes in Baden-Württemberg

NASA Astrophysics Data System (ADS)

Kjellgren, S.

2013-07-01

In response to the EU Floods Directive (2007/60/EC), flood hazard maps are currently produced all over Europe, reflecting a wider shift in focus from "flood protection" to "risk management", for which not only public authorities but also populations at risk are seen as responsible. By providing a visual image of the foreseen consequences of flooding, flood hazard maps can enhance people's knowledge about flood risk, making them more capable of an adequate response. Current literature, however, questions the maps' awareness raising capacity, arguing that their content and design are rarely adjusted to laypeople's needs. This paper wants to complement this perspective with a focus on risk communication by studying how these tools are disseminated and marketed to the public in the first place. Judging from communication theory, simply making hazard maps publicly available is unlikely to lead to attitudinal or behavioral effects, since this typically requires two-way communication and material or symbolic incentives. Consequently, it is relevant to investigate whether and how local risk managers, who are well positioned to interact with the local population, make use of flood hazard maps for risk communication purposes. A qualitative case study of this issue in the German state of Baden-Württemberg suggests that many municipalities lack a clear strategy for using this new information tool for hazard and risk communication. Four barriers in this regard are identified: perceived disinterest/sufficient awareness on behalf of the population at risk; unwillingness to cause worry or distress; lack of skills and resources; and insufficient support. These barriers are important to address - in research as well as in practice - since it is only if flood hazard maps are used to enhance local knowledge resources that they can be expected to contribute to social capacity building.

[Risk maps. The concept and the methodology for their development].

PubMed

García Gómez, M M

1994-01-01

In this article the concept of risk map is revised. It is considered as an instrument for the knowledge of risks and damages in a certain environment. A historic revision is made analyzing the birth and evolution of the concept. Different experiences and types of maps in different countries are described. Finally the operative steps, the data sources and the risk indicators which should be used in Spain are included.

Participatory Risk Mapping of Malaria Vector Exposure in Northern South America using Environmental and Population Data

PubMed Central

Fuller, D.O.; Troyo, A.; Alimi, T.O.; Beier, J.C.

2014-01-01

Malaria elimination remains a major public health challenge in many tropical regions, including large areas of northern South America. In this study, we present a new high spatial resolution (90 × 90 m) risk map for Colombia and surrounding areas based on environmental and human population data. The map was created through a participatory multi-criteria decision analysis in which expert opinion was solicited to determine key environmental and population risk factors, different fuzzy functions to standardize risk factor inputs, and variable factor weights to combine risk factors in a geographic information system. The new risk map was compared to a map of malaria cases in which cases were aggregated to the municipio (municipality) level. The relationship between mean municipio risk scores and total cases by muncípio showed a weak correlation. However, the relationship between pixel-level risk scores and vector occurrence points for two dominant vector species, Anopheles albimanus and An. darlingi, was significantly different (p < 0.05) from a random point distribution, as was a pooled point distribution for these two vector species and An. nuneztovari. Thus, we conclude that the new risk map derived based on expert opinion provides an accurate spatial representation of risk of potential vector exposure rather than malaria transmission as shown by the pattern of malaria cases, and therefore it may be used to inform public health authorities as to where vector control measures should be prioritized to limit human-vector contact in future malaria outbreaks. PMID:24976656

Spatial vulnerability assessments by regression kriging

NASA Astrophysics Data System (ADS)

Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor

2016-04-01

Two fairly different complex environmental phenomena, causing natural hazard were mapped based on a combined spatial inference approach. The behaviour is related to various environmental factors and the applied approach enables the inclusion of several, spatially exhaustive auxiliary variables that are available for mapping. Inland excess water (IEW) is an interrelated natural and human induced phenomenon causes several problems in the flat-land regions of Hungary, which cover nearly half of the country. The term 'inland excess water' refers to the occurrence of inundations outside the flood levee that originate from sources differing from flood overflow, it is surplus surface water forming due to the lack of runoff, insufficient absorption capability of soil or the upwelling of groundwater. There is a multiplicity of definitions, which indicate the complexity of processes that govern this phenomenon. Most of the definitions have a common part, namely, that inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources. Radon gas is produced in the radioactive decay chain of uranium, which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on soil physical and meteorological parameters and can enter and accumulate in the buildings. Health risk originating from indoor radon concentration attributed to natural factors is characterized by geogenic radon potential (GRP). In addition to geology and meteorology, physical soil properties play significant role in the determination of GRP. Identification of areas with high risk requires spatial modelling, that is mapping of specific natural hazards. In both cases external environmental factors determine the behaviour of the target process (occurrence/frequncy of IEW and grade of GRP respectively). Spatial auxiliary information representing IEW or GRP forming environmental factors were taken into account to support the spatial inference of the locally experienced IEW frequency and measured GRP values respectively. An efficient spatial prediction methodology was applied to construct reliable maps, namely regression kriging (RK) using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Application of RK also provides the possibility of inherent accuracy assessment. The resulting maps are characterized by global and local measures of its accuracy. Additionally the method enables interval estimation for spatial extension of the areas of predefined risk categories. All of these outputs provide useful contribution to spatial planning, action planning and decision making. Acknowledgement: Our work was partly supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Mapping potential carbon and timber losses from hurricanes using a decision tree and ecosystem services driver model.

PubMed

Delphin, S; Escobedo, F J; Abd-Elrahman, A; Cropper, W

2013-11-15

Information on the effect of direct drivers such as hurricanes on ecosystem services is relevant to landowners and policy makers due to predicted effects from climate change. We identified forest damage risk zones due to hurricanes and estimated the potential loss of 2 key ecosystem services: aboveground carbon storage and timber volume. Using land cover, plot-level forest inventory data, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and a decision tree-based framework; we determined potential damage to subtropical forests from hurricanes in the Lower Suwannee River (LS) and Pensacola Bay (PB) watersheds in Florida, US. We used biophysical factors identified in previous studies as being influential in forest damage in our decision tree and hurricane wind risk maps. Results show that 31% and 0.5% of the total aboveground carbon storage in the LS and PB, respectively was located in high forest damage risk (HR) zones. Overall 15% and 0.7% of the total timber net volume in the LS and PB, respectively, was in HR zones. This model can also be used for identifying timber salvage areas, developing ecosystem service provision and management scenarios, and assessing the effect of other drivers on ecosystem services and goods. Copyright © 2013 Elsevier Ltd. All rights reserved.

An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing

NASA Astrophysics Data System (ADS)

Sareen, Sanjay; Gupta, Sunil Kumar; Sood, Sandeep K.

2017-10-01

Zika virus is a mosquito-borne disease that spreads very quickly in different parts of the world. In this article, we proposed a system to prevent and control the spread of Zika virus disease using integration of Fog computing, cloud computing, mobile phones and the Internet of things (IoT)-based sensor devices. Fog computing is used as an intermediary layer between the cloud and end users to reduce the latency time and extra communication cost that is usually found high in cloud-based systems. A fuzzy k-nearest neighbour is used to diagnose the possibly infected users, and Google map web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each Zika virus (ZikaV)-infected user, mosquito-dense sites and breeding sites on the Google map that help the government healthcare authorities to control such risk-prone areas effectively and efficiently. The proposed system is deployed on Amazon EC2 cloud to evaluate its performance and accuracy using data set for 2 million users. Our system provides high accuracy of 94.5% for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment.

Model-Based Geostatistical Mapping of the Prevalence of Onchocerca volvulus in West Africa

PubMed Central

O’Hanlon, Simon J.; Slater, Hannah C.; Cheke, Robert A.; Boatin, Boakye A.; Coffeng, Luc E.; Pion, Sébastien D. S.; Boussinesq, Michel; Zouré, Honorat G. M.; Stolk, Wilma A.; Basáñez, María-Gloria

2016-01-01

Background The initial endemicity (pre-control prevalence) of onchocerciasis has been shown to be an important determinant of the feasibility of elimination by mass ivermectin distribution. We present the first geostatistical map of microfilarial prevalence in the former Onchocerciasis Control Programme in West Africa (OCP) before commencement of antivectorial and antiparasitic interventions. Methods and Findings Pre-control microfilarial prevalence data from 737 villages across the 11 constituent countries in the OCP epidemiological database were used as ground-truth data. These 737 data points, plus a set of statistically selected environmental covariates, were used in a Bayesian model-based geostatistical (B-MBG) approach to generate a continuous surface (at pixel resolution of 5 km x 5km) of microfilarial prevalence in West Africa prior to the commencement of the OCP. Uncertainty in model predictions was measured using a suite of validation statistics, performed on bootstrap samples of held-out validation data. The mean Pearson’s correlation between observed and estimated prevalence at validation locations was 0.693; the mean prediction error (average difference between observed and estimated values) was 0.77%, and the mean absolute prediction error (average magnitude of difference between observed and estimated values) was 12.2%. Within OCP boundaries, 17.8 million people were deemed to have been at risk, 7.55 million to have been infected, and mean microfilarial prevalence to have been 45% (range: 2–90%) in 1975. Conclusions and Significance This is the first map of initial onchocerciasis prevalence in West Africa using B-MBG. Important environmental predictors of infection prevalence were identified and used in a model out-performing those without spatial random effects or environmental covariates. Results may be compared with recent epidemiological mapping efforts to find areas of persisting transmission. These methods may be extended to areas where data are sparse, and may be used to help inform the feasibility of elimination with current and novel tools. PMID:26771545

Co-endemicity of Pulmonary Tuberculosis and Intestinal Helminth Infection in the People’s Republic of China

PubMed Central

Li, Xin-Xu; Ren, Zhou-Peng; Wang, Li-Xia; Zhang, Hui; Jiang, Shi-Wen; Chen, Jia-Xu; Wang, Jin-Feng; Zhou, Xiao-Nong

2016-01-01

Both pulmonary tuberculosis (PTB) and intestinal helminth infection (IHI) affect millions of individuals every year in China. However, the national-scale estimation of prevalence predictors and prevalence maps for these diseases, as well as co-endemic relative risk (RR) maps of both diseases’ prevalence are not well developed. There are co-endemic, high prevalence areas of both diseases, whose delimitation is essential for devising effective control strategies. Bayesian geostatistical logistic regression models including socio-economic, climatic, geographical and environmental predictors were fitted separately for active PTB and IHI based on data from the national surveys for PTB and major human parasitic diseases that were completed in 2010 and 2004, respectively. Prevalence maps and co-endemic RR maps were constructed for both diseases by means of Bayesian Kriging model and Bayesian shared component model capable of appraising the fraction of variance of spatial RRs shared by both diseases, and those specific for each one, under an assumption that there are unobserved covariates common to both diseases. Our results indicate that gross domestic product (GDP) per capita had a negative association, while rural regions, the arid and polar zones and elevation had positive association with active PTB prevalence; for the IHI prevalence, GDP per capita and distance to water bodies had a negative association, the equatorial and warm zones and the normalized difference vegetation index had a positive association. Moderate to high prevalence of active PTB and low prevalence of IHI were predicted in western regions, low to moderate prevalence of active PTB and low prevalence of IHI were predicted in north-central regions and the southeast coastal regions, and moderate to high prevalence of active PTB and high prevalence of IHI were predicted in the south-western regions. Thus, co-endemic areas of active PTB and IHI were located in the south-western regions of China, which might be determined by socio-economic factors, such as GDP per capita. PMID:27088504

An Attempt to Develop AN Environmental Information System of Ecological Infrastructure for Evaluating Functions of Ecosystem-Based Solutions for Disaster Risk Reduction Eco-Drr

NASA Astrophysics Data System (ADS)

Doko, T.; Chen, W.; Sasaki, K.; Furutani, T.

2016-06-01

"Ecological Infrastructure (EI)" are defined as naturally functioning ecosystems that deliver valuable services to people, such as healthy mountain catchments, rivers, wetlands, coastal dunes, and nodes and corridors of natural habitat, which together form a network of interconnected structural elements in the landscape. On the other hand, natural disaster occur at the locations where habitat was reduced due to the changes of land use, in which the land was converted to the settlements and agricultural cropland. Hence, habitat loss and natural disaster are linked closely. Ecological infrastructure is the nature-based equivalent of built or hard infrastructure, and is as important for providing services and underpinning socio-economic development. Hence, ecological infrastructure is expected to contribute to functioning as ecological disaster reduction, which is termed Ecosystem-based Solutions for Disaster Risk Reduction (Eco-DRR). Although ecological infrastructure already exists in the landscape, it might be degraded, needs to be maintained and managed, and in some cases restored. Maintenance and restoration of ecological infrastructure is important for security of human lives. Therefore, analytical tool and effective visualization tool in spatially explicit way for the past natural disaster and future prediction of natural disaster in relation to ecological infrastructure is considered helpful. Hence, Web-GIS based Ecological Infrastructure Environmental Information System (EI-EIS) has been developed. This paper aims to describe the procedure of development and future application of EI-EIS. The purpose of the EI-EIS is to evaluate functions of Eco-DRR. In order to analyse disaster data, collection of past disaster information, and disaster-prone area is effective. First, a number of digital maps and analogue maps in Japan and Europe were collected. In total, 18,572 maps over 100 years were collected. The Japanese data includes Future-Pop Data Series (1,736 maps), JMC dataset 50m grid (elevation) (13,071 maps), Old Edition Maps: Topographic Map (325 maps), Digital Base Map at a scale of 2500 for reconstruction planning (808 maps), Detailed Digital Land Use Information for Metropolitan Area (10 m land use) (2,436 maps), and Digital Information by GSI (national large scale map) (71 maps). Old Edition Maps: Topographic Map were analogue maps, and were scanned and georeferenced. These geographical area covered 1) Tohoku area, 2) Five Lakes of Mikata area (Fukui), 3) Ooshima Island (Tokyo), 4) Hiroshima area (Hiroshima), 5) Okushiri Island (Hokkaido), and 6) Toyooka City area (Hyogo). The European data includes topographic map in Germany (8 maps), old topographic map in Germany (31 maps), ancient map in Germany (23 maps), topographic map in Austria (9 maps), old topographic map in Austria (17 maps), and ancient map in Austria (37 maps). Second, focusing on Five Lakes of Mikata area as an example, these maps were integrated into the ArcGIS Online® (ESRI). These data can be overlaid, and time-series data can be visualized by a time slider function of ArcGIS Online.

sfDM: Open-Source Software for Temporal Analysis and Visualization of Brain Tumor Diffusion MR Using Serial Functional Diffusion Mapping.

PubMed

Ceschin, Rafael; Panigrahy, Ashok; Gopalakrishnan, Vanathi

2015-01-01

A major challenge in the diagnosis and treatment of brain tumors is tissue heterogeneity leading to mixed treatment response. Additionally, they are often difficult or at very high risk for biopsy, further hindering the clinical management process. To overcome this, novel advanced imaging methods are increasingly being adapted clinically to identify useful noninvasive biomarkers capable of disease stage characterization and treatment response prediction. One promising technique is called functional diffusion mapping (fDM), which uses diffusion-weighted imaging (DWI) to generate parametric maps between two imaging time points in order to identify significant voxel-wise changes in water diffusion within the tumor tissue. Here we introduce serial functional diffusion mapping (sfDM), an extension of existing fDM methods, to analyze the entire tumor diffusion profile along the temporal course of the disease. sfDM provides the tools necessary to analyze a tumor data set in the context of spatiotemporal parametric mapping: the image registration pipeline, biomarker extraction, and visualization tools. We present the general workflow of the pipeline, along with a typical use case for the software. sfDM is written in Python and is freely available as an open-source package under the Berkley Software Distribution (BSD) license to promote transparency and reproducibility.

Identification of residue pairing in interacting β-strands from a predicted residue contact map.

PubMed

Mao, Wenzhi; Wang, Tong; Zhang, Wenxuan; Gong, Haipeng

2018-04-19

Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β-β interactions. This information may benefit the tertiary structure prediction of mainly β proteins. In this work, we propose a novel ridge-detection-based β-β contact predictor to identify residue pairing in β strands from any predicted residue contact map. Our algorithm RDb 2 C adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb 2 C remarkably outperforms all state-of-the-art methods on two conventional test sets of β proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~ 62% and ~ 76% at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb 2 C achieves impressively higher performance, with F1-scores reaching ~ 76% and ~ 86% at the residue level and strand level, respectively. In a test of structural modeling using the top 1 L predicted contacts as constraints, for 61 mainly β proteins, the average TM-score achieves 0.442 when using the raw RaptorX-Contact prediction, but increases to 0.506 when using the improved prediction by RDb 2 C. Our method can significantly improve the prediction of β-β contacts from any predicted residue contact maps. Prediction results of our algorithm could be directly applied to effectively facilitate the practical structure prediction of mainly β proteins. All source data and codes are available at http://166.111.152.91/Downloads.html or the GitHub address of https://github.com/wzmao/RDb2C .

Group-regularized individual prediction: theory and application to pain.

PubMed

Lindquist, Martin A; Krishnan, Anjali; López-Solà, Marina; Jepma, Marieke; Woo, Choong-Wan; Koban, Leonie; Roy, Mathieu; Atlas, Lauren Y; Schmidt, Liane; Chang, Luke J; Reynolds Losin, Elizabeth A; Eisenbarth, Hedwig; Ashar, Yoni K; Delk, Elizabeth; Wager, Tor D

2017-01-15

Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or 'decode' psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction-based on population-level predictive maps from prior groups-and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker-in this case, the Neurologic Pain Signature (NPS)-improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study. Copyright © 2015 Elsevier Inc. All rights reserved.

Enhancing the Characterization of Epistemic Uncertainties in PM2.5 Risk Analyses.

PubMed

Smith, Anne E; Gans, Will

2015-03-01

The Environmental Benefits Mapping and Analysis Program (BenMAP) is a software tool developed by the U.S. Environmental Protection Agency (EPA) that is widely used inside and outside of EPA to produce quantitative estimates of public health risks from fine particulate matter (PM2.5 ). This article discusses the purpose and appropriate role of a risk analysis tool to support risk management deliberations, and evaluates the functions of BenMAP in this context. It highlights the importance in quantitative risk analyses of characterization of epistemic uncertainty, or outright lack of knowledge, about the true risk relationships being quantified. This article describes and quantitatively illustrates sensitivities of PM2.5 risk estimates to several key forms of epistemic uncertainty that pervade those calculations: the risk coefficient, shape of the risk function, and the relative toxicity of individual PM2.5 constituents. It also summarizes findings from a review of U.S.-based epidemiological evidence regarding the PM2.5 risk coefficient for mortality from long-term exposure. That review shows that the set of risk coefficients embedded in BenMAP substantially understates the range in the literature. We conclude that BenMAP would more usefully fulfill its role as a risk analysis support tool if its functions were extended to better enable and prompt its users to characterize the epistemic uncertainties in their risk calculations. This requires expanded automatic sensitivity analysis functions and more recognition of the full range of uncertainty in risk coefficients. © 2014 Society for Risk Analysis.

UTE-T2* mapping detects sub-clinical meniscus injury after anterior cruciate ligament tear

PubMed Central

Williams, A.; Qian, Y.; Golla, S.; Chu, C.R.

2018-01-01

SUMMARY Objective Meniscus tear is a known risk factor for osteoarthritis (OA). Quantitative assessment of meniscus degeneration, prior to surface break-down, is important to identification of early disease potentially amenable to therapeutic interventions. This work examines the diagnostic potential of ultrashort echo time-enhanced T2* (UTE-T2*) mapping to detect human meniscus degeneration in vitro and in vivo in subjects at risk of developing OA. Design UTE-T2* maps of 16 human cadaver menisci were compared to histological evaluations of meniscal structural integrity and clinical magnetic resonance imaging (MRI) assessment by a musculoskeletal radiologist. In vivo UTE-T2* maps were compared in 10 asymptomatic subjects and 25 ACL-injured patients with and without concomitant meniscal tear. Results In vitro, UTE-T2* values tended to be lower in histologically and clinically normal meniscus tissue and higher in torn or degenerate tissue. UTE-T2* map heterogeneity reflected collagen disorganization. In vivo, asymptomatic meniscus UTE-T2* values were repeatable within 9% (root-mean-square average coefficient of variation). Posteromedial meniscus UTE-T2* values in ACL-injured subjects with clinically diagnosed medial meniscus tear (n = 10) were 87% higher than asymptomatics (n = 10, P < 0.001). Posteromedial menisci UTE-T2* values of ACL-injured subjects without concomitant medial meniscal tear (n = 15) were 33% higher than asymptomatics (P = 0.001). Posterolateral menisci UTE-T2* values also varied significantly with degree of joint pathology (P = 0.001). Conclusion Significant elevations of UTE-T2* values in the menisci of ACL-injured subjects without clinical evidence of subsurface meniscal abnormality suggest that UTE-T2* mapping is sensitive to subclinical meniscus degeneration. Further study is needed to determine whether elevated subsurface meniscus UTE-T2* values predict progression of meniscal degeneration and development of OA. PMID:22306000

Developing a smartphone software package for predicting atmospheric pollutant concentrations at mobile locations

PubMed Central

Larkin, Andrew; Williams, David E.; Kile, Molly L.; Baird, William M.

2014-01-01

Background There is considerable evidence that exposure to air pollution is harmful to health. In the U.S., ambient air quality is monitored by Federal and State agencies for regulatory purposes. There are limited options, however, for people to access this data in real-time which hinders an individual's ability to manage their own risks. This paper describes a new software package that models environmental concentrations of fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone concentrations for the state of Oregon and calculates personal health risks at the smartphone's current location. Predicted air pollution risk levels can be displayed on mobile devices as interactive maps and graphs color-coded to coincide with EPA air quality index (AQI) categories. Users have the option of setting air quality warning levels via color-coded bars and were notified whenever warning levels were exceeded by predicted levels within 10 km. We validated the software using data from participants as well as from simulations which showed that the application was capable of identifying spatial and temporal air quality trends. This unique application provides a potential low-cost technology for reducing personal exposure to air pollution which can improve quality of life particularly for people with health conditions, such as asthma, that make them more susceptible to these hazards. PMID:26146409

Volcanic Hazard Map as a Tool of City Planning: Experiences at Galeras Volcano and the county of Pasto, Colombia.

NASA Astrophysics Data System (ADS)

Calvache, M. L.

2001-12-01

Large populated areas located near active volcanoes emphasize the importance to take effective actions towards risk reduction. A volcanic hazard map is believed to be the first step in order to inform government officials, private institutions and community about the danger that poses a particular volcano. The hazard map is a tool that must be used to evaluate risk and elaborate risk map. The risk map must be used by decision makers to take measurements about the land-use accordingly with the hazard present in the area and to prepare contingency plans. In 1998 and 1999 the Colombian government pass a law, where every county of the country has to have a plan of land-use and development (POT) for the following 10 years. The POT must consider natural hazard and risk such as seismicity, landslide and volcanic activity. Without the plan, the county will not receive any economical support from the central government. In the county of Pasto, the largest city in the influence zone of Galeras volcano, the hazard map has been used to promote educational plan in schools, increasing public awareness of Galeras and its hazard, advise and persuade decision makers to consider Galeras hazard in the city development plans. On the other hand, the hazard map has been mistaken as a risk map and it has originated opposition due to the measurements taken as a consequence of the map. This presentation deal with the gain experience of using the hazard map as a tool of information and planing and the confrontation that any decision implies with political, social and economic interest.

The Dokuchaev hypothesis as a basis for predictive digital soil mapping (on the 125th anniversary of its publication)

NASA Astrophysics Data System (ADS)

Florinsky, I. V.

2012-04-01

Predictive digital soil mapping is widely used in soil science. Its objective is the prediction of the spatial distribution of soil taxonomic units and quantitative soil properties via the analysis of spatially distributed quantitative characteristics of soil-forming factors. Western pedometrists stress the scientific priority and principal importance of Hans Jenny's book (1941) for the emergence and development of predictive soil mapping. In this paper, we demonstrate that Vasily Dokuchaev explicitly defined the central idea and statement of the problem of contemporary predictive soil mapping in the year 1886. Then, we reconstruct the history of the soil formation equation from 1899 to 1941. We argue that Jenny adopted the soil formation equation from Sergey Zakharov, who published it in a well-known fundamental textbook in 1927. It is encouraging that this issue was clarified in 2011, the anniversary year for publications of Dokuchaev and Jenny.

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects.

PubMed

Ziegler, G; Ridgway, G R; Dahnke, R; Gaser, C

2014-08-15

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects

PubMed Central

Ziegler, G.; Ridgway, G.R.; Dahnke, R.; Gaser, C.

2014-01-01

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18–94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. PMID:24742919

Values of Flood Hazard Mapping for Disaster Risk Assessment and Communication

NASA Astrophysics Data System (ADS)

Sayama, T.; Takara, K. T.

2015-12-01

Flood plains provide tremendous benefits for human settlements. Since olden days people have lived with floods and attempted to control them if necessary. Modern engineering works such as building embankment have enabled people to live even in flood prone areas, and over time population and economic assets have concentrated in these areas. In developing countries also, rapid land use change alters exposure and vulnerability to floods and consequently increases disaster risk. Flood hazard mapping is an essential step for any counter measures. It has various objectives including raising awareness of residents, finding effective evacuation routes and estimating potential damages through flood risk mapping. Depending on the objectives and data availability, there are also many possible approaches for hazard mapping including simulation basis, community basis and remote sensing basis. In addition to traditional paper-based hazard maps, Information and Communication Technology (ICT) promotes more interactive hazard mapping such as movable hazard map to demonstrate scenario simulations for risk communications and real-time hazard mapping for effective disaster responses and safe evacuations. This presentation first summarizes recent advancement of flood hazard mapping by focusing on Japanese experiences and other examples from Asian countries. Then it introduces a flood simulation tool suitable for hazard mapping at the river basin scale even in data limited regions. In the past few years, the tool has been practiced by local officers responsible for disaster management in Asian countries. Through the training activities of hazard mapping and risk assessment, we conduct comparative analysis to identify similarity and uniqueness of estimated economic damages depending on topographic and land use conditions.

Research frontiers for improving our understanding of drought‐induced tree and forest mortality

USGS Publications Warehouse

Hartmann, Henrik; Moura, Catarina; Anderegg, William R. L.; Ruehr, Nadine; Salmon, Yann; Allen, Craig D.; Arndt, Stefan K.; Breshears, David D.; Davi, Hendrik; Galbraith, David; Ruthrof, Katinka X.; Wunder, Jan; Adams, Henry D.; Bloemen, Jasper; Cailleret, Maxime; Cobb, Richard; Gessler, Arthur; Grams, Thorsten E. E.; Jansen, Steven; Kautz, Markus; Lloret, Francisco; O’Brien, Michael

2018-01-01

Accumulating evidence highlights increased mortality risks for trees during severe drought, particularly under warmer temperatures and increasing vapour pressure deficit (VPD). Resulting forest die‐off events have severe consequences for ecosystem services, biophysical and biogeochemical land–atmosphere processes. Despite advances in monitoring, modelling and experimental studies of the causes and consequences of tree death from individual tree to ecosystem and global scale, a general mechanistic understanding and realistic predictions of drought mortality under future climate conditions are still lacking. We update a global tree mortality map and present a roadmap to a more holistic understanding of forest mortality across scales. We highlight priority research frontiers that promote: (1) new avenues for research on key tree ecophysiological responses to drought; (2) scaling from the tree/plot level to the ecosystem and region; (3) improvements of mortality risk predictions based on both empirical and mechanistic insights; and (4) a global monitoring network of forest mortality. In light of recent and anticipated large forest die‐off events such a research agenda is timely and needed to achieve scientific understanding for realistic predictions of drought‐induced tree mortality. The implementation of a sustainable network will require support by stakeholders and political authorities at the international level.

Factors affecting paddy soil arsenic concentration in Bangladesh: prediction and uncertainty of geostatistical risk mapping.

PubMed

Ahmed, Zia U; Panaullah, Golam M; DeGloria, Stephen D; Duxbury, John M

2011-12-15

Knowledge of the spatial correlation of soil arsenic (As) concentrations with environmental variables is needed to assess the nature and extent of the risk of As contamination from irrigation water in Bangladesh. We analyzed 263 paired groundwater and paddy soil samples covering highland (HL) and medium highland-1 (MHL-1) land types for geostatistical mapping of soil As and delineation of As contaminated areas in Tala Upazilla, Satkhira district. We also collected 74 non-rice soil samples to assess the baseline concentration of soil As for this area. The mean soil As concentrations (mg/kg) for different land types under rice and non-rice crops were: rice-MHL-1 (21.2)>rice-HL (14.1)>non-rice-MHL-1 (11.9)>non-rice-HL (7.2). Multiple regression analyses showed that irrigation water As, Fe, land elevation and years of tubewell operation are the important factors affecting the concentrations of As in HL paddy soils. Only years of tubewell operation affected As concentration in the MHL-1 paddy soils. Quantitatively similar increases in soil As above the estimated baseline-As concentration were observed for rice soils on HL and MHL-1 after 6-8 years of groundwater irrigation, implying strong retention of As added in irrigation water in both land types. Application of single geostatistical methods with secondary variables such as regression kriging (RK) and ordinary co-kriging (OCK) gave little improvement in prediction of soil As over ordinary kriging (OK). Comparing single prediction methods, kriging within strata (KWS), the combination of RK for HL and OCK for MHL-1, gave more accurate soil As predictions and showed the lowest misclassification of declaring a location "contaminated" with respect to 14.8 mg As/kg, the highest value obtained for the baseline soil As concentration. Prediction of soil As buildup over time indicated that 75% or the soils cropped to rice would contain at least 30 mg/L As by the year 2020. Copyright © 2011 Elsevier B.V. All rights reserved.

Bayesian Geostatistical Model-Based Estimates of Soil-Transmitted Helminth Infection in Nigeria, Including Annual Deworming Requirements

PubMed Central

Oluwole, Akinola S.; Ekpo, Uwem F.; Karagiannis-Voules, Dimitrios-Alexios; Abe, Eniola M.; Olamiju, Francisca O.; Isiyaku, Sunday; Okoronkwo, Chukwu; Saka, Yisa; Nebe, Obiageli J.; Braide, Eka I.; Mafiana, Chiedu F.; Utzinger, Jürg; Vounatsou, Penelope

2015-01-01

Background The acceleration of the control of soil-transmitted helminth (STH) infections in Nigeria, emphasizing preventive chemotherapy, has become imperative in light of the global fight against neglected tropical diseases. Predictive risk maps are an important tool to guide and support control activities. Methodology STH infection prevalence data were obtained from surveys carried out in 2011 using standard protocols. Data were geo-referenced and collated in a nationwide, geographic information system database. Bayesian geostatistical models with remotely sensed environmental covariates and variable selection procedures were utilized to predict the spatial distribution of STH infections in Nigeria. Principal Findings We found that hookworm, Ascaris lumbricoides, and Trichuris trichiura infections are endemic in 482 (86.8%), 305 (55.0%), and 55 (9.9%) locations, respectively. Hookworm and A. lumbricoides infection co-exist in 16 states, while the three species are co-endemic in 12 states. Overall, STHs are endemic in 20 of the 36 states of Nigeria, including the Federal Capital Territory of Abuja. The observed prevalence at endemic locations ranged from 1.7% to 51.7% for hookworm, from 1.6% to 77.8% for A. lumbricoides, and from 1.0% to 25.5% for T. trichiura. Model-based predictions ranged from 0.7% to 51.0% for hookworm, from 0.1% to 82.6% for A. lumbricoides, and from 0.0% to 18.5% for T. trichiura. Our models suggest that day land surface temperature and dense vegetation are important predictors of the spatial distribution of STH infection in Nigeria. In 2011, a total of 5.7 million (13.8%) school-aged children were predicted to be infected with STHs in Nigeria. Mass treatment at the local government area level for annual or bi-annual treatment of the school-aged population in Nigeria in 2011, based on World Health Organization prevalence thresholds, were estimated at 10.2 million tablets. Conclusions/Significance The predictive risk maps and estimated deworming needs presented here will be helpful for escalating the control and spatial targeting of interventions against STH infections in Nigeria. PMID:25909633

Bayesian geostatistical model-based estimates of soil-transmitted helminth infection in Nigeria, including annual deworming requirements.

PubMed

Oluwole, Akinola S; Ekpo, Uwem F; Karagiannis-Voules, Dimitrios-Alexios; Abe, Eniola M; Olamiju, Francisca O; Isiyaku, Sunday; Okoronkwo, Chukwu; Saka, Yisa; Nebe, Obiageli J; Braide, Eka I; Mafiana, Chiedu F; Utzinger, Jürg; Vounatsou, Penelope

2015-04-01

The acceleration of the control of soil-transmitted helminth (STH) infections in Nigeria, emphasizing preventive chemotherapy, has become imperative in light of the global fight against neglected tropical diseases. Predictive risk maps are an important tool to guide and support control activities. STH infection prevalence data were obtained from surveys carried out in 2011 using standard protocols. Data were geo-referenced and collated in a nationwide, geographic information system database. Bayesian geostatistical models with remotely sensed environmental covariates and variable selection procedures were utilized to predict the spatial distribution of STH infections in Nigeria. We found that hookworm, Ascaris lumbricoides, and Trichuris trichiura infections are endemic in 482 (86.8%), 305 (55.0%), and 55 (9.9%) locations, respectively. Hookworm and A. lumbricoides infection co-exist in 16 states, while the three species are co-endemic in 12 states. Overall, STHs are endemic in 20 of the 36 states of Nigeria, including the Federal Capital Territory of Abuja. The observed prevalence at endemic locations ranged from 1.7% to 51.7% for hookworm, from 1.6% to 77.8% for A. lumbricoides, and from 1.0% to 25.5% for T. trichiura. Model-based predictions ranged from 0.7% to 51.0% for hookworm, from 0.1% to 82.6% for A. lumbricoides, and from 0.0% to 18.5% for T. trichiura. Our models suggest that day land surface temperature and dense vegetation are important predictors of the spatial distribution of STH infection in Nigeria. In 2011, a total of 5.7 million (13.8%) school-aged children were predicted to be infected with STHs in Nigeria. Mass treatment at the local government area level for annual or bi-annual treatment of the school-aged population in Nigeria in 2011, based on World Health Organization prevalence thresholds, were estimated at 10.2 million tablets. The predictive risk maps and estimated deworming needs presented here will be helpful for escalating the control and spatial targeting of interventions against STH infections in Nigeria.

Exploring prediction uncertainty of spatial data in geostatistical and machine learning Approaches

NASA Astrophysics Data System (ADS)

Klump, J. F.; Fouedjio, F.

2017-12-01

Geostatistical methods such as kriging with external drift as well as machine learning techniques such as quantile regression forest have been intensively used for modelling spatial data. In addition to providing predictions for target variables, both approaches are able to deliver a quantification of the uncertainty associated with the prediction at a target location. Geostatistical approaches are, by essence, adequate for providing such prediction uncertainties and their behaviour is well understood. However, they often require significant data pre-processing and rely on assumptions that are rarely met in practice. Machine learning algorithms such as random forest regression, on the other hand, require less data pre-processing and are non-parametric. This makes the application of machine learning algorithms to geostatistical problems an attractive proposition. The objective of this study is to compare kriging with external drift and quantile regression forest with respect to their ability to deliver reliable prediction uncertainties of spatial data. In our comparison we use both simulated and real world datasets. Apart from classical performance indicators, comparisons make use of accuracy plots, probability interval width plots, and the visual examinations of the uncertainty maps provided by the two approaches. By comparing random forest regression to kriging we found that both methods produced comparable maps of estimated values for our variables of interest. However, the measure of uncertainty provided by random forest seems to be quite different to the measure of uncertainty provided by kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. These preliminary results raise questions about assessing the risks associated with decisions based on the predictions from geostatistical and machine learning algorithms in a spatial context, e.g. mineral exploration.

Assessing, mapping and validating site-specific ecotoxicological risk for pesticide mixtures: a case study for small scale hot spots in aquatic and terrestrial environments.

PubMed

Vaj, Claudia; Barmaz, Stefania; Sørensen, Peter Borgen; Spurgeon, David; Vighi, Marco

2011-11-01

Mixture toxicity is a real world problem and as such requires risk assessment solutions that can be applied within different geographic regions, across different spatial scales and in situations where the quantity of data available for the assessment varies. Moreover, the need for site specific procedures for assessing ecotoxicological risk for non-target species in non-target ecosystems also has to be recognised. The work presented in the paper addresses the real world effects of pesticide mixtures on natural communities. Initially, the location of risk hotspots is theoretically estimated through exposure modelling and the use of available toxicity data to predict potential community effects. The concept of Concentration Addition (CA) is applied to describe responses resulting from exposure of multiple pesticides The developed and refined exposure models are georeferenced (GIS-based) and include environmental and physico-chemical parameters, and site specific information on pesticide usage and land use. As a test of the risk assessment framework, the procedures have been applied on a suitable study areas, notably the River Meolo basin (Northern Italy), a catchment characterised by intensive agriculture, as well as comparative area for some assessments. Within the studied areas, the risks for individual chemicals and complex mixtures have been assessed on aquatic and terrestrial aboveground and belowground communities. Results from ecological surveys have been used to validate these risk assessment model predictions. Value and limitation of the approaches are described and the possibilities for larger scale applications in risk assessment are also discussed. Copyright © 2011 Elsevier Inc. All rights reserved.

Mapping Infected Area after a Flash-Flooding Storm Using Multi Criteria Analysis and Spectral Indices

NASA Astrophysics Data System (ADS)

Al-Akad, S.; Akensous, Y.; Hakdaoui, M.

2017-11-01

This research article is summarize the applications of remote sensing and GIS to study the urban floods risk in Al Mukalla. Satellite acquisition of a flood event on October 2015 in Al Mukalla (Yemen) by using flood risk mapping techniques illustrate the potential risk present in this city. Satellite images (The Landsat and DEM images data were atmospherically corrected, radiometric corrected, and geometric and topographic distortions rectified.) are used for flood risk mapping to afford a hazard (vulnerability) map. This map is provided by applying image-processing techniques and using geographic information system (GIS) environment also the application of NDVI, NDWI index, and a method to estimate the flood-hazard areas. Four factors were considered in order to estimate the spatial distribution of the hazardous areas: flow accumulation, slope, land use, geology and elevation. The multi-criteria analysis, allowing to deal with vulnerability to flooding, as well as mapping areas at the risk of flooding of the city Al Mukalla. The main object of this research is to provide a simple and rapid method to reduce and manage the risks caused by flood in Yemen by take as example the city of Al Mukalla.

Effects of urban microcellular environments on ray-tracing-based coverage predictions.

PubMed

Liu, Zhongyu; Guo, Lixin; Guan, Xiaowei; Sun, Jiejing

2016-09-01

The ray-tracing (RT) algorithm, which is based on geometrical optics and the uniform theory of diffraction, has become a typical deterministic approach of studying wave-propagation characteristics. Under urban microcellular environments, the RT method highly depends on detailed environmental information. The aim of this paper is to provide help in selecting the appropriate level of accuracy required in building databases to achieve good tradeoffs between database costs and prediction accuracy. After familiarization with the operating procedures of the RT-based prediction model, this study focuses on the effect of errors in environmental information on prediction results. The environmental information consists of two parts, namely, geometric and electrical parameters. The geometric information can be obtained from a digital map of a city. To study the effects of inaccuracies in geometry information (building layout) on RT-based coverage prediction, two different artificial erroneous maps are generated based on the original digital map, and systematic analysis is performed by comparing the predictions with the erroneous maps and measurements or the predictions with the original digital map. To make the conclusion more persuasive, the influence of random errors on RMS delay spread results is investigated. Furthermore, given the electrical parameters' effect on the accuracy of the predicted results of the RT model, the dielectric constant and conductivity of building materials are set with different values. The path loss and RMS delay spread under the same circumstances are simulated by the RT prediction model.

Predicting chromosomal locations of genetically mapped loci in maize using the Morgan2McClintock Translator.

PubMed

Lawrence, Carolyn J; Seigfried, Trent E; Bass, Hank W; Anderson, Lorinda K

2006-03-01

The Morgan2McClintock Translator permits prediction of meiotic pachytene chromosome map positions from recombination-based linkage data using recombination nodule frequency distributions. Its outputs permit estimation of DNA content between mapped loci and help to create an integrated overview of the maize nuclear genome structure.

Should coastal planners have concern over where land ice is melting?

PubMed Central

Larour, Eric; Ivins, Erik R.; Adhikari, Surendra

2017-01-01

There is a general consensus among Earth scientists that melting of land ice greatly contributes to sea-level rise (SLR) and that future warming will exacerbate the risks posed to human civilization. As land ice is lost to the oceans, both the Earth’s gravitational and rotational potentials are perturbed, resulting in strong spatial patterns in SLR, termed sea-level fingerprints. We lack robust forecasting models for future ice changes, which diminishes our ability to use these fingerprints to accurately predict local sea-level (LSL) changes. We exploit an advanced mathematical property of adjoint systems and determine the exact gradient of sea-level fingerprints with respect to local variations in the ice thickness of all of the world’s ice drainage systems. By exhaustively mapping these fingerprint gradients, we form a new diagnosis tool, henceforth referred to as gradient fingerprint mapping (GFM), that readily allows for improved assessments of future coastal inundation or emergence. We demonstrate that for Antarctica and Greenland, changes in the predictions of inundation at major port cities depend on the location of the drainage system. For example, in London, GFM shows LSL that is significantly affected by changes on the western part of the Greenland Ice Sheet (GrIS), whereas in New York, LSL change predictions are greatly sensitive to changes in the northeastern portions of the GrIS. We apply GFM to 293 major port cities to allow coastal planners to readily calculate LSL change as more reliable predictions of cryospheric mass changes become available. PMID:29152565

Short communication: Investigation into Mycobacterium avium ssp. paratuberculosis in pasteurized milk in Italy.

PubMed

Serraino, A; Bonilauri, P; Giacometti, F; Ricchi, M; Cammi, G; Piva, S; Zambrini, V; Canever, A; Arrigoni, N

2017-01-01

This study investigated the presence of viable Mycobacterium avium ssp. paratuberculosis (MAP) in pasteurized milk produced by Italian industrial dairy plants to verify the prediction of a previously performed risk assessment. The study analyzed 160 one-liter bottles of pasteurized milk from 2 dairy plants located in 2 different regions. Traditional cultural protocols were applied to 500mL of pasteurized milk for each sample. The investigation focused also on the pasteurization parameters and data on the microbiological characteristics of raw milk (total bacterial count) and pasteurized milk (Enterobacteriaceae and Listeria monocytogenes). No sample was positive for MAP, the pasteurization parameters complied with European Union legislation, and the microbiological analysis of raw and pasteurized milk showed good microbiological quality. The results show that a 7-log (or >7) reduction could be a plausible value for commercial pasteurization. The combination of hygiene practices at farm level and commercial pasteurization yield very low or absent levels of MAP contamination in pasteurized milk, suggesting that pasteurized milk is not a significant source of human exposure to MAP in the dairies investigated. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Combination Testing Using a Single MSH5 Variant alongside HLA Haplotypes Improves the Sensitivity of Predicting Coeliac Disease Risk in the Polish Population.

PubMed

Paziewska, Agnieszka; Cukrowska, Bozena; Dabrowska, Michalina; Goryca, Krzysztof; Piatkowska, Magdalena; Kluska, Anna; Mikula, Michal; Karczmarski, Jakub; Oralewska, Beata; Rybak, Anna; Socha, Jerzy; Balabas, Aneta; Zeber-Lubecka, Natalia; Ambrozkiewicz, Filip; Konopka, Ewa; Trojanowska, Ilona; Zagroba, Malgorzata; Szperl, Malgorzata; Ostrowski, Jerzy

2015-01-01

Assessment of non-HLA variants alongside standard HLA testing was previously shown to improve the identification of potential coeliac disease (CD) patients. We intended to identify new genetic variants associated with CD in the Polish population that would improve CD risk prediction when used alongside HLA haplotype analysis. DNA samples of 336 CD and 264 unrelated healthy controls were used to create DNA pools for a genome wide association study (GWAS). GWAS findings were validated with individual HLA tag single nucleotide polymorphism (SNP) typing of 473 patients and 714 healthy controls. Association analysis using four HLA-tagging SNPs showed that, as was found in other populations, positive predicting genotypes (HLA-DQ2.5/DQ2.5, HLA-DQ2.5/DQ2.2, and HLA-DQ2.5/DQ8) were found at higher frequencies in CD patients than in healthy control individuals in the Polish population. Both CD-associated SNPs discovered by GWAS were found in the CD susceptibility region, confirming the previously-determined association of the major histocompatibility (MHC) region with CD pathogenesis. The two most significant SNPs from the GWAS were rs9272346 (HLA-dependent; localized within 1 Kb of DQA1) and rs3130484 (HLA-independent; mapped to MSH5). Specificity of CD prediction using the four HLA-tagging SNPs achieved 92.9%, but sensitivity was only 45.5%. However, when a testing combination of the HLA-tagging SNPs and the MSH5 SNP was used, specificity decreased to 80%, and sensitivity increased to 74%. This study confirmed that improvement of CD risk prediction sensitivity could be achieved by including non-HLA SNPs alongside HLA SNPs in genetic testing.

Modifications to risk-targeted seismic design maps for subduction and near-fault hazards

USGS Publications Warehouse

Liel, Abbie B.; Luco, Nicolas; Raghunandan, Meera; Champion, C.; Haukaas, Terje

2015-01-01

ASCE 7-10 introduced new seismic design maps that define risk-targeted ground motions such that buildings designed according to these maps will have 1% chance of collapse in 50 years. These maps were developed by iterative risk calculation, wherein a generic building collapse fragility curve is convolved with the U.S. Geological Survey hazard curve until target risk criteria are met. Recent research shows that this current approach may be unconservative at locations where the tectonic environment is much different than that used to develop the generic fragility curve. This study illustrates how risk-targeted ground motions at selected sites would change if generic building fragility curve and hazard assessment were modified to account for seismic risk from subduction earthquakes and near-fault pulses. The paper also explores the difficulties in implementing these changes.

Geographic information systems and logistic regression for high-resolution malaria risk mapping in a rural settlement of the southern Brazilian Amazon.

PubMed

de Oliveira, Elaine Cristina; dos Santos, Emerson Soares; Zeilhofer, Peter; Souza-Santos, Reinaldo; Atanaka-Santos, Marina

2013-11-15

In Brazil, 99% of the cases of malaria are concentrated in the Amazon region, with high level of transmission. The objectives of the study were to use geographic information systems (GIS) analysis and logistic regression as a tool to identify and analyse the relative likelihood and its socio-environmental determinants of malaria infection in the Vale do Amanhecer rural settlement, Brazil. A GIS database of georeferenced malaria cases, recorded in 2005, and multiple explanatory data layers was built, based on a multispectral Landsat 5 TM image, digital map of the settlement blocks and a SRTM digital elevation model. Satellite imagery was used to map the spatial patterns of land use and cover (LUC) and to derive spectral indices of vegetation density (NDVI) and soil/vegetation humidity (VSHI). An Euclidian distance operator was applied to measure proximity of domiciles to potential mosquito breeding habitats and gold mining areas. The malaria risk model was generated by multiple logistic regression, in which environmental factors were considered as independent variables and the number of cases, binarized by a threshold value was the dependent variable. Out of a total of 336 cases of malaria, 133 positive slides were from inhabitants at Road 08, which corresponds to 37.60% of the notifications. The southern region of the settlement presented 276 cases and a greater number of domiciles in which more than ten cases/home were notified. From these, 102 (30.36%) cases were caused by Plasmodium falciparum and 174 (51.79%) cases by Plasmodium vivax. Malaria risk is the highest in the south of the settlement, associated with proximity to gold mining sites, intense land use, high levels of soil/vegetation humidity and low vegetation density. Mid-resolution, remote sensing data and GIS-derived distance measures can be successfully combined with digital maps of the housing location of (non-) infected inhabitants to predict relative likelihood of disease infection through the analysis by logistic regression. Obtained findings on the relation between malaria cases and environmental factors should be applied in the future for land use planning in rural settlements in the Southern Amazon to minimize risks of disease transmission.

Topsoil organic carbon content of Europe, a new map based on a generalised additive model

NASA Astrophysics Data System (ADS)

de Brogniez, Delphine; Ballabio, Cristiano; Stevens, Antoine; Jones, Robert J. A.; Montanarella, Luca; van Wesemael, Bas

2014-05-01

There is an increasing demand for up-to-date spatially continuous organic carbon (OC) data for global environment and climatic modeling. Whilst the current map of topsoil organic carbon content for Europe (Jones et al., 2005) was produced by applying expert-knowledge based pedo-transfer rules on large soil mapping units, the aim of this study was to replace it by applying digital soil mapping techniques on the first European harmonised geo-referenced topsoil (0-20 cm) database, which arises from the LUCAS (land use/cover area frame statistical survey) survey. A generalized additive model (GAM) was calibrated on 85% of the dataset (ca. 17 000 soil samples) and a backward stepwise approach selected slope, land cover, temperature, net primary productivity, latitude and longitude as environmental covariates (500 m resolution). The validation of the model (applied on 15% of the dataset), gave an R2 of 0.27. We observed that most organic soils were under-predicted by the model and that soils of Scandinavia were also poorly predicted. The model showed an RMSE of 42 g kg-1 for mineral soils and of 287 g kg-1 for organic soils. The map of predicted OC content showed the lowest values in Mediterranean countries and in croplands across Europe, whereas highest OC content were predicted in wetlands, woodlands and in mountainous areas. The map of standard error of the OC model predictions showed high values in northern latitudes, wetlands, moors and heathlands, whereas low uncertainty was mostly found in croplands. A comparison of our results with the map of Jones et al. (2005) showed a general agreement on the prediction of mineral soils' OC content, most probably because the models use some common covariates, namely land cover and temperature. Our model however failed to predict values of OC content greater than 200 g kg-1, which we explain by the imposed unimodal distribution of our model, whose mean is tilted towards the majority of soils, which are mineral. Finally, average OC content predictions for each land cover class compared well between models, with our model always showing smaller standard deviations. We concluded that the chosen model and covariates are appropriate for the prediction of OC content in European mineral soils. We presented in this work the first map of topsoil OC content at European scale based on a harmonised soil dataset. The associated uncertainty map shall support the end-users in a careful use of the predictions.

Application of a GIS-/remote sensing-based approach for predicting groundwater potential zones using a multi-criteria data mining methodology.

PubMed

Mogaji, Kehinde Anthony; Lim, Hwee San

2017-07-01

This study integrates the application of Dempster-Shafer-driven evidential belief function (DS-EBF) methodology with remote sensing and geographic information system techniques to analyze surface and subsurface data sets for the spatial prediction of groundwater potential in Perak Province, Malaysia. The study used additional data obtained from the records of the groundwater yield rate of approximately 28 bore well locations. The processed surface and subsurface data produced sets of groundwater potential conditioning factors (GPCFs) from which multiple surface hydrologic and subsurface hydrogeologic parameter thematic maps were generated. The bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training to 30% (9 wells) for model testing. Application results of the DS-EBF relationship model algorithms of the surface- and subsurface-based GPCF thematic maps and the bore well locations produced two groundwater potential prediction (GPP) maps based on surface hydrologic and subsurface hydrogeologic characteristics which established that more than 60% of the study area falling within the moderate-high groundwater potential zones and less than 35% falling within the low potential zones. The estimated uncertainty values within the range of 0 to 17% for the predicted potential zones were quantified using the uncertainty algorithm of the model. The validation results of the GPP maps using relative operating characteristic curve method yielded 80 and 68% success rates and 89 and 53% prediction rates for the subsurface hydrogeologic factor (SUHF)- and surface hydrologic factor (SHF)-based GPP maps, respectively. The study results revealed that the SUHF-based GPP map accurately delineated groundwater potential zones better than the SHF-based GPP map. However, significant information on the low degree of uncertainty of the predicted potential zones established the suitability of the two GPP maps for future development of groundwater resources in the area. The overall results proved the efficacy of the data mining model and the geospatial technology in groundwater potential mapping.

Predictive model of outcome of targeted nodal assessment in colorectal cancer.

PubMed

Nissan, Aviram; Protic, Mladjan; Bilchik, Anton; Eberhardt, John; Peoples, George E; Stojadinovic, Alexander

2010-02-01

Improvement in staging accuracy is the principal aim of targeted nodal assessment in colorectal carcinoma. Technical factors independently predictive of false negative (FN) sentinel lymph node (SLN) mapping should be identified to facilitate operative decision making. To define independent predictors of FN SLN mapping and to develop a predictive model that could support surgical decisions. Data was analyzed from 2 completed prospective clinical trials involving 278 patients with colorectal carcinoma undergoing SLN mapping. Clinical outcome of interest was FN SLN(s), defined as one(s) with no apparent tumor cells in the presence of non-SLN metastases. To assess the independent predictive effect of a covariate for a nominal response (FN SLN), a logistic regression model was constructed and parameters estimated using maximum likelihood. A probabilistic Bayesian model was also trained and cross validated using 10-fold train-and-test sets to predict FN SLN mapping. Area under the curve (AUC) from receiver operating characteristics curves of these predictions was calculated to determine the predictive value of the model. Number of SLNs (<3; P = 0.03) and tumor-replaced nodes (P < 0.01) independently predicted FN SLN. Cross validation of the model created with Bayesian Network Analysis effectively predicted FN SLN (area under the curve = 0.84-0.86). The positive and negative predictive values of the model are 83% and 97%, respectively. This study supports a minimum threshold of 3 nodes for targeted nodal assessment in colorectal cancer, and establishes sufficient basis to conclude that SLN mapping and biopsy cannot be justified in the presence of clinically apparent tumor-replaced nodes.

An Integrated Approach for Urban Earthquake Vulnerability Analyses

NASA Astrophysics Data System (ADS)

Düzgün, H. S.; Yücemen, M. S.; Kalaycioglu, H. S.

2009-04-01

The earthquake risk for an urban area has increased over the years due to the increasing complexities in urban environments. The main reasons are the location of major cities in hazard prone areas, growth in urbanization and population and rising wealth measures. In recent years physical examples of these factors are observed through the growing costs of major disasters in urban areas which have stimulated a demand for in-depth evaluation of possible strategies to manage the large scale damaging effects of earthquakes. Understanding and formulation of urban earthquake risk requires consideration of a wide range of risk aspects, which can be handled by developing an integrated approach. In such an integrated approach, an interdisciplinary view should be incorporated into the risk assessment. Risk assessment for an urban area requires prediction of vulnerabilities related to elements at risk in the urban area and integration of individual vulnerability assessments. However, due to complex nature of an urban environment, estimating vulnerabilities and integrating them necessities development of integrated approaches in which vulnerabilities of social, economical, structural (building stock and infrastructure), cultural and historical heritage are estimated for a given urban area over a given time period. In this study an integrated urban earthquake vulnerability assessment framework, which considers vulnerability of urban environment in a holistic manner and performs the vulnerability assessment for the smallest administrative unit, namely at neighborhood scale, is proposed. The main motivation behind this approach is the inability to implement existing vulnerability assessment methodologies for countries like Turkey, where the required data are usually missing or inadequate and decision makers seek for prioritization of their limited resources in risk reduction in the administrative districts from which they are responsible. The methodology integrates socio-economical, structural, coastal, ground condition, organizational vulnerabilities, as well as accessibility to critical services within the framework. The proposed framework has the following eight components: Seismic hazard analysis, soil response analysis, tsunami inundation analysis, structural vulnerability analysis, socio-economic vulnerability analysis, accessibility to critical services, GIS-based integrated vulnerability assessment, and visualization of vulnerabilities in 3D virtual city model The integrated model for various vulnerabilities obtained for the urban area is developed in GIS environment by using individual vulnerability assessments for considered elements at risk and serve for establishing the backbone of the spatial decision support system. The stages followed in the model are: Determination of a common mapping unit for each aspect of urban earthquake vulnerability, formation of a geo-database for the vulnerabilities, evaluation of urban vulnerability based on multi attribute utility theory with various weighting algorithms, mapping of the evaluated integrated earthquake risk in geographic information systems (GIS) in the neighborhood scale. The framework is also applicable to larger geographical mapping scales, for example, the building scale. When illustrating the results in building scale, 3-D visualizations with remote sensing data is used so that decision-makers can easily interpret the outputs. The proposed vulnerability assessment framework is flexible and can easily be applied to urban environments at various geographical scales with different mapping units. The obtained total vulnerability maps for the urban area provide a baseline for the development of risk reduction strategies for the decision makers. Moreover, as several aspects of elements at risk for an urban area is considered through vulnerability analyses, effect on changes in vulnerability conditions on the total can easily be determined. The developed approach also enables decision makers to monitor temporal and spatial changes in the urban environment due to implementation of risk reduction strategies.

Estimating Hantavirus Risk in Southern Argentina: A GIS-Based Approach Combining Human Cases and Host Distribution

PubMed Central

Andreo, Veronica; Neteler, Markus; Rocchini, Duccio; Provensal, Cecilia; Levis, Silvana; Porcasi, Ximena; Rizzoli, Annapaola; Lanfri, Mario; Scavuzzo, Marcelo; Pini, Noemi; Enria, Delia; Polop, Jaime

2014-01-01

We use a Species Distribution Modeling (SDM) approach along with Geographic Information Systems (GIS) techniques to examine the potential distribution of hantavirus pulmonary syndrome (HPS) caused by Andes virus (ANDV) in southern Argentina and, more precisely, define and estimate the area with the highest infection probability for humans, through the combination with the distribution map for the competent rodent host (Oligoryzomys longicaudatus). Sites with confirmed cases of HPS in the period 1995–2009 were mostly concentrated in a narrow strip (~90 km × 900 km) along the Andes range from northern Neuquén to central Chubut province. This area is characterized by high mean annual precipitation (~1,000 mm on average), but dry summers (less than 100 mm), very low percentages of bare soil (~10% on average) and low temperatures in the coldest month (minimum average temperature −1.5 °C), as compared to the HPS-free areas, features that coincide with sub-Antarctic forests and shrublands (especially those dominated by the invasive plant Rosa rubiginosa), where rodent host abundances and ANDV prevalences are known to be the highest. Through the combination of predictive distribution maps of the reservoir host and disease cases, we found that the area with the highest probability for HPS to occur overlaps only 28% with the most suitable habitat for O. longicaudatus. With this approach, we made a step forward in the understanding of the risk factors that need to be considered in the forecasting and mapping of risk at the regional/national scale. We propose the implementation and use of thematic maps, such as the one built here, as a basic tool allowing public health authorities to focus surveillance efforts and normally scarce resources for prevention and control actions in vast areas like southern Argentina. PMID:24424500

Electrocorticographic high gamma activity versus electrical cortical stimulation mapping of naming.

PubMed

Sinai, Alon; Bowers, Christopher W; Crainiceanu, Ciprian M; Boatman, Dana; Gordon, Barry; Lesser, Ronald P; Lenz, Frederick A; Crone, Nathan E

2005-07-01

Subdural electrocorticographic (ECoG) recordings in patients undergoing epilepsy surgery have shown that functional activation is associated with event-related broadband gamma activity in a higher frequency range (>70 Hz) than previously studied in human scalp EEG. To investigate the utility of this high gamma activity (HGA) for mapping language cortex, we compared its neuroanatomical distribution with functional maps derived from electrical cortical stimulation (ECS), which remains the gold standard for predicting functional impairment after surgery for epilepsy, tumours or vascular malformations. Thirteen patients had undergone subdural electrode implantation for the surgical management of intractable epilepsy. Subdural ECoG signals were recorded while each patient verbally named sequentially presented line drawings of objects, and estimates of event-related HGA (80-100 Hz) were made at each recording site. Routine clinical ECS mapping used a subset of the same naming stimuli at each cortical site. If ECS disrupted mouth-related motor function, i.e. if it affected the mouth, lips or tongue, naming could not be tested with ECS at the same cortical site. Because naming during ECoG involved these muscles of articulation, the sensitivity and specificity of ECoG HGA were estimated relative to both ECS-induced impairments of naming and ECS disruption of mouth-related motor function. When these estimates were made separately for 12 electrode sites per patient (the average number with significant HGA), the specificity of ECoG HGA with respect to ECS was 78% for naming and 81% for mouth-related motor function, and equivalent sensitivities were 38% and 46%, respectively. When ECS maps of naming and mouth-related motor function were combined, the specificity and sensitivity of ECoG HGA with respect to ECS were 84% and 43%, respectively. This study indicates that event-related ECoG HGA during confrontation naming predicts ECS interference with naming and mouth-related motor function with good specificity but relatively low sensitivity. Its favourable specificity suggests that ECoG HGA can be used to construct a preliminary functional map that may help identify cortical sites of lower priority for ECS mapping. Passive recordings of ECoG gamma activity may be done simultaneously at all electrode sites without the risk of after-discharges associated with ECS mapping, which must be done sequentially at pairs of electrodes. We discuss the relative merits of these two functional mapping techniques.

Application of Data-Driven Evidential Belief Functions to Prospectivity Mapping for Aquamarine-Bearing Pegmatites, Lundazi District, Zambia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carranza, E. J. M., E-mail: carranza@itc.nl; Woldai, T.; Chikambwe, E. M.

A case application of data-driven estimation of evidential belief functions (EBFs) is demonstrated to prospectivity mapping in Lundazi district (eastern Zambia). Spatial data used to represent recognition criteria of prospectivity for aquamarine-bearing pegmatites include mapped granites, mapped faults/fractures, mapped shear zones, and radioelement concentration ratios derived from gridded airborne radiometric data. Data-driven estimates EBFs take into account not only (a) spatial association between an evidential map layer and target deposits but also (b) spatial relationships between classes of evidences in an evidential map layer. Data-driven estimates of EBFs can indicate which spatial data provide positive or negative evidence of prospectivity.more » Data-driven estimates of EBFs of only spatial data providing positive evidence of prospectivity were integrated according to Dempster's rule of combination. Map of integrated degrees of belief was used to delineate zones of relative degress of prospectivity for aquamarine-bearing pegmatites. The predictive map has at least 85% prediction rate and at least 79% success rate of delineating training and validation deposits, respectively. The results illustrate usefulness of data-driven estimation of EBFs in GIS-based predictive mapping of mineral prospectivity. The results also show usefulness of EBFs in managing uncertainties associated with evidential maps.« less

Human Factors Risk Analyses of a Doffing Protocol for Ebola-Level Personal Protective Equipment: Mapping Errors to Contamination.

PubMed

Mumma, Joel M; Durso, Francis T; Ferguson, Ashley N; Gipson, Christina L; Casanova, Lisa; Erukunuakpor, Kimberly; Kraft, Colleen S; Walsh, Victoria L; Zimring, Craig; DuBose, Jennifer; Jacob, Jesse T

2018-03-05

Doffing protocols for personal protective equipment (PPE) are critical for keeping healthcare workers (HCWs) safe during care of patients with Ebola virus disease. We assessed the relationship between errors and self-contamination during doffing. Eleven HCWs experienced with doffing Ebola-level PPE participated in simulations in which HCWs donned PPE marked with surrogate viruses (ɸ6 and MS2), completed a clinical task, and were assessed for contamination after doffing. Simulations were video recorded, and a failure modes and effects analysis and fault tree analyses were performed to identify errors during doffing, quantify their risk (risk index), and predict contamination data. Fifty-one types of errors were identified, many having the potential to spread contamination. Hand hygiene and removing the powered air purifying respirator (PAPR) hood had the highest total risk indexes (111 and 70, respectively) and number of types of errors (9 and 13, respectively). ɸ6 was detected on 10% of scrubs and the fault tree predicted a 10.4% contamination rate, likely occurring when the PAPR hood inadvertently contacted scrubs during removal. MS2 was detected on 10% of hands, 20% of scrubs, and 70% of inner gloves and the predicted rates were 7.3%, 19.4%, 73.4%, respectively. Fault trees for MS2 and ɸ6 contamination suggested similar pathways. Ebola-level PPE can both protect and put HCWs at risk for self-contamination throughout the doffing process, even among experienced HCWs doffing with a trained observer. Human factors methodologies can identify error-prone steps, delineate the relationship between errors and self-contamination, and suggest remediation strategies.

Terra and Aqua satellites track tiger mosquito invasion: modelling the potential distribution of Aedes albopictus in north-eastern Italy

PubMed Central

2011-01-01

Background The continuing spread of the Asian tiger mosquito Aedes albopictus in Europe is of increasing public health concern due to the potential risk of new outbreaks of exotic vector-borne diseases that this species can transmit as competent vector. We predicted the most favorable areas for a short term invasion of Ae. albopictus in north-eastern Italy using reconstructed daily satellite data time series (MODIS Land Surface Temperature maps, LST). We reconstructed more than 11,000 daily MODIS LST maps for the period 2001-09 (i.e. performed spatial and temporal gap-filling) in an Open Source GIS framework. We aggregated these LST maps over time and identified the potential distribution areas of Ae. albopictus by adapting published temperature threshold values using three variables as predictors (0°C for mean January temperatures, 11°C for annual mean temperatures and 1350 growing degree days filtered for areas with autumnal mean temperatures > 11°C). The resulting maps were integrated into the final potential distribution map and this was compared with the known current distribution of Ae. albopictus in north-eastern Italy. Results LST maps show the microclimatic characteristics peculiar to complex terrains, which would not be visible in maps commonly derived from interpolated meteorological station data. The patterns of the three indicator variables partially differ from each other, while winter temperature is the determining limiting factor for the distribution of Ae. albopictus. All three variables show a similar spatial pattern with some local differences, in particular in the northern part of the study area (upper Adige valley). Conclusions Reconstructed daily land surface temperature data from satellites can be used to predict areas of short term invasion of the tiger mosquito with sufficient accuracy (200 m pixel resolution size). Furthermore, they may be applied to other species of arthropod of medical interest for which temperature is a relevant limiting factor. The results indicate that, during the next few years, the tiger mosquito will probably spread toward northern latitudes and higher altitudes in north-eastern Italy, which will considerably expand the range of the current distribution of this species. PMID:21812983

Gait Rather Than Cognition Predicts Decline in Specific Cognitive Domains in Early Parkinson's Disease.

PubMed

Morris, Rosie; Lord, Sue; Lawson, Rachael A; Coleman, Shirley; Galna, Brook; Duncan, Gordon W; Khoo, Tien K; Yarnall, Alison J; Burn, David J; Rochester, Lynn

2017-11-09

Dementia is significant in Parkinson's disease (PD) with personal and socioeconomic impact. Early identification of risk is of upmost importance to optimize management. Gait precedes and predicts cognitive decline and dementia in older adults. We aimed to evaluate gait characteristics as predictors of cognitive decline in newly diagnosed PD. One hundred and nineteen participants recruited at diagnosis were assessed at baseline, 18 and 36 months. Baseline gait was characterized by variables that mapped to five domains: pace, rhythm, variability, asymmetry, and postural control. Cognitive assessment included attention, fluctuating attention, executive function, visual memory, and visuospatial function. Mixed-effects models tested independent gait predictors of cognitive decline. Gait characteristics of pace, variability, and postural control predicted decline in fluctuating attention and visual memory, whereas baseline neuropsychological assessment performance did not predict decline. This provides novel evidence for gait as a clinical biomarker for PD cognitive decline in early disease. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America.

Is there more valuable information in PWI datasets for a voxel-wise acute ischemic stroke tissue outcome prediction than what is represented by typical perfusion maps?

NASA Astrophysics Data System (ADS)

Forkert, Nils Daniel; Siemonsen, Susanne; Dalski, Michael; Verleger, Tobias; Kemmling, Andre; Fiehler, Jens

2014-03-01

The acute ischemic stroke is a leading cause for death and disability in the industry nations. In case of a present acute ischemic stroke, the prediction of the future tissue outcome is of high interest for the clinicians as it can be used to support therapy decision making. Within this context, it has already been shown that the voxel-wise multi-parametric tissue outcome prediction leads to more promising results compared to single channel perfusion map thresholding. Most previously published multi-parametric predictions employ information from perfusion maps derived from perfusion-weighted MRI together with other image sequences such as diffusion-weighted MRI. However, it remains unclear if the typically calculated perfusion maps used for this purpose really include all valuable information from the PWI dataset for an optimal tissue outcome prediction. To investigate this problem in more detail, two different methods to predict tissue outcome using a k-nearest-neighbor approach were developed in this work and evaluated based on 18 datasets of acute stroke patients with known tissue outcome. The first method integrates apparent diffusion coefficient and perfusion parameter (Tmax, MTT, CBV, CBF) information for the voxel-wise prediction, while the second method employs also apparent diffusion coefficient information but the complete perfusion information in terms of the voxel-wise residue functions instead of the perfusion parameter maps for the voxel-wise prediction. Overall, the comparison of the results of the two prediction methods for the 18 patients using a leave-one-out cross validation revealed no considerable differences. Quantitatively, the parameter-based prediction of tissue outcome led to a mean Dice coefficient of 0.474, while the prediction using the residue functions led to a mean Dice coefficient of 0.461. Thus, it may be concluded from the results of this study that the perfusion parameter maps typically derived from PWI datasets include all valuable perfusion information required for a voxel-based tissue outcome prediction, while the complete analysis of the residue functions does not add further benefits for the voxel-wise tissue outcome prediction and is also computationally more expensive.

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2013-02-01

The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.

Development of a Low Cost Earthquake Early Warning System in Taiwan

NASA Astrophysics Data System (ADS)

Wu, Y. M.

2017-12-01

The National Taiwan University (NTU) developed an earthquake early warning (EEW) system for research purposes using low-cost accelerometers (P-Alert) since 2010. As of 2017, a total of 650 stations have been deployed and configured. The NTU system can provide earthquake information within 15 s of an earthquake occurrence. Thus, this system may provide early warnings for cities located more than 50 km from the epicenter. Additionally, the NTU system also has an onsite alert function that triggers a warning for incoming P-waves greater than a certain magnitude threshold, thus providing a 2-3 s lead time before peak ground acceleration (PGA) for regions close to an epicenter. Detailed shaking maps are produced by the NTU system within one or two minutes after an earthquake. Recently, a new module named ShakeAlarm has been developed. Equipped with real-time acceleration signals and the time-dependent anisotropic attenuation relationship of the PGA, ShakingAlarm can provide an accurate PGA estimation immediately before the arrival of the observed PGA. This unique advantage produces sufficient lead time for hazard assessment and emergency response, which is unavailable for traditional shakemap, which are based on only the PGA observed in real time. The performance of ShakingAlarm was tested with six M > 5.5 inland earthquakes from 2013 to 2016. Taking the 2016 M6.4 Meinong earthquake simulation as an example, the predicted PGA converges to a stable value and produces a predicted shake map and an isocontour map of the predicted PGA within 16 seconds of earthquake occurrence. Compared with traditional regional EEW system, ShakingAlarm can effectively identify possible damage regions and provide valuable early warning information (magnitude and PGA) for risk mitigation.

Predicting and mapping malaria under climate change scenarios: the potential redistribution of malaria vectors in Africa.

PubMed

Tonnang, Henri E Z; Kangalawe, Richard Y M; Yanda, Pius Z

2010-04-23

Malaria is rampant in Africa and causes untold mortality and morbidity. Vector-borne diseases are climate sensitive and this has raised considerable concern over the implications of climate change on future disease risk. The problem of malaria vectors (Anopheles mosquitoes) shifting from their traditional locations to invade new zones is an important concern. The vision of this study was to exploit the sets of information previously generated by entomologists, e.g. on geographical range of vectors and malaria distribution, to build models that will enable prediction and mapping the potential redistribution of Anopheles mosquitoes in Africa. The development of the modelling tool was carried out through calibration of CLIMEX parameters. The model helped estimate the potential geographical distribution and seasonal abundance of the species in relation to climatic factors. These included temperature, rainfall and relative humidity, which characterized the living environment for Anopheles mosquitoes. The same parameters were used in determining the ecoclimatic index (EI). The EI values were exported to a GIS package for special analysis and proper mapping of the potential future distribution of Anopheles gambiae and Anophles arabiensis within the African continent under three climate change scenarios. These results have shown that shifts in these species boundaries southward and eastward of Africa may occur rather than jumps into quite different climatic environments. In the absence of adequate control, these predictions are crucial in understanding the possible future geographical range of the vectors and the disease, which could facilitate planning for various adaptation options. Thus, the outputs from this study will be helpful at various levels of decision making, for example, in setting up of an early warning and sustainable strategies for climate change and climate change adaptation for malaria vectors control programmes in Africa.

Perceptions of risk in communities near parks in an African biodiversity hotspot.

PubMed

Hartter, Joel; Dowhaniuk, Nicholas; MacKenzie, Catrina A; Ryan, Sadie J; Diem, Jeremy E; Palace, Michael W; Chapman, Colin A

2016-10-01

Understanding conservation and livelihood threats in park landscapes is important to informing conservation policy. To identify threats, we examined perceived risks of residents living near three national parks in Uganda. We used cross-sectional household data to document, rank, and measure severity of perceived risks. Three risk categories, grouped into protected area, climate, and health, were cited by 80 % of respondents and received the highest severity scores. Elevation, proximity to the park, local forest loss, recent population change, and measures of poverty were the most important variables in predicting whether or not an individual identified these risks as the most or second most severe risk. Health issues were cited throughout the landscape, while problems attributed to climate (mainly insufficient rainfall) were reported to be most severe farther from the park. Increased population density was associated with increased perceived risk of health challenges, but decreased perceived risks attributed to the park and climate. Participatory risk mapping provides the opportunity to make standardized comparisons across sites, to help identify commonalities and differences, as a first step to examining the degree to which conservation management might address some of these local challenges and where mitigation techniques might be transferable between different sites or conflict scenarios.

Current and future distribution of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in WHO Eastern Mediterranean Region.

PubMed

Ducheyne, Els; Tran Minh, Nhu Nguyen; Haddad, Nabil; Bryssinckx, Ward; Buliva, Evans; Simard, Frédéric; Malik, Mamunur Rahman; Charlier, Johannes; De Waele, Valérie; Mahmoud, Osama; Mukhtar, Muhammad; Bouattour, Ali; Hussain, Abdulhafid; Hendrickx, Guy; Roiz, David

2018-02-14

Aedes-borne diseases as dengue, zika, chikungunya and yellow fever are an emerging problem worldwide, being transmitted by Aedes aegypti and Aedes albopictus. Lack of up to date information about the distribution of Aedes species hampers surveillance and control. Global databases have been compiled but these did not capture data in the WHO Eastern Mediterranean Region (EMR), and any models built using these datasets fail to identify highly suitable areas where one or both species may occur. The first objective of this study was therefore to update the existing Ae. aegypti (Linnaeus, 1762) and Ae. albopictus (Skuse, 1895) compendia and the second objective was to generate species distribution models targeted to the EMR. A final objective was to engage the WHO points of contacts within the region to provide feedback and hence validate all model outputs. The Ae. aegypti and Ae. albopictus compendia provided by Kraemer et al. (Sci Data 2:150035, 2015; Dryad Digit Repos, 2015) were used as starting points. These datasets were extended with more recent species and disease data. In the next step, these sets were filtered using the Köppen-Geiger classification and the Mahalanobis distance. The occurrence data were supplemented with pseudo-absence data as input to Random Forests. The resulting suitability and maximum risk of establishment maps were combined into hard-classified maps per country for expert validation. The EMR datasets consisted of 1995 presence locations for Ae. aegypti and 2868 presence locations for Ae. albopictus. The resulting suitability maps indicated that there exist areas with high suitability and/or maximum risk of establishment for these disease vectors in contrast with previous model output. Precipitation and host availability, expressed as population density and night-time lights, were the most important variables for Ae. aegypti. Host availability was the most important predictor in case of Ae. albopictus. Internal validation was assessed geographically. External validation showed high agreement between the predicted maps and the experts' extensive knowledge of the terrain. Maps of distribution and maximum risk of establishment were created for Ae. aegypti and Ae. albopictus for the WHO EMR. These region-specific maps highlighted data gaps and these gaps will be filled using targeted monitoring and surveillance. This will increase the awareness and preparedness of the different countries for Aedes borne diseases.

Satellite freeze forecast system: Executive summary

NASA Technical Reports Server (NTRS)

Martsolf, J. D. (Principal Investigator)

1983-01-01

A satellite-based temperature monitoring and prediction system consisting of a computer controlled acquisition, processing, and display system and the ten automated weather stations called by that computer was developed and transferred to the national weather service. This satellite freeze forecasting system (SFFS) acquires satellite data from either one of two sources, surface data from 10 sites, displays the observed data in the form of color-coded thermal maps and in tables of automated weather station temperatures, computes predicted thermal maps when requested and displays such maps either automatically or manually, archives the data acquired, and makes comparisons with historical data. Except for the last function, SFFS handles these tasks in a highly automated fashion if the user so directs. The predicted thermal maps are the result of two models, one a physical energy budget of the soil and atmosphere interface and the other a statistical relationship between the sites at which the physical model predicts temperatures and each of the pixels of the satellite thermal map.

Simulating wildfire spread behavior between two NASA Active Fire data timeframes

NASA Astrophysics Data System (ADS)

Adhikari, B.; Hodza, P.; Xu, C.; Minckley, T. A.

2017-12-01

Although NASA's Active Fire dataset is considered valuable in mapping the spatial distribution and extent of wildfires across the world, the data is only available at approximately 12-hour time intervals, creating uncertainties and risks associated with fire spread and behavior between the two Visible Infrared Imaging Radiometer Satellite (VIIRS) data collection timeframes. Our study seeks to close the information gap for the United States by using the latest Active Fire data collected for instance around 0130 hours as an ignition source and critical inputs to a wildfire model by uniquely incorporating forecasted and real-time weather conditions for predicting fire perimeter at the next 12 hour reporting time (i.e. around 1330 hours). The model ingests highly dynamic variables such as fuel moisture, temperature, relative humidity, wind among others, and prompts a Monte Carlo simulation exercise that uses a varying range of possible values for evaluating all possible wildfire behaviors. The Monte Carlo simulation implemented in this model provides a measure of the relative wildfire risk levels at various locations based on the number of times those sites are intersected by simulated fire perimeters. Model calibration is achieved using data at next reporting time (i.e. after 12 hours) to enhance the predictive quality at further time steps. While initial results indicate that the calibrated model can predict the overall geometry and direction of wildland fire spread, the model seems to over-predict the sizes of most fire perimeters possibly due to unaccounted fire suppression activities. Nonetheless, the results of this study show great promise in aiding wildland fire tracking, fighting and risk management.

Recent advances in understanding idiopathic pulmonary fibrosis

PubMed Central

Daccord, Cécile; Maher, Toby M.

2016-01-01

Despite major research efforts leading to the recent approval of pirfenidone and nintedanib, the dismal prognosis of idiopathic pulmonary fibrosis (IPF) remains unchanged. The elaboration of international diagnostic criteria and disease stratification models based on clinical, physiological, radiological, and histopathological features has improved the accuracy of IPF diagnosis and prediction of mortality risk. Nevertheless, given the marked heterogeneity in clinical phenotype and the considerable overlap of IPF with other fibrotic interstitial lung diseases (ILDs), about 10% of cases of pulmonary fibrosis remain unclassifiable. Moreover, currently available tools fail to detect early IPF, predict the highly variable course of the disease, and assess response to antifibrotic drugs. Recent advances in understanding the multiple interrelated pathogenic pathways underlying IPF have identified various molecular phenotypes resulting from complex interactions among genetic, epigenetic, transcriptional, post-transcriptional, metabolic, and environmental factors. These different disease endotypes appear to confer variable susceptibility to the condition, differing risks of rapid progression, and, possibly, altered responses to therapy. The development and validation of diagnostic and prognostic biomarkers are necessary to enable a more precise and earlier diagnosis of IPF and to improve prediction of future disease behaviour. The availability of approved antifibrotic therapies together with potential new drugs currently under evaluation also highlights the need for biomarkers able to predict and assess treatment responsiveness, thereby allowing individualised treatment based on risk of progression and drug response. This approach of disease stratification and personalised medicine is already used in the routine management of many cancers and provides a potential road map for guiding clinical care in IPF. PMID:27303645

Insights into earthquake hazard map performance from shaking history simulations

NASA Astrophysics Data System (ADS)

Stein, S.; Vanneste, K.; Camelbeeck, T.; Vleminckx, B.

2017-12-01

Why recent large earthquakes caused shaking stronger than predicted by earthquake hazard maps is under debate. This issue has two parts. Verification involves how well maps implement probabilistic seismic hazard analysis (PSHA) ("have we built the map right?"). Validation asks how well maps forecast shaking ("have we built the right map?"). We explore how well a map can ideally perform by simulating an area's shaking history and comparing "observed" shaking to that predicted by a map generated for the same parameters. The simulations yield shaking distributions whose mean is consistent with the map, but individual shaking histories show large scatter. Infrequent large earthquakes cause shaking much stronger than mapped, as observed. Hence, PSHA seems internally consistent and can be regarded as verified. Validation is harder because an earthquake history can yield shaking higher or lower than that predicted while being consistent with the hazard map. The scatter decreases for longer observation times because the largest earthquakes and resulting shaking are increasingly likely to have occurred. For the same reason, scatter is much less for the more active plate boundary than for a continental interior. For a continental interior, where the mapped hazard is low, even an M4 event produces exceedances at some sites. Larger earthquakes produce exceedances at more sites. Thus many exceedances result from small earthquakes, but infrequent large ones may cause very large exceedances. However, for a plate boundary, an M6 event produces exceedance at only a few sites, and an M7 produces them in a larger, but still relatively small, portion of the study area. As reality gives only one history, and a real map involves assumptions about more complicated source geometries and occurrence rates, which are unlikely to be exactly correct and thus will contribute additional scatter, it is hard to assess whether misfit between actual shaking and a map — notably higher-than-mapped shaking — arises by chance or reflects biases in the map. Due to this problem, there are limits to how well we can expect hazard maps to predict future shaking, as well as to our ability to test the performance of a hazard map based on available observations.

Forest/non-forest mapping using inventory data and satellite imagery

Treesearch

Ronald E. McRoberts

2002-01-01

For two study areas in Minnesota, USA, one heavily forested and one sparsely forested, maps of predicted proportion forest area were created using Landsat Thematic Mapper imagery, forest inventory plot data, and two prediction techniques, logistic regression and a k-Nearest Neighbours technique. The maps were used to increase the precision of forest area estimates by...

Predicting Chromosomal Locations of Genetically Mapped Loci in Maize Using the Morgan2McClintock Translator

PubMed Central

Lawrence, Carolyn J.; Seigfried, Trent E.; Bass, Hank W.; Anderson, Lorinda K.

2006-01-01

The Morgan2McClintock Translator permits prediction of meiotic pachytene chromosome map positions from recombination-based linkage data using recombination nodule frequency distributions. Its outputs permit estimation of DNA content between mapped loci and help to create an integrated overview of the maize nuclear genome structure. PMID:16387866

Structure and data consistency of a GIS database for geological risk analysis in S. Miguel Island (Azores)

NASA Astrophysics Data System (ADS)

Queiroz, G.; Goulart, C.; Gaspar, J. L.; Gomes, A.; Resendes, J. P.; Marques, R.; Gonçalves, P.; Silveira, D.; Valadão, P.

2003-04-01

The Geographic Information Systems (GIS) are becoming a major tool in the domain of geological hazard assessment and risk mitigation. When available, hazard and vulnerability data can easily be represented in a GIS and a great diversity of risk maps can be produced following the implementation of specific predicting models. A major difficulty for those that deal with GIS is to obtain high quality, well geo-referenced and validated data. This situation is particularly evident in the scope of risk analysis due to the diversity of data that need to be considered. In order to develop a coherent database for the geological risk analysis of the Azores archipelago it was decided to use the digital maps edited in 2001 by the Instituto Geográfico do Exército de Portugal (scale 1:25000), comprising altimetry, urban areas, roads and streams network. For the particular case of S. Miguel Island the information contained in these layers was revised and rectifications were made whenever needed. Moreover basic additional layers were added to the system, including counties and parishes administrative limits, agriculture and forested areas. For detailed studies all the edifices (e.g. houses, public buildings, monuments) are being individualized and characterized taking in account several parameters that can become crucial to assess their direct vulnerability to geological hazards (e.g. type of construction, number of floors, roof stability). Geological data obtained (1) through the interpretation of historical documents, (2) during recent fieldwork campaigns (e.g. mapping of volcanic centres and associated deposits, faults, dikes, soil degassing anomalies, landslides) and (3) by the existent monitoring networks (e.g. seismic, geodetic, fluid geochemistry) are also being digitised. The acquisition, storage and maintenance of all this information following the same criteria of quality are critical to guarantee the accuracy and consistency of the GIS database through time. In this work we notice the GIS-based methodologies aimed to assure the development of a GIS database directed to the geological risk analysis in S. Miguel Island. In a long-term programme the same strategy is being extended to the other Azorean islands.

RISK COMMUNICATION IN ACTION: THE TOOLS OF MESSAGE MAPPING

EPA Science Inventory

Risk Communication in Action: The Tools of Message Mapping, is a workbook designed to guide risk communicators in crisis situations. The first part of this workbook will review general guidelines for risk communication. The second part will focus on one of the most robust tools o...

OS090. Performance of candidate clinical and biochemical markers in screening early in pregnancy to detect women at high risk to develop preeclampsia.

PubMed

Forest, J-C; Massé, J; Bujold, E; Rousseau, F; Charland, M; Thériault, S; Lafond, J; Giguère, Y

2012-07-01

The advent of early preventive measures, such as low-dose aspirin targeting women at high risk of preeclampsia (PE), emphasizes the need for better detection. Despite the emergence of promising biochemical markers linked to the pathophysiological processes, systematic reviews have shown that, until now, no single tests fulfill the criteria set by WHO for biomarkers to screen for a disease. However, recent literature reveals that by combining various clinical, biophysical and biochemical markers into multivariate algorithms, one can envisage to estimate the risk of PE with a performance that would reach clinical utility and cost-effectiveness, but this remains to be demonstrated in various environments and health care settings. To investigate, in a prospective study, the clinical utility of candidate biomarkers and clinical data to detect, early in pregnancy, women at risk to develop PE and to propose a multivariate prediction algorithm combining clinical parameters to biochemical markers. 7929 pregnant women prospectively recruited at the first prenatal visit, provided blood samples, clinical and sociodemographic information. 214 pregnant women developed hypertensive disorders of pregnancy (HDP) of which 88 had PE (1.2%), including 44 with severe PE (0.6%). A nested case-control study was performed including for each case of HDP two normal pregnancies matched for maternal age, gestational age at recruitment, ethnicity, parity, and smoking status. Based on the literature we selected the most promising markers in a multivariate logistic regression model: mean arterial pressure (MAP), BMI, placental growth factor (PlGF), soluble Flt-1, inhibin A and PAPP-A. Biomarker results measured between 10-18 weeks gestation were expressed as multiples of the median. Medians were determined for each gestational week. When combined with MAP at the time of blood sampling and BMI at the beginning of pregnancy, the four biochemical markers discriminate normal pregnancies from those with HDP. At a 5% false positive rate, 37% of the affected pregnancies would have been detected. However, considering the prevalence of HDP in our population, the positive predictive value would have been only 15%. If all the predicted positive women would have been proposed a preventive intervention, only one out 6.7 women could have potentially benefited. In the case of severe PE, performance was not improved, sensitivity was the same, but the positive predictive value decreased to 3% (lower prevalence of severe PE). In our low-risk Caucasian population, neither individual candidate markers nor multivariate risk algorithm using an a priori combination of selected markers reached a performance justifying implementation. This also emphasizes the necessity to take into consideration characteristics of the population and environment influencing prevalence before promoting wide implementation of such screening strategies. In a perspective of personalized medicine, it appears more than ever mandatory to tailor recommendations for HDP screening according not only to individual but also to population characteristics. Copyright © 2012. Published by Elsevier B.V.

Assessing Changes in High School Students' Conceptual Understanding through Concept Maps before and after the Computer-Based Predict-Observe-Explain (CB-POE) Tasks on Acid-Base Chemistry at the Secondary Level

ERIC Educational Resources Information Center

Yaman, Fatma; Ayas, Alipasa

2015-01-01

Although concept maps have been used as alternative assessment methods in education, there has been an ongoing debate on how to evaluate students' concept maps. This study discusses how to evaluate students' concept maps as an assessment tool before and after 15 computer-based Predict-Observe-Explain (CB-POE) tasks related to acid-base chemistry.…

Aortic-Brachial Pulse Wave Velocity Ratio: A Measure of Arterial Stiffness Gradient Not Affected by Mean Arterial Pressure.

PubMed

Fortier, Catherine; Desjardins, Marie-Pier; Agharazii, Mohsen

2018-03-01

Aortic stiffness, measured by carotid-femoral pulse wave velocity (cf-PWV), is used for the prediction of cardiovascular risk. This mini-review describes the nonlinear relationship between cf-PWV and operational blood pressure, presents the proposed methods to adjust for this relationship, and discusses a potential place for aortic-brachial PWV ratio (a measure of arterial stiffness gradient) as a blood pressure-independent measure of vascular aging. PWV is inherently dependent on the operational blood pressure. In cross-sectional studies, PWV adjustment for mean arterial pressure (MAP) is preferred, but still remains a nonoptimal approach, as the relationship between PWV and blood pressure is nonlinear and varies considerably among individuals due to heterogeneity in genetic background, vascular tone, and vascular remodeling. Extrapolations from the blood pressure-independent stiffness parameter β (β 0 ) have led to the creation of stiffness index β, which can be used for local stiffness. A similar approach has been used for cardio-ankle PWV to generate a blood pressure-independent cardio-ankle vascular index (CAVI). It was recently demonstrated that stiffness index β and CAVI remain slightly blood pressure-dependent, and a more appropriate formula has been proposed to make the proper adjustments. On the other hand, the negative impact of aortic stiffness on clinical outcomes is thought to be mediated through attenuation or reversal of the arterial stiffness gradient, which can also be influenced by a reduction in peripheral medium-sized muscular arteries in conditions that predispose to accelerate vascular aging. Arterial stiffness gradient, assessed by aortic-brachial PWV ratio, is emerging to be at least as good as cf-PWV for risk prediction, but has the advantage of not being affected by operating MAP. The negative impacts of aortic stiffness on clinical outcomes are proposed to be mediated through attenuation or reversal of arterial stiffness gradient. Aortic-brachial PWV ratio, a measure of arterial stiffness gradient, is independent of MAP.

Lava flow risk maps at Mount Cameroon volcano

NASA Astrophysics Data System (ADS)

Favalli, M.; Fornaciai, A.; Papale, P.; Tarquini, S.

2009-04-01

Mount Cameroon, in the southwest Cameroon, is one of the most active volcanoes in Africa. Rising 4095 m asl, it has erupted nine times since the beginning of the past century, more recently in 1999 and 2000. Mount Cameroon documented eruptions are represented by moderate explosive and effusive eruptions occurred from both summit and flank vents. A 1922 SW-flank eruption produced a lava flow that reached the Atlantic coast near the village of Biboundi, and a lava flow from a 1999 south-flank eruption stopped only 200 m from the sea, threatening the villages of Bakingili and Dibunscha. More than 450,000 people live or work around the volcano, making the risk from lava flow invasion a great concern. In this work we propose both conventional hazard and risk maps and novel quantitative risk maps which relate vent locations to the expected total damage on existing buildings. These maps are based on lava flow simulations starting from 70,000 different vent locations, a probability distribution of vent opening, a law for the maximum length of lava flows, and a database of buildings. The simulations were run over the SRTM Digital Elevation Model (DEM) using DOWNFLOW, a fast DEM-driven model that is able to compute detailed invasion areas of lava flows from each vent. We present three different types of risk maps (90-m-pixel) for buildings around Mount Cameroon volcano: (1) a conventional risk map that assigns a probability of devastation by lava flows to each pixel representing buildings; (2) a reversed risk map where each pixel expresses the total damage expected as a consequence of vent opening in that pixel (the damage is expressed as the total surface of urbanized areas invaded); (3) maps of the lava catchments of the main towns around the volcano, within every catchment the pixels are classified according to the expected impact they might produce on the relative town in the case of a vent opening in that pixel. Maps of type (1) and (3) are useful for long term planning. Maps of type (2) and (3) are useful at the onset of a new eruption, when a vent forms. The combined use of these maps provides an efficient tool for lava flow risk assessment at Mount Cameroon.

Geovisualization in the HydroProg web map service

NASA Astrophysics Data System (ADS)

Spallek, Waldemar; Wieczorek, Malgorzata; Szymanowski, Mariusz; Niedzielski, Tomasz; Swierczynska, Malgorzata

2016-04-01

The HydroProg system, built at the University of Wroclaw (Poland) in frame of the research project no. 2011/01/D/ST10/04171 financed by the National Science Centre of Poland, has been designed for computing predictions of river stages in real time on a basis of multimodelling. This experimental system works on the upper Nysa Klodzka basin (SW Poland) above the gauge in the town of Bardo, with the catchment area of 1744 square kilometres. The system operates in association with the Local System for Flood Monitoring of Klodzko County (LSOP), and produces hydrograph prognoses as well as inundation predictions. For presenting the up-to-date predictions and their statistics in the online mode, the dedicated real-time web map service has been designed. Geovisualisation in the HydroProg map service concerns: interactive maps of study area, interactive spaghetti hydrograms of water level forecasts along with observed river stages, animated images of inundation. The LSOP network offers a high spatial and temporal resolution of observations, as the length of the sampling interval is equal to 15 minutes. The main environmental elements related to hydrological modelling are shown on the main map. This includes elevation data (hillshading and hypsometric tints), rivers and reservoirs as well as catchment boundaries. Furthermore, we added main towns, roads as well as political and administrative boundaries for better map understanding. The web map was designed as a multi-scale representation, with levels of detail and zooming according to scales: 1:100 000, 1:250 000 and 1:500 000. Observations of water level in LSOP are shown on interactive hydrographs for each gauge. Additionally, predictions and some of their statistical characteristics (like prediction errors and Nash-Sutcliffe efficiency) are shown for selected gauges. Finally, predictions of inundation are presented on animated maps which have been added for four experimental sites. The HydroProg system is a strictly scientific project, but the web map service has been designed for all web users. The main objective of the paper is to present the design process of the web map service, following the cartographic and graphic principles.

Development of a Frost Risk Assessment Tool in Agriculture for a Mediterranean ecosystem Utilizing MODIS satellite observations Geomatics and Surface Data

NASA Astrophysics Data System (ADS)

Louka, Panagiota; Papanikolaou, Ioannis; Petropoulos, George; Migiros, George; Tsiros, Ioannis

2014-05-01

Frost risk in Mediterranean countries is a critical factor in agricultural planning and management. Nowadays, the rapid technological developments in Earth Observation (EO) technology have improved dramatically our ability to map the spatiotemporal distribution of frost conditions over a given area and evaluate its impacts on the environment and society. In this study, a frost risk model for agricultural crops cultivated in a Mediterranean environment has been developed, based primarily on Earth Observation (EO) data from MODIS sensor and ancillary spatial and point data. The ability of the model to predict frost conditions has been validated for selected days on which frost conditions had been observed for a region in Northwestern Greece according to ground observations obtained by the Agricultural Insurance Organization (ELGA). An extensive evaluation of the frost risk model predictions has been performed herein to evaluate objectively its ability to predict the spatio-temporal distribution of frost risk in the studied region, including comparisons against physiographical factors of the study area. The topographical characteristics that were taken under consideration were latitude, altitude, slope steepness, topographic convergence and the extend of the areas influenced by water bodies (such as lake and sea) existing in the study area. Additional data were also used concerning land use data and vegetation classification (type and density). Our results showed that the model was able to produce reasonably the spatio-temporal distribution of the frost conditions in our study area, following largely explainable patterns in respect to the study site and local weather conditions characteristics. All in all, the methodology implemented herein proved capable in obtaining rapidly and cost-effectively cartography of the frost risk in a Mediterranean environment, making it potentially a very useful tool for agricultural management and planning. The model presented here has also a potential to enhance conventional field-based surveying for monitoring frost changes over long timescales. KEYWORDS: Earth Observation, MODIS, frost, risk assessment, Greece

The spatial domain of wildfire risk and response in the wildland urban interface in Sydney, Australia

NASA Astrophysics Data System (ADS)

Price, O. F.; Bradstock, R. A.

2013-12-01

In order to quantify the risks from fire at the wildland urban interface (WUI), it is important to understand where fires occur and their likelihood of spreading to the WUI. For each of the 999 fires in the Sydney region we calculated the distance between the ignition and the WUI, the fire's weather and wind direction and whether it spread to the WUI. The likelihood of burning the WUI was analysed using binomial regression. Weather and distance interacted such that under mild weather conditions, the model predicted only a 5% chance that a fire starting >2.5 km from the interface would reach it, whereas when the conditions are extreme the predicted chance remained above 30% even at distances >10 km. Fires were more likely to spread to the WUI if the wind was from the west and in the western side of the region. We examined whether the management responses to wildfires are commensurate with risk by comparing the distribution of distance to the WUI of wildfires with roads and prescribed fires. Prescribed fires and roads were concentrated nearer to the WUI than wildfires as a whole, but further away than wildfires that burnt the WUI under extreme weather conditions (high risk fires). Overall, 79% of these high risk fires started within 2 km of the WUI, so there is some argument for concentrating more management effort near the WUI. By substituting climate change scenario weather into the statistical model, we predicted a small increase in the risk of fires spreading to the WUI, but the increase will be greater under extreme weather. This approach has a variety of uses, including mapping fire risk and improving the ability to match fire management responses to the threat from each fire. They also provide a baseline from which a cost-benefit analysis of complementary fire management strategies can be conducted.

The spatial domain of wildfire risk and response in the Wildland Urban Interface in Sydney, Australia

NASA Astrophysics Data System (ADS)

Price, O. F.; Bradstock, R. A.

2013-09-01

In order to quantify the risks from fire at the Wildland Urban Interface (WUI), it is important to understand where fires occur and their likelihood of spreading to the WUI. For each of 999 fires in the Sydney region we calculated the distance between the ignition and the WUI, the fire weather and wind direction and whether it spread to the WUI. The likelihood of burning the WUI was analysed using binomial regression. Weather and distance interacted such that under mild weather conditions, the model predicted only a 5% chance that a fire starting more than 2.5 km from the interface would reach it, whereas when the conditions are extreme the predicted chance remained above 30% even at distances further than 10 km. Fires were more likely to spread to the WUI if the wind was from the west and in the western side of the region. We examined whether the management responses to wildfires are commensurate with risk by comparing the distribution of distance to the WUI of wildfires with roads and prescribed fires. Prescribed fires and roads were concentrated nearer to the WUI than wildfires as a whole, but further away than wildfires that burnt the WUI under extreme weather conditions (high risk fires). 79% of these high risk fires started within 2 km of the WUI, so there is some argument for concentrating more management effort near the WUI. By substituting climate change scenario weather into the statistical model, we predicted a small increase in the risk of fires spreading to the WUI, but the increase will be greater under extreme weather. This approach has a variety of uses, including mapping fire risk and improving the ability to match fire management responses to the threat from each fire. They also provide a baseline from which a cost-benefit analysis of complementary fire management strategies can be conducted.

Method for the Preparation of Hazard Map in Urban Area Using Soil Depth and Groundwater Level

NASA Astrophysics Data System (ADS)

Kim, Sung-Wook; Choi, Eun-Kyeong; Cho, Jin Woo; Lee, Ju-Hyoung

2017-04-01

The hazard maps for predicting collapse on natural slopes consists of a combination of topographic, hydrological, and geological factors. Topographic factors are extracted from DEM, including aspect, slope, curvature, and topographic index. Hydrological factors, such as distance to drainage, drainage density, stream-power index, and wetness index are most important factors for slope instability. However, most of the urban areas are located on the plains and it is difficult to apply the hazard map using the topography and hydrological factors. In order to evaluate the risk of collapse of flat and low slope areas, soil depth and groundwater level data were collected and used as a factor for interpretation. In addition, the reliability of the hazard map was compared with the disaster history of the study area (Gangnam-gu and Yeouido district). In the disaster map of the disaster prevention agency, the urban area was mostly classified as the stable area and did not reflect the collapse history. Soil depth, drainage conditions and groundwater level obtained from boreholes were added as input data of hazard map, and disaster vulnerability increased at the location where the actual collapse points. In the study area where damage occurred, the moderate and low grades of the vulnerability of previous hazard map were 12% and 88%, respectively. While, the improved map showed 2% high grade, moderate grade 29%, low grade 66% and very low grade 2%. These results were similar to actual damage. Keywords: hazard map, urban area, soil depth, ground water level Acknowledgement This research was supported by a Grant from a Strategic Research Project (Horizontal Drilling and Stabilization Technologies for Urban Search and Rescue (US&R) Operation) funded by the Korea Institute of Civil Engineering and Building Technology.

Mapping and Modelling Malaria Risk Areas Using Climate, Socio-Demographic and Clinical Variables in Chimoio, Mozambique.

PubMed

Ferrao, Joao L; Niquisse, Sergio; Mendes, Jorge M; Painho, Marco

2018-04-19

Background : Malaria continues to be a major public health concern in Africa. Approximately 3.2 billion people worldwide are still at risk of contracting malaria, and 80% of deaths caused by malaria are concentrated in only 15 countries, most of which are in Africa. These high-burden countries have achieved a lower than average reduction of malaria incidence and mortality, and Mozambique is among these countries. Malaria eradication is therefore one of Mozambique’s main priorities. Few studies on malaria have been carried out in Chimoio, and there is no malaria map risk of the area. This map is important to identify areas at risk for application of Public Precision Health approaches. By using GIS-based spatial modelling techniques, the research goal of this article was to map and model malaria risk areas using climate, socio-demographic and clinical variables in Chimoio, Mozambique. Methods : A 30 m × 30 m Landsat image, ArcGIS 10.2 and BioclimData were used. A conceptual model for spatial problems was used to create the final risk map. The risks factors used were: the mean temperature, precipitation, altitude, slope, distance to water bodies, distance to roads, NDVI, land use and land cover, malaria prevalence and population density. Layers were created in a raster dataset. For class value comparisons between layers, numeric values were assigned to classes within each map layer, giving them the same importance. The input dataset were ranked, with different weights according to their suitability. The reclassified outputs of the data were combined. Results : Chimoio presented 96% moderate risk and 4% high-risk areas. The map showed that the central and south-west “Residential areas”, namely, Centro Hipico, Trangapsso, Bairro 5 and 1° de Maio, had a high risk of malaria, while the rest of the residential areas had a moderate risk. Conclusions : The entire Chimoio population is at risk of contracting malaria, and the precise estimation of malaria risk, therefore, has important precision public health implications and for the planning of effective control measures, such as the proper time and place to spray to combat vectors, distribution of bed nets and other control measures.

Decoding the Formation of New Semantics: MVPA Investigation of Rapid Neocortical Plasticity during Associative Encoding through Fast Mapping.

PubMed

Atir-Sharon, Tali; Gilboa, Asaf; Hazan, Hananel; Koilis, Ester; Manevitz, Larry M

2015-01-01

Neocortical structures typically only support slow acquisition of declarative memory; however, learning through fast mapping may facilitate rapid learning-induced cortical plasticity and hippocampal-independent integration of novel associations into existing semantic networks. During fast mapping the meaning of new words and concepts is inferred, and durable novel associations are incidentally formed, a process thought to support early childhood's exuberant learning. The anterior temporal lobe, a cortical semantic memory hub, may critically support such learning. We investigated encoding of semantic associations through fast mapping using fMRI and multivoxel pattern analysis. Subsequent memory performance following fast mapping was more efficiently predicted using anterior temporal lobe than hippocampal voxels, while standard explicit encoding was best predicted by hippocampal activity. Searchlight algorithms revealed additional activity patterns that predicted successful fast mapping semantic learning located in lateral occipitotemporal and parietotemporal neocortex and ventrolateral prefrontal cortex. By contrast, successful explicit encoding could be classified by activity in medial and dorsolateral prefrontal and parahippocampal cortices. We propose that fast mapping promotes incidental rapid integration of new associations into existing neocortical semantic networks by activating related, nonoverlapping conceptual knowledge. In healthy adults, this is better captured by unique anterior and lateral temporal lobe activity patterns, while hippocampal involvement is less predictive of this kind of learning.

Relative risk estimation of Chikungunya disease in Malaysia: An analysis based on Poisson-gamma model

NASA Astrophysics Data System (ADS)

Samat, N. A.; Ma'arof, S. H. Mohd Imam

2015-05-01

Disease mapping is a method to display the geographical distribution of disease occurrence, which generally involves the usage and interpretation of a map to show the incidence of certain diseases. Relative risk (RR) estimation is one of the most important issues in disease mapping. This paper begins by providing a brief overview of Chikungunya disease. This is followed by a review of the classical model used in disease mapping, based on the standardized morbidity ratio (SMR), which we then apply to our Chikungunya data. We then fit an extension of the classical model, which we refer to as a Poisson-Gamma model, when prior distributions for the relative risks are assumed known. Both results are displayed and compared using maps and we reveal a smoother map with fewer extremes values of estimated relative risk. The extensions of this paper will consider other methods that are relevant to overcome the drawbacks of the existing methods, in order to inform and direct government strategy for monitoring and controlling Chikungunya disease.

Arterial spin labeling-based Z-maps have high specificity and positive predictive value for neurodegenerative dementia compared to FDG-PET.

PubMed

Fällmar, David; Haller, Sven; Lilja, Johan; Danfors, Torsten; Kilander, Lena; Tolboom, Nelleke; Egger, Karl; Kellner, Elias; Croon, Philip M; Verfaillie, Sander C J; van Berckel, Bart N M; Ossenkoppele, Rik; Barkhof, Frederik; Larsson, Elna-Marie

2017-10-01

Cerebral perfusion analysis based on arterial spin labeling (ASL) MRI has been proposed as an alternative to FDG-PET in patients with neurodegenerative disease. Z-maps show normal distribution values relating an image to a database of controls. They are routinely used for FDG-PET to demonstrate disease-specific patterns of hypometabolism at the individual level. This study aimed to compare the performance of Z-maps based on ASL to FDG-PET. Data were combined from two separate sites, each cohort consisting of patients with Alzheimer's disease (n = 18 + 7), frontotemporal dementia (n = 12 + 8) and controls (n = 9 + 29). Subjects underwent pseudocontinuous ASL and FDG-PET. Z-maps were created for each subject and modality. Four experienced physicians visually assessed the 166 Z-maps in random order, blinded to modality and diagnosis. Discrimination of patients versus controls using ASL-based Z-maps yielded high specificity (84%) and positive predictive value (80%), but significantly lower sensitivity compared to FDG-PET-based Z-maps (53% vs. 96%, p < 0.001). Among true-positive cases, correct diagnoses were made in 76% (ASL) and 84% (FDG-PET) (p = 0.168). ASL-based Z-maps can be used for visual assessment of neurodegenerative dementia with high specificity and positive predictive value, but with inferior sensitivity compared to FDG-PET. • ASL-based Z-maps yielded high specificity and positive predictive value in neurodegenerative dementia. • ASL-based Z-maps had significantly lower sensitivity compared to FDG-PET-based Z-maps. • FDG-PET might be reserved for ASL-negative cases where clinical suspicion persists. • Findings were similar at two study sites.

Using the basic reproduction number to assess the effects of climate change in the risk of Chagas disease transmission in Colombia.

PubMed

Cordovez, Juan M; Rendon, Lina Maria; Gonzalez, Camila; Guhl, Felipe

2014-01-01

The dynamics of vector-borne diseases has often been linked to climate change. However the commonly complex dynamics of vector-borne diseases make it very difficult to predict risk based on vector or host distributions. The basic reproduction number (R0) integrates all factors that determine whether a pathogen can establish or not. To obtain R0 for complex vector-borne diseases one can use the next-generation matrix (NGM) approach. We used the NGM to compute R0 for Chagas disease in Colombia incorporating the effect of temperature in some of the transmission routes of Trypanosoma cruzi. We used R0 to generate a risk map of present conditions and a forecast risk map at 20 years from now based on mean annual temperature (data obtained from Worldclim). In addition we used the model to compute elasticity and sensitivity indexes on all model parameters and routes of transmission. We present this work as an approach to indicate which transmission pathways are more critical for disease transmission but acknowledge the fact that results and projections strongly depend on better knowledge of entomological parameters and transmission routes. We concluded that the highest contribution to R0 comes from transmission of the parasites from humans to vectors, which is a surprising result. In addition,parameters related to contacts between human and vectors and the efficiency of parasite transmission between them also show a prominent effect on R0.

Ecological Forecasting of West Nile Virus Outbreaks in a High-Risk Area of the North-Central United States

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Merkord, C. L.; Kightlinger, L.; Vincent, G.; Hildreth, M. B.

2015-12-01

West Nile virus (WNV) is the most widespread and important mosquito-borne pathogen in North America. Since its emergence in the western hemisphere in 1999, human WNV disease has continued to exhibit recurrent outbreaks. Perplexingly, the incidence of this tropical disease has been highest in the cold-temperate climates of the Northern Great Plains (NGP). The spatial and temporal distributions of the vector mosquitoes and bird hosts, and consequently the risk of disease in humans, are strongly influenced by temperature, precipitation, vegetation, soils, and land use. We have utilized satellite remote sensing to map these environmental factors through time and develop models of disease risk. Outbreak years in South Dakota were preceded by warm winters, and WNV cases were most likely to occur during the hottest weeks of summer. Hot spots of persistent WNV transmission within the state were associated with rural land cover as well as patterns of physiography and climate. These models are currently being integrated into the South Dakota Mosquito Early Warning system (SDMIS), an automated WNV outbreak detection system that integrates remotely-sensed environmental indicators with vector abundance and infection data from a statewide mosquito surveillance network. The major goal of this effort is to leverage global environmental monitoring datasets to provide up-to-date, locally relevant information that can improve the effectiveness of mosquito control and disease prevention activities. This system was implemented for the first time during the summer of 2015. We will review the outcomes of this implementation, including the underlying influences of temperature on WNV risk, a preliminary statewide WNV risk map, and dynamic risk predictions made during the 2015 WNV season. Lessons learned as well as plans for future years will be discussed.

Designing Antibacterial Peptides with Enhanced Killing Kinetics

PubMed Central

Waghu, Faiza H.; Joseph, Shaini; Ghawali, Sanket; Martis, Elvis A.; Madan, Taruna; Venkatesh, Kareenhalli V.; Idicula-Thomas, Susan

2018-01-01

Antimicrobial peptides (AMPs) are gaining attention as substitutes for antibiotics in order to combat the risk posed by multi-drug resistant pathogens. Several research groups are engaged in design of potent anti-infective agents using natural AMPs as templates. In this study, a library of peptides with high sequence similarity to Myeloid Antimicrobial Peptide (MAP) family were screened using popular online prediction algorithms. These peptide variants were designed in a manner to retain the conserved residues within the MAP family. The prediction algorithms were found to effectively classify peptides based on their antimicrobial nature. In order to improve the activity of the identified peptides, molecular dynamics (MD) simulations, using bilayer and micellar systems could be used to design and predict effect of residue substitution on membranes of microbial and mammalian cells. The inference from MD simulation studies well corroborated with the wet-lab observations indicating that MD-guided rational design could lead to discovery of potent AMPs. The effect of the residue substitution on membrane activity was studied in greater detail using killing kinetic analysis. Killing kinetics studies on Gram-positive, negative and human erythrocytes indicated that a single residue change has a drastic effect on the potency of AMPs. An interesting outcome was a switch from monophasic to biphasic death rate constant of Staphylococcus aureus due to a single residue mutation in the peptide. PMID:29527201

Targeting Forest Management through Fire and Erosion Modeling

NASA Astrophysics Data System (ADS)

Elliot, William J.; Miller, Mary Ellen; MacDonald, Lee H.

2013-04-01

Forests deliver a number of ecosystem services, including clean water. When forests are disturbed by wildfire, the timing and quantity of runoff can be altered, and the quality can be severely degraded. A modeling study for about 1500 km2 in the Upper Mokelumne River Watershed in California was conducted to determine the risk of wildfire and the associated potential sediment delivery should a wildfire occur, and to calculate the potential reduction in sediment delivery that might result from fuel reduction treatments. The first step was to predict wildfire severity and probability of occurrence under current vegetation conditions with FlamMap fire prediction tool. FlamMap uses current vegetation, topography, and wind characteristics to predict the speed, flame length, and direction of a simulated flame front for each 30-m pixel. As the first step in the erosion modeling, a geospatial interface for the WEPP model (GeoWEPP) was used to delineate approximately 6-ha hillslope polygons for the study area. The flame length values from FlamMap were then aggregated for each hillslope polygon to yield a predicted fire intensity. Fire intensity and pre-fire vegetation conditions were used to estimate fire severity (either unburned, low, moderate or high). The fire severity was combined with soil properties from the STATSGO database to build the vegetation and soil files needed to run WEPP for each polygon. Eight different stochastic climates were generated to account for the weather variability within the basin. A modified batching version of GeoWEPP was used to predict the first-year post-fire sediment yield from each hillslope and subwatershed. Estimated sediment yields ranged from 0 to more than 100 Mg/ha, and were typical of observed values. The polygons that generated the greatest amount of sediment or that were critical for reducing fire spread were identified, and these were "treated" by reducing the amount of fuel available for a wildfire. The erosion associated with these fuel treatments was estimated using WEPP. FlamMap and WEPP were run a second time to determine the extent to which the imposed treatments reduced fire intensity, fire severity, and the predicted sediment yields. The results allowed managers to quantify the net reduction in sediment delivery due to the prescribed treatments. The modeling also identified those polygons with the greatest net decline in sediment delivery, with the expectation that these polygons would have the highest priority for fuel reduction treatments. An economic value can be assigned to the predicted net change in sediment delivered to a reservoir or a specified decline in water quality. The estimated avoided costs due to the reduction in sediment delivery can help justify the optimized fuel treatments.

Solar Energetic Proton Nowcast for Low Earth Orbits

NASA Astrophysics Data System (ADS)

Winter, L. M.; Quinn, R. A.

2013-12-01

Solar energetic proton flux levels above > 10 pfu can damage spacecraft and pose a hazard to astronauts as well as passengers and crew on polar commercial flights. While the GOES satellites provide real-time data of SEP levels in geosynchronous orbit, it is also important to determine the risk to objects in lower altitude orbits. To assess this risk in real-time, we created a web-based nowcast of SEP flux. The tool determines the current solar energetic proton flux level given input position (latitude, longitude, and altitude) and energy of the protons (e.g., > 10 MeV). The effective cutoff energy is calculated for the location and current geomagnetic storm level (i.e., the Kp value from SWPC) using the Shea & Smart (e.g., Smart et al. 1999abc, 2000) geomagnetic cutoff model, which uses a trajectory tracing technique through the Tsyganenko magnetospheric model for the geomagnetic field. With the cutoff energy and GOES proton flux measurements, a map of the current predicted proton flux level at the input energy is displayed along with the calculated integral spectrum for the input position. This operational tool is a powerful new diagnostic for assessing the risk to spacecraft from current solar proton levels, with easy to read color-coded maps generated for all GOES integral proton flux energies and a range of altitudes (1000 - 35000 km). The figures show example maps over a ';'quiet'' (03-26-13) and active (10-30-03) time, with high proton levels easily distinguishable at or above the NOAA warning level (yellow-orange-red). The tool also displays the current GOES integral spectrum and fit, and the estimated spectrum at a user-defined location and altitude.

Vulnerabilities to Rock-Slope Failure Impacts from Christchurch, NZ Case History Analysis

NASA Astrophysics Data System (ADS)

Grant, A.; Wartman, J.; Massey, C. I.; Olsen, M. J.; Motley, M. R.; Hanson, D.; Henderson, J.

2015-12-01

Rock-slope failures during the 2010/11 Canterbury (Christchurch), New Zealand Earthquake Sequence resulted in 5 fatalities and caused an estimated US$400 million of damage to buildings and infrastructure. Reducing losses from rock-slope failures requires consideration of both hazard (i.e. likelihood of occurrence) and risk (i.e. likelihood of losses given an occurrence). Risk assessment thus requires information on the vulnerability of structures to rock or boulder impacts. Here we present 32 case histories of structures impacted by boulders triggered during the 2010/11 Canterbury earthquake sequence, in the Port Hills region of Christchurch, New Zealand. The consequences of rock fall impacts on structures, taken as penetration distance into structures, are shown to follow a power-law distribution with impact energy. Detailed mapping of rock fall sources and paths from field mapping, aerial lidar digital elevation model (DEM) data, and high-resolution aerial imagery produced 32 well-constrained runout paths of boulders that impacted structures. Impact velocities used for structural analysis were developed using lumped mass 2-D rock fall runout models using 1-m resolution lidar elevation data. Model inputs were based on calibrated surface parameters from mapped runout paths of 198 additional boulder runouts. Terrestrial lidar scans and structure from motion (SfM) imagery generated 3-D point cloud data used to measure structural damage and impacting boulders. Combining velocity distributions from 2-D analysis and high-precision boulder dimensions, kinetic energy distributions were calculated for all impacts. Calculated impact energy versus penetration distance for all cases suggests a power-law relationship between damage and impact energy. These case histories and resulting fragility curve should serve as a foundation for future risk analysis of rock fall hazards by linking vulnerability data to the predicted energy distributions from the hazard analysis.

Diel predator activity drives a dynamic landscape of fear

USGS Publications Warehouse

Kohl, Michel T.; Stahler, Daniel R.; Metz, Matthew C.; Forester, James D.; Kauffman, Matthew J.; Varley, Nathan; White, P.J.; Smith, Douglas W.; MacNulty, Daniel R.

2017-01-01

A "landscape of fear" (LOF) is a map that describes continuous spatial variation in an animal's perception of predation risk. The relief on this map reflects, for example, places that an animal avoids to minimize risk. Although the LOF concept is a potential unifying theme in ecology that is often invoked to explain the ecological and conservation significance of fear, quantified examples of a LOF over large spatial scales are lacking as is knowledge about the daily dynamics of a LOF. Despite theory and data to the contrary, investigators often assume, implicitly or explicitly, that a LOF is a static consequence of a predator's mere presence. We tested the prediction that a LOF in a large-scale, free-living system is a highly-dynamic map with "peaks" and "valleys" that alternate across the diel (24-hour) cycle in response to daily lulls in predator activity. We did so with extensive data from the case study of Yellowstone elk (Cervus elaphus) and wolves (Canis lupus) that was the original basis for the LOF concept. We quantified the elk LOF, defined here as spatial allocation of time away from risky places and times, across nearly 1000-km2 of northern Yellowstone National Park and found that it fluctuated with the crepuscular activity pattern of wolves, enabling elk to use risky places during wolf downtimes. This may help explain evidence that wolf predation risk has no effect on elk stress levels, body condition, pregnancy, or herbivory. The ability of free-living animals to adaptively allocate habitat use across periods of high and low predator activity within the diel cycle is an underappreciated aspect of animal behavior that helps explain why strong antipredator responses may trigger weak ecological effects, and why a LOF may have less conceptual and practical importance than direct killing.

Applications systems verification and transfer project. Volume 8: Satellite snow mapping and runoff prediction handbook

NASA Technical Reports Server (NTRS)

Bowley, C. J.; Barnes, J. C.; Rango, A.

1981-01-01

The purpose of the handbook is to update the various snowcover interpretation techniques, document the snow mapping techniques used in the various ASVT study areas, and describe the ways snowcover data have been applied to runoff prediction. Through documentation in handbook form, the methodology developed in the Snow Mapping ASVT can be applied to other areas.

[Diabetes and predictive medicine--parallax of the present time].

PubMed

Rybka, J

2010-04-01

Predictive genetics uses genetic testing to estimate the risk in asymptomatic persons. Since in the case of multifactorial diseases predictive genetic analysis deals with findings which allow wider interpretation, it has a higher predictive value in expressly qualified diseases (monogenous) with high penetration compared to multifactorial (polygenous) diseases with high participation of environmental factors. In most "civilisation" (multifactorial) diseases including diabetes, heredity and environmental factors do not play two separate, independent roles. Instead, their interactions play a principal role. The new classification of diabetes is based on the implementation of not only ethiopathogenetic, but also genetic research. Diabetes mellitus type 1 (DM1T) is a polygenous multifactorial disease with the genetic component carrying about one half of the risk, the non-genetic one the other half. The study of the autoimmune nature of DM1T in connection with genetic analysis is going to bring about new insights in DM1T prediction. The author presents new pieces of knowledge on molecular genetics concerning certain specific types of diabetes. Issues relating to heredity in diabetes mellitus type 2 (DM2T) are even more complex. The disease has a polygenous nature, and the phenotype of a patient with DM2T, in addition to environmental factors, involves at least three, perhaps even tens of different genetic variations. At present, results at the genom-wide level appear to be most promising. The current concept of prediabetes is a realistic foundation for our prediction and prevention of DM2T. A multifactorial, multimarker approach based on our understanding of new pathophysiological factors of DM2T, tries to outline a "map" of prediabetes physiology, and if these tests are combined with sophisticated methods of genetic forecasting of DM2T, this may represent a significant step in our methodology of diabetes prediction. So far however, predictive genetics is limited by the interpretation of genetic predisposition and individualisation of the level of risk. There is no doubt that interpretation calls for co-operation with clinicians, while results of genetic analyses should presently be not uncritically overestimated. Predictive medicine, however, unquestionably fulfills the preventive focus of modern medicine, and genetic analysis is a perspective diagnostic method.

Potential for using regional and global datasets for national scale ecosystem service modelling

NASA Astrophysics Data System (ADS)

Maxwell, Deborah; Jackson, Bethanna

2016-04-01

Ecosystem service models are increasingly being used by planners and policy makers to inform policy development and decisions about national-level resource management. Such models allow ecosystem services to be mapped and quantified, and subsequent changes to these services to be identified and monitored. In some cases, the impact of small scale changes can be modelled at a national scale, providing more detailed information to decision makers about where to best focus investment and management interventions that could address these issues, while moving toward national goals and/or targets. National scale modelling often uses national (or local) data (for example, soils, landcover and topographical information) as input. However, there are some places where fine resolution and/or high quality national datasets cannot be easily obtained, or do not even exist. In the absence of such detailed information, regional or global datasets could be used as input to such models. There are questions, however, about the usefulness of these coarser resolution datasets and the extent to which inaccuracies in this data may degrade predictions of existing and potential ecosystem service provision and subsequent decision making. Using LUCI (the Land Utilisation and Capability Indicator) as an example predictive model, we examine how the reliability of predictions change when national datasets of soil, landcover and topography are substituted with coarser scale regional and global datasets. We specifically look at how LUCI's predictions of where water services, such as flood risk, flood mitigation, erosion and water quality, change when national data inputs are replaced by regional and global datasets. Using the Conwy catchment, Wales, as a case study, the land cover products compared are the UK's Land Cover Map (2007), the European CORINE land cover map and the ESA global land cover map. Soils products include the National Soil Map of England and Wales (NatMap) and the European Soils Database. NEXTmap elevation data, which covers the UK and parts of continental Europe, are compared to global AsterDEM and SRTM30 topographical products. While the regional and global datasets can be used to fill gaps in data requirements, the coarser resolution of these datasets means that there is greater aggregation of information over larger areas. This loss of detail impacts on the reliability of model output, particularly where significant discrepancies between datasets exist. The implications of this loss of detail in terms of spatial planning and decision making is discussed. Finally, in the context of broader development the need for better nationally and globally available data to allow LUCI and other ecosystem models to become more globally applicable is highlighted.

Spatially varying density dependence drives a shifting mosaic of survival in a recovering apex predator (Canis lupus).

PubMed

O'Neil, Shawn T; Bump, Joseph K; Beyer, Dean E

2017-11-01

Understanding landscape patterns in mortality risk is crucial for promoting recovery of threatened and endangered species. Humans affect mortality risk in large carnivores such as wolves ( Canis lupus ), but spatiotemporally varying density dependence can significantly influence the landscape of survival. This potentially occurs when density varies spatially and risk is unevenly distributed. We quantified spatiotemporal sources of variation in survival rates of gray wolves ( C. lupus ) during a 21-year period of population recovery in the Upper Peninsula of Michigan, USA. We focused on mapping risk across time using Cox Proportional Hazards (CPH) models with time-dependent covariates, thus exploring a shifting mosaic of survival. Extended CPH models and time-dependent covariates revealed influences of seasonality, density dependence and experience, as well as individual-level factors and landscape predictors of risk. We used results to predict the shifting landscape of risk at the beginning, middle, and end of the wolf recovery time series. Survival rates varied spatially and declined over time. Long-term change was density-dependent, with landscape predictors such as agricultural land cover and edge densities contributing negatively to survival. Survival also varied seasonally and depended on individual experience, sex, and resident versus transient status. The shifting landscape of survival suggested that increasing density contributed to greater potential for human conflict and wolf mortality risk. Long-term spatial variation in key population vital rates is largely unquantified in many threatened, endangered, and recovering species. Variation in risk may indicate potential for source-sink population dynamics, especially where individuals preemptively occupy suitable territories, which forces new individuals into riskier habitat types as density increases. We encourage managers to explore relationships between adult survival and localized changes in population density. Density-dependent risk maps can identify increasing conflict areas or potential habitat sinks which may persist due to high recruitment in adjacent habitats.

Toward uniform probabilistic seismic hazard assessments for Southeast Asia

NASA Astrophysics Data System (ADS)

Chan, C. H.; Wang, Y.; Shi, X.; Ornthammarath, T.; Warnitchai, P.; Kosuwan, S.; Thant, M.; Nguyen, P. H.; Nguyen, L. M.; Solidum, R., Jr.; Irsyam, M.; Hidayati, S.; Sieh, K.

2017-12-01

Although most Southeast Asian countries have seismic hazard maps, various methodologies and quality result in appreciable mismatches at national boundaries. We aim to conduct a uniform assessment across the region by through standardized earthquake and fault databases, ground-shaking scenarios, and regional hazard maps. Our earthquake database contains earthquake parameters obtained from global and national seismic networks, harmonized by removal of duplicate events and the use of moment magnitude. Our active-fault database includes fault parameters from previous studies and from the databases implemented for national seismic hazard maps. Another crucial input for seismic hazard assessment is proper evaluation of ground-shaking attenuation. Since few ground-motion prediction equations (GMPEs) have used local observations from this region, we evaluated attenuation by comparison of instrumental observations and felt intensities for recent earthquakes with predicted ground shaking from published GMPEs. We then utilize the best-fitting GMPEs and site conditions into our seismic hazard assessments. Based on the database and proper GMPEs, we have constructed regional probabilistic seismic hazard maps. The assessment shows highest seismic hazard levels near those faults with high slip rates, including the Sagaing Fault in central Myanmar, the Sumatran Fault in Sumatra, the Palu-Koro, Matano and Lawanopo Faults in Sulawesi, and the Philippine Fault across several islands of the Philippines. In addition, our assessment demonstrates the important fact that regions with low earthquake probability may well have a higher aggregate probability of future earthquakes, since they encompass much larger areas than the areas of high probability. The significant irony then is that in areas of low to moderate probability, where building codes are usually to provide less seismic resilience, seismic risk is likely to be greater. Infrastructural damage in East Malaysia during the 2015 Sabah earthquake offers a case in point.

Development of a simulation of the surficial groundwater system for the CONUS

NASA Astrophysics Data System (ADS)

Zell, W.; Sanford, W. E.

2016-12-01

Water resource and environmental managers across the country face a variety of questions involving groundwater availability and/or groundwater transport pathways. Emerging management questions require prediction of groundwater response to changing climate regimes (e.g., how drought-induced water-table recession may degrade near-stream vegetation and result in increased wildfire risks), while existing questions can require identification of current groundwater contributions to surface water (e.g., groundwater linkages between landscape contaminant inputs and receiving streams may help explain in-stream phenomena such as fish intersex). At present, few national-coverage simulation tools exist to help characterize groundwater contributions to receiving streams and predict potential changes in base-flow regimes under changing climate conditions. We will describe the Phase 1 development of a simulation of the water table and shallow groundwater system for the entire CONUS. We use national-scale datasets such as the National Recharge Map and the Map Database for Surficial Materials in the CONUS to develop groundwater flow (MODFLOW) and transport (MODPATH) models that are calibrated against groundwater level and stream elevation data from NWIS and NHD, respectively. Phase 1 includes the development of a national transmissivity map for the surficial groundwater system and examines the impact of model-grid resolution on the simulated steady-state discharge network (and associated recharge areas) and base-flow travel time distributions for different HUC scales. In the course of developing the transmissivity map we show that transmissivity in fractured bedrock systems is dependent on depth to water. Subsequent phases of this work will simulate water table changes at a monthly time step (using MODIS-dependent recharge estimates) and serve as a critical complement to surface-water-focused USGS efforts to provide national coverage hydrologic modeling tools.

Increasing resilience through participative flood risk map design

NASA Astrophysics Data System (ADS)

Fuchs, Sven; Spira, Yvonne; Stickler, Therese

2013-04-01

In recent years, an increasing number of flood hazards has shown to the European Commission and the Member States of the European Union the importance of flood risk management strategies in order to reduce losses and to protect the environment and the citizens. Exposure to floods as well as flood vulnerability might increase across Europe due to the ongoing economic development in many EU countries. Thus even without taking climate change into account an increase of flood disasters in Europe might be foreseeable. These circumstances have produced a reaction in the European Commission, and a Directive on the Assessment and Management of Flood Risks was issued as one of the three components of the European Action Programme on Flood Risk Management. Floods have the potential to jeopardise economic development, above all due to an increase of human activities in floodplains and the reduction of natural water retention by land use activities. As a result, an increase in the likelihood and adverse impacts of flood events is expected. Therefore, concentrated action is needed at the European level to avoid severe impacts on human life and property. In order to have an effective tool available for gathering information, as well as a valuable basis for priority setting and further technical, financial and political decisions regarding flood risk mitigation and management, it is necessary to provide for the establishment of flood risk maps which show the potential adverse consequences associated with different flood scenarios. So far, hazard and risk maps are compiled in terms of a top-down linear approach: planning authorities take the responsibility to create and implement these maps on different national and local scales, and the general public will only be informed about the outcomes (EU Floods Directive, Article 10). For the flood risk management plans, however, an "active involvement of interested parties" is required, which means at least some kind of multilateral consultation on the management plans that allows stakeholders to discuss relevant issues and to contribute to arguments and propositions put forward by the stakeholders. Through a wider stakeholder participation and more effective communication, awareness of flood risks should be raised. With the term participation diverse voluntary and informal forms of inclusion are summarized (in contrast to legal forms of participation like the status as a party). When discussing the theoretical and practical implications of participation in flood risk management, it is important to make a clear distinction between public and stakeholder participation. The broad public is "everybody" and refers to the participation by non-organised individuals as members of the general public, and specifically to individuals whose profession is not connected to flood risk management. As such, they have to be regarded as lay persons, which, nevertheless, does not mean that these individuals do not have any idea about the hazard they are exposed to or can contribute to the quality of an decision making process. In contrast to professionally interested parties, this group is typically comprised of individuals with different individual perspectives on flood risk management. It is argued that including practical knowledge and perceptions (reflecting values and preferences) into the flood risk management process is - apart from professional assessments (as systematic knowledge) - a milestone towards adequate governance structures in any institutional process with political legitimacy. Neither normative concepts like sustainable development or "Good Governance" nor the European Water Framework Directive 2000/60/EC do specify what public participation or the participation of user means in detail. As also scientific literature offers no consistent definition of public participation and stakeholder participation we developed an innovative approach used in the pilot project Krems, Austria. The most innovative step regarding participation was not the methods used for participation but the involvement of concerned lay persons not only in the design of the hazard and risk maps or the risk assessments itself but the cooperative elaboration of the risk assessment approach especially for the harbour area. Following these principles, flood risk maps were created in the underlying EU-project DANUBE FLOODRISK. In this ETC SEE project "DANUBE FLOODRISK - Stakeholder Oriented Assessment of the Danube Floodplains" (2009-2012), hazard and risk maps harmonized across borders for the Danube main stream were produced. This way the overall DANUBE FLOODRISK project contributed to Article 6 of the EU Floods Directive, the hazard and risk maps for international river basins, and provides with the involvement of the national and regional stakeholders the first step to the implementation of Article 7, the Flood Risk Management Plans. By testing the involvement of the broad public and local stakeholders, first exemplary steps were taken for local flood risk management planning. A first set of maps was created for an underlying hazard scenario of a 1-in-100 year flood affecting the city of Krems assuming a failure of the temporal flood protection due to the impact of a ship in the area of the pier. Moreover, both, hazard scenarios with and without a second line of defence were visualised. The set of maps includes (a) an evaluative risk map showing the risk qualitatively aggregated for each building exposed and the number of affected citizens, (b) an evaluative risk map showing the risk qualitatively aggregated per square footage for each building exposed and the number of affected citizens, (c) an evaluative risk map showing the risk quantitatively in monetary units per square footage for each building exposed and the number of affected citizens, and (d) as well as (e) risk maps according to (a) and (b) without the second line of defence in order to communicate the effectiveness of temporal flood protection. For the harbour of Krems, a risk map was compiled based on a self-evaluation of the effects of flooding by the harbour companies. This risk map was based on the assumption of a failure of the harbour gate during a flood event. The self-evaluation was undertaken based on a developed risk matrix which includes significant adverse impacts on human health, the environment, cultural heritage and economic activity. Insights on stakeholder-oriented risk communication were gained with respect to the design and the layout of the maps. Specific elements of semiology for the cartographic representation were deduced. The pilot initiative discussed in this paper is brought added value to all involved parties so far. All participants brought in knowledge, data and time resources. The project team was involved in a social learning process and gained additional know-how about adequate stakeholder involvement and communication as well as about risk assessment methods and mapping. It could be shown that it is possible to involve lay persons in topics such as risk assessments so far only defined by technical experts. Stakeholders from the harbour area were not only involved in the risk assessment but also in the development of the methods for this risk assessment. Such approaches may be increasingly used to develop a better understanding of flood risk within affected communities, and thus increase flood resilience.

The psychological four-color mapping problem.

PubMed

Francis, Gregory; Bias, Keri; Shive, Joshua

2010-06-01

Mathematicians have proven that four colors are sufficient to color 2-D maps so that no neighboring regions share the same color. Here we consider the psychological 4-color problem: Identifying which 4 colors should be used to make a map easy to use. We build a model of visual search for this design task and demonstrate how to apply it to the task of identifying the optimal colors for a map. We parameterized the model with a set of 7 colors using a visual search experiment in which human participants found a target region on a small map. We then used the model to predict search times for new maps and identified the color assignments that minimize or maximize average search time. The differences between these maps were predicted to be substantial. The model was then tested with a larger set of 31 colors on a map of English counties under conditions in which participants might memorize some aspects of the map. Empirical tests of the model showed that an optimally best colored version of this map is searched 15% faster than the correspondingly worst colored map. Thus, the color assignment seems to affect search times in a way predicted by the model, and this effect persists even when participants might use other sources of knowledge about target location. PsycINFO Database Record (c) 2010 APA, all rights reserved.

Groundwater pollution risk assessment. Application to different carbonate aquifers in south Spain

NASA Astrophysics Data System (ADS)

Jimenez Madrid, A.; Martinez Navarrete, C.; Carrasco Cantos, F.

2009-04-01

Water protection has been considered one of the most important environmental goals in the European politics since the 2000/60/CE Water Framework Directive came into force in 2000, and more specifically in 2006 with the 2006/118/CE Directive on groundwater protection. As one of the necessary requirements to tackle groundwater protection, a pollution risk assessment has been made through the analysis of both the existing hazard human activities map and the intrinsic aquifer vulnerability map, by applying the methodologies proposed by COST Action 620 in an experimental study site in south Spain containing different carbonated aquifers, which supply 8 towns ranging from 2000 to 2500 inhabitants. In order to generate both maps it was necessary to make a field inventory over a 1:10000 topographic base map, followed by Geographic Information System (GIS) processing. The outcome maps show a clear spatial distribution of both pollution risk and intrinsic vulnerability of the carbonated aquifers studied. As a final result, a map of the intensity of groundwater pollution risk is presented, representing and important base for the development of a proper methodology for the protection of groundwater resources for human consumption protection. Keywords. Hazard, Vulnerability, Risk, SIG, Protection

Mapping flood and flooding potential indices: a methodological approach to identifying areas susceptible to flood and flooding risk. Case study: the Prahova catchment (Romania)

NASA Astrophysics Data System (ADS)

Zaharia, Liliana; Costache, Romulus; Prăvălie, Remus; Ioana-Toroimac, Gabriela

2017-04-01

Given that floods continue to cause yearly significant worldwide human and material damages, flood risk mitigation is a key issue and a permanent challenge in developing policies and strategies at various spatial scales. Therefore, a basic phase is elaborating hazard and flood risk maps, documents which are an essential support for flood risk management. The aim of this paper is to develop an approach that allows for the identification of flash-flood and flood-prone susceptible areas based on computing and mapping of two indices: FFPI (Flash-Flood Potential Index) and FPI (Flooding Potential Index). These indices are obtained by integrating in a GIS environment several geographical variables which control runoff (in the case of the FFPI) and favour flooding (in the case of the FPI). The methodology was applied in the upper (mountainous) and middle (hilly) catchment of the Prahova River, a densely populated and socioeconomically well-developed area which has been affected repeatedly by water-related hazards over the past decades. The resulting maps showing the spatialization of the FFPI and FPI allow for the identification of areas with high susceptibility to flashfloods and flooding. This approach can provide useful mapped information, especially for areas (generally large) where there are no flood/hazard risk maps. Moreover, the FFPI and FPI maps can constitute a preliminary step for flood risk and vulnerability assessment.

In Silico Identification of Epitopes in Mycobacterium avium subsp. paratuberculosis Proteins That Were Upregulated under Stress Conditions

PubMed Central

Gurung, Ratna B.; Purdie, Auriol C.; Begg, Douglas J.

2012-01-01

Johne's disease in ruminants is caused by Mycobacterium avium subsp. paratuberculosis. Diagnosis of M. avium subsp. paratuberculosis infection is difficult, especially in the early stages. To date, ideal antigen candidates are not available for efficient immunization or immunodiagnosis. This study reports the in silico selection and subsequent analysis of epitopes of M. avium subsp. paratuberculosis proteins that were found to be upregulated under stress conditions as a means to identify immunogenic candidate proteins. Previous studies have reported differential regulation of proteins when M. avium subsp. paratuberculosis is exposed to stressors which induce a response similar to dormancy. Dormancy may be involved in evading host defense mechanisms, and the host may also mount an immune response against these proteins. Twenty-five M. avium subsp. paratuberculosis proteins that were previously identified as being upregulated under in vitro stress conditions were analyzed for B and T cell epitopes by use of the prediction tools at the Immune Epitope Database and Analysis Resource. Major histocompatibility complex class I T cell epitopes were predicted using an artificial neural network method, and class II T cell epitopes were predicted using the consensus method. Conformational B cell epitopes were predicted from the relevant three-dimensional structure template for each protein. Based on the greatest number of predicted epitopes, eight proteins (MAP2698c [encoded by desA2], MAP2312c [encoded by fadE19], MAP3651c [encoded by fadE3_2], MAP2872c [encoded by fabG5_2], MAP3523c [encoded by oxcA], MAP0187c [encoded by sodA], and the hypothetical proteins MAP3567 and MAP1168c) were identified as potential candidates for study of antibody- and cell-mediated immune responses within infected hosts. PMID:22496492

Neural networks for satellite remote sensing and robotic sensor interpretation

NASA Astrophysics Data System (ADS)

Martens, Siegfried

Remote sensing of forests and robotic sensor fusion can be viewed, in part, as supervised learning problems, mapping from sensory input to perceptual output. This dissertation develops ARTMAP neural networks for real-time category learning, pattern recognition, and prediction tailored to remote sensing and robotics applications. Three studies are presented. The first two use ARTMAP to create maps from remotely sensed data, while the third uses an ARTMAP system for sensor fusion on a mobile robot. The first study uses ARTMAP to predict vegetation mixtures in the Plumas National Forest based on spectral data from the Landsat Thematic Mapper satellite. While most previous ARTMAP systems have predicted discrete output classes, this project develops new capabilities for multi-valued prediction. On the mixture prediction task, the new network is shown to perform better than maximum likelihood and linear mixture models. The second remote sensing study uses an ARTMAP classification system to evaluate the relative importance of spectral and terrain data for map-making. This project has produced a large-scale map of remotely sensed vegetation in the Sierra National Forest. Network predictions are validated with ground truth data, and maps produced using the ARTMAP system are compared to a map produced by human experts. The ARTMAP Sierra map was generated in an afternoon, while the labor intensive expert method required nearly a year to perform the same task. The robotics research uses an ARTMAP system to integrate visual information and ultrasonic sensory information on a B14 mobile robot. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. ARTMAP effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.

Robustness of risk maps and survey networks to knowledge gaps about a new invasive pest

Treesearch

Denys Yemshanov; Frank H. Koch; Yakov Ben-Haim; William D. Smith

2010-01-01

In pest risk assessment it is frequently necessary to make management decisions regarding emerging threats under severe uncertainty. Although risk maps provide useful decision support for invasive alien species, they rarely address knowledge gaps associated with the underlying risk model or how they may change the risk estimates. Failure to recognize uncertainty leads...

Slope Hazard and Risk Assessment in the Tropics: Malaysia' Experience

NASA Astrophysics Data System (ADS)

Mohamad, Zakaria; Azahari Razak, Khamarrul; Ahmad, Ferdaus; Manap, Mohamad Abdul; Ramli, Zamri; Ahmad, Azhari; Mohamed, Zainab

2015-04-01

The increasing number of geological hazards in Malaysia has often resulted in casualties and extensive devastation with high mitigation cost. Given the destructive capacity and high frequency of disaster, Malaysia has taken a step forward to address the multi-scale landslide risk reduction emphasizing pre-disaster action rather than post-disaster reaction. Slope hazard and risk assessment in a quantitative manner at regional and national scales remains challenging in Malaysia. This paper presents the comprehensive methodology framework and operational needs driven by modern and advanced geospatial technology to address the aforementioned issues in the tropics. The Slope Hazard and Risk Mapping, the first national project in Malaysia utilizing the multi-sensor LIDAR has been critically implemented with the support of multi- and trans-disciplinary partners. The methodological model has been formulated and evaluated given the complexity of risk scenarios in this knowledge driven project. Instability slope problems in the urban, mountainous and tectonic landscape are amongst them, and their spatial information is of crucial for regional landslide assessment. We develop standard procedures with optimal parameterization for susceptibility, hazard and risk assessment in the selected regions. Remarkably, we are aiming at producing an utmost complete landslide inventory in both space and time. With the updated reliable terrain and landscape models, the landslide conditioning factor maps can be accurately derived depending on the landslide types and failure mechanisms which crucial for hazard and risk assessment. We also aim to improve the generation of elements at risk for landslide and promote integrated approaches for a better disaster risk analysis. As a result, a new tool, notably multi-sensor LIDAR technology is a very promising tool for an old geological problem and its derivative data for hazard and risk analysis is an effective preventive measure in Malaysia. Geological, morphological, and physical factors coupled with anthropogenic activities made the spatiotemporal prediction of possible slope failures very challenging. Changing climate and land-use-and-land-cover required a dynamic geo-system approach for assessing multi-hazard in Malaysia and it is still a great challenge to be dealt with. We also critically discussed the capability, limitation and future direction of geo-information tools particularly the active sensors for systematically providing the spatial input towards landslide hazard and possible risk. The cost-and-benefit of developed methods compared to traditional mapping techniques is also elaborated. This paper put forth the critical and practical framework ranging from updating landslide inventory to mitigating landslide risk as an attempt to support the establishment of a comprehensive landslide risk management in Malaysia. The advancement of multistage processing sequence based on airborne-, and ground-based laser remote sensing technology coupling with the sophisticated satellite positioning system, advanced geographical information system and expert knowledge leading to a better understanding of the landslide processes and their dynamics in time and space. Given the state-of-the-art of multi-sensor-LIDAR and complexity of tropical environment, this first landslide project carried out at the national scale provides a better indication and recommendation on the use of modern and advanced mapping technology for assessing tropical landslide geomorphology in an objective, reproducible and quantitative manner.

A new multicriteria risk mapping approach based on a multiattribute frontier concept

Treesearch

Denys Yemshanov; Frank H. Koch; Yakov Ben-Haim; Marla Downing; Frank Sapio; Marty Siltanen

2013-01-01

Invasive species risk maps provide broad guidance on where to allocate resources for pest monitoring and regulation, but they often present individual risk components (such as climatic suitability, host abundance, or introduction potential) as independent entities. These independent risk components are integrated using various multicriteria analysis techniques that...

Fine mapping of breast cancer genome-wide association studies loci in women of African ancestry identifies novel susceptibility markers

PubMed Central

Huo, Dezheng

2013-01-01

Numerous single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility have been identified by genome-wide association studies (GWAS). However, these SNPs were primarily discovered and validated in women of European and Asian ancestry. Because linkage disequilibrium is ancestry-dependent and heterogeneous among racial/ethnic populations, we evaluated common genetic variants at 22 GWAS-identified breast cancer susceptibility loci in a pooled sample of 1502 breast cancer cases and 1378 controls of African ancestry. None of the 22 GWAS index SNPs could be validated, challenging the direct generalizability of breast cancer risk variants identified in Caucasians or Asians to other populations. Novel breast cancer risk variants for women of African ancestry were identified in regions including 5p12 (odds ratio [OR] = 1.40, 95% confidence interval [CI] = 1.11–1.76; P = 0.004), 5q11.2 (OR = 1.22, 95% CI = 1.09–1.36; P = 0.00053) and 10p15.1 (OR = 1.22, 95% CI = 1.08–1.38; P = 0.0015). We also found positive association signals in three regions (6q25.1, 10q26.13 and 16q12.1–q12.2) previously confirmed by fine mapping in women of African ancestry. In addition, polygenic model indicated that eight best markers in this study, compared with 22 GWAS-identified SNPs, could better predict breast cancer risk in women of African ancestry (per-allele OR = 1.21, 95% CI = 1.16–1.27; P = 9.7 × 10–16). Our results demonstrate that fine mapping is a powerful approach to better characterize the breast cancer risk alleles in diverse populations. Future studies and new GWAS in women of African ancestry hold promise to discover additional variants for breast cancer susceptibility with clinical implications throughout the African diaspora. PMID:23475944

A Rb1 promoter variant with reduced activity contributes to osteosarcoma susceptibility in irradiated mice

PubMed Central

2014-01-01

Background Syndromic forms of osteosarcoma (OS) account for less than 10% of all recorded cases of this malignancy. An individual OS predisposition is also possible by the inheritance of low penetrance alleles of tumor susceptibility genes, usually without evidence of a syndromic condition. Genetic variants involved in such a non-syndromic form of tumor predisposition are difficult to identify, given the low incidence of osteosarcoma cases and the genetic heterogeneity of patients. We recently mapped a major OS susceptibility QTL to mouse chromosome 14 by comparing alpha-radiation induced osteosarcoma in mouse strains which differ in their tumor susceptibility. Methods Tumor-specific allelic losses in murine osteosacoma were mapped along chromosome 14 using microsatellite markers and SNP allelotyping. Candidate gene search in the mapped interval was refined using PosMed data mining and mRNA expression analysis in normal osteoblasts. A strain-specific promoter variant in Rb1 was tested for its influence on mRNA expression using reporter assay. Results A common Rb1 allele derived from the BALB/cHeNhg strain was identified as the major determinant of radiation-induced OS risk at this locus. Increased OS-risk is linked with a hexanucleotide deletion in the promoter region which is predicted to change WT1 and SP1 transcription factor-binding sites. Both in-vitro reporter and in-vivo expression assays confirmed an approx. 1.5 fold reduced gene expression by this promoter variant. Concordantly, the 50% reduction in Rb1 expression in mice bearing a conditional hemizygous Rb1 deletion causes a significant rise of OS incidence following alpha-irradiation. Conclusion This is the first experimental demonstration of a functional and genetic link between reduced Rb1 expression from a common promoter variant and increased tumor risk after radiation exposure. We propose that a reduced Rb1 expression by common variants in regulatory regions can modify the risk for a malignant transformation of bone cells after radiation exposure. PMID:25092376

Coastal Hazard Vulnerability Assessment: A Case Study of Erosion and Flooding on Herschel Island, Yukon Territory, Canada

NASA Astrophysics Data System (ADS)

Radosavljevic, B.; Lantuit, H.; Overduin, P. P.; Fritz, M.

2015-12-01

Coastal infrastructure, cultural, and archeological sites are increasingly vulnerable to erosion and flooding along permafrost coasts. Amplified warming of the Arctic, sea level rise, lengthening of the open water period, and a predicted increase in frequency of major storms compound these threats. Mitigation necessitates decision-making tools at an appropriate scale. We present a study of coastal erosion combining it with a flooding risk assessment for the culturally important historic settlement on Herschel Island, a UNESCO World Heritage candidate site. The resulting map may help local stakeholders devise management strategies to cope with rapidly changing environmental conditions. We analyzed shoreline movement using the Digital Shoreline Analysis System (DSAS) after digitizing shorelines from 1952, 1970, and 2011. Using these data, forecasts of shoreline positions were made for 20 and 50 years into the future. Flooding risk was assessed using a cost-distance map based on a high-resolution Light Detection and Ranging (LiDAR) dataset and current Intergovernmental Panel on Climate Change sea level estimates. Widespread erosion characterizes the study area. The rate of shoreline movement for different periods of the study ranges from -5.5 to 2.7 m·a-1 (mean -0.6 m·a-1). Mean coastal retreat decreased from -0.6 m·a-1 to -0.5 m·a-1, for 1952-1970 and 1970-2000, respectively, and increased to -1.3 m·a-1 in the period 2000-2011. Ice-rich coastal sections, and coastal sections most exposed to wave attack exhibited the highest rates of coastal retreat. The geohazard map resulting from shoreline projections and flood risk analysis indicates that most of the area occupied by the historic settlement is at extreme or very high risk of flooding, and some buildings are vulnerable to coastal erosion. The results of this study indicate a greater threat by coastal flooding than erosion. Our assessment may be applied in other locations where limited data are available.