WorldWideScience

Sample records for predictive risk maps

  1. Risk predicting of macropore flow using pedotransfer functions, textural maps and modeling

    DEFF Research Database (Denmark)

    Iversen, Bo Vangsø; Børgesen, Christen Duus; Lægdsmand, Mette

    2011-01-01

    of this study were first to develop pedotransfer functions (PTFs) predicting near-saturated [k(−1)] and saturated (Ks) hydraulic conductivity using simple soil parameters as predictors and second to use this information and a newly developed rasterbased soil property map of Denmark to identify risk areas...... modeling were used to construct a new map dividing Denmark into risk categories for macropore flow. This map can be combined with other tools to identify areas where there is a high risk of contaminants leaching out of the root zone....

  2. Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria

    Directory of Open Access Journals (Sweden)

    Solarin Adewale RT

    2008-05-01

    Full Text Available Abstract Background The control of urinary schistosomiasis in Ogun State, Nigeria remains inert due to lack of reliable data on the geographical distribution of the disease and the population at risk. To help in developing a control programme, delineating areas of risk, geographical information system and remotely sensed environmental images were used to developed predictive risk maps of the probability of occurrence of the disease and quantify the risk for infection in Ogun State, Nigeria. Methods Infection data used were derived from carefully validated morbidity questionnaires among primary school children in 2001–2002, in which school children were asked among other questions if they have experienced "blood in urine" or urinary schistosomiasis. The infection data from 1,092 schools together with remotely sensed environmental data such as rainfall, vegetation, temperature, soil-types, altitude and land cover were analysis using binary logistic regression models to identify environmental features that influence the spatial distribution of the disease. The final regression equations were then used in Arc View 3.2a GIS software to generate predictive risk maps of the distribution of the disease and population at risk in the state. Results Logistic regression analysis shows that the only significant environmental variable in predicting the presence and absence of urinary schistosomiasis in any area of the State was Land Surface Temperature (LST (B = 0.308, p = 0.013. While LST (B = -0.478, p = 0.035, rainfall (B = -0.006, p = 0.0005, ferric luvisols (B = 0.539, p = 0.274, dystric nitosols (B = 0.133, p = 0.769 and pellic vertisols (B = 1.386, p = 0.008 soils types were the final variables in the model for predicting the probability of an area having an infection prevalence equivalent to or more than 50%. The two predictive risk maps suggest that urinary schistosomiasis is widely distributed and occurring in all the Local Government Areas (LGAs

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

    Science.gov (United States)

    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.

  4. Differential maps, difference maps, interpolated maps, and long term prediction

    International Nuclear Information System (INIS)

    Talman, R.

    1988-06-01

    Mapping techniques may be thought to be attractive for the long term prediction of motion in accelerators, especially because a simple map can approximately represent an arbitrarily complicated lattice. The intention of this paper is to develop prejudices as to the validity of such methods by applying them to a simple, exactly solveable, example. It is shown that a numerical interpolation map, such as can be generated in the accelerator tracking program TEAPOT, predicts the evolution more accurately than an analytically derived differential map of the same order. Even so, in the presence of ''appreciable'' nonlinearity, it is shown to be impractical to achieve ''accurate'' prediction beyond some hundreds of cycles of oscillation. This suggests that the value of nonlinear maps is restricted to the parameterization of only the ''leading'' deviation from linearity. 41 refs., 6 figs

  5. Mapping of nitrogen risks

    DEFF Research Database (Denmark)

    Blicher-Mathiesen, Gitte; Andersen, Hans Estrup; Carstensen, Jacob

    2014-01-01

    risk mapping part of the tool, we combined a modelled root zone N leaching with a catchment-specific N reduction factor which in combination determines the N load to the marine recipient. N leaching was calculated using detailed information of agricultural management from national databases as well...... will be more effective if they are implemented in N loss hot spots or risk areas. Additionally, the highly variable N reduction in groundwater and surface waters needs to be taken into account as this strongly influences the resulting effect of mitigation measures. The objectives of this study were to develop...... and apply an N risk tool to the entire agricultural land area in Denmark. The purpose of the tool is to identify high risk areas, i.e. areas which contribute disproportionately much to diffuse N losses to the marine recipient, and to suggest cost-effective measures to reduce losses from risk areas. In the N...

  6. The prediction of the in-hospital mortality of acutely ill medical patients by electrocardiogram (ECG) dispersion mapping compared with established risk factors and predictive scores--a pilot study.

    LENUS (Irish Health Repository)

    Kellett, John

    2011-08-01

    ECG dispersion mapping (ECG-DM) is a novel technique that analyzes low amplitude ECG oscillations and reports them as the myocardial micro-alternation index (MMI). This study compared the ability of ECG-DM to predict in-hospital mortality with traditional risk factors such as age, vital signs and co-morbid diagnoses, as well as three predictive scores: the Simple Clinical Score (SCS)--based on clinical and ECG findings, and two Medical Admission Risk System scores--one based on vital signs and laboratory data (MARS), and one only on laboratory data (LD).

  7. Predictive Mapping of Anti-Social Behaviour

    NARCIS (Netherlands)

    Smit, S.K.; Vecht, B. van der; Lebesque, L.H.E.M.

    2014-01-01

    Predictive mapping of crime and anti-social behaviour is becoming more and more popular as a tool to support police and policy makers. Important ingredients of such models are often demographic and economic characteristics of the area. Since those are hard to influence, we propose to use the

  8. Seismic risk maps of Switzerland

    International Nuclear Information System (INIS)

    Saegesser, R.; Rast, B.; Merz, H.

    1977-01-01

    Seismic Risk Maps of Switzerland have been developed under the auspices of the Swiss Federal Division on Nuclear Safety. They are primarily destined for the use of owners of future nuclear power plants. The results will be mandatory for these future sites. The results will be shown as contourmaps of equal intensities for average return periods of 500, 1000, 10 000... years. This general form will not restrict the use of the results to nuclear power plants only, rather allows their applicability to any site or installation of public interest (such as r.a. waste deposits, hydropower plants, etc.). This follows the recommendations of the UNESCO World Conference (Paris, February 1976). In the study MSK 64 INTENSITY was chosen. The detailed scale allowed a precise handling of historical data and separates the results from continuously changing state-of-the-art correlations to acceleration and other input motion parameters. The method used is the probabilistic theory developed by C.A. Cornell and others at MIT in the late 1960's with the program in the version of the US Geological Survey by R. McGuire. In the study, the program was extended for the use of the continuous attenuation law by Sponheuer, azimuth-dependency in the attenuation relation, a quadratic intensity-frequency relation, large number of gross sources and output modifications with respect to the mapping program used. To determine the basic parameters, more than 3000 independent events in an area of approximately 240 000km 2 -Switzerland with its neighbouring parts of Italy, Austria, Germany and France- were systematically classified (and relocated where necessary)

  9. 2011 FEMA Risk Mapping, Assessment, and Planning (Risk MAP) Lidar: Nashua River Watershed (Massachusetts, New Hampshire)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — These data are the lidar points collected for FEMA Risk Mapping, Assessment, and Planning (Risk MAP) for the Nashua River Watershed. This area falls in portions of...

  10. Crop Biometric Maps: The Key to Prediction

    Directory of Open Access Journals (Sweden)

    Francisco Rovira-Más

    2013-09-01

    Full Text Available The sustainability of agricultural production in the twenty-first century, both in industrialized and developing countries, benefits from the integration of farm management with information technology such that individual plants, rows, or subfields may be endowed with a singular “identity.” This approach approximates the nature of agricultural processes to the engineering of industrial processes. In order to cope with the vast variability of nature and the uncertainties of agricultural production, the concept of crop biometrics is defined as the scientific analysis of agricultural observations confined to spaces of reduced dimensions and known position with the purpose of building prediction models. This article develops the idea of crop biometrics by setting its principles, discussing the selection and quantization of biometric traits, and analyzing the mathematical relationships among measured and predicted traits. Crop biometric maps were applied to the case of a wine-production vineyard, in which vegetation amount, relative altitude in the field, soil compaction, berry size, grape yield, juice pH, and grape sugar content were selected as biometric traits. The enological potential of grapes was assessed with a quality-index map defined as a combination of titratable acidity, sugar content, and must pH. Prediction models for yield and quality were developed for high and low resolution maps, showing the great potential of crop biometric maps as a strategic tool for vineyard growers as well as for crop managers in general, due to the wide versatility of the methodology proposed.

  11. Crop biometric maps: the key to prediction.

    Science.gov (United States)

    Rovira-Más, Francisco; Sáiz-Rubio, Verónica

    2013-09-23

    The sustainability of agricultural production in the twenty-first century, both in industrialized and developing countries, benefits from the integration of farm management with information technology such that individual plants, rows, or subfields may be endowed with a singular "identity." This approach approximates the nature of agricultural processes to the engineering of industrial processes. In order to cope with the vast variability of nature and the uncertainties of agricultural production, the concept of crop biometrics is defined as the scientific analysis of agricultural observations confined to spaces of reduced dimensions and known position with the purpose of building prediction models. This article develops the idea of crop biometrics by setting its principles, discussing the selection and quantization of biometric traits, and analyzing the mathematical relationships among measured and predicted traits. Crop biometric maps were applied to the case of a wine-production vineyard, in which vegetation amount, relative altitude in the field, soil compaction, berry size, grape yield, juice pH, and grape sugar content were selected as biometric traits. The enological potential of grapes was assessed with a quality-index map defined as a combination of titratable acidity, sugar content, and must pH. Prediction models for yield and quality were developed for high and low resolution maps, showing the great potential of crop biometric maps as a strategic tool for vineyard growers as well as for crop managers in general, due to the wide versatility of the methodology proposed.

  12. Seismic risk maps of Switzerland

    International Nuclear Information System (INIS)

    Saegesser, R.; Rast, B.; Merz, H.

    1977-01-01

    Seismic Risk Maps of Switzerland have been developed under the auspices of the Swiss Federal Division on Nuclear Safety. They are primarily destined for the use of owners of future nuclear power plants. The results will be mandatory for these future sites. The results will be shown as contourmaps of equal intensities for average return periods of 500, 1 000, 10 000... years. This general form will not restrict the use of the results to nuclear power plants only, rather allows their applicability to any site or installation of public interest (such as r.a. waste deposits, hydropower plants, etc.). This follows the recommendations of the UNESCO World Conference (Paris, February 1976). In the study MSK 64 INTENSITY was chosen. The detailed scale allowed a precise handling of historical data and separates the results from continuously changing state of the art correlations to acceleration and other input motion parameters. The method used is the probabilistic theory developed by C.A. Cornell and others at MIT in the late 1960's with the program in the version of the US Geological Survey by R. McGuire. (Auth.)

  13. Modeling Research Project Risks with Fuzzy Maps

    Science.gov (United States)

    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…

  14. Breast cancer risks and risk prediction models.

    Science.gov (United States)

    Engel, Christoph; Fischer, Christine

    2015-02-01

    BRCA1/2 mutation carriers have a considerably increased risk to develop breast and ovarian cancer. The personalized clinical management of carriers and other at-risk individuals depends on precise knowledge of the cancer risks. In this report, we give an overview of the present literature on empirical cancer risks, and we describe risk prediction models that are currently used for individual risk assessment in clinical practice. Cancer risks show large variability between studies. Breast cancer risks are at 40-87% for BRCA1 mutation carriers and 18-88% for BRCA2 mutation carriers. For ovarian cancer, the risk estimates are in the range of 22-65% for BRCA1 and 10-35% for BRCA2. The contralateral breast cancer risk is high (10-year risk after first cancer 27% for BRCA1 and 19% for BRCA2). Risk prediction models have been proposed to provide more individualized risk prediction, using additional knowledge on family history, mode of inheritance of major genes, and other genetic and non-genetic risk factors. User-friendly software tools have been developed that serve as basis for decision-making in family counseling units. In conclusion, further assessment of cancer risks and model validation is needed, ideally based on prospective cohort studies. To obtain such data, clinical management of carriers and other at-risk individuals should always be accompanied by standardized scientific documentation.

  15. Cardiovascular risk prediction

    DEFF Research Database (Denmark)

    Graversen, Peter; Abildstrøm, Steen Z.; Jespersen, Lasse

    2016-01-01

    Aim European society of cardiology (ESC) guidelines recommend that cardiovascular disease (CVD) risk stratification in asymptomatic individuals is based on the Systematic Coronary Risk Evaluation (SCORE) algorithm, which estimates individual 10-year risk of death from CVD. We assessed the potential...

  16. Flood Risk and Flood hazard maps - Visualisation of hydrological risks

    International Nuclear Information System (INIS)

    Spachinger, Karl; Dorner, Wolfgang; Metzka, Rudolf; Serrhini, Kamal; Fuchs, Sven

    2008-01-01

    Hydrological models are an important basis of flood forecasting and early warning systems. They provide significant data on hydrological risks. In combination with other modelling techniques, such as hydrodynamic models, they can be used to assess the extent and impact of hydrological events. The new European Flood Directive forces all member states to evaluate flood risk on a catchment scale, to compile maps of flood hazard and flood risk for prone areas, and to inform on a local level about these risks. Flood hazard and flood risk maps are important tools to communicate flood risk to different target groups. They provide compiled information to relevant public bodies such as water management authorities, municipalities, or civil protection agencies, but also to the broader public. For almost each section of a river basin, run-off and water levels can be defined based on the likelihood of annual recurrence, using a combination of hydrological and hydrodynamic models, supplemented by an analysis of historical records and mappings. In combination with data related to the vulnerability of a region risk maps can be derived. The project RISKCATCH addressed these issues of hydrological risk and vulnerability assessment focusing on the flood risk management process. Flood hazard maps and flood risk maps were compiled for Austrian and German test sites taking into account existing national and international guidelines. These maps were evaluated by eye-tracking using experimental graphic semiology. Sets of small-scale as well as large-scale risk maps were presented to 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 risk communication. A cognitive survey asking for negative and positive aspects and complexity of each single map complemented the experimental graphic semiology. The results indicate how risk maps can be improved to fit the needs of different user

  17. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  18. Predictive geochemical mapping using environmental correlation

    International Nuclear Information System (INIS)

    Wilford, John; Caritat, Patrice de; Bui, Elisabeth

    2016-01-01

    The distribution of chemical elements at and near the Earth's surface, the so-called critical zone, is complex and reflects the geochemistry and mineralogy of the original substrate modified by environmental factors that include physical, chemical and biological processes over time. Geochemical data typically is illustrated in the form of plan view maps or vertical cross-sections, where the composition of regolith, soil, bedrock or any other material is represented. These are primarily point observations that frequently are interpolated to produce rasters of element distributions. Here we propose the application of environmental or covariate regression modelling to predict and better understand the controls on major and trace element geochemistry within the regolith. Available environmental covariate datasets (raster or vector) representing factors influencing regolith or soil composition are intersected with the geochemical point data in a spatial statistical correlation model to develop a system of multiple linear correlations. The spatial resolution of the environmental covariates, which typically is much finer (e.g. ∼90 m pixel) than that of geochemical surveys (e.g. 1 sample per 10-10,000 km 2 ), carries over to the predictions. Therefore the derived predictive models of element concentrations take the form of continuous geochemical landscape representations that are potentially much more informative than geostatistical interpolations. Environmental correlation is applied to the Sir Samuel 1:250,000 scale map sheet in Western Australia to produce distribution models of individual elements describing the geochemical composition of the regolith and exposed bedrock. As an example we model the distribution of two elements – chromium and sodium. We show that the environmental correlation approach generates high resolution predictive maps that are statistically more accurate and effective than ordinary kriging and inverse distance weighting interpolation

  19. Quantifying prognosis with risk predictions.

    Science.gov (United States)

    Pace, Nathan L; Eberhart, Leopold H J; Kranke, Peter R

    2012-01-01

    Prognosis is a forecast, based on present observations in a patient, of their probable outcome from disease, surgery and so on. Research methods for the development of risk probabilities may not be familiar to some anaesthesiologists. We briefly describe methods for identifying risk factors and risk scores. A probability prediction rule assigns a risk probability to a patient for the occurrence of a specific event. Probability reflects the continuum between absolute certainty (Pi = 1) and certified impossibility (Pi = 0). Biomarkers and clinical covariates that modify risk are known as risk factors. The Pi as modified by risk factors can be estimated by identifying the risk factors and their weighting; these are usually obtained by stepwise logistic regression. The accuracy of probabilistic predictors can be separated into the concepts of 'overall performance', 'discrimination' and 'calibration'. Overall performance is the mathematical distance between predictions and outcomes. Discrimination is the ability of the predictor to rank order observations with different outcomes. Calibration is the correctness of prediction probabilities on an absolute scale. Statistical methods include the Brier score, coefficient of determination (Nagelkerke R2), C-statistic and regression calibration. External validation is the comparison of the actual outcomes to the predicted outcomes in a new and independent patient sample. External validation uses the statistical methods of overall performance, discrimination and calibration and is uniformly recommended before acceptance of the prediction model. Evidence from randomised controlled clinical trials should be obtained to show the effectiveness of risk scores for altering patient management and patient outcomes.

  20. Seismic risk map of Korea

    International Nuclear Information System (INIS)

    Lee, S.H.; Lee, Y.K.; Eum, S.H.; Yang, S.J.; Chun, M.S.

    1983-01-01

    A study on seismic hazard level in Korea has been performed and the main results of the study are summarized as follows: 1. Historians suggest that the quality of historical earthquake data may be accurate in some degree and the data should be used in seismic risk analysis. 2. The historical damage events are conformed in historical literatures and their intensities are re-evaluated by joint researchers. The maximum MM intensity of them is VIII evaluated for 17 events. 3. The relation of earthquakes to surface fault is not clear. It seems resonable to related them to tectonic provinces. 4. Statistical seismic risk analysis shows that the acceleration expected within 50O year return period is less than 0.25G when only instrumental earthquakes are used and less than 0.10G if all of instrumental and historical earthquakes are used. The acceleration in Western Coast and Kyungsang area is higher than the other regions in Korea. 5. The maximum horizontal acceleration determined by conservative method is 0.26G when historical earthquake data are used and less than 0.20G if only instrumental earthquakes are used. The return period of 0.26G is 240 years in Kyungsang province and longer in other provinces. (Author)

  1. Climate Prediction Center - Forecasts & Outlook Maps, Graphs and Tables

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site Map News list below The Climate Prediction Center (CPC) is responsible for issuing seasonal climate outlook maps , and National Centers for Environmental Prediction). These weather and climate products comprise the

  2. Developmental dyslexia: predicting individual risk.

    Science.gov (United States)

    Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

    2015-09-01

    Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as 'dyslexic' or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. © 2015 The Authors. Journal of Child Psychology and Psychiatry published by

  3. Mapping Risks of Indonesian Tuna Supply Chain

    Science.gov (United States)

    Karningsih, P. D.; Anggrahini, D.; Kurniati, N.; Suef, M.; Fachrur, A. R.; Syahroni, N.

    2018-04-01

    Due to its high economic value and is produced by many countries, Tuna is considered as one of the world’s popular fish. Demand for Tuna species are very high and it usually sells in three form: fresh, frozen or canned. Competition in Tuna trading is challengin with the potential risk of price and supply fluctuations. With recent focus of Indonesia government that see the future of Indonesia civilization depend on the oceans and as the three biggest Tuna producing country, Ministry of Marine Affairs and Fisheries should ensure sustainability and competitiveness of Indonesian tuna. Therefore, there is a great need to develop a proper and effective strategy to manage potential risks in Indonesian Tuna supply chain. This paper is aimed at identifying and mapping potential Tuna supply chain risks and its interrelationships that would assist government in determining proper strategies to manage Indonesian Tuna. A framework for identifying Tuna supply chain risks is proposed. Generic risk structure of Supply Chain Risk Identification System is adopted and modified to match with particular object, which is Indonesian Tuna. The proposed model consists of hierarchical and causal structure that encompass potential risks of Tuna supply chain operations from fishing, trading, processing and distribution. The causal structure consist of risk events and its risk agents which is the cause of risk events. To ensure the root cause of risk events are identified properly, five why’s analysis is utilized to obtain risk agents. This proposed model also captures risk interrelationship between internal and external environment of Tuna supply chain. Preliminary result of this study identifies 15 risk events and 13 risk factors on fishing and trading operations and maps their interrelationships.

  4. Interpreting predictive maps of disease: highlighting the pitfalls of distribution models in epidemiology

    Directory of Open Access Journals (Sweden)

    Nicola A. Wardrop

    2014-11-01

    Full Text Available The application of spatial modelling to epidemiology has increased significantly over the past decade, delivering enhanced understanding of the environmental and climatic factors affecting disease distributions and providing spatially continuous representations of disease risk (predictive maps. These outputs provide significant information for disease control programmes, allowing spatial targeting and tailored interventions. However, several factors (e.g. sampling protocols or temporal disease spread can influence predictive mapping outputs. This paper proposes a conceptual framework which defines several scenarios and their potential impact on resulting predictive outputs, using simulated data to provide an exemplar. It is vital that researchers recognise these scenarios and their influence on predictive models and their outputs, as a failure to do so may lead to inaccurate interpretation of predictive maps. As long as these considerations are kept in mind, predictive mapping will continue to contribute significantly to epidemiological research and disease control planning.

  5. Risk-targeted maps for Romania

    Science.gov (United States)

    Vacareanu, Radu; Pavel, Florin; Craciun, Ionut; Coliba, Veronica; Arion, Cristian; Aldea, Alexandru; Neagu, Cristian

    2018-03-01

    Romania has one of the highest seismic hazard levels in Europe. The seismic hazard is due to a combination of local crustal seismic sources, situated mainly in the western part of the country and the Vrancea intermediate-depth seismic source, which can be found at the bend of the Carpathian Mountains. Recent seismic hazard studies have shown that there are consistent differences between the slopes of the seismic hazard curves for sites situated in the fore-arc and back-arc of the Carpathian Mountains. Consequently, in this study we extend this finding to the evaluation of the probability of collapse of buildings and finally to the development of uniform risk-targeted maps. The main advantage of uniform risk approach is that the target probability of collapse will be uniform throughout the country. Finally, the results obtained are discussed in the light of a recent study with the same focus performed at European level using the hazard data from SHARE project. The analyses performed in this study have pointed out to a dominant influence of the quantile of peak ground acceleration used for anchoring the fragility function. This parameter basically alters the shape of the risk-targeted maps shifting the areas which have higher collapse probabilities from eastern Romania to western Romania, as its exceedance probability increases. Consequently, a uniform procedure for deriving risk-targeted maps appears as more than necessary.

  6. Climate Prediction Center - Outlooks: Current UV Index Forecast Map

    Science.gov (United States)

    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

  7. Health risks maps. Modelling of air quality as a tool to map health risks

    International Nuclear Information System (INIS)

    Van Doorn, R.; Hegger, C.

    2000-01-01

    Environmental departments consider geographical maps with information on air quality as the final product of a complicated process of measuring, modelling and presentation. Municipal health departments consider such maps a useful starting point to solve the problem whether air pollution causes health risks for citizens. The answer to this question cannot be reduced to checking if threshold limit values are exceeded. Based on the results of measurements and modelling of concentrations of nitrogen dioxide in air, the health significance of air pollution caused by nitrogen dioxide is illuminated. A proposal is presented to map health risks of air pollution by using the results of measurements and modelling of air pollution. 7 refs

  8. Radon risk map of the city Brno

    International Nuclear Information System (INIS)

    Jansky, J.

    2000-01-01

    Data of radon risk mapping of the city Brno area from 1992 to 1999 were collected from databases of six private companies measuring radon risk there. The data sets are completed now. The first results are presented in this paper. In the city Brno area only low (385 measured sites) and medium (300) radon risk categories were found. The largest number of measured areas were situated in places with loess and loess loam (total quantity 344 sites, with medium radon risk category 158 sites), recent fluvial sediments (64, 32) and anthropogenous deposits (61, 23). High values of radon volume activity in soil gas were found predominantly in Quaternary sediments and in granodiorite, type Veverska Bityska, low values in leucotonalite and metabasalt. (author)

  9. Seismic risk map for Southeastern Brazil

    International Nuclear Information System (INIS)

    Mioto, J.A.

    1984-01-01

    During the last few years, some studies regarding seismic risk were prepared for three regions of Brazil. They were carried on account of two basic interests: first, toward the seismic history and recurrence of Brazilian seismic events; second, in a way as to provide seismic parameters for the design and construction of hydro and nuclear power plants. The first seismic risk map prepared for the southeastern region was elaborated in 1979 by 6he Universidade de Brasilia (UnB-Brasilia Seismological Station). In 1981 another seismic risk map was completed on the basis of seismotectonic studies carried out for the design and construction of the Nuclear power plants of Itaorna Beach (Angra dos Reis, Rio de Janeiro) by IPT (Mining and Applied Geology Division). In Brazil, until 1984, seismic studies concerning hydro and nuclear power plants and other civil construction of larger size did not take into account the seismic events from the point of view of probabilities of seismic recurrences. Such analysis in design is more important than the choice of a level of intensity or magnitude, or adoption of a seismicity level ased on deterministic methods. In this way, some considerations were made, concerning the use of seisms in Brazilian designs of hydro and nuclear power plants, as far as seismic analysis is concerned, recently altered over the current seismic risk panorama. (D.J.M.) [pt

  10. Smoky River coal flood risk mapping study

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2004-06-01

    The Canada-Alberta Flood Damage Reduction Program (FDRP) is designed to reduce flood damage by identifying areas susceptible to flooding and by encouraging application of suitable land use planning, zoning, and flood preparedness and proofing. The purpose of this study is to define flood risk and floodway limits along the Smoky River near the former Smoky River Coal (SRC) plant. Alberta Energy has been responsible for the site since the mine and plant closed in 2000. The study describes flooding history, available data, features of the river and valley, calculation of flood levels, and floodway determination, and includes flood risk maps. The HEC-RAS program is used for the calculations. The flood risk area was calculated using the 1:100 year return period flood as the hydrological event. 7 refs., 11 figs., 7 tabs., 3 apps.

  11. Detailed predictive mapping of acid sulfate soil occurrence using electromagnetic induction data

    DEFF Research Database (Denmark)

    Beucher, Amélie; Boman, A; Mattbäck, S

    impact through the resulting corrosion of concrete and steel infrastructures, or their poor geotechnical qualities. Therefore, mapping acid sulfate soil occurrence constitutes a key step to target the strategic areas for subsequent environmental risk management and mitigation. Conventional mapping (i...... obtained from a EM38 proximal sensor enabled the refined mapping of acid sulfate soils over a field (Huang et al. 2014). The present study aims at developing an efficient and reliable method for the detailed predictive mapping of acid sulfate soil occurrence in a field located in western Finland. Different...

  12. Tsunami risk mapping simulation for Malaysia

    Science.gov (United States)

    Teh, S.Y.; Koh, H. L.; Moh, Y.T.; De Angelis, D. L.; Jiang, J.

    2011-01-01

    The 26 December 2004 Andaman mega tsunami killed about a quarter of a million people worldwide. Since then several significant tsunamis have recurred in this region, including the most recent 25 October 2010 Mentawai tsunami. These tsunamis grimly remind us of the devastating destruction that a tsunami might inflict on the affected coastal communities. There is evidence that tsunamis of similar or higher magnitudes might occur again in the near future in this region. Of particular concern to Malaysia are tsunamigenic earthquakes occurring along the northern part of the Sunda Trench. Further, the Manila Trench in the South China Sea has been identified as another source of potential tsunamigenic earthquakes that might trigger large tsunamis. To protect coastal communities that might be affected by future tsunamis, an effective early warning system must be properly installed and maintained to provide adequate time for residents to be evacuated from risk zones. Affected communities must be prepared and educated in advance regarding tsunami risk zones, evacuation routes as well as an effective evacuation procedure that must be taken during a tsunami occurrence. For these purposes, tsunami risk zones must be identified and classified according to the levels of risk simulated. This paper presents an analysis of tsunami simulations for the South China Sea and the Andaman Sea for the purpose of developing a tsunami risk zone classification map for Malaysia based upon simulated maximum wave heights. ?? 2011 WIT Press.

  13. 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.

    Science.gov (United States)

    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

  14. Radon risk mapping of the Czech Republic on a scale 1:50000

    International Nuclear Information System (INIS)

    Barnet, I.; Miksova, J.; Tomas, R.; Karenova, J.

    2000-01-01

    A new type of radon risk maps on a scale 1:50000 was published in the Czech Republic. Maps are based on the vectorized contours of' geological units and rock types and field soil gas radon measurements from the radon database. Radon risk is expressed in four categories. More detailed topography enables to predict the radon risk from bedrock in the intravilans of villages and towns. (author)

  15. Tail Risk Premia and Return Predictability

    DEFF Research Database (Denmark)

    Bollerslev, Tim; Todorov, Viktor; Xu, Lai

    The variance risk premium, defined as the difference between actual and risk-neutralized expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may be attribu......The variance risk premium, defined as the difference between actual and risk-neutralized expectations of the forward aggregate market variation, helps predict future market returns. Relying on new essentially model-free estimation procedure, we show that much of this predictability may......-varying economic uncertainty and changes in risk aversion, or market fears, respectively....

  16. Mapping and predicting mortality from systemic sclerosis

    DEFF Research Database (Denmark)

    Elhai, Muriel; Meune, Christophe; Boubaya, Marouane

    2017-01-01

    OBJECTIVES: To determine the causes of death and risk factors in systemic sclerosis (SSc). METHODS: Between 2000 and 2011, we examined the death certificates of all French patients with SSc to determine causes of death. Then we examined causes of death and developed a score associated with all-ca....... With the emergence of new therapies, these important observations should help caregivers plan and refine the monitoring and management to prolong these patients' survival....

  17. Can Selforganizing Maps Accurately Predict Photometric Redshifts?

    Science.gov (United States)

    Way, Michael J.; Klose, Christian

    2012-01-01

    We present an unsupervised machine-learning approach that can be employed for estimating photometric redshifts. The proposed method is based on a vector quantization called the self-organizing-map (SOM) approach. A variety of photometrically derived input values were utilized from the Sloan Digital Sky Survey's main galaxy sample, luminous red galaxy, and quasar samples, along with the PHAT0 data set from the Photo-z Accuracy Testing project. Regression results obtained with this new approach were evaluated in terms of root-mean-square error (RMSE) to estimate the accuracy of the photometric redshift estimates. The results demonstrate competitive RMSE and outlier percentages when compared with several other popular approaches, such as artificial neural networks and Gaussian process regression. SOM RMSE results (using delta(z) = z(sub phot) - z(sub spec)) are 0.023 for the main galaxy sample, 0.027 for the luminous red galaxy sample, 0.418 for quasars, and 0.022 for PHAT0 synthetic data. The results demonstrate that there are nonunique solutions for estimating SOM RMSEs. Further research is needed in order to find more robust estimation techniques using SOMs, but the results herein are a positive indication of their capabilities when compared with other well-known methods

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

    Science.gov (United States)

    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

  19. National Fire Risk Map for Continental USA: Creation and Validation

    International Nuclear Information System (INIS)

    Zhang, Q; Wollersheim, M; Griffiths, S; Maddox, I

    2014-01-01

    A nation-wide fire risk map for the continental USA has been created based on a hybrid fire risk model, incorporating a combination of static risk indicators which change very slowly over time, and dynamic risk indicators that may vary significantly from week-to-week. Static risk indicators include: terrain elevation, terrain slope, terrain aspect, and distance from roads and settlements. Each of the static risk indicators are derived from Intermap's high-accuracy NEXTMap ® USA database. The dynamic risk indicators are derived from satellite-based multi-spectral imagery and provide a snapshot of the fuel-moisture conditions during fire seasons. Each of these risk indicators are combined to produce a map provided at 5m posting and normalized to the range of 0 (very low risk) and 255 (very high risk). The map has been validated in two selected areas using historical fire information

  20. Predictive mapping of the acidifying potential for acid sulfate soils

    DEFF Research Database (Denmark)

    Boman, A; Beucher, Amélie; Mattbäck, S

    Developing methods for the predictive mapping of the potential environmental impact from acid sulfate soils is important because recent studies (e.g. Mattbäck et al., under revision) have shown that the environmental hazards (e.g. leaching of acidity) related to acid sulfate soils vary depending...... on their texture (clay, silt, sand etc.). Moreover, acidity correlates, not only with the sulfur content, but also with the electrical conductivity (EC) measured after incubation. Electromagnetic induction (EMI) data collected from an EM38 proximal sensor also enabled the detailed mapping of acid sulfate soils...... over a field (Huang et al., 2014).This study aims at assessing the use of EMI data for the predictive mapping of the acidifying potential in an acid sulfate soil area in western Finland. Different supervised classification modelling techniques, such as Artificial Neural Networks (Beucher et al., 2015...

  1. Climate Prediction Center - Monitoring and Data - Regional Climate Maps:

    Science.gov (United States)

    National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site government Web resources and services. HOME > Monitoring and Data > U.S. Climate Data > ; Precipitation & Temperature > Regional Climate Maps: USA Menu Weekly 1-Month 3-Month 12-Month Weekly

  2. Lipoprotein metabolism indicators improve cardiovascular risk prediction.

    Directory of Open Access Journals (Sweden)

    Daniël B van Schalkwijk

    Full Text Available BACKGROUND: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to investigate whether lipoprotein metabolism indicators can improve cardiovascular risk prediction and therapy management. METHODS AND RESULTS: We calculated lipoprotein metabolism indicators for 1981 subjects (145 cases, 1836 controls from the Framingham Heart Study offspring cohort in which NMR lipoprotein profiles were measured. We applied a statistical learning algorithm using a support vector machine to select conventional risk factors and lipoprotein metabolism indicators that contributed to predicting risk for general cardiovascular disease. Risk prediction was quantified by the change in the Area-Under-the-ROC-Curve (ΔAUC and by risk reclassification (Net Reclassification Improvement (NRI and Integrated Discrimination Improvement (IDI. Two VLDL lipoprotein metabolism indicators (VLDLE and VLDLH improved cardiovascular risk prediction. We added these indicators to a multivariate model with the best performing conventional risk markers. Our method significantly improved both CVD prediction and risk reclassification. CONCLUSIONS: Two calculated VLDL metabolism indicators significantly improved cardiovascular risk prediction. These indicators may help to reduce prescription of unnecessary cholesterol-lowering medication, reducing costs and possible side-effects. For clinical application, further validation is required.

  3. Predictive spatial modelling for mapping soil salinity at continental scale

    Science.gov (United States)

    Bui, Elisabeth; Wilford, John; de Caritat, Patrice

    2017-04-01

    Soil salinity is a serious limitation to agriculture and one of the main causes of land degradation. Soil is considered saline if its electrical conductivity (EC) is > 4 dS/m. Maps of saline soil distribution are essential for appropriate land development. Previous attempts to map soil salinity over extensive areas have relied on satellite imagery, aerial electromagnetic (EM) and/or proximally sensed EM data; other environmental (climate, topographic, geologic or soil) datasets are generally not used. Having successfully modelled and mapped calcium carbonate distribution over the 0-80 cm depth in Australian soils using machine learning with point samples from the National Geochemical Survey of Australia (NGSA), we took a similar approach to map soil salinity at 90-m resolution over the continent. The input data were the EC1:5 measurements on the randomly sampled trees were built using the training data. The results were good with an average internal correlation (r) of 0.88 between predicted and measured logEC1:5 (training data), an average external correlation of 0.48 (test subset), and a Lin's concordance correlation coefficient (which evaluates the 1:1 fit) of 0.61. Therefore, the rules derived were mapped and the mean prediction for each 90-m pixel was used for the final logEC1:5 map. This is the most detailed picture of soil salinity over Australia since the 2001 National Land and Water Resources Audit and is generally consistent with it. Our map will be useful as a baseline salinity map circa 2008, when the NGSA samples were collected, for future State of the Environment reports.

  4. Prostate Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  5. Colorectal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  6. Esophageal Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Bladder Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Lung Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Breast Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Pancreatic Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Ovarian Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Developmental Dyslexia: Predicting Individual Risk

    Science.gov (United States)

    Thompson, Paul A.; Hulme, Charles; Nash, Hannah M.; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J.

    2015-01-01

    Background: Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods: The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6…

  13. Liver Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  14. Testicular Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Cervical Cancer Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. High resolution fire risk mapping in Italy

    Science.gov (United States)

    Fiorucci, Paolo; Biondi, Guido; Campo, Lorenzo; D'Andrea, Mirko

    2014-05-01

    extinguishing actions, leaving more resources to improve safety in areas at risk. With the availability of fire perimeters mapped over a period spanning from 5 to 10 years, depending by the region, a procedure was defined in order to assess areas at risk with high spatial resolution (900 m2) based on objective criteria by observing past fire events. The availability of fire perimeters combined with a detailed knowledge of topography and land cover allowed to understand which are the main features involved in forest fire occurrences and their behaviour. The seasonality of the fire regime was also considered, partitioning the analysis in two macro season (November- April and May- October). In addition, the total precipitation obtained from the interpolation of 30 years-long time series from 460 raingauges and the average air temperature obtained downscaling 30 years ERA-INTERIM data series were considered. About 48000 fire perimeters which burnt about 5500 km2 were considered in the analysis. The analysis has been carried out at 30 m spatial resolution. Some important considerations relating to climate and the territorial features that characterize the fire regime at national level contribute to better understand the forest fire phenomena. These results allow to define new strategies for forest fire prevention and management extensible to other geographical areas.

  17. Cardiovascular risk prediction in the Netherlands

    NARCIS (Netherlands)

    Dis, van S.J.

    2011-01-01

    Background: In clinical practice, Systematic COronary Risk Evaluation (SCORE) risk prediction functions and charts are used to identify persons at high risk for cardiovascular diseases (CVD), who are considered eligible for drug treatment of elevated blood pressure and serum cholesterol. These

  18. [Risk maps. The concept and the methodology for their development].

    Science.gov (United States)

    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.

  19. Prediction of eyespot infection risks

    Directory of Open Access Journals (Sweden)

    M. Váòová

    2012-12-01

    Full Text Available The objective of the study was to design a prediction model for eyespot (Tapesia yallundae infection based on climatic factors (temperature, precipitation, air humidity. Data from experiment years 1994-2002 were used to study correlations between the eyespot infection index and individual weather characteristics. The model of prediction was constructed using multiple regression when a separate parameter is assigned to each factor, i.e. the frequency of days with optimum temperatures, humidity, and precipitation. The correlation between relative air humidity and precipitation and the infection index is significant.

  20. CNNcon: improved protein contact maps prediction using cascaded neural networks.

    Directory of Open Access Journals (Sweden)

    Wang Ding

    Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective

  1. The necessity of flood risk maps on Timis River

    International Nuclear Information System (INIS)

    Aldescu, Geogr Catalin

    2008-01-01

    The paper aims to clarify the necessity of risk reduction in flood prone areas along the Timis River. Different methods to reduce risk in flood prone areas are analyzed as well. According to the EU Flood Directive it is mandatory for the European countries to develop flood maps and flood risk maps. The maps help to assess the vulnerable zones in the floodable (i.e. flood prone) areas. Many European countries have produced maps which identify areas prone to flooding events for specific known return periods. In Romania the flood risk maps have not been yet produced, but the process has been started to be implemented at the national and regional level, therefore the first results will be soon available. Banat Hydrographical Area was affected by severe floods on Timis River in 2000, 2005 and 2006. The 2005 flood was the most devastating one with large economic losses. As a result of these catastrophes the need for generating flood risk maps along the Timis. River was clearly stated. The water management experts can use these maps in order to identify the 'hot spots' in Timis catchment, give the people a better understanding of flood risk issues and help reducing flood risk more efficient in the identified vulnerable areas.

  2. T2 map signal variation predicts symptomatic osteoarthritis progression: data from the Osteoarthritis Initiative

    Energy Technology Data Exchange (ETDEWEB)

    Zhong, Haoti; Miller, David J. [The Pennsylvania State University, Department of Electrical Engineering, University Park, PA (United States); Urish, Kenneth L. [Magee Womens Hospital of the University of Pittsburgh Medical Center, The Bone and Joint Center, Pittsburgh, PA (United States); University of Pittsburgh School of Medicine, Department of Orthopaedic Surgery, Pittsburgh, PA (United States)

    2016-07-15

    The aim of this work is to use quantitative magnetic resonance imaging (MRI) to identify patients at risk for symptomatic osteoarthritis (OA) progression. We hypothesized that classification of signal variation on T2 maps might predict symptomatic OA progression. Patients were selected from the Osteoarthritis Initiative (OAI), a prospective cohort. Two groups were identified: a symptomatic OA progression group and a control group. At baseline, both groups were asymptomatic (Western Ontario and McMaster Universities Arthritis [WOMAC] pain score total <10) with no radiographic evidence of OA (Kellgren-Lawrence [KL] score ≤ 1). The OA progression group (n = 103) had a change in total WOMAC score greater than 10 by the 3-year follow-up. The control group (n = 79) remained asymptomatic, with a change in total WOMAC score less than 10 at the 3-year follow-up. A classifier was designed to predict OA progression in an independent population based on T2 map cartilage signal variation. The classifier was designed using a nearest neighbor classification based on a Gaussian Mixture Model log-likelihood fit of T2 map cartilage voxel intensities. The use of T2 map signal variation to predict symptomatic OA progression in asymptomatic individuals achieved a specificity of 89.3 %, a sensitivity of 77.2 %, and an overall accuracy rate of 84.2 %. T2 map signal variation can predict symptomatic knee OA progression in asymptomatic individuals, serving as a possible early OA imaging biomarker. (orig.)

  3. Forest fire risk zonation mapping using remote sensing technology

    Science.gov (United States)

    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.

  4. RISK COMMUNICATION IN ACTION: THE TOOLS OF MESSAGE MAPPING

    Science.gov (United States)

    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...

  5. Risk mapping in the customs field

    Directory of Open Access Journals (Sweden)

    Emilia Iordache

    2008-12-01

    Full Text Available One of customs main tasks is to assess risks in the flow of goods. Risks are those factors that could influence Customs objectives. In pursuing those objectives it is important to have good knowledge of the risks that we face and the impact they might have on the objectives. The Customs administrations in the Member Stateshave opted to base the control on economic operators on the basis of risk management.The purpose of using risk management is to aim Customs’ control activities on risks rather then on random selected aspects or declarations.

  6. Cognitive structure of occupational risks represented by a perceptual map.

    Science.gov (United States)

    Cardoso-Junior, M M; Scarpel, R A

    2012-01-01

    The main focus of risk management is technical and rational analysis about the operational risks and by those imposed by the occupational environment. In this work one seeks to contribute to the risk perception study and to better comprehend how a group of occupational safety students assesses a set of activities and environmental agents. In this way it was used theory sustained by psychometric paradigm and multivariate analysis tools, mainly multidimensional scaling, generalized Procrustes analysis and facets theory, in order to construct the perceptual map of occupational risks. The results obtained showed that the essential characteristics of risks, which were initially splited in 4 facets were detected and maintained in the perceptual map. It was not possible to reveal the cognitive structure of the group, because the variability of the students was too high. Differences among the risks analyzed could not be detected as well in the perceptual map of the group.

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

    Science.gov (United States)

    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

  8. Predicting and mapping soil available water capacity in Korea

    Directory of Open Access Journals (Sweden)

    Suk Young Hong

    2013-04-01

    Full Text Available The knowledge on the spatial distribution of soil available water capacity at a regional or national extent is essential, as soil water capacity is a component of the water and energy balances in the terrestrial ecosystem. It controls the evapotranspiration rate, and has a major impact on climate. This paper demonstrates a protocol for mapping soil available water capacity in South Korea at a fine scale using data available from surveys. The procedures combined digital soil mapping technology with the available soil map of 1:25,000. We used the modal profile data from the Taxonomical Classification of Korean Soils. The data consist of profile description along with physical and chemical analysis for the modal profiles of the 380 soil series. However not all soil samples have measured bulk density and water content at −10 and −1500 kPa. Thus they need to be predicted using pedotransfer functions. Furthermore, water content at −10 kPa was measured using ground samples. Thus a correction factor is derived to take into account the effect of bulk density. Results showed that Andisols has the highest mean water storage capacity, followed by Entisols and Inceptisols which have loamy texture. The lowest water retention is Entisols which are dominated by sandy materials. Profile available water capacity to a depth of 1 m was calculated and mapped for Korea. The western part of the country shows higher available water capacity than the eastern part which is mountainous and has shallower soils. The highest water storage capacity soils are the Ultisols and Alfisols (mean of 206 and 205 mm, respectively. Validation of the maps showed promising results. The map produced can be used as an indication of soil physical quality of Korean soils.

  9. Predicting and mapping soil available water capacity in Korea.

    Science.gov (United States)

    Hong, Suk Young; Minasny, Budiman; Han, Kyung Hwa; Kim, Yihyun; Lee, Kyungdo

    2013-01-01

    The knowledge on the spatial distribution of soil available water capacity at a regional or national extent is essential, as soil water capacity is a component of the water and energy balances in the terrestrial ecosystem. It controls the evapotranspiration rate, and has a major impact on climate. This paper demonstrates a protocol for mapping soil available water capacity in South Korea at a fine scale using data available from surveys. The procedures combined digital soil mapping technology with the available soil map of 1:25,000. We used the modal profile data from the Taxonomical Classification of Korean Soils. The data consist of profile description along with physical and chemical analysis for the modal profiles of the 380 soil series. However not all soil samples have measured bulk density and water content at -10 and -1500 kPa. Thus they need to be predicted using pedotransfer functions. Furthermore, water content at -10 kPa was measured using ground samples. Thus a correction factor is derived to take into account the effect of bulk density. Results showed that Andisols has the highest mean water storage capacity, followed by Entisols and Inceptisols which have loamy texture. The lowest water retention is Entisols which are dominated by sandy materials. Profile available water capacity to a depth of 1 m was calculated and mapped for Korea. The western part of the country shows higher available water capacity than the eastern part which is mountainous and has shallower soils. The highest water storage capacity soils are the Ultisols and Alfisols (mean of 206 and 205 mm, respectively). Validation of the maps showed promising results. The map produced can be used as an indication of soil physical quality of Korean soils.

  10. MAPPING FRAUD RISK IN FINANCIAL AUDIT – SALES OPERATIONS

    Directory of Open Access Journals (Sweden)

    Ioan RADU

    2013-10-01

    Full Text Available This paper provides an effective tool «fraud risk map« for organizations and auditors in mathematic assessment procedures, with real, tangible results, regarding the organization’s fraud risk exposure. Overcoming the fade and purely theoretical risk assessment methods («based on professional judgement – risk: high, medium, low«, the paper presents risk indicators as warning signals and quantitative and qualitative methods for risk assessment. Illustrating scalability methods and developping a practical tools for material misstatement of the economic entity financial statements, the case study presented, on sales operations, brigs added value to auditors work in the fraud risk assessment procedures.

  11. Mapping of interconnection of climate risks

    Science.gov (United States)

    Yokohata, Tokuta; Tanaka, Katsumasa; Nishina, Kazuya; Takanashi, Kiyoshi; Emori, Seita; Kiguchi, Masashi; Iseri, Yoshihiko; Honda, Yasushi; Okada, Masashi; Masaki, Yoshimitsu; Yamamoto, Akitomo; Shigemitsu, Masahito; Yoshimori, Masakazu; Sueyoshi, Tetsuo; Iwase, Kenta; Hanasaki, Naota; Ito, Akihiko; Sakurai, Gen; Iizumi, Toshichika; Oki, Taikan

    2015-04-01

    Anthropogenic climate change possibly causes various impacts on human society and ecosystem. Here, we call possible damages or benefits caused by the future climate change as "climate risks". Many climate risks are closely interconnected with each other by direct cause-effect relationship. In this study, the major climate risks are comprehensively summarized based on the survey of studies in the literature using IPCC AR5 etc, and their cause-effect relationship are visualized by a "network diagram". This research is conducted by the collaboration between the experts of various fields, such as water, energy, agriculture, health, society, and eco-system under the project called ICA-RUS (Integrated Climate Assessment - Risks, Uncertainties and Society). First, the climate risks are classified into 9 categories (water, energy, food, health, disaster, industry, society, ecosystem, and tipping elements). Second, researchers of these fields in our project survey the research articles, and pick up items of climate risks, and possible cause-effect relationship between the risk items. A long list of the climate risks is summarized into ~130, and that of possible cause-effect relationship between the risk items is summarized into ~300, because the network diagram would be illegible if the number of the risk items and cause-effect relationship is too large. Here, we only consider the risks that could occur if climate mitigation policies are not conducted. Finally, the chain of climate risks is visualized by creating a "network diagram" based on a network graph theory (Fruchtman & Reingold algorithm). Through the analysis of network diagram, we find that climate risks at various sectors are closely related. For example, the decrease in the precipitation under the global climate change possibly causes the decrease in river runoff and the decrease in soil moisture, which causes the changes in crop production. The changes in crop production can have an impact on society by

  12. Predicting impacts of climate change on Fasciola hepatica risk.

    Science.gov (United States)

    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.

  13. Predicting impacts of climate change on Fasciola hepatica risk.

    Directory of Open Access Journals (Sweden)

    Naomi J Fox

    2011-01-01

    Full Text Available 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.

  14. Macromolecular target prediction by self-organizing feature maps.

    Science.gov (United States)

    Schneider, Gisbert; Schneider, Petra

    2017-03-01

    Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the 'de-orphaning' of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes. Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique. Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.

  15. Malaria in Africa: vector species' niche models and relative risk maps.

    Directory of Open Access Journals (Sweden)

    Alexander Moffett

    2007-09-01

    Full Text Available A central theoretical goal of epidemiology is the construction of spatial models of disease prevalence and risk, including maps for the potential spread of infectious disease. We provide three continent-wide maps representing the relative risk of malaria in Africa based on ecological niche models of vector species and risk analysis at a spatial resolution of 1 arc-minute (9 185 275 cells of approximately 4 sq km. Using a maximum entropy method we construct niche models for 10 malaria vector species based on species occurrence records since 1980, 19 climatic variables, altitude, and land cover data (in 14 classes. For seven vectors (Anopheles coustani, A. funestus, A. melas, A. merus, A. moucheti, A. nili, and A. paludis these are the first published niche models. We predict that Central Africa has poor habitat for both A. arabiensis and A. gambiae, and that A. quadriannulatus and A. arabiensis have restricted habitats in Southern Africa as claimed by field experts in criticism of previous models. The results of the niche models are incorporated into three relative risk models which assume different ecological interactions between vector species. The "additive" model assumes no interaction; the "minimax" model assumes maximum relative risk due to any vector in a cell; and the "competitive exclusion" model assumes the relative risk that arises from the most suitable vector for a cell. All models include variable anthrophilicity of vectors and spatial variation in human population density. Relative risk maps are produced from these models. All models predict that human population density is the critical factor determining malaria risk. Our method of constructing relative risk maps is equally general. We discuss the limits of the relative risk maps reported here, and the additional data that are required for their improvement. The protocol developed here can be used for any other vector-borne disease.

  16. CpG island mapping by epigenome prediction.

    Directory of Open Access Journals (Sweden)

    Christoph Bock

    2007-06-01

    Full Text Available CpG islands were originally identified by epigenetic and functional properties, namely, absence of DNA methylation and frequent promoter association. However, this concept was quickly replaced by simple DNA sequence criteria, which allowed for genome-wide annotation of CpG islands in the absence of large-scale epigenetic datasets. Although widely used, the current CpG island criteria incur significant disadvantages: (1 reliance on arbitrary threshold parameters that bear little biological justification, (2 failure to account for widespread heterogeneity among CpG islands, and (3 apparent lack of specificity when applied to the human genome. This study is driven by the idea that a quantitative score of "CpG island strength" that incorporates epigenetic and functional aspects can help resolve these issues. We construct an epigenome prediction pipeline that links the DNA sequence of CpG islands to their epigenetic states, including DNA methylation, histone modifications, and chromatin accessibility. By training support vector machines on epigenetic data for CpG islands on human Chromosomes 21 and 22, we identify informative DNA attributes that correlate with open versus compact chromatin structures. These DNA attributes are used to predict the epigenetic states of all CpG islands genome-wide. Combining predictions for multiple epigenetic features, we estimate the inherent CpG island strength for each CpG island in the human genome, i.e., its inherent tendency to exhibit an open and transcriptionally competent chromatin structure. We extensively validate our results on independent datasets, showing that the CpG island strength predictions are applicable and informative across different tissues and cell types, and we derive improved maps of predicted "bona fide" CpG islands. The mapping of CpG islands by epigenome prediction is conceptually superior to identifying CpG islands by widely used sequence criteria since it links CpG island detection to

  17. CpG island mapping by epigenome prediction.

    Science.gov (United States)

    Bock, Christoph; Walter, Jörn; Paulsen, Martina; Lengauer, Thomas

    2007-06-01

    CpG islands were originally identified by epigenetic and functional properties, namely, absence of DNA methylation and frequent promoter association. However, this concept was quickly replaced by simple DNA sequence criteria, which allowed for genome-wide annotation of CpG islands in the absence of large-scale epigenetic datasets. Although widely used, the current CpG island criteria incur significant disadvantages: (1) reliance on arbitrary threshold parameters that bear little biological justification, (2) failure to account for widespread heterogeneity among CpG islands, and (3) apparent lack of specificity when applied to the human genome. This study is driven by the idea that a quantitative score of "CpG island strength" that incorporates epigenetic and functional aspects can help resolve these issues. We construct an epigenome prediction pipeline that links the DNA sequence of CpG islands to their epigenetic states, including DNA methylation, histone modifications, and chromatin accessibility. By training support vector machines on epigenetic data for CpG islands on human Chromosomes 21 and 22, we identify informative DNA attributes that correlate with open versus compact chromatin structures. These DNA attributes are used to predict the epigenetic states of all CpG islands genome-wide. Combining predictions for multiple epigenetic features, we estimate the inherent CpG island strength for each CpG island in the human genome, i.e., its inherent tendency to exhibit an open and transcriptionally competent chromatin structure. We extensively validate our results on independent datasets, showing that the CpG island strength predictions are applicable and informative across different tissues and cell types, and we derive improved maps of predicted "bona fide" CpG islands. The mapping of CpG islands by epigenome prediction is conceptually superior to identifying CpG islands by widely used sequence criteria since it links CpG island detection to their characteristic

  18. Predicting Maps of Green Growth in Košice

    Science.gov (United States)

    Poorova, Zuzana; Vranayova, Zuzana

    2017-10-01

    The paper deals with the changing of the traditional roofs in the city of Košice into green roofs. Possible areas of city housing estates, after taking into account the conditions of each of them (types of buildings, statics of buildings), are listed in the paper. The research is picturing the prediction maps of Košice city from 2017 to 2042 in 5-years interval. The paper is a segment of a dissertation work focusing on changing traditional roofs into green roofs with the aim to retain water, calculate the amount of retained water and show possibilities how to use this water.

  19. 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)

    Science.gov (United States)

    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

  20. Mapping the receptivity of malaria risk to plan the future of control in Somalia.

    Science.gov (United States)

    Noor, Abdisalan Mohamed; Alegana, Victor Adagi; Patil, Anand Prabhakar; Moloney, Grainne; Borle, Mohammed; Yusuf, Fahmi; Amran, Jamal; Snow, Robert William

    2012-01-01

    To measure the receptive risks of malaria in Somalia and compare decisions on intervention scale-up based on this map and the more widely used contemporary risk maps. Cross-sectional community Plasmodium falciparum parasite rate (PfPR) data for the period 2007-2010 corrected to a standard age range of 2 to contemporary (2010) mean PfPR(2-10) and the maximum annual mean PfPR(2-10) (receptive) from the highest predicted PfPR(2-10) value over the study period as an estimate of receptivity. Randomly sampled communities in Somalia. Randomly sampled individuals of all ages. Cartographic descriptions of malaria receptivity and contemporary risks in Somalia at the district level. The contemporary annual PfPR(2-10) map estimated that all districts (n=74) and population (n=8.4 million) in Somalia were under hypoendemic transmission (≤10% PfPR(2-10)). Of these, 23% of the districts, home to 13% of the population, were under transmission of 10%-50% PfPR(2-10)) and the rest as hypoendemic. Compared with maps of receptive risks, contemporary maps of transmission mask disparities of malaria risk necessary to prioritise and sustain future control. As malaria risk declines across Africa, efforts must be invested in measuring receptivity for efficient control planning.

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

    International Nuclear Information System (INIS)

    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

    2015-01-01

    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. - Highlights: • Predictions of trace metal concentration use geographically weighted regression • Human health risk

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

    Energy Technology Data Exchange (ETDEWEB)

    Bhowmik, Avit Kumar [Institute for Environmental Sciences, University of Koblenz-Landau, Fortstrasse 7, D-76829 Landau in der Pfalz (Germany); Alamdar, Ambreen [Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021 (China); Katsoyiannis, Ioannis [Aristotle University of Thessaloniki, Department of Chemistry, Division of Chemical Technology, Box 116, Thessaloniki 54124 (Greece); Shen, Heqing [Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021 (China); Ali, Nadeem [Department of Environmental Sciences, FBAS, International Islamic University, Islamabad (Pakistan); Ali, Syeda Maria [Center of Excellence in Environmental Studies, King Abdulaziz University, Jeddah (Saudi Arabia); Bokhari, Habib [Public Health and Environment Division, Department of Biosciences, COMSATS Institute of Information Technology, Islamabad (Pakistan); Schäfer, Ralf B. [Institute for Environmental Sciences, University of Koblenz-Landau, Fortstrasse 7, D-76829 Landau in der Pfalz (Germany); Eqani, Syed Ali Musstjab Akber Shah, E-mail: ali_ebl2@yahoo.com [Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021 (China); Public Health and Environment Division, Department of Biosciences, COMSATS Institute of Information Technology, Islamabad (Pakistan)

    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. - Highlights: • Predictions of trace metal concentration use geographically weighted regression • Human health risk

  3. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  4. Artificial Intelligence Techniques for Predicting and Mapping Daily Pan Evaporation

    Science.gov (United States)

    Arunkumar, R.; Jothiprakash, V.; Sharma, Kirty

    2017-09-01

    In this study, Artificial Intelligence techniques such as Artificial Neural Network (ANN), Model Tree (MT) and Genetic Programming (GP) are used to develop daily pan evaporation time-series (TS) prediction and cause-effect (CE) mapping models. Ten years of observed daily meteorological data such as maximum temperature, minimum temperature, relative humidity, sunshine hours, dew point temperature and pan evaporation are used for developing the models. For each technique, several models are developed by changing the number of inputs and other model parameters. The performance of each model is evaluated using standard statistical measures such as Mean Square Error, Mean Absolute Error, Normalized Mean Square Error and correlation coefficient (R). The results showed that daily TS-GP (4) model predicted better with a correlation coefficient of 0.959 than other TS models. Among various CE models, CE-ANN (6-10-1) resulted better than MT and GP models with a correlation coefficient of 0.881. Because of the complex non-linear inter-relationship among various meteorological variables, CE mapping models could not achieve the performance of TS models. From this study, it was found that GP performs better for recognizing single pattern (time series modelling), whereas ANN is better for modelling multiple patterns (cause-effect modelling) in the data.

  5. Rice crop risk map in Babahoyo canton (Ecuador)

    Science.gov (United States)

    Valverde Arias, Omar; Tarquis, Ana; Garrido, Alberto

    2016-04-01

    It is widely known that extreme climatic phenomena occur with more intensity and frequency. This fact has put more pressure over farming, making agricultural and livestock production riskier. In order to reduce hazards and economic loses that could jeopardize farmer's incomes and even its business continuity, it is very important to implement agriculture risk management plans by governments and institutions. One of the main strategies is transfer risk by agriculture insurance. Agriculture insurance based in indexes has a significant growth in the last decade. And consist in a comparison between measured index values with a defined threshold that triggers damage losses. However, based index insurance could not be based on an isolated measurement. It is necessary to be integrated in a complete monitoring system that uses many sources of information and tools. For example, index influence areas, crop production risk maps, crop yields, claim statistics, and so on. Crop production risk is related with yield variation of crops and livestock, due to weather, pests, diseases, and other factors that affect both the quantity and quality of commodities produced. This is the risk which farmers invest more time managing, and it is completely under their control. The aim of this study is generate a crop risk map of rice that can provide risk manager important information about the status of crop facing production risks. Then, based on this information, it will be possible to make best decisions to deal with production risk. The rice crop risk map was generated qualifying a 1:25000 scale soil and climatic map of Babahoyo canton, which is located in coast region of Ecuador, where rice is one of the main crops. The methodology to obtain crop risk map starts by establishing rice crop requirements and indentifying the risks associated with this crop. A second step is to evaluate soil and climatic conditions of the study area related to optimal crop requirements. Based on it, we can

  6. Subclinical organ damage and cardiovascular risk prediction

    DEFF Research Database (Denmark)

    Sehestedt, Thomas; Olsen, Michael H

    2010-01-01

    Traditional cardiovascular risk factors have poor prognostic value for individuals and screening for subclinical organ damage has been recommended in hypertension in recent guidelines. The aim of this review was to investigate the clinical impact of the additive prognostic information provided...... by measuring subclinical organ damage. We have (i) reviewed recent studies linking markers of subclinical organ damage in the heart, blood vessels and kidney to cardiovascular risk; (ii) discussed the evidence for improvement in cardiovascular risk prediction using markers of subclinical organ damage; (iii...

  7. Cognitive mapping tools: review and risk management needs.

    Science.gov (United States)

    Wood, Matthew D; Bostrom, Ann; Bridges, Todd; Linkov, Igor

    2012-08-01

    Risk managers are increasingly interested in incorporating stakeholder beliefs and other human factors into the planning process. Effective risk assessment and management requires understanding perceptions and beliefs of involved stakeholders, and how these beliefs give rise to actions that influence risk management decisions. Formal analyses of risk manager and stakeholder cognitions represent an important first step. Techniques for diagramming stakeholder mental models provide one tool for risk managers to better understand stakeholder beliefs and perceptions concerning risk, and to leverage this new understanding in developing risk management strategies. This article reviews three methodologies for assessing and diagramming stakeholder mental models--decision-analysis-based mental modeling, concept mapping, and semantic web analysis--and assesses them with regard to their ability to address risk manager needs. © 2012 Society for Risk Analysis.

  8. Combining disparate data sources for improved poverty prediction and mapping.

    Science.gov (United States)

    Pokhriyal, Neeti; Jacques, Damien Christophe

    2017-11-14

    More than 330 million people are still living in extreme poverty in Africa. Timely, accurate, and spatially fine-grained baseline data are essential to determining policy in favor of reducing poverty. The potential of "Big Data" to estimate socioeconomic factors in Africa has been proven. However, most current studies are limited to using a single data source. We propose a computational framework to accurately predict the Global Multidimensional Poverty Index (MPI) at a finest spatial granularity and coverage of 552 communes in Senegal using environmental data (related to food security, economic activity, and accessibility to facilities) and call data records (capturing individualistic, spatial, and temporal aspects of people). Our framework is based on Gaussian Process regression, a Bayesian learning technique, providing uncertainty associated with predictions. We perform model selection using elastic net regularization to prevent overfitting. Our results empirically prove the superior accuracy when using disparate data (Pearson correlation of 0.91). Our approach is used to accurately predict important dimensions of poverty: health, education, and standard of living (Pearson correlation of 0.84-0.86). All predictions are validated using deprivations calculated from census. Our approach can be used to generate poverty maps frequently, and its diagnostic nature is, likely, to assist policy makers in designing better interventions for poverty eradication. Copyright © 2017 the Author(s). Published by PNAS.

  9. Increasing resilience through participative flood risk map design

    Science.gov (United States)

    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

  10. Carbon emissions risk map from deforestation in the tropical Amazon

    Science.gov (United States)

    Ometto, J.; Soler, L. S.; Assis, T. D.; Oliveira, P. V.; Aguiar, A. P.

    2011-12-01

    Assis, Pedro Valle This work aims to estimate the carbon emissions from tropical deforestation in the Brazilian Amazon associated to the risk assessment of future land use change. The emissions are estimated by incorporating temporal deforestation dynamics, accounting for the biophysical and socioeconomic heterogeneity in the region, as well secondary forest growth dynamic in abandoned areas. The land cover change model that supported the risk assessment of deforestation, was run based on linear regressions. This method takes into account spatial heterogeneity of deforestation as the spatial variables adopted to fit the final regression model comprise: environmental aspects, economic attractiveness, accessibility and land tenure structure. After fitting a suitable regression models for each land cover category, the potential of each cell to be deforested (25x25km and 5x5 km of resolution) in the near future was used to calculate the risk assessment of land cover change. The carbon emissions model combines high-resolution new forest clear-cut mapping and four alternative sources of spatial information on biomass distribution for different vegetation types. The risk assessment map of CO2 emissions, was obtained by crossing the simulation results of the historical land cover changes to a map of aboveground biomass contained in the remaining forest. This final map represents the risk of CO2 emissions at 25x25km and 5x5 km until 2020, under a scenario of carbon emission reduction target.

  11. Mapping and predictive variations of soil bacterial richness across France.

    Directory of Open Access Journals (Sweden)

    Sébastien Terrat

    Full Text Available Although numerous studies have demonstrated the key role of bacterial diversity in soil functions and ecosystem services, little is known about the variations and determinants of such diversity on a nationwide scale. The overall objectives of this study were i to describe the bacterial taxonomic richness variations across France, ii to identify the ecological processes (i.e. selection by the environment and dispersal limitation influencing this distribution, and iii to develop a statistical predictive model of soil bacterial richness. We used the French Soil Quality Monitoring Network (RMQS, which covers all of France with 2,173 sites. The soil bacterial richness (i.e. OTU number was determined by pyrosequencing 16S rRNA genes and related to the soil characteristics, climatic conditions, geomorphology, land use and space. Mapping of bacterial richness revealed a heterogeneous spatial distribution, structured into patches of about 111km, where the main drivers were the soil physico-chemical properties (18% of explained variance, the spatial descriptors (5.25%, 1.89% and 1.02% for the fine, medium and coarse scales, respectively, and the land use (1.4%. Based on these drivers, a predictive model was developed, which allows a good prediction of the bacterial richness (R2adj of 0.56 and provides a reference value for a given pedoclimatic condition.

  12. Mapping and predictive variations of soil bacterial richness across France.

    Science.gov (United States)

    Terrat, Sébastien; Horrigue, Walid; Dequiedt, Samuel; Saby, Nicolas P A; Lelièvre, Mélanie; Nowak, Virginie; Tripied, Julie; Régnier, Tiffanie; Jolivet, Claudy; Arrouays, Dominique; Wincker, Patrick; Cruaud, Corinne; Karimi, Battle; Bispo, Antonio; Maron, Pierre Alain; Chemidlin Prévost-Bouré, Nicolas; Ranjard, Lionel

    2017-01-01

    Although numerous studies have demonstrated the key role of bacterial diversity in soil functions and ecosystem services, little is known about the variations and determinants of such diversity on a nationwide scale. The overall objectives of this study were i) to describe the bacterial taxonomic richness variations across France, ii) to identify the ecological processes (i.e. selection by the environment and dispersal limitation) influencing this distribution, and iii) to develop a statistical predictive model of soil bacterial richness. We used the French Soil Quality Monitoring Network (RMQS), which covers all of France with 2,173 sites. The soil bacterial richness (i.e. OTU number) was determined by pyrosequencing 16S rRNA genes and related to the soil characteristics, climatic conditions, geomorphology, land use and space. Mapping of bacterial richness revealed a heterogeneous spatial distribution, structured into patches of about 111km, where the main drivers were the soil physico-chemical properties (18% of explained variance), the spatial descriptors (5.25%, 1.89% and 1.02% for the fine, medium and coarse scales, respectively), and the land use (1.4%). Based on these drivers, a predictive model was developed, which allows a good prediction of the bacterial richness (R2adj of 0.56) and provides a reference value for a given pedoclimatic condition.

  13. Risk prediction of hepatotoxicity in paracetamol poisoning.

    Science.gov (United States)

    Wong, Anselm; Graudins, Andis

    2017-09-01

    Paracetamol (acetaminophen) poisoning is the most common cause of acute liver failure in the developed world. A paracetamol treatment nomogram has been used for over four decades to help determine whether patients will develop hepatotoxicity without acetylcysteine treatment, and thus indicates those needing treatment. Despite this, a small proportion of patients still develop hepatotoxicity. More accurate risk predictors would be useful to increase the early detection of patients with the potential to develop hepatotoxicity despite acetylcysteine treatment. Similarly, there would be benefit in early identification of those with a low likelihood of developing hepatotoxicity, as this group may be safely treated with an abbreviated acetylcysteine regimen. To review the current literature related to risk prediction tools that can be used to identify patients at increased risk of hepatotoxicity. A systematic literature review was conducted using the search terms: "paracetamol" OR "acetaminophen" AND "overdose" OR "toxicity" OR "risk prediction rules" OR "hepatotoxicity" OR "psi parameter" OR "multiplication product" OR "half-life" OR "prothrombin time" OR "AST/ALT (aspartate transaminase/alanine transaminase)" OR "dose" OR "biomarkers" OR "nomogram". The search was limited to human studies without language restrictions, of Medline (1946 to May 2016), PubMed and EMBASE. Original articles pertaining to the theme were identified from January 1974 to May 2016. Of the 13,975 articles identified, 60 were relevant to the review. Paracetamol treatment nomograms: Paracetamol treatment nomograms have been used for decades to help decide the need for acetylcysteine, but rarely used to determine the risk of hepatotoxicity with treatment. Reported paracetamol dose and concentration: A dose ingestion >12 g or serum paracetamol concentration above the treatment thresholds on the paracetamol nomogram are associated with a greater risk of hepatotoxicity. Paracetamol elimination half

  14. Functional digital soil mapping for the prediction of available water capacity in Nigeria using legacy data

    NARCIS (Netherlands)

    Ugbaje, S.U.; Reuter, H.I.

    2013-01-01

    Soil information, particularly water storage capacity, is of utmost importance for assessing and managing land resources for sustainable land management. We investigated using digital soil mapping (DSM) and digital soil functional mapping (DSFM) procedures to predict available water capacity (AWC)

  15. Shoulder dystocia: risk factors, predictability, and preventability.

    Science.gov (United States)

    Mehta, Shobha H; Sokol, Robert J

    2014-06-01

    Shoulder dystocia remains an unpredictable obstetric emergency, striking fear in the hearts of obstetricians both novice and experienced. While outcomes that lead to permanent injury are rare, almost all obstetricians with enough years of practice have participated in a birth with a severe shoulder dystocia and are at least aware of cases that have resulted in significant neurologic injury or even neonatal death. This is despite many years of research trying to understand the risk factors associated with it, all in an attempt primarily to characterize when the risk is high enough to avoid vaginal delivery altogether and prevent a shoulder dystocia, whose attendant morbidities are estimated to be at a rate as high as 16-48%. The study of shoulder dystocia remains challenging due to its generally retrospective nature, as well as dependence on proper identification and documentation. As a result, the prediction of shoulder dystocia remains elusive, and the cost of trying to prevent one by performing a cesarean delivery remains high. While ultimately it is the injury that is the key concern, rather than the shoulder dystocia itself, it is in the presence of an identified shoulder dystocia that occurrence of injury is most common. The majority of shoulder dystocia cases occur without major risk factors. Moreover, even the best antenatal predictors have a low positive predictive value. Shoulder dystocia therefore cannot be reliably predicted, and the only preventative measure is cesarean delivery. Copyright © 2014 Elsevier Inc. All rights reserved.

  16. New methods for fall risk prediction.

    Science.gov (United States)

    Ejupi, Andreas; Lord, Stephen R; Delbaere, Kim

    2014-09-01

    Accidental falls are the leading cause of injury-related death and hospitalization in old age, with over one-third of the older adults experiencing at least one fall or more each year. Because of limited healthcare resources, regular objective fall risk assessments are not possible in the community on a large scale. New methods for fall prediction are necessary to identify and monitor those older people at high risk of falling who would benefit from participating in falls prevention programmes. Technological advances have enabled less expensive ways to quantify physical fall risk in clinical practice and in the homes of older people. Recently, several studies have demonstrated that sensor-based fall risk assessments of postural sway, functional mobility, stepping and walking can discriminate between fallers and nonfallers. Recent research has used low-cost, portable and objective measuring instruments to assess fall risk in older people. Future use of these technologies holds promise for assessing fall risk accurately in an unobtrusive manner in clinical and daily life settings.

  17. Nonparametric predictive pairwise comparison with competing risks

    International Nuclear Information System (INIS)

    Coolen-Maturi, Tahani

    2014-01-01

    In reliability, failure data often correspond to competing risks, where several failure modes can cause a unit to fail. This paper presents nonparametric predictive inference (NPI) for pairwise comparison with competing risks data, assuming that the failure modes are independent. These failure modes could be the same or different among the two groups, and these can be both observed and unobserved failure modes. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. The focus is on the lower and upper probabilities for the event that the lifetime of a future unit from one group, say Y, is greater than the lifetime of a future unit from the second group, say X. The paper also shows how the two groups can be compared based on particular failure mode(s), and the comparison of the two groups when some of the competing risks are combined is discussed

  18. The prediction of the bankruptcy risk

    Directory of Open Access Journals (Sweden)

    Gheorghe DUMITRESCU

    2010-04-01

    Full Text Available The study research results of the bankruptcy risk in the actual economic crisis are very weak. This issue is very important for the economy of every country, no matter what their actual development level.The necessity of bankruptcy risk prediction appears in every company,but also in the related institutions like financial companies, investors, suppliers, customers.The bankruptcy risk made and makes the object of many studies of research that want to identify: the moment of the appearance of the bankruptcy, the factors that compete at the reach of this state, the indicators that express the best this orientation (to the bankruptcy.The threats to the firms impose the knowledge by the managers,permanently of the economic-financial situations, of the vulnerable areas and of those with potential of development. Thus, these must identify and gesture the threats that would stop the fulfillment of the established purposes.

  19. Korean risk assessment model for breast cancer risk prediction.

    Science.gov (United States)

    Park, Boyoung; Ma, Seung Hyun; Shin, Aesun; Chang, Myung-Chul; Choi, Ji-Yeob; Kim, Sungwan; Han, Wonshik; Noh, Dong-Young; Ahn, Sei-Hyun; Kang, Daehee; Yoo, Keun-Young; Park, Sue K

    2013-01-01

    We evaluated the performance of the Gail model for a Korean population and developed a Korean breast cancer risk assessment tool (KoBCRAT) based upon equations developed for the Gail model for predicting breast cancer risk. Using 3,789 sets of cases and controls, risk factors for breast cancer among Koreans were identified. Individual probabilities were projected using Gail's equations and Korean hazard data. We compared the 5-year and lifetime risk produced using the modified Gail model which applied Korean incidence and mortality data and the parameter estimators from the original Gail model with those produced using the KoBCRAT. We validated the KoBCRAT based on the expected/observed breast cancer incidence and area under the curve (AUC) using two Korean cohorts: the Korean Multicenter Cancer Cohort (KMCC) and National Cancer Center (NCC) cohort. The major risk factors under the age of 50 were family history, age at menarche, age at first full-term pregnancy, menopausal status, breastfeeding duration, oral contraceptive usage, and exercise, while those at and over the age of 50 were family history, age at menarche, age at menopause, pregnancy experience, body mass index, oral contraceptive usage, and exercise. The modified Gail model produced lower 5-year risk for the cases than for the controls (p = 0.017), while the KoBCRAT produced higher 5-year and lifetime risk for the cases than for the controls (pKorean women, especially urban women.

  20. Using mental mapping to unpack perceived cycling risk.

    Science.gov (United States)

    Manton, Richard; Rau, Henrike; Fahy, Frances; Sheahan, Jerome; Clifford, Eoghan

    2016-03-01

    Cycling is the most energy-efficient mode of transport and can bring extensive environmental, social and economic benefits. Research has highlighted negative perceptions of safety as a major barrier to the growth of cycling. Understanding these perceptions through the application of novel place-sensitive methodological tools such as mental mapping could inform measures to increase cyclist numbers and consequently improve cyclist safety. Key steps to achieving this include: (a) the design of infrastructure to reduce actual risks and (b) targeted work on improving safety perceptions among current and future cyclists. This study combines mental mapping, a stated-preference survey and a transport infrastructure inventory to unpack perceptions of cycling risk and to reveal both overlaps and discrepancies between perceived and actual characteristics of the physical environment. Participants translate mentally mapped cycle routes onto hard-copy base-maps, colour-coding road sections according to risk, while a transport infrastructure inventory captures the objective cycling environment. These qualitative and quantitative data are matched using Geographic Information Systems and exported to statistical analysis software to model the individual and (infra)structural determinants of perceived cycling risk. This method was applied to cycling conditions in Galway City (Ireland). Participants' (n=104) mental maps delivered data-rich perceived safety observations (n=484) and initial comparison with locations of cycling collisions suggests some alignment between perception and reality, particularly relating to danger at roundabouts. Attributing individual and (infra)structural characteristics to each observation, a Generalised Linear Mixed Model statistical analysis identified segregated infrastructure, road width, the number of vehicles as well as gender and cycling experience as significant, and interactions were found between individual and infrastructural variables. The paper

  1. Risk-based fault detection using Self-Organizing Map

    International Nuclear Information System (INIS)

    Yu, Hongyang; Khan, Faisal; Garaniya, Vikram

    2015-01-01

    The complexity of modern systems is increasing rapidly and the dominating relationships among system variables have become highly non-linear. This results in difficulty in the identification of a system's operating states. In turn, this difficulty affects the sensitivity of fault detection and imposes a challenge on ensuring the safety of operation. In recent years, Self-Organizing Maps has gained popularity in system monitoring as a robust non-linear dimensionality reduction tool. Self-Organizing Map is able to capture non-linear variations of the system. Therefore, it is sensitive to the change of a system's states leading to early detection of fault. In this paper, a new approach based on Self-Organizing Map is proposed to detect and assess the risk of fault. In addition, probabilistic analysis is applied to characterize the risk of fault into different levels according to the hazard potential to enable a refined monitoring of the system. The proposed approach is applied on two experimental systems. The results from both systems have shown high sensitivity of the proposed approach in detecting and identifying the root cause of faults. The refined monitoring facilitates the determination of the risk of fault and early deployment of remedial actions and safety measures to minimize the potential impact of fault. - Highlights: • A new approach based on Self-Organizing Map is proposed to detect faults. • Integration of fault detection with risk assessment methodology. • Fault risk characterization into different levels to enable focused system monitoring

  2. Estimating and mapping the population at risk of sleeping sickness.

    Directory of Open Access Journals (Sweden)

    Pere P Simarro

    Full Text Available Human African trypanosomiasis (HAT, also known as sleeping sickness, persists as a public health problem in several sub-Saharan countries. Evidence-based, spatially explicit estimates of population at risk are needed to inform planning and implementation of field interventions, monitor disease trends, raise awareness and support advocacy. Comprehensive, geo-referenced epidemiological records from HAT-affected countries were combined with human population layers to map five categories of risk, ranging from "very high" to "very low," and to estimate the corresponding at-risk population.Approximately 70 million people distributed over a surface of 1.55 million km(2 are estimated to be at different levels of risk of contracting HAT. Trypanosoma brucei gambiense accounts for 82.2% of the population at risk, the remaining 17.8% being at risk of infection from T. b. rhodesiense. Twenty-one million people live in areas classified as moderate to very high risk, where more than 1 HAT case per 10,000 inhabitants per annum is reported.Updated estimates of the population at risk of sleeping sickness were made, based on quantitative information on the reported cases and the geographic distribution of human population. Due to substantial methodological differences, it is not possible to make direct comparisons with previous figures for at-risk population. By contrast, it will be possible to explore trends in the future. The presented maps of different HAT risk levels will help to develop site-specific strategies for control and surveillance, and to monitor progress achieved by ongoing efforts aimed at the elimination of sleeping sickness.

  3. Epidemiological geomatics in evaluation of mine risk education in Afghanistan: introducing population weighted raster maps

    Directory of Open Access Journals (Sweden)

    Andersson Neil

    2006-01-01

    Full Text Available Abstract Evaluation of mine risk education in Afghanistan used population weighted raster maps as an evaluation tool to assess mine education performance, coverage and costs. A stratified last-stage random cluster sample produced representative data on mine risk and exposure to education. Clusters were weighted by the population they represented, rather than the land area. A "friction surface" hooked the population weight into interpolation of cluster-specific indicators. The resulting population weighted raster contours offer a model of the population effects of landmine risks and risk education. Five indicator levels ordered the evidence from simple description of the population-weighted indicators (level 0, through risk analysis (levels 1–3 to modelling programme investment and local variations (level 4. Using graphic overlay techniques, it was possible to metamorphose the map, portraying the prediction of what might happen over time, based on the causality models developed in the epidemiological analysis. Based on a lattice of local site-specific predictions, each cluster being a small universe, the "average" prediction was immediately interpretable without losing the spatial complexity.

  4. Mapping spatial patterns of people's risk perception of landslides

    Science.gov (United States)

    Kofler, Christian; Pedoth, Lydia; Elzbieta Stawinoga, Agnieszka; Schneiderbauer, Stefan

    2016-04-01

    The resilience of communities against natural hazards is largely influenced by how the individuals perceive risk. A good understanding of people's risk perception, awareness and hazard knowledge is crucial for developing and improving risk management and communication strategies between authorities and the affected population. A lot of research has been done in investigating the social aspects of risks to natural hazards by means of interviews or questionnaires. However, there is still a lack of research in the investigation of the influence of the spatial distance to a hazard event on peoples risk perception. While the spatial dimension of a natural hazard event is always assessed in works with a natural science approach, it is often neglected in works on social aspects of natural hazards. In the present study, we aimed to overcome these gaps by combining methods from different disciplines and assessing and mapping the spatial pattern of risk perception through multivariate statistical approaches based on empirical data from questionnaires. We will present results from a case study carried out in Badia, located in the Province of South Tyrol- Italy, where in December 2012 a landslide destroyed four residential buildings and led to the evacuation of 36 people. By means of questionnaires distributed to all adults living in the case study area we assessed people's risk perception and asked respondents to allocate their place of residence on a map of the case study area subdivided in 7 zones. Based on the data of the questionnaire results we developed a risk perception factor in order to express various assessed aspects linked to risk perception with one metric. We analyzed and mapped this factor according to the different zones reflecting the spatial distance to the event. Furthermore, a cluster analysis identified various risk behavior profiles within the population. We also investigated the spatial patterns of these risk profiles. We revealed that the residential

  5. Mapping monomeric threading to protein-protein structure prediction.

    Science.gov (United States)

    Guerler, Aysam; Govindarajoo, Brandon; Zhang, Yang

    2013-03-25

    The key step of template-based protein-protein structure prediction is the recognition of complexes from experimental structure libraries that have similar quaternary fold. Maintaining two monomer and dimer structure libraries is however laborious, and inappropriate library construction can degrade template recognition coverage. We propose a novel strategy SPRING to identify complexes by mapping monomeric threading alignments to protein-protein interactions based on the original oligomer entries in the PDB, which does not rely on library construction and increases the efficiency and quality of complex template recognitions. SPRING is tested on 1838 nonhomologous protein complexes which can recognize correct quaternary template structures with a TM score >0.5 in 1115 cases after excluding homologous proteins. The average TM score of the first model is 60% and 17% higher than that by HHsearch and COTH, respectively, while the number of targets with an interface RMSD benchmark proteins. Although the relative performance of SPRING and ZDOCK depends on the level of homology filters, a combination of the two methods can result in a significantly higher model quality than ZDOCK at all homology thresholds. These data demonstrate a new efficient approach to quaternary structure recognition that is ready to use for genome-scale modeling of protein-protein interactions due to the high speed and accuracy.

  6. New "Risk-Targeted" Seismic Maps Introduced into Building Codes

    Science.gov (United States)

    Luco, Nicholas; Garrett, B.; Hayes, J.

    2012-01-01

    Throughout most municipalities of the United States, structural engineers design new buildings using the U.S.-focused International Building Code (IBC). Updated editions of the IBC are published every 3 years. The latest edition (2012) contains new "risk-targeted maximum considered earthquake" (MCER) ground motion maps, which are enabling engineers to incorporate a more consistent and better defined level of seismic safety into their building designs.

  7. Development of erosion risk map using fuzzy logic approach

    Directory of Open Access Journals (Sweden)

    Fauzi Manyuk

    2017-01-01

    Full Text Available Erosion-hazard assessment is an important aspect in the management of a river basin such as Siak River Basin, Riau Province, Indonesia. This study presents an application of fuzzy logic approach to develop erosion risk map based on geographic information system. Fuzzy logic is a computing approach based on “degrees of truth” rather than the usual “true or false” (1 or 0 Boolean logic on which the modern computer is based. The results of the erosion risk map were verified by using field measurements. The verification result shows that the parameter of soil-erodibility (K indicates a good agreement with field measurement data. The classification of soil-erodibility (K as the result of validation were: very low (0.0–0.1, medium (0.21-0.32, high (0.44-0.55 and very high (0.56-0.64. The results obtained from this study show that the erosion risk map of Siak River Basin were dominantly classified as medium level which cover about 68.54%. The other classifications were high and very low erosion level which cover about 28.84% and 2.61% respectively.

  8. Agent-based mapping of credit risk for sustainable microfinance.

    Directory of Open Access Journals (Sweden)

    Joung-Hun Lee

    Full Text Available By drawing analogies with independent research areas, we propose an unorthodox framework for mapping microfinance credit risk--a major obstacle to the sustainability of lenders outreaching to the poor. Specifically, using the elements of network theory, we constructed an agent-based model that obeys the stylized rules of microfinance industry. We found that in a deteriorating economic environment confounded with adverse selection, a form of latent moral hazard may cause a regime shift from a high to a low loan payment probability. An after-the-fact recovery, when possible, required the economic environment to improve beyond that which led to the shift in the first place. These findings suggest a small set of measurable quantities for mapping microfinance credit risk and, consequently, for balancing the requirements to reasonably price loans and to operate on a fully self-financed basis. We illustrate how the proposed mapping works using a 10-year monthly data set from one of the best-known microfinance representatives, Grameen Bank in Bangladesh. Finally, we discuss an entirely new perspective for managing microfinance credit risk based on enticing spontaneous cooperation by building social capital.

  9. Agent-based mapping of credit risk for sustainable microfinance.

    Science.gov (United States)

    Lee, Joung-Hun; Jusup, Marko; Podobnik, Boris; Iwasa, Yoh

    2015-01-01

    By drawing analogies with independent research areas, we propose an unorthodox framework for mapping microfinance credit risk--a major obstacle to the sustainability of lenders outreaching to the poor. Specifically, using the elements of network theory, we constructed an agent-based model that obeys the stylized rules of microfinance industry. We found that in a deteriorating economic environment confounded with adverse selection, a form of latent moral hazard may cause a regime shift from a high to a low loan payment probability. An after-the-fact recovery, when possible, required the economic environment to improve beyond that which led to the shift in the first place. These findings suggest a small set of measurable quantities for mapping microfinance credit risk and, consequently, for balancing the requirements to reasonably price loans and to operate on a fully self-financed basis. We illustrate how the proposed mapping works using a 10-year monthly data set from one of the best-known microfinance representatives, Grameen Bank in Bangladesh. Finally, we discuss an entirely new perspective for managing microfinance credit risk based on enticing spontaneous cooperation by building social capital.

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

    Science.gov (United States)

    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.

  11. Risk-maps informing land-use planning processes

    Energy Technology Data Exchange (ETDEWEB)

    Basta, Claudia [DIRC Sustainable Urban Areas, Section of Material Science and Sustainable Construction, Delft University of Technology, Stevinweg 1, 2600 GA, Delft (Netherlands)]. E-mail: c.basta@citg.tudelft.nl; Neuvel, Jeroen M.M. [Land Use Planning, Wageningen University, Droevendaalsesteeg 3, Postbus 47, 6700 AA Wageningen (Netherlands)]. E-mail: jeroen.neuvel@wur.nl; Zlatanova, Sisi [Section GISt, OTB Research Institute for Housing, Urban and Mobility Studies, Delft University of Technology, Jaffalaan 9, P.O. Box 5030, 2600 GA, Delft (Netherlands)]. E-mail: s.zlatanova@otb.tudelft.nl; Ale, Ben [Safety Science Group, TBM Faculty, Delft University of Technology, Jaffalaan 5, 2600 GA, Delft (Netherlands)

    2007-06-25

    The definition of safety distances as required by Art 12 of the Seveso II Directive on dangerous substances (96/82/EC) is necessary to minimize the consequences of potential major accidents. As they affect the land-use destinations of involved areas, safety distances can be considered as risk tolerability criteria with a territorial reflection. Recent studies explored the suitability of using Geographical Information System technologies to support their elaboration and visual rendering. In particular, the elaboration of GIS 'risk-maps' has been recognized as functional to two objectives: connecting spatial planners and safety experts during decision making processes and communicating risk to non-experts audiences. In order to elaborate on these findings and to verify their reflection on European practices, the article presents the result of a comparative study between the United Kingdom and the Netherlands recent developments. Their land-use planning practices for areas falling under Seveso II requirements are explored. The role of GIS risk-maps within decisional processes is analyzed and the reflection on the transparency and accessibility of risk-information is commented. Recommendations for further developments are given.

  12. Risk-maps informing land-use planning processes

    International Nuclear Information System (INIS)

    Basta, Claudia; Neuvel, Jeroen M.M.; Zlatanova, Sisi; Ale, Ben

    2007-01-01

    The definition of safety distances as required by Art 12 of the Seveso II Directive on dangerous substances (96/82/EC) is necessary to minimize the consequences of potential major accidents. As they affect the land-use destinations of involved areas, safety distances can be considered as risk tolerability criteria with a territorial reflection. Recent studies explored the suitability of using Geographical Information System technologies to support their elaboration and visual rendering. In particular, the elaboration of GIS 'risk-maps' has been recognized as functional to two objectives: connecting spatial planners and safety experts during decision making processes and communicating risk to non-experts audiences. In order to elaborate on these findings and to verify their reflection on European practices, the article presents the result of a comparative study between the United Kingdom and the Netherlands recent developments. Their land-use planning practices for areas falling under Seveso II requirements are explored. The role of GIS risk-maps within decisional processes is analyzed and the reflection on the transparency and accessibility of risk-information is commented. Recommendations for further developments are given

  13. Managing the total cost of risk exposures through risk mapping techniques

    International Nuclear Information System (INIS)

    Unione, A.J.; Rode, D.M.

    1998-01-01

    In a competitive power market, power producers are exposed to an increasingly broad spectrum of financial risks. The cumulative impact of these financial risks is known collectively as the Total of Cost of Risk. The concept of Total of Cost of Risk presents the business reality of a company's exposure to potentially devastating financial consequences in an integrated and useful way. In this way, a strategy of managing Total Cost of Risk in the most cost effective way can become a means of ensuring long term business health and security. This paper will examine the use of risk mapping as a tool for visually understanding Total Cost of Risk, thus creating an enhanced situational awareness and an integrated basis for risk management decision. The evaluation process, available through the use of risk maps allows the power producers to pro-actively implement prudent business decisions concerning the design, operation and maintenance of power plants. Risk mapping is thus a means for harmonizing operational objectives, such as improved plant reliability, with corporate strategies and goals in terms of an effective risk management program

  14. 2012 FEMA Risk Map Lidar: Merrimack River Watershed (Massachusetts, New Hampshire)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — These data are the lidar points collected for FEMA Risk Mapping, Assessment, and Planning (Risk MAP) for the Merrimack River Watershed. This area falls in portions...

  15. Spatial analysis and mapping of malaria risk in Malawi using point-referenced prevalence of infection data

    Directory of Open Access Journals (Sweden)

    Kazembe Lawrence N

    2006-09-01

    Full Text Available Abstract Background Current malaria control initiatives aim at reducing malaria burden by half by the year 2010. Effective control requires evidence-based utilisation of resources. Characterizing spatial patterns of risk, through maps, is an important tool to guide control programmes. To this end an analysis was carried out to predict and map malaria risk in Malawi using empirical data with the aim of identifying areas where greatest effort should be focussed. Methods Point-referenced prevalence of infection data for children aged 1–10 years were collected from published and grey literature and geo-referenced. The model-based geostatistical methods were applied to analyze and predict malaria risk in areas where data were not observed. Topographical and climatic covariates were added in the model for risk assessment and improved prediction. A Bayesian approach was used for model fitting and prediction. Results Bivariate models showed a significant association of malaria risk with elevation, annual maximum temperature, rainfall and potential evapotranspiration (PET. However in the prediction model, the spatial distribution of malaria risk was associated with elevation, and marginally with maximum temperature and PET. The resulting map broadly agreed with expert opinion about the variation of risk in the country, and further showed marked variation even at local level. High risk areas were in the low-lying lake shore regions, while low risk was along the highlands in the country. Conclusion The map provided an initial description of the geographic variation of malaria risk in Malawi, and might help in the choice and design of interventions, which is crucial for reducing the burden of malaria in Malawi.

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

    Science.gov (United States)

    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...

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

    Science.gov (United States)

    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...

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

    Science.gov (United States)

    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

  19. Flood Impacts on People: from Hazard to Risk Maps

    Science.gov (United States)

    Arrighi, C.; Castelli, F.

    2017-12-01

    The mitigation of adverse consequences of floods on people is crucial for civil protection and public authorities. According to several studies, in the developed countries the majority of flood-related fatalities occurs due to inappropriate high risk behaviours such as driving and walking in floodwaters. In this work both the loss of stability of vehicles and pedestrians in floodwaters are analysed. Flood hazard is evaluated, based on (i) a 2D inundation model of an urban area, (ii) 3D hydrodynamic simulations of water flows around vehicles and human body and (iii) a dimensional analysis of experimental activity. Exposure and vulnerability of vehicles and population are assessed exploiting several sources of open GIS data in order to produce risk maps for a testing case study. The results show that a significant hazard to vehicles and pedestrians exists in the study area. Particularly high is the hazard to vehicles, which are likely to be swept away by flood flow, possibly aggravate damages to structures and infrastructures and locally alter the flood propagation. Exposure and vulnerability analysis identifies some structures such as schools and public facilities, which may attract several people. Moreover, some shopping facilities in the area, which attract both vehicular and pedestrians' circulation are located in the highest flood hazard zone.The application of the method demonstrates that, at municipal level, such risk maps can support civil defence strategies and education to active citizenship, thus contributing to flood impact reduction to population.

  20. A two-stage approach for improved prediction of residue contact maps

    Directory of Open Access Journals (Sweden)

    Pollastri Gianluca

    2006-03-01

    Full Text Available Abstract Background Protein topology representations such as residue contact maps are an important intermediate step towards ab initio prediction of protein structure. Although improvements have occurred over the last years, the problem of accurately predicting residue contact maps from primary sequences is still largely unsolved. Among the reasons for this are the unbalanced nature of the problem (with far fewer examples of contacts than non-contacts, the formidable challenge of capturing long-range interactions in the maps, the intrinsic difficulty of mapping one-dimensional input sequences into two-dimensional output maps. In order to alleviate these problems and achieve improved contact map predictions, in this paper we split the task into two stages: the prediction of a map's principal eigenvector (PE from the primary sequence; the reconstruction of the contact map from the PE and primary sequence. Predicting the PE from the primary sequence consists in mapping a vector into a vector. This task is less complex than mapping vectors directly into two-dimensional matrices since the size of the problem is drastically reduced and so is the scale length of interactions that need to be learned. Results We develop architectures composed of ensembles of two-layered bidirectional recurrent neural networks to classify the components of the PE in 2, 3 and 4 classes from protein primary sequence, predicted secondary structure, and hydrophobicity interaction scales. Our predictor, tested on a non redundant set of 2171 proteins, achieves classification performances of up to 72.6%, 16% above a base-line statistical predictor. We design a system for the prediction of contact maps from the predicted PE. Our results show that predicting maps through the PE yields sizeable gains especially for long-range contacts which are particularly critical for accurate protein 3D reconstruction. The final predictor's accuracy on a non-redundant set of 327 targets is 35

  1. Use of paleogeochemical topographic maps for prediction of epigenetic uranium deposits

    International Nuclear Information System (INIS)

    Perel'man, A.I.

    1985-01-01

    The role of paleogeochemical maps for prospecting for and predicting uranium deposits is considered. The method of paleogeochemical landscape mapping is based on the landscape geochemistry, modern notions of geochemical condition evolution during geologic history, on the general principles of geochemical mapping. The use of the above-mentioned maps for predicting epigenetic uranium deposits is based on prospecting criteria and signs, which follow from epigenetic theory of the deposit genesis. According to the above theory a number of signs, favourable for the formation of deposits of this class (aride climate, granitoids and other rocks in the area of artesian water source, depression shapes of relief, etc.), is established

  2. Cancer Risk Map for the Surface of Mars

    Science.gov (United States)

    Kim, Myung-Hee Y.; Cucinotta, Francis A.

    2011-01-01

    We discuss calculations of the median and 95th percentile cancer risks on the surface of Mars for different solar conditions. The NASA Space Radiation Cancer Risk 2010 model is used to estimate gender and age specific cancer incidence and mortality risks for astronauts exploring Mars. Organ specific fluence spectra and doses for large solar particle events (SPE) and galactic cosmic rays (GCR) at various levels of solar activity are simulated using the HZETRN/QMSFRG computer code, and the 2010 version of the Badhwar and O Neill GCR model. The NASA JSC propensity model of SPE fluence and occurrence is used to consider upper bounds on SPE fluence for increasing mission lengths. In the transport of particles through the Mars atmosphere, a vertical distribution of Mars atmospheric thickness is calculated from the temperature and pressure data of Mars Global Surveyor, and the directional cosine distribution is implemented to describe the spherically distributed atmospheric distance along the slant path at each elevation on Mars. The resultant directional shielding by Mars atmosphere at each elevation is coupled with vehicle and body shielding for organ dose estimates. Astronaut cancer risks are mapped on the global topography of Mars, which was measured by the Mars Orbiter Laser Altimeter. Variation of cancer risk on the surface of Mars is due to a 16-km elevation range, and the large difference is obtained between the Tharsis Montes (Ascraeus, Pavonis, and Arsia) and the Hellas impact basin. Cancer incidence risks are found to be about 2-fold higher than mortality risks with a disproportionate increase in skin and thyroid cancers for all astronauts and breast cancer risk for female astronauts. The number of safe days on Mars to be below radiation limits at the 95th percent confidence level is reported for several Mission design scenarios.

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

    Science.gov (United States)

    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...

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

    International Nuclear Information System (INIS)

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

    2015-01-01

    Purpose: 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. Method: 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). Results: 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. Conclusion: 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

  5. A Case of Quality Prediction of Architecture Knowledge Sharing through Model Mapping

    NARCIS (Netherlands)

    Liang, Peng; Jansen, Anton; Avgeriou, Paris

    2008-01-01

    In this report, we introduce the AK sharing activity with a query-based scenario, and the motivation for the prediction of AK sharing quality prediction. In the end, a concrete case of quality prediction of AK sharing through model mapping was presented with assumptions.

  6. Supplementing predictive mapping of acid sulfate soil occurrence with Vis-NIR spectroscopy

    DEFF Research Database (Denmark)

    Beucher, Amélie; Peng, Yi; Knadel, Maria

    , including geology, landscape type and terrain parameters. Visible-Near-Infrared (Vis-NIR) spectroscopy constitutes a rapid and cheap alternative to soil analysis, and was successfully utilized for the prediction of soil chemical, physical and biological properties. In particular, the Vis-NIR spectra contain......Releasing acidity and metals into watercourses, acid sulfate soils represent a critical environmental problem worldwide. Identifying the spatial distribution of these soils enables to target the strategic areas for risk management. In Denmark, the occurrence of acid sulfate soils was first studied...... during the 1980’s through conventional mapping (i.e. soil sampling and the subsequent determination of pH at the time of sampling and after incubation, the pyrite content and the acid-neutralizing capacity). Since acid sulfate soils mostly occur in wetlands, the survey specifically targeted these areas...

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

    Science.gov (United States)

    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.

  8. Development of an attrition risk prediction tool.

    Science.gov (United States)

    Fowler, John; Norrie, Peter

    To review lecturers' and students' perceptions of the factors that may lead to attrition from pre-registration nursing and midwifery programmes and to identify ways to reduce the impact of such factors on the student's experience. Comparable attrition rates for nursing and midwifery students across various universities are difficult to monitor accurately; however, estimates that there is approximately a 25% national attrition rate are not uncommon. The financial and human implications of this are significant and worthy of investigation. A study was carried out in one medium-sized UK school of nursing and midwifery, aimed at identifying perceived factors associated with attrition and retention. Thirty-five lecturers were interviewed individually; 605 students completed a questionnaire, and of these, 10 were individually interviewed. Attrition data kept by the student service department were reviewed. Data were collected over an 18-month period in 2007-2008. Regression analysis of the student data identified eight significant predictors. Four of these were 'positive' factors in that they aided student retention and four were 'negative' in that they were associated with students' thoughts of resigning. Student attrition and retention is multifactorial, and, as such, needs to be managed holistically. One aspect of this management could be an attrition risk prediction tool.

  9. Using risk maps to link land value damage and risk as basis of flexible risk management for brownfield redevelopment.

    Science.gov (United States)

    Chen, I-chun; Ma, Hwong-wen

    2013-02-01

    Brownfield redevelopment involves numerous uncertain financial risks associated with market demand and land value. To reduce the uncertainty of the specific impact of land value and social costs, this study develops small-scale risk maps to determine the relationship between population risk (PR) and damaged land value (DLV) to facilitate flexible land reutilisation plans. This study used the spatial variability of exposure parameters in each village to develop the contaminated site-specific risk maps. In view of the combination of risk and cost, risk level that most affected land use was mainly 1.00×10(-6) to 1.00×10(-5) in this study area. Village 2 showed the potential for cost-effective conversion with contaminated land development. If the risk of remediation target was set at 5.00×10(-6), the DLV could be reduced by NT$15,005 million for the land developer. The land developer will consider the net benefit by quantifying the trade-off between the changes of land value and the cost of human health. In this study, small-scale risk maps can illuminate the economic incentive potential for contaminated site redevelopment through the adjustment of land value damage and human health risk. Copyright © 2012 Elsevier Ltd. All rights reserved.

  10. Flood risk assessment and mapping for the Lebanese watersheds

    Science.gov (United States)

    Abdallah, Chadi; Hdeib, Rouya

    2016-04-01

    Of all natural disasters, floods affect the greatest number of people worldwide and have the greatest potential to cause damage. Nowadays, with the emerging global warming phenomenon, this number is expected to increase. The Eastern Mediterranean area, including Lebanon (10452 Km2, 4.5 M habitant), has witnessed in the past few decades an increase frequency of flooding events. This study profoundly assess the flood risk over Lebanon covering all the 17 major watersheds and a number of small sub-catchments. It evaluate the physical direct tangible damages caused by floods. The risk assessment and evaluation process was carried out over three stages; i) Evaluating Assets at Risk, where the areas and assets vulnerable to flooding are identified, ii) Vulnerability Assessment, where the causes of vulnerability are assessed and the value of the assets are provided, iii) Risk Assessment, where damage functions are established and the consequent damages of flooding are estimated. A detailed Land CoverUse map was prepared at a scale of 1/ 1 000 using 0.4 m resolution satellite images within the flood hazard zones. The detailed field verification enabled to allocate and characterize all elements at risk, identify hotspots, interview local witnesses, and to correlate and calibrate previous flood damages with the utilized models. All filed gathered information was collected through Mobile Application and transformed to be standardized and classified under GIS environment. Consequently; the general damage evaluation and risk maps at different flood recurrence periods (10, 50, 100 years) were established. Major results showed that floods in a winter season (December, January, and February) of 10 year recurrence and of water retention ranging from 1 to 3 days can cause total damages (losses) that reach 1.14 M for crop lands and 2.30 M for green houses. Whereas, it may cause 0.2 M to losses in fruit trees for a flood retention ranging from 3 to 5 days. These numbers differs

  11. Predictive analytics for supply chain collaboration, risk management ...

    African Journals Online (AJOL)

    kirstam

    management, and (2) supply chain risk management predicted financial .... overhead costs, delivery of ever-increasing customer value, flexibility with superior ... risk exposure, relationship longevity, trust and communication are considered as.

  12. Improving flood risk mapping in Italy: the FloodRisk open-source software

    Science.gov (United States)

    Albano, Raffaele; Mancusi, Leonardo; Craciun, Iulia; Sole, Aurelia; Ozunu, Alexandru

    2017-04-01

    Time and again, floods around the world illustrate the devastating impact they can have on societies. Furthermore, the expectation that the flood damages can increase over time with climate, land-use change and social growth in flood prone-areas has raised the public and other stakeholders' (governments, international organization, re-insurance companies and emergency responders) awareness for the need to manage risks in order to mitigate their causes and consequences. In this light, the choice of appropriate measures, the assessment of the costs and effects of such measures, and their prioritization are crucial for decision makers. As a result, a priori flood risk assessment has become a key part of flood management practices with the aim of minimizing the total costs related to the risk management cycle. In this context, The EU Flood Directive 2007/60 requires the delineation of flood risk maps on the bases of most appropriate and advanced tools, with particular attention on limiting required economic efforts. The main aim of these risk maps is to provide the required knowledge for the development of flood risk management plans (FRMPs) by considering both costs and benefits of alternatives and results from consultation with all interested parties. In this context, this research project developed a free and open-source (FOSS) GIS software, called FloodRisk, to operatively support stakeholders in their compliance with the FRMPs. FloodRisk aims to facilitate the development of risk maps and the evaluation and management of current and future flood risk for multi-purpose applications. This new approach overcomes the limits of the expert-drive qualitative (EDQ) approach currently adopted in several European countries, such as Italy, which does not permit a suitable evaluation of the effectiveness of risk mitigation strategies, because the vulnerability component cannot be properly assessed. Moreover, FloodRisk is also able to involve the citizens in the flood

  13. Elderly fall risk prediction using static posturography

    Science.gov (United States)

    2017-01-01

    Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP) and medial-lateral (ML) center of pressure (CoP) motion; AP and ML CoP root mean square distance from mean (RMS); and AP, ML, and vector sum magnitude (VSM) CoP velocity were calculated. Romberg Quotients (RQ) were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24), prospective all fallers (42), prospective fallers (22 single, 6 multiple), and prospective non-fallers (47). Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity—0.114 x Eyes Closed Vector Sum Magnitude Velocity—2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity) and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for older

  14. Elderly fall risk prediction using static posturography.

    Science.gov (United States)

    Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan; McIlroy, William E

    2017-01-01

    Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP) and medial-lateral (ML) center of pressure (CoP) motion; AP and ML CoP root mean square distance from mean (RMS); and AP, ML, and vector sum magnitude (VSM) CoP velocity were calculated. Romberg Quotients (RQ) were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24), prospective all fallers (42), prospective fallers (22 single, 6 multiple), and prospective non-fallers (47). Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity-0.114 x Eyes Closed Vector Sum Magnitude Velocity-2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity) and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for older

  15. Elderly fall risk prediction using static posturography.

    Directory of Open Access Journals (Sweden)

    Jennifer Howcroft

    Full Text Available Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years stood quietly with eyes open and then eyes closed while Wii Balance Board data were collected. Range in anterior-posterior (AP and medial-lateral (ML center of pressure (CoP motion; AP and ML CoP root mean square distance from mean (RMS; and AP, ML, and vector sum magnitude (VSM CoP velocity were calculated. Romberg Quotients (RQ were calculated for all parameters. Participants reported six-month fall history and six-month post-assessment fall occurrence. Groups were retrospective fallers (24, prospective all fallers (42, prospective fallers (22 single, 6 multiple, and prospective non-fallers (47. Non-faller RQ AP range and RQ AP RMS differed from prospective all fallers, fallers, and single fallers. Non-faller eyes closed AP velocity, eyes closed VSM velocity, RQ AP velocity, and RQ VSM velocity differed from multi-fallers. RQ calculations were particularly relevant for elderly fall risk assessments. Cut-off scores from Clinical Cut-off Score, ROC curves, and discriminant functions were clinically viable for multi-faller classification and provided better accuracy than single-faller classification. RQ AP range with cut-off score 1.64 could be used to screen for older people who may fall once. Prospective multi-faller classification with a discriminant function (-1.481 + 0.146 x Eyes Closed AP Velocity-0.114 x Eyes Closed Vector Sum Magnitude Velocity-2.027 x RQ AP Velocity + 2.877 x RQ Vector Sum Magnitude Velocity and cut-off score 0.541 achieved an accuracy of 84.9% and is viable as a screening tool for

  16. Detailed predictive mapping of acid sulfate soil occurrence using electromagnetic induction data

    DEFF Research Database (Denmark)

    Beucher, Amélie; Boman, A; Mattbäck, S

    impact through the resulting corrosion of concrete and steel infrastructures, or their poor geotechnical qualities.Mapping acid sulfate soil occurrence thus constitutes a key step to target the strategic areas for subsequent environmental risk management and mitigation. Conventional mapping (i.e. soil...

  17. InterMap3D: predicting and visualizing co-evolving protein residues

    DEFF Research Database (Denmark)

    Oliveira, Rodrigo Gouveia; Roque, francisco jose sousa simôes almeida; Wernersson, Rasmus

    2009-01-01

    InterMap3D predicts co-evolving protein residues and plots them on the 3D protein structure. Starting with a single protein sequence, InterMap3D automatically finds a set of homologous sequences, generates an alignment and fetches the most similar 3D structure from the Protein Data Bank (PDB......). It can also accept a user-generated alignment. Based on the alignment, co-evolving residues are then predicted using three different methods: Row and Column Weighing of Mutual Information, Mutual Information/Entropy and Dependency. Finally, InterMap3D generates high-quality images of the protein...

  18. A novel risk score to predict cardiovascular disease risk in national populations (Globorisk)

    DEFF Research Database (Denmark)

    Hajifathalian, Kaveh; Ueda, Peter; Lu, Yuan

    2015-01-01

    BACKGROUND: Treatment of cardiovascular risk factors based on disease risk depends on valid risk prediction equations. We aimed to develop, and apply in example countries, a risk prediction equation for cardiovascular disease (consisting here of coronary heart disease and stroke) that can be reca...

  19. 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.

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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

  1. Update of the volcanic risk map of Colima volcano, Mexico

    Science.gov (United States)

    Suarez-Plascencia, C.; Nuñez Cornu, F. J.; Marquez-Azua, B.

    2010-12-01

    The Colima volcano, located in western Mexico (19° 30.696 N, 103° 37.026 W) began its current eruptive process in February 10, 1999. This event was the basis for the development of two volcanic hazard maps: one for ballistics (rock fall) lahars, and another one for ash fall. During the period of 2003 to 2008 this volcano has had an intense effusive-explosive activity, similar to the one that took place during the period of 1890 through 1900. Intense pre-Plinian eruption in January 20, 1913, generated little economic losses in the lower parts of the volcano thanks to the low population density and low socio-economic activities at the time The current volcanic activity has triggered ballistic projections, pyroclastic and ash flows, and lahars, all have exceeded the maps limits established in 1999. Vulnerable elements within these areas have gradually changed due to the expansion of the agricultural frontier on the east and southeast sides of the Colima volcano. On the slopes of the northwest side, new blue agave Tequilana weber and avocado orchard crops have emerged along with important production of greenhouse tomato, alfalfa and fruit (citrus) crops that will eventually be processed and dried for exportation to the United States and Europe. Also, in addition to the above, large expanses of corn and sugar cane have been planted on the slopes of the volcano since the nineteenth century. The increased agricultural activity has had a direct impact in the reduction of the available forest land area. Coinciding with this increased activity, the 0.8% growth population during the period of 2000 - 2005, - due to the construction of the Guadalajara-Colima highway-, also increased this impact. The growth in vulnerability changed the level of risk with respect to the one identified in the year 1999 (Suarez, 2000), thus motivating us to perform an update to the risk map at 1:25,000 using vector models of the INEGI, SPOT images of different dates, and fieldwork done in order

  2. Drug response prediction in high-risk multiple myeloma

    DEFF Research Database (Denmark)

    Vangsted, A J; Helm-Petersen, S; Cowland, J B

    2018-01-01

    from high-risk patients by GEP70 at diagnosis from Total Therapy 2 and 3A to predict the response by the DRP score of drugs used in the treatment of myeloma patients. The DRP score stratified patients further. High-risk myeloma with a predicted sensitivity to melphalan by the DRP score had a prolonged...

  3. Risk stratification in upper gastrointestinal bleeding; prediction, prevention and prognosis

    NARCIS (Netherlands)

    de Groot, N.L.

    2013-01-01

    In the first part of this thesis we developed a novel prediction score for predicting upper gastrointestinal (GI) bleeding in both NSAID and low-dose aspirin users. Both for NSAIDs and low-dose aspirin use risk scores were developed by identifying the five most dominant predictors. The risk of upper

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

    Science.gov (United States)

    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.

  5. Mapping risk of plague in Qinghai-Tibetan Plateau, China.

    Science.gov (United States)

    Qian, Quan; Zhao, Jian; Fang, Liqun; Zhou, Hang; Zhang, Wenyi; Wei, Lan; Yang, Hong; Yin, Wenwu; Cao, Wuchun; Li, Qun

    2014-07-10

    Qinghai-Tibetan Plateau of China is known to be the plague endemic region where marmot (Marmota himalayana) is the primary host. Human plague cases are relatively low incidence but high mortality, which presents unique surveillance and public health challenges, because early detection through surveillance may not always be feasible and infrequent clinical cases may be misdiagnosed. Based on plague surveillance data and environmental variables, Maxent was applied to model the presence probability of plague host. 75% occurrence points were randomly selected for training model, and the rest 25% points were used for model test and validation. Maxent model performance was measured as test gain and test AUC. The optimal probability cut-off value was chosen by maximizing training sensitivity and specificity simultaneously. We used field surveillance data in an ecological niche modeling (ENM) framework to depict spatial distribution of natural foci of plague in Qinghai-Tibetan Plateau. Most human-inhabited areas at risk of exposure to enzootic plague are distributed in the east and south of the Plateau. Elevation, temperature of land surface and normalized difference vegetation index play a large part in determining the distribution of the enzootic plague. This study provided a more detailed view of spatial pattern of enzootic plague and human-inhabited areas at risk of plague. The maps could help public health authorities decide where to perform plague surveillance and take preventive measures in Qinghai-Tibetan Plateau.

  6. Local dimension and finite time prediction in coupled map lattices

    Indian Academy of Sciences (India)

    In most of the cases these systems often exhibit highly complex type of ... tical applications bred vectors are different in two aspects. Firstly, for bred .... prediction 〈y| x〉 using the conditional distribution obtained from the joint distri- bution p(y, x) ...

  7. Local dimension and finite time prediction in coupled map lattices

    Indian Academy of Sciences (India)

    Forecasting, for obvious reasons, often become the most important goal to be achieved. For spatially extended systems (e.g. atmospheric system) where the local nonlinearities lead to the most unpredictable chaotic evolution, it is highly desirable to have a simple diagnostic tool to identify regions of predictable behaviour.

  8. Matching methods to produce maps for pest risk analysis to resources

    Directory of Open Access Journals (Sweden)

    Richard Baker

    2013-09-01

    Full Text Available Decision support systems (DSSs for pest risk mapping are invaluable for guiding pest risk analysts seeking to add maps to pest risk analyses (PRAs. Maps can help identify the area of potential establishment, the area at highest risk and the endangered area for alien plant pests. However, the production of detailed pest risk maps may require considerable time and resources and it is important to match the methods employed to the priority, time and detail required. In this paper, we apply PRATIQUE DSSs to Phytophthora austrocedrae, a pathogen of the Cupressaceae, Thaumetopoea pityocampa, the pine processionary moth, Drosophila suzukii, spotted wing Drosophila, and Thaumatotibia leucotreta, the false codling moth. We demonstrate that complex pest risk maps are not always a high priority and suggest that simple methods may be used to determine the geographic variation in relative risks posed by invasive alien species within an area of concern.

  9. Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker’s perspective

    Science.gov (United States)

    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...

  10. Predictability of cardiovascular risks by psychological measures

    Czech Academy of Sciences Publication Activity Database

    Šolcová, Iva; Kebza, V.

    2008-01-01

    Roč. 23, č. 1 (2008), s. 241-241 ISSN 0887-0446 R&D Projects: GA ČR GA406/06/0747 Institutional research plan: CEZ:AV0Z70250504 Keywords : CVD risks * psychological measures * physiological risks Subject RIV: AN - Psychology

  11. Predicting risk of cancer during HIV infection

    DEFF Research Database (Denmark)

    Borges, Álvaro H; Silverberg, Michael J; Wentworth, Deborah

    2013-01-01

    To investigate the relationship between inflammatory [interleukin-6 (IL-6) and C-reactive protein (CRP)] and coagulation (D-dimer) biomarkers and cancer risk during HIV infection.......To investigate the relationship between inflammatory [interleukin-6 (IL-6) and C-reactive protein (CRP)] and coagulation (D-dimer) biomarkers and cancer risk during HIV infection....

  12. Evaluation of flood hazard maps in print and web mapping services as information tools in flood risk communication

    Science.gov (United States)

    Hagemeier-Klose, M.; Wagner, K.

    2009-04-01

    Flood risk communication with the general public and the population at risk is getting increasingly important for flood risk management, especially as a precautionary measure. This is also underlined by the EU Flood Directive. The flood related authorities therefore have to develop adjusted information tools which meet the demands of different user groups. This article presents the formative evaluation of flood hazard maps and web mapping services according to the specific requirements and needs of the general public using the dynamic-transactional approach as a theoretical framework. The evaluation was done by a mixture of different methods; an analysis of existing tools, a creative workshop with experts and laymen and an online survey. The currently existing flood hazard maps or web mapping services or web GIS still lack a good balance between simplicity and complexity with adequate readability and usability for the public. Well designed and associative maps (e.g. using blue colours for water depths) which can be compared with past local flood events and which can create empathy in viewers, can help to raise awareness, to heighten the activity and knowledge level or can lead to further information seeking. Concerning web mapping services, a linkage between general flood information like flood extents of different scenarios and corresponding water depths and real time information like gauge levels is an important demand by users. Gauge levels of these scenarios are easier to understand than the scientifically correct return periods or annualities. The recently developed Bavarian web mapping service tries to integrate these requirements.

  13. GlobalSoilMap and Global Carbon Predictions

    DEFF Research Database (Denmark)

    Hempel, Jonathan; McBratney, Alex B.; Arrouays, Dominique

    consistently produced soil property information at 100 m resolution across the world. This information will aid in solving some of the key environment and societal issues of the day, including food security, global climate change land degradation and carbon sequestration. Data would be produced using mostly...... the storehouse of existing legacy soils data along with geographic information and a range of covariates. A range of modeling techniques is used dependant on the complexity of the background soil survey information. The key soil properties that would be most useful to the modeling community and other users are...... of soil property values throughout the depth of each profile. Maps have been produced at the country level in the Australia, Canada, Denmark, Nigeria, South Korea and the US and work is on-going in many other parts of the world....

  14. Global Variance Risk Premium and Forex Return Predictability

    OpenAIRE

    Aloosh, Arash

    2014-01-01

    In a long-run risk model with stochastic volatility and frictionless markets, I express expected forex returns as a function of consumption growth variances and stock variance risk premiums (VRPs)—the difference between the risk-neutral and statistical expectations of market return variation. This provides a motivation for using the forward-looking information available in stock market volatility indices to predict forex returns. Empirically, I find that stock VRPs predict forex returns at a ...

  15. Risk Prediction Model for Severe Postoperative Complication in Bariatric Surgery.

    Science.gov (United States)

    Stenberg, Erik; Cao, Yang; Szabo, Eva; Näslund, Erik; Näslund, Ingmar; Ottosson, Johan

    2018-01-12

    Factors associated with risk for adverse outcome are important considerations in the preoperative assessment of patients for bariatric surgery. As yet, prediction models based on preoperative risk factors have not been able to predict adverse outcome sufficiently. This study aimed to identify preoperative risk factors and to construct a risk prediction model based on these. Patients who underwent a bariatric surgical procedure in Sweden between 2010 and 2014 were identified from the Scandinavian Obesity Surgery Registry (SOReg). Associations between preoperative potential risk factors and severe postoperative complications were analysed using a logistic regression model. A multivariate model for risk prediction was created and validated in the SOReg for patients who underwent bariatric surgery in Sweden, 2015. Revision surgery (standardized OR 1.19, 95% confidence interval (CI) 1.14-0.24, p prediction model. Despite high specificity, the sensitivity of the model was low. Revision surgery, high age, low BMI, large waist circumference, and dyspepsia/GERD were associated with an increased risk for severe postoperative complication. The prediction model based on these factors, however, had a sensitivity that was too low to predict risk in the individual patient case.

  16. Predictive risk factors for persistent postherniotomy pain

    DEFF Research Database (Denmark)

    Aasvang, Eske K; Gmaehle, Eliza; Hansen, Jeanette B

    2010-01-01

    BACKGROUND: Persistent postherniotomy pain (PPP) affects everyday activities in 5-10% of patients. Identification of predisposing factors may help to identify the risk groups and guide anesthetic or surgical procedures in reducing risk for PPP. METHODS: A prospective study was conducted in 464...... patients undergoing open or laparoscopic transabdominal preperitoneal elective groin hernia repair. Primary outcome was identification of risk factors for substantial pain-related functional impairment at 6 months postoperatively assessed by the validated Activity Assessment Scale (AAS). Data on potential...... risk factors for PPP were collected preoperatively (pain from the groin hernia, preoperative AAS score, pain from other body regions, and psychometric assessment). Pain scores were collected on days 7 and 30 postoperatively. Sensory functions including pain response to tonic heat stimulation were...

  17. Lipoprotein metabolism indicators improve cardiovascular risk prediction

    NARCIS (Netherlands)

    Schalkwijk, D.B. van; Graaf, A.A. de; Tsivtsivadze, E.; Parnell, L.D.; Werff-van der Vat, B.J.C. van der; Ommen, B. van; Greef, J. van der; Ordovás, J.M.

    2014-01-01

    Background: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to

  18. Develop mental dyslexia: predicting individual risk

    OpenAIRE

    Thompson, PA; Hulme, C; Nash, HM; Gooch, Deborah; Hayiou-Thomas, E; Snowling, MJ

    2015-01-01

    Background Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited...

  19. Using NDVI and guided sampling to develop yield prediction maps of processing tomato crop

    Energy Technology Data Exchange (ETDEWEB)

    Fortes, A.; Henar Prieto, M. del; García-Martín, A.; Córdoba, A.; Martínez, L.; Campillo, C.

    2015-07-01

    The use of yield prediction maps is an important tool for the delineation of within-field management zones. Vegetation indices based on crop reflectance are of potential use in the attainment of this objective. There are different types of vegetation indices based on crop reflectance, the most commonly used of which is the NDVI (normalized difference vegetation index). NDVI values are reported to have good correlation with several vegetation parameters including the ability to predict yield. The field research was conducted in two commercial farms of processing tomato crop, Cantillana and Enviciados. An NDVI prediction map developed through ordinary kriging technique was used for guided sampling of processing tomato yield. Yield was studied and related with NDVI, and finally a prediction map of crop yield for the entire plot was generated using two geostatistical methodologies (ordinary and regression kriging). Finally, a comparison was made between the yield obtained at validation points and the yield values according to the prediction maps. The most precise yield maps were obtained with the regression kriging methodology with RRMSE values of 14% and 17% in Cantillana and Enviciados, respectively, using the NDVI as predictor. The coefficient of correlation between NDVI and yield was correlated in the point samples taken in the two locations, with values of 0.71 and 0.67 in Cantillana and Enviciados, respectively. The results suggest that the use of a massive sampling parameter such as NDVI is a good indicator of the distribution of within-field yield variation. (Author)

  20. Calibration plots for risk prediction models in the presence of competing risks

    DEFF Research Database (Denmark)

    Gerds, Thomas A; Andersen, Per K; Kattan, Michael W

    2014-01-01

    A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks...... prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves...

  1. Water erosion risk prediction in eucalyptus plantations

    Directory of Open Access Journals (Sweden)

    Mayesse Aparecida da Silva

    2014-04-01

    Full Text Available Eucalyptus plantations are normally found in vulnerable ecosystems such as steep slope, soil with low natural fertility and lands that were degraded by agriculture. The objective of this study was to obtain Universal Soil Loss Equation (USLE factors and use them to estimate water erosion risk in regions with eucalyptus planted. The USLE factors were obtained in field plots under natural rainfall in the Rio Doce Basin, MG, Brazil, and the model applied to assess erosion risk using USLE in a Geographic Information System. The study area showed rainfall-runoff erosivity values from 10,721 to 10,642 MJ mm ha-1 h-1 yr-1. Some soils (Latosols had very low erodibility values (2.0 x 10-4 and 1.0 x 10-4t h MJ-1 mm-1, the topographic factor ranged from 0.03 to 10.57 and crop and management factor values obtained for native forest, eucalyptus and planted pasture were 0.09, 0.12 and 0.22, respectively. Water erosion risk estimates for current land use indicated that the areas where should receive more attention were mainly areas with greater topographic factors and those with Cambisols. Planning of forestry activities in this region should consider implementation of other conservation practices beyond those already used, reducing areas with a greater risk of soil erosion and increasing areas with very low risk.

  2. Predicting risk and the emergence of schizophrenia.

    LENUS (Irish Health Repository)

    Clarke, Mary C

    2012-09-01

    This article gives an overview of genetic and environmental risk factors for schizophrenia. The presence of certain molecular, biological, and psychosocial factors at certain points in the life span, has been linked to later development of schizophrenia. All need to be considered in the context of schizophrenia as a lifelong brain disorder. Research interest in schizophrenia is shifting to late childhood\\/early adolescence for screening and preventative measures. This article discusses those environmental risk factors for schizophrenia for which there is the largest evidence base.

  3. Identifying environmental risk factors and mapping the risk of human West Nile virus in South Dakota.

    Science.gov (United States)

    Hess, A.; Davis, J. K.; Wimberly, M. C.

    2017-12-01

    Human West Nile virus (WNV) first arrived in the USA in 1999 and has since then spread across the country. Today, the highest incidence rates are found in the state of South Dakota. The disease occurrence depends on the complex interaction between the mosquito vector, the bird host and the dead-end human host. Understanding the spatial domain of this interaction and being able to identify disease transmission hotspots is crucial for effective disease prevention and mosquito control. In this study we use geospatial environmental information to understand what drives the spatial distribution of cases of human West Nile virus in South Dakota and to map relative infection risk across the state. To map the risk of human West Nile virus in South Dakota, we used geocoded human case data from the years 2004-2016. Satellite data from the Landsat ETM+ and MODIS for the years 2003 to 2016 were used to characterize environmental patterns. From these datasets we calculated indices, such as the normalized differenced vegetation index (NDVI) and the normalized differenced water index (NDWI). In addition, datasets such as the National Land Data Assimilation System (NLDAS), National Land Cover Dataset (NLCD), National Wetland inventory (NWI), National Elevation Dataset (NED) and Soil Survey Geographic Database (SSURGO) were utilized. Environmental variables were summarized for a buffer zone around the case and control points. We used a boosted regression tree model to identify the most important variables describing the risk of WNV infection. We generated a risk map by applying this model across the entire state. We found that the highest relative risk is present in the James River valley in northeastern South Dakota. Factors that were identified as influencing the transmission risk include inter-annual variability of vegetation cover, water availability and temperature. Land covers such as grasslands, low developed areas and wetlands were also found to be good predictors for human

  4. A decision-support scheme for mapping endangered areas in pest risk analysis

    NARCIS (Netherlands)

    Baker, R.H.A.; Benninga, J.; Bremmer, J.; Brunel, S.; Dupin, M.; Eyre, D.; Ilieva, Z.; Jarosik, V.; Kehlenbeck, H.; Kriticos, D.J.; Makowski, D.; Pergl, J.; Reynaud, P.; Robinet, C.; Soliman, T.; Werf, van der W.; Worner, S.

    2012-01-01

    This paper describes a decision-support scheme (DSS) for mapping the area where economically important loss is likely to occur (the endangered area). It has been designed by the PRATIQUE project to help pest risk analysts address the numerous risk mapping challenges and decide on the most suitable

  5. Risk assessment methodologies for predicting phosphorus losses

    NARCIS (Netherlands)

    Schoumans, O.F.; Chardon, W.J.

    2003-01-01

    Risk assessment parameters are needed to assess the contribution of phosphorus (P) losses from soil to surface water, and the effectiveness of nutrient and land management strategies for the reduction of P loss. These parameters need to take into account the large temporal and spatial variation in P

  6. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

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

    Science.gov (United States)

    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.

  8. Prediction of Banking Systemic Risk Based on Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Shouwei Li

    2013-01-01

    Full Text Available Banking systemic risk is a complex nonlinear phenomenon and has shed light on the importance of safeguarding financial stability by recent financial crisis. According to the complex nonlinear characteristics of banking systemic risk, in this paper we apply support vector machine (SVM to the prediction of banking systemic risk in an attempt to suggest a new model with better explanatory power and stability. We conduct a case study of an SVM-based prediction model for Chinese banking systemic risk and find the experiment results showing that support vector machine is an efficient method in such case.

  9. Spatial epidemiological techniques in cholera mapping and analysis towards a local scale predictive modelling

    Science.gov (United States)

    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.

  10. Spatial epidemiological techniques in cholera mapping and analysis towards a local scale predictive modelling

    International Nuclear Information System (INIS)

    Rasam, A R A; Ghazali, R; Noor, A M M; Mohd, W M N W; Hamid, J R A; Bazlan, M J; Ahmad, N

    2014-01-01

    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

  11. Elderly fall risk prediction using static posturography

    OpenAIRE

    Howcroft, Jennifer; Lemaire, Edward D.; Kofman, Jonathan; McIlroy, William E.

    2017-01-01

    Maintaining and controlling postural balance is important for activities of daily living, with poor postural balance being predictive of future falls. This study investigated eyes open and eyes closed standing posturography with elderly adults to identify differences and determine appropriate outcome measure cut-off scores for prospective faller, single-faller, multi-faller, and non-faller classifications. 100 older adults (75.5 ± 6.7 years) stood quietly with eyes open and then eyes closed w...

  12. Identification of residue pairing in interacting β-strands from a predicted residue contact map.

    Science.gov (United States)

    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 .

  13. Does the Risk Assessment and Prediction Tool Predict Discharge Disposition After Joint Replacement?

    DEFF Research Database (Denmark)

    Hansen, Viktor J.; Gromov, Kirill; Lebrun, Lauren M

    2015-01-01

    BACKGROUND: Payers of health services and policymakers place a major focus on cost containment in health care. Studies have shown that early planning of discharge is essential in reducing length of stay and achieving financial benefit; tools that can help predict discharge disposition would...... populations is unknown. A low RAPT score is reported to indicate a high risk of needing any form of inpatient rehabilitation after TJA, including short-term nursing facilities. QUESTIONS/PURPOSES: This study attempts (1) to assess predictive accuracy of the RAPT on US patients undergoing total hip and knee....... Based on our findings, the risk categories in our populations should be high risk intermediate risk 7 to 10, and low risk > 10. CONCLUSIONS: The RAPT accurately predicted discharge disposition for high- and low-risk patients in our cohort. Based on our data, intermediate-risk patients should...

  14. PREDICTION OF SURGICAL TREATMENT WITH POUR PERITONITIS QUANTIFYING RISK FACTORS

    Directory of Open Access Journals (Sweden)

    І. К. Churpiy

    2012-11-01

    Full Text Available Explored the possibility of quantitative assessment of risk factors of complications in the treatment of diffuse peritonitis. Highlighted 53 groups of features that are important in predicting the course of diffuse peritonitis. The proposed scheme of defining the risk of clinical course of diffuse peritonitis can quantify the severity of the source of patients and in most cases correctly predict the results of treatment of disease.

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

    Science.gov (United States)

    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...

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

    Science.gov (United States)

    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

  17. Predicting child maltreatment: A meta-analysis of the predictive validity of risk assessment instruments.

    Science.gov (United States)

    van der Put, Claudia E; Assink, Mark; Boekhout van Solinge, Noëlle F

    2017-11-01

    Risk assessment is crucial in preventing child maltreatment since it can identify high-risk cases in need of child protection intervention. Despite widespread use of risk assessment instruments in child welfare, it is unknown how well these instruments predict maltreatment and what instrument characteristics are associated with higher levels of predictive validity. Therefore, a multilevel meta-analysis was conducted to examine the predictive accuracy of (characteristics of) risk assessment instruments. A literature search yielded 30 independent studies (N=87,329) examining the predictive validity of 27 different risk assessment instruments. From these studies, 67 effect sizes could be extracted. Overall, a medium significant effect was found (AUC=0.681), indicating a moderate predictive accuracy. Moderator analyses revealed that onset of maltreatment can be better predicted than recurrence of maltreatment, which is a promising finding for early detection and prevention of child maltreatment. In addition, actuarial instruments were found to outperform clinical instruments. To bring risk and needs assessment in child welfare to a higher level, actuarial instruments should be further developed and strengthened by distinguishing risk assessment from needs assessment and by integrating risk assessment with case management. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DODGE COUNTY, WISCONSIN, USA - MIP Dodge Portion Upper Rock River Watershed RiskMap DFIRM Update

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk;...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLUMBIA COUNTY, WISCONSIN, USA - MIP Columbia Portion Baraboo River Watershed RiskMap DFIRM Update

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk;...

  20. Topography and geology site effects from the intensity prediction model (ShakeMap) for Austria

    Science.gov (United States)

    del Puy Papí Isaba, María; Jia, Yan; Weginger, Stefan

    2017-04-01

    The seismicity in Austria can be categorized as moderated. Despite the fact that the hazard seems to be rather low, earthquakes can cause great damage and losses, specially in densely populated and industrialized areas. It is well known, that equations which predict intensity as a function of magnitude and distance, among other parameters, are useful tool for hazard and risk assessment. Therefore, this study aims to determine an empirical model of the ground shaking intensities (ShakeMap) of a series of earthquakes occurred in Austria between 1000 and 2014. Furthermore, the obtained empirical model will lead to further interpretation of both, contemporary and historical earthquakes. A total of 285 events, which epicenters were located in Austria, and a sum of 22.739 reported macreoseismic data points from Austria and adjoining countries, were used. These events are enclosed in the period 1000-2014 and characterized by having a local magnitude greater than 3. In the first state of the model development, the data was careful selected, e.g. solely intensities equal or greater than III were used. In a second state the data was adjusted to the selected empirical model. Finally, geology and topography corrections were obtained by means of the model residuals in order to derive intensity-based site amplification effects.

  1. Obesity Risk Prediction among Women of Upper Egypt: The impact ...

    African Journals Online (AJOL)

    Obesity Risk Prediction among Women of Upper Egypt: The impact of FTO ... with increased obesity risk but there is a lack of association with diabetes. ... (as certain foods or gene therapy) will prevent the percentage of women who is affected ...

  2. Evaluation of flood hazard maps in print and web mapping services as information tools in flood risk communication

    Directory of Open Access Journals (Sweden)

    M. Hagemeier-Klose

    2009-04-01

    Full Text Available Flood risk communication with the general public and the population at risk is getting increasingly important for flood risk management, especially as a precautionary measure. This is also underlined by the EU Flood Directive. The flood related authorities therefore have to develop adjusted information tools which meet the demands of different user groups. This article presents the formative evaluation of flood hazard maps and web mapping services according to the specific requirements and needs of the general public using the dynamic-transactional approach as a theoretical framework. The evaluation was done by a mixture of different methods; an analysis of existing tools, a creative workshop with experts and laymen and an online survey.

    The currently existing flood hazard maps or web mapping services or web GIS still lack a good balance between simplicity and complexity with adequate readability and usability for the public. Well designed and associative maps (e.g. using blue colours for water depths which can be compared with past local flood events and which can create empathy in viewers, can help to raise awareness, to heighten the activity and knowledge level or can lead to further information seeking. Concerning web mapping services, a linkage between general flood information like flood extents of different scenarios and corresponding water depths and real time information like gauge levels is an important demand by users. Gauge levels of these scenarios are easier to understand than the scientifically correct return periods or annualities. The recently developed Bavarian web mapping service tries to integrate these requirements.

  3. Determining Coastal Hazards Risk Perception to Enhance Local Mitigation Planning through a Participatory Mapping Approach

    Science.gov (United States)

    Bethel, M.; Braud, D.; Lambeth, T.; Biber, P.; Wu, W.

    2017-12-01

    Coastal community leaders, government officials, and natural resource managers must be able to accurately assess and predict a given coastal landscape's sustainability and/or vulnerability as coastal habitat continues to undergo rapid and dramatic changes associated with natural and anthropogenic activities such as accelerated relative sea level rise (SLR). To help address this information need, a multi-disciplinary project team conducted Sea Grant sponsored research in Louisiana and Mississippi with traditional ecosystem users and natural resource managers to determine a method for producing localized vulnerability and sustainability maps for projected SLR and storm surge impacts, and determine how and whether the results of such an approach can provide more useful information to enhance hazard mitigation planning. The goals of the project are to develop and refine SLR visualization tools for local implementation in areas experiencing subsidence and erosion, and discover the different ways stakeholder groups evaluate risk and plan mitigation strategies associated with projected SLR and storm surge. Results from physical information derived from data and modeling of subsidence, erosion, engineered restoration and coastal protection features, historical land loss, and future land projections under SLR are integrated with complimentary traditional ecological knowledge (TEK) offered by the collaborating local ecosystem users for these assessments. The data analysis involves interviewing stakeholders, coding the interviews for themes, and then converting the themes into vulnerability and sustainability factors. Each factor is weighted according to emphasis by the TEK experts and number of experts who mention it to determine which factors are the highest priority. The priority factors are then mapped with emphasis on the perception of contributing to local community vulnerability or sustainability to SLR and storm surge. The maps are used by the collaborators to benefit

  4. The Role of Risk Aversion in Predicting Individual Behaviours

    OpenAIRE

    Guiso, Luigi; Paiella, Monica

    2004-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers’ decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways cons...

  5. The Role of Risk Aversion in Predicting Individual Behaviour

    OpenAIRE

    Monica Paiella; Luigi Guiso

    2004-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers' decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways cons...

  6. Risk avoidance in sympatric large carnivores: reactive or predictive?

    Science.gov (United States)

    Broekhuis, Femke; Cozzi, Gabriele; Valeix, Marion; McNutt, John W; Macdonald, David W

    2013-09-01

    1. Risks of predation or interference competition are major factors shaping the distribution of species. An animal's response to risk can either be reactive, to an immediate risk, or predictive, based on preceding risk or past experiences. The manner in which animals respond to risk is key in understanding avoidance, and hence coexistence, between interacting species. 2. We investigated whether cheetahs (Acinonyx jubatus), known to be affected by predation and competition by lions (Panthera leo) and spotted hyaenas (Crocuta crocuta), respond reactively or predictively to the risks posed by these larger carnivores. 3. We used simultaneous spatial data from Global Positioning System (GPS) radiocollars deployed on all known social groups of cheetahs, lions and spotted hyaenas within a 2700 km(2) study area on the periphery of the Okavango Delta in northern Botswana. The response to risk of encountering lions and spotted hyaenas was explored on three levels: short-term or immediate risk, calculated as the distance to the nearest (contemporaneous) lion or spotted hyaena, long-term risk, calculated as the likelihood of encountering lions and spotted hyaenas based on their cumulative distributions over a 6-month period and habitat-associated risk, quantified by the habitat used by each of the three species. 4. We showed that space and habitat use by cheetahs was similar to that of lions and, to a lesser extent, spotted hyaenas. However, cheetahs avoided immediate risks by positioning themselves further from lions and spotted hyaenas than predicted by a random distribution. 5. Our results suggest that cheetah spatial distribution is a hierarchical process, first driven by resource acquisition and thereafter fine-tuned by predator avoidance; thus suggesting a reactive, rather than a predictive, response to risk. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

  7. Prediction of Poly(A Sites by Poly(A Read Mapping.

    Directory of Open Access Journals (Sweden)

    Thomas Bonfert

    Full Text Available RNA-seq reads containing part of the poly(A tail of transcripts (denoted as poly(A reads provide the most direct evidence for the position of poly(A sites in the genome. However, due to reduced coverage of poly(A tails by reads, poly(A reads are not routinely identified during RNA-seq mapping. Nevertheless, recent studies for several herpesviruses successfully employed mapping of poly(A reads to identify herpesvirus poly(A sites using different strategies and customized programs. To more easily allow such analyses without requiring additional programs, we integrated poly(A read mapping and prediction of poly(A sites into our RNA-seq mapping program ContextMap 2. The implemented approach essentially generalizes previously used poly(A read mapping approaches and combines them with the context-based approach of ContextMap 2 to take into account information provided by other reads aligned to the same location. Poly(A read mapping using ContextMap 2 was evaluated on real-life data from the ENCODE project and compared against a competing approach based on transcriptome assembly (KLEAT. This showed high positive predictive value for our approach, evidenced also by the presence of poly(A signals, and considerably lower runtime than KLEAT. Although sensitivity is low for both methods, we show that this is in part due to a high extent of spurious results in the gold standard set derived from RNA-PET data. Sensitivity improves for poly(A sites of known transcripts or determined with a more specific poly(A sequencing protocol and increases with read coverage on transcript ends. Finally, we illustrate the usefulness of the approach in a high read coverage scenario by a re-analysis of published data for herpes simplex virus 1. Thus, with current trends towards increasing sequencing depth and read length, poly(A read mapping will prove to be increasingly useful and can now be performed automatically during RNA-seq mapping with ContextMap 2.

  8. THE EVOLUTION OF RISK MANAGEMENT RESEARCH: CHANGES IN KNOWLEDGE MAPS

    OpenAIRE

    Iwona Gorzeń-Mitka

    2017-01-01

    One of the leading trends in modern academic research is risk management. Over the years, the approach to risk management has changed and affected many different areas. This study aims to investigate changes in risk management and trends of risk management in the past 20 years. Risk management related publications from 1990 to 2016 were retrieved from the Web of Science and Scopus databases. VOS viewer software was used to analyse the research trend. Literature growth related to risk manageme...

  9. MSD-MAP: A Network-Based Systems Biology Platform for Predicting Disease-Metabolite Links.

    Science.gov (United States)

    Wathieu, Henri; Issa, Naiem T; Mohandoss, Manisha; Byers, Stephen W; Dakshanamurthy, Sivanesan

    2017-01-01

    Cancer-associated metabolites result from cell-wide mechanisms of dysregulation. The field of metabolomics has sought to identify these aberrant metabolites as disease biomarkers, clues to understanding disease mechanisms, or even as therapeutic agents. This study was undertaken to reliably predict metabolites associated with colorectal, esophageal, and prostate cancers. Metabolite and disease biological action networks were compared in a computational platform called MSD-MAP (Multi Scale Disease-Metabolite Association Platform). Using differential gene expression analysis with patient-based RNAseq data from The Cancer Genome Atlas, genes up- or down-regulated in cancer compared to normal tissue were identified. Relational databases were used to map biological entities including pathways, functions, and interacting proteins, to those differential disease genes. Similar relational maps were built for metabolites, stemming from known and in silico predicted metabolite-protein associations. The hypergeometric test was used to find statistically significant relationships between disease and metabolite biological signatures at each tier, and metabolites were assessed for multi-scale association with each cancer. Metabolite networks were also directly associated with various other diseases using a disease functional perturbation database. Our platform recapitulated metabolite-disease links that have been empirically verified in the scientific literature, with network-based mapping of jointly-associated biological activity also matching known disease mechanisms. This was true for colorectal, esophageal, and prostate cancers, using metabolite action networks stemming from both predicted and known functional protein associations. By employing systems biology concepts, MSD-MAP reliably predicted known cancermetabolite links, and may serve as a predictive tool to streamline conventional metabolomic profiling methodologies. Copyright© Bentham Science Publishers; For any

  10. Predicting Risk-Mitigating Behaviors From Indecisiveness and Trait Anxiety

    DEFF Research Database (Denmark)

    Mcneill, Ilona M.; Dunlop, Patrick D.; Skinner, Timothy C.

    2016-01-01

    Past research suggests that indecisiveness and trait anxiety may both decrease the likelihood of performing risk-mitigating preparatory behaviors (e.g., preparing for natural hazards) and suggests two cognitive processes (perceived control and worrying) as potential mediators. However, no single...... control over wildfire-related outcomes. Trait anxiety did not uniquely predict preparedness or perceived control, but it did uniquely predict worry, with higher trait anxiety predicting more worrying. Also, worry trended toward uniquely predicting preparedness, albeit in an unpredicted positive direction...

  11. Cardiovascular risk prediction tools for populations in Asia.

    Science.gov (United States)

    Barzi, F; Patel, A; Gu, D; Sritara, P; Lam, T H; Rodgers, A; Woodward, M

    2007-02-01

    Cardiovascular risk equations are traditionally derived from the Framingham Study. The accuracy of this approach in Asian populations, where resources for risk factor measurement may be limited, is unclear. To compare "low-information" equations (derived using only age, systolic blood pressure, total cholesterol and smoking status) derived from the Framingham Study with those derived from the Asian cohorts, on the accuracy of cardiovascular risk prediction. Separate equations to predict the 8-year risk of a cardiovascular event were derived from Asian and Framingham cohorts. The performance of these equations, and a subsequently "recalibrated" Framingham equation, were evaluated among participants from independent Chinese cohorts. Six cohort studies from Japan, Korea and Singapore (Asian cohorts); six cohort studies from China; the Framingham Study from the US. 172,077 participants from the Asian cohorts; 25,682 participants from Chinese cohorts and 6053 participants from the Framingham Study. In the Chinese cohorts, 542 cardiovascular events occurred during 8 years of follow-up. Both the Asian cohorts and the Framingham equations discriminated cardiovascular risk well in the Chinese cohorts; the area under the receiver-operator characteristic curve was at least 0.75 for men and women. However, the Framingham risk equation systematically overestimated risk in the Chinese cohorts by an average of 276% among men and 102% among women. The corresponding average overestimation using the Asian cohorts equation was 11% and 10%, respectively. Recalibrating the Framingham risk equation using cardiovascular disease incidence from the non-Chinese Asian cohorts led to an overestimation of risk by an average of 4% in women and underestimation of risk by an average of 2% in men. A low-information Framingham cardiovascular risk prediction tool, which, when recalibrated with contemporary data, is likely to estimate future cardiovascular risk with similar accuracy in Asian

  12. Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India

    Science.gov (United States)

    Kumar, Deepak; Thakur, Manoj; Dubey, Chandra S.; Shukla, Dericks P.

    2017-10-01

    In recent years, various machine learning techniques have been applied for landslide susceptibility mapping. In this study, three different variants of support vector machine viz., SVM, Proximal Support Vector Machine (PSVM) and L2-Support Vector Machine - Modified Finite Newton (L2-SVM-MFN) have been applied on the Mandakini River Basin in Uttarakhand, India to carry out the landslide susceptibility mapping. Eight thematic layers such as elevation, slope, aspect, drainages, geology/lithology, buffer of thrusts/faults, buffer of streams and soil along with the past landslide data were mapped in GIS environment and used for landslide susceptibility mapping in MATLAB. The study area covering 1625 km2 has merely 0.11% of area under landslides. There are 2009 pixels for past landslides out of which 50% (1000) landslides were considered as training set while remaining 50% as testing set. The performance of these techniques has been evaluated and the computational results show that L2-SVM-MFN obtains higher prediction values (0.829) of receiver operating characteristic curve (AUC-area under the curve) as compared to 0.807 for PSVM model and 0.79 for SVM. The results obtained from L2-SVM-MFN model are found to be superior than other SVM prediction models and suggest the usefulness of this technique to problem of landslide susceptibility mapping where training data is very less. However, these techniques can be used for satisfactory determination of susceptible zones with these inputs.

  13. An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

    Science.gov (United States)

    Hua, Hong-Li; Zhang, Fa-Zhan; Labena, Abraham Alemayehu; Dong, Chuan; Jin, Yan-Ting; Guo, Feng-Biao

    Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus , which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

  14. Aliens in Transylvania: risk maps of invasive alien plant species in Central Romania

    Directory of Open Access Journals (Sweden)

    Heike Zimmermann

    2015-01-01

    Full Text Available Using the MAXENT algorithm, we developed risk maps for eight invasive plant species in southern Transylvania, Romania, a region undergoing drastic land-use changes. Our findings show that invasion risk increased with landscape heterogeneity. Roads and agricultural areas were most prone to invasion, whereas forests were least at risk.

  15. Computational Prediction of Atomic Structures of Helical Membrane Proteins Aided by EM Maps

    Science.gov (United States)

    Kovacs, Julio A.; Yeager, Mark; Abagyan, Ruben

    2007-01-01

    Integral membrane proteins pose a major challenge for protein-structure prediction because only ≈100 high-resolution structures are available currently, thereby impeding the development of rules or empirical potentials to predict the packing of transmembrane α-helices. However, when an intermediate-resolution electron microscopy (EM) map is available, it can be used to provide restraints which, in combination with a suitable computational protocol, make structure prediction feasible. In this work we present such a protocol, which proceeds in three stages: 1), generation of an ensemble of α-helices by flexible fitting into each of the density rods in the low-resolution EM map, spanning a range of rotational angles around the main helical axes and translational shifts along the density rods; 2), fast optimization of side chains and scoring of the resulting conformations; and 3), refinement of the lowest-scoring conformations with internal coordinate mechanics, by optimizing the van der Waals, electrostatics, hydrogen bonding, torsional, and solvation energy contributions. In addition, our method implements a penalty term through a so-called tethering map, derived from the EM map, which restrains the positions of the α-helices. The protocol was validated on three test cases: GpA, KcsA, and MscL. PMID:17496035

  16. Risk mapping of Rinderpest sero-prevalence in Central and Southern Somalia based on spatial and network risk factors.

    Science.gov (United States)

    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

  17. Updating risk prediction tools: a case study in prostate cancer.

    Science.gov (United States)

    Ankerst, Donna P; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J; Feng, Ziding; Sanda, Martin G; Partin, Alan W; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M

    2012-01-01

    Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Enhanced clinical pharmacy service targeting tools: risk-predictive algorithms.

    Science.gov (United States)

    El Hajji, Feras W D; Scullin, Claire; Scott, Michael G; McElnay, James C

    2015-04-01

    This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management (IMM) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut-off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. Numbers of previous emergency admissions and admission medicines together with age-adjusted co-morbidity and diuretic receipt formed a 12-month post-discharge and/or readmission risk algorithm. Age-adjusted co-morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk-adjusted mortality index (RAMI). Algorithms created were valid in predicting risk of in-hospital and post-discharge mortality and risk of hospital readmission 3, 6 and 12 months post-discharge. The provision of ward-based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized. © 2014 John Wiley & Sons, Ltd.

  19. Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps

    DEFF Research Database (Denmark)

    Jørgensen, Dan Richter; Dam, Erik Bjørnager; Lillholm, Martin

    2013-01-01

    This study investigates whether measures of knee cartilage thickness can predict future loss of knee cartilage. A slow and a rapid progressor group was determined using longitudinal data, and anatomically aligned cartilage thickness maps were extracted from MRI at baseline. A novel machine learning...... framework was then trained using these maps. Compared to measures of mean cartilage plate thickness, group separation was increased by focusing on local cartilage differences. This result is central for clinical trials where inclusion of rapid progressors may help reduce the period needed to study effects...

  20. Pest risk maps for invasive alien species: a roadmap for improvement

    Science.gov (United States)

    Robert C. Venette; Darren J. Kriticos; Roger D. Magarey; Frank H. Koch; Richard H.A. Baker; Susan P. Worner; Nadilia N. Gomez Raboteaux; Daniel W. McKenney; Erhard J. Dobesberger; Denys Yemshanov; Paul J. De Barro; William D. Hutchison; Glenn Fowler; Tom M. Kalaris; John. Pedlar

    2010-01-01

    Pest risk maps are powerful visual communication tools to describe where invasive alien species might arrive, establish, spread, or cause harmful impacts. These maps inform strategic and tactical pest management decisions, such as potential restrictions on international trade or the design of pest surveys and domestic quarantines. Diverse methods are available to...

  1. Pest risk maps for invasive alien species: a roadmap for improvement

    Science.gov (United States)

    Robert C. Venette; Darren J. Kriticos; Roger D. Magarey; Frank H. Koch; Richard H. A. Baker; Susan P. Worner; Nadila N. Gomez Raboteaux; Daniel W. McKenney; Erhard J. Dobesberger; Denys Yemshanov; Paul J. De Barro; William D. Hutchinson; Glenn Fowler; Tom M. Kalaris; John. Pedlar

    2010-01-01

    Pest risk maps are powerful visual communication tools to describe where invasive alien species might arrive, establish, spread, or cause harmful impacts. These maps inform strategic and tactical pest management decisions, such as potential restrictions on international trade or the design of pest surveys and domestic quarantines. Diverse methods are available to...

  2. Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation

    NARCIS (Netherlands)

    Castellanos Abella, E.A.

    2007-01-01

    his paper explains the procedure for the generation of a landslide risk index map at national level in Cuba, using a semiquantitative model with ten indicator maps and a cell size of 90× 90 m. The model was designed and implemented using spatial multi-criteria evaluation techniques in a GIS system.

  3. Marine oil spill risk mapping for accidental pollution and its application in a coastal city.

    Science.gov (United States)

    Lan, Dongdong; Liang, Bin; Bao, Chenguang; Ma, Minghui; Xu, Yan; Yu, Chunyan

    2015-07-15

    Accidental marine oil spill pollution can result in severe environmental, ecological, economic and other consequences. This paper discussed the model of Marine Oil Spill Risk Mapping (MOSRM), which was constructed as follows: (1) proposing a marine oil spill risk system based on the typical marine oil spill pollution accidents and prevailing risk theories; (2) identifying suitable indexes that are supported by quantitative sub-indexes; (3) constructing the risk measuring models according to the actual interactions between the factors in the risk system; and (4) assessing marine oil spill risk on coastal city scale with GIS to map the overall risk. The case study of accidental marine oil spill pollution in the coastal area of Dalian, China was used to demonstrate the effectiveness of the model. The coastal areas of Dalian were divided into three zones with risk degrees of high, medium, and low. And detailed countermeasures were proposed for specific risk zones. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    the expectations hypothesis (EH) out-ofsample: the forecasts do not add economic value compared to using the average historical excess return as an EH-consistent estimate of constant risk premia. We show that in general statistical signicance does not necessarily translate into economic signicance because EH...... deviations mainly matter at short horizons and standard predictability metrics are not compatible with common measures of economic value. Overall, the EH remains the benchmark for investment decisions and should be considered an economic prior in models of bond risk premia.......This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for bond investors. We show that ane term structure models (ATSMs) estimated by jointly tting yields and bond excess returns capture this predictive information otherwise hidden...

  5. Flood-risk mapping: contributions towards an enhanced assessment of extreme events and associated risks

    Directory of Open Access Journals (Sweden)

    B. Büchele

    2006-01-01

    Full Text Available Currently, a shift from classical flood protection as engineering task towards integrated flood risk management concepts can be observed. In this context, a more consequent consideration of extreme events which exceed the design event of flood protection structures and failure scenarios such as dike breaches have to be investigated. Therefore, this study aims to enhance existing methods for hazard and risk assessment for extreme events and is divided into three parts. In the first part, a regionalization approach for flood peak discharges was further developed and substantiated, especially regarding recurrence intervals of 200 to 10 000 years and a large number of small ungauged catchments. Model comparisons show that more confidence in such flood estimates for ungauged areas and very long recurrence intervals may be given as implied by statistical analysis alone. The hydraulic simulation in the second part is oriented towards hazard mapping and risk analyses covering the whole spectrum of relevant flood events. As the hydrodynamic simulation is directly coupled with a GIS, the results can be easily processed as local inundation depths for spatial risk analyses. For this, a new GIS-based software tool was developed, being presented in the third part, which enables estimations of the direct flood damage to single buildings or areas based on different established stage-damage functions. Furthermore, a new multifactorial approach for damage estimation is presented, aiming at the improvement of damage estimation on local scale by considering factors like building quality, contamination and precautionary measures. The methods and results from this study form the base for comprehensive risk analyses and flood management strategies.

  6. Correlation of spatial climate/weather maps and the advantages of using the Mahalanobis metric in predictions

    OpenAIRE

    Stephenson, D. B.

    2011-01-01

    he skill in predicting spatially varying weather/climate maps depends on the definition of the measure of similarity between the maps. Under the justifiable approximation that the anomaly maps are distributed multinormally, it is shown analytically that the choice of weighting metric, used in defining the anomaly correlation between spatial maps, can change the resulting probability distribution of the correlation coefficient. The estimate of the numbers of degrees of freedom based on the var...

  7. Identification of the high risk emergency surgical patient: Which risk prediction model should be used?

    Science.gov (United States)

    Stonelake, Stephen; Thomson, Peter; Suggett, Nigel

    2015-09-01

    National guidance states that all patients having emergency surgery should have a mortality risk assessment calculated on admission so that the 'high risk' patient can receive the appropriate seniority and level of care. We aimed to assess if peri-operative risk scoring tools could accurately calculate mortality and morbidity risk. Mortality risk scores for 86 consecutive emergency laparotomies, were calculated using pre-operative (ASA, Lee index) and post-operative (POSSUM, P-POSSUM and CR-POSSUM) risk calculation tools. Morbidity risk scores were calculated using the POSSUM predicted morbidity and compared against actual morbidity according to the Clavien-Dindo classification. The actual mortality was 10.5%. The average predicted risk scores for all laparotomies were: ASA 26.5%, Lee Index 2.5%, POSSUM 29.5%, P-POSSUM 18.5%, CR-POSSUM 10.5%. Complications occurred following 67 laparotomies (78%). The majority (51%) of complications were classified as Clavien-Dindo grade 2-3 (non-life-threatening). Patients having a POSSUM morbidity risk of greater than 50% developed significantly more life-threatening complications (CD 4-5) compared with those who predicted less than or equal to 50% morbidity risk (P = 0.01). Pre-operative risk stratification remains a challenge because the Lee Index under-predicts and ASA over-predicts mortality risk. Post-operative risk scoring using the CR-POSSUM is more accurate and we suggest can be used to identify patients who require intensive care post-operatively. In the absence of accurate risk scoring tools that can be used on admission to hospital it is not possible to reliably audit the achievement of national standards of care for the 'high-risk' patient.

  8. Predicting disease risk using bootstrap ranking and classification algorithms.

    Directory of Open Access Journals (Sweden)

    Ohad Manor

    Full Text Available Genome-wide association studies (GWAS are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a "black box" in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF, suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.

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

    Science.gov (United States)

    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.

  10. National Insect and Disease Risk Map (NIDRM)--cutting edge software for rapid insect and disease risk model development

    Science.gov (United States)

    Frank J. Krist

    2010-01-01

    The Forest Health Technology Enterprise Team (FHTET) of the U.S. Forest Service is leading an effort to produce the next version of the National Insect and Disease Risk Map (NIDRM) for targeted release in 2011. The goal of this effort is to update spatial depictions of risk of tree mortality based on: (1) newly derived 240-m geospatial information depicting the...

  11. Investigation on Cardiovascular Risk Prediction Using Physiological Parameters

    Directory of Open Access Journals (Sweden)

    Wan-Hua Lin

    2013-01-01

    Full Text Available Cardiovascular disease (CVD is the leading cause of death worldwide. Early prediction of CVD is urgently important for timely prevention and treatment. Incorporation or modification of new risk factors that have an additional independent prognostic value of existing prediction models is widely used for improving the performance of the prediction models. This paper is to investigate the physiological parameters that are used as risk factors for the prediction of cardiovascular events, as well as summarizing the current status on the medical devices for physiological tests and discuss the potential implications for promoting CVD prevention and treatment in the future. The results show that measures extracted from blood pressure, electrocardiogram, arterial stiffness, ankle-brachial blood pressure index (ABI, and blood glucose carry valuable information for the prediction of both long-term and near-term cardiovascular risk. However, the predictive values should be further validated by more comprehensive measures. Meanwhile, advancing unobtrusive technologies and wireless communication technologies allow on-site detection of the physiological information remotely in an out-of-hospital setting in real-time. In addition with computer modeling technologies and information fusion. It may allow for personalized, quantitative, and real-time assessment of sudden CVD events.

  12. Rift Valley Fever Risk Map Model and Seroprevalence in Selected Wild Ungulates and Camels from Kenya

    Science.gov (United States)

    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.; hide

    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.

  13. Machine learning application in online lending risk prediction

    OpenAIRE

    Yu, Xiaojiao

    2017-01-01

    Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with various format and size were collected from public website, third-parties and assembled with client's loan application information data. Ensemble machine learning models, random fo...

  14. Predictive risk factors for moderate to severe hyperbilirubinemia

    OpenAIRE

    Gláucia Macedo de Lima; Maria Amélia Sayeg Campos Porto; Arnaldo Prata Barbosa; Antonio José Ledo Alves da Cunha

    2007-01-01

    Objective: to describe predictive factors for severity of neonataljaundice in newborn infants treated at the University Neonatal Clinic,highlighting maternal, obstetric and neonatal factors. Methods: Acohort retrospective study by means of review of medical charts todefine risk factors associated with moderate and severe jaundice.The cohort consisted of newborns diagnosed with indirect neonatalhyperbilirubinemia and submitted to phototherapy. Risk was classifiedas maternal, prenatal, obstetri...

  15. Challenges to mapping the health risk of hepatitis A virus infection

    Directory of Open Access Journals (Sweden)

    Wiersma Steven T

    2011-10-01

    Full Text Available Abstract Background World maps are among the most effective ways to convey public health messages such as recommended vaccinations, but creating a useful and valid map requires careful deliberation. The changing epidemiology of hepatitis A virus (HAV in many world regions heightens the need for up-to-date risk maps. HAV infection is usually asymptomatic in children, so low-income areas with high incidence rates usually have a low burden of disease. In higher-income areas, many adults remain susceptible to the virus and, if infected, often experience severe disease. Results Several challenges associated with presenting hepatitis A risk using maps were identified, including the need to decide whether prior infection or continued susceptibility more aptly indicates risk, whether to display incidence or prevalence, how to distinguish between different levels of risk, how to display changes in risk over time, how to present complex information to target audiences, and how to handle missing or obsolete data. Conclusion For future maps to be comparable across place and time, we propose the use of the age at midpoint of population susceptibility as a standard indicator for the level of hepatitis A endemicity within a world region. We also call for the creation of an accessible active database for population-based age-specific HAV seroprevalence and incidence studies. Health risk maps for other conditions with rapidly changing epidemiology would benefit from similar strategies.

  16. NOAA predicts moderate flood potential in Midwest, elevated risk of ice

    Science.gov (United States)

    March 20, 2014 U.S. Spring Flood Risk Map for 2014. U.S. Spring Flood Risk Map for 2014. (Credit: NOAA moderate flood potential in Midwest, elevated risk of ice jams; California and Southwest stuck with drought minor or moderate risk of exceeding flood levels this spring with the highest threat in the southern

  17. Risk assessment and remedial policy evaluation using predictive modeling

    International Nuclear Information System (INIS)

    Linkov, L.; Schell, W.R.

    1996-01-01

    As a result of nuclear industry operation and accidents, large areas of natural ecosystems have been contaminated by radionuclides and toxic metals. Extensive societal pressure has been exerted to decrease the radiation dose to the population and to the environment. Thus, in making abatement and remediation policy decisions, not only economic costs but also human and environmental risk assessments are desired. This paper introduces a general framework for risk assessment and remedial policy evaluation using predictive modeling. Ecological risk assessment requires evaluation of the radionuclide distribution in ecosystems. The FORESTPATH model is used for predicting the radionuclide fate in forest compartments after deposition as well as for evaluating the efficiency of remedial policies. Time of intervention and radionuclide deposition profile was predicted as being crucial for the remediation efficiency. Risk assessment conducted for a critical group of forest users in Belarus shows that consumption of forest products (berries and mushrooms) leads to about 0.004% risk of a fatal cancer annually. Cost-benefit analysis for forest cleanup suggests that complete removal of organic layer is too expensive for application in Belarus and a better methodology is required. In conclusion, FORESTPATH modeling framework could have wide applications in environmental remediation of radionuclides and toxic metals as well as in dose reconstruction and, risk-assessment

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

    Science.gov (United States)

    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.

  19. Exploring local risk managers' use of flood hazard maps for risk communication purposes in Baden-Württemberg

    Directory of Open Access Journals (Sweden)

    S. Kjellgren

    2013-07-01

    Full Text Available 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.

  20. Exploring local risk managers' use of flood hazard maps for risk communication purposes in Baden-Württemberg

    Science.gov (United States)

    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.

  1. Cartographic Design in Flood Risk Mapping - A Challenge for Communication and Stakeholder Involvement

    Science.gov (United States)

    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.

  2. THE ROLE OF RISK AVERSION IN PREDICTING INDIVIDUAL BEHAVIOR

    OpenAIRE

    Luigi Guiso; Monica Paiella

    2005-01-01

    We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to buy a risky asset. We relate this measure to a set of consumers� decisions that in theory should vary with attitude towards risk. We find that elicited risk aversion has considerable predictive power for a number of key household decisions such as choice of occupation, portfolio selection, moving decisions and exposure to chronic diseases in ways co...

  3. Improved failure prediction in forming simulations through pre-strain mapping

    Science.gov (United States)

    Upadhya, Siddharth; Staupendahl, Daniel; Heuse, Martin; Tekkaya, A. Erman

    2018-05-01

    The sensitivity of sheared edges of advanced high strength steel (AHSS) sheets to cracking during subsequent forming operations and the difficulty to predict this failure with any degree of accuracy using conventionally used FLC based failure criteria is a major problem plaguing the manufacturing industry. A possible method that allows for an accurate prediction of edge cracks is the simulation of the shearing operation and carryover of this model into a subsequent forming simulation. But even with an efficient combination of a solid element shearing operation and a shell element forming simulation, the need for a fine mesh, and the resulting high computation time makes this approach not viable from an industry point of view. The crack sensitivity of sheared edges is due to work hardening in the shear-affected zone (SAZ). A method to predict plastic strains induced by the shearing process is to measure the hardness after shearing and calculate the ultimate tensile strength as well as the flow stress. In combination with the flow curve, the relevant strain data can be obtained. To eliminate the time-intensive shearing simulation necessary to obtain the strain data in the SAZ, a new pre-strain mapping approach is proposed. The pre-strains to be mapped are, hereby, determined from hardness values obtained in the proximity of the sheared edge. To investigate the performance of this approach the ISO/TS 16630 hole expansion test was simulated with shell elements for different materials, whereby the pre-strains were mapped onto the edge of the hole. The hole expansion ratios obtained from such pre-strain mapped simulations are in close agreement with the experimental results. Furthermore, the simulations can be carried out with no increase in computation time, making this an interesting and viable solution for predicting edge failure due to shearing.

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

    Science.gov (United States)

    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.

  5. Deformation and fracture map methodology for predicting cladding behavior during dry storage

    International Nuclear Information System (INIS)

    Chin, B.A.; Khan, M.A.; Tarn, J.C.L.

    1986-09-01

    The licensing of interim dry storage of light-water reactor spent fuel requires assurance that release limits of radioactive materials are not exceeded. The extent to which Zircaloy cladding can be relied upon as a barrier to prevent release of radioactive spent fuel and fission products depends upon its integrity. The internal pressure from helium and fission gases could become a source of hoop stress for creep rupture if pressures and temperatures were sufficiently high. Consequently, it is of interest to predict the condition of spent fuel cladding during interim storage for periods up to 40 years. To develop this prediction, deformation and fracture theories were used to develop maps. Where available, experimental deformation and fracture data were used to test the validity of the maps. Predictive equations were then developed and cumulative damage methodology was used to take credit for the declining temperature of spent fuel during storage. This methodology was then used to predict storage temperatures below which creep rupture would not be expected to occur except in fuel rods with pre-existing flaws. Predictions were also made and compared with results from tests conducted under abnormal conditions

  6. Dynamic Bayesian modeling for risk prediction in credit operations

    DEFF Research Database (Denmark)

    Borchani, Hanen; Martinez, Ana Maria; Masegosa, Andres

    2015-01-01

    Our goal is to do risk prediction in credit operations, and as data is collected continuously and reported on a monthly basis, this gives rise to a streaming data classification problem. Our analysis reveals some practical problems that have not previously been thoroughly analyzed in the context...

  7. Prediction of postpartum blood transfusion – risk factors and recurrence

    DEFF Research Database (Denmark)

    Wikkelsø, Anne J; Hjortøe, Sofie; Gerds, Thomas A

    2014-01-01

    OBJECTIVE: The aim was to find clinically useful risk factors for postpartum transfusion and to assess the joint predictive value in a population of women with a first and second delivery. METHODS: All Danish women with a first and second delivery from January 2001 to September 2009 who gave birt...

  8. Predicting the risk of mineral deficiencies in grazing animals

    African Journals Online (AJOL)

    lambs to mineral supplements can be used to predict risks of deficiency will be demonstrated. In both cases .... between body size and appetite, the onset of lactation or the feeding of ... possible importance of this in the aetiology of milk fever.

  9. Mountain Risks: From Prediction to Management and Governance

    Directory of Open Access Journals (Sweden)

    David Petley

    2015-05-01

    Full Text Available Reviewed: Mountain Risks: From Prediction to Management and Governance. Edited by Theo Van Asch, Jordi Corominas, Stefan Greiving, Jean-Philippe Malet, and Sterlacchini Simone. Dordrecht, The Netherlands: Springer, 2014. xi + 413 pp. US$ 129.00, € 90.00, € 104.00. Also available as an e-book. ISBN 978-94-007-6768-3.

  10. Predicting disease risks from highly imbalanced data using random forest

    Directory of Open Access Journals (Sweden)

    Chakraborty Sounak

    2011-07-01

    Full Text Available Abstract Background We present a method utilizing Healthcare Cost and Utilization Project (HCUP dataset for predicting disease risk of individuals based on their medical diagnosis history. The presented methodology may be incorporated in a variety of applications such as risk management, tailored health communication and decision support systems in healthcare. Methods We employed the National Inpatient Sample (NIS data, which is publicly available through Healthcare Cost and Utilization Project (HCUP, to train random forest classifiers for disease prediction. Since the HCUP data is highly imbalanced, we employed an ensemble learning approach based on repeated random sub-sampling. This technique divides the training data into multiple sub-samples, while ensuring that each sub-sample is fully balanced. We compared the performance of support vector machine (SVM, bagging, boosting and RF to predict the risk of eight chronic diseases. Results We predicted eight disease categories. Overall, the RF ensemble learning method outperformed SVM, bagging and boosting in terms of the area under the receiver operating characteristic (ROC curve (AUC. In addition, RF has the advantage of computing the importance of each variable in the classification process. Conclusions In combining repeated random sub-sampling with RF, we were able to overcome the class imbalance problem and achieve promising results. Using the national HCUP data set, we predicted eight disease categories with an average AUC of 88.79%.

  11. Sandwich mapping of schistosomiasis risk in Anhui Province, China.

    Science.gov (United States)

    Hu, Yi; Bergquist, Robert; Lynn, Henry; Gao, Fenghua; Wang, Qizhi; Zhang, Shiqing; Li, Rui; Sun, Liqian; Xia, Congcong; Xiong, Chenglong; Zhang, Zhijie; Jiang, Qingwu

    2015-06-03

    Schistosomiasis mapping using data obtained from parasitological surveys is frequently used in planning and evaluation of disease control strategies. The available geostatistical approaches are, however, subject to the assumption of stationarity, a stochastic process whose joint probability distribution does not change when shifted in time. As this is impractical for large areas, we introduce here the sandwich method, the basic idea of which is to divide the study area (with its attributes) into homogeneous subareas and estimate the values for the reporting units using spatial stratified sampling. The sandwich method was applied to map the county-level prevalence of schistosomiasis japonica in Anhui Province, China based on parasitological data collected from sample villages and land use data. We first mapped the county-level prevalence using the sandwich method, then compared our findings with block Kriging. The sandwich estimates ranged from 0.17 to 0.21% with a lower level of uncertainty, while the Kriging estimates varied from 0 to 0.97% with a higher level of uncertainty, indicating that the former is more smoothed and stable compared to latter. Aside from various forms of reporting units, the sandwich method has the particular merit of simple model assumption coupled with full utilization of sample data. It performs well when a disease presents stratified heterogeneity over space.

  12. Sandwich mapping of schistosomiasis risk in Anhui Province, China

    Directory of Open Access Journals (Sweden)

    Yi Hu

    2015-06-01

    Full Text Available Schistosomiasis mapping using data obtained from parasitological surveys is frequently used in planning and evaluation of disease control strategies. The available geostatistical approaches are, however, subject to the assumption of stationarity, a stochastic process whose joint probability distribution does not change when shifted in time. As this is impractical for large areas, we introduce here the sandwich method, the basic idea of which is to divide the study area (with its attributes into homogeneous subareas and estimate the values for the reporting units using spatial stratified sampling. The sandwich method was applied to map the county-level prevalence of schistosomiasis japonica in Anhui Province, China based on parasitological data collected from sample villages and land use data. We first mapped the county-level prevalence using the sandwich method, then compared our findings with block Kriging. The sandwich estimates ranged from 0.17 to 0.21% with a lower level of uncertainty, while the Kriging estimates varied from 0 to 0.97% with a higher level of uncertainty, indicating that the former is more smoothed and stable compared to latter. Aside from various forms of reporting units, the sandwich method has the particular merit of simple model assumption coupled with full utilization of sample data. It performs well when a disease presents stratified heterogeneity over space.

  13. Provision of a wildfire risk map: informing residents in the wildland urban interface.

    Science.gov (United States)

    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.

  14. The role of risk propensity in predicting self-employment.

    Science.gov (United States)

    Nieß, Christiane; Biemann, Torsten

    2014-09-01

    This study aims to untangle the role of risk propensity as a predictor of self-employment entry and self-employment survival. More specifically, it examines whether the potentially positive effect of risk propensity on the decision to become self-employed turns curvilinear when it comes to the survival of the business. Building on a longitudinal sample of 4,973 individuals from the German Socio-Economic Panel, we used event history analyses to evaluate the influence of risk propensity on self-employment over a 7-year time period. Results indicated that whereas high levels of risk propensity positively predicted the decision to become self-employed, the relationship between risk propensity and self-employment survival followed an inverted U-shaped curve. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  15. Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation

    International Nuclear Information System (INIS)

    Wang, Yan; Zhou, Jiliu; Zhang, Pei; An, Le; Ma, Guangkai; Kang, Jiayin; Shi, Feng; Shen, Dinggang; Wu, Xi; Lalush, David S; Lin, Weili

    2016-01-01

    Positron emission tomography (PET) has been widely used in clinical diagnosis for diseases and disorders. To obtain high-quality PET images requires a standard-dose radionuclide (tracer) injection into the human body, which inevitably increases risk of radiation exposure. One possible solution to this problem is to predict the standard-dose PET image from its low-dose counterpart and its corresponding multimodal magnetic resonance (MR) images. Inspired by the success of patch-based sparse representation (SR) in super-resolution image reconstruction, we propose a mapping-based SR (m-SR) framework for standard-dose PET image prediction. Compared with the conventional patch-based SR, our method uses a mapping strategy to ensure that the sparse coefficients, estimated from the multimodal MR images and low-dose PET image, can be applied directly to the prediction of standard-dose PET image. As the mapping between multimodal MR images (or low-dose PET image) and standard-dose PET images can be particularly complex, one step of mapping is often insufficient. To this end, an incremental refinement framework is therefore proposed. Specifically, the predicted standard-dose PET image is further mapped to the target standard-dose PET image, and then the SR is performed again to predict a new standard-dose PET image. This procedure can be repeated for prediction refinement of the iterations. Also, a patch selection based dictionary construction method is further used to speed up the prediction process. The proposed method is validated on a human brain dataset. The experimental results show that our method can outperform benchmark methods in both qualitative and quantitative measures. (paper)

  16. Predicting risk of violence through a self-appraisal questionnaire

    Directory of Open Access Journals (Sweden)

    José Manuel Andreu-Rodríguez

    2016-07-01

    Full Text Available The Self-Appraisal Questionnaire (SAQ is a self-report that predicts the risk of violence and recidivism and provides relevant information about treatment needs for incarcerated populations. The objective of the present study was to evaluate the concurrent and predictive validity of this self-report in Spanish offenders. The SAQ was administered to 276 offenders recruited from several prisons in Madrid (Spain. SAQ total scores presented high levels of internal consistency (alpha = .92. Correlations of the instrument with violence risk instruments were statistically significant and showed a moderate magnitude, indicating a reasonable degree of concurrent validity. The ROC analysis carried out on the SAQ total score revealed an AUC of .80, showing acceptable accuracy discriminating between violent and nonviolent recidivist groups. It is concluded that the SAQ total score is a reliable and valid measure to estimate violence and recidivism risk in Spanish offenders.

  17. Risk predictive modelling for diabetes and cardiovascular disease.

    Science.gov (United States)

    Kengne, Andre Pascal; Masconi, Katya; Mbanya, Vivian Nchanchou; Lekoubou, Alain; Echouffo-Tcheugui, Justin Basile; Matsha, Tandi E

    2014-02-01

    Absolute risk models or clinical prediction models have been incorporated in guidelines, and are increasingly advocated as tools to assist risk stratification and guide prevention and treatments decisions relating to common health conditions such as cardiovascular disease (CVD) and diabetes mellitus. We have reviewed the historical development and principles of prediction research, including their statistical underpinning, as well as implications for routine practice, with a focus on predictive modelling for CVD and diabetes. Predictive modelling for CVD risk, which has developed over the last five decades, has been largely influenced by the Framingham Heart Study investigators, while it is only ∼20 years ago that similar efforts were started in the field of diabetes. Identification of predictive factors is an important preliminary step which provides the knowledge base on potential predictors to be tested for inclusion during the statistical derivation of the final model. The derived models must then be tested both on the development sample (internal validation) and on other populations in different settings (external validation). Updating procedures (e.g. recalibration) should be used to improve the performance of models that fail the tests of external validation. Ultimately, the effect of introducing validated models in routine practice on the process and outcomes of care as well as its cost-effectiveness should be tested in impact studies before wide dissemination of models beyond the research context. Several predictions models have been developed for CVD or diabetes, but very few have been externally validated or tested in impact studies, and their comparative performance has yet to be fully assessed. A shift of focus from developing new CVD or diabetes prediction models to validating the existing ones will improve their adoption in routine practice.

  18. Calibration plots for risk prediction models in the presence of competing risks.

    Science.gov (United States)

    Gerds, Thomas A; Andersen, Per K; Kattan, Michael W

    2014-08-15

    A predicted risk of 17% can be called reliable if it can be expected that the event will occur to about 17 of 100 patients who all received a predicted risk of 17%. Statistical models can predict the absolute risk of an event such as cardiovascular death in the presence of competing risks such as death due to other causes. For personalized medicine and patient counseling, it is necessary to check that the model is calibrated in the sense that it provides reliable predictions for all subjects. There are three often encountered practical problems when the aim is to display or test if a risk prediction model is well calibrated. The first is lack of independent validation data, the second is right censoring, and the third is that when the risk scale is continuous, the estimation problem is as difficult as density estimation. To deal with these problems, we propose to estimate calibration curves for competing risks models based on jackknife pseudo-values that are combined with a nearest neighborhood smoother and a cross-validation approach to deal with all three problems. Copyright © 2014 John Wiley & Sons, Ltd.

  19. Mapping the receptivity of malaria risk to plan the future of control in Somalia

    Science.gov (United States)

    Alegana, Victor Adagi; Patil, Anand Prabhakar; Moloney, Grainne; Borle, Mohammed; Yusuf, Fahmi; Amran, Jamal; Snow, Robert William

    2012-01-01

    Objectives To measure the receptive risks of malaria in Somalia and compare decisions on intervention scale-up based on this map and the more widely used contemporary risk maps. Design Cross-sectional community Plasmodium falciparum parasite rate (PfPR) data for the period 2007–2010 corrected to a standard age range of 2 to Somalia. Participants Randomly sampled individuals of all ages. Main outcome measure Cartographic descriptions of malaria receptivity and contemporary risks in Somalia at the district level. Results The contemporary annual PfPR2–10 map estimated that all districts (n=74) and population (n=8.4 million) in Somalia were under hypoendemic transmission (≤10% PfPR2–10). Of these, 23% of the districts, home to 13% of the population, were under transmission of 10%–50% PfPR2–10) and the rest as hypoendemic. Conclusion Compared with maps of receptive risks, contemporary maps of transmission mask disparities of malaria risk necessary to prioritise and sustain future control. As malaria risk declines across Africa, efforts must be invested in measuring receptivity for efficient control planning. PMID:22855625

  20. Providing access to risk prediction tools via the HL7 XML-formatted risk web service.

    Science.gov (United States)

    Chipman, Jonathan; Drohan, Brian; Blackford, Amanda; Parmigiani, Giovanni; Hughes, Kevin; Bosinoff, Phil

    2013-07-01

    Cancer risk prediction tools provide valuable information to clinicians but remain computationally challenging. Many clinics find that CaGene or HughesRiskApps fit their needs for easy- and ready-to-use software to obtain cancer risks; however, these resources may not fit all clinics' needs. The HughesRiskApps Group and BayesMendel Lab therefore developed a web service, called "Risk Service", which may be integrated into any client software to quickly obtain standardized and up-to-date risk predictions for BayesMendel tools (BRCAPRO, MMRpro, PancPRO, and MelaPRO), the Tyrer-Cuzick IBIS Breast Cancer Risk Evaluation Tool, and the Colorectal Cancer Risk Assessment Tool. Software clients that can convert their local structured data into the HL7 XML-formatted family and clinical patient history (Pedigree model) may integrate with the Risk Service. The Risk Service uses Apache Tomcat and Apache Axis2 technologies to provide an all Java web service. The software client sends HL7 XML information containing anonymized family and clinical history to a Dana-Farber Cancer Institute (DFCI) server, where it is parsed, interpreted, and processed by multiple risk tools. The Risk Service then formats the results into an HL7 style message and returns the risk predictions to the originating software client. Upon consent, users may allow DFCI to maintain the data for future research. The Risk Service implementation is exemplified through HughesRiskApps. The Risk Service broadens the availability of valuable, up-to-date cancer risk tools and allows clinics and researchers to integrate risk prediction tools into their own software interface designed for their needs. Each software package can collect risk data using its own interface, and display the results using its own interface, while using a central, up-to-date risk calculator. This allows users to choose from multiple interfaces while always getting the latest risk calculations. Consenting users contribute their data for future

  1. The Reliability and Predictive Validity of the Stalking Risk Profile.

    Science.gov (United States)

    McEwan, Troy E; Shea, Daniel E; Daffern, Michael; MacKenzie, Rachel D; Ogloff, James R P; Mullen, Paul E

    2018-03-01

    This study assessed the reliability and validity of the Stalking Risk Profile (SRP), a structured measure for assessing stalking risks. The SRP was administered at the point of assessment or retrospectively from file review for 241 adult stalkers (91% male) referred to a community-based forensic mental health service. Interrater reliability was high for stalker type, and moderate-to-substantial for risk judgments and domain scores. Evidence for predictive validity and discrimination between stalking recidivists and nonrecidivists for risk judgments depended on follow-up duration. Discrimination was moderate (area under the curve = 0.66-0.68) and positive and negative predictive values good over the full follow-up period ( Mdn = 170.43 weeks). At 6 months, discrimination was better than chance only for judgments related to stalking of new victims (area under the curve = 0.75); however, high-risk stalkers still reoffended against their original victim(s) 2 to 4 times as often as low-risk stalkers. Implications for the clinical utility and refinement of the SRP are discussed.

  2. Seismic risk maps of Switzerland; description of the probabilistic method and discussion of some input parameters

    International Nuclear Information System (INIS)

    Mayer-Rosa, D.; Merz, H.A.

    1976-01-01

    The probabilistic model used in a seismic risk mapping project for Switzerland is presented. Some of its advantages and limitations are spelled out. In addition some earthquake parameters which should be carefully investigated before using them in a seismic risk analysis are discussed

  3. Mapping cumulative environmental risks: examples from the EU NoMiracle project

    NARCIS (Netherlands)

    Pistocchi, A.; Groenwold, J.; Lahr, J.; Loos, M.; Mujica, M.; Ragas, A.M.J.; Rallo, R.; Sala, S.; Schlink, U.; Strebel, K.; Vighi, M.; Vizcaino, P.

    2011-01-01

    We present examples of cumulative chemical risk mapping methods developed within the NoMiracle project. The different examples illustrate the application of the concentration addition (CA) approach to pesticides at different scale, the integration in space of cumulative risks to individual organisms

  4. Fine mapping in the MHC region accounts for 18% additional genetic risk for celiac disease

    NARCIS (Netherlands)

    Gutierrez-Achury, Javier; Zhernakova, Alexandra; Pulit, Sara L.; Trynka, Gosia; Hunt, Karen A.; Romanos, Jihane; Raychaudhuri, Soumya; van Heel, David A.; Wijmenga, Cisca; de Balcker, Paul I. W.

    Although dietary gluten is the trigger for celiac disease, risk is strongly influenced by genetic variation in the major histocompatibility complex (MHC) region. We fine mapped the MHC association signal to identify additional risk factors independent of the HLA-DQA1 and HLA-DQB1 alleles and

  5. Environmental risk mapping of pollutants: state of the art and communication aspects

    NARCIS (Netherlands)

    Lahr, J.; Kooistra, L.

    2010-01-01

    Risk maps help risk analysts and scientists to explore the spatial nature of the effects of environmental stressors such as pollutants. The development of Geographic Information Systems over the past few decades has greatly improved spatial representation and analysis of environmental information

  6. Mapping between Classical Risk Management and Game Theoretical Approaches

    OpenAIRE

    Rajbhandari , Lisa; Snekkenes , Einar ,

    2011-01-01

    Part 2: Work in Progress; International audience; In a typical classical risk assessment approach, the probabilities are usually guessed and not much guidance is provided on how to get the probabilities right. When coming up with probabilities, people are generally not well calibrated. History may not always be a very good teacher. Hence, in this paper, we explain how game theory can be integrated into classical risk management. Game theory puts emphasis on collecting representative data on h...

  7. Estimation of risk map for cohort study of Hiroshima atomic bomb survivors. 1970-2010

    International Nuclear Information System (INIS)

    Tonda, Tetsuji; Satoh, Kenichi; Otani, Keiko; Sato, Yuya; Maruyama, Hirofomi; Kawakami, Hideshi; Tashiro, Satoshi; Hoshi, Masaharu; Ohtaki, Megu

    2012-01-01

    A risk map (map I) involving the effects of direct A-bomb exposure and of other confounding factors was estimated to analyze the death risk in the geographic distribution, and another risk map (map II) was also made by subtracting the direct exposure effect to see the confounder effect. The cohort was 37,382/157,327 survivors at Jan. 1, 1970, whose positional coordinates at the exposure were known, and was followed up until Dec. 31, 2009. For survival analysis, the endpoint was defined to be death (total 19,119) by regarding other 18,263 as censoring. Confounding factors were sex, age at the exposure, exposed dose and shielded condition. Maps I and II were depicted using the hazard ratio at the exposed position relative to the hypocenter, which was estimated by previously reported hazard model functions. Map I was found to be rather similar to concentric circle of the hypocenter, but to be tended a bit distorted toward northwest area. The distortion was clearer in the map II, indicating that death causes other than direct exposure existed. The confounder was thought to be the indirect exposure through the black rain, residual radiation and/or internal exposure, which awaiting future investigation. (T.T.)

  8. Mapping soil erosion risk in Serra de Grândola (Portugal)

    Science.gov (United States)

    Neto Paixão, H. M.; Granja Martins, F. M.; Zavala, L. M.; Jordán, A.; Bellinfante, N.

    2012-04-01

    Geomorphological processes can pose environmental risks to people and economical activities. Information and a better knowledge of the genesis of these processes is important for environmental planning, since it allows to model, quantify and classify risks, what can mitigate the threats. The objective of this research is to assess the soil erosion risk in Serra de Grândola, which is a north-south oriented mountain ridge with an altitude of 383 m, located in southwest of Alentejo (southern Portugal). The study area is 675 km2, including the councils of Grândola, Santiago do Cacém and Sines. The process for mapping of erosive status was based on the guidelines for measuring and mapping the processes of erosion of coastal areas of the Mediterranean proposed by PAP/RAC (1997), developed and later modified by other authors in different areas. This method is based on the application of a geographic information system that integrates different types of spatial information inserted into a digital terrain model and in their derivative models. Erosive status are classified using information from soil erodibility, slope, land use and vegetation cover. The rainfall erosivity map was obtained using the modified Fournier index, calculated from the mean monthly rainfall, as recorded in 30 meteorological stations with influence in the study area. Finally, the soil erosion risk map was designed by ovelaying the erosive status map and the rainfall erosivity map.

  9. Feasibility study of geospatial mapping of chronic disease risk to inform public health commissioning.

    Science.gov (United States)

    Noble, Douglas; Smith, Dianna; Mathur, Rohini; Robson, John; Greenhalgh, Trisha

    2012-01-01

    To explore the feasibility of producing small-area geospatial maps of chronic disease risk for use by clinical commissioning groups and public health teams. Cross-sectional geospatial analysis using routinely collected general practitioner electronic record data. Tower Hamlets, an inner-city district of London, UK, characterised by high socioeconomic and ethnic diversity and high prevalence of non-communicable diseases. The authors used type 2 diabetes as an example. The data set was drawn from electronic general practice records on all non-diabetic individuals aged 25-79 years in the district (n=163 275). The authors used a validated instrument, QDScore, to calculate 10-year risk of developing type 2 diabetes. Using specialist mapping software (ArcGIS), the authors produced visualisations of how these data varied by lower and middle super output area across the district. The authors enhanced these maps with information on examples of locality-based social determinants of health (population density, fast food outlets and green spaces). Data were piloted as three types of geospatial map (basic, heat and ring). The authors noted practical, technical and information governance challenges involved in producing the maps. Usable data were obtained on 96.2% of all records. One in 11 adults in our cohort was at 'high risk' of developing type 2 diabetes with a 20% or more 10-year risk. Small-area geospatial mapping illustrated 'hot spots' where up to 17.3% of all adults were at high risk of developing type 2 diabetes. Ring maps allowed visualisation of high risk for type 2 diabetes by locality alongside putative social determinants in the same locality. The task of downloading, cleaning and mapping data from electronic general practice records posed some technical challenges, and judgement was required to group data at an appropriate geographical level. Information governance issues were time consuming and required local and national consultation and agreement. Producing

  10. Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool.

    Science.gov (United States)

    Veeravagu, Anand; Li, Amy; Swinney, Christian; Tian, Lu; Moraff, Adrienne; Azad, Tej D; Cheng, Ivan; Alamin, Todd; Hu, Serena S; Anderson, Robert L; Shuer, Lawrence; Desai, Atman; Park, Jon; Olshen, Richard A; Ratliff, John K

    2017-07-01

    OBJECTIVE The ability to assess the risk of adverse events based on known patient factors and comorbidities would provide more effective preoperative risk stratification. Present risk assessment in spine surgery is limited. An adverse event prediction tool was developed to predict the risk of complications after spine surgery and tested on a prospective patient cohort. METHODS The spinal Risk Assessment Tool (RAT), a novel instrument for the assessment of risk for patients undergoing spine surgery that was developed based on an administrative claims database, was prospectively applied to 246 patients undergoing 257 spinal procedures over a 3-month period. Prospectively collected data were used to compare the RAT to the Charlson Comorbidity Index (CCI) and the American College of Surgeons National Surgery Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator. Study end point was occurrence and type of complication after spine surgery. RESULTS The authors identified 69 patients (73 procedures) who experienced a complication over the prospective study period. Cardiac complications were most common (10.2%). Receiver operating characteristic (ROC) curves were calculated to compare complication outcomes using the different assessment tools. Area under the curve (AUC) analysis showed comparable predictive accuracy between the RAT and the ACS NSQIP calculator (0.670 [95% CI 0.60-0.74] in RAT, 0.669 [95% CI 0.60-0.74] in NSQIP). The CCI was not accurate in predicting complication occurrence (0.55 [95% CI 0.48-0.62]). The RAT produced mean probabilities of 34.6% for patients who had a complication and 24% for patients who did not (p = 0.0003). The generated predicted values were stratified into low, medium, and high rates. For the RAT, the predicted complication rate was 10.1% in the low-risk group (observed rate 12.8%), 21.9% in the medium-risk group (observed 31.8%), and 49.7% in the high-risk group (observed 41.2%). The ACS NSQIP calculator consistently

  11. Machine learning derived risk prediction of anorexia nervosa.

    Science.gov (United States)

    Guo, Yiran; Wei, Zhi; Keating, Brendan J; Hakonarson, Hakon

    2016-01-20

    Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children's Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC's of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.

  12. Predictive risk factors for moderate to severe hyperbilirubinemia

    Directory of Open Access Journals (Sweden)

    Gláucia Macedo de Lima

    2007-12-01

    Full Text Available Objective: to describe predictive factors for severity of neonataljaundice in newborn infants treated at the University Neonatal Clinic,highlighting maternal, obstetric and neonatal factors. Methods: Acohort retrospective study by means of review of medical charts todefine risk factors associated with moderate and severe jaundice.The cohort consisted of newborns diagnosed with indirect neonatalhyperbilirubinemia and submitted to phototherapy. Risk was classifiedas maternal, prenatal, obstetric and neonatal factors; risk estimationwas based on the odds ratio (95% confidence interval; a bi-variantmultivariate regression logistic analysis was applied to variables forp < 0.1. Results: Of 818 babies born during the studied period, 94(11% had jaundice prior to hospital discharge. Phototherapy was usedon 69 (73% patients. Predictive factors for severity were multiparity;prolonged rupture of membranes, dystocia, cephalohematoma, a lowApgar score, prematurity and small-for-date babies. Following birth,breastfeeding, sepsis, Rh incompatibility, and jaundice presentingbefore the third day of life were associated with an increased risk ofhyperbilirubinemia and the need for therapy. Conclusion: Other thanthose characteristics that are singly associated with phototherapy,we concluded that multiparity, presumed neonatal asphyxia, low birthweight and infection are the main predictive factors leading to moderateand severe jaundice in newborn infants in our neonatal unit.

  13. A risk prediction model for xerostomia: a retrospective cohort study.

    Science.gov (United States)

    Villa, Alessandro; Nordio, Francesco; Gohel, Anita

    2016-12-01

    We investigated the prevalence of xerostomia in dental patients and built a xerostomia risk prediction model by incorporating a wide range of risk factors. Socio-demographic data, past medical history, self-reported dry mouth and related symptoms were collected retrospectively from January 2010 to September 2013 for all new dental patients. A logistic regression framework was used to build a risk prediction model for xerostomia. External validation was performed using an independent data set to test the prediction power. A total of 12 682 patients were included in this analysis (54.3%, females). Xerostomia was reported by 12.2% of patients. The proportion of people reporting xerostomia was higher among those who were taking more medications (OR = 1.11, 95% CI = 1.08-1.13) or recreational drug users (OR = 1.4, 95% CI = 1.1-1.9). Rheumatic diseases (OR = 2.17, 95% CI = 1.88-2.51), psychiatric diseases (OR = 2.34, 95% CI = 2.05-2.68), eating disorders (OR = 2.28, 95% CI = 1.55-3.36) and radiotherapy (OR = 2.00, 95% CI = 1.43-2.80) were good predictors of xerostomia. For the test model performance, the ROC-AUC was 0.816 and in the external validation sample, the ROC-AUC was 0.799. The xerostomia risk prediction model had high accuracy and discriminated between high- and low-risk individuals. Clinicians could use this model to identify the classes of medications and systemic diseases associated with xerostomia. © 2015 John Wiley & Sons A/S and The Gerodontology Association. Published by John Wiley & Sons Ltd.

  14. Lung cancer in never smokers Epidemiology and risk prediction models

    Science.gov (United States)

    McCarthy, William J.; Meza, Rafael; Jeon, Jihyoun; Moolgavkar, Suresh

    2012-01-01

    In this chapter we review the epidemiology of lung cancer incidence and mortality among never smokers/ nonsmokers and describe the never smoker lung cancer risk models used by CISNET modelers. Our review focuses on those influences likely to have measurable population impact on never smoker risk, such as secondhand smoke, even though the individual-level impact may be small. Occupational exposures may also contribute importantly to the population attributable risk of lung cancer. We examine the following risk factors in this chapter: age, environmental tobacco smoke, cooking fumes, ionizing radiation including radon gas, inherited genetic susceptibility, selected occupational exposures, preexisting lung disease, and oncogenic viruses. We also compare the prevalence of never smokers between the three CISNET smoking scenarios and present the corresponding lung cancer mortality estimates among never smokers as predicted by a typical CISNET model. PMID:22882894

  15. Using participatory risk mapping (PRM to identify and understand people's perceptions of crop loss to animals in Uganda.

    Directory of Open Access Journals (Sweden)

    Amanda D Webber

    Full Text Available 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

  16. Cumulative risk hypothesis: Predicting and preventing child maltreatment recidivism.

    Science.gov (United States)

    Solomon, David; Åsberg, Kia; Peer, Samuel; Prince, Gwendolyn

    2016-08-01

    Although Child Protective Services (CPS) and other child welfare agencies aim to prevent further maltreatment in cases of child abuse and neglect, recidivism is common. Having a better understanding of recidivism predictors could aid in preventing additional instances of maltreatment. A previous study identified two CPS interventions that predicted recidivism: psychotherapy for the parent, which was related to a reduced risk of recidivism, and temporary removal of the child from the parent's custody, which was related to an increased recidivism risk. However, counter to expectations, this previous study did not identify any other specific risk factors related to maltreatment recidivism. For the current study, it was hypothesized that (a) cumulative risk (i.e., the total number of risk factors) would significantly predict maltreatment recidivism above and beyond intervention variables in a sample of CPS case files and that (b) therapy for the parent would be related to a reduced likelihood of recidivism. Because it was believed that the relation between temporary removal of a child from the parent's custody and maltreatment recidivism is explained by cumulative risk, the study also hypothesized that that the relation between temporary removal of the child from the parent's custody and recidivism would be mediated by cumulative risk. After performing a hierarchical logistic regression analysis, the first two hypotheses were supported, and an additional predictor, psychotherapy for the child, also was related to reduced chances of recidivism. However, Hypothesis 3 was not supported, as risk did not significantly mediate the relation between temporary removal and recidivism. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

    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.

  18. Predicting risk and human reliability: a new approach

    International Nuclear Information System (INIS)

    Duffey, R.; Ha, T.-S.

    2009-01-01

    Learning from experience describes human reliability and skill acquisition, and the resulting theory has been validated by comparison against millions of outcome data from multiple industries and technologies worldwide. The resulting predictions were used to benchmark the classic first generation human reliability methods adopted in probabilistic risk assessments. The learning rate, probabilities and response times are also consistent with the existing psychological models for human learning and error correction. The new approach also implies a finite lower bound probability that is not predicted by empirical statistical distributions that ignore the known and fundamental learning effects. (author)

  19. Mapping the nursing care with the NIC for patients in risk for pressure ulcer

    Directory of Open Access Journals (Sweden)

    Ana Gabriela Silva Pereira

    2014-06-01

    Full Text Available Objective:To identify the nursing care prescribed for patients in risk for pressure ulcer (PU and to compare those with the Nursing Interventions Classification (NIC interventions. Method: Cross mapping study conducted in a university hospital. The sample was composed of 219 adult patients hospitalized in clinical and surgical units. The inclusion criteria were: score ≤ 13 in the Braden Scale and one of the nursing diagnoses, Self-Care deficit syndrome, Impaired physical mobility, Impaired tissue integrity, Impaired skin integrity, Risk for impaired skin integrity. The data were collected retrospectively in a nursing prescription system and statistically analyzed by crossed mapping. Result: It was identified 32 different nursing cares to prevent PU, mapped in 17 different NIC interventions, within them: Skin surveillance, Pressure ulcer prevention and Positioning. Conclusion: The cross mapping showed similarities between the prescribed nursing care and the NIC interventions.

  20. Risk prediction of cardiovascular death based on the QTc interval

    DEFF Research Database (Denmark)

    Nielsen, Jonas B; Graff, Claus; Rasmussen, Peter V

    2014-01-01

    electrocardiograms from 173 529 primary care patients aged 50-90 years were collected during 2001-11. The Framingham formula was used for heart rate-correction of the QT interval. Data on medication, comorbidity, and outcomes were retrieved from administrative registries. During a median follow-up period of 6......AIMS: Using a large, contemporary primary care population we aimed to provide absolute long-term risks of cardiovascular death (CVD) based on the QTc interval and to test whether the QTc interval is of value in risk prediction of CVD on an individual level. METHODS AND RESULTS: Digital...

  1. Risk determination after an acute myocardial infarction: review of 3 clinical risk prediction tools.

    Science.gov (United States)

    Scruth, Elizabeth Ann; Page, Karen; Cheng, Eugene; Campbell, Michelle; Worrall-Carter, Linda

    2012-01-01

    The objective of the study was to provide comprehensive information for the clinical nurse specialist (CNS) on commonly used clinical prediction (risk assessment) tools used to estimate risk of a secondary cardiac or noncardiac event and mortality in patients undergoing primary percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction (STEMI). The evolution and widespread adoption of primary PCI represent major advances in the treatment of acute myocardial infarction, specifically STEMI. The American College of Cardiology and the American Heart Association have recommended early risk stratification for patients presenting with acute coronary syndromes using several clinical risk scores to identify patients' mortality and secondary event risk after PCI. Clinical nurse specialists are integral to any performance improvement strategy. Their knowledge and understandings of clinical prediction tools will be essential in carrying out important assessment, identifying and managing risk in patients who have sustained a STEMI, and enhancing discharge education including counseling on medications and lifestyle changes. Over the past 2 decades, risk scores have been developed from clinical trials to facilitate risk assessment. There are several risk scores that can be used to determine in-hospital and short-term survival. This article critiques the most common tools: the Thrombolytic in Myocardial Infarction risk score, the Global Registry of Acute Coronary Events risk score, and the Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications risk score. The importance of incorporating risk screening assessment tools (that are important for clinical prediction models) to guide therapeutic management of patients cannot be underestimated. The ability to forecast secondary risk after a STEMI will assist in determining which patients would require the most aggressive level of treatment and monitoring postintervention including

  2. Nonparametric predictive inference for combined competing risks data

    International Nuclear Information System (INIS)

    Coolen-Maturi, Tahani; Coolen, Frank P.A.

    2014-01-01

    The nonparametric predictive inference (NPI) approach for competing risks data has recently been presented, in particular addressing the question due to which of the competing risks the next unit will fail, and also considering the effects of unobserved, re-defined, unknown or removed competing risks. In this paper, we introduce how the NPI approach can be used to deal with situations where units are not all at risk from all competing risks. This may typically occur if one combines information from multiple samples, which can, e.g. be related to further aspects of units that define the samples or groups to which the units belong or to different applications where the circumstances under which the units operate can vary. We study the effect of combining the additional information from these multiple samples, so effectively borrowing information on specific competing risks from other units, on the inferences. Such combination of information can be relevant to competing risks scenarios in a variety of application areas, including engineering and medical studies

  3. Predicting adolescent's cyberbullying behavior: A longitudinal risk analysis.

    Science.gov (United States)

    Barlett, Christopher P

    2015-06-01

    The current study used the risk factor approach to test the unique and combined influence of several possible risk factors for cyberbullying attitudes and behavior using a four-wave longitudinal design with an adolescent US sample. Participants (N = 96; average age = 15.50 years) completed measures of cyberbullying attitudes, perceptions of anonymity, cyberbullying behavior, and demographics four times throughout the academic school year. Several logistic regression equations were used to test the contribution of these possible risk factors. Results showed that (a) cyberbullying attitudes and previous cyberbullying behavior were important unique risk factors for later cyberbullying behavior, (b) anonymity and previous cyberbullying behavior were valid risk factors for later cyberbullying attitudes, and (c) the likelihood of engaging in later cyberbullying behavior increased with the addition of risk factors. Overall, results show the unique and combined influence of such risk factors for predicting later cyberbullying behavior. Results are discussed in terms of theory. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  4. Improving default risk prediction using Bayesian model uncertainty techniques.

    Science.gov (United States)

    Kazemi, Reza; Mosleh, Ali

    2012-11-01

    Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from "nominal predictions" due to "upsetting events" such as the 2008 global banking crisis. © 2012 Society for Risk Analysis.

  5. Representing human-mediated pathways in forest pest risk mapping

    Science.gov (United States)

    Frank H. Koch; William D. Smith

    2010-01-01

    Historically, U.S. forests have been invaded by a variety of nonindigenous insects and pathogens. Some of these pests have catastrophically impacted important species over a relatively short timeframe. To curtail future changes of this magnitude, agencies such as the U.S. Department of Agriculture Forest Service have devoted substantial resources to assessing the risks...

  6. analysis and mapping of climate change risk and vulnerability

    African Journals Online (AJOL)

    ACSS

    Rift Valley (CRV) of Ethiopia, to determine the degree of climate risk and the relative vulnerability of the districts, to climate .... widely used index for quantifying drought, was extracted from ... semivariogram/Covariance model) in ArcGIS 9.3.

  7. Probabilistic landslide hazards and risk mapping on Penang Island ...

    Indian Academy of Sciences (India)

    This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geo- .... require a priori knowledge of the main causes of landslides .... Soil. Rengam-bukit. 289450. 10.03. 96. 20.73. 2.07 temiang association. Selangor-kangkong. 34197. 1.18. 0. 0.00. 0.00 association. Local alluvium-. 373655.

  8. Mapping Malaria Transmission Risk in Northern Morocco Using Entomological and Environmental Data

    Directory of Open Access Journals (Sweden)

    E. Adlaoui

    2011-01-01

    Full Text Available 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.

  9. nuMap: a web platform for accurate prediction of nucleosome positioning.

    Science.gov (United States)

    Alharbi, Bader A; Alshammari, Thamir H; Felton, Nathan L; Zhurkin, Victor B; Cui, Feng

    2014-10-01

    Nucleosome positioning is critical for gene expression and of major biological interest. The high cost of experimentally mapping nucleosomal arrangement signifies the need for computational approaches to predict nucleosome positions at high resolution. Here, we present a web-based application to fulfill this need by implementing two models, YR and W/S schemes, for the translational and rotational positioning of nucleosomes, respectively. Our methods are based on sequence-dependent anisotropic bending that dictates how DNA is wrapped around a histone octamer. This application allows users to specify a number of options such as schemes and parameters for threading calculation and provides multiple layout formats. The nuMap is implemented in Java/Perl/MySQL and is freely available for public use at http://numap.rit.edu. The user manual, implementation notes, description of the methodology and examples are available at the site. Copyright © 2014 The Authors. Production and hosting by Elsevier Ltd.. All rights reserved.

  10. nuMap: A Web Platform for Accurate Prediction of Nucleosome Positioning

    Directory of Open Access Journals (Sweden)

    Bader A. Alharbi

    2014-10-01

    Full Text Available Nucleosome positioning is critical for gene expression and of major biological interest. The high cost of experimentally mapping nucleosomal arrangement signifies the need for computational approaches to predict nucleosome positions at high resolution. Here, we present a web-based application to fulfill this need by implementing two models, YR and W/S schemes, for the translational and rotational positioning of nucleosomes, respectively. Our methods are based on sequence-dependent anisotropic bending that dictates how DNA is wrapped around a histone octamer. This application allows users to specify a number of options such as schemes and parameters for threading calculation and provides multiple layout formats. The nuMap is implemented in Java/Perl/MySQL and is freely available for public use at http://numap.rit.edu. The user manual, implementation notes, description of the methodology and examples are available at the site.

  11. Effect of Co-segregating Markers on High-Density Genetic Maps and Prediction of Map Expansion Using Machine Learning Algorithms.

    Science.gov (United States)

    N'Diaye, Amidou; Haile, Jemanesh K; Fowler, D Brian; Ammar, Karim; Pozniak, Curtis J

    2017-01-01

    Advances in sequencing and genotyping methods have enable cost-effective production of high throughput single nucleotide polymorphism (SNP) markers, making them the choice for linkage mapping. As a result, many laboratories have developed high-throughput SNP assays and built high-density genetic maps. However, the number of markers may, by orders of magnitude, exceed the resolution of recombination for a given population size so that only a minority of markers can accurately be ordered. Another issue attached to the so-called 'large p, small n' problem is that high-density genetic maps inevitably result in many markers clustering at the same position (co-segregating markers). While there are a number of related papers, none have addressed the impact of co-segregating markers on genetic maps. In the present study, we investigated the effects of co-segregating markers on high-density genetic map length and marker order using empirical data from two populations of wheat, Mohawk × Cocorit (durum wheat) and Norstar × Cappelle Desprez (bread wheat). The maps of both populations consisted of 85% co-segregating markers. Our study clearly showed that excess of co-segregating markers can lead to map expansion, but has little effect on markers order. To estimate the inflation factor (IF), we generated a total of 24,473 linkage maps (8,203 maps for Mohawk × Cocorit and 16,270 maps for Norstar × Cappelle Desprez). Using seven machine learning algorithms, we were able to predict with an accuracy of 0.7 the map expansion due to the proportion of co-segregating markers. For example in Mohawk × Cocorit, with 10 and 80% co-segregating markers the length of the map inflated by 4.5 and 16.6%, respectively. Similarly, the map of Norstar × Cappelle Desprez expanded by 3.8 and 11.7% with 10 and 80% co-segregating markers. With the increasing number of markers on SNP-chips, the proportion of co-segregating markers in high-density maps will continue to increase making map expansion

  12. Effect of Co-segregating Markers on High-Density Genetic Maps and Prediction of Map Expansion Using Machine Learning Algorithms

    Directory of Open Access Journals (Sweden)

    Amidou N’Diaye

    2017-08-01

    Full Text Available Advances in sequencing and genotyping methods have enable cost-effective production of high throughput single nucleotide polymorphism (SNP markers, making them the choice for linkage mapping. As a result, many laboratories have developed high-throughput SNP assays and built high-density genetic maps. However, the number of markers may, by orders of magnitude, exceed the resolution of recombination for a given population size so that only a minority of markers can accurately be ordered. Another issue attached to the so-called ‘large p, small n’ problem is that high-density genetic maps inevitably result in many markers clustering at the same position (co-segregating markers. While there are a number of related papers, none have addressed the impact of co-segregating markers on genetic maps. In the present study, we investigated the effects of co-segregating markers on high-density genetic map length and marker order using empirical data from two populations of wheat, Mohawk × Cocorit (durum wheat and Norstar × Cappelle Desprez (bread wheat. The maps of both populations consisted of 85% co-segregating markers. Our study clearly showed that excess of co-segregating markers can lead to map expansion, but has little effect on markers order. To estimate the inflation factor (IF, we generated a total of 24,473 linkage maps (8,203 maps for Mohawk × Cocorit and 16,270 maps for Norstar × Cappelle Desprez. Using seven machine learning algorithms, we were able to predict with an accuracy of 0.7 the map expansion due to the proportion of co-segregating markers. For example in Mohawk × Cocorit, with 10 and 80% co-segregating markers the length of the map inflated by 4.5 and 16.6%, respectively. Similarly, the map of Norstar × Cappelle Desprez expanded by 3.8 and 11.7% with 10 and 80% co-segregating markers. With the increasing number of markers on SNP-chips, the proportion of co-segregating markers in high-density maps will continue to increase

  13. Risk prediction, safety analysis and quantitative probability methods - a caveat

    International Nuclear Information System (INIS)

    Critchley, O.H.

    1976-01-01

    Views are expressed on the use of quantitative techniques for the determination of value judgements in nuclear safety assessments, hazard evaluation, and risk prediction. Caution is urged when attempts are made to quantify value judgements in the field of nuclear safety. Criteria are given the meaningful application of reliability methods but doubts are expressed about their application to safety analysis, risk prediction and design guidances for experimental or prototype plant. Doubts are also expressed about some concomitant methods of population dose evaluation. The complexities of new designs of nuclear power plants make the problem of safety assessment more difficult but some possible approaches are suggested as alternatives to the quantitative techniques criticized. (U.K.)

  14. Public Health and Epidemiological Considerations For Avian Influenza Risk Mapping and Risk Assessment

    Directory of Open Access Journals (Sweden)

    Joseph P. Dudley

    2008-12-01

    Full Text Available Avian influenza viruses are now widely recognized as important threats to agricultural biosecurity and public health, and as the potential source for pandemic human influenza viruses. Human infections with avian influenza viruses have been reported from Asia (H5N1, H5N2, H9N2, Africa (H5N1, H10N7, Europe (H7N7, H7N3, H7N2, and North America (H7N3, H7N2, H11N9. Direct and indirect public health risks from avian influenzas are not restricted to the highly pathogenic H5N1 "bird flu" virus, and include low pathogenic as well as high pathogenic strains of other avian influenza virus subtypes, e.g., H1N1, H7N2, H7N3, H7N7, and H9N2. Research has shown that the 1918 Spanish Flu pandemic was caused by an H1N1 influenza virus of avian origins, and during the past decade, fatal human disease and human-to-human transmission has been confirmed among persons infected with H5N1 and H7N7 avian influenza viruses. Our ability to accurately assess and map the potential economic and public health risks associated with avian influenza outbreaks is currently constrained by uncertainties regarding key aspects of the ecology and epidemiology of avian influenza viruses in birds and humans, and the mechanisms by which highly pathogenic avian influenza viruses are transmitted between and among wild birds, domestic poultry, mammals, and humans. Key factors needing further investigation from a risk management perspective include identification of the driving forces behind the emergence and persistence of highly pathogenic avian influenza viruses within poultry populations, and a comprehensive understanding of the mechanisms regulating transmission of highly pathogenic avian influenza viruses between industrial poultry farms and backyard poultry flocks. More information is needed regarding the extent to which migratory bird populations to contribute to the transnational and transcontinental spread of highly pathogenic avian influenza viruses, and the potential for wild bird

  15. Predictions of space radiation fatality risk for exploration missions.

    Science.gov (United States)

    Cucinotta, Francis A; To, Khiet; Cacao, Eliedonna

    2017-05-01

    In this paper we describe revisions to the NASA Space Cancer Risk (NSCR) model focusing on updates to probability distribution functions (PDF) representing the uncertainties in the radiation quality factor (QF) model parameters and the dose and dose-rate reduction effectiveness factor (DDREF). We integrate recent heavy ion data on liver, colorectal, intestinal, lung, and Harderian gland tumors with other data from fission neutron experiments into the model analysis. In an earlier work we introduced distinct QFs for leukemia and solid cancer risk predictions, and here we consider liver cancer risks separately because of the higher RBE's reported in mouse experiments compared to other tumors types, and distinct risk factors for liver cancer for astronauts compared to the U.S. The revised model is used to make predictions of fatal cancer and circulatory disease risks for 1-year deep space and International Space Station (ISS) missions, and a 940 day Mars mission. We analyzed the contribution of the various model parameter uncertainties to the overall uncertainty, which shows that the uncertainties in relative biological effectiveness (RBE) factors at high LET due to statistical uncertainties and differences across tissue types and mouse strains are the dominant uncertainty. NASA's exposure limits are approached or exceeded for each mission scenario considered. Two main conclusions are made: 1) Reducing the current estimate of about a 3-fold uncertainty to a 2-fold or lower uncertainty will require much more expansive animal carcinogenesis studies in order to reduce statistical uncertainties and understand tissue, sex and genetic variations. 2) Alternative model assumptions such as non-targeted effects, increased tumor lethality and decreased latency at high LET, and non-cancer mortality risks from circulatory diseases could significantly increase risk estimates to several times higher than the NASA limits. Copyright © 2017 The Committee on Space Research (COSPAR

  16. Predicting epidemic risk from past temporal contact data.

    Directory of Open Access Journals (Sweden)

    Eugenio Valdano

    2015-03-01

    Full Text Available Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system's functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system's pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node's loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node's epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies.

  17. Application of Fuzzy Logic Inference System, Interval Numbers and Mapping Operator for Determination of Risk Level

    Directory of Open Access Journals (Sweden)

    Mohsen Omidvar

    2015-12-01

    Full Text Available Background & objective: Due to the features such as intuitive graphical appearance, ease of perception and straightforward applicability, risk matrix has become as one of the most used risk assessment tools. On the other hand, features such as the lack of precision in the classification of risk index, as well as subjective computational process, has limited its use. In order to solve this problem, in the current study we used fuzzy logic inference systems and mathematical operators (interval numbers and mapping operator. Methods: In this study, first 10 risk scenarios in the excavation and piping process were selected, then the outcome of the risk assessment were studied using four types of matrix including traditional (ORM, displaced cells (RCM , extended (ERM and fuzzy (FRM risk matrixes. Results: The results showed that the use of FRM and ERM matrix have prority, due to the high level of " Risk Tie Density" (RTD and "Risk Level Density" (RLD in the ORM and RCM matrix, as well as more accurate results presented in FRM and ERM, in risk assessment. While, FRM matrix provides more reliable results due to the application of fuzzy membership functions. Conclusion: Using new mathematical issues such as fuzzy sets and arithmetic and mapping operators for risk assessment could improve the accuracy of risk matrix and increase the reliability of the risk assessment results, when the accurate data are not available, or its data are avaliable in a limit range.

  18. Predicting chromosomal locations of genetically mapped loci in maize using the Morgan2McClintock Translator.

    Science.gov (United States)

    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.

  19. The FTLD risk factor TMEM106B and MAP6 control dendritic trafficking of lysosomes

    Science.gov (United States)

    Schwenk, Benjamin M; Lang, Christina M; Hogl, Sebastian; Tahirovic, Sabina; Orozco, Denise; Rentzsch, Kristin; Lichtenthaler, Stefan F; Hoogenraad, Casper C; Capell, Anja; Haass, Christian; Edbauer, Dieter

    2014-01-01

    TMEM106B is a major risk factor for frontotemporal lobar degeneration with TDP-43 pathology. TMEM106B localizes to lysosomes, but its function remains unclear. We show that TMEM106B knockdown in primary neurons affects lysosomal trafficking and blunts dendritic arborization. We identify microtubule-associated protein 6 (MAP6) as novel interacting protein for TMEM106B. MAP6 over-expression inhibits dendritic branching similar to TMEM106B knockdown. MAP6 knockdown fully rescues the dendritic phenotype of TMEM106B knockdown, supporting a functional interaction between TMEM106B and MAP6. Live imaging reveals that TMEM106B knockdown and MAP6 overexpression strongly increase retrograde transport of lysosomes in dendrites. Downregulation of MAP6 in TMEM106B knockdown neurons restores the balance of anterograde and retrograde lysosomal transport and thereby prevents loss of dendrites. To strengthen the link, we enhanced anterograde lysosomal transport by expressing dominant-negative Rab7-interacting lysosomal protein (RILP), which also rescues the dendrite loss in TMEM106B knockdown neurons. Thus, TMEM106B/MAP6 interaction is crucial for controlling dendritic trafficking of lysosomes, presumably by acting as a molecular brake for retrograde transport. Lysosomal misrouting may promote neurodegeneration in patients with TMEM106B risk variants. PMID:24357581

  20. Participatory three dimensional mapping for the preparation of landslide disaster risk reduction program

    Science.gov (United States)

    Kusratmoko, Eko; Wibowo, Adi; Cholid, Sofyan; Pin, Tjiong Giok

    2017-07-01

    This paper presents the results of applications of participatory three dimensional mapping (P3DM) method for fqcilitating the people of Cibanteng' village to compile a landslide disaster risk reduction program. Physical factors, as high rainfall, topography, geology and land use, and coupled with the condition of demographic and social-economic factors, make up the Cibanteng region highly susceptible to landslides. During the years 2013-2014 has happened 2 times landslides which caused economic losses, as a result of damage to homes and farmland. Participatory mapping is one part of the activities of community-based disaster risk reduction (CBDRR)), because of the involvement of local communities is a prerequisite for sustainable disaster risk reduction. In this activity, participatory mapping method are done in two ways, namely participatory two-dimensional mapping (P2DM) with a focus on mapping of disaster areas and participatory three-dimensional mapping (P3DM) with a focus on the entire territory of the village. Based on the results P3DM, the ability of the communities in understanding the village environment spatially well-tested and honed, so as to facilitate the preparation of the CBDRR programs. Furthermore, the P3DM method can be applied to another disaster areas, due to it becomes a medium of effective dialogue between all levels of involved communities.

  1. How to make predictions about future infectious disease risks

    Science.gov (United States)

    Woolhouse, Mark

    2011-01-01

    Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models. PMID:21624924

  2. Predictive Brain Mechanisms in Sound-to-Meaning Mapping during Speech Processing.

    Science.gov (United States)

    Lyu, Bingjiang; Ge, Jianqiao; Niu, Zhendong; Tan, Li Hai; Gao, Jia-Hong

    2016-10-19

    Spoken language comprehension relies not only on the identification of individual words, but also on the expectations arising from contextual information. A distributed frontotemporal network is known to facilitate the mapping of speech sounds onto their corresponding meanings. However, how prior expectations influence this efficient mapping at the neuroanatomical level, especially in terms of individual words, remains unclear. Using fMRI, we addressed this question in the framework of the dual-stream model by scanning native speakers of Mandarin Chinese, a language highly dependent on context. We found that, within the ventral pathway, the violated expectations elicited stronger activations in the left anterior superior temporal gyrus and the ventral inferior frontal gyrus (IFG) for the phonological-semantic prediction of spoken words. Functional connectivity analysis showed that expectations were mediated by both top-down modulation from the left ventral IFG to the anterior temporal regions and enhanced cross-stream integration through strengthened connections between different subregions of the left IFG. By further investigating the dynamic causality within the dual-stream model, we elucidated how the human brain accomplishes sound-to-meaning mapping for words in a predictive manner. In daily communication via spoken language, one of the core processes is understanding the words being used. Effortless and efficient information exchange via speech relies not only on the identification of individual spoken words, but also on the contextual information giving rise to expected meanings. Despite the accumulating evidence for the bottom-up perception of auditory input, it is still not fully understood how the top-down modulation is achieved in the extensive frontotemporal cortical network. Here, we provide a comprehensive description of the neural substrates underlying sound-to-meaning mapping and demonstrate how the dual-stream model functions in the modulation of

  3. The Economic Value of Predicting Bond Risk Premia

    DEFF Research Database (Denmark)

    Sarno, Lucio; Schneider, Paul; Wagner, Christian

    2016-01-01

    evaluation. More specifically, the model mostly generates positive (negative) economic value during times of high (low) macroeconomic uncertainty. Overall, the expectations hypothesis remains a useful benchmark for investment decisions in bond markets, especially in low uncertainty states.......This paper studies whether the evident statistical predictability of bond risk premia translates into economic gains for investors. We propose a novel estimation strategy for affine term structure models that jointly fits yields and bond excess returns, thereby capturing predictive information...... otherwise hidden to standard estimations. The model predicts excess returns with high regression R2s and high forecast accuracy but cannot outperform the expectations hypothesis out-of-sample in terms of economic value, showing a general contrast between statistical and economic metrics of forecast...

  4. Predicted cancer risks induced by computed tomography examinations during childhood, by a quantitative risk assessment approach.

    Science.gov (United States)

    Journy, Neige; Ancelet, Sophie; Rehel, Jean-Luc; Mezzarobba, Myriam; Aubert, Bernard; Laurier, Dominique; Bernier, Marie-Odile

    2014-03-01

    The potential adverse effects associated with exposure to ionizing radiation from computed tomography (CT) in pediatrics must be characterized in relation to their expected clinical benefits. Additional epidemiological data are, however, still awaited for providing a lifelong overview of potential cancer risks. This paper gives predictions of potential lifetime risks of cancer incidence that would be induced by CT examinations during childhood in French routine practices in pediatrics. Organ doses were estimated from standard radiological protocols in 15 hospitals. Excess risks of leukemia, brain/central nervous system, breast and thyroid cancers were predicted from dose-response models estimated in the Japanese atomic bomb survivors' dataset and studies of medical exposures. Uncertainty in predictions was quantified using Monte Carlo simulations. This approach predicts that 100,000 skull/brain scans in 5-year-old children would result in eight (90 % uncertainty interval (UI) 1-55) brain/CNS cancers and four (90 % UI 1-14) cases of leukemia and that 100,000 chest scans would lead to 31 (90 % UI 9-101) thyroid cancers, 55 (90 % UI 20-158) breast cancers, and one (90 % UI risks without exposure). Compared to background risks, radiation-induced risks would be low for individuals throughout life, but relative risks would be highest in the first decades of life. Heterogeneity in the radiological protocols across the hospitals implies that 5-10 % of CT examinations would be related to risks 1.4-3.6 times higher than those for the median doses. Overall excess relative risks in exposed populations would be 1-10 % depending on the site of cancer and the duration of follow-up. The results emphasize the potential risks of cancer specifically from standard CT examinations in pediatrics and underline the necessity of optimization of radiological protocols.

  5. Predictive Modelling Risk Calculators and the Non Dialysis Pathway.

    Science.gov (United States)

    Robins, Jennifer; Katz, Ivor

    2013-04-16

    This guideline will review the current prediction models and survival/mortality scores available for decision making in patients with advanced kidney disease who are being considered for a non-dialysis treatment pathway. Risk prediction is gaining increasing attention with emerging literature suggesting improved patient outcomes through individualised risk prediction (1). Predictive models help inform the nephrologist and the renal palliative care specialists in their discussions with patients and families about suitability or otherwise of dialysis. Clinical decision making in the care of end stage kidney disease (ESKD) patients on a non-dialysis treatment pathway is currently governed by several observational trials (3). Despite the paucity of evidence based medicine in this field, it is becoming evident that the survival advantages associated with renal replacement therapy in these often elderly patients with multiple co-morbidities and limited functional status may be negated by loss of quality of life (7) (6), further functional decline (5, 8), increased complications and hospitalisations. This article is protected by copyright. All rights reserved.

  6. Adige river in Trento flooding map, 1892: private or public risk transfer?

    Science.gov (United States)

    Ranzi, Roberto

    2016-04-01

    For the determination of the flood risk hydrologist and hydraulic engineers focuse their attention mainly to the estimation of physical factors determining the flood hazard, while economists and experts of social sciences deal mainly with the estimation of vulnerability and exposure. The fact that flood zoning involves both hydrological and socio-economic aspects, however, was clear already in the XIX century when the impact of floods on inundated areas started to appear in flood maps, for instance in the UK and in Italy. A pioneering 'flood risk' map for the Adige river in Trento, Italy, was already published in 1892, taking into account in detail both hazard intensity in terms of velocity and depth, frequency of occurrence, vulnerability and economic costs for flood protection with river embankments. This map is likely to be the reinterpreted certainly as a pioneering, and possibly as the first flood risk map for an Italian river and worldwide. Risk levels were divided in three categories and seven sub-categories, depending on flood water depth, velocity, frequency and damage costs. It is interesting to notice the fact that at that time the map was used to share the cost of levees' reparation and enhancement after the severe September 1882 flood as a function of the estimated level of protection of the respective areas against the flood risk. The sharing of costs between public bodies, the railway company and private owners was debated for about 20 years and at the end the public sustained the major costs. This shows how already at that time the economic assessment of structural flood protections was based on objective and rational cost-benefit criteria, that hydraulic risk mapping was perceived by the society as fundamental for the design of flood protection systems and that a balanced cost sharing between public and private was an accepted approach although some protests arose at that time.

  7. Prediction of health risks from accidents: A comprehensive assessment methodology

    International Nuclear Information System (INIS)

    MacFarlane, D.R.; Yuan, Y.C.

    1992-01-01

    We have developed two computer programs to predict radiation risks to individuals and/or the collective population from exposures to accidental releases of radioactive materials. When used together, these two codes provide a consistent, comprehensive tool to estimate not only the risks to specific individuals but also the distribution of risks in the exposed population and the total number of individuals within a specific level of risk. Prompt and latent fatalities are estimated for the exposed population, and from these, the risk to an average individual can be derived. Uncertainty in weather conditions is considered by estimating both the ''median'' and the ''maximum'' population doses based on the frequency distribution of wind speeds and stabilities for a given site. The importance of including all dispersible particles (particles smaller than about 100 μm) for dose and health risk analyses from nonfiltered releases for receptor locations within about 10 km from a release has been investigated. The dose contribution of the large particles (> 10 μm) has been shown to be substantially greater than those from the small particles for the dose receptors in various release and exposure conditions. These conditions include, particularly, elevated releases, strong wind weather, and exposure pathways associated with ground-deposited material over extended periods of time

  8. Predicting risk behaviors: development and validation of a diagnostic scale.

    Science.gov (United States)

    Witte, K; Cameron, K A; McKeon, J K; Berkowitz, J M

    1996-01-01

    The goal of this study was to develop and validate the Risk Behavior Diagnosis (RBD) Scale for use by health care providers and practitioners interested in promoting healthy behaviors. Theoretically guided by the Extended Parallel Process Model (EPPM; a fear appeal theory), the RBD scale was designed to work in conjunction with an easy-to-use formula to determine which types of health risk messages would be most appropriate for a given individual or audience. Because some health risk messages promote behavior change and others backfire, this type of scale offers guidance to practitioners on how to develop the best persuasive message possible to motivate healthy behaviors. The results of the study demonstrate the RBD scale to have a high degree of content, construct, and predictive validity. Specific examples and practical suggestions are offered to facilitate use of the scale for health practitioners.

  9. Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.

    Science.gov (United States)

    Mahony, Shaun; McInerney, James O; Smith, Terry J; Golden, Aaron

    2004-03-05

    Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

  10. A new multicriteria risk mapping approach based on a multiattribute frontier concept.

    Science.gov (United States)

    Yemshanov, Denys; Koch, Frank H; Ben-Haim, Yakov; Downing, Marla; Sapio, Frank; Siltanen, Marty

    2013-09-01

    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 typically require prior knowledge of the risk components' importance. Such information is often nonexistent for many invasive pests. This study proposes a new approach for building integrated risk maps using the principle of a multiattribute efficient frontier and analyzing the partial order of elements of a risk map as distributed in multidimensional criteria space. The integrated risks are estimated as subsequent multiattribute frontiers in dimensions of individual risk criteria. We demonstrate the approach with the example of Agrilus biguttatus Fabricius, a high-risk pest that may threaten North American oak forests in the near future. Drawing on U.S. and Canadian data, we compare the performance of the multiattribute ranking against a multicriteria linear weighted averaging technique in the presence of uncertainties, using the concept of robustness from info-gap decision theory. The results show major geographic hotspots where the consideration of tradeoffs between multiple risk components changes integrated risk rankings. Both methods delineate similar geographical regions of high and low risks. Overall, aggregation based on a delineation of multiattribute efficient frontiers can be a useful tool to prioritize risks for anticipated invasive pests, which usually have an extremely poor prior knowledge base. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

  11. Integrated mapping of establishment risk for emerging vector-borne infections: a case study of canine leishmaniasis in southwest France.

    Directory of Open Access Journals (Sweden)

    Nienke Hartemink

    Full Text Available BACKGROUND: Zoonotic visceral leishmaniasis is endemic in the Mediterranean Basin, where the dog is the main reservoir host. The disease's causative agent, Leishmania infantum, is transmitted by blood-feeding female sandflies. This paper reports an integrative study of canine leishmaniasis in a region of France spanning the southwest Massif Central and the northeast Pyrenees, where the vectors are the sandflies Phlebotomus ariasi and P. perniciosus. METHODS: Sandflies were sampled in 2005 using sticky traps placed uniformly over an area of approximately 100 by 150 km. High- and low-resolution satellite data for the area were combined to construct a model of the sandfly data, which was then used to predict sandfly abundance throughout the area on a pixel by pixel basis (resolution of c. 1 km. Using literature- and expert-derived estimates of other variables and parameters, a spatially explicit R(0 map for leishmaniasis was constructed within a Geographical Information System. R(0 is a measure of the risk of establishment of a disease in an area, and it also correlates with the amount of control needed to stop transmission. CONCLUSIONS: To our knowledge, this is the first analysis that combines a vector abundance prediction model, based on remotely-sensed variables measured at different levels of spatial resolution, with a fully mechanistic process-based temperature-dependent R(0 model. The resulting maps should be considered as proofs-of-principle rather than as ready-to-use risk maps, since validation is currently not possible. The described approach, based on integrating several modeling methods, provides a useful new set of tools for the study of the risk of outbreaks of vector-borne diseases.

  12. Modeling the Ecological Niche of Bacillus anthracis to Map Anthrax Risk in Kyrgyzstan.

    Science.gov (United States)

    Blackburn, Jason K; Matakarimov, Saitbek; Kozhokeeva, Sabira; Tagaeva, Zhyldyz; Bell, Lindsay K; Kracalik, Ian T; Zhunushov, Asankadyr

    2017-03-01

    AbstractAnthrax, caused by the environmental bacterium Bacillus anthracis , is an important zoonosis nearly worldwide. In Central Asia, anthrax represents a major veterinary and public health concern. In the Republic of Kyrgyzstan, ongoing anthrax outbreaks have been reported in humans associated with handling infected livestock and contaminated animal by-products such as meat or hides. The current anthrax situation has prompted calls for improved insights into the epidemiology, ecology, and spatial distribution of the disease in Kyrgyzstan to better inform control and surveillance. Disease control for both humans and livestock relies on annual livestock vaccination ahead of outbreaks. Toward this, we used a historic database of livestock anthrax reported from 1932 to 2006 mapped at high resolution to develop an ecological niche model-based prediction of B. anthracis across Kyrgyzstan and identified spatial clusters of livestock anthrax using a cluster morphology statistic. We also defined the seasonality of outbreaks in livestock. Cattle were the most frequently reported across the time period, with the greatest number of cases in late summer months. Our niche models defined four areas as suitable to support pathogen persistence, the plateaus near Talas and Bishkek, the valleys of western Kyrgyzstan along the Fergana Valley, and the low-lying areas along the shore of Lake Isyk-Kul. These areas should be considered "at risk" for livestock anthrax and subsequent human cases. Areas defined by the niche models can be used to prioritize anthrax surveillance and inform efforts to target livestock vaccination campaigns.

  13. An invasion risk map for non-native aquatic macrophytes of the Iberian Peninsula

    Directory of Open Access Journals (Sweden)

    Argantonio Rodríguez-Merino

    2017-05-01

    Full Text Available Freshwater systems are particularly susceptible to non-native organisms, owing to their high sensitivity to the impacts that are caused by these organisms. Species distribution models, which are based on both environmental and socio-economic variables, facilitate the identification of the most vulnerable areas for the spread of non-native species. We used MaxEnt to predict the potential distribution of 20 non-native aquatic macrophytes in the Iberian Peninsula. Some selected variables, such as the temperature seasonality and the precipitation in the driest quarter, highlight the importance of the climate on their distribution. Notably, the human influence in the territory appears as a key variable in the distribution of studied species. The model discriminated between favorable and unfavorable areas with high accuracy. We used the model to build an invasion risk map of aquatic macrophytes for the Iberian Peninsula that included results from 20 individual models. It showed that the most vulnerable areas are located near to the sea, the major rivers basins, and the high population density areas. These facts suggest the importance of the human impact on the colonization and distribution of non-native aquatic macrophytes in the Iberian Peninsula, and more precisely agricultural development during the Green Revolution at the end of the 70’s. Our work also emphasizes the utility of species distribution models for the prevention and management of biological invasions.

  14. Mapping regional risks from climate change for rainfed rice cultivation in India.

    Science.gov (United States)

    Singh, Kuntal; McClean, Colin J; Büker, Patrick; Hartley, Sue E; Hill, Jane K

    2017-09-01

    Global warming is predicted to increase in the future, with detrimental consequences for rainfed crops that are dependent on natural rainfall (i.e. non-irrigated). Given that many crops grown under rainfed conditions support the livelihoods of low-income farmers, it is important to highlight the vulnerability of rainfed areas to climate change in order to anticipate potential risks to food security. In this paper, we focus on India, where ~ 50% of rice is grown under rainfed conditions, and we employ statistical models (climate envelope models (CEMs) and boosted regression trees (BRTs)) to map changes in climate suitability for rainfed rice cultivation at a regional level (~ 18 × 18 km cell resolution) under projected future (2050) climate change (IPCC RCPs 2.6 and 8.5, using three GCMs: BCC-CSM1.1, MIROC-ESM-CHEM, and HadGEM2-ES). We quantify the occurrence of rice (whether or not rainfed rice is commonly grown, using CEMs) and rice extent (area under cultivation, using BRTs) during the summer monsoon in relation to four climate variables that affect rice growth and yield namely ratio of precipitation to evapotranspiration ( PER ), maximum and minimum temperatures ( T max and T min ), and total rainfall during harvesting. Our models described the occurrence and extent of rice very well (CEMs for occurrence, ensemble AUC = 0.92; BRTs for extent, Pearson's r = 0.87). PER was the most important predictor of rainfed rice occurrence, and it was positively related to rainfed rice area, but all four climate variables were important for determining the extent of rice cultivation. Our models project that 15%-40% of current rainfed rice growing areas will be at risk (i.e. decline in climate suitability or become completely unsuitable). However, our models project considerable variation across India in the impact of future climate change: eastern and northern India are the locations most at risk, but parts of central and western India may benefit from increased

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

    Science.gov (United States)

    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.

  16. A Novel Risk Scoring System Reliably Predicts Readmission Following Pancreatectomy

    Science.gov (United States)

    Valero, Vicente; Grimm, Joshua C.; Kilic, Arman; Lewis, Russell L.; Tosoian, Jeffrey J.; He, Jin; Griffin, James; Cameron, John L.; Weiss, Matthew J.; Vollmer, Charles M.; Wolfgang, Christopher L.

    2015-01-01

    Background Postoperative readmissions have been proposed by Medicare as a quality metric and may impact provider reimbursement. Since readmission following pancreatectomy is common, we sought to identify factors associated with readmission in order to establish a predictive risk scoring system (RSS). Study Design A retrospective analysis of 2,360 pancreatectomies performed at nine, high-volume pancreatic centers between 2005 and 2011 was performed. Forty-five factors strongly associated with readmission were identified. To derive and validate a RSS, the population was randomly divided into two cohorts in a 4:1 fashion. A multivariable logistic regression model was constructed and scores were assigned based on the relative odds ratio of each independent predictor. A composite Readmission After Pancreatectomy (RAP) score was generated and then stratified to create risk groups. Results Overall, 464 (19.7%) patients were readmitted within 90-days. Eight pre- and postoperative factors, including prior myocardial infarction (OR 2.03), ASA Class ≥ 3 (OR 1.34), dementia (OR 6.22), hemorrhage (OR 1.81), delayed gastric emptying (OR 1.78), surgical site infection (OR 3.31), sepsis (OR 3.10) and short length of stay (OR 1.51), were independently predictive of readmission. The 32-point RAP score generated from the derivation cohort was highly predictive of readmission in the validation cohort (AUC 0.72). The low (0-3), intermediate (4-7) and high risk (>7) groups correlated to 11.7%, 17.5% and 45.4% observed readmission rates, respectively (preadmission following pancreatectomy. Identification of patients with increased risk of readmission using the RAP score will allow efficient resource allocation aimed to attenuate readmission rates. It also has potential to serve as a new metric for comparative research and quality assessment. PMID:25797757

  17. Prediction of tension-type headache risk in adolescents

    Directory of Open Access Journals (Sweden)

    K. A. Stepanchenko

    2016-08-01

    Full Text Available Tension-type headache is the actual problem of adolescent neurology, which is associated with the prevalence of the disease, the tendency of the disease to the chronic course and a negative impact on performance in education, work capacity and quality of patients’ life. The aim. To develop a method for prediction of tension-type headache occurrence in adolescents. Materials and methods. 2342 adolescent boys and girls at the age of 13-17 years in schools of Kharkiv were examined. We used questionnaire to identify the headache. A group of adolescents with tension-type headache - 1430 people (61.1% was selected. The control group included 246 healthy adolescents. Possible risk factors for tension-type headache formation were divided into 4 groups: genetic, biomedical, psychosocial and social. Mathematical prediction of tension-type headache risk in adolescents was performed using the method of intensive indicators normalization of E.N. Shigan, which was based on probabilistic Bayesian’s method. The result was presented in the form of prognostic coefficients. Results. The most informative risk factors for tension-type headache development were the diseases, from which the teenager suffered after 1 year (sleep disorders, gastrointestinal diseases, autonomic disorders in the family history, traumatic brain injury, physical inactivity, poor adaptation of the patient in the kindergarten and school, stresses. Diagnostic scale has been developed to predict the risk of tension-type headache. It includes 23 prognostic factors with their gradation and meaning of integrated risk indicator, depending on individual factor strength influence. The risk of tension-type headache development ranged from 25,27 to 81,43 values of prognostic coefficient (low probability (25,27-43,99, the average probability (43,99-62,71 and high probability (62,71- 81,43. Conclusion. The study of tension-type headache risk factors, which were obtained by using an assessed and

  18. Developing a climate-based risk map of fascioliasis outbreaks in Iran

    Directory of Open Access Journals (Sweden)

    Mansour Halimi

    2015-09-01

    Full Text Available Summary: The strong relationship between climate and fascioliasis outbreaks enables the development of climate-based models to estimate the potential risk of fascioliasis outbreaks. This work aims to develop a climate-based risk map of fascioliasis outbreaks in Iran using Ollerenshaw's fascioliasis risk index incorporating geographical information system (GIS. Using this index, a risk map of fascioliasis outbreaks for the entire country was developed. We determined that the country can be divided into 4 fascioliasis outbreak risk categories. Class 1, in which the Mt value is less than 100, includes more than 0.91 of the country's area. The climate in this class is not conducive to fascioliasis outbreaks in any month. Dryness and low temperature in the wet season (December to April are the key barriers against fascioliasis outbreaks in this class. The risk map developed based on climatic factors indicated that only 0.03 of the country's area, including Gilan province in the northern region of Iran, is highly suitable to fascioliasis outbreaks during September to January. The Mt value is greater than 500 in this class. Heavy rainfall in the summer and fall, especially in Rasht, Astara and Bandar Anzaly (≥1000 mm/year, creates more suitable breeding places for snail intermediate hosts. Keywords: Ollerenshaw fascioliasis risk index, Climate, Gilan province, Iran

  19. Spatial analysis and risk mapping of Fasciola hepatica infection in dairy herds in Ireland

    Directory of Open Access Journals (Sweden)

    Nikolaos Selemetas

    2015-03-01

    Full Text Available Fasciolosis is generally a subclinical infection of dairy cows and can cause marked economic losses. This study investigated the prevalence and spatial distribution of fasciolosis in dairy cow herds in Ireland using an in-house antibodydetection enzyme-linked immunosorbent assay applied to bulk tank milk (BTM samples collected during the autumn of 2012. A total of 5,116 BTM samples were collected from 4,602 different herds, with 514 farmers submitting BTM samples in two consecutive months. Analysis of the BTM samples showed that 82% (n = 3,764 of the dairy herds had been exposed to Fasciola hepatica. A total of 108 variables, including averaged climatic data for the period 1981-2010 and contemporary meteorological data for the year 2012, such as soil, subsoil, land cover and habitat maps, were investigated for a possible role as predictor of fasciolosis. Using mainly climatic variables as the major predictors, a model of the predicted risk of fasciolosis was created by Random Forest modelling that had 95% sensitivity and 100% specificity. The most important predictors in descending order of importance were: average of annual total number of rain-days for the period 1981-2010, total rainfall during September, winter and autumn of 2012, average of annual total number of wet-days for the period 1981- 2010 and annual mean temperature of 2012. The findings of this study confirm the high prevalence of fasciolosis in Irish dairy herds and suggest that specific weather and environmental risk factors support a robust and precise distribution model.

  20. Dynamical prediction and pattern mapping in short-term load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Aguirre, Luis Antonio; Rodrigues, Daniela D.; Lima, Silvio T. [Departamento de Engenharia Eletronica, Universidade Federal de Minas Gerais, Av. Antonio Carlos, 6627, 31270-901 Belo Horizonte, MG (Brazil); Martinez, Carlos Barreira [Departamento de Engenharia Hidraulica e Recursos Hidricos, Universidade Federal de Minas Gerais, Av. Antonio Carlos, 6627, 31270-901 Belo Horizonte, MG (Brazil)

    2008-01-15

    This work will not put forward yet another scheme for short-term load forecasting but rather will provide evidences that may improve our understanding about fundamental issues which underlay load forecasting problems. In particular, load forecasting will be decomposed into two main problems, namely dynamical prediction and pattern mapping. It is argued that whereas the latter is essentially static and becomes nonlinear when weekly features in the data are taken into account, the former might not be deterministic at all. In such cases there is no determinism (serial correlations) in the data apart from the average cycle and the best a model can do is to perform pattern mapping. Moreover, when there is determinism in addition to the average cycle, the underlying dynamics are sometimes linear, in which case there is no need to resort to nonlinear models to perform dynamical prediction. Such conclusions were confirmed using real load data and surrogate data analysis. In a sense, the paper details and organizes some general beliefs found in the literature on load forecasting. This sheds some light on real model-building and forecasting problems and helps understand some apparently conflicting results reported in the literature. (author)

  1. Predictive cytogenetic biomarkers for colorectal neoplasia in medium risk patients.

    Science.gov (United States)

    Ionescu, E M; Nicolaie, T; Ionescu, M A; Becheanu, G; Andrei, F; Diculescu, M; Ciocirlan, M

    2015-01-01

    DNA damage and chromosomal alterations in peripheral lymphocytes parallels DNA mutations in tumor tissues. The aim of our study was to predict the presence of neoplastic colorectal lesions by specific biomarkers in "medium risk" individuals (age 50 to 75, with no personal or family of any colorectal neoplasia). We designed a prospective cohort observational study including patients undergoing diagnostic or opportunistic screening colonoscopy. Specific biomarkers were analyzed for each patient in peripheral lymphocytes - presence of micronuclei (MN), nucleoplasmic bridges (NPB) and the Nuclear Division Index (NDI) by the cytokinesis-blocked micronucleus assay (CBMN). Of 98 patients included, 57 were "medium risk" individuals. MN frequency and NPB presence were not significantly different in patients with neoplastic lesions compared to controls. In "medium risk" individuals, mean NDI was significantly lower for patients with any neoplastic lesions (adenomas and adenocarcinomas, AUROC 0.668, p 00.5), for patients with advanced neoplasia (advanced adenoma and adenocarcinoma, AUROC 0.636 p 0.029) as well as for patients with adenocarcinoma (AUROC 0.650, p 0.048), for each comparison with the rest of the population. For a cut-off of 1.8, in "medium risk" individuals, an NDI inferior to that value may predict any neoplastic lesion with a sensitivity of 97.7%, an advanced neoplastic lesion with a sensitivity of 97% and adenocarcinoma with a sensitivity of 94.4%. NDI score may have a role as a colorectal cancer-screening test in "medium risk" individuals. DNA = deoxyribonucleic acid; CRC = colorectal cancer; EU = European Union; WHO = World Health Organization; FOBT = fecal occult blood test; CBMN = cytokinesis-blocked micronucleus assay; MN = micronuclei; NPB = nucleoplasmic bridges; NDI = Nuclear Division Index; FAP = familial adenomatous polyposis; HNPCC = hereditary non-polypoid colorectal cancer; IBD = inflammatory bowel diseases; ROC = receiver operating

  2. Risk Assessment and Mapping of Fecal Contamination in the Ohio River Basin

    Science.gov (United States)

    Cabezas, A.; Morehead, D.; Teklitz, A.; Yeghiazarian, L.

    2014-12-01

    Decisions in many problems in engineering planning are invariably made under conditions of uncertainty imposed by the inherent randomness of natural phenomena. Water quality is one such problem. For example, the leading cause of surface-water impairment in the US is fecal microbial contamination, which can potentially trigger massive outbreaks of gastrointestinal disease. It is well known that the difficulty in prediction of water contamination is rooted in the stochastic variability of microbes in the environment, and in the complexity of environmental systems.To address these issues, we employ a risk-based design format to compute the variability in microbial concentrations and the probability of exceeding the E. Coli target in the Ohio River Basin (ORB). This probability is then mapped onto the basin's stream network within the ArcGIS environment. We demonstrate how spatial risk maps can be used in support of watershed management decisions, in particular in the assessment of best management practices for reduction of E. Coli load in surface water. The modeling environment selected for the analysis is the Schematic Processor (SP), a suite of geoprocessing ArcGIS tools. SP operates on a schematic, link-and-node network model of the watershed. The National Hydrography Dataset (NHD) is used as the basis for this representation, as it provides the stream network, lakes, and catchment definitions. Given the schematic network of the watershed, SP adds the capability to perform mathematical computations along the links and at the nodes. This enables modeling fate and transport of any entity over the network. Data from various sources have been integrated for this analysis. Catchment boundaries, lake locations, the stream network and flow data have been retrieved from the NHDPlus. Land use data come from the National Land Cover Database (NLCD), and microbial observations data from the Ohio River Sanitation Committee. The latter dataset is a result of a 2003

  3. Efficient prediction of ground noise from helicopters and parametric studies based on acoustic mapping

    Directory of Open Access Journals (Sweden)

    Fei WANG

    2018-02-01

    Full Text Available Based on the acoustic mapping, a prediction model for the ground noise radiated from an in-flight helicopter is established. For the enhancement of calculation efficiency, a high-efficiency second-level acoustic radiation model capable of taking the influence of atmosphere absorption on noise into account is first developed by the combination of the point-source idea and the rotor noise radiation characteristics. The comparison between the present model and the direct computation method of noise is done and the high efficiency of the model is validated. Rotor free-wake analysis method and Ffowcs Williams-Hawkings (FW-H equation are applied to the aerodynamics and noise prediction in the present model. Secondly, a database of noise spheres with the characteristic parameters of advance ratio and tip-path-plane angle is established by the helicopter trim model together with a parametric modeling approach. Furthermore, based on acoustic mapping, a method of rapid simulation for the ground noise radiated from an in-flight helicopter is developed. The noise footprint for AH-1 rotor is then calculated and the influence of some parameters including advance ratio and flight path angle on ground noise is deeply analyzed using the developed model. The results suggest that with the increase of advance ratio and flight path angle, the peak noise levels on the ground first increase and then decrease, in the meantime, the maximum Sound Exposure Level (SEL noise on the ground shifts toward the advancing side of rotor. Besides, through the analysis of the effects of longitudinal forces on miss-distance and rotor Blade-Vortex Interaction (BVI noise in descent flight, some meaningful results for reducing the BVI noise on the ground are obtained. Keywords: Acoustic mapping, Helicopter, Noise footprint, Rotor noise, Second-level acoustic radiation model

  4. Peak Pc Prediction in Conjunction Analysis: Conjunction Assessment Risk Analysis. Pc Behavior Prediction Models

    Science.gov (United States)

    Vallejo, J.J.; Hejduk, M.D.; Stamey, J. D.

    2015-01-01

    Satellite conjunction risk typically evaluated through the probability of collision (Pc). Considers both conjunction geometry and uncertainties in both state estimates. Conjunction events initially discovered through Joint Space Operations Center (JSpOC) screenings, usually seven days before Time of Closest Approach (TCA). However, JSpOC continues to track objects and issue conjunction updates. Changes in state estimate and reduced propagation time cause Pc to change as event develops. These changes a combination of potentially predictable development and unpredictable changes in state estimate covariance. Operationally useful datum: the peak Pc. If it can reasonably be inferred that the peak Pc value has passed, then risk assessment can be conducted against this peak value. If this value is below remediation level, then event intensity can be relaxed. Can the peak Pc location be reasonably predicted?

  5. Threat and error management for anesthesiologists: a predictive risk taxonomy

    Science.gov (United States)

    Ruskin, Keith J.; Stiegler, Marjorie P.; Park, Kellie; Guffey, Patrick; Kurup, Viji; Chidester, Thomas

    2015-01-01

    Purpose of review Patient care in the operating room is a dynamic interaction that requires cooperation among team members and reliance upon sophisticated technology. Most human factors research in medicine has been focused on analyzing errors and implementing system-wide changes to prevent them from recurring. We describe a set of techniques that has been used successfully by the aviation industry to analyze errors and adverse events and explain how these techniques can be applied to patient care. Recent findings Threat and error management (TEM) describes adverse events in terms of risks or challenges that are present in an operational environment (threats) and the actions of specific personnel that potentiate or exacerbate those threats (errors). TEM is a technique widely used in aviation, and can be adapted for the use in a medical setting to predict high-risk situations and prevent errors in the perioperative period. A threat taxonomy is a novel way of classifying and predicting the hazards that can occur in the operating room. TEM can be used to identify error-producing situations, analyze adverse events, and design training scenarios. Summary TEM offers a multifaceted strategy for identifying hazards, reducing errors, and training physicians. A threat taxonomy may improve analysis of critical events with subsequent development of specific interventions, and may also serve as a framework for training programs in risk mitigation. PMID:24113268

  6. Risk prediction of major complications in individuals with diabetes: the Atherosclerosis Risk in Communities Study.

    Science.gov (United States)

    Parrinello, C M; Matsushita, K; Woodward, M; Wagenknecht, L E; Coresh, J; Selvin, E

    2016-09-01

    To develop a prediction equation for 10-year risk of a combined endpoint (incident coronary heart disease, stroke, heart failure, chronic kidney disease, lower extremity hospitalizations) in people with diabetes, using demographic and clinical information, and a panel of traditional and non-traditional biomarkers. We included in the study 654 participants in the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort study, with diagnosed diabetes (visit 2; 1990-1992). Models included self-reported variables (Model 1), clinical measurements (Model 2), and glycated haemoglobin (Model 3). Model 4 tested the addition of 12 blood-based biomarkers. We compared models using prediction and discrimination statistics. Successive stages of model development improved risk prediction. The C-statistics (95% confidence intervals) of models 1, 2, and 3 were 0.667 (0.64, 0.70), 0.683 (0.65, 0.71), and 0.694 (0.66, 0.72), respectively (p < 0.05 for differences). The addition of three traditional and non-traditional biomarkers [β-2 microglobulin, creatinine-based estimated glomerular filtration rate (eGFR), and cystatin C-based eGFR] to Model 3 significantly improved discrimination (C-statistic = 0.716; p = 0.003) and accuracy of 10-year risk prediction for major complications in people with diabetes (midpoint percentiles of lowest and highest deciles of predicted risk changed from 18-68% to 12-87%). These biomarkers, particularly those of kidney filtration, may help distinguish between people at low versus high risk of long-term major complications. © 2016 John Wiley & Sons Ltd.

  7. Groundwater pollution risk mapping for the Eocene aquifer of the Oum Er-Rabia basin, Morocco

    Science.gov (United States)

    Ettazarini, Said

    2006-11-01

    Sustainable development requires the management and preservation of water resources indispensable for all human activities. When groundwater constitutes the main water resource, vulnerability maps therefore are an important tool for identifying zones of high pollution risk and taking preventive measures in potential pollution sites. The vulnerability assessment for the Eocene aquifer in the Moroccan basin of Oum Er-Rabia is based on the DRASTIC method that uses seven parameters summarizing climatic, geological, and hydrogeological conditions controlling the seepage of pollutant substances to groundwater. Vulnerability maps were produced by using GIS techniques and applying the “generic” and “agricultural” models according to the DRASTIC charter. Resulting maps revealed that the aquifer is highly vulnerable in the western part of the basin and areas being under high contamination risk are more extensive when the “agricultural” model was applied.

  8. Developing a climate-based risk map of fascioliasis outbreaks in Iran.

    Science.gov (United States)

    Halimi, Mansour; Farajzadeh, Manuchehr; Delavari, Mahdi; Arbabi, Mohsen

    2015-01-01

    The strong relationship between climate and fascioliasis outbreaks enables the development of climate-based models to estimate the potential risk of fascioliasis outbreaks. This work aims to develop a climate-based risk map of fascioliasis outbreaks in Iran using Ollerenshaw's fascioliasis risk index incorporating geographical information system (GIS). Using this index, a risk map of fascioliasis outbreaks for the entire country was developed. We determined that the country can be divided into 4 fascioliasis outbreak risk categories. Class 1, in which the Mt value is less than 100, includes more than 0.91 of the country's area. The climate in this class is not conducive to fascioliasis outbreaks in any month. Dryness and low temperature in the wet season (December to April) are the key barriers against fascioliasis outbreaks in this class. The risk map developed based on climatic factors indicated that only 0.03 of the country's area, including Gilan province in the northern region of Iran, is highly suitable to fascioliasis outbreaks during September to January. The Mt value is greater than 500 in this class. Heavy rainfall in the summer and fall, especially in Rasht, Astara and Bandar Anzaly (≥ 1000 mm/year), creates more suitable breeding places for snail intermediate hosts. Copyright © 2015 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

  9. Adolescent expectations of early death predict adult risk behaviors.

    Directory of Open Access Journals (Sweden)

    Quynh C Nguyen

    Full Text Available Only a handful of public health studies have investigated expectations of early death among adolescents. Associations have been found between these expectations and risk behaviors in adolescence. However, these beliefs may not only predict worse adolescent outcomes, but worse trajectories in health with ties to negative outcomes that endure into young adulthood. The objectives of this study were to investigate perceived chances of living to age 35 (Perceived Survival Expectations, PSE as a predictor of suicidal ideation, suicide attempt and substance use in young adulthood. We examined the predictive capacity of PSE on future suicidal ideation/attempt after accounting for sociodemographics, depressive symptoms, and history of suicide among family and friends to more fully assess its unique contribution to suicide risk. We investigated the influence of PSE on legal and illegal substance use and varying levels of substance use. We utilized the National Longitudinal Study of Adolescent Health (Add Health initiated in 1994-95 among 20,745 adolescents in grades 7-12 with follow-up interviews in 1996 (Wave II, 2001-02 (Wave III and 2008 (Wave IV; ages 24-32. Compared to those who were almost certain of living to age 35, perceiving a 50-50 or less chance of living to age 35 at Waves I or III predicted suicide attempt and ideation as well as regular substance use (i.e., exceeding daily limits for moderate drinking; smoking ≥ a pack/day; and using illicit substances other than marijuana at least weekly at Wave IV. Associations between PSE and detrimental adult outcomes were particularly strong for those reporting persistently low PSE at both Waves I and III. Low PSE at Wave I or Wave III was also related to a doubling and tripling, respectively, of death rates in young adulthood. Long-term and wide-ranging ties between PSE and detrimental outcomes suggest these expectations may contribute to identifying at-risk youth.

  10. Mapping Entomological Dengue Risk Levels in Martinique Using High-Resolution Remote-Sensing Environmental Data

    Directory of Open Access Journals (Sweden)

    Vanessa Machault

    2014-12-01

    Full Text Available Controlling dengue virus transmission mainly involves integrated vector management. Risk maps at appropriate scales can provide valuable information for assessing entomological risk levels. Here, results from a spatio-temporal model of dwellings potentially harboring Aedes aegypti larvae from 2009 to 2011 in Tartane (Martinique, French Antilles using high spatial resolution remote-sensing environmental data and field entomological and meteorological information are presented. This tele-epidemiology methodology allows monitoring the dynamics of diseases closely related to weather/climate and environment variability. A Geoeye-1 image was processed to extract landscape elements that could surrogate societal or biological information related to the life cycle of Aedes vectors. These elements were subsequently included into statistical models with random effect. Various environmental and meteorological conditions have indeed been identified as risk/protective factors for the presence of Aedes aegypti immature stages in dwellings at a given date. These conditions were used to produce dynamic high spatio-temporal resolution maps from the presence of most containers harboring larvae. The produced risk maps are examples of modeled entomological maps at the housing level with daily temporal resolution. This finding is an important contribution to the development of targeted operational control systems for dengue and other vector-borne diseases, such as chikungunya, which is also present in Martinique.

  11. Mapping of elements at risk for landslides in the tropics using airborne laser scanning

    NARCIS (Netherlands)

    Razak, Khamarrul Azahari; van Westen, C.J.; Straatsma, Menno; ... [et al.],

    2011-01-01

    Mapping elements at risk for landslides in the tropics pose as a challenging task. Aerial-photograph, satellite imagery, and synthetic perture radar images are less effective to accurately provide physical presence of objects in a relatively short time. In this paper, we utilized an airborne laser

  12. Mapping sudden oak death risk nationally using host, climate, and pathways data

    Science.gov (United States)

    Frank H. Koch; William D. Smith

    2008-01-01

    In 2002, a team of United States Department of Agriculture-Forest Service (USDA-FS) scientists developed a preliminary risk map to serve as the foundation for an efficient, cost effective sample design for the national sudden oak death detection survey. At the time, a need to initiate rapid detection in the face of limited information on Phytophthora ramorum...

  13. Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions.

    Directory of Open Access Journals (Sweden)

    Tomislav Hengl

    Full Text Available 80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management--organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na. We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring

  14. Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion

    Science.gov (United States)

    Rahmati, Omid; Tahmasebipour, Nasser; Haghizadeh, Ali; Pourghasemi, Hamid Reza; Feizizadeh, Bakhtiar

    2017-12-01

    Gully erosion constitutes a serious problem for land degradation in a wide range of environments. The main objective of this research was to compare the performance of seven state-of-the-art machine learning models (SVM with four kernel types, BP-ANN, RF, and BRT) to model the occurrence of gully erosion in the Kashkan-Poldokhtar Watershed, Iran. In the first step, a gully inventory map consisting of 65 gully polygons was prepared through field surveys. Three different sample data sets (S1, S2, and S3), including both positive and negative cells (70% for training and 30% for validation), were randomly prepared to evaluate the robustness of the models. To model the gully erosion susceptibility, 12 geo-environmental factors were selected as predictors. Finally, the goodness-of-fit and prediction skill of the models were evaluated by different criteria, including efficiency percent, kappa coefficient, and the area under the ROC curves (AUC). In terms of accuracy, the RF, RBF-SVM, BRT, and P-SVM models performed excellently both in the degree of fitting and in predictive performance (AUC values well above 0.9), which resulted in accurate predictions. Therefore, these models can be used in other gully erosion studies, as they are capable of rapidly producing accurate and robust gully erosion susceptibility maps (GESMs) for decision-making and soil and water management practices. Furthermore, it was found that performance of RF and RBF-SVM for modelling gully erosion occurrence is quite stable when the learning and validation samples are changed.

  15. The "polyenviromic risk score": Aggregating environmental risk factors predicts conversion to psychosis in familial high-risk subjects.

    Science.gov (United States)

    Padmanabhan, Jaya L; Shah, Jai L; Tandon, Neeraj; Keshavan, Matcheri S

    2017-03-01

    Young relatives of individuals with schizophrenia (i.e. youth at familial high-risk, FHR) are at increased risk of developing psychotic disorders, and show higher rates of psychiatric symptoms, cognitive and neurobiological abnormalities than non-relatives. It is not known whether overall exposure to environmental risk factors increases risk of conversion to psychosis in FHR subjects. Subjects consisted of a pilot longitudinal sample of 83 young FHR subjects. As a proof of principle, we examined whether an aggregate score of exposure to environmental risk factors, which we term a 'polyenviromic risk score' (PERS), could predict conversion to psychosis. The PERS combines known environmental risk factors including cannabis use, urbanicity, season of birth, paternal age, obstetric and perinatal complications, and various types of childhood adversity, each weighted by its odds ratio for association with psychosis in the literature. A higher PERS was significantly associated with conversion to psychosis in young, familial high-risk subjects (OR=1.97, p=0.009). A model combining the PERS and clinical predictors had a sensitivity of 27% and specificity of 96%. An aggregate index of environmental risk may help predict conversion to psychosis in FHR subjects. Copyright © 2016 Elsevier B.V. All rights reserved.

  16. A Spatial Framework to Map Heat Health Risks at Multiple Scales.

    Science.gov (United States)

    Ho, Hung Chak; Knudby, Anders; Huang, Wei

    2015-12-18

    In the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming centuries as the result of climate-driven global increases in the severity and frequency of extreme heat events. Spatial information on heat exposure and population vulnerability may be combined to map the areas of highest risk and focus mitigation efforts there. However, a mismatch in spatial resolution between heat exposure and vulnerability data can cause spatial scale issues such as the Modifiable Areal Unit Problem (MAUP). We used a raster-based model to integrate heat exposure and vulnerability data in a multi-criteria decision analysis, and compared it to the traditional vector-based model. We then used the Getis-Ord G(i) index to generate spatially smoothed heat risk hotspot maps from fine to coarse spatial scales. The raster-based model allowed production of maps at spatial resolution, more description of local-scale heat risk variability, and identification of heat-risk areas not identified with the vector-based approach. Spatial smoothing with the Getis-Ord G(i) index produced heat risk hotspots from local to regional spatial scale. The approach is a framework for reducing spatial scale issues in future heat risk mapping, and for identifying heat risk hotspots at spatial scales ranging from the block-level to the municipality level.

  17. Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks

    Directory of Open Access Journals (Sweden)

    Martin Alberto JM

    2009-01-01

    Full Text Available Abstract Background Prediction of protein structures from their sequences is still one of the open grand challenges of computational biology. Some approaches to protein structure prediction, especially ab initio ones, rely to some extent on the prediction of residue contact maps. Residue contact map predictions have been assessed at the CASP competition for several years now. Although it has been shown that exact contact maps generally yield correct three-dimensional structures, this is true only at a relatively low resolution (3–4 Å from the native structure. Another known weakness of contact maps is that they are generally predicted ab initio, that is not exploiting information about potential homologues of known structure. Results We introduce a new class of distance restraints for protein structures: multi-class distance maps. We show that Cα trace reconstructions based on 4-class native maps are significantly better than those from residue contact maps. We then build two predictors of 4-class maps based on recursive neural networks: one ab initio, or relying on the sequence and on evolutionary information; one template-based, or in which homology information to known structures is provided as a further input. We show that virtually any level of sequence similarity to structural templates (down to less than 10% yields more accurate 4-class maps than the ab initio predictor. We show that template-based predictions by recursive neural networks are consistently better than the best template and than a number of combinations of the best available templates. We also extract binary residue contact maps at an 8 Å threshold (as per CASP assessment from the 4-class predictors and show that the template-based version is also more accurate than the best template and consistently better than the ab initio one, down to very low levels of sequence identity to structural templates. Furthermore, we test both ab-initio and template-based 8

  18. Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker’s perspective

    Directory of Open Access Journals (Sweden)

    Denys Yemshanov

    2013-09-01

    Full Text Available 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 that assesses pest invasion risk from the perspective of a risk-averse decision maker.We demonstrated the method by evaluating the likelihood that an invasive forest pest will be transported to one of the U.S. states or Canadian provinces in infested firewood by visitors to U.S. federal campgrounds. We tested the impact of the risk aversion assumption using distributions of plausible pest arrival scenarios generated with a geographically explicit model developed from data documenting camper travel across the study area. Next, we prioritized regions of high and low pest arrival risk via application of two stochastic ordering techniques that employed, respectively, first- and second-degree stochastic dominance rules, the latter of which incorporated the notion of risk aversion. We then identified regions in the study area where the pest risk value changed considerably after incorporating risk aversion.While both methods identified similar areas of highest and lowest risk, they differed in how they demarcated moderate-risk areas. In general, the second-order stochastic dominance method assigned lower risk rankings to moderate-risk areas. Overall, this new method offers a better strategy to deal with the uncertainty typically associated with risk assessments and provides a tractable way to incorporate decision-making preferences into final risk estimates, and thus helps to better align these estimates with particular decision-making scenarios about a pest organism of concern. Incorporation of risk aversion also helps prioritize the set of locations to target for inspections and

  19. Melanoma risk prediction using a multilocus genetic risk score in the Women's Health Initiative cohort.

    Science.gov (United States)

    Cho, Hyunje G; Ransohoff, Katherine J; Yang, Lingyao; Hedlin, Haley; Assimes, Themistocles; Han, Jiali; Stefanick, Marcia; Tang, Jean Y; Sarin, Kavita Y

    2018-07-01

    Single-nucleotide polymorphisms (SNPs) associated with melanoma have been identified though genome-wide association studies. However, the combined impact of these SNPs on melanoma development remains unclear, particularly in postmenopausal women who carry a lower melanoma risk. We examine the contribution of a combined polygenic risk score on melanoma development in postmenopausal women. Genetic risk scores were calculated using 21 genome-wide association study-significant SNPs. Their combined effect on melanoma development was evaluated in 19,102 postmenopausal white women in the clinical trial and observational study arms of the Women's Health Initiative dataset. Compared to the tertile of weighted genetic risk score with the lowest genetic risk, the women in the tertile with the highest genetic risk were 1.9 times more likely to develop melanoma (95% confidence interval 1.50-2.42). The incremental change in c-index from adding genetic risk scores to age were 0.075 (95% confidence interval 0.041-0.109) for incident melanoma. Limitations include a lack of information on nevi count, Fitzpatrick skin type, family history of melanoma, and potential reporting and selection bias in the Women's Health Initiative cohort. Higher genetic risk is associated with increased melanoma prevalence and incidence in postmenopausal women, but current genetic information may have a limited role in risk prediction when phenotypic information is available. Copyright © 2018 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

  20. Mapping intra-urban transmission risk of dengue fever with big hourly cellphone data.

    Science.gov (United States)

    Mao, Liang; Yin, Ling; Song, Xiaoqing; Mei, Shujiang

    2016-10-01

    Cellphone tracking has been recently integrated into risk assessment of disease transmission, because travel behavior of disease carriers can be depicted in unprecedented details. Still in its infancy, such an integration has been limited to: 1) risk assessment only at national and provincial scales, where intra-urban human movements are neglected, and 2) using irregularly logged cellphone data that miss numerous user movements. Furthermore, few risk assessments have considered positional uncertainty of cellphone data. This study proposed a new framework for mapping intra-urban disease risk with regularly logged cellphone tracking data, taking the dengue fever in Shenzhen city as an example. Hourly tracking records of 5.85 million cellphone users, combined with the random forest classification and mosquito activities, were utilized to estimate the local transmission risk of dengue fever and the importation risk through travels. Stochastic simulations were further employed to quantify the uncertainty of risk. The resultant maps suggest targeted interventions to maximally reduce dengue cases exported to other places, as well as appropriate interventions to contain risk in places that import them. Given the popularity of cellphone use in urbanized areas, this framework can be adopted by other cities to design spatio-temporally resolved programs for disease control. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: An application to Switzerland

    Energy Technology Data Exchange (ETDEWEB)

    Kropat, Georg, E-mail: georg.kropat@chuv.ch [Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, 1007 Lausanne (Switzerland); Bochud, Francois [Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, 1007 Lausanne (Switzerland); Jaboyedoff, Michel [Faculty of Geosciences and Environment, University of Lausanne, GEOPOLIS — 3793, 1015 Lausanne (Switzerland); Laedermann, Jean-Pascal [Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, 1007 Lausanne (Switzerland); Murith, Christophe; Palacios, Martha [Swiss Federal Office of Public Health, Schwarzenburgstrasse 165, 3003 Berne (Switzerland); Baechler, Sébastien [Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, 1007 Lausanne (Switzerland); Swiss Federal Office of Public Health, Schwarzenburgstrasse 165, 3003 Berne (Switzerland)

    2015-02-01

    Purpose: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. Methods: We looked at about 240 000 IRC measurements carried out in about 150 000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m{sup 3}. Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. Results: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. Conclusions: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements

  2. Predicting Ambulance Time of Arrival to the Emergency Department Using Global Positioning System and Google Maps

    Science.gov (United States)

    Fleischman, Ross J.; Lundquist, Mark; Jui, Jonathan; Newgard, Craig D.; Warden, Craig

    2014-01-01

    Objective To derive and validate a model that accurately predicts ambulance arrival time that could be implemented as a Google Maps web application. Methods This was a retrospective study of all scene transports in Multnomah County, Oregon, from January 1 through December 31, 2008. Scene and destination hospital addresses were converted to coordinates. ArcGIS Network Analyst was used to estimate transport times based on street network speed limits. We then created a linear regression model to improve the accuracy of these street network estimates using weather, patient characteristics, use of lights and sirens, daylight, and rush-hour intervals. The model was derived from a 50% sample and validated on the remainder. Significance of the covariates was determined by p times recorded by computer-aided dispatch. We then built a Google Maps-based web application to demonstrate application in real-world EMS operations. Results There were 48,308 included transports. Street network estimates of transport time were accurate within 5 minutes of actual transport time less than 16% of the time. Actual transport times were longer during daylight and rush-hour intervals and shorter with use of lights and sirens. Age under 18 years, gender, wet weather, and trauma system entry were not significant predictors of transport time. Our model predicted arrival time within 5 minutes 73% of the time. For lights and sirens transports, accuracy was within 5 minutes 77% of the time. Accuracy was identical in the validation dataset. Lights and sirens saved an average of 3.1 minutes for transports under 8.8 minutes, and 5.3 minutes for longer transports. Conclusions An estimate of transport time based only on a street network significantly underestimated transport times. A simple model incorporating few variables can predict ambulance time of arrival to the emergency department with good accuracy. This model could be linked to global positioning system data and an automated Google Maps web

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

    Directory of Open Access Journals (Sweden)

    P. Akhavan

    2014-10-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  5. [Nursing care mapping for patients at risk of falls in the Nursing Interventions Classification].

    Science.gov (United States)

    Luzia, Melissa de Freitas; Almeida, Miriam de Abreu; Lucena, Amália de Fátima

    2014-08-01

    Identifying the prescribed nursing care for hospitalized patients at risk of falls and comparing them with the interventions of the Nursing Interventions Classifications (NIC). A cross-sectional study carried out in a university hospital in southern Brazil. It was a retrospective data collection in the nursing records system. The sample consisted of 174 adult patients admitted to medical and surgical units with the Nursing Diagnosis of Risk for falls. The prescribed care were compared with the NIC interventions by the cross-mapping method. The most prevalent care were the following: keeping the bed rails, guiding patients/family regarding the risks and prevention of falls, keeping the bell within reach of patients, and maintaining patients' belongings nearby, mapped in the interventions Environmental Management: safety and Fall Prevention. The treatment prescribed in clinical practice was corroborated by the NIC reference.

  6. Mapping child maltreatment risk: a 12-year spatio-temporal analysis of neighborhood influences.

    Science.gov (United States)

    Gracia, Enrique; López-Quílez, Antonio; Marco, Miriam; Lila, Marisol

    2017-10-18

    'Place' matters in understanding prevalence variations and inequalities in child maltreatment risk. However, most studies examining ecological variations in child maltreatment risk fail to take into account the implications of the spatial and temporal dimensions of neighborhoods. In this study, we conduct a high-resolution small-area study to analyze the influence of neighborhood characteristics on the spatio-temporal epidemiology of child maltreatment risk. We conducted a 12-year (2004-2015) small-area Bayesian spatio-temporal epidemiological study with all families with child maltreatment protection measures in the city of Valencia, Spain. As neighborhood units, we used 552 census block groups. Cases were geocoded using the family address. Neighborhood-level characteristics analyzed included three indicators of neighborhood disadvantage-neighborhood economic status, neighborhood education level, and levels of policing activity-, immigrant concentration, and residential instability. Bayesian spatio-temporal modelling and disease mapping methods were used to provide area-specific risk estimations. Results from a spatio-temporal autoregressive model showed that neighborhoods with low levels of economic and educational status, with high levels of policing activity, and high immigrant concentration had higher levels of substantiated child maltreatment risk. Disease mapping methods were used to analyze areas of excess risk. Results showed chronic spatial patterns of high child maltreatment risk during the years analyzed, as well as stability over time in areas of low risk. Areas with increased or decreased child maltreatment risk over the years were also observed. A spatio-temporal epidemiological approach to study the geographical patterns, trends over time, and the contextual determinants of child maltreatment risk can provide a useful method to inform policy and action. This method can offer a more accurate description of the problem, and help to inform more

  7. Urban-hazard risk analysis: mapping of heat-related risks in the elderly in major Italian cities.

    Directory of Open Access Journals (Sweden)

    Marco Morabito

    Full Text Available Short-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks.Development of high-resolution, heat-related urban risk maps regarding the elderly population (≥ 65.A long time-series (2001-2013 of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m daytime and night-time land surface temperatures (LST. LST was estimated pixel-wise by applying two statistical model approaches: 1 the Linear Regression Model (LRM; 2 the Generalized Additive Model (GAM. Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m from the 2001 census (Eurostat source, and processed together using "Crichton's Risk Triangle" hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI.The GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities.This study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public health operators and facilitate coordination for heat

  8. Urban-hazard risk analysis: mapping of heat-related risks in the elderly in major Italian cities.

    Science.gov (United States)

    Morabito, Marco; Crisci, Alfonso; Gioli, Beniamino; Gualtieri, Giovanni; Toscano, Piero; Di Stefano, Valentina; Orlandini, Simone; Gensini, Gian Franco

    2015-01-01

    Short-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks. Development of high-resolution, heat-related urban risk maps regarding the elderly population (≥ 65). A long time-series (2001-2013) of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m) daytime and night-time land surface temperatures (LST). LST was estimated pixel-wise by applying two statistical model approaches: 1) the Linear Regression Model (LRM); 2) the Generalized Additive Model (GAM). Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m) from the 2001 census (Eurostat source), and processed together using "Crichton's Risk Triangle" hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI). The GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk) were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities. This study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public health operators and facilitate coordination for heat

  9. Compositional cokriging for mapping the probability risk of groundwater contamination by nitrates.

    Science.gov (United States)

    Pardo-Igúzquiza, Eulogio; Chica-Olmo, Mario; Luque-Espinar, Juan A; Rodríguez-Galiano, Víctor

    2015-11-01

    Contamination by nitrates is an important cause of groundwater pollution and represents a potential risk to human health. Management decisions must be made using probability maps that assess the nitrate concentration potential of exceeding regulatory thresholds. However these maps are obtained with only a small number of sparse monitoring locations where the nitrate concentrations have been measured. It is therefore of great interest to have an efficient methodology for obtaining those probability maps. In this paper, we make use of the fact that the discrete probability density function is a compositional variable. The spatial discrete probability density function is estimated by compositional cokriging. There are several advantages in using this approach: (i) problems of classical indicator cokriging, like estimates outside the interval (0,1) and order relations, are avoided; (ii) secondary variables (e.g. aquifer parameters) can be included in the estimation of the probability maps; (iii) uncertainty maps of the probability maps can be obtained; (iv) finally there are modelling advantages because the variograms and cross-variograms of real variables that do not have the restrictions of indicator variograms and indicator cross-variograms. The methodology was applied to the Vega de Granada aquifer in Southern Spain and the advantages of the compositional cokriging approach were demonstrated. Copyright © 2015 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    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.

  11. Alternative Testing Methods for Predicting Health Risk from Environmental Exposures

    Directory of Open Access Journals (Sweden)

    Annamaria Colacci

    2014-08-01

    Full Text Available Alternative methods to animal testing are considered as promising tools to support the prediction of toxicological risks from environmental exposure. Among the alternative testing methods, the cell transformation assay (CTA appears to be one of the most appropriate approaches to predict the carcinogenic properties of single chemicals, complex mixtures and environmental pollutants. The BALB/c 3T3 CTA shows a good degree of concordance with the in vivo rodent carcinogenesis tests. Whole-genome transcriptomic profiling is performed to identify genes that are transcriptionally regulated by different kinds of exposures. Its use in cell models representative of target organs may help in understanding the mode of action and predicting the risk for human health. Aiming at associating the environmental exposure to health-adverse outcomes, we used an integrated approach including the 3T3 CTA and transcriptomics on target cells, in order to evaluate the effects of airborne particulate matter (PM on toxicological complex endpoints. Organic extracts obtained from PM2.5 and PM1 samples were evaluated in the 3T3 CTA in order to identify effects possibly associated with different aerodynamic diameters or airborne chemical components. The effects of the PM2.5 extracts on human health were assessed by using whole-genome 44 K oligo-microarray slides. Statistical analysis by GeneSpring GX identified genes whose expression was modulated in response to the cell treatment. Then, modulated genes were associated with pathways, biological processes and diseases through an extensive biological analysis. Data derived from in vitro methods and omics techniques could be valuable for monitoring the exposure to toxicants, understanding the modes of action via exposure-associated gene expression patterns and to highlight the role of genes in key events related to adversity.

  12. Large-Scale Mapping and Predictive Modeling of Submerged Aquatic Vegetation in a Shallow Eutrophic Lake

    Directory of Open Access Journals (Sweden)

    Karl E. Havens

    2002-01-01

    Full Text Available A spatially intensive sampling program was developed for mapping the submerged aquatic vegetation (SAV over an area of approximately 20,000 ha in a large, shallow lake in Florida, U.S. The sampling program integrates Geographic Information System (GIS technology with traditional field sampling of SAV and has the capability of producing robust vegetation maps under a wide range of conditions, including high turbidity, variable depth (0 to 2 m, and variable sediment types. Based on sampling carried out in AugustœSeptember 2000, we measured 1,050 to 4,300 ha of vascular SAV species and approximately 14,000 ha of the macroalga Chara spp. The results were similar to those reported in the early 1990s, when the last large-scale SAV sampling occurred. Occurrence of Chara was strongly associated with peat sediments, and maximal depths of occurrence varied between sediment types (mud, sand, rock, and peat. A simple model of Chara occurrence, based only on water depth, had an accuracy of 55%. It predicted occurrence of Chara over large areas where the plant actually was not found. A model based on sediment type and depth had an accuracy of 75% and produced a spatial map very similar to that based on observations. While this approach needs to be validated with independent data in order to test its general utility, we believe it may have application elsewhere. The simple modeling approach could serve as a coarse-scale tool for evaluating effects of water level management on Chara populations.

  13. Large-scale mapping and predictive modeling of submerged aquatic vegetation in a shallow eutrophic lake.

    Science.gov (United States)

    Havens, Karl E; Harwell, Matthew C; Brady, Mark A; Sharfstein, Bruce; East, Therese L; Rodusky, Andrew J; Anson, Daniel; Maki, Ryan P

    2002-04-09

    A spatially intensive sampling program was developed for mapping the submerged aquatic vegetation (SAV) over an area of approximately 20,000 ha in a large, shallow lake in Florida, U.S. The sampling program integrates Geographic Information System (GIS) technology with traditional field sampling of SAV and has the capability of producing robust vegetation maps under a wide range of conditions, including high turbidity, variable depth (0 to 2 m), and variable sediment types. Based on sampling carried out in August-September 2000, we measured 1,050 to 4,300 ha of vascular SAV species and approximately 14,000 ha of the macroalga Chara spp. The results were similar to those reported in the early 1990s, when the last large-scale SAV sampling occurred. Occurrence of Chara was strongly associated with peat sediments, and maximal depths of occurrence varied between sediment types (mud, sand, rock, and peat). A simple model of Chara occurrence, based only on water depth, had an accuracy of 55%. It predicted occurrence of Chara over large areas where the plant actually was not found. A model based on sediment type and depth had an accuracy of 75% and produced a spatial map very similar to that based on observations. While this approach needs to be validated with independent data in order to test its general utility, we believe it may have application elsewhere. The simple modeling approach could serve as a coarse-scale tool for evaluating effects of water level management on Chara populations.

  14. Value of multiple risk factors in predicting coronary artery disease

    International Nuclear Information System (INIS)

    Zhu Zhengbin; Zhang Ruiyan; Zhang Qi; Yang Zhenkun; Hu Jian; Zhang Jiansheng; Shen Weifeng

    2008-01-01

    Objective: This study sought to assess the relationship between correlative comprehension risk factors and coronary arterial disease and to build up a simple mathematical model to evaluate the extension of coronary artery lesion in patients with stable angina. Methods: A total of 1024 patients with chest pain who underwent coronary angiography were divided into CAD group(n=625)and control group(n=399) based on at least one significant coronary artery narrowing more than 50% in diameter. Independent risk factors for CAD were evaluated and multivariate logistic regression model and receiver-operating characteristic(ROC) curves were used to estimate the independent influence factor for CAD and built up a simple formula for clinical use. Results: Multivariate regression analysis revealed that UACR > 7.25 μg/mg(OR=3.6; 95% CI 2.6-4.9; P 20 mmol/L(OR=3.2; 95% CI 2.3-4.4; P 2 (OR=2.3; 95% CI 1.4-3.8; P 2.6 mmol/L (OR 2.141; 95% CI 1.586-2.890; P 7.25 μg/mg + 1.158 x hsCRP > 20 mmol/L + 0.891 GFR 2 + 0.831 x LVEF 2.6 mmol/L + 0.676 x smoking history + 0.594 x male + 0.459 x diabetes + 0.425 x hypertension). Area under the curve was 0.811 (P < 0.01), and the optimal probability value for predicting severe stage of CAD was 0.977 (sensitivity 49.0%, specificity 92.7% ). Conclusions: Risk factors including renal insufficiency were the main predictors for CAD. The logistic regression model is the non-invasive method of choice for predicting the extension of coronary artery lesion in patients with stable agiana. (authors)

  15. Prediction of dose and field mapping around a shielded plutonium fuel fabrication glovebox

    International Nuclear Information System (INIS)

    Strode, J.N.; Soldat, K.L.; Brackenbush, L.W.

    1984-01-01

    Westinghouse Hanford Company, as the Department of Energy's (DOE) prime contractor for the operation of the Hanford Engineering Development Laboratory (HEDL), is responsible for the development of the Secure Automated Fabrication (SAF) Line which is to be installed in the recently constructed Fuels and Materials Examination Facility (FMEF). The SAF Line will fabricate mixed-oxide (MOX) fuel pins for the Fast Flux Test Facility (FFTF) at an annual throughput rate of six (6) metric tons (MT) of MOX. The SAF Line will also demonstrate the automated manufacture of fuel pins on a production-scale. This paper describes some of the techniques used to reduce personnel exposure on the SAF Line, as well as the prediction and field mapping of doses from a shielded fuel fabrication glovebox. Tables are also presented from which exposure rate estimates can be made for plutonium recovered from fuels having different isotopic compositions as a result of varied burnup

  16. Predictive modeling and mapping of Malayan Sun Bear (Helarctos malayanus) distribution using maximum entropy.

    Science.gov (United States)

    Nazeri, Mona; Jusoff, Kamaruzaman; Madani, Nima; Mahmud, Ahmad Rodzi; Bahman, Abdul Rani; Kumar, Lalit

    2012-01-01

    One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt) is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus) in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear's population.

  17. Predictive modeling and mapping of Malayan Sun Bear (Helarctos malayanus distribution using maximum entropy.

    Directory of Open Access Journals (Sweden)

    Mona Nazeri

    Full Text Available One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear's population.

  18. Mapping and predicting sinkholes by integration of remote sensing and spectroscopy methods

    Science.gov (United States)

    Goldshleger, N.; Basson, U.; Azaria, I.

    2013-08-01

    The Dead Sea coastal area is exposed to the destructive process of sinkhole collapse. The increase in sinkhole activity in the last two decades has been substantial, resulting from the continuous decrease in the Dead Sea's level, with more than 1,000 sinkholes developing as a result of upper layer collapse. Large sinkholes can reach 25 m in diameter. They are concentrated mainly in clusters in several dozens of sites with different characteristics. In this research, methods for mapping, monitoring and predicting sinkholes were developed using active and passive remote-sensing methods: field spectrometer, geophysical ground penetration radar (GPR) and a frequency domain electromagnetic instrument (FDEM). The research was conducted in three stages: 1) literature review and data collection; 2) mapping regions abundant with sinkholes in various stages and regions vulnerable to sinkholes; 3) analyzing the data and translating it into cognitive and accessible scientific information. Field spectrometry enabled a comparison between the spectral signatures of soil samples collected near active or progressing sinkholes, and those collected in regions with no visual sign of sinkhole occurrence. FDEM and GPR investigations showed that electrical conductivity and soil moisture are higher in regions affected by sinkholes. Measurements taken at different time points over several seasons allowed monitoring the progress of an 'embryonic' sinkhole.

  19. The Functional Diffusion Map: An Imaging Biomarker for the Early Prediction of Cancer Treatment Outcome

    Directory of Open Access Journals (Sweden)

    Bradford A. Moffat

    2006-04-01

    Full Text Available Functional diffusion map (fDM has been recently reported as an early and quantitative biomarker of clinical brain tumor treatment outcome. This MRI approach spatially maps and quantifies treatment-induced changes in tumor water diffusion values resulting from alterations in cell density/cell membrane function and microenvironment. This current study was designed to evaluate the capability of fDM for preclinical evaluation of dose escalation studies and to determine if these changes were correlated with outcome measures (cell kill and overall survival. Serial T2-weighted and diffusion MRI were carried out on rodents with orthotopically implanted 9L brain tumors receiving three doses of 1,3-bis(2-chloroethyl-1-nitrosourea (6.65, 13.3, and 26.6 mg/kg, i.p.. All images were coregistered to baseline T2-weighted images for fDM analysis. Analysis of tumor fDM data on day 4 posttreatment detected dosedependent changes in tumor diffusion values, which were also found to be spatially dependent. Histologic analysis of treated tumors confirmed spatial changes in cellularity as observed by fDM. Early changes in tumor diffusion values were found to be highly correlative with drug dose and independent biologic outcome measures (cell kill and survival. Therefore, the fDM imaging biomarker for early prediction of treatment efficacy can be used in the drug development process.

  20. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer

    NARCIS (Netherlands)

    Petersen, Japke F.; Stuiver, Martijn M.; Timmermans, Adriana J.; Chen, Amy; Zhang, Hongzhen; O'Neill, James P.; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T.; Koch, Wayne; van den Brekel, Michiel W. M.

    2017-01-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442

  1. Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks.

    Science.gov (United States)

    Coiera, Enrico; Wang, Ying; Magrabi, Farah; Concha, Oscar Perez; Gallego, Blanca; Runciman, William

    2014-05-21

    Current prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models. Patients admitted between 2000 and 2006 from 501 public and private hospitals in NSW, Australia were used for training and 2007 data for evaluation. The impact of hospital care delivered over different days of the week and or times of the day was modeled by separating hospitalization risk into 21 separate time periods (morning, day, night across the days of the week). Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/disease specific models (Charlson co-morbidity index, DRG). Combining these three generated a full model. Models were evaluated by accuracy, AUC, Akaike and Bayesian information criteria. There was a clear diurnal rhythm to hospital mortality in the data set, peaking in the evening, as well as the well-known 'weekend-effect' where mortality peaks with weekend admissions. Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively). The combined model which included time-varying risk however yielded an average AUC of 0.92. This model performed best for stays up to 7-days (93% of admissions), peaking at days 3 to 5 (AUC 0.94). Risks of hospitalization vary not just with the day of the week but also time of the day, and can be used to make predictions about the cumulative risk of death associated with an individual's hospitalization. Combining disease specific models with such time varying- estimates appears to

  2. Hazard, Vulnerability and Capacity Mapping for Landslides Risk Analysis using Geographic Information System (GIS)

    Science.gov (United States)

    Sari, D. A. P.; Innaqa, S.; Safrilah

    2017-06-01

    This research analyzed the levels of disaster risk in the Citeureup sub-District, Bogor Regency, West Java, based on its potential hazard, vulnerability and capacity, using map to represent the results, then Miles and Huberman analytical techniques was used to analyze the qualitative interviews. The analysis conducted in this study is based on the concept of disaster risk by Wisner. The result shows that the Citeureup sub-District has medium-low risk of landslides. Of the 14 villages, three villages have a moderate risk level, namely Hambalang, Tajur, and Tangkil, or 49.58% of the total land area. Eleven villages have a low level of risk, namely Pasir Mukti, Sanja, Tarikolot, Gunung Sari, Puspasari, East Karang Asem, Citeureup, Leuwinutug, Sukahati, West Karang Asem West and Puspanegara, or 48.68% of the total land area, for high-risk areas only around 1.74%, which is part of Hambalang village. The analysis using Geographic Information System (GIS) prove that areas with a high risk potential does not necessarily have a high level of risk. The capacity of the community plays an important role to minimize the risk of a region. Disaster risk reduction strategy is done by creating a safe condition, which intensified the movement of disaster risk reduction.

  3. Violence risk prediction. Clinical and actuarial measures and the role of the Psychopathy Checklist.

    Science.gov (United States)

    Dolan, M; Doyle, M

    2000-10-01

    Violence risk prediction is a priority issue for clinicians working with mentally disordered offenders. To review the current status of violence risk prediction research. Literature search (Medline). Key words: violence, risk prediction, mental disorder. Systematic/structured risk assessment approaches may enhance the accuracy of clinical prediction of violent outcomes. Data on the predictive validity of available clinical risk assessment tools are based largely on American and North American studies and further validation is required in British samples. The Psychopathy Checklist appears to be a key predictor of violent recidivism in a variety of settings. Violence risk prediction is an inexact science and as such will continue to provoke debate. Clinicians clearly need to be able to demonstrate the rationale behind their decisions on violence risk and much can be learned from recent developments in research on violence risk prediction.

  4. Predicting Hip Fracture Type With Cortical Bone Mapping (CBM) in the Osteoporotic Fractures in Men (MrOS) Study.

    Science.gov (United States)

    Treece, Graham M; Gee, Andrew H; Tonkin, Carol; Ewing, Susan K; Cawthon, Peggy M; Black, Dennis M; Poole, Kenneth E S

    2015-11-01

    Hip fracture risk is known to be related to material properties of the proximal femur, but fracture prediction studies adding richer quantitative computed tomography (QCT) measures to dual-energy X-ray (DXA)-based methods have shown limited improvement. Fracture types have distinct relationships to predictors, but few studies have subdivided fracture into types, because this necessitates regional measurements and more fracture cases. This work makes use of cortical bone mapping (CBM) to accurately assess, with no prior anatomical presumptions, the distribution of properties related to fracture type. CBM uses QCT data to measure the cortical and trabecular properties, accurate even for thin cortices below the imaging resolution. The Osteoporotic Fractures in Men (MrOS) study is a predictive case-cohort study of men over 65 years old: we analyze 99 fracture cases (44 trochanteric and 55 femoral neck) compared to a cohort of 308, randomly selected from 5994. To our knowledge, this is the largest QCT-based predictive hip fracture study to date, and the first to incorporate CBM analysis into fracture prediction. We show that both cortical mass surface density and endocortical trabecular BMD are significantly different in fracture cases versus cohort, in regions appropriate to fracture type. We incorporate these regions into predictive models using Cox proportional hazards regression to estimate hazard ratios, and logistic regression to estimate area under the receiver operating characteristic curve (AUC). Adding CBM to DXA-based BMD leads to a small but significant (p fracture, with AUC increasing from 0.78 to 0.79, assessed using leave-one-out cross-validation. For specific fracture types, the improvement is more significant (p trochanteric fractures and 0.76 to 0.82 for femoral neck fractures. In contrast, adding DXA-based BMD to a CBM-based predictive model does not result in any significant improvement. © 2015 The Authors. Journal of Bone and Mineral Research

  5. Mapping regional soil water erosion risk in the Brittany-Loire basin for water management agency

    Science.gov (United States)

    Degan, Francesca; Cerdan, Olivier; Salvador-Blanes, Sébastien; Gautier, Jean-Noël

    2014-05-01

    Soil water erosion is one of the main degradation processes that affect soils through the removal of soil particles from the surface. The impacts for environment and agricultural areas are diverse, such as water pollution, crop yield depression, organic matter loss and reduction in water storage capacity. There is therefore a strong need to produce maps at the regional scale to help environmental policy makers and soil and water management bodies to mitigate the effect of water and soil pollution. Our approach aims to model and map soil erosion risk at regional scale (155 000 km²) and high spatial resolution (50 m) in the Brittany - Loire basin. The factors responsible for soil erosion are different according to the spatial and time scales considered. The regional scale entails challenges about homogeneous data sets availability, spatial resolution of results, various erosion processes and agricultural practices. We chose to improve the MESALES model (Le Bissonnais et al., 2002) to map soil erosion risk, because it was developed specifically for water erosion in agricultural fields in temperate areas. The MESALES model consists in a decision tree which gives for each combination of factors the corresponding class of soil erosion risk. Four factors that determine soil erosion risk are considered: soils, land cover, climate and topography. The first main improvement of the model consists in using newly available datasets that are more accurate than the initial ones. The datasets used cover all the study area homogeneously. Soil dataset has a 1/1 000 000 scale and attributes such as texture, soil type, rock fragment and parent material are used. The climate dataset has a spatial resolution of 8 km and a temporal resolution of mm/day for 12 years. Elevation dataset has a spatial resolution of 50 m. Three different land cover datasets are used where the finest spatial resolution is 50 m over three years. Using these datasets, four erosion factors are characterized and

  6. RAPID-N: Assessing and mapping the risk of natural-hazard impact at industrial installations

    Science.gov (United States)

    Girgin, Serkan; Krausmann, Elisabeth

    2015-04-01

    Natural hazard-triggered technological accidents (so-called Natech accidents) at hazardous installations can have major consequences due to the potential for release of hazardous materials, fires and explosions. Effective Natech risk reduction requires the identification of areas where this risk is high. However, recent studies have shown that there are hardly any methodologies and tools that would allow authorities to identify these areas. To work towards closing this gap, the European Commission's Joint Research Centre has developed the rapid Natech risk assessment and mapping framework RAPID-N. The tool, which is implemented in an online web-based environment, is unique in that it contains all functionalities required for running a full Natech risk analysis simulation (natural hazards severity estimation, equipment damage probability and severity calculation, modeling of the consequences of loss of containment scenarios) and for visualizing its results. The output of RAPID-N are risk summary reports and interactive risk maps which can be used for decision making. Currently, the tool focuses on Natech risk due to earthquakes at industrial installations. However, it will be extended to also analyse and map Natech risk due to floods in the near future. RAPID-N is available at http://rapidn.jrc.ec.europa.eu. This presentation will discuss the results of case-study calculations performed for selected flammable and toxic substances to test the capabilities of RAPID-N both for single- and multi-site earthquake Natech risk assessment. For this purpose, an Istanbul earthquake scenario provided by the Turkish government was used. The results of the exercise show that RAPID-N is a valuable decision-support tool that assesses the Natech risk and maps the consequence end-point distances. These end-point distances are currently defined by 7 kPa overpressure for Vapour Cloud Explosions, 2nd degree burns for pool fire (which is equivalent to a heat radiation of 5 kW/m2 for 40s

  7. WILD FIRE RISK MAP IN THE EASTERN STEPPE OF MONGOLIA USING SPATIAL MULTI-CRITERIA ANALYSIS

    Directory of Open Access Journals (Sweden)

    E. Nasanbat

    2016-06-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  9. Young Children’s Risk-Taking: Mothers’ Authoritarian Parenting Predicts Risk-Taking by Daughters but Not Sons

    OpenAIRE

    Wood, Erin E.; Kennison, Shelia M.

    2017-01-01

    We investigated how mothers’ parenting behaviors and personal characteristics were related to risk-taking by young children. We tested contrasting predictions from evolutionary and social role theories with the former predicting higher risk-taking by boys compared to girls and the latter predicting that mothers would influence children’s gender role development with risk-taking occurring more in children parented with higher levels of harshness (i.e., authoritarian parenting style). In our st...

  10. The utility of absolute risk prediction using FRAX® and Garvan Fracture Risk Calculator in daily practice.

    Science.gov (United States)

    van Geel, Tineke A C M; Eisman, John A; Geusens, Piet P; van den Bergh, Joop P W; Center, Jacqueline R; Dinant, Geert-Jan

    2014-02-01

    There are two commonly used fracture risk prediction tools FRAX(®) and Garvan Fracture Risk Calculator (GARVAN-FRC). The objective of this study was to investigate the utility of these tools in daily practice. A prospective population-based 5-year follow-up study was conducted in ten general practice centres in the Netherlands. For the analyses, the FRAX(®) and GARVAN-FRC 10-year absolute risks (FRAX(®) does not have 5-year risk prediction) for all fractures were used. Among 506 postmenopausal women aged ≥60 years (mean age: 67.8±5.8 years), 48 (9.5%) sustained a fracture during follow-up. Both tools, using BMD values, distinguish between women who did and did not fracture (10.2% vs. 6.8%, respectively for FRAX(®) and 32.4% vs. 39.1%, respectively for GARVAN-FRC, pbetter for women who sustained a fracture (higher sensitivity) and FRAX(®) for women who did not sustain a fracture (higher specificity). Similar results were obtained using age related cut off points. The discriminant value of both models is at least as good as models used in other medical conditions; hence they can be used to communicate the fracture risk to patients. However, given differences in the estimated risks between FRAX(®) and GARVAN-FRC, the significance of the absolute risk must be related to country-specific recommended intervention thresholds to inform the patient. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  11. Geopan AT@S: a Brokering Based Gateway to Georeferenced Historical Maps for Risk Analysis

    Science.gov (United States)

    Previtali, M.

    2017-08-01

    Importance of ancient and historical maps is nowadays recognized in many applications (e.g., urban planning, landscape valorisation and preservation, land changes identification, etc.). In the last years a great effort has been done by different institutions, such as Geographical Institutes, Public Administrations, and collaborative communities, for digitizing and publishing online collections of historical maps. In spite of this variety and availability of data, information overload makes difficult their discovery and management: without knowing the specific repository where the data are stored, it is difficult to find the information required. In addition, problems of interconnection between different data sources and their restricted interoperability may arise. This paper describe a new brokering based gateway developed to assure interoperability between data, in particular georeferenced historical maps and geographic data, gathered from different data providers, with various features and referring to different historical periods. The developed approach is exemplified by a new application named GeoPAN Atl@s that is aimed at linking in Northern Italy area land changes with risk analysis (local seismicity amplification and flooding risk) by using multi-temporal data sources and historic maps.

  12. Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers

    DEFF Research Database (Denmark)

    Kuchenbaecker, Karoline B; McGuffog, Lesley; Barrowdale, Daniel

    2017-01-01

    Background: Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic ...... risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management....

  13. Groundwater vulnerability and risk mapping using GIS, modeling and a fuzzy logic tool.

    Science.gov (United States)

    Nobre, R C M; Rotunno Filho, O C; Mansur, W J; Nobre, M M M; Cosenza, C A N

    2007-12-07

    A groundwater vulnerability and risk mapping assessment, based on a source-pathway-receptor approach, is presented for an urban coastal aquifer in northeastern Brazil. A modified version of the DRASTIC methodology was used to map the intrinsic and specific groundwater vulnerability of a 292 km(2) study area. A fuzzy hierarchy methodology was adopted to evaluate the potential contaminant source index, including diffuse and point sources. Numerical modeling was performed for delineation of well capture zones, using MODFLOW and MODPATH. The integration of these elements provided the mechanism to assess groundwater pollution risks and identify areas that must be prioritized in terms of groundwater monitoring and restriction on use. A groundwater quality index based on nitrate and chloride concentrations was calculated, which had a positive correlation with the specific vulnerability index.

  14. Development and distribution of radon risk maps in New York State

    International Nuclear Information System (INIS)

    Kitto, M.E.; Kunz, C.O.; New York State Univ., Albany, NY; Green, J.G.

    2001-01-01

    Radon maps for each county in New York State have been developed on the township level indicating the percent of homes with ≥ 148 Bq/m 3 (4 pCi/l) in the indoor air of the basement and living area. Estimates are based on a combination of nearly 45,000 basement-screening measurements and correlations to surficial geology. Many of the towns and cities in the State with the highest average indoor radon concentrations are located on highly-permeable gravelly soils formed during the retreat of the Wisconsinan Glaciation. As many towns (32% of total) had ≤ 5 measurements, a project to obtain additional measurements in high-risk towns produced results comparable to estimates based on correlations to surficial geology. Radon risk maps for each county have been distributed to municipal governments, schools, and professionals in activities related to homes, buildings, and indoor air quality. (author)

  15. Uncertainty estimation and risk prediction in air quality

    International Nuclear Information System (INIS)

    Garaud, Damien

    2011-01-01

    This work is about uncertainty estimation and risk prediction in air quality. Firstly, we build a multi-model ensemble of air quality simulations which can take into account all uncertainty sources related to air quality modeling. Ensembles of photochemical simulations at continental and regional scales are automatically generated. Then, these ensemble are calibrated with a combinatorial optimization method. It selects a sub-ensemble which is representative of uncertainty or shows good resolution and reliability for probabilistic forecasting. This work shows that it is possible to estimate and forecast uncertainty fields related to ozone and nitrogen dioxide concentrations or to improve the reliability of threshold exceedance predictions. The approach is compared with Monte Carlo simulations, calibrated or not. The Monte Carlo approach appears to be less representative of the uncertainties than the multi-model approach. Finally, we quantify the observational error, the representativeness error and the modeling errors. The work is applied to the impact of thermal power plants, in order to quantify the uncertainty on the impact estimates. (author) [fr

  16. Cardiovascular risk prediction: the old has given way to the new but at what risk-benefit ratio?

    Directory of Open Access Journals (Sweden)

    Yeboah J

    2014-10-01

    Full Text Available Joseph Yeboah Heart and Vascular Center of Excellence, Wake Forest University School of Medicine, Winston-Salem, NC, USA Abstract: The ultimate goal of cardiovascular risk prediction is to identify individuals in the population to whom the application or administration of current proven lifestyle modifications and medicinal therapies will result in reduction in cardiovascular disease events and minimal adverse effects (net benefit to society. The use of cardiovascular risk prediction tools dates back to 1976 when the Framingham coronary heart disease risk score was published. Since then a lot of novel risk markers have been identified and other cardiovascular risk prediction tools have been developed to either improve or replace the Framingham Risk Score (FRS. In 2013, the new atherosclerotic cardiovascular disease risk estimator was published by the American College of Cardiology and the American Heart Association to replace the FRS for cardiovascular risk prediction. It is too soon to know the performance of the new atherosclerotic cardiovascular disease risk estimator. The risk-benefit ratio for preventive therapy (lifestyle modifications, statin +/− aspirin based on cardiovascular disease risk assessed using the FRS is unknown but it was assumed to be a net benefit. Should we also assume the risk-benefit ratio for the new atherosclerotic cardiovascular disease risk estimator is also a net benefit? Keywords: risk prediction, prevention, cardiovascular disease

  17. Predicting infection risk of airborne foot-and-mouth disease.

    Science.gov (United States)

    Schley, David; Burgin, Laura; Gloster, John

    2009-05-06

    Foot-and-mouth disease is a highly contagious disease of cloven-hoofed animals, the control and eradication of which is of significant worldwide socio-economic importance. The virus may spread by direct contact between animals or via fomites as well as through airborne transmission, with the latter being the most difficult to control. Here, we consider the risk of infection to flocks or herds from airborne virus emitted from a known infected premises. We show that airborne infection can be predicted quickly and with a good degree of accuracy, provided that the source of virus emission has been determined and reliable geo-referenced herd data are available. A simple model provides a reliable tool for estimating risk from known sources and for prioritizing surveillance and detection efforts. The issue of data information management systems was highlighted as a lesson to be learned from the official inquiry into the UK 2007 foot-and-mouth outbreak: results here suggest that the efficacy of disease control measures could be markedly improved through an accurate livestock database incorporating flock/herd size and location, which would enable tactical as well as strategic modelling.

  18. Mapping Disaster Risk Reduction and Climate Change Adaptation: progress in South Africa

    Science.gov (United States)

    Storie, Judith M.

    2018-05-01

    Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA) strategies in Africa are on the increase. South Africa is no different, and a number of strategies have seen the light in aid of reducing disaster risk and adapting to cli-mate change. The DRR and CCA processes include the mapping of location and extent of known and potential hazards, vulnerable communities and environments, and opportunities that may exist to manage these risks. However, the mapping of often fast-changing urban and rural spaces in a standardized manner presents challenges that relate to processes, scales of data capture, level of detail recorded, software and compatibility related to data formats and net-works, human resources skills and understanding, as well as differences in approaches to the nature in which the map-ping processes are executed and spatial data is managed. As a result, projects and implementation of strategies that re-late to the use of such data is affected, and the success of activities based on the data may therefore be uncertain. This paper investigates data custodianship and data categories that is processed and managed across South Africa. It explores the process and content management of disaster risk and climate change related information and defines the challenges that exist in terms of governance. The paper also comments on the challenges and potential solutions for the situation as it gives rise to varying degrees of accuracy, effectiveness for use, and applicability of the spatial data available to affect DRR and improve the value of CCA programmes in the region.

  19. Can Probability Maps of Swept-Source Optical Coherence Tomography Predict Visual Field Changes in Preperimetric Glaucoma?

    Science.gov (United States)

    Lee, Won June; Kim, Young Kook; Jeoung, Jin Wook; Park, Ki Ho

    2017-12-01

    To determine the usefulness of swept-source optical coherence tomography (SS-OCT) probability maps in detecting locations with significant reduction in visual field (VF) sensitivity or predicting future VF changes, in patients with classically defined preperimetric glaucoma (PPG). Of 43 PPG patients, 43 eyes were followed-up on every 6 months for at least 2 years were analyzed in this longitudinal study. The patients underwent wide-field SS-OCT scanning and standard automated perimetry (SAP) at the time of enrollment. With this wide-scan protocol, probability maps originating from the corresponding thickness map and overlapped with SAP VF test points could be generated. We evaluated the vulnerable VF points with SS-OCT probability maps as well as the prevalence of locations with significant VF reduction or subsequent VF changes observed in the corresponding damaged areas of the probability maps. The vulnerable VF points were shown in superior and inferior arcuate patterns near the central fixation. In 19 of 43 PPG eyes (44.2%), significant reduction in baseline VF was detected within the areas of structural change on the SS-OCT probability maps. In 16 of 43 PPG eyes (37.2%), subsequent VF changes within the areas of SS-OCT probability map change were observed over the course of the follow-up. Structural changes on SS-OCT probability maps could detect or predict VF changes using SAP, in a considerable number of PPG eyes. Careful comparison of probability maps with SAP results could be useful in diagnosing and monitoring PPG patients in the clinical setting.

  20. Risk score to predict gastrointestinal bleeding after acute ischemic stroke.

    Science.gov (United States)

    Ji, Ruijun; Shen, Haipeng; Pan, Yuesong; Wang, Penglian; Liu, Gaifen; Wang, Yilong; Li, Hao; Singhal, Aneesh B; Wang, Yongjun

    2014-07-25

    Gastrointestinal bleeding (GIB) is a common and often serious complication after stroke. Although several risk factors for post-stroke GIB have been identified, no reliable or validated scoring system is currently available to predict GIB after acute stroke in routine clinical practice or clinical trials. In the present study, we aimed to develop and validate a risk model (acute ischemic stroke associated gastrointestinal bleeding score, the AIS-GIB score) to predict in-hospital GIB after acute ischemic stroke. The AIS-GIB score was developed from data in the China National Stroke Registry (CNSR). Eligible patients in the CNSR were randomly divided into derivation (60%) and internal validation (40%) cohorts. External validation was performed using data from the prospective Chinese Intracranial Atherosclerosis Study (CICAS). Independent predictors of in-hospital GIB were obtained using multivariable logistic regression in the derivation cohort, and β-coefficients were used to generate point scoring system for the AIS-GIB. The area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration, respectively. A total of 8,820, 5,882, and 2,938 patients were enrolled in the derivation, internal validation and external validation cohorts. The overall in-hospital GIB after AIS was 2.6%, 2.3%, and 1.5% in the derivation, internal, and external validation cohort, respectively. An 18-point AIS-GIB score was developed from the set of independent predictors of GIB including age, gender, history of hypertension, hepatic cirrhosis, peptic ulcer or previous GIB, pre-stroke dependence, admission National Institutes of Health stroke scale score, Glasgow Coma Scale score and stroke subtype (Oxfordshire). The AIS-GIB score showed good discrimination in the derivation (0.79; 95% CI, 0.764-0.825), internal (0.78; 95% CI, 0.74-0.82) and external (0.76; 95% CI, 0.71-0.82) validation cohorts

  1. Landscape epidemiology: An emerging perspective in the mapping and modelling of disease and disease risk factors

    Directory of Open Access Journals (Sweden)

    Nnadi Nnaemeka Emmanuel

    2011-09-01

    Full Text Available Landscape epidemiology describes how the temporal dynamics of host, vector, and pathogen populations interact spatially within a permissive environment to enable transmission. It also aims at understanding the vegetation and geologic conditions that are necessary for the maintenance and transmission of a particular pathogen. The current review describes the evolution of landscape epidemiology. As a science, it also highlights the various methods of mapping and modeling diseases and disease risk factors. The key tool to characterize landscape is satellite remote sensing and these data are used as inputs to drive spatial models of transmission risk.

  2. Gis-Based Multi-Criteria Decision Analysis for Forest Fire Risk Mapping

    Science.gov (United States)

    Akay, A. E.; Erdoğan, A.

    2017-11-01

    The forested areas along the coastal zone of the Mediterranean region in Turkey are classified as first-degree fire sensitive areas. Forest fires are major environmental disaster that affects the sustainability of forest ecosystems. Besides, forest fires result in important economic losses and even threaten human lives. Thus, it is critical to determine the forested areas with fire risks and thereby minimize the damages on forest resources by taking necessary precaution measures in these areas. The risk of forest fire can be assessed based on various factors such as forest vegetation structures (tree species, crown closure, tree stage), topographic features (slope and aspect), and climatic parameters (temperature, wind). In this study, GIS-based Multi-Criteria Decision Analysis (MCDA) method was used to generate forest fire risk map. The study was implemented in the forested areas within Yayla Forest Enterprise Chiefs at Dursunbey Forest Enterprise Directorate which is classified as first degree fire sensitive area. In the solution process, "extAhp 2.0" plug-in running Analytic Hierarchy Process (AHP) method in ArcGIS 10.4.1 was used to categorize study area under five fire risk classes: extreme risk, high risk, moderate risk, and low risk. The results indicated that 23.81 % of the area was of extreme risk, while 25.81 % was of high risk. The result indicated that the most effective criterion was tree species, followed by tree stages. The aspect had the least effective criterion on forest fire risk. It was revealed that GIS techniques integrated with MCDA methods are effective tools to quickly estimate forest fire risk at low cost. The integration of these factors into GIS can be very useful to determine forested areas with high fire risk and also to plan forestry management after fire.

  3. A genetic risk score combining ten psoriasis risk loci improves disease prediction.

    Directory of Open Access Journals (Sweden)

    Haoyan Chen

    2011-04-01

    Full Text Available Psoriasis is a chronic, immune-mediated skin disease affecting 2-3% of Caucasians. Recent genetic association studies have identified multiple psoriasis risk loci; however, most of these loci contribute only modestly to disease risk. In this study, we investigated whether a genetic risk score (GRS combining multiple loci could improve psoriasis prediction. Two approaches were used: a simple risk alleles count (cGRS and a weighted (wGRS approach. Ten psoriasis risk SNPs were genotyped in 2815 case-control samples and 858 family samples. We found that the total number of risk alleles in the cases was significantly higher than in controls, mean 13.16 (SD 1.7 versus 12.09 (SD 1.8, p = 4.577×10(-40. The wGRS captured considerably more risk than any SNP considered alone, with a psoriasis OR for high-low wGRS quartiles of 10.55 (95% CI 7.63-14.57, p = 2.010×10(-65. To compare the discriminatory ability of the GRS models, receiver operating characteristic curves were used to calculate the area under the curve (AUC. The AUC for wGRS was significantly greater than for cGRS (72.0% versus 66.5%, p = 2.13×10(-8. Additionally, the AUC for HLA-C alone (rs10484554 was equivalent to the AUC for all nine other risk loci combined (66.2% versus 63.8%, p = 0.18, highlighting the dominance of HLA-C as a risk locus. Logistic regression revealed that the wGRS was significantly associated with two subphenotypes of psoriasis, age of onset (p = 4.91×10(-6 and family history (p = 0.020. Using a liability threshold model, we estimated that the 10 risk loci account for only 11.6% of the genetic variance in psoriasis. In summary, we found that a GRS combining 10 psoriasis risk loci captured significantly more risk than any individual SNP and was associated with early onset of disease and a positive family history. Notably, only a small fraction of psoriasis heritability is captured by the common risk variants identified to date.

  4. Genetic risk prediction and neurobiological understanding of alcoholism.

    Science.gov (United States)

    Levey, D F; Le-Niculescu, H; Frank, J; Ayalew, M; Jain, N; Kirlin, B; Learman, R; Winiger, E; Rodd, Z; Shekhar, A; Schork, N; Kiefer, F; Kiefe, F; Wodarz, N; Müller-Myhsok, B; Dahmen, N; Nöthen, M; Sherva, R; Farrer, L; Smith, A H; Kranzler, H R; Rietschel, M; Gelernter, J; Niculescu, A B

    2014-05-20

    We have used a translational Convergent Functional Genomics (CFG) approach to discover genes involved in alcoholism, by gene-level integration of genome-wide association study (GWAS) data from a German alcohol dependence cohort with other genetic and gene expression data, from human and animal model studies, similar to our previous work in bipolar disorder and schizophrenia. A panel of all the nominally significant P-value SNPs in the top candidate genes discovered by CFG  (n=135 genes, 713 SNPs) was used to generate a genetic  risk prediction score (GRPS), which showed a trend towards significance (P=0.053) in separating  alcohol dependent individuals from controls in an independent German test cohort. We then validated and prioritized our top findings from this discovery work, and subsequently tested them in three independent cohorts, from two continents. A panel of all the nominally significant P-value single-nucleotide length polymorphisms (SNPs) in the top candidate genes discovered by CFG (n=135 genes, 713 SNPs) were used to generate a Genetic Risk Prediction Score (GRPS), which showed a trend towards significance (P=0.053) in separating alcohol-dependent individuals from controls in an independent German test cohort. In order to validate and prioritize the key genes that drive behavior without some of the pleiotropic environmental confounds present in humans, we used a stress-reactive animal model of alcoholism developed by our group, the D-box binding protein (DBP) knockout mouse, consistent with the surfeit of stress theory of addiction proposed by Koob and colleagues. A much smaller panel (n=11 genes, 66 SNPs) of the top CFG-discovered genes for alcoholism, cross-validated and prioritized by this stress-reactive animal model showed better predictive ability in the independent German test cohort (P=0.041). The top CFG scoring gene for alcoholism from the initial discovery step, synuclein alpha (SNCA) remained the top gene after the stress

  5. Comparison of the large-scale radon risk map for southern Belgium with results of high resolution surveys

    International Nuclear Information System (INIS)

    Zhu, H.-C.; Charlet, J.M.; Poffijn, A.

    2000-01-01

    A large-scale radon survey consisting of long-term measurements in about 5200 singe-family houses in the southern part of Belgium was carried from 1995 to 1999. A radon risk map for the region was produced using geostatistical and GIS approaches. Some communes or villages situated within high risk areas were chosen for detailed surveys. A high resolution radon survey with about 330 measurements was performed in half part of the commune of Burg-Reuland. Comparison of radon maps on quite different scales shows that the general Rn risk map has similar pattern as the radon map for the detailed study area. Another detailed radon survey in the village of Hatrival, situated in a high radon area, found very high proportion of houses with elevated radon concentrations. The results of this detailed survey are comparable to the expectation for high risk areas on the large-scale radon risk map. The good correspondence between the findings of the general risk map and the analysis of the limited detailed surveys, suggests that the large-scale radon risk map is likely reliable. (author)

  6. Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

    Science.gov (United States)

    Iglesias, Adriana I; Mihaescu, Raluca; Ioannidis, John P A; Khoury, Muin J; Little, Julian; van Duijn, Cornelia M; Janssens, A Cecile J W

    2014-05-01

    Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement. We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. Structural Bioinformatics-Based Prediction of Exceptional Selectivity of p38 MAP Kinase Inhibitor PH-797804

    Energy Technology Data Exchange (ETDEWEB)

    Xing, Li; Shieh, Huey S.; Selness, Shaun R.; Devraj, Rajesh V.; Walker, John K.; Devadas, Balekudru; Hope, Heidi R.; Compton, Robert P.; Schindler, John F.; Hirsch, Jeffrey L.; Benson, Alan G.; Kurumbail, Ravi G.; Stegeman, Roderick A.; Williams, Jennifer M.; Broadus, Richard M.; Walden, Zara; Monahan, Joseph B.; Pfizer

    2009-07-24

    PH-797804 is a diarylpyridinone inhibitor of p38{alpha} mitogen-activated protein (MAP) kinase derived from a racemic mixture as the more potent atropisomer (aS), first proposed by molecular modeling and subsequently confirmed by experiments. On the basis of structural comparison with a different biaryl pyrazole template and supported by dozens of high-resolution crystal structures of p38{alpha} inhibitor complexes, PH-797804 is predicted to possess a high level of specificity across the broad human kinase genome. We used a structural bioinformatics approach to identify two selectivity elements encoded by the TXXXG sequence motif on the p38{alpha} kinase hinge: (i) Thr106 that serves as the gatekeeper to the buried hydrophobic pocket occupied by 2,4-difluorophenyl of PH-797804 and (ii) the bidentate hydrogen bonds formed by the pyridinone moiety with the kinase hinge requiring an induced 180{sup o} rotation of the Met109-Gly110 peptide bond. The peptide flip occurs in p38{alpha} kinase due to the critical glycine residue marked by its conformational flexibility. Kinome-wide sequence mining revealed rare presentation of the selectivity motif. Corroboratively, PH-797804 exhibited exceptionally high specificity against MAP kinases and the related kinases. No cross-reactivity was observed in large panels of kinase screens (selectivity ratio of >500-fold). In cellular assays, PH-797804 demonstrated superior potency and selectivity consistent with the biochemical measurements. PH-797804 has met safety criteria in human phase I studies and is under clinical development for several inflammatory conditions. Understanding the rationale for selectivity at the molecular level helps elucidate the biological function and design of specific p38{alpha} kinase inhibitors.

  8. Bayesian risk maps for Schistosoma mansoni and hookworm mono-infections in a setting where both parasites co-exist

    Directory of Open Access Journals (Sweden)

    Giovanna Raso

    2007-11-01

    Full Text Available There is growing interest in the use of Bayesian geostatistical models for predicting the spatial distribution of parasitic infections, including hookworm, Schistosoma mansoni and co-infections with both parasites. The aim of this study was to predict the spatial distribution of mono-infections with either hookworm or S. mansoni in a setting where both parasites co-exist. School-based cross-sectional parasitological and questionnaire surveys were carried out in 57 rural schools in the Man region, western Côte d’Ivoire. A single stool specimen was obtained from each schoolchild attending grades 3-5. Stool specimens were processed by the Kato-Katz technique and an ether concentration method and examined for the presence of hookworm and S. mansoni eggs. The combined results from the two diagnostic approaches were considered for the infection status of each child. Demographic data (i.e. age and sex were obtained from readily available school registries. Each child’s socio-economic status was estimated, using the questionnaire data following a household-based asset approach. Environmental data were extracted from satellite imagery. The different data sources were incorporated into a geographical information system. Finally, a Bayesian spatial multinomial regression model was constructed and the spatial patterns of S. mansoni and hookworm mono-infections were investigated using Bayesian kriging. Our approach facilitated the production of smooth risk maps for hookworm and S. mansoni mono-infections that can be utilized for targeting control interventions. We argue that in settings where S. mansoni and hookworm co-exist and control efforts are under way, there is a need for both mono- and co-infection risk maps to enhance the cost-effectiveness of control programmes.

  9. Fuzzy Cognitive Maps for Glacier Hazards Assessment: Application to Predicting the Potential for Glacier Lake Outbursts

    Science.gov (United States)

    Furfaro, R.; Kargel, J. S.; Fink, W.; Bishop, M. P.

    2010-12-01

    Glaciers and ice sheets are among the largest unstable parts of the solid Earth. Generally, glaciers are devoid of resources (other than water), are dangerous, are unstable and no infrastructure is normally built directly on their surfaces. Areas down valley from large alpine glaciers are also commonly unstable due to landslide potential of moraines, debris flows, snow avalanches, outburst floods from glacier lakes, and other dynamical alpine processes; yet there exists much development and human occupation of some disaster-prone areas. Satellite remote sensing can be extremely effective in providing cost-effective and time- critical information. Space-based imagery can be used to monitor glacier outlines and their lakes, including processes such as iceberg calving and debris accumulation, as well as changing thicknesses and flow speeds. Such images can also be used to make preliminary identifications of specific hazardous spots and allows preliminary assessment of possible modes of future disaster occurrence. Autonomous assessment of glacier conditions and their potential for hazards would present a major advance and permit systematized analysis of more data than humans can assess. This technical leap will require the design and implementation of Artificial Intelligence (AI) algorithms specifically designed to mimic glacier experts’ reasoning. Here, we introduce the theory of Fuzzy Cognitive Maps (FCM) as an AI tool for predicting and assessing natural hazards in alpine glacier environments. FCM techniques are employed to represent expert knowledge of glaciers physical processes. A cognitive model embedded in a fuzzy logic framework is constructed via the synergistic interaction between glaciologists and AI experts. To verify the effectiveness of the proposed AI methodology as applied to predicting hazards in glacier environments, we designed and implemented a FCM that addresses the challenging problem of autonomously assessing the Glacier Lake Outburst Flow

  10. An app for climate-based Chikungunya risk monitoring and mapping

    Science.gov (United States)

    Soebiyanto, R. P.; Rama, X.; Jepsen, R.; Bijoria, S.; Linthicum, K. J.; Anyamba, A.

    2017-12-01

    There is an increasing concern for reemergence and spread of chikungunya in the last 10 years in Africa, the Indian Ocean, and Asia, and range expansion that now reaches the Caribbean, South America and threatens North America. The outbreak of Chikungunya in 2013 and its spread throughout the Americas has so far resulted in more than 1.7 million suspected cases. This has demonstrated the importance of readiness in assessing potential risk of the emergence of vector-borne diseases. Climate and ecological conditions are now recognized as major contributors to the emergence and re-emergence of various vector-borne diseases including Chikungunya. Variations and persistence of extreme climate conditions provide suitable environment for the increase of certain disease vector populations, which then further amplify vector-borne disease transmission. This highlights the importance of climate anomaly information in assessing regions at risk for Chikungunya. In order to address such issue, we are developing a climate-based app, CHIKRISK, which will help decision makers to answer three critical questions: (i) Where has Chikungunya activity occurred; (ii) Where it is occurring now; (iii) Which regions are currently at risk for Chikungunya. We first develop a database of historical Chikungunya outbreak locations compiled from publicly available information. These records are used to map where Chikungunya activity has occurred over time. We leverage on various satellite-based climate data records - such as rainfall, land surface and near surface temperature to characterize evolving conditions prior to and during Chikungunya activity. Chikungunya outbreak data, climate and ancillary (i.e. population and elevation) data are used to develop analytics capability that will produce risk maps. The CHIKRISK app has the capability to visualize historical Chikungunya activity locations, climate anomaly conditions and Chikungunya risk maps. Currently, the focus of the development is on the

  11. Fine-Mapping of Common Genetic Variants Associated with Colorectal Tumor Risk Identified Potential Functional Variants.

    Directory of Open Access Journals (Sweden)

    Mengmeng Du

    Full Text Available Genome-wide association studies (GWAS have identified many common single nucleotide polymorphisms (SNPs associated with colorectal cancer risk. These SNPs may tag correlated variants with biological importance. Fine-mapping around GWAS loci can facilitate detection of functional candidates and additional independent risk variants. We analyzed 11,900 cases and 14,311 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry. To fine-map genomic regions containing all known common risk variants, we imputed high-density genetic data from the 1000 Genomes Project. We tested single-variant associations with colorectal tumor risk for all variants spanning genomic regions 250-kb upstream or downstream of 31 GWAS-identified SNPs (index SNPs. We queried the University of California, Santa Cruz Genome Browser to examine evidence for biological function. Index SNPs did not show the strongest association signals with colorectal tumor risk in their respective genomic regions. Bioinformatics analysis of SNPs showing smaller P-values in each region revealed 21 functional candidates in 12 loci (5q31.1, 8q24, 11q13.4, 11q23, 12p13.32, 12q24.21, 14q22.2, 15q13, 18q21, 19q13.1, 20p12.3, and 20q13.33. We did not observe evidence of additional independent association signals in GWAS-identified regions. Our results support the utility of integrating data from comprehensive fine-mapping with expanding publicly available genomic databases to help clarify GWAS associations and identify functional candidates that warrant more onerous laboratory follow-up. Such efforts may aid the eventual discovery of disease-causing variant(s.

  12. CADYRI, a dynamic mapping tool of human risk associated with flooding in urban areas

    Science.gov (United States)

    Tanguy, M.; Chokmani, K.; Bernier, M.; Poulin, J.

    2013-12-01

    When a flood affects an urban area, the managers and services responsible for public safety need precise and real time information on the localization of the flooded areas, on the submersion heights in those areas, but also on the vulnerability of people exposed to this hazard. Such information is essential for an effective crisis management. Despite a growing interest in this topic over the last 15 years, the development of flood risk assessment tools mainly focused on quantitative modeling of the monetary damages caused by floods to residential buildings or to critical infrastructures. Little attention was paid to the vulnerability of people exposed to flooding but also to the effects of the failure or destruction of critical infrastructures and residential building on people health and security during the disaster. Moreover, these models do not integrate the dynamic features of the flood (extent, submersion heights) and the evolution of human vulnerability in the same mapping tool. Thus, an accurate and precise evaluation of human risk induced by urban flooding is hardly feasible using such models. This study presents CADYRI, a dynamic mapping tool of human risk associated with flooding in urban areas, which fills the actual needs in terms of flood risk evaluation and management. This innovative tool integrates a methodology of flood hazard mapping that simulates, for a given discharge, the associated water level, and subsequently determines the extent of the flooded area and the submersion heights at each point of the flooded area, using a DEM. The dynamics of human vulnerability is then mapped at the household level, according to the characteristics of the flood hazard. Three key components of human vulnerability have been identified and are integrated to CADYRI: 1, the intrinsic vulnerability of the population, estimated by specific socio-economic indicators; 2, the vulnerability of buildings, assessed by their structural features; 3, the vulnerability of

  13. Predicting the distribution of four species of raptors (Aves: Accipitridae) in southern Spain: statistical models work better than existing maps

    OpenAIRE

    Bustamante, Javier; Seoane, Javier

    2004-01-01

    Aim To test the effectiveness of statistical models based on explanatory environmental variables vs. existing distribution information (maps and breeding atlas), for predicting the distribution of four species of raptors (family Accipitridae): common buzzard Buteo buteo (Linnaeus, 1758), short-toed eagle Circaetus gallicus (Gmelin, 1788), booted eagle Hieraaetus pennatus (Gmelin, 1788) and black kite Milvus migrans (Boddaert, 1783). Location Andalusia, southe...

  14. Irrigation salinity hazard assessment and risk mapping in the lower Macintyre Valley, Australia.

    Science.gov (United States)

    Huang, Jingyi; Prochazka, Melissa J; Triantafilis, John

    2016-05-01

    In the Murray-Darling Basin of Australia, secondary soil salinization occurs due to excessive deep drainage and the presence of shallow saline water tables. In order to understand the cause and best management, soil and vadose zone information is necessary. This type of information has been generated in the Toobeah district but owing to the state border an inconsistent methodology was used. This has led to much confusion from stakeholders who are unable to understand the ambiguity of the results in terms of final overall risk of salinization. In this research, a digital soil mapping method that employs various ancillary data is presented. Firstly, an electromagnetic induction survey using a Geonics EM34 and EM38 was used to characterise soil and vadose zone stratigraphy. From the apparent electrical conductivity (ECa) collected, soil sampling locations were selected and with laboratory analysis carried out to determine average (2-12m) clay and EC of a saturated soil-paste extract (ECe). EM34 ECa, land surface parameters derived from a digital elevation model and measured soil data were used to establish multiple linear regression models, which allowed for mapping of various hazard factors, including clay and ECe. EM38 ECa data were calibrated to deep drainage obtained from Salt and Leaching Fraction (SaLF) modelling of soil data. Expert knowledge and indicator kriging were used to determine critical values where the salinity hazard factors were likely to contribute to a shallow saline water table (i.e., clay ≤35%; ECe>2.5dS/m, and deep drainage >100mm/year). This information was combined to produce an overall salinity risk map for the Toobeah district using indicator kriging. The risk map shows potential salinization areas and where detailed information is required and where targeted research can be conducted to monitor soil conditions and water table heights and determine best management strategies. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. Risk approximation in decision making: approximative numeric abilities predict advantageous decisions under objective risk.

    Science.gov (United States)

    Mueller, Silke M; Schiebener, Johannes; Delazer, Margarete; Brand, Matthias

    2018-01-22

    Many decision situations in everyday life involve mathematical considerations. In decisions under objective risk, i.e., when explicit numeric information is available, executive functions and abilities to handle exact numbers and ratios are predictors of objectively advantageous choices. Although still debated, exact numeric abilities, e.g., normative calculation skills, are assumed to be related to approximate number processing skills. The current study investigates the effects of approximative numeric abilities on decision making under objective risk. Participants (N = 153) performed a paradigm measuring number-comparison, quantity-estimation, risk-estimation, and decision-making skills on the basis of rapid dot comparisons. Additionally, a risky decision-making task with exact numeric information was administered, as well as tasks measuring executive functions and exact numeric abilities, e.g., mental calculation and ratio processing skills, were conducted. Approximative numeric abilities significantly predicted advantageous decision making, even beyond the effects of executive functions and exact numeric skills. Especially being able to make accurate risk estimations seemed to contribute to superior choices. We recommend approximation skills and approximate number processing to be subject of future investigations on decision making under risk.

  16. Urban-Hazard Risk Analysis: Mapping of Heat-Related Risks in the Elderly in Major Italian Cities

    Science.gov (United States)

    Morabito, Marco; Crisci, Alfonso; Gioli, Beniamino; Gualtieri, Giovanni; Toscano, Piero; Di Stefano, Valentina; Orlandini, Simone; Gensini, Gian Franco

    2015-01-01

    Background Short-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks. Objectives Development of high-resolution, heat-related urban risk maps regarding the elderly population (≥65). Methods A long time-series (2001–2013) of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m) daytime and night-time land surface temperatures (LST). LST was estimated pixel-wise by applying two statistical model approaches: 1) the Linear Regression Model (LRM); 2) the Generalized Additive Model (GAM). Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m) from the 2001 census (Eurostat source), and processed together using “Crichton’s Risk Triangle” hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI). Results The GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk) were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities. Conclusions This study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public

  17. Malignancy risk prediction for primary jejunum-ileal tumors

    Directory of Open Access Journals (Sweden)

    MARQUES Ruy Garcia

    2000-01-01

    Full Text Available This work is aimed at identifying factors associated with primary jejunum-ileal tumors malignancy, defining a prediction model with sensitivity, specificity and accuracy to distinguish malign from benign neoplasms. These tumors are rare, have highly unspecific presentation and, frequently, are diagnosed late. We reviewed the charts of 42 patients with primary jejunum-ileal tumors treated in the Department of General Surgery of Rio de Janeiro State University Hospital, Rio de Janeiro, RJ, Brazil, from 1969 to 1998. We performed bivariate analyses, based on chi² test, searching associations between tumors malignancy and demographic and clinical variables. Then logistic regression was employed to consider the independent effect of variables previously identified on malignancy risk. The malign tumors included 11 adenocarcinomas, 7 leiomyosarcomas, 5 carcinoids and 4 lymphomas; the benign tumors included 10 leiomyomas, 2 hamartomas, and single cases of adenoma, multiple neurilemoma and choristoma. The bivariate analyses indicated the association between malignancy and palpable abdominal mass (P = 0.003, period from signs and symptoms onset to diagnosis (P = 0.016, anemia (P = 0.020, anorexia (P = 0.003, abdominal pain (P = 0.031, weight loss (P = 0.001, nausea and vomit (P = 0.094, and intestinal obstruction (P = 0.066; no association with patients demographic characteristics were found. In the final logistic regression model, weight loss, anemia and intestinal obstruction were statistically associated with the dependent variable of interest. Based only on three variables -- weight loss, anemia and intestinal obstruction -- the model defined was able to predict primary jejunum-ileal tumors malignancy with sensitivity of 85.2%, specificity of 80.0%, and accuracy of 83.3%.

  18. Predicting geomorphically-induced flood risk for the Nepalese Terai communities

    Science.gov (United States)

    Dingle, Elizabeth; Creed, Maggie; Attal, Mikael; Sinclair, Hugh; Mudd, Simon; Borthwick, Alistair; Dugar, Sumit; Brown, Sarah

    2017-04-01

    Rivers sourced from the Himalaya irrigate the Indo-Gangetic Plain via major river networks that support 10% of the global population. However, many of these rivers are also the source of devastating floods. During the 2014 Karnali River floods in west Nepal, the Karnali rose to around 16 m at Chisapani (where it enters the Indo-Gangetic Plain), 1 m higher than the previous record in 1983; the return interval for this event was estimated to be 1000 years. Flood risk may currently be underestimated in this region, primarily because changes to the channel bed are not included when identifying areas at risk of flooding from events of varying recurrence intervals. Our observations in the field, corroborated by satellite imagery, show that river beds are highly mobile and constantly evolve through each monsoon. Increased bed levels due to sediment aggradation decreases the capacity of the river, increasing significantly the risk of devastating flood events; we refer to these as 'geomorphically-induced floods'. Major, short-lived episodes of sediment accumulation in channels are caused by stochastic variability in sediment flux generated by storms, earthquakes and glacial outburst floods from upstream parts of the catchment. Here, we generate a field-calibrated, geomorphic flood risk model for varying upstream scenarios, and predict changing flood risk for the Karnali River. A numerical model is used to carry out a sensitivity analysis of changes in channel geometry (particularly aggradation or degradation) based on realistic flood scenarios. In these scenarios, water and sediment discharge are varied within a range of plausible values, up to extreme sediment and water fluxes caused by widespread landsliding and/or intense monsoon precipitation based on existing records. The results of this sensitivity analysis will be used to inform flood hazard maps of the Karnali River floodplain and assess the vulnerability of the populations in the region.

  19. Environmental sensitivity mapping and risk assessment for oil spill along the Chennai Coast in India.

    Science.gov (United States)

    Kankara, R S; Arockiaraj, S; Prabhu, K

    2016-05-15

    Integration of oil spill modeling with coastal resource information could be useful for protecting the coastal environment from oil spills. A scenario-based risk assessment and sensitivity indexing were performed for the Chennai coast by integrating a coastal resource information system and an oil spill trajectory model. The fate analysis of spilled oil showed that 55% of oil out of a total volume of 100m(3) remained in the water column, affecting 800m of the shoreline. The seasonal scenarios show major impact during the southwest (SW) and northeast (NE) monsoons and more fatal effects on marine pelagic organisms during SW monsoon. The Oil Spill Risk Assessment Modeler tool was constructed in a geographic information systems (GIS) platform to analyze the risks, sensitivity mapping, and priority indexing of resources that are likely to be affected by oil spills along the Chennai coast. The results of sensitivity mapping and the risk assessment results can help organizations take measures to combat oil spills in a timely manner. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. A method for mapping fire hazard and risk across multiple scales and its application in fire management

    Science.gov (United States)

    Robert E. Keane; Stacy A. Drury; Eva C. Karau; Paul F. Hessburg; Keith M. Reynolds

    2010-01-01

    This paper presents modeling methods for mapping fire hazard and fire risk using a research model called FIREHARM (FIRE Hazard and Risk Model) that computes common measures of fire behavior, fire danger, and fire effects to spatially portray fire hazard over space. FIREHARM can compute a measure of risk associated with the distribution of these measures over time using...

  1. ECG dispersion mapping predicts clinical deterioration, measured by increase in the Simple Clinical Score.

    LENUS (Irish Health Repository)

    Kellett, J

    2012-01-01

    Objective: ECG dispersion mapping (ECG-DM) is a novel technique that reports abnormal ECG microalternations. We report the ability of ECG-DM to predict clinical deterioration of acutely ill medical patients, as measured by an increase in the Simple Clinical Score (SCS) the day after admission to hospital. Methods: 453 acutely ill medical patients (mean age 69.7 +\\/- 14.0 years) had the SCS recorded and ECGDM performed immediately after admission to hospital. Results: 46 patients had an SCS increase 20.8 +\\/- 7.6 hours after admission. Abnormal micro-alternations during left ventricular re-polarization had the highest association with SCS increase (p=0.0005). Logistic regression showed that only nursing home residence and abnormal micro-alternations during re-polarization of the left ventricle were independent predictors of SCS increase with an odds ratio of 2.84 and 3.01, respectively. Conclusion: ECG-DM changes during left ventricular re-polarization are independent predictors of clinical deterioration the day after hospital admission.

  2. Event-related potentials during word mapping to object shape predict toddlers’ vocabulary size

    Directory of Open Access Journals (Sweden)

    Kristina eBorgström

    2015-02-01

    Full Text Available What role does attention to different object properties play in early vocabulary development? This longitudinal study using event-related potentials in combination with behavioral measures investigated 20- and 24-month-olds’ (n = 38; n = 34; overlapping n = 24 ability to use object shape and object part information in word-object mapping. The N400 component was used to measure semantic priming by images containing shape or detail information. At 20 months, the N400 to words primed by object shape varied in topography and amplitude depending on vocabulary size, and these differences predicted productive vocabulary size at 24 months. At 24 months, when most of the children had vocabularies of several hundred words, the relation between vocabulary size and the N400 effect in a shape context was weaker. Detached object parts did not function as word primes regardless of age or vocabulary size, although the part-objects were identified behaviorally. The behavioral measure, however, also showed relatively poor recognition of the part-objects compared to the shape-objects. These three findings provide new support for the link between shape recognition and early vocabulary development.

  3. Using self-organizing maps to determine observation threshold limit predictions in highly variant data

    Science.gov (United States)

    Paganoni, C.A.; Chang, K.C.; Robblee, M.B.

    2006-01-01

    A significant data quality challenge for highly variant systems surrounds the limited ability to quantify operationally reasonable limits on the data elements being collected and provide reasonable threshold predictions. In many instances, the number of influences that drive a resulting value or operational range is too large to enable physical sampling for each influencer, or is too complicated to accurately model in an explicit simulation. An alternative method to determine reasonable observation thresholds is to employ an automation algorithm that would emulate a human analyst visually inspecting data for limits. Using the visualization technique of self-organizing maps (SOM) on data having poorly understood relationships, a methodology for determining threshold limits was developed. To illustrate this approach, analysis of environmental influences that drive the abundance of a target indicator species (the pink shrimp, Farfantepenaeus duorarum) provided a real example of applicability. The relationship between salinity and temperature and abundance of F. duorarum is well documented, but the effect of changes in water quality upstream on pink shrimp abundance is not well understood. The highly variant nature surrounding catch of a specific number of organisms in the wild, and the data available from up-stream hydrology measures for salinity and temperature, made this an ideal candidate for the approach to provide a determination about the influence of changes in hydrology on populations of organisms.

  4. Mapping and evaluation of snow avalanche risk using GIS technique in Rodnei National Park

    Science.gov (United States)

    Covǎsnianu, Adrian; Grigoraş, Ioan-Rǎducu; Covǎsnianu, Liliana-Elena; Iordache, Iulian; Balin, Daniela

    2010-05-01

    The study consisted in a precise mapping project (GPS field campaign, on-screen digitization of the topographic maps at 1:25.000 scale and updated with ASTER mission) of the Rodnei National Park area (Romanian Carpathians) with a focus on snow avalanche risk survey. Parameters taken into account were slope, aspect, altitude, landforms and roughness resulted from a high resolute numerical terrain model obtained by ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) mission. The resulted digital surface model with a spatial resolution of 10 m covered a total area of 187 square kilometers and was improved by the help of Topo to Raster tool. All these parameters were calibrated after a model applied onto Tatra Massive and also Ceahlău Mountain. The results were adapted and interpreted in accordance with European avalanche hazard scale. This work was made in the context of the elaboration of Risk Map and is directly concerning both the security of tourism activities but also the management of the Rodnei Natural Park. The extension of this method to similar mountain areas is ongoing.

  5. Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature

    Science.gov (United States)

    Marques da Silva, J. R.; Damásio, C. V.; Sousa, A. M. O.; Bugalho, L.; Pessanha, L.; Quaresma, P.

    2015-06-01

    Pest risk maps for agricultural use are usually constructed from data obtained from in-situ meteorological weather stations, which are relatively sparsely distributed and are often quite expensive to install and difficult to maintain. This leads to the creation of maps with relatively low spatial resolution, which are very much dependent on interpolation methodologies. Considering that agricultural applications typically require a more detailed scale analysis than has traditionally been available, remote sensing technology can offer better monitoring at increasing spatial and temporal resolutions, thereby, improving pest management results and reducing costs. This article uses ground temperature, or land surface temperature (LST), data distributed by EUMETSAT/LSASAF (with a spatial resolution of 3 × 3 km (nadir resolution) and a revisiting time of 15 min) to generate one of the most commonly used parameters in pest modeling and monitoring: "thermal integral over air temperature (accumulated degree-days)". The results show a clear association between the accumulated LST values over a threshold and the accumulated values computed from meteorological stations over the same threshold (specific to a particular tomato pest). The results are very promising and enable the production of risk maps for agricultural pests with a degree of spatial and temporal detail that is difficult to achieve using in-situ meteorological stations.

  6. Mitigating Zika Risk: Aedes Aegypti and Aedes Albopictus Risk Mapping in Brownsville, TX

    Science.gov (United States)

    The presentation is intended to (1) engage audiences on a “System’s” approach to model Aedes species risks at a community level, (2) highlight US EPA EnviroAtlas ecosystem services and data rich capacity, and (3) present ORD research that is directly support EPA...

  7. Developing a scientific procedure for community based hazard mapping and risk mitigation

    Science.gov (United States)

    Verrier, M.

    2011-12-01

    As an international exchange student from the Geological Sciences Department at San Diego State University (SDSU), I joined the KKN-PPM program at Universitas Gadjah Mada (UGM), Yogyakarta, Indonesia, in July 2011 for 12 days (July 4th to July 16th) of its two month duration (July 4th to August 25th). The KKN-PPM group I was attached was designated 154 and was focused in Plosorejo Village, Karanganyar, Kerjo, Central Java, Indonesia. The mission of KKN-PPM 154 was to survey Plosorejo village for existing landslides, to generate a simple hazard susceptibility map that can be understood by local villagers, and then to begin dissemination of that map into the community. To generate our susceptibility map we first conducted a geological survey of the existing landslides in the field study area, with a focus on determining landslide triggers and gauging areas for susceptibility for future landslides. The methods for gauging susceptibility included lithological observation, the presence of linear cracking, visible loss of structural integrity in structures such as villager homes, as well as collaboration with local residents and with the local rescue and response team. There were three color distinctions used in representing susceptibility which were green, where there is no immediate danger of landslide damage; orange, where transportation routes are at risk of being disrupted by landslides; and red, where imminent landslide potential puts a home in direct danger. The landslide inventory and susceptibility data was compiled into digital mediums such as CorelDraw, ArcGIS and Google Earth. Once a technical map was generated, we presented it to the village leadership for confirmation and modification based on their experience. Finally, we began to use the technical susceptibility map to draft evacuation routes and meeting points in the event of landslides, as well as simple susceptibility maps that can be understood and utilized by local villagers. Landslide mitigation

  8. Predictive Accuracy of a Cardiovascular Disease Risk Prediction Model in Rural South India – A Community Based Retrospective Cohort Study

    Directory of Open Access Journals (Sweden)

    Farah N Fathima

    2015-03-01

    Full Text Available Background: Identification of individuals at risk of developing cardiovascular diseases by risk stratification is the first step in primary prevention. Aims & Objectives: To assess the five year risk of developing a cardiovascular event from retrospective data and to assess the predictive accuracy of the non laboratory based National Health and Nutrition Examination Survey (NHANES risk prediction model among individuals in a rural South Indian population. Materials & Methods: A community based retrospective cohort study was conducted in three villages where risk stratification was done for all eligible adults aged between 35-74 years at the time of initial assessment using the NHANES risk prediction charts. Household visits were made after a period of five years by trained doctors to determine cardiovascular outcomes. Results: 521 people fulfilled the eligibility criteria of whom 486 (93.3% could be traced after five years. 56.8% were in low risk, 36.6% were in moderate risk and 6.6% were in high risk categories. 29 persons (5.97% had had cardiovascular events over the last five years of which 24 events (82.7% were nonfatal and five (17.25% were fatal. The mean age of the people who developed cardiovascular events was 57.24 ± 9.09 years. The odds ratios for the three levels of risk showed a linear trend with the odds ratios for the moderate risk and high risk category being 1.35 and 1.94 respectively with the low risk category as baseline. Conclusion: The non laboratory based NHANES charts did not accurately predict the occurrence of cardiovascular events in any of the risk categories.

  9. Application of the Risk Map for Assessment Destructive Effects of Essential Environment on the Information System

    Directory of Open Access Journals (Sweden)

    Vladlena Sergeevna Atkina

    2013-12-01

    Full Text Available The article is devoted to the problem of the business continuity as a necessary element of the strategy of information security. The analysis of the requirements of federal legislation, standards, recommendations and guidelines to ensure the availability, disaster recovery, and recovery of data and information structures of organizations in their operation was conducted. The proposed approach to assessing the possible destabilizing factors and emergency situations includes the model of the environment where the information systems are operational processing key business processes of the organization, and the method for risk assessment to each of the destabilizing effects. Technique of risk assessment expects to use quantitative and qualitative approach to drawing three zone risk maps.

  10. Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking.

    Directory of Open Access Journals (Sweden)

    Ian C Scott

    Full Text Available The improved characterisation of risk factors for rheumatoid arthritis (RA suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated. We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA (YORA. Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles, 31 single nucleotide polymorphisms (SNPs and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation. Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women. We developed multiple models to evaluate each risk factor's impact on prediction. Each model's ability to discriminate anti-citrullinated protein antibody (ACPA-positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium (WTCCC: 1,516 cases; 1,647 controls; UK RA Genetics Group Consortium (UKRAGG: 2,623 cases; 1,500 controls. HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve (AUC value of 0.813 in both WTCCC and UKRAGG. SNPs provided minimal prediction (AUC 0.660 WTCCC/0.617 UKRAGG. Whilst high individual risks were identified, with some cases having estimated lifetime risks of 86%, only a minority overall had substantially increased odds for RA. High risks from the HLA model were associated with YORA (P<0.0001; ever-smoking associated with older onset disease. This latter finding suggests smoking's impact on RA risk manifests later in life. Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA, respectively.

  11. Indoor Tanning and the MC1R Genotype: Risk Prediction for Basal Cell Carcinoma Risk in Young People

    OpenAIRE

    Molinaro, Annette M.; Ferrucci, Leah M.; Cartmel, Brenda; Loftfield, Erikka; Leffell, David J.; Bale, Allen E.; Mayne, Susan T.

    2015-01-01

    Basal cell carcinoma (BCC) incidence is increasing, particularly in young people, and can be associated with significant morbidity and treatment costs. To identify young individuals at risk of BCC, we assessed existing melanoma or overall skin cancer risk prediction models and built a novel risk prediction model, with a focus on indoor tanning and the melanocortin 1 receptor gene, MC1R. We evaluated logistic regression models among 759 non-Hispanic whites from a case-control study of patients...

  12. Genetic risk prediction and neurobiological understanding of alcoholism

    Science.gov (United States)

    Levey, D F; Le-Niculescu, H; Frank, J; Ayalew, M; Jain, N; Kirlin, B; Learman, R; Winiger, E; Rodd, Z; Shekhar, A; Schork, N; Kiefe, F; Wodarz, N; Müller-Myhsok, B; Dahmen, N; Nöthen, M; Sherva, R; Farrer, L; Smith, A H; Kranzler, H R; Rietschel, M; Gelernter, J; Niculescu, A B

    2014-01-01

    We have used a translational Convergent Functional Genomics (CFG) approach to discover genes involved in alcoholism, by gene-level integration of genome-wide association study (GWAS) data from a German alcohol dependence cohort with other genetic and gene expression data, from human and animal model studies, similar to our previous work in bipolar disorder and schizophrenia. A panel of all the nominally significant P-value SNPs in the top candidate genes discovered by CFG  (n=135 genes, 713 SNPs) was used to generate a genetic  risk prediction score (GRPS), which showed a trend towards significance (P=0.053) in separating  alcohol dependent individuals from controls in an independent German test cohort. We then validated and prioritized our top findings from this discovery work, and subsequently tested them in three independent cohorts, from two continents. In order to validate and prioritize the key genes that drive behavior without some of the pleiotropic environmental confounds present in humans, we used a stress-reactive animal model of alcoholism developed by our group, the D-box binding protein (DBP) knockout mouse, consistent with the surfeit of stress theory of addiction proposed by Koob and colleagues. A much smaller panel (n=11 genes, 66 SNPs) of the top CFG-discovered genes for alcoholism, cross-validated and prioritized by this stress-reactive animal model showed better predictive ability in the independent German test cohort (P=0.041). The top CFG scoring gene for alcoholism from the initial discovery step, synuclein alpha (SNCA) remained the top gene after the stress-reactive animal model cross-validation. We also tested this small panel of genes in two other independent test cohorts from the United States, one with alcohol dependence (P=0.00012) and one with alcohol abuse (a less severe form of alcoholism; P=0.0094). SNCA by itself was able to separate alcoholics from controls in the alcohol-dependent cohort (P=0.000013) and the alcohol abuse

  13. Implementation of landslide susceptibility maps in Lower Austria as part of risk governance

    Science.gov (United States)

    Bell, Rainer; Petschko, Helene; Bauer, Christian; Glade, Thomas; Granica, Klaus; Heiss, Gerhard; Leopold, Philip; Pomaroli, Gilbert; Proske, Herwig; Schweigl, Joachim

    2013-04-01

    Landslides frequently cause damage to agricultural land and infrastructure in Lower Austria - a province of Austria. Also settlements and people are threatened by landslides. To reduce landslide risks and to prevent the establishment of new settlements in highly landslide prone areas, the project "MoNOE" (Method development for landslide susceptibility modeling in Lower Austria) was set up by the provincial government. The main aim of the project is the development of methods to model rock fall and slide susceptibility for an area of approx. 15,900 km2 and to implement the resulting susceptibility maps into the spatial planning strategies of the state. Right from the beginning of the project a close cooperation between the involved scientists and the stakeholders from the Geological Survey of Lower Austria and the Department of Spatial Planning and Regional Policy of Lower Austria was established to ensure that method development and final susceptibility maps meet exactly the needs and demands of the stakeholders. This posed huge challenges, together with its realization in the large study area and a (heterogeneous) complex geological situation,. Limitations were given by restricted data availability (e.g. for geology or landslide inventories) in such a large study area. Rock fall susceptibility was modeled by a combined approach of determining rock fall release areas by empirical slope thresholds (dependent on geology) followed by empirical run-out modeling. Slide susceptibility was modeled based on the statistical approaches of weights of evidence (WofE) and generalized additive models (GAM) by two different research groups. Huge efforts were spent on the validation of all susceptibility models. In a later stage of the project we found that the best scientific maps are not necessarily the best maps to be implemented in spatial planning strategies. Thus, in close cooperation with the stakeholders, decisions had to be taken to find the best resolution of the maps

  14. Decision-Making Competence Predicts Domain-Specific Risk Attitudes

    Directory of Open Access Journals (Sweden)

    Joshua eWeller

    2015-05-01

    Full Text Available Decision Making Competence (DMC reflects individual differences in rational responding across several classic behavioral decision-making tasks. Although it has been associated with real-world risk behavior, less is known about the degree to which DMC contributes to specific components of risk attitudes. Utilizing a psychological risk-return framework, we examined the associations between risk attitudes and DMC. Italian community residents (n = 804 completed an online DMC measure, using a subset of the original Adult-DMC battery (A-DMC; Bruine de Bruin, Parker, & Fischhoff, 2007. Participants also completed a self-reported risk attitude measure for three components of risk attitudes (risk-taking, risk perceptions, and expected benefits across six risk domains. Overall, greater performance on the DMC component scales were inversely, albeit modestly, associated with risk-taking tendencies. Structural equation modeling results revealed that DMC was associated with lower perceived expected benefits for all domains. In contrast, its association with perceived risks was more domain-specific. These analyses also revealed stronger indirect effects for the DMC  expected benefits  risk-taking than the DMC  perceived riskrisk-taking path, especially for risk behaviors that may be considered more antisocial in nature. These results suggest that DMC performance differentially impacts specific components of risk attitudes, and may be more strongly related to the evaluation of expected value of the given behavior.

  15. Evidence-based risk assessment and communication: a new global dengue-risk map for travellers and clinicians.

    Science.gov (United States)

    Jentes, Emily S; Lash, R Ryan; Johansson, Michael A; Sharp, Tyler M; Henry, Ronnie; Brady, Oliver J; Sotir, Mark J; Hay, Simon I; Margolis, Harold S; Brunette, Gary W

    2016-06-01

    International travel can expose travellers to pathogens not commonly found in their countries of residence, like dengue virus. Travellers and the clinicians who advise and treat them have unique needs for understanding the geographic extent of risk for dengue. Specifically, they should assess the need for prevention measures before travel and ensure appropriate treatment of illness post-travel. Previous dengue-risk maps published in the Centers for Disease Control and Prevention's Yellow Book lacked specificity, as there was a binary (risk, no risk) classification. We developed a process to compile evidence, evaluate it and apply more informative risk classifications. We collected more than 839 observations from official reports, ProMED reports and published scientific research for the period 2005-2014. We classified each location as frequent/continuous risk if there was evidence of more than 10 dengue cases in at least three of the previous 10 years. For locations that did not fit this criterion, we classified locations as sporadic/uncertain risk if the location had evidence of at least one locally acquired dengue case during the last 10 years. We used expert opinion in limited instances to augment available data in areas where data were sparse. Initial categorizations classified 134 areas as frequent/continuous and 140 areas as sporadic/uncertain. CDC subject matter experts reviewed all initial frequent/continuous and sporadic/uncertain categorizations and the previously uncategorized areas. From this review, most categorizations stayed the same; however, 11 categorizations changed from the initial determinations. These new risk classifications enable detailed consideration of dengue risk, with clearer meaning and a direct link to the evidence that supports the specific classification. Since many infectious diseases have dynamic risk, strong geographical heterogeneities and varying data quality and availability, using this approach for other diseases can

  16. The Theory-based Influence of Map Features on Risk Beliefs: Self-reports of What is Seen and Understood for Maps Depicting an Environmental Health Hazard

    OpenAIRE

    Severtson, Dolores J.; Vatovec, Christine

    2012-01-01

    Theory-based research is needed to understand how maps of environmental health risk information influence risk beliefs and protective behavior. Using theoretical concepts from multiple fields of study including visual cognition, semiotics, health behavior, and learning and memory supports a comprehensive assessment of this influence. We report results from thirteen cognitive interviews that provide theory-based insights into how visual features influenced what participants saw ...

  17. An acarologic survey and Amblyomma americanum distribution map with implications for tularemia risk in Missouri

    Science.gov (United States)

    Brown, H.E.; Yates, K.F.; Dietrich, G.; MacMillan, K.; Graham, C.B.; Reese, S.M.; Helterbrand, Wm. S.; Nicholson, W.L.; Blount, K.; Mead, P.S.; Patrick, S.L.; Eisen, R.J.

    2011-01-01

    In the United States, tickborne diseases occur focally. Missouri represents a major focus of several tickborne diseases that includes spotted fever rickettsiosis, tularemia, and ehrlichiosis. Our study sought to determine the potential risk of human exposure to human-biting vector ticks in this area. We collected ticks in 79 sites in southern Missouri during June 7-10, 2009, which yielded 1,047 adult and 3,585 nymphal Amblyomma americanum, 5 adult Amblyomma maculatum, 19 adult Dermacentor variabilis, and 5 nymphal Ixodes brunneus. Logistic regression analysis showed that areas posing an elevated risk of exposure to A. americanum nymphs or adults were more likely to be classified as forested than grassland, and the probability of being classified as elevated risk increased with increasing relative humidity during the month of June (30-year average). Overall accuracy of each of the two models was greater than 70% and showed that 20% and 30% of the state were classified as elevated risk for human exposure to nymphs and adults, respectively. We also found a significant positive association between heightened acarologic risk and counties reporting tularemia cases. Our study provides an updated distribution map for A. americanum in Missouri and suggests a wide-spread risk of human exposure to A. americanum and their associated pathogens in this region. Copyright ?? 2011 by The American Society of Tropical Medicine and Hygiene.

  18. Focus maps: a means of prioritizing data collection for efficient geo-risk assessment

    Directory of Open Access Journals (Sweden)

    Massimiliano Pittore

    2015-04-01

    Full Text Available  Efficient Disaster Risk Reduction (DRR management constantly calls upon high-quality information to be collected or updated for vulnerability monitoring and risk assessment. This process is often resource- and time-intensive, which many economically developing states (including most Central Asian countries can seldom afford. In this paper, we introduce the concept of focus maps as a useful tool to quantify the spatial probability of sampling. Focus maps allow for a data collection prioritization scheme to be put in place, enabling the realization of optimized spatial sampling which assigns a higher priority to locations where the need for high-quality information is greater. In practice, smaller samples can be drawn with the same (or better resulting accuracy of the estimates, resulting in a more efficient use of time and resources. The factors that affect such a spatial sampling scheme include the usual components of risk assessment (hazard, exposure, vulnerability where available, as well as other potentially critical factors, such as the extent and quality of previously collected data. The practical application of the proposed approach to the case of Central Asia will be exemplified and discussed.

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

    Science.gov (United States)

    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.

  20. Value of routine blood tests for prediction of mortality risk in hip fracture patients

    DEFF Research Database (Denmark)

    Mosfeldt, Mathias; Pedersen, Ole Birger Vesterager; Riis, Troels

    2012-01-01

    There is a 5- to 8-fold increased risk of mortality during the first 3 months after a hip fracture. Several risk factors are known. We studied the predictive value (for mortality) of routine blood tests taken on admission.......There is a 5- to 8-fold increased risk of mortality during the first 3 months after a hip fracture. Several risk factors are known. We studied the predictive value (for mortality) of routine blood tests taken on admission....

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

    Science.gov (United States)

    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.

  2. Subsidence Induced Faulting Hazard risk maps in Mexico City and Morelia, central Mexico

    Science.gov (United States)

    Cabral-Cano, E.; Solano-Rojas, D.; Hernández-Espriu, J.; Cigna, F.; Wdowinski, S.; Osmanoglu, B.; Falorni, G.; Bohane, A.; Colombo, D.

    2012-12-01

    Subsidence and surface faulting have affected urban areas in Central Mexico for decades and the process has intensified as a consequence of urban sprawl and economic growth. This process causes substantial damages to the urban infrastructure and housing structures and in several cities it is becoming a major factor to be considered when planning urban development, land use zoning and hazard mitigation strategies in the next decades. Subsidence is usually associated with aggressive groundwater extraction rates and a general decrease of aquifer static level that promotes soil consolidation, deformation and ultimately, surface faulting. However, local stratigraphic and structural conditions also play an important role in the development and extension of faults. Despite its potential for damaging housing, and other urban infrastructure, the economic impact of this phenomena is poorly known, in part because detailed, city-wide subsidence induced faulting risk maps have not been published before. Nevertheless, modern remote sensing techniques are most suitable for this task. We present the results of a risk analysis for subsidence induced surface faulting in two cities in central Mexico: Morelia and Mexico City. Our analysis in Mexico City and Morelia is based on a risk matrix using the horizontal subsidence gradient from a Persistent Scatterer InSAR (Morelia) and SqueeSAR (Mexico City) analysis and 2010 census population distribution data from Mexico's National Institute of Statistics and Geography. Defining subsidence induced surface faulting vulnerability within these urbanized areas is best determined using both magnitude and horizontal subsidence gradient. Our Morelia analysis (597,000 inhabitants with localized subsidence rates up to 80 mm/yr) shows that 7% of the urbanized area is under a high to very high risk level, and 14% of its population (11.7% and 2.3% respectively) lives within these areas. In the case of the Mexico City (15'490,000 inhabitants for the

  3. XenDB: Full length cDNA prediction and cross species mapping in Xenopus laevis

    Directory of Open Access Journals (Sweden)

    Giegerich Robert

    2005-09-01

    Full Text Available Abstract Background Research using the model system Xenopus laevis has provided critical insights into the mechanisms of early vertebrate development and cell biology. Large scale sequencing efforts have provided an increasingly important resource for researchers. To provide full advantage of the available sequence, we have analyzed 350,468 Xenopus laevis Expressed Sequence Tags (ESTs both to identify full length protein encoding sequences and to develop a unique database system to support comparative approaches between X. laevis and other model systems. Description Using a suffix array based clustering approach, we have identified 25,971 clusters and 40,877 singleton sequences. Generation of a consensus sequence for each cluster resulted in 31,353 tentative contig and 4,801 singleton sequences. Using both BLASTX and FASTY comparison to five model organisms and the NR protein database, more than 15,000 sequences are predicted to encode full length proteins and these have been matched to publicly available IMAGE clones when available. Each sequence has been compared to the KOG database and ~67% of the sequences have been assigned a putative functional category. Based on sequence homology to mouse and human, putative GO annotations have been determined. Conclusion The results of the analysis have been stored in a publicly available database XenDB http://bibiserv.techfak.uni-bielefeld.de/xendb/. A unique capability of the database is the ability to batch upload cross species queries to identify potential Xenopus homologues and their associated full length clones. Examples are provided including mapping of microarray results and application of 'in silico' analysis. The ability to quickly translate the results of various species into 'Xenopus-centric' information should greatly enhance comparative embryological approaches. Supplementary material can be found at http://bibiserv.techfak.uni-bielefeld.de/xendb/.

  4. Hygrothermal risk on building heritage a methodology for a risk map

    CERN Document Server

    Delgado, João M P Q; Freitas, Vasco Peixoto

    2015-01-01

    This book presents a critical review of a criterion of risk, created to assess the flood risk to heritage buildings, and evaluates this criterion by applying it to the sample Portuguese heritage buildings. In a first approach, the total number of potential parameters is effectively reduced and the selected criteria are divided into two different groups: the monument’s location in relation to a waterway, and the behaviour of its construction material in contact with water. Above all, the book discusses the importance of architectural heritage and argues for the need to safeguard it from extreme climatic phenomena such as floods. As such, the book vividly reminds the scientific community that the intensification of the global warming and climate change will worsen throughout the 21st century, and that it is therefore necessary to adopt preventive measures to minimize, mitigate and control these adverse effects if we hope to avoid catastrophic consequences. At the same time, the book takes into account a broad...

  5. Driving Strategic Risk Planning With Predictive Modelling For Managerial Accounting

    DEFF Research Database (Denmark)

    Nielsen, Steen; Pontoppidan, Iens Christian

    for managerial accounting and shows how it can be used to determine the impact of different types of risk assessment input parameters on the variability of important outcome measures. The purpose is to: (i) point out the theoretical necessity of a stochastic risk framework; (ii) present a stochastic framework......Currently, risk management in management/managerial accounting is treated as deterministic. Although it is well-known that risk estimates are necessarily uncertain or stochastic, until recently the methodology required to handle stochastic risk-based elements appear to be impractical and too...... mathematical. The ultimate purpose of this paper is to “make the risk concept procedural and analytical” and to argue that accountants should now include stochastic risk management as a standard tool. Drawing on mathematical modelling and statistics, this paper methodically develops risk analysis approach...

  6. On the Use of Second-Order Descriptors To Predict Queueing Behavior of MAPs

    DEFF Research Database (Denmark)

    Andersen, Allan T.; Nielsen, Bo Friis

    2002-01-01

    The contributions of this paper are the following: We derive a formula for the IDI (Index of Dispersion for Intervals) for the Markovian Arrival Process (MAP). We show that two-state MAPs with identical fundamental rate, IDI and IDC (Index of Dispersion for Counts), define interval stationary poi...

  7. The General Urban Plan of Casimcea territorial administrative unit, map of natural and anthropogenic risks

    Directory of Open Access Journals (Sweden)

    Sorin BĂNICĂ

    2013-08-01

    Full Text Available The General Urban Plan represents the legal ground for any development action proposed. After endorsement and approval as required by law, GUP is act of authority of local government for the area in which it applies. The aim is to establish priorities regulations applied in land use planning and construction of structures. In terms of geographical location, the administrative territory of Casimcea, Tulcea county, falls in the central Northwest Plateau Casimcei. This is the second unit of the Central Dobrogea Plateau. Geographical location in southeastern Romania, climatic and relief conditions and anthropogenic pressure, expose the village administrative territorial unit Casimcea, permanent susceptibility to produce natural and antropogenical risks. In this context, we identified the following categories of natural and anthropogenic risks: i natural risk phenomena (earthquakes, strong winds, heavy rains, floods caused by overflowing or precipitation, erosion of river banks and torrents, gravitational processes, rain droplet erosion and surface soil erosion; and ii anthropogenic risk phenomena (overgrazing, chemicals use in agriculture, road transport infrastructure and electricity, wind turbines for electricity production, waste deposits, agro-zootechnical complexs, and human cemeteries. Extending their surface was materialized by creating a map of natural and anthropogenic risk on Casimcea territorial administrative unit, explaining the share of potentially affected areas as territorial balance

  8. Risk of vicarious trauma in nursing research: a focused mapping review and synthesis.

    Science.gov (United States)

    Taylor, Julie; Bradbury-Jones, Caroline; Breckenridge, Jenna P; Jones, Christine; Herber, Oliver Rudolf

    2016-10-01

    To provide a snapshot of how vicarious trauma is considered within the published nursing research literature. Vicarious trauma (secondary traumatic stress) has been the focus of attention in nursing practice for many years. The most pertinent areas to invoke vicarious trauma in research have been suggested as abuse/violence and death/dying. What is not known is how researchers account for the risks of vicarious trauma in research. Focused mapping review and synthesis. Empirical studies meeting criteria for abuse/violence or death/dying in relevant Scopus ranked top nursing journals (n = 6) January 2009 to December 2014. Relevant papers were scrutinised for the extent to which researchers discussed the risk of vicarious trauma. Aspects of the studies were mapped systematically to a pre-defined template, allowing patterns and gaps in authors' reporting to be determined. These were synthesised into a coherent profile of current reporting practices and from this, a new conceptualisation seeking to anticipate and address the risk of vicarious trauma was developed. Two thousand five hundred and three papers were published during the review period, of which 104 met the inclusion criteria. Studies were distributed evenly by method (52 qualitative; 51 quantitative; one mixed methods) and by focus (54 abuse/violence; 50 death/dying). The majority of studies (98) were carried out in adult populations. Only two papers reported on vicarious trauma. The conceptualisation of vicarious trauma takes account of both sensitivity of the substantive data collected, and closeness of those involved with the research. This might assist researchers in designing ethical and protective research and foreground the importance of managing risks of vicarious trauma. Vicarious trauma is not well considered in research into clinically important topics. Our proposed framework allows for consideration of these so that precautionary measures can be put in place to minimise harm to staff. © 2016

  9. Mapping risk of cadmium and lead contamination to human health in soils of Central Iran

    International Nuclear Information System (INIS)

    Amini, M.; Afyuni, M.; Khademi, H.; Abbaspour, K.C.; Schulin, R.

    2005-01-01

    In order to map Cd and Pb contamination in the soils of the region of Isfahan, Central Iran, we performed indicator kriging on a set of 255 topsoil samples (0-20 cm) gathered irregularly from an area of 6800 km 2 . The measured Cd concentrations exceeded the Swiss guide value in more than 80% of the samples whereas Pb concentrations exceeded the respective guide value only in 2% of the samples. Based on the simulated conditional distribution functions, the probability of exceeding the concentration of Cd and Pb from the specific threshold was computed. The results indicated that in most parts of the region the probability of contamination by Cd is very large (>0.95) whereas it is small (<0.5) for Pb. Based on a misclassification analysis, we chose the probability of 0.45 as optimum probability threshold to delineate the polluted from unpolluted areas for Cd. In addition, we performed a loss analysis to separate risks to human health from potential losses due to remediation costs. Based on this analysis a probability threshold of 0.8 was found to be the optimum threshold for the classification of polluted and unpolluted areas in the case of Cd. Health risks were found to be larger in the western parts of the region. Misclassification analysis was sufficient for risk mapping for Pb as its concentration did not reach risk levels for human health. A probability of 0.7 for Pb was found to be the optimum threshold for the delineation of polluted and unpolluted lands

  10. Mapping heat wave risk in the UK: Proactive planning for the 2050s

    Science.gov (United States)

    Oven, Katie; Reaney, Sim; Ohlemüller, Ralf; Nodwell, Sarah; Curtis, Sarah; Riva, Mylène; Dunn, Christine; Val, Dimitri; Burkhard, Roland

    2010-05-01

    Climate change projections suggest an increased frequency of heat waves in the UK over the coming decades. Such extreme events pose a serious threat to human health and are likely to impact upon health and social care systems and the infrastructures supporting them. This stress will result from both increased demands upon healthcare services and the ability of the infrastructure to cope, such as sufficient climate control in hospitals. Certain sectors of the population, such as older people, have an increased susceptibility to heat waves and hence are the focus of this research. There is no universal definition of a heat wave, reflecting the acclimatisation of a population. Based on a review of the literature, this research therefore sets out a series of working definitions of a heat wave in the UK context from a human health perspective. Drawing on these definitions, the UK heat wave hazard was mapped for the 2050s (2040-2069) using daily minimum and maximum temperature data derived from the UKCP09 Weather Generator at 50 km resolution. The analysis was undertaken for the three different greenhouse gas emissions scenarios within UKCP09 (low, medium and high). Hot spots of increased heat wave risk were identified and comparisons made between the various model outputs. These data were then combined with demographic forecasts for the 2050s enabling the identification of areas with an ageing population. Results are presented showing the scale of the projected change in heat wave risk across the UK and the location of older people. These results will be used in proactive planning to help policymakers and practitioners respond more appropriately to the needs of vulnerable populations in the coming decades. Key words: climate change; heat wave; risk mapping; vulnerability; risk reduction.

  11. Mapping of leptospirosis risk factor based on remote sensing image in Tembalang, Semarang City, Central Java

    Directory of Open Access Journals (Sweden)

    Sunaryo Sunaryo

    2012-09-01

    of leptospirosis, physical environment of risk factor analysis.Methods: This cross sectional design consisted of 246 leptospirosis subjects mapped with GPS, and processed by using ArcGis 92 program. Leptospirosis case was overlaid with remote sensing (Quickbird image, then is done interpretation of spatial feature, and digitation on screen to visual identify of risk factor.Results: Based on digital visualization leptospirosis cases in 2009 were clustered in Tembalang with shortest distance index 0,009 km and is furthermost 18 km. More case distribution found at children and men adolescent. Temporally, case increased in the dry season, among of July and August. Result of visual interpretation and digitation can obtain land use map, water body, settlement, fl oods area, vegetation index and height.Conclusion: Spatial high resolution remote sensing image is very good for mapping of leptospirosis risk factor. Leptospirosis case distribution forms the cluster in Tembalang; case is predominated by children andmen adolescent. (Health Science Indones 2012;1:45-50 

  12. Young Children’s Risk-Taking: Mothers’ Authoritarian Parenting Predicts Risk-Taking by Daughters but Not Sons

    Directory of Open Access Journals (Sweden)

    Erin E. Wood

    2017-01-01

    Full Text Available We investigated how mothers’ parenting behaviors and personal characteristics were related to risk-taking by young children. We tested contrasting predictions from evolutionary and social role theories with the former predicting higher risk-taking by boys compared to girls and the latter predicting that mothers would influence children’s gender role development with risk-taking occurring more in children parented with higher levels of harshness (i.e., authoritarian parenting style. In our study, mothers reported their own gender roles and parenting styles as well as their children’s risk-taking and activities related to gender roles. The results were only partially consistent with the two theories, as the amount of risk-taking by sons and daughters did not differ significantly and risk-taking by daughters, but not sons, was positively related to mothers’ use of the authoritarian parenting style and the girls’ engagement in masculine activities. Risk-taking by sons was not predicted by any combination of mother-related variables. Overall, mothers who were higher in femininity used more authoritative and less authoritarian parenting styles. Theoretical implications as well as implications for predicting and reducing children’s risk-taking are discussed.

  13. Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults.

    Science.gov (United States)

    Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

    2015-08-07

    Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

  14. Distribution of Short-Term and Lifetime Predicted Risks of Cardiovascular Diseases in Peruvian Adults

    Science.gov (United States)

    Quispe, Renato; Bazo-Alvarez, Juan Carlos; Burroughs Peña, Melissa S; Poterico, Julio A; Gilman, Robert H; Checkley, William; Bernabé-Ortiz, Antonio; Huffman, Mark D; Miranda, J Jaime

    2015-01-01

    Background Short-term risk assessment tools for prediction of cardiovascular disease events are widely recommended in clinical practice and are used largely for single time-point estimations; however, persons with low predicted short-term risk may have higher risks across longer time horizons. Methods and Results We estimated short-term and lifetime cardiovascular disease risk in a pooled population from 2 studies of Peruvian populations. Short-term risk was estimated using the atherosclerotic cardiovascular disease Pooled Cohort Risk Equations. Lifetime risk was evaluated using the algorithm derived from the Framingham Heart Study cohort. Using previously published thresholds, participants were classified into 3 categories: low short-term and low lifetime risk, low short-term and high lifetime risk, and high short-term predicted risk. We also compared the distribution of these risk profiles across educational level, wealth index, and place of residence. We included 2844 participants (50% men, mean age 55.9 years [SD 10.2 years]) in the analysis. Approximately 1 of every 3 participants (34% [95% CI 33 to 36]) had a high short-term estimated cardiovascular disease risk. Among those with a low short-term predicted risk, more than half (54% [95% CI 52 to 56]) had a high lifetime predicted risk. Short-term and lifetime predicted risks were higher for participants with lower versus higher wealth indexes and educational levels and for those living in urban versus rural areas (PPeruvian adults were classified as low short-term risk but high lifetime risk. Vulnerable adults, such as those from low socioeconomic status and those living in urban areas, may need greater attention regarding cardiovascular preventive strategies. PMID:26254303

  15. A PROPOSAL FOR MAKING A RISK MAP OF ROCKFALL BY EVALUATING THE INFLUENCE TO RAILWAY TRACK

    Science.gov (United States)

    Fukata, Takahiro; Mori, Taiki; Shibuya, Satoru

    An incident of rockfall may trigger serious damage to the safety as well as the after-the-event maintenance of railway transportation. In an attempt to minimize such damage caused by rockfall incident, the railway companies regularly in spect the concerned slopes along the railway, and take necessary measures. In the current practice, however, it is very difficult to make a full control of slopes in danger spreading over the wide area due to the limited human and financial resources. Accordingly, it is urgently needed for the railway firms to establish cost-effective as well as reliable system for the slope management. A risk map based on the impact statement of rockfall to railway track is of great use for not only enhancing the efficiency of slope inspection, but also planning the disaster prevention of railway. In this paper, a methodology for establishing a risk map by considering the impact statement to railway truck in a quantitative manner is proposed. In so doing, the rockfall movement on a slope was numerically simulated in match with each categorized type of slopes. The applicability of this proposed method for the slope management practice is examined based on a number of case histories.

  16. VESsel GENeration Analysis (VESGEN): Innovative Vascular Mappings for Astronaut Exploration Health Risks and Human Terrestrial Medicine

    Science.gov (United States)

    Parsons-Wingerter, Patricia; Kao, David; Valizadegan, Hamed; Martin, Rodney; Murray, Matthew C.; Ramesh, Sneha; Sekaran, Srinivaas

    2017-01-01

    Currently, astronauts face significant health risks in future long-duration exploration missions such as colonizing the Moon and traveling to Mars. Numerous risks include greatly increased radiation exposures beyond the low earth orbit (LEO) of the ISS, and visual and ocular impairments in response to microgravity environments. The cardiovascular system is a key mediator in human physiological responses to radiation and microgravity. Moreover, blood vessels are necessarily involved in the progression and treatment of vascular-dependent terrestrial diseases such as cancer, coronary vessel disease, wound-healing, reproductive disorders, and diabetes. NASA developed an innovative, globally requested beta-level software, VESsel GENeration Analysis (VESGEN) to map and quantify vascular remodeling for application to astronaut and terrestrial health challenges. VESGEN mappings of branching vascular trees and networks are based on a weighted multi-parametric analysis derived from vascular physiological branching rules. Complex vascular branching patterns are determined by biological signaling mechanisms together with the fluid mechanics of multi-phase laminar blood flow.

  17. Correlation of spatial climate/weather maps and the advantages of using the Mahalanobis metric in predictions

    Science.gov (United States)

    Stephenson, D. B.

    1997-10-01

    The skill in predicting spatially varying weather/climate maps depends on the definition of the measure of similarity between the maps. Under the justifiable approximation that the anomaly maps are distributed multinormally, it is shown analytically that the choice of weighting metric, used in defining the anomaly correlation between spatial maps, can change the resulting probability distribution of the correlation coefficient. The estimate of the numbers of degrees of freedom based on the variance of the correlation distribution can vary from unity up to the number of grid points depending on the choice of weighting metric. The (pseudo-) inverse of the sample covariance matrix acts as a special choice for the metric in that it gives a correlation distribution which has minimal kurtosis and maximum dimension. Minimal kurtosis suggests that the average predictive skill might be improved due to the rarer occurrence of troublesome outlier patterns far from the mean state. Maximum dimension has a disadvantage for analogue prediction schemes in that it gives the minimum number of analogue states. This metric also has an advantage in that it allows one to powerfully test the null hypothesis of multinormality by examining the second and third moments of the correlation coefficient which were introduced by Mardia as invariant measures of multivariate kurtosis and skewness. For these reasons, it is suggested that this metric could be usefully employed in the prediction of weather/climate and in fingerprinting anthropogenic climate change. The ideas are illustrated using the bivariate example of the observed monthly mean sea-level pressures at Darwin and Tahitifrom 1866 1995.

  18. The precision of mapping between number words and the approximate number system predicts children's formal math abilities.

    Science.gov (United States)

    Libertus, Melissa E; Odic, Darko; Feigenson, Lisa; Halberda, Justin

    2016-10-01

    Children can represent number in at least two ways: by using their non-verbal, intuitive approximate number system (ANS) and by using words and symbols to count and represent numbers exactly. Furthermore, by the time they are 5years old, children can map between the ANS and number words, as evidenced by their ability to verbally estimate numbers of items without counting. How does the quality of the mapping between approximate and exact numbers relate to children's math abilities? The role of the ANS-number word mapping in math competence remains controversial for at least two reasons. First, previous work has not examined the relation between verbal estimation and distinct subtypes of math abilities. Second, previous work has not addressed how distinct components of verbal estimation-mapping accuracy and variability-might each relate to math performance. Here, we addressed these gaps by measuring individual differences in ANS precision, verbal number estimation, and formal and informal math abilities in 5- to 7-year-old children. We found that verbal estimation variability, but not estimation accuracy, predicted formal math abilities, even when controlling for age, expressive vocabulary, and ANS precision, and that it mediated the link between ANS precision and overall math ability. These findings suggest that variability in the ANS-number word mapping may be especially important for formal math abilities. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. The Precision of Mapping Between Number Words and the Approximate Number System Predicts Children’s Formal Math Abilities

    Science.gov (United States)

    Libertus, Melissa E.; Odic, Darko; Feigenson, Lisa; Halberda, Justin

    2016-01-01

    Children can represent number in at least two ways: by using their non-verbal, intuitive Approximate Number System (ANS), and by using words and symbols to count and represent numbers exactly. Further, by the time they are five years old, children can map between the ANS and number words, as evidenced by their ability to verbally estimate numbers of items without counting. How does the quality of the mapping between approximate and exact numbers relate to children’s math abilities? The role of the ANS-number word mapping in math competence remains controversial for at least two reasons. First, previous work has not examined the relation between verbal estimation and distinct subtypes of math abilities. Second, previous work has not addressed how distinct components of verbal estimation – mapping accuracy and variability – might each relate to math performance. Here, we address these gaps by measuring individual differences in ANS precision, verbal number estimation, and formal and informal math abilities in 5- to 7-year-old children. We found that verbal estimation variability, but not estimation accuracy, predicted formal math abilities even when controlling for age, expressive vocabulary, and ANS precision, and that it mediated the link between ANS precision and overall math ability. These findings suggest that variability in the ANS-number word mapping may be especially important for formal math abilities. PMID:27348475

  20. Extensions of the Rosner-Colditz breast cancer prediction model to include older women and type-specific predicted risk.

    Science.gov (United States)

    Glynn, Robert J; Colditz, Graham A; Tamimi, Rulla M; Chen, Wendy Y; Hankinson, Susan E; Willett, Walter W; Rosner, Bernard

    2017-08-01

    A breast cancer risk prediction rule previously developed by Rosner and Colditz has reasonable predictive ability. We developed a re-fitted version of this model, based on more than twice as many cases now including women up to age 85, and further extended it to a model that distinguished risk factor prediction of tumors with different estrogen/progesterone receptor status. We compared the calibration and discriminatory ability of the original, the re-fitted, and the type-specific models. Evaluation used data from the Nurses' Health Study during the period 1980-2008, when 4384 incident invasive breast cancers occurred over 1.5 million person-years. Model development used two-thirds of study subjects and validation used one-third. Predicted risks in the validation sample from the original and re-fitted models were highly correlated (ρ = 0.93), but several parameters, notably those related to use of menopausal hormone therapy and age, had different estimates. The re-fitted model was well-calibrated and had an overall C-statistic of 0.65. The extended, type-specific model identified several risk factors with varying associations with occurrence of tumors of different receptor status. However, this extended model relative to the prediction of any breast cancer did not meaningfully reclassify women who developed breast cancer to higher risk categories, nor women remaining cancer free to lower risk categories. The re-fitted Rosner-Colditz model has applicability to risk prediction in women up to age 85, and its discrimination is not improved by consideration of varying associations across tumor subtypes.

  1. The potential of large studies for building genetic risk prediction models

    Science.gov (United States)

    NCI scientists have developed a new paradigm to assess hereditary risk prediction in common diseases, such as prostate cancer. This genetic risk prediction concept is based on polygenic analysis—the study of a group of common DNA sequences, known as singl

  2. The Efficacy of Violence Prediction: A Meta-Analytic Comparison of Nine Risk Assessment Tools

    Science.gov (United States)

    Yang, Min; Wong, Stephen C. P.; Coid, Jeremy

    2010-01-01

    Actuarial risk assessment tools are used extensively to predict future violence, but previous studies comparing their predictive accuracies have produced inconsistent findings as a result of various methodological issues. We conducted meta-analyses of the effect sizes of 9 commonly used risk assessment tools and their subscales to compare their…

  3. Predictive Risk Factors in the Treatment of Gestational Diabetes Mellitus

    Directory of Open Access Journals (Sweden)

    Lebriz Hale Aktun

    2015-01-01

    Full Text Available Objective This study aims to investigate predictive risk factors in the treatment of gestational diabetes mellitus (GDM. Patients and Methods A total of 256 pregnant women who underwent 75 g oral glucose tolerance test (OGTT during 24–28 weeks of pregnancy were included according to the World Health Organization criteria. Demographic characteristics of the patients, including age, parity, family history of diabetes, body weight before pregnancy, and body weight at the diagnosis of GDM, were recorded. Fasting insulin and hemoglobin A1c (HbA1c values at the time of diagnosis were evaluated. The patients were divided into two groups: those requiring insulin treatment (insulin group, n = 89 and those receiving diet therapy (diet group, n = 167 during pregnancy according to the American Diabetes Association recommendations. Results A total of 34.76% of the pregnant women with GDM required insulin treatment. The mean age of these patients was significantly higher compared to the diet group (34.9 ± 0.6 years vs. 31.9 ± 0.6 years; P = 0.004. Body mass index before pregnancy was also significantly higher in the insulin group than that in the diet group (32 ± 0.9 kg/m 2 vs. 29 ± 0.7 kg/m 2 ; P = 0.004. Fasting blood glucose (FBG during OGTT was 105.6 ± 2.1 mg/dL and 96.7 ± 1.1 mg/dL in the insulin group and diet group, respectively ( P < 0.001. There was no significant difference in fasting plasma glucose during OGTT between the groups ( P = 0.069, while plasma glucose at two hours was 161.1 ± 6.8 mg/dL in the insulin group and 145.1 ± 3.7 mg/dL in the diet group ( P = 0.027. At the time of diagnosis, HbA1c values were significantly higher in the insulin group compared to the diet group (5.3 ± 0.1 vs. 4.9 ± 0.1; P = 0.001. There was no significant difference in FBG and homeostasis model assessment-insulin resistance values between the groups ( P = 0.908, P = 0.073. Conclusion Our study results suggest that age, family history of diabetes, body

  4. Using the Remote Sensing and GIS Technology for Erosion Risk Mapping of Kartalkaya Dam Watershed in Kahramanmaras, Turkey

    Directory of Open Access Journals (Sweden)

    Abdullah E. Akay

    2008-08-01

    Full Text Available The soil erosion is the most serious environmental problem in watershed areas in Turkey. The main factors affecting the amount of soil erosion include vegetation cover, topography, soil, and climate. In order to describe the areas with high soil erosion risks and to develop adequate erosion prevention measures in the watersheds of dams, erosion risk maps should be generated considering these factors. Remote Sensing (RS and Geographic Information System (GIS technologies were used for erosion risk mapping in Kartalkaya Dam Watershed of Kahramanmaras, Turkey, based on the methodology implemented in COoRdination of INformation on the Environment (CORINE model. ASTER imagery was used to generate a land use/cover classification in ERDAS Imagine. The digital maps of the other factors (topography, soil types, and climate were generated in ArcGIS v9.2, and were then integrated as CORINE input files to produce erosion risk maps. The results indicate that 33.82%, 35.44%, and 30.74% of the study area were under low, moderate, and high actual erosion risks, respectively. The CORINE model integrated with RS and GIS technologies has great potential for producing accurate and inexpensive erosion risk maps in Turkey.

  5. Predictive risk modelling under different data access scenarios: who is identified as high risk and for how long?

    Science.gov (United States)

    Johnson, Tracy L; Kaldor, Jill; Sutherland, Kim; Humphries, Jacob; Jorm, Louisa R; Levesque, Jean-Frederic

    2018-01-01

    Objective This observational study critically explored the performance of different predictive risk models simulating three data access scenarios, comparing: (1) sociodemographic and clinical profiles; (2) consistency in high-risk designation across models; and (3) persistence of high-risk status over time. Methods Cross-sectional health survey data (2006–2009) for more than 260 000 Australian adults 45+ years were linked to longitudinal individual hospital, primary care, pharmacy and mortality data. Three risk models predicting acute emergency hospitalisations were explored, simulating conditions where data are accessed through primary care practice management systems, or through hospital-based electronic records, or through a hypothetical ‘full’ model using a wider array of linked data. High-risk patients were identified using different risk score thresholds. Models were reapplied monthly for 24 months to assess persistence in high-risk categorisation. Results The three models displayed similar statistical performance. Three-quarters of patients in the high-risk quintile from the ‘full’ model were also identified using the primary care or hospital-based models, with the remaining patients differing according to age, frailty, multimorbidity, self-rated health, polypharmacy, prior hospitalisations and imminent mortality. The use of higher risk prediction thresholds resulted in lower levels of agreement in high-risk designation across models and greater morbidity and mortality in identified patient populations. Persistence of high-risk status varied across approaches according to updated information on utilisation history, with up to 25% of patients reassessed as lower risk within 1 year. Conclusion/implications Small differences in risk predictors or risk thresholds resulted in comparatively large differences in who was classified as high risk and for how long. Pragmatic predictive risk modelling design decisions based on data availability or projected

  6. Commentary on Holmes et al. (2007): resolving the debate on when extinction risk is predictable.

    Science.gov (United States)

    Ellner, Stephen P; Holmes, Elizabeth E

    2008-08-01

    We reconcile the findings of Holmes et al. (Ecology Letters, 10, 2007, 1182) that 95% confidence intervals for quasi-extinction risk were narrow for many vertebrates of conservation concern, with previous theory predicting wide confidence intervals. We extend previous theory, concerning the precision of quasi-extinction estimates as a function of population dynamic parameters, prediction intervals and quasi-extinction thresholds, and provide an approximation that specifies the prediction interval and threshold combinations where quasi-extinction estimates are precise (vs. imprecise). This allows PVA practitioners to define the prediction interval and threshold regions of safety (low risk with high confidence), danger (high risk with high confidence), and uncertainty.

  7. Predictive value of updating Framingham risk scores with novel risk markers in the U.S. general population.

    Directory of Open Access Journals (Sweden)

    Bart S Ferket

    Full Text Available BACKGROUND: According to population-based cohort studies CT coronary calcium score (CTCS, carotid intima-media thickness (cIMT, high-sensitivity C- reactive protein (CRP, and ankle-brachial index (ABI are promising novel risk markers for improving cardiovascular risk assessment. Their impact in the U.S. general population is however uncertain. Our aim was to estimate the predictive value of four novel cardiovascular risk markers for the U.S. general population. METHODS AND FINDINGS: Risk profiles, CRP and ABI data of 3,736 asymptomatic subjects aged 40 or older from the National Health and Nutrition Examination Survey (NHANES 2003-2004 exam were used along with predicted CTCS and cIMT values. For each subject, we calculated 10-year cardiovascular risks with and without each risk marker. Event rates adjusted for competing risks were obtained by microsimulation. We assessed the impact of updated 10-year risk scores by reclassification and C-statistics. In the study population (mean age 56±11 years, 48% male, 70% (80% were at low (<10%, 19% (14% at intermediate (≥10-<20%, and 11% (6% at high (≥20% 10-year CVD (CHD risk. Net reclassification improvement was highest after updating 10-year CVD risk with CTCS: 0.10 (95%CI 0.02-0.19. The C-statistic for 10-year CVD risk increased from 0.82 by 0.02 (95%CI 0.01-0.03 with CTCS. Reclassification occurred most often in those at intermediate risk: with CTCS, 36% (38% moved to low and 22% (30% to high CVD (CHD risk. Improvements with other novel risk markers were limited. CONCLUSIONS: Only CTCS appeared to have significant incremental predictive value in the U.S. general population, especially in those at intermediate risk. In future research, cost-effectiveness analyses should be considered for evaluating novel cardiovascular risk assessment strategies.

  8. Predictive Value of Updating Framingham Risk Scores with Novel Risk Markers in the U.S. General Population

    Science.gov (United States)

    Hunink, M. G. Myriam; Agarwal, Isha; Kavousi, Maryam; Franco, Oscar H.; Steyerberg, Ewout W.; Max, Wendy; Fleischmann, Kirsten E.

    2014-01-01

    Background According to population-based cohort studies CT coronary calcium score (CTCS), carotid intima-media thickness (cIMT), high-sensitivity C- reactive protein (CRP), and ankle-brachial index (ABI) are promising novel risk markers for improving cardiovascular risk assessment. Their impact in the U.S. general population is however uncertain. Our aim was to estimate the predictive value of four novel cardiovascular risk markers for the U.S. general population. Methods and Findings Risk profiles, CRP and ABI data of 3,736 asymptomatic subjects aged 40 or older from the National Health and Nutrition Examination Survey (NHANES) 2003–2004 exam were used along with predicted CTCS and cIMT values. For each subject, we calculated 10-year cardiovascular risks with and without each risk marker. Event rates adjusted for competing risks were obtained by microsimulation. We assessed the impact of updated 10-year risk scores by reclassification and C-statistics. In the study population (mean age 56±11 years, 48% male), 70% (80%) were at low (risk. Net reclassification improvement was highest after updating 10-year CVD risk with CTCS: 0.10 (95%CI 0.02–0.19). The C-statistic for 10-year CVD risk increased from 0.82 by 0.02 (95%CI 0.01–0.03) with CTCS. Reclassification occurred most often in those at intermediate risk: with CTCS, 36% (38%) moved to low and 22% (30%) to high CVD (CHD) risk. Improvements with other novel risk markers were limited. Conclusions Only CTCS appeared to have significant incremental predictive value in the U.S. general population, especially in those at intermediate risk. In future research, cost-effectiveness analyses should be considered for evaluating novel cardiovascular risk assessment strategies. PMID:24558385

  9. From Risk Assessment to Knowledge Mapping: Science, Precaution, and Participation in Disease Ecology

    Directory of Open Access Journals (Sweden)

    Andy C. Stirling

    2009-12-01

    Full Text Available Governance of infectious disease risks requires understanding of often indeterminate interactions between diverse, complex, open, and dynamic human and natural systems. In the face of these challenges, worldwide policy making affords disproportionate status to " science-based" risk-assessment methods. These reduce multiple, complex dimensions to simple quantitative parameters of "outcomes" and "probabilities," and then re-aggregate across diverse metrics, contexts, and perspectives to yield a single ostensibly definitive picture of risk. In contrast, more precautionary or participatory approaches are routinely portrayed as less rigorous, complete, or robust. Yet, although conventional reductive-aggregative techniques provide powerful responses to a narrow state of risk, they are not applicable to less tractable conditions of uncertainty, ambiguity, and ignorance. Strong sensitivities to divergent framings can render results highly variable. Reductive aggregation can marginalize important perspectives and compound exposure to surprise. The value of more broad-based precautionary and participatory approaches may be appreciated. These offer ways to be more rigorous and complete in the mapping of different framings. They may also be more robust than reductive-aggregative appraisal methods, in "opening up" greater accountability for intrinsically normative judgements in decision making on threats like pandemic avian influenza.

  10. SIMULATION AND PREDICTION OF THE PROCESS BASED ON THE GENERAL LOGISTIC MAPPING

    Directory of Open Access Journals (Sweden)

    V. V. Skalozub

    2013-11-01

    Full Text Available Purpose. The aim of the research is to build a model of the generalzed logistic mapping and assessment of the possibilities of its use for the formation of the mathematical description, as well as operational forecasts of parameters of complex dynamic processes described by the time series. Methodology. The research results are obtained on the basis of mathematical modeling and simulation of nonlinear systems using the tools of chaotic dynamics. Findings. A model of the generalized logistic mapping, which is used to interpret the characteristics of dynamic processes was proposed. We consider some examples of representations of processes based on enhanced logistic mapping varying the values of model parameters. The procedures of modeling and interpretation of the data on the investigated processes, represented by the time series, as well as the operational forecasting of parameters using the generalized model of logistic mapping were proposed. Originality. The paper proposes an improved mathematical model, generalized logistic mapping, designed for the study of nonlinear discrete dynamic processes. Practical value. The carried out research using the generalized logistic mapping of railway transport processes, in particular, according to assessment of the parameters of traffic volumes, indicate the great potential of its application in practice for solving problems of analysis, modeling and forecasting complex nonlinear discrete dynamical processes. The proposed model can be used, taking into account the conditions of uncertainty, irregularity, the manifestations of the chaotic nature of the technical, economic and other processes, including the railway ones.

  11. Testing the Predictive Validity of the Hendrich II Fall Risk Model.

    Science.gov (United States)

    Jung, Hyesil; Park, Hyeoun-Ae

    2018-03-01

    Cumulative data on patient fall risk have been compiled in electronic medical records systems, and it is possible to test the validity of fall-risk assessment tools using these data between the times of admission and occurrence of a fall. The Hendrich II Fall Risk Model scores assessed during three time points of hospital stays were extracted and used for testing the predictive validity: (a) upon admission, (b) when the maximum fall-risk score from admission to falling or discharge, and (c) immediately before falling or discharge. Predictive validity was examined using seven predictive indicators. In addition, logistic regression analysis was used to identify factors that significantly affect the occurrence of a fall. Among the different time points, the maximum fall-risk score assessed between admission and falling or discharge showed the best predictive performance. Confusion or disorientation and having a poor ability to rise from a sitting position were significant risk factors for a fall.

  12. Comparison between frailty index of deficit accumulation and fracture risk assessment tool (FRAX) in prediction of risk of fractures.

    Science.gov (United States)

    Li, Guowei; Thabane, Lehana; Papaioannou, Alexandra; Adachi, Jonathan D

    2015-08-01

    A frailty index (FI) of deficit accumulation could quantify and predict the risk of fractures based on the degree of frailty in the elderly. We aimed to compare the predictive powers between the FI and the fracture risk assessment tool (FRAX) in predicting risk of major osteoporotic fracture (hip, upper arm or shoulder, spine, or wrist) and hip fracture, using the data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) 3-year Hamilton cohort. There were 3985 women included in the study, with the mean age of 69.4 years (standard deviation [SD] = 8.89). During the follow-up, there were 149 (3.98%) incident major osteoporotic fractures and 18 (0.48%) hip fractures reported. The FRAX and FI were significantly related to each other. Both FRAX and FI significantly predicted risk of major osteoporotic fracture, with a hazard ratio (HR) of 1.03 (95% confidence interval [CI]: 1.02-1.05) and 1.02 (95% CI: 1.01-1.04) for per-0.01 increment for the FRAX and FI respectively. The HRs were 1.37 (95% CI: 1.19-1.58) and 1.26 (95% CI: 1.12-1.42) for an increase of per-0.10 (approximately one SD) in the FRAX and FI respectively. Similar discriminative ability of the models was found: c-index = 0.62 for the FRAX and c-index = 0.61 for the FI. When cut-points were chosen to trichotomize participants into low-risk, medium-risk and high-risk groups, a significant increase in fracture risk was found in the high-risk group (HR = 2.04, 95% CI: 1.36-3.07) but not in the medium-risk group (HR = 1.23, 95% CI: 0.82-1.84) compared with the low-risk women for the FI, while for FRAX the medium-risk (HR = 2.00, 95% CI: 1.09-3.68) and high-risk groups (HR = 2.61, 95% CI: 1.48-4.58) predicted risk of major osteoporotic fracture significantly only when survival time exceeded 18months (550 days). Similar findings were observed for hip fracture and in sensitivity analyses. In conclusion, the FI is comparable with FRAX in the prediction of risk of future fractures, indicating that

  13. The theory-based influence of map features on risk beliefs: self-reports of what is seen and understood for maps depicting an environmental health hazard.

    Science.gov (United States)

    Severtson, Dolores J; Vatovec, Christine

    2012-08-01

    Theory-based research is needed to understand how maps of environmental health risk information influence risk beliefs and protective behavior. Using theoretical concepts from multiple fields of study including visual cognition, semiotics, health behavior, and learning and memory supports a comprehensive assessment of this influence. The authors report results from 13 cognitive interviews that provide theory-based insights into how visual features influenced what participants saw and the meaning of what they saw as they viewed 3 formats of water test results for private wells (choropleth map, dot map, and a table). The unit of perception, color, proximity to hazards, geographic distribution, and visual salience had substantial influences on what participants saw and their resulting risk beliefs. These influences are explained by theoretical factors that shape what is seen, properties of features that shape cognition (preattentive, symbolic, visual salience), information processing (top-down and bottom-up), and the strength of concrete compared with abstract information. Personal relevance guided top-down attention to proximal and larger hazards that shaped stronger risk beliefs. Meaning was more local for small perceptual units and global for large units. Three aspects of color were important: preattentive "incremental risk" meaning of sequential shading, symbolic safety meaning of stoplight colors, and visual salience that drew attention. The lack of imagery, geographic information, and color diminished interest in table information. Numeracy and prior beliefs influenced comprehension for some participants. Results guided the creation of an integrated conceptual framework for application to future studies. Ethics should guide the selection of map features that support appropriate communication goals.

  14. Dynamics of Variance Risk Premia, Investors' Sentiment and Return Predictability

    DEFF Research Database (Denmark)

    Rombouts, Jerome V.K.; Stentoft, Lars; Violante, Francesco

    We develop a joint framework linking the physical variance and its risk neutral expectation implying variance risk premia that are persistent, appropriately reacting to changes in level and variability of the variance and naturally satisfying the sign constraint. Using option market data and real...... events and only marginally by the premium associated with normal price fluctuations....

  15. Risk Prediction Models for Other Cancers or Multiple Sites

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing other multiple cancers over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  16. Evaluating and predicting overall process risk using event logs

    NARCIS (Netherlands)

    Pika, A.; Van Der Aalst, W.M.P.; Wynn, M.T.; Fidge, C.J.; Ter Hofstede, A.H.M.

    2016-01-01

    Companies standardise and automate their business processes in order to improve process efficiency and minimise operational risks. However, it is difficult to eliminate all process risks during the process design stage due to the fact that processes often run in complex and changeable environments

  17. Genetically Predicted Body Mass Index and Breast Cancer Risk

    DEFF Research Database (Denmark)

    Guo, Yan; Warren Andersen, Shaneda; Shu, Xiao-Ou

    2016-01-01

    BACKGROUND: Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or enviro...

  18. Resistance training and predicted risk of coronary heart disease in ...

    African Journals Online (AJOL)

    The purpose of this study was to determine the impact of resistance training, designed to prevent the development of coronary heart disease (CHD) based on the Framingham Risk Assessment (FRA) score. Twenty-five healthy sedentary men with low CHD risk were assigned to participate in a 16-week (three days per week) ...

  19. Application of cardiovascular disease risk prediction models and the relevance of novel biomarkers to risk stratification in Asian Indians.

    Science.gov (United States)

    Kanjilal, S; Rao, V S; Mukherjee, M; Natesha, B K; Renuka, K S; Sibi, K; Iyengar, S S; Kakkar, Vijay V

    2008-01-01

    The increasing pressure on health resources has led to the emergence of risk assessment as an essential tool in the management of cardiovascular disease (CVD). Concern exists regarding the validity of their generalization to all populations. Existing risk scoring models do not incorporate emerging 'novel' risk factors. In this context, the aim of the study was to examine the relevance of British, European, and Framingham predictive CVD risk scores to the asymptomatic high risk Indian population. Blood samples drawn from the participants were analyzed for various 'traditional' and 'novel' biomarkers, and their CVD risk factor profiling was also done. The Framingham model defined only 5% of the study cohort to be at high risk, which appears to be an underestimation of CVD risk in this genetically predisposed population. These subjects at high risk had significantly elevated levels of lipid, pro-inflammatory, pro-thrombotic, and serological markers. It is more relevant to develop risk predictive scores for application to the Indian population. This study substantiates the argument that alternative approaches to risk stratification are required in order to make them more adaptable and applicable to different populations with varying risk factor and disease patterns.

  20. US EPA Office of Research and Development Community-Focused Exposure and Risk Screening Tool (C-FERST) Air Pollutants 2011 web mapping service

    Data.gov (United States)

    U.S. Environmental Protection Agency — This map service displays all air-related layers used in the USEPA Community/Tribal-Focused Exposure and Risk Screening Tool (C/T-FERST) mapping application...

  1. US EPA Office of Research and Development Community-Focused Exposure and Risk Screening Tool (C-FERST) Air web mapping service

    Data.gov (United States)

    U.S. Environmental Protection Agency — This map service displays all air-related layers used in the USEPA Community/Tribal-Focused Exposure and Risk Screening Tool (C/T-FERST) mapping application...

  2. Predicting reattendance at a high-risk breast cancer clinic.

    Science.gov (United States)

    Ormseth, Sarah R; Wellisch, David K; Aréchiga, Adam E; Draper, Taylor L

    2015-10-01

    The research about follow-up patterns of women attending high-risk breast-cancer clinics is sparse. This study sought to profile daughters of breast-cancer patients who are likely to return versus those unlikely to return for follow-up care in a high-risk clinic. Our investigation included 131 patients attending the UCLA Revlon Breast Center High Risk Clinic. Predictor variables included age, computed breast-cancer risk, participants' perceived personal risk, clinically significant depressive symptomatology (CES-D score ≥ 16), current level of anxiety (State-Trait Anxiety Inventory), and survival status of participants' mothers (survived or passed away from breast cancer). A greater likelihood of reattendance was associated with older age (adjusted odds ratio [AOR] = 1.07, p = 0.004), computed breast-cancer risk (AOR = 1.10, p = 0.017), absence of depressive symptomatology (AOR = 0.25, p = 0.009), past psychiatric diagnosis (AOR = 3.14, p = 0.029), and maternal loss to breast cancer (AOR = 2.59, p = 0.034). Also, an interaction was found between mother's survival and perceived risk (p = 0.019), such that reattendance was associated with higher perceived risk among participants whose mothers survived (AOR = 1.04, p = 0.002), but not those whose mothers died (AOR = 0.99, p = 0.685). Furthermore, a nonlinear inverted "U" relationship was observed between state anxiety and reattendance (p = 0.037); participants with moderate anxiety were more likely to reattend than those with low or high anxiety levels. Demographic, medical, and psychosocial factors were found to be independently associated with reattendance to a high-risk breast-cancer clinic. Explication of the profiles of women who may or may not reattend may serve to inform the development and implementation of interventions to increase the likelihood of follow-up care.

  3. From the lab - Predicting Autism in High-Risk Infants | NIH MedlinePlus the Magazine

    Science.gov (United States)

    ... High-Risk Infants Follow us Photo: iStock Predicting Autism in High-Risk Infants AN NIH-SUPPORTED STUDY ... high-risk, 6-month-old infants will develop autism spectrum disorder by age 2. Such a tool ...

  4. A comparison of the predictive properties of nine sex offender risk assessment instruments

    NARCIS (Netherlands)

    Smid, W.J.; Kamphuis, J.H.; Wever, E.C.; van Beek, D.J.

    2014-01-01

    Sex offender treatment is most effective when tailored to risk-need-responsivity principles, which dictate that treatment levels should match risk levels as assessed by structured risk assessment instruments. The predictive properties, missing values, and interrater agreement of the scores of 9

  5. Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction.

    Science.gov (United States)

    Luo, Gang

    2016-01-01

    Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year. For the champion machine learning model of the competition, our method explained prediction results for 87.4 % of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year. Our demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.

  6. Mapping resilience not risk: Turning the tide in New York City and Jamaica Bay

    Science.gov (United States)

    Parris, A. S.; Sanderson, E. W.

    2015-12-01

    Resilience in urban coastal areas is affected by actions at multiple levels from individuals to community groups to city, state and federal governments. At any level, actions can be a response to immediate hazards (e.g. flooding of coastal homes) or long-term drivers of change (e.g. sea level rise). Jamaica Bay, a highly urbanized estuary within New York City, exemplifies the Nation's coastal zone challenges. Prior to Hurricane Sandy, city, state, and federal governments had made the estuary a major focal point for habitat restoration, improvements to public access and outdoor recreation, and sustainable development. Sandy caused the highest flood level in the recorded history of New York City, eventually claiming 44 lives and costing over $19 billion. Electrical system failure caused four of NYCs wastewater pollution control plants to shutdown, discharging untreated sewage into Jamaica Bay. The Sea Level Rise Tool for Sandy Recovery (the Tool), a flood mapping tool developed by several government agencies including FEMA, NYC, and the Executive Branch, integrated science from the National Flood Insurance Program and the New York City Panel on Climate Change (NPCC). While compound flooding hazards (stormwater plus coastal flooding) remain an important uncertainty, the Tool and subsequent NPCC mapping efforts provide sufficient evidence for science-based discourse around coastal flood risks in Jamaica Bay. But toward what outcome? Coastal flood risk reduction measures and other management actions are managed within existing regulatory frameworks. Disaster relief funds appropriated by Congress in the immediate aftermath of Sandy have provided critical resources to the Jamaica Bay region. However, the challenge now is to transition from the short-term response to long-term resilience planning, a challenge which requires new institutional capacity. This transition to resilience planning and implementation is not only critical in New York City, but in other coastal

  7. Risk Prediction of New Adjacent Vertebral Fractures After PVP for Patients with Vertebral Compression Fractures: Development of a Prediction Model

    International Nuclear Information System (INIS)

    Zhong, Bin-Yan; He, Shi-Cheng; Zhu, Hai-Dong; Wu, Chun-Gen; Fang, Wen; Chen, Li; Guo, Jin-He; Deng, Gang; Zhu, Guang-Yu; Teng, Gao-Jun

    2017-01-01

    PurposeWe aim to determine the predictors of new adjacent vertebral fractures (AVCFs) after percutaneous vertebroplasty (PVP) in patients with osteoporotic vertebral compression fractures (OVCFs) and to construct a risk prediction score to estimate a 2-year new AVCF risk-by-risk factor condition.Materials and MethodsPatients with OVCFs who underwent their first PVP between December 2006 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included in this study. In training cohort, we assessed the independent risk predictors and developed the probability of new adjacent OVCFs (PNAV) score system using the Cox proportional hazard regression analysis. The accuracy of this system was then validated in both training and validation cohorts by concordance (c) statistic.Results421 patients (training cohort: n = 256; validation cohort: n = 165) were included in this study. In training cohort, new AVCFs after the first PVP treatment occurred in 33 (12.9%) patients. The independent risk factors were intradiscal cement leakage and preexisting old vertebral compression fracture(s). The estimated 2-year absolute risk of new AVCFs ranged from less than 4% in patients with neither independent risk factors to more than 45% in individuals with both factors.ConclusionsThe PNAV score is an objective and easy approach to predict the risk of new AVCFs.

  8. Risk Prediction of New Adjacent Vertebral Fractures After PVP for Patients with Vertebral Compression Fractures: Development of a Prediction Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhong, Bin-Yan; He, Shi-Cheng; Zhu, Hai-Dong [Southeast University, Department of Radiology, Medical School, Zhongda Hospital (China); Wu, Chun-Gen [Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Department of Diagnostic and Interventional Radiology (China); Fang, Wen; Chen, Li; Guo, Jin-He; Deng, Gang; Zhu, Guang-Yu; Teng, Gao-Jun, E-mail: gjteng@vip.sina.com [Southeast University, Department of Radiology, Medical School, Zhongda Hospital (China)

    2017-02-15

    PurposeWe aim to determine the predictors of new adjacent vertebral fractures (AVCFs) after percutaneous vertebroplasty (PVP) in patients with osteoporotic vertebral compression fractures (OVCFs) and to construct a risk prediction score to estimate a 2-year new AVCF risk-by-risk factor condition.Materials and MethodsPatients with OVCFs who underwent their first PVP between December 2006 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included in this study. In training cohort, we assessed the independent risk predictors and developed the probability of new adjacent OVCFs (PNAV) score system using the Cox proportional hazard regression analysis. The accuracy of this system was then validated in both training and validation cohorts by concordance (c) statistic.Results421 patients (training cohort: n = 256; validation cohort: n = 165) were included in this study. In training cohort, new AVCFs after the first PVP treatment occurred in 33 (12.9%) patients. The independent risk factors were intradiscal cement leakage and preexisting old vertebral compression fracture(s). The estimated 2-year absolute risk of new AVCFs ranged from less than 4% in patients with neither independent risk factors to more than 45% in individuals with both factors.ConclusionsThe PNAV score is an objective and easy approach to predict the risk of new AVCFs.

  9. Shallow landslide prediction and analysis with risk assessment using a spatial model in the coastal region in the state of São Paulo, Brazil

    Science.gov (United States)

    Camarinha, P. I. M.; Canavesi, V.; Alvalá, R. C. S.

    2013-10-01

    In Brazil, most of the disasters involving landslide occur in coastal regions, with population density concentrated on steep slopes. Thus, different approaches have been used to evaluate the landslide risk, although the greatest difficulty is related to the scarcity of spatial data with good quality. In this context, four cities located on the southeast coast of Brazil - Santos, Cubatão, Caraguatatuba and Ubatuba - in a region with the rough reliefs of the Serra do Mar and with a history of natural disasters were evaluated. Spatial prediction by fuzzy gamma technique was used for the landslide susceptibility mapping, considering environmental variables from data and software in the public domain. To validate the susceptibility mapping results, it was overlapped with risk sectors provided by the Geological Survey of Brazil (CPRM). A positive correlation was observed between the classes most susceptible and the location of these sectors. The results were also analyzed from the categorization of risk levels provided by CPRM. To compare the approach with other studies using landslide-scar maps, correlated indexes were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas and can be replicated to municipalities that do not have risk areas mapped.

  10. The Irma-sponge Project Frhymap: Flood Risk and Hydrological Mapping

    Science.gov (United States)

    Hoffmann, L.; Pfister, L.

    potential damage of flood scenarios was evaluated via flood risk mapping, based on monetary cost assessment on the one hand and on security deficit analysis on the other hand. The uncertainty analysis re- veals that the reliability of these risk maps primarily depends on data quality. In order to increase public awareness about flood issues, an experimental hydro-climatological atlas has been developed, which contains information on the whole chain of processes that are relevant in terms of flood genesis.

  11. A case study for the integration of predictive mineral potential maps

    Science.gov (United States)

    Lee, Saro; Oh, Hyun-Joo; Heo, Chul-Ho; Park, Inhye

    2014-09-01

    This study aims to elaborate on the mineral potential maps using various models and verify the accuracy for the epithermal gold (Au) — silver (Ag) deposits in a Geographic Information System (GIS) environment assuming that all deposits shared a common genesis. The maps of potential Au and Ag deposits were produced by geological data in Taebaeksan mineralized area, Korea. The methodological framework consists of three main steps: 1) identification of spatial relationships 2) quantification of such relationships and 3) combination of multiple quantified relationships. A spatial database containing 46 Au-Ag deposits was constructed using GIS. The spatial association between training deposits and 26 related factors were identified and quantified by probabilistic and statistical modelling. The mineral potential maps were generated by integrating all factors using the overlay method and recombined afterwards using the likelihood ratio model. They were verified by comparison with test mineral deposit locations. The verification revealed that the combined mineral potential map had the greatest accuracy (83.97%), whereas it was 72.24%, 65.85%, 72.23% and 71.02% for the likelihood ratio, weight of evidence, logistic regression and artificial neural network models, respectively. The mineral potential map can provide useful information for the mineral resource development.

  12. Prediction of CT Substitutes from MR Images Based on Local Diffeomorphic Mapping for Brain PET Attenuation Correction.

    Science.gov (United States)

    Wu, Yao; Yang, Wei; Lu, Lijun; Lu, Zhentai; Zhong, Liming; Huang, Meiyan; Feng, Yanqiu; Feng, Qianjin; Chen, Wufan

    2016-10-01

    Attenuation correction is important for PET reconstruction. In PET/MR, MR intensities are not directly related to attenuation coefficients that are needed in PET imaging. The attenuation coefficient map can be derived from CT images. Therefore, prediction of CT substitutes from MR images is desired for attenuation correction in PET/MR. This study presents a patch-based method for CT prediction from MR images, generating attenuation maps for PET reconstruction. Because no global relation exists between MR and CT intensities, we propose local diffeomorphic mapping (LDM) for CT prediction. In LDM, we assume that MR and CT patches are located on 2 nonlinear manifolds, and the mapping from the MR manifold to the CT manifold approximates a diffeomorphism under a local constraint. Locality is important in LDM and is constrained by the following techniques. The first is local dictionary construction, wherein, for each patch in the testing MR image, a local search window is used to extract patches from training MR/CT pairs to construct MR and CT dictionaries. The k-nearest neighbors and an outlier detection strategy are then used to constrain the locality in MR and CT dictionaries. Second is local linear representation, wherein, local anchor embedding is used to solve MR dictionary coefficients when representing the MR testing sample. Under these local constraints, dictionary coefficients are linearly transferred from the MR manifold to the CT manifold and used to combine CT training samples to generate CT predictions. Our dataset contains 13 healthy subjects, each with T1- and T2-weighted MR and CT brain images. This method provides CT predictions with a mean absolute error of 110.1 Hounsfield units, Pearson linear correlation of 0.82, peak signal-to-noise ratio of 24.81 dB, and Dice in bone regions of 0.84 as compared with real CTs. CT substitute-based PET reconstruction has a regression slope of 1.0084 and R 2 of 0.9903 compared with real CT-based PET. In this method, no

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

    Science.gov (United States)

    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.

  14. Cardiovascular risk prediction in chronic kidney disease patients

    Directory of Open Access Journals (Sweden)

    Santiago Cedeño Mora

    2017-05-01

    Conclusion: The cardiovascular risk scores (FRS-CVD and ASCVD [AHA/ACC 2013] can estimate the probability of atherosclerotic cardiovascular events in patients with CKD regardless of renal function, albuminuria and previous cardiovascular events.

  15. Waiting for chikungunya fever in Argentina: spatio-temporal risk maps

    Directory of Open Access Journals (Sweden)

    Aníbal E Carbajo

    2015-04-01

    Full Text Available Chikungunya virus (CHIKV transmission has been detected in America in 2013 and recently reached south up to Bolivia, Brazil and Paraguay, bordering countries of Argentina. The presence of the mosquito Aedes aegypti in half of the country together with the regional context drove us to make a rapid assessment of transmission risk. Temperature thresholds for vector breeding and for virus transmission, together with adult activity from the literature, were mapped on a monthly basis to estimate risk. Transmission of chikungunya by Ae. aegypti in the world was seen at monthly mean temperatures from 21-34ºC, with the majority occurring between 26-28ºC. In Argentina temperatures above 21ºC are observed since September in the northeast, expanding south until January and retreating back to the northeast in April. The maximum area under risk encompasses more than half the country and around 32 million inhabitants. Vector adult activity was registered where monthly means temperatures exceeded 13ºC, in the northeast all over the year and in the northern half from September-May. The models herein proposed show that conditions for transmission are already present. Considering the regional context and the historic inability to control dengue in the region, chikungunya fever illness seems unavoidable.

  16. Mapping eutrophication risk from climate change: Future phosphorus concentrations in English rivers.

    Science.gov (United States)

    Charlton, Matthew B; Bowes, Michael J; Hutchins, Michael G; Orr, Harriet G; Soley, Rob; Davison, Paul

    2018-02-01

    Climate change is expected to increase eutrophication risk in rivers yet few studies identify the timescale or spatial extent of such impacts. Phosphorus concentration, considered the primary driver of eutrophication risk in English rivers, may increase through reduced dilution particularly if river flows are lower in summer. Detailed models can indicate change in catchment phosphorus concentrations but targeted support for mitigation measures requires a national scale evaluation of risk. In this study, a load apportionment model is used to describe the current relationship between flow and total reactive phosphorus (TRP) at 115 river sites across England. These relationships are used to estimate TRP concentrations for the 2050s under 11 climate change driven scenarios of future river flows and under scenarios of both current and higher levels of sewage treatment. National maps of change indicate a small but inconsistent increase in annual average TRP concentrations with a greater change in summer. Reducing the TRP concentration of final sewage effluent to 0.5mg/L P for all upstream sewage treatment works was inadequate to meet existing P standards required through the EU Water Framework Directive, indicating that more needs to be done, including efforts to reduce diffuse pollution. Copyright © 2017 Elsevier B.V. All rights reserved.

  17. Mapping multiple components of malaria risk for improved targeting of elimination interventions.

    Science.gov (United States)

    Cohen, Justin M; Le Menach, Arnaud; Pothin, Emilie; Eisele, Thomas P; Gething, Peter W; Eckhoff, Philip A; Moonen, Bruno; Schapira, Allan; Smith, David L

    2017-11-13

    There is a long history of considering the constituent components of malaria risk and the malaria transmission cycle via the use of mathematical models, yet strategic planning in endemic countries tends not to take full advantage of available disease intelligence to tailor interventions. National malaria programmes typically make operational decisions about where to implement vector control and surveillance activities based upon simple categorizations of annual parasite incidence. With technological advances, an enormous opportunity exists to better target specific malaria interventions to the places where they will have greatest impact by mapping and evaluating metrics related to a variety of risk components, each of which describes a different facet of the transmission cycle. Here, these components and their implications for operational decision-making are reviewed. For each component, related mappable malaria metrics are also described which may be measured and evaluated by malaria programmes seeking to better understand the determinants of malaria risk. Implementing tailored programmes based on knowledge of the heterogeneous distribution of the drivers of malaria transmission rather than only consideration of traditional metrics such as case incidence has the potential to result in substantial improvements in decision-making. As programmes improve their ability to prioritize their available tools to the places where evidence suggests they will be most effective, elimination aspirations may become increasingly feasible.

  18. Estimating surface soil erosion losses and mapping erosion risk for Yusufeli micro-catchment (Artvin

    Directory of Open Access Journals (Sweden)

    Mustafa Tüfekçioğlu

    2016-10-01

    Full Text Available Sheet erosion, one of the most important types of water erosion, takes place on the top soil as tiny soil layer movement that affects lake and stream ecosystem. This type of erosion is very important because the productive soil layer on the top soil can be lost in a very short period of time. The goal of this study was to quantify the amount of surface (sheet and rill soil erosion, and to identify areas under high erosion risk within the study area at Yusufeli province in Artvin by using RUSLE erosion methodology. As a result of the study it was found that the average annual potential soil loss by surface erosion was 3.6 ton ha-1yr-1. Additionally, the maps produced and conclusions reached by the study revealed that the areas of high erosion risk were identified spatially and measures to control erosion on some of these high risk areas can be possible with appropriate erosion control techniques.

  19. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.

    Directory of Open Access Journals (Sweden)

    Yiming Hu

    2017-06-01

    Full Text Available Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn's disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

  20. Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling

    DEFF Research Database (Denmark)

    Ekpo, Uwem F.; Hürlimann, Eveline; Schur, Nadine

    2013-01-01

    Schistosomiasis prevalence data for Nigeria were extracted from peer-reviewed journals and reports, geo-referenced and collated in a nationwide geographical information system database for the generation of point prevalence maps. This exercise revealed that the disease is endemic in 35 of the cou......Schistosomiasis prevalence data for Nigeria were extracted from peer-reviewed journals and reports, geo-referenced and collated in a nationwide geographical information system database for the generation of point prevalence maps. This exercise revealed that the disease is endemic in 35...

  1. Multimethod prediction of child abuse risk in an at-risk sample of male intimate partner violence offenders.

    Science.gov (United States)

    Rodriguez, Christina M; Gracia, Enrique; Lila, Marisol

    2016-10-01

    The vast majority of research on child abuse potential has concentrated on women demonstrating varying levels of risk of perpetrating physical child abuse. In contrast, the current study considered factors predictive of physical child abuse potential in a group of 70 male intimate partner violence offenders, a group that would represent a likely high risk group. Elements of Social Information Processing theory were evaluated, including pre-existing schemas of empathy, anger, and attitudes approving of parent-child aggression considered as potential moderators of negative attributions of child behavior. To lend methodological rigor, the study also utilized multiple measures and multiple methods, including analog tasks, to predict child abuse risk. Contrary to expectations, findings did not support the role of anger independently predicting child abuse risk in this sample of men. However, preexisting beliefs approving of parent-child aggression, lower empathy, and more negative child behavior attributions independently predicted abuse potential; in addition, greater anger, poorer empathy, and more favorable attitudes toward parent-child aggression also exacerbated men's negative child attributions to further elevate their child abuse risk. Future work is encouraged to consider how factors commonly considered in women parallel or diverge from those observed to elevate child abuse risk in men of varying levels of risk. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Prediction of Adulthood Obesity Using Genetic and Childhood Clinical Risk Factors in the Cardiovascular Risk in Young Finns Study.

    Science.gov (United States)

    Seyednasrollah, Fatemeh; Mäkelä, Johanna; Pitkänen, Niina; Juonala, Markus; Hutri-Kähönen, Nina; Lehtimäki, Terho; Viikari, Jorma; Kelly, Tanika; Li, Changwei; Bazzano, Lydia; Elo, Laura L; Raitakari, Olli T

    2017-06-01

    Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods. A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, P obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity. © 2017 American Heart Association, Inc.

  3. Mapping risk of avian influenza transmission at the interface of domestic poultry and wild birds

    Science.gov (United States)

    Prosser, Diann J.; Hungerford, Laura L.; Erwin, R. Michael; Ottinger, Mary Ann; Takekawa, John Y.; Ellis, Erle C.

    2013-01-01

    Emergence of avian influenza viruses with high lethality to humans, such as the currently circulating highly pathogenic A(H5N1) (emerged in 1996) and A(H7N9) cause serious concern for the global economic and public health sectors. Understanding the spatial and temporal interface between wild and domestic populations, from which these viruses emerge, is fundamental to taking action. This information, however, is rarely considered in influenza risk models, partly due to a lack of data. We aim to identify areas of high transmission risk between domestic poultry and wild waterfowl in China, the epicenter of both viruses. Two levels of models were developed: one that predicts hotspots of novel virus emergence between domestic and wild birds, and one that incorporates H5N1 risk factors, for which input data exists. Models were produced at 1 and 30 km spatial resolution, and two temporal seasons. Patterns of risk varied between seasons with higher risk in the northeast, central-east, and western regions of China during spring and summer, and in the central and southeastern regions during winter. Monte-Carlo uncertainty analyses indicated varying levels of model confidence, with lowest errors in the densely populated regions of eastern and southern China. Applications and limitations of the models are discussed within.

  4. Genetic variants demonstrating flip-flop phenomenon and breast cancer risk prediction among women of African ancestry.

    Science.gov (United States)

    Wang, Shengfeng; Qian, Frank; Zheng, Yonglan; Ogundiran, Temidayo; Ojengbede, Oladosu; Zheng, Wei; Blot, William; Nathanson, Katherine L; Hennis, Anselm; Nemesure, Barbara; Ambs, Stefan; Olopade, Olufunmilayo I; Huo, Dezheng

    2018-04-01

    Few studies have evaluated the performance of existing breast cancer risk prediction models among women of African ancestry. In replication studies of genetic variants, a change in direction of the risk association is a common phenomenon. Termed flip-flop, it means that a variant is risk factor in one population but protective in another, affecting the performance of risk prediction models. We used data from the genome-wide association study (GWAS) of breast cancer in the African diaspora (The Root consortium), which included 3686 participants of African ancestry from Nigeria, USA, and Barbados. Polygenic risk scores (PRSs) were constructed from the published odds ratios (ORs) of four sets of susceptibility loci for breast cancer. Discrimination capacity was measured using the area under the receiver operating characteristic curve (AUC). Flip-flop phenomenon was observed among 30~40% of variants across studies. Using the 34 variants with consistent directionality among previous studies, we constructed a PRS with AUC of 0.531 (95% confidence interval [CI]: 0.512-0.550), which is similar to the PRS using 93 variants and ORs from European ancestry populations (AUC = 0.525, 95% CI: 0.506-0.544). Additionally, we found the 34-variant PRS has good discriminative accuracy in women with family history of breast cancer (AUC = 0.586, 95% CI: 0.532-0.640). We found that PRS based on variants identified from prior GWASs conducted in women of European and Asian ancestries did not provide a comparable degree of risk stratification for women of African ancestry. Further large-scale fine-mapping studies in African ancestry populations are desirable to discover population-specific genetic risk variants.

  5. Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery

    DEFF Research Database (Denmark)

    Meretoja, Tuomo J; Andersen, Kenneth Geving; Bruce, Julie

    2017-01-01

    are missing. The aim was to develop a clinically applicable risk prediction tool. Methods The prediction models were developed and tested using three prospective data sets from Finland (n = 860), Denmark (n = 453), and Scotland (n = 231). Prediction models for persistent pain of moderate to severe intensity......), high body mass index ( P = .039), axillary lymph node dissection ( P = .008), and more severe acute postoperative pain intensity at the seventh postoperative day ( P = .003) predicted persistent pain in the final prediction model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC......-AUC, 0.740) cohorts. At the 20% risk level, the model had 32.8% and 47.4% sensitivity and 94.4% and 82.4% specificity in the Danish and Scottish cohorts, respectively. Conclusion Our validated prediction models and an online risk calculator provide clinicians and researchers with a simple tool to screen...

  6. Aedes aegypti Global Suitability Maps Using a Water Container Energy Balance Model for Dengue Risk Applications

    Science.gov (United States)

    Steinhoff, D.

    2015-12-01

    Dengue infections are estimated to total nearly 400 million per year worldwide, with both the geographic range and the magnitude of infections having increased in the past 50 years. The primary dengue vector mosquito Aedes aegypti is closely associated with humans. It lives exclusively in urban and semi-urban areas, preferentially bites humans, and spends its developmental stages in artificial water containers. Climate regulates the development of Ae. aegypti immature mosquitoes in artificial containers. Potential containers for Ae. aegypti immature development include, but are not limited to, small sundry items (e.g., bottles, cans, plastic containers), buckets, tires, barrels, tanks, and cisterns. Successful development of immature mosquitoes from eggs to larvae, pupae, and eventually adults is largely dependent on the availability of water and the thermal properties of the water in the containers. Recent work has shown that physics-based approaches toward modeling container water properties are promising for resolving the complexities of container water dynamics and the effects on immature mosquito development. An energy balance container model developed by the author, termed the Water Height And Temperature in Container Habitats Energy Model (WHATCH'EM), solves for water temperature and height for user-specified containers with readily available weather data. Here we use WHATCH'EM with NASA Earth Science products used as input to construct global suitability maps based on established water temperature ranges for immature Ae. aegypti mosquitoes. A proxy for dengue risk is provided from habitat suitability, but also population estimates, as Ae. aegypti is closely associated with human activity. NASA gridded Global Population of the World data is used to mask out rural areas with low dengue risk. Suitability maps are illustrated for a variety of containers (size, material, color) and shading scenarios.

  7. Mapping the Risk of Snakebite in Sri Lanka - A National Survey with Geospatial Analysis.

    Directory of Open Access Journals (Sweden)

    Dileepa Senajith Ediriweera

    2016-07-01

    Full Text Available There is a paucity of robust epidemiological data on snakebite, and data available from hospitals and localized or time-limited surveys have major limitations. No study has investigated the incidence of snakebite across a whole country. We undertook a community-based national survey and model based geostatistics to determine incidence, envenoming, mortality and geographical pattern of snakebite in Sri Lanka.The survey was designed to sample a population distributed equally among the nine provinces of the country. The number of data collection clusters was divided among districts in proportion to their population. Within districts clusters were randomly selected. Population based incidence of snakebite and significant envenoming were estimated. Model-based geostatistics was used to develop snakebite risk maps for Sri Lanka. 1118 of the total of 14022 GN divisions with a population of 165665 (0.8%of the country's population were surveyed. The crude overall community incidence of snakebite, envenoming and mortality were 398 (95% CI: 356-441, 151 (130-173 and 2.3 (0.2-4.4 per 100000 population, respectively. Risk maps showed wide variation in incidence within the country, and snakebite hotspots and cold spots were determined by considering the probability of exceeding the national incidence.This study provides community based incidence rates of snakebite and envenoming for Sri Lanka. The within-country spatial variation of bites can inform healthcare decision making and highlights the limitations associated with estimates of incidence from hospital data or localized surveys. Our methods are replicable, and these models can be adapted to other geographic regions after re-estimating spatial covariance parameters for the particular region.

  8. GIS and local knowledge in disaster management: a case study of flood risk mapping in Viet Nam.

    Science.gov (United States)

    Tran, Phong; Shaw, Rajib; Chantry, Guillaume; Norton, John

    2009-03-01

    Linking community knowledge with modern techniques to record and analyse risk related data is one way of engaging and mobilising community capacity. This paper discusses the use of the Geographic Information System (GIS) at the local level and the need for integrating modern technology and indigenous knowledge into disaster management. It suggests a way to mobilise available human and technical resources in order to strengthen a good partnership between local communities and local and national institutions. The paper also analyses the current vulnerability of t