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Sample records for disease regions predicting

  1. Switch region for pathogenic structural change in conformational disease and its prediction.

    Directory of Open Access Journals (Sweden)

    Xin Liu

    2010-01-01

    Full Text Available Many diseases are believed to be related to abnormal protein folding. In the first step of such pathogenic structural changes, misfolding occurs in regions important for the stability of the native structure. This destabilizes the normal protein conformation, while exposing the previously hidden aggregation-prone regions, leading to subsequent errors in the folding pathway. Sites involved in this first stage can be deemed switch regions of the protein, and can represent perfect binding targets for drugs to block the abnormal folding pathway and prevent pathogenic conformational changes. In this study, a prediction algorithm for the switch regions responsible for the start of pathogenic structural changes is introduced. With an accuracy of 94%, this algorithm can successfully find short segments covering sites significant in triggering conformational diseases (CDs and is the first that can predict switch regions for various CDs. To illustrate its effectiveness in dealing with urgent public health problems, the reason of the increased pathogenicity of H5N1 influenza virus is analyzed; the mechanisms of the pandemic swine-origin 2009 A(H1N1 influenza virus in overcoming species barriers and in infecting large number of potential patients are also suggested. It is shown that the algorithm is a potential tool useful in the study of the pathology of CDs because: (1 it can identify the origin of pathogenic structural conversion with high sensitivity and specificity, and (2 it provides an ideal target for clinical treatment.

  2. Regional white matter lesions predict falls in patients with amnestic mild cognitive impairment and Alzheimer's disease.

    Science.gov (United States)

    Ogama, Noriko; Sakurai, Takashi; Shimizu, Atsuya; Toba, Kenji

    2014-01-01

    Preventive strategy for falls in demented elderly is a clinical challenge. From early-stage of Alzheimer's disease (AD), patients show impaired balance and gait. The purpose of this study is to determine whether regional white matter lesions (WMLs) can predict balance/gait disturbance and falls in elderly with amnestic mild cognitive impairment (aMCI) or AD. Cross-sectional. Hospital out-patient clinic. One hundred sixty-three patients diagnosed with aMCI or AD were classified into groups having experienced falls (n = 63) or not (n = 100) in the previous year. Cognition, depression, behavior and psychological symptoms of dementia, medication, and balance/gait function were evaluated. Regional WMLs were visually analyzed as periventricular hyperintensity in frontal caps, bands, and occipital caps, and as deep white matter hyperintensity in frontal, parietal, temporal, and occipital lobes, basal ganglia, thalamus, and brain stem. Brain atrophy was linearly measured. The fallers had a greater volume of WMLs and their posture/gait performance tended to be worse than nonfallers. Several WMLs in particular brain regions were closely associated with balance and gait impairment. Besides polypharmacy, periventricular hyperintensity in frontal caps and occipital WMLs were strong predictors for falls, even after potential risk factors for falls were considered. Regional white matter burden, independent of cognitive decline, correlates with balance/gait disturbance and predicts falls in elderly with aMCI and AD. Careful insight into regional WMLs on brain magnetic resonance may greatly help to diagnose demented elderly with a higher risk of falls. Copyright © 2014 American Medical Directors Association, Inc. Published by Elsevier Inc. All rights reserved.

  3. Models to Predict the Burden of Cardiovascular Disease Risk in a Rural Mountainous Region of Vietnam

    NARCIS (Netherlands)

    Nguyen, Thi Phuong Lan; Schuiling-Veninga, Nynke; Nguyen, Thi Bach Yen; Hang, Vu Thi Thu; Wright, E. Pamela; Postma, M.J.

    2014-01-01

    Objective: To compare and identify the most appropriate model to predict cardiovascular disease (CVD) in a rural area in Northern Vietnam, using data on hypertension from the communities. Methods: A cross-sectional survey was conducted including all residents in selected communities, aged 34 to 65

  4. Using Machine Learning to Predict Swine Movements within a Regional Program to Improve Control of Infectious Diseases in the US.

    Science.gov (United States)

    Valdes-Donoso, Pablo; VanderWaal, Kimberly; Jarvis, Lovell S; Wayne, Spencer R; Perez, Andres M

    2017-01-01

    Between-farm animal movement is one of the most important factors influencing the spread of infectious diseases in food animals, including in the US swine industry. Understanding the structural network of contacts in a food animal industry is prerequisite to planning for efficient production strategies and for effective disease control measures. Unfortunately, data regarding between-farm animal movements in the US are not systematically collected and thus, such information is often unavailable. In this paper, we develop a procedure to replicate the structure of a network, making use of partial data available, and subsequently use the model developed to predict animal movements among sites in 34 Minnesota counties. First, we summarized two networks of swine producing facilities in Minnesota, then we used a machine learning technique referred to as random forest, an ensemble of independent classification trees, to estimate the probability of pig movements between farms and/or markets sites located in two counties in Minnesota. The model was calibrated and tested by comparing predicted data and observed data in those two counties for which data were available. Finally, the model was used to predict animal movements in sites located across 34 Minnesota counties. Variables that were important in predicting pig movements included between-site distance, ownership, and production type of the sending and receiving farms and/or markets. Using a weighted-kernel approach to describe spatial variation in the centrality measures of the predicted network, we showed that the south-central region of the study area exhibited high aggregation of predicted pig movements. Our results show an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome, which is believed to be transmitted, at least in part, though animal movements. While the correspondence of movements and disease is not a causal test, it suggests that the predicted network may approximate

  5. Regional differences in fiber tractography predict neurodevelopmental outcomes in neonates with infantile Krabbe disease

    Directory of Open Access Journals (Sweden)

    A. Gupta

    2015-01-01

    Interpretation: Neonatal microstructural abnormalities correlate with neurodevelopmental treatment outcomes in patients treated for infantile Krabbe disease. DTI with quantitative tractography is an excellent biomarker for evaluating infants with Krabbe disease identified through newborn screening.

  6. Prediction of survival by texture-based automated quantitative assessment of regional disease patterns on CT in idiopathic pulmonary fibrosis

    International Nuclear Information System (INIS)

    Lee, Sang Min; Seo, Joon Beom; Oh, Sang Young; Lee, Sang Min; Kim, Namkug; Kim, Tae Hoon; Song, Jin Woo

    2018-01-01

    To retrospectively investigate whether the baseline extent and 1-year change in regional disease patterns on CT can predict survival of patients with idiopathic pulmonary fibrosis (IPF). A total of 144 IPF patients with CT scans at the time of diagnosis and 1 year later were included. The extents of five regional disease patterns were quantified using an in-house texture-based automated system. The fibrosis score was defined as the sum of the extent of honeycombing and reticular opacity. The Cox proportional hazard model was used to determine the independent predictors of survival. A total of 106 patients (73.6%) died during the follow-up period. Univariate analysis revealed that age, baseline forced vital capacity, total lung capacity, diffusing capacity of the lung for carbon monoxide, six-minute walk distance, desaturation , honeycombing, reticular opacity, fibrosis score, and interval changes in honeycombing and fibrosis score were significantly associated with survival. Multivariate analysis revealed that age, desaturation, fibrosis score and interval change in fibrosis score were significant independent predictors of survival (p = 0.003, <0.001, 0.001 and <0.001). The C-index for the developed model was 0.768. Texture-based, automated CT quantification of fibrosis can be used as an independent predictor of survival in IPF patients. (orig.)

  7. Prediction of survival by texture-based automated quantitative assessment of regional disease patterns on CT in idiopathic pulmonary fibrosis

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Sang Min; Seo, Joon Beom; Oh, Sang Young; Lee, Sang Min; Kim, Namkug [University of Ulsan College of Medicine, Asan Medical Center, Department of Radiology and Research Institute of Radiology, Seoul (Korea, Republic of); Kim, Tae Hoon; Song, Jin Woo [University of Ulsan College of Medicine, Asan Medical Center, Department of Pulmonary and Critical Care Medicine, Seoul (Korea, Republic of)

    2018-03-15

    To retrospectively investigate whether the baseline extent and 1-year change in regional disease patterns on CT can predict survival of patients with idiopathic pulmonary fibrosis (IPF). A total of 144 IPF patients with CT scans at the time of diagnosis and 1 year later were included. The extents of five regional disease patterns were quantified using an in-house texture-based automated system. The fibrosis score was defined as the sum of the extent of honeycombing and reticular opacity. The Cox proportional hazard model was used to determine the independent predictors of survival. A total of 106 patients (73.6%) died during the follow-up period. Univariate analysis revealed that age, baseline forced vital capacity, total lung capacity, diffusing capacity of the lung for carbon monoxide, six-minute walk distance, desaturation{sub ,} honeycombing, reticular opacity, fibrosis score, and interval changes in honeycombing and fibrosis score were significantly associated with survival. Multivariate analysis revealed that age, desaturation, fibrosis score and interval change in fibrosis score were significant independent predictors of survival (p = 0.003, <0.001, 0.001 and <0.001). The C-index for the developed model was 0.768. Texture-based, automated CT quantification of fibrosis can be used as an independent predictor of survival in IPF patients. (orig.)

  8. Predicting coronary heart disease

    DEFF Research Database (Denmark)

    Sillesen, Henrik; Fuster, Valentin

    2012-01-01

    Atherosclerosis is the leading cause of death and disabling disease. Whereas risk factors are well known and constitute therapeutic targets, they are not useful for prediction of risk of future myocardial infarction, stroke, or death. Therefore, methods to identify atherosclerosis itself have bee...

  9. Genetic Predictions of Prion Disease Susceptibility in Carnivore Species Based on Variability of the Prion Gene Coding Region

    Science.gov (United States)

    Stewart, Paula; Campbell, Lauren; Skogtvedt, Susan; Griffin, Karen A.; Arnemo, Jon M.; Tryland, Morten; Girling, Simon; Miller, Michael W.; Tranulis, Michael A.; Goldmann, Wilfred

    2012-01-01

    Mammalian species vary widely in their apparent susceptibility to prion diseases. For example, several felid species developed prion disease (feline spongiform encephalopathy or FSE) during the bovine spongiform encephalopathy (BSE) epidemic in the United Kingdom, whereas no canine BSE cases were detected. Whether either of these or other groups of carnivore species can contract other prion diseases (e.g. chronic wasting disease or CWD) remains an open question. Variation in the host-encoded prion protein (PrPC) largely explains observed disease susceptibility patterns within ruminant species, and may explain interspecies differences in susceptibility as well. We sequenced and compared the open reading frame of the PRNP gene encoding PrPC protein from 609 animal samples comprising 29 species from 22 genera of the Order Carnivora; amongst these samples were 15 FSE cases. Our analysis revealed that FSE cases did not encode an identifiable disease-associated PrP polymorphism. However, all canid PrPs contained aspartic acid or glutamic acid at codon 163 which we propose provides a genetic basis for observed susceptibility differences between canids and felids. Among other carnivores studied, wolverine (Gulo gulo) and pine marten (Martes martes) were the only non-canid species to also express PrP-Asp163, which may impact on their prion diseases susceptibility. Populations of black bear (Ursus americanus) and mountain lion (Puma concolor) from Colorado showed little genetic variation in the PrP protein and no variants likely to be highly resistant to prions in general, suggesting that strain differences between BSE and CWD prions also may contribute to the limited apparent host range of the latter. PMID:23236380

  10. Genetic predictions of prion disease susceptibility in carnivore species based on variability of the prion gene coding region.

    Directory of Open Access Journals (Sweden)

    Paula Stewart

    Full Text Available Mammalian species vary widely in their apparent susceptibility to prion diseases. For example, several felid species developed prion disease (feline spongiform encephalopathy or FSE during the bovine spongiform encephalopathy (BSE epidemic in the United Kingdom, whereas no canine BSE cases were detected. Whether either of these or other groups of carnivore species can contract other prion diseases (e.g. chronic wasting disease or CWD remains an open question. Variation in the host-encoded prion protein (PrP(C largely explains observed disease susceptibility patterns within ruminant species, and may explain interspecies differences in susceptibility as well. We sequenced and compared the open reading frame of the PRNP gene encoding PrP(C protein from 609 animal samples comprising 29 species from 22 genera of the Order Carnivora; amongst these samples were 15 FSE cases. Our analysis revealed that FSE cases did not encode an identifiable disease-associated PrP polymorphism. However, all canid PrPs contained aspartic acid or glutamic acid at codon 163 which we propose provides a genetic basis for observed susceptibility differences between canids and felids. Among other carnivores studied, wolverine (Gulo gulo and pine marten (Martes martes were the only non-canid species to also express PrP-Asp163, which may impact on their prion diseases susceptibility. Populations of black bear (Ursus americanus and mountain lion (Puma concolor from Colorado showed little genetic variation in the PrP protein and no variants likely to be highly resistant to prions in general, suggesting that strain differences between BSE and CWD prions also may contribute to the limited apparent host range of the latter.

  11. Measurement of temporal regional cerebral perfusion with single-photon emission tomography predicts rate of decline in language function and survival in early Alzheimer`s disease

    Energy Technology Data Exchange (ETDEWEB)

    Claus, J.J.; Walstra, G.J.M.; Hijdra, A.; Gool, W.A. van [Department of Neurology, Academic Medical Center, University of Amsterdam, Amsterdam (Netherlands); Royen, E.A. van [Department of Nuclear Medicine, Academic Medical Center, University of Amsterdam (Netherlands); Verbeeten, B. Jr. [Department of Radiology, Academic Medical Center, University of Amsterdam (Netherlands)

    1999-03-01

    We determined the relationship between regional cerebral blood flow (rCBF) measured with single-photon emission tomography (SPET) and decline in cognitive function and survival in Alzheimer`s disease. In a prospective follow-up study, 69 consecutively referred patients with early probable Alzheimer`s disease (NINCDS/ADRDA criteria) underwent SPET performed at the time of initial diagnosis using technetium-99m-labelled hexamethylpropylene amine oxime. Neuropsychological function was assessed at baseline and after 6 months and survival data were available on all patients, extending to 5.5 years of follow-up. Lower left temporal (P<0.01) and lower left parietal (P<0.01) rCBF were statistically significantly related to decline in language function after 6 months. The association between left temporal rCBF and survival was also statistically significant (P<0.05) using Cox proportional hazards regression analysis. Performing analysis with quartiles of the distribution, we found a threshold effect for low left temporal rCBF (rCBF<73.7%, P<0.01) and high risk of mortality. In this lowest quartile, median survival time was 2.7 years (follow-up to 5.2 years), compared with 4.4 years in the other quartiles (follow-up to 5.5 years). Kaplan-Meier survival curves showed statistically significant (P<0.05, log rank test) survival curves for the lowest versus other quartiles of left temporal rCBF. All results were unaffected by adjustment for age, sex, dementia severity, duration of symptoms, education and ratings of local cortical atrophy. We conclude that left temporal rCBF predicts decline in language function and survival in patients with early probable Alzheimer`s disease, with a threshold effect of low rCBF and high risk of mortality. (orig.) With 3 figs., 3 tabs., 44 refs.

  12. Measurement of temporal regional cerebral perfusion with single-photon emission tomography predicts rate of decline in language function and survival in early Alzheimer's disease

    International Nuclear Information System (INIS)

    Claus, J.J.; Walstra, G.J.M.; Hijdra, A.; Gool, W.A. van; Royen, E.A. van; Verbeeten, B. Jr.

    1999-01-01

    We determined the relationship between regional cerebral blood flow (rCBF) measured with single-photon emission tomography (SPET) and decline in cognitive function and survival in Alzheimer's disease. In a prospective follow-up study, 69 consecutively referred patients with early probable Alzheimer's disease (NINCDS/ADRDA criteria) underwent SPET performed at the time of initial diagnosis using technetium-99m-labelled hexamethylpropylene amine oxime. Neuropsychological function was assessed at baseline and after 6 months and survival data were available on all patients, extending to 5.5 years of follow-up. Lower left temporal (P<0.01) and lower left parietal (P<0.01) rCBF were statistically significantly related to decline in language function after 6 months. The association between left temporal rCBF and survival was also statistically significant (P<0.05) using Cox proportional hazards regression analysis. Performing analysis with quartiles of the distribution, we found a threshold effect for low left temporal rCBF (rCBF<73.7%, P<0.01) and high risk of mortality. In this lowest quartile, median survival time was 2.7 years (follow-up to 5.2 years), compared with 4.4 years in the other quartiles (follow-up to 5.5 years). Kaplan-Meier survival curves showed statistically significant (P<0.05, log rank test) survival curves for the lowest versus other quartiles of left temporal rCBF. All results were unaffected by adjustment for age, sex, dementia severity, duration of symptoms, education and ratings of local cortical atrophy. We conclude that left temporal rCBF predicts decline in language function and survival in patients with early probable Alzheimer's disease, with a threshold effect of low rCBF and high risk of mortality. (orig.)

  13. Ellipsoidal prediction regions for multivariate uncertainty characterization

    DEFF Research Database (Denmark)

    Golestaneh, Faranak; Pinson, Pierre; Azizipanah-Abarghooee, Rasoul

    2018-01-01

    , for classes of decision-making problems based on robust, interval chance-constrained optimization, necessary inputs take the form of multivariate prediction regions rather than scenarios. The current literature is at very primitive stage of characterizing multivariate prediction regions to be employed...... in these classes of optimization problems. To address this issue, we introduce a new class of multivariate forecasts which form as multivariate ellipsoids for non-Gaussian variables. We propose a data-driven systematic framework to readily generate and evaluate ellipsoidal prediction regions, with predefined...... probability guarantees and minimum conservativeness. A skill score is proposed for quantitative assessment of the quality of prediction ellipsoids. A set of experiments is used to illustrate the discrimination ability of the proposed scoring rule for potential misspecification of ellipsoidal prediction regions...

  14. Pre-existing IgG antibodies cross-reacting with the Fab region of infliximab predict efficacy and safety of infliximab therapy in inflammatory bowel disease

    DEFF Research Database (Denmark)

    Steenholdt, Casper; Palarasah, Yaseelan; Bendtzen, Klaus

    2013-01-01

    are common and may cross-react with the murine part of IFX. AIM: To investigate if Abs binding to IFX's Fab region (IFX-Fab) are present in IBD patients before exposure to IFX, and whether they predict efficacy and safety of IFX therapy. METHODS: Observational, retrospective cohort study of patients with CD...... (n = 29) and UC (n = 22). RESULTS: Pre-treatment levels of IFX-Fab reactive IgG Abs were significantly lower in CD patients in remission after 1 year of maintenance IFX (median 91 mU/L, n = 8) than in the rest of the patients (639 mU/L, n = 21; P ...

  15. Nonparametric conditional predictive regions for time series

    NARCIS (Netherlands)

    de Gooijer, J.G.; Zerom Godefay, D.

    2000-01-01

    Several nonparametric predictors based on the Nadaraya-Watson kernel regression estimator have been proposed in the literature. They include the conditional mean, the conditional median, and the conditional mode. In this paper, we consider three types of predictive regions for these predictors — the

  16. Huntington's disease : Psychological aspects of predictive testing

    NARCIS (Netherlands)

    Timman, Reinier

    2005-01-01

    Predictive testing for Huntington's disease appears to have long lasting psychological effects. The predictive test for Huntington's disease (HD), a hereditary disease of the nervous system, was introduced in the Netherlands in the late eighties. As adverse consequences of the test were

  17. Ellipsoidal prediction regions for multivariate uncertainty characterization

    DEFF Research Database (Denmark)

    Golestaneh, Faranak; Pinson, Pierre; Azizipanah-Abarghooee, Rasoul

    2018-01-01

    While substantial advances are observed in probabilistic forecasting for power system operation and electricity market applications, most approaches are still developed in a univariate framework. This prevents from informing about the interdependence structure among locations, lead times and vari......While substantial advances are observed in probabilistic forecasting for power system operation and electricity market applications, most approaches are still developed in a univariate framework. This prevents from informing about the interdependence structure among locations, lead times...... probability guarantees and minimum conservativeness. A skill score is proposed for quantitative assessment of the quality of prediction ellipsoids. A set of experiments is used to illustrate the discrimination ability of the proposed scoring rule for potential misspecification of ellipsoidal prediction regions...

  18. Design Expert® supported optimization and predictive analysis of selegiline nanoemulsion via the olfactory region with enhanced behavioural performance in Parkinson’s disease

    Science.gov (United States)

    Kumar, Shobhit; Ali, Javed; Baboota, Sanjula

    2016-10-01

    Selegiline is a monoamine oxidase B (MAO-B) inhibitor and is used in the treatment of Parkinson’s disease. The main problem associated with its oral administration is its low oral bioavailability (10%) due to its poor aqueous solubility and extensive first pass metabolism. The aim of the present research work was to develop a nanoemulsion loaded with selegiline for direct nose-to-brain delivery for the better management of Parkinson’s disease. A quality by design (QbD) approach was used in a statistical multivariate method for the preparation and optimization of nanoemulsion. In this study, four independent variables were chosen, in which two were compositions and two were process variables, while droplet size, transmittance, zeta potential and drug release were selected as response variables. The optimized formulation was assessed for efficacy in Parkinson’s disease using behavioural studies, namely forced swimming, locomotor, catalepsy, muscle coordination, akinesia and bradykinesia or pole test in Wistar rats. The observed droplet size, polydispersity index (PDI), refractive index, transmittance, zeta potential and viscosity of selegiline nanoemulsion were found to be 61.43 ± 4.10 nm, 0.203 ± 0.005, 1.30 ± 0.01, 99.80 ± 0.04%, -34 mV and 31.85 ± 0.24 mPas respectively. Surface characterization studies demonstrated a spherical shape of nanoemulsion which showed 3.7 times enhancement in drug permeation as compared to drug suspension. The results of behaviour studies showed that treatment of haloperidol induced Parkinson’s disease in rats with selegiline nanoemulsion (administered intranasally) showed significant improvement in behavioural activities in comparison to orally administered drug. These findings demonstrate that nanoemulsion could be a promising new drug delivery carrier for intranasal delivery of selegiline in the treatment of Parkinson’s disease.

  19. Is the disease course predictable in inflammatory bowel diseases?

    Science.gov (United States)

    Lakatos, Peter Laszlo; Kiss, Lajos S

    2010-01-01

    During the course of the disease, most patients with Crohn’s disease (CD) may eventually develop a stricturing or a perforating complication, and a significant number of patients with both CD and ulcerative colitis will undergo surgery. In recent years, research has focused on the determination of factors important in the prediction of disease course in inflammatory bowel diseases to improve stratification of patients, identify individual patient profiles, including clinical, laboratory and molecular markers, which hopefully will allow physicians to choose the most appropriate management in terms of therapy and intensity of follow-up. This review summarizes the available evidence on clinical, endoscopic variables and biomarkers in the prediction of short and long-term outcome in patients with inflammatory bowel diseases. PMID:20518079

  20. Predicting global variation in infectious disease severity

    DEFF Research Database (Denmark)

    Jensen, Per Moestrup; de Fine Licht, Henrik Hjarvard

    2016-01-01

    demographic and population data. Results: Birth rates were the best predictor for mumps and malaria CFR. For tuberculosis CFR death rates were the best predictor and for leptospirosis population density was a significant predictor. Conclusions and implications: CFR predictors differed among diseases according...... and leptospirosis and assessed these for association with a range of population characteristics, such as crude birth and death rates, median age of the population, mean body mass index, proportion living in urban areas and tuberculosis vaccine coverage. We then tested this predictive model on Danish his- torical...... have the opposite effect. Accordingly changes in CFR may occur in parallel with demographic transitions. Methodology: We explored the predictability of CFR using data obtained from the World Health Organization (WHO) disease databases for four human diseases: mumps, malaria, tuberculosis...

  1. [Predictive ocular motor control in Parkinson's disease].

    Science.gov (United States)

    Ying, Li; Liu, Zhen-Guo; Chen, Wei; Gan, Jing; Wang, Wen-An

    2008-02-19

    To investigate the changes of predictive ocular motor function in the patients with Parkinson's disease (PD), and to discuss its clinical value. Videonystagmography (VNG) was used to examine 24 patients with idiopathic Parkinson's disease, 15 males and 9 females, aged 61 +/- 6 (50-69), and 24 sex and age-matched healthy control subjects on random ocular saccade (with the target moving at random intervals to random positions) and predictive ocular saccade (with the 1.25-second light target moving 10 degrees right or left from the center). In the random ocular saccade program, the latency of saccade of the PD patients was 284 ms +/- 58 ms, significantly longer than that of the healthy controls (236 ms +/- 37 ms, P = 0.003). In the predictive ocular saccade pattern, the latency of saccades the PD patients was 150 ms +/- 138 ms, significantly longer than that of the healthy controls (59 ms +/- 102 ms, P = 0.002). The appearance rate of predictive saccades (with the latency of saccade <80 ms) in the PD group was 21%, significantly lower than that in the control group (31%, P = 0.003). There is dysfunction of predictive ocular motor control in the PD patients, and the cognitive function may be impaired at the early stage of PD.

  2. Assessment of protein disorder region predictions in CASP10

    KAUST Repository

    Monastyrskyy, Bohdan; Kryshtafovych, Andriy; Moult, John; Tramontano, Anna; Fidelis, Krzysztof

    2013-01-01

    The article presents the assessment of disorder region predictions submitted to CASP10. The evaluation is based on the three measures tested in previous CASPs: (i) balanced accuracy, (ii) the Matthews correlation coefficient for the binary predictions, and (iii) the area under the curve in the receiver operating characteristic (ROC) analysis of predictions using probability annotation. We also performed new analyses such as comparison of the submitted predictions with those obtained with a Naïve disorder prediction method and with predictions from the disorder prediction databases D2P2 and MobiDB. On average, the methods participating in CASP10 demonstrated slightly better performance than those in CASP9.

  3. Assessment of protein disorder region predictions in CASP10

    KAUST Repository

    Monastyrskyy, Bohdan

    2013-11-22

    The article presents the assessment of disorder region predictions submitted to CASP10. The evaluation is based on the three measures tested in previous CASPs: (i) balanced accuracy, (ii) the Matthews correlation coefficient for the binary predictions, and (iii) the area under the curve in the receiver operating characteristic (ROC) analysis of predictions using probability annotation. We also performed new analyses such as comparison of the submitted predictions with those obtained with a Naïve disorder prediction method and with predictions from the disorder prediction databases D2P2 and MobiDB. On average, the methods participating in CASP10 demonstrated slightly better performance than those in CASP9.

  4. Ground Motion Prediction Models for Caucasus Region

    Science.gov (United States)

    Jorjiashvili, Nato; Godoladze, Tea; Tvaradze, Nino; Tumanova, Nino

    2016-04-01

    Ground motion prediction models (GMPMs) relate ground motion intensity measures to variables describing earthquake source, path, and site effects. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration or spectral acceleration because this parameter gives useful information for Seismic Hazard Assessment. Since 2003 development of Georgian Digital Seismic Network has started. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models is obtained by classical, statistical way, regression analysis. In this study site ground conditions are additionally considered because the same earthquake recorded at the same distance may cause different damage according to ground conditions. Empirical ground-motion prediction models (GMPMs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This work presents a holistic framework for the development of a peak ground acceleration (PGA) or spectral acceleration (SA) GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain.

  5. Ensemble-based Regional Climate Prediction: Political Impacts

    Science.gov (United States)

    Miguel, E.; Dykema, J.; Satyanath, S.; Anderson, J. G.

    2008-12-01

    Accurate forecasts of regional climate, including temperature and precipitation, have significant implications for human activities, not just economically but socially. Sub Saharan Africa is a region that has displayed an exceptional propensity for devastating civil wars. Recent research in political economy has revealed a strong statistical relationship between year to year fluctuations in precipitation and civil conflict in this region in the 1980s and 1990s. To investigate how climate change may modify the regional risk of civil conflict in the future requires a probabilistic regional forecast that explicitly accounts for the community's uncertainty in the evolution of rainfall under anthropogenic forcing. We approach the regional climate prediction aspect of this question through the application of a recently demonstrated method called generalized scalar prediction (Leroy et al. 2009), which predicts arbitrary scalar quantities of the climate system. This prediction method can predict change in any variable or linear combination of variables of the climate system averaged over a wide range spatial scales, from regional to hemispheric to global. Generalized scalar prediction utilizes an ensemble of model predictions to represent the community's uncertainty range in climate modeling in combination with a timeseries of any type of observational data that exhibits sensitivity to the scalar of interest. It is not necessary to prioritize models in deriving with the final prediction. We present the results of the application of generalized scalar prediction for regional forecasts of temperature and precipitation and Sub Saharan Africa. We utilize the climate predictions along with the established statistical relationship between year-to-year rainfall variability in Sub Saharan Africa to investigate the potential impact of climate change on civil conflict within that region.

  6. New technologies in predicting, preventing and controlling emerging infectious diseases.

    Science.gov (United States)

    Christaki, Eirini

    2015-01-01

    Surveillance of emerging infectious diseases is vital for the early identification of public health threats. Emergence of novel infections is linked to human factors such as population density, travel and trade and ecological factors like climate change and agricultural practices. A wealth of new technologies is becoming increasingly available for the rapid molecular identification of pathogens but also for the more accurate monitoring of infectious disease activity. Web-based surveillance tools and epidemic intelligence methods, used by all major public health institutions, are intended to facilitate risk assessment and timely outbreak detection. In this review, we present new methods for regional and global infectious disease surveillance and advances in epidemic modeling aimed to predict and prevent future infectious diseases threats.

  7. Prediction of disease course in inflammatory bowel diseases.

    Science.gov (United States)

    Lakatos, Peter Laszlo

    2010-06-07

    Clinical presentation at diagnosis and disease course of both Crohn's disease (CD) and ulcerative colitis are heterogeneous and variable over time. Since most patients have a relapsing course and most CD patients develop complications (e.g. stricture and/or perforation), much emphasis has been placed in the recent years on the determination of important predictive factors. The identification of these factors may eventually lead to a more personalized, tailored therapy. In this TOPIC HIGHLIGHT series, we provide an update on the available literature regarding important clinical, endoscopic, fecal, serological/routine laboratory and genetic factors. Our aim is to assist clinicians in the everyday practical decision-making when choosing the treatment strategy for their patients suffering from inflammatory bowel diseases.

  8. Predicting human age using regional morphometry and inter-regional morphological similarity

    Science.gov (United States)

    Wang, Xun-Heng; Li, Lihua

    2016-03-01

    The goal of this study is predicting human age using neuro-metrics derived from structural MRI, as well as investigating the relationships between age and predictive neuro-metrics. To this end, a cohort of healthy subjects were recruited from 1000 Functional Connectomes Project. The ages of the participations were ranging from 7 to 83 (36.17+/-20.46). The structural MRI for each subject was preprocessed using FreeSurfer, resulting in regional cortical thickness, mean curvature, regional volume and regional surface area for 148 anatomical parcellations. The individual age was predicted from the combination of regional and inter-regional neuro-metrics. The prediction accuracy is r = 0.835, p Pearson correlation coefficient between predicted ages and actual ages. Moreover, the LASSO linear regression also found certain predictive features, most of which were inter-regional features. The turning-point of the developmental trajectories in human brain was around 40 years old based on regional cortical thickness. In conclusion, structural MRI could be potential biomarkers for the aging in human brain. The human age could be successfully predicted from the combination of regional morphometry and inter-regional morphological similarity. The inter-regional measures could be beneficial to investigating human brain connectome.

  9. Moment-ration imaging of seismic regions for earthquake prediction

    Science.gov (United States)

    Lomnitz, Cinna

    1993-10-01

    An algorithm for predicting large earthquakes is proposed. The reciprocal ratio (mri) of the residual seismic moment to the total moment release in a region is used for imaging seismic moment precursors. Peaks in mri predict recent major earthquakes, including the 1985 Michoacan, 1985 central Chile, and 1992 Eureka, California earthquakes.

  10. Prediction of fibre architecture and adaptation in diseased carotid bifurcations.

    LENUS (Irish Health Repository)

    Creane, Arthur

    2011-12-01

    Many studies have used patient-specific finite element models to estimate the stress environment in atherosclerotic plaques, attempting to correlate the magnitude of stress to plaque vulnerability. In complex geometries, few studies have incorporated the anisotropic material response of arterial tissue. This paper presents a fibre remodelling algorithm to predict the fibre architecture, and thus anisotropic material response in four patient-specific models of the carotid bifurcation. The change in fibre architecture during disease progression and its affect on the stress environment in the plaque were predicted. The mean fibre directions were assumed to lie at an angle between the two positive principal strain directions. The angle and the degree of dispersion were assumed to depend on the ratio of principal strain values. Results were compared with experimental observations and other numerical studies. In non-branching regions of each model, the typical double helix arterial fibre pattern was predicted while at the bifurcation and in regions of plaque burden, more complex fibre architectures were found. The predicted change in fibre architecture in the arterial tissue during plaque progression was found to alter the stress environment in the plaque. This suggests that the specimen-specific anisotropic response of the tissue should be taken into account to accurately predict stresses in the plaque. Since determination of the fibre architecture in vivo is a difficult task, the system presented here provides a useful method of estimating the fibre architecture in complex arterial geometries.

  11. Predicting Surface Runoff from Catchment to Large Region

    Directory of Open Access Journals (Sweden)

    Hongxia Li

    2015-01-01

    Full Text Available Predicting surface runoff from catchment to large region is a fundamental and challenging task in hydrology. This paper presents a comprehensive review for various studies conducted for improving runoff predictions from catchment to large region in the last several decades. This review summarizes the well-established methods and discusses some promising approaches from the following four research fields: (1 modeling catchment, regional and global runoff using lumped conceptual rainfall-runoff models, distributed hydrological models, and land surface models, (2 parameterizing hydrological models in ungauged catchments, (3 improving hydrological model structure, and (4 using new remote sensing precipitation data.

  12. Poor Positive Predictive Value of Lyme Disease Serologic Testing in an Area of Low Disease Incidence.

    Science.gov (United States)

    Lantos, Paul M; Branda, John A; Boggan, Joel C; Chudgar, Saumil M; Wilson, Elizabeth A; Ruffin, Felicia; Fowler, Vance; Auwaerter, Paul G; Nigrovic, Lise E

    2015-11-01

    Lyme disease is diagnosed by 2-tiered serologic testing in patients with a compatible clinical illness, but the significance of positive test results in low-prevalence regions has not been investigated. We reviewed the medical records of patients who tested positive for Lyme disease with standardized 2-tiered serologic testing between 2005 and 2010 at a single hospital system in a region with little endemic Lyme disease. Based on clinical findings, we calculated the positive predictive value of Lyme disease serology. Next, we reviewed the outcome of serologic testing in patients with select clinical syndromes compatible with disseminated Lyme disease (arthritis, cranial neuropathy, or meningitis). During the 6-year study period 4723 patients were tested for Lyme disease, but only 76 (1.6%) had positive results by established laboratory criteria. Among 70 seropositive patients whose medical records were available for review, 12 (17%; 95% confidence interval, 9%-28%) were found to have Lyme disease (6 with documented travel to endemic regions). During the same time period, 297 patients with a clinical illness compatible with disseminated Lyme disease underwent 2-tiered serologic testing. Six of them (2%; 95% confidence interval, 0.7%-4.3%) were seropositive, 3 with documented travel and 1 who had an alternative diagnosis that explained the clinical findings. In this low-prevalence cohort, fewer than 20% of positive Lyme disease tests are obtained from patients with clinically likely Lyme disease. Positive Lyme disease test results may have little diagnostic value in this setting. © The Author 2015. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  13. Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

    Science.gov (United States)

    Wessler, Benjamin S; Lai Yh, Lana; Kramer, Whitney; Cangelosi, Michael; Raman, Gowri; Lutz, Jennifer S; Kent, David M

    2015-07-01

    Clinical prediction models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision making and individualize care. For patients with cardiovascular disease, there are numerous CPMs available although the extent of this literature is not well described. We conducted a systematic review for articles containing CPMs for cardiovascular disease published between January 1990 and May 2012. Cardiovascular disease includes coronary heart disease, heart failure, arrhythmias, stroke, venous thromboembolism, and peripheral vascular disease. We created a novel database and characterized CPMs based on the stage of development, population under study, performance, covariates, and predicted outcomes. There are 796 models included in this database. The number of CPMs published each year is increasing steadily over time. Seven hundred seventeen (90%) are de novo CPMs, 21 (3%) are CPM recalibrations, and 58 (7%) are CPM adaptations. This database contains CPMs for 31 index conditions, including 215 CPMs for patients with coronary artery disease, 168 CPMs for population samples, and 79 models for patients with heart failure. There are 77 distinct index/outcome pairings. Of the de novo models in this database, 450 (63%) report a c-statistic and 259 (36%) report some information on calibration. There is an abundance of CPMs available for a wide assortment of cardiovascular disease conditions, with substantial redundancy in the literature. The comparative performance of these models, the consistency of effects and risk estimates across models and the actual and potential clinical impact of this body of literature is poorly understood. © 2015 American Heart Association, Inc.

  14. Ambient air quality predictions in the Athabasca oil sands region

    International Nuclear Information System (INIS)

    1996-01-01

    This report presents dispersion modelling predictions for SO 2 , NOx, CO, HC and particulate matter (PM), which complement regional monitoring observations. The air quality simulation models provide a scientific means of relating industrial emissions to changes in ambient air quality. The four models applied to the emission sources in the region were: (1) SCREEN3, (2) ISC3BE, (3) ADEPT2, and (4) the box model. Model predictions were compared to air quality guidelines. It was concluded that the largest SO 2 concentrations were associated with intermittent flaring, and with the Suncor Powerhouse whose emissions are continuous. 45 refs., 36 tabs., 40 figs

  15. Initializing decadal climate predictions over the North Atlantic region

    Science.gov (United States)

    Matei, Daniela Mihaela; Pohlmann, Holger; Jungclaus, Johann; Müller, Wolfgang; Haak, Helmuth; Marotzke, Jochem

    2010-05-01

    Decadal climate prediction aims to predict the internally-generated decadal climate variability in addition to externally-forced climate change signal. In order to achieve this it is necessary to start the predictions from the current climate state. In this study we investigate the forecast skill of the North Atlantic decadal climate predictions using two different ocean initialization strategies. First we apply an assimilation of ocean synthesis data provided by the GECCO project (Köhl and Stammer, 2008) as initial conditions for the coupled model ECHAM5/MPI-OM. Hindcast experiments are then performed over the period 1952-2001. An alternative approach is one in which the subsurface ocean temperature and salinity are diagnosed from an ensemble of ocean model runs forced by the NCEP-NCAR atmospheric reanalyzes for the period 1948-2007, then nudge into the coupled model to produce initial conditions for the hindcast experiments. An anomaly coupling scheme is used in both approaches to avoid the hindcast drift and the associated initial shock. Differences between the two assimilation approaches are discussed by comparing them with the observational data in key regions and processes. We asses the skill of the initialized decadal hindcast experiments against the prediction skill of the non-initialized hindcasts simulation. We obtain an overview of the regions with the highest predictability from the regional distribution of the anomaly correlation coefficients and RMSE for the SAT. For the first year the hindcast skill is increased over almost all ocean regions in the NCEP-forced approach. This increase in the hindcast skill for the 1 year lead time is somewhat reduced in the GECCO approach. At lead time 5yr and 10yr, the skill enhancement is still found over the North Atlantic and North Pacific regions. We also consider the potential predictability of the Atlantic Meridional Overturning Circulation (AMOC) and Nordic Seas Overflow by comparing the predicted values to

  16. Conditional predictive inference for online surveillance of spatial disease incidence

    Science.gov (United States)

    Corberán-Vallet, Ana; Lawson, Andrew B.

    2012-01-01

    This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of multiple comparisons, we incorporate a common probability that each small area signals an alarm when no change in the risk pattern of disease takes place into the analysis. We investigate the performance of the proposed surveillance technique within the framework of Bayesian hierarchical Poisson models using a simulation study. Finally, we present a case study of salmonellosis in South Carolina. PMID:21898522

  17. Poisson Mixture Regression Models for Heart Disease Prediction.

    Science.gov (United States)

    Mufudza, Chipo; Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

  18. 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

  19. HotRegion: a database of predicted hot spot clusters.

    Science.gov (United States)

    Cukuroglu, Engin; Gursoy, Attila; Keskin, Ozlem

    2012-01-01

    Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. We propose a database called HotRegion, which provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. HotRegion is accessible at http://prism.ccbb.ku.edu.tr/hotregion.

  20. PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations.

    Directory of Open Access Journals (Sweden)

    Jaroslav Bendl

    2014-01-01

    Full Text Available Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

  1. Using MODIS Data to Predict Regional Corn Yields

    Directory of Open Access Journals (Sweden)

    Ho-Young Ban

    2016-12-01

    Full Text Available A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data, which are eight-day interval surface reflectance data, were obtained from day of the year (DOY 89 to 337 to calculate the leaf area index (LAI. The sum of the LAI from early in the season to a given date in the season (end of DOY (EOD was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD. A simple phenology model was derived to estimate the dates of emergence and maturity using the LAD-logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF were established using LAD as the variable to predict corn yield. The yield model YP used LAD from predicted emergence to maturity, and the yield model YF used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model, including the predicted phenology, performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed similar or better performance, even for the much earlier pre-harvest yield prediction at DOY 257. In addition, the model performance showed no difference between the two study regions with very different climates and cultivation methods, including cultivar and irrigation management. These results suggested that the approach described in this paper has potential for application to

  2. Some considerations regarding earthquake prediction - The case of Vrancea region -

    International Nuclear Information System (INIS)

    Enescu, Bogdan; Enescu, Dumitru

    2000-01-01

    Earthquake prediction research has been conducted for over 100 years with no obvious success. In the last year, the new modern concepts regarding the earthquake dynamics added another source of skepticism regarding the possibility of predicting earthquakes. However there are some recognizable trends, optimized in the recent years, which may give rise to more reliable and solid approaches to deal with this complex subject. In the light of these trends, emphasized by Aki, we try to analyze the new developments in the field, especially concerning the Vrancea region. (authors)

  3. Networked Predictive Control for Nonlinear Systems With Arbitrary Region Quantizers.

    Science.gov (United States)

    Yang, Hongjiu; Xu, Yang; Xia, Yuanqing; Zhang, Jinhui

    2017-04-06

    In this paper, networked predictive control is investigated for planar nonlinear systems with quantization by an extended state observer (ESO). The ESO is used not only to deal with nonlinear terms but also to generate predictive states for dealing with network-induced delays. Two arbitrary region quantizers are applied to take effective values of signals in forward channel and feedback channel, respectively. Based on a "zoom" strategy, sufficient conditions are given to guarantee stabilization of the closed-loop networked control system with quantization. A simulation example is proposed to exhibit advantages and availability of the results.

  4. MHC Region and Its Related Disease Study

    DEFF Research Database (Denmark)

    Cao, Hongzhi

    The major histocompatibility complex (MHC) is one of the most gene dense regions in the human genome and many disorders, including primary immune deficiencies, autoimmune conditions, infections, cancers and mental disorder have been found to be associated with this region. However, due to a high ...

  5. Quantitative Earthquake Prediction on Global and Regional Scales

    International Nuclear Information System (INIS)

    Kossobokov, Vladimir G.

    2006-01-01

    for mega-earthquakes of M9.0+. The monitoring at regional scales may require application of a recently proposed scheme for the spatial stabilization of the intermediate-term middle-range predictions. The scheme guarantees a more objective and reliable diagnosis of times of increased probability and is less restrictive to input seismic data. It makes feasible reestablishment of seismic monitoring aimed at prediction of large magnitude earthquakes in Caucasus and Central Asia, which to our regret, has been discontinued in 1991. The first results of the monitoring (1986-1990) were encouraging, at least for M6.5+

  6. Quantitative Earthquake Prediction on Global and Regional Scales

    Science.gov (United States)

    Kossobokov, Vladimir G.

    2006-03-01

    for mega-earthquakes of M9.0+. The monitoring at regional scales may require application of a recently proposed scheme for the spatial stabilization of the intermediate-term middle-range predictions. The scheme guarantees a more objective and reliable diagnosis of times of increased probability and is less restrictive to input seismic data. It makes feasible reestablishment of seismic monitoring aimed at prediction of large magnitude earthquakes in Caucasus and Central Asia, which to our regret, has been discontinued in 1991. The first results of the monitoring (1986-1990) were encouraging, at least for M6.5+.

  7. A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities

    Directory of Open Access Journals (Sweden)

    Wenqiong Xue

    2018-03-01

    Full Text Available Relating disease status to imaging data stands to increase the clinical significance of neuroimaging studies. Many neurological and psychiatric disorders involve complex, systems-level alterations that manifest in functional and structural properties of the brain and possibly other clinical and biologic measures. We propose a Bayesian hierarchical model to predict disease status, which is able to incorporate information from both functional and structural brain imaging scans. We consider a two-stage whole brain parcellation, partitioning the brain into 282 subregions, and our model accounts for correlations between voxels from different brain regions defined by the parcellations. Our approach models the imaging data and uses posterior predictive probabilities to perform prediction. The estimates of our model parameters are based on samples drawn from the joint posterior distribution using Markov Chain Monte Carlo (MCMC methods. We evaluate our method by examining the prediction accuracy rates based on leave-one-out cross validation, and we employ an importance sampling strategy to reduce the computation time. We conduct both whole-brain and voxel-level prediction and identify the brain regions that are highly associated with the disease based on the voxel-level prediction results. We apply our model to multimodal brain imaging data from a study of Parkinson's disease. We achieve extremely high accuracy, in general, and our model identifies key regions contributing to accurate prediction including caudate, putamen, and fusiform gyrus as well as several sensory system regions.

  8. Regional Disease Surveillance Meeting - Final Paper

    Energy Technology Data Exchange (ETDEWEB)

    Lesperance, Ann M.; Mahy, Heidi A.

    2006-08-08

    On June 1, 2006, public health officials working in surveillance, epidemiological modeling, and information technology communities from the Seattle/Tacoma area and State of Washington met with members of the Pacific Northwest National Laboratory (PNNL) to discuss the current state of disease surveillance and gaps and needs to improve the current systems. The meeting also included a discussion of PNNL initiatives that might be appropriate to enhance disease surveillance and the current tools being used for disease surveillance. Participants broke out into two groups to identify critical gaps and needs for improving a surveillance system, and discuss the requirements for developing improved surveillance. Each group developed a list of key priorities summarizing the requirements for improved surveillance. The objective of this meeting was to work towards the development of an improved disease surveillance system.

  9. Regional prediction of basin-scale brown trout habitat suitability

    Directory of Open Access Journals (Sweden)

    S. Ceola

    2014-09-01

    Full Text Available In this study we propose a novel method for the estimation of ecological indices describing the habitat suitability of brown trout (Salmo trutta. Traditional hydrological tools are coupled with an innovative regional geostatistical technique, aiming at the prediction of the brown trout habitat suitability index where partial or totally ungauged conditions occur. Several methods for the assessment of ecological indices are already proposed in the scientific literature, but the possibility of exploiting a geostatistical prediction model, such as Topological Kriging, has never been investigated before. In order to develop a regional habitat suitability model we use the habitat suitability curve, obtained from measured data of brown trout adult individuals collected in several river basins across the USA. The Top-kriging prediction model is then employed to assess the spatial correlation between upstream and downstream habitat suitability indices. The study area is the Metauro River basin, located in the central part of Italy (Marche region, for which both water depth and streamflow data were collected. The present analysis focuses on discharge values corresponding to the 0.1-, 0.5-, 0.9-empirical quantiles derived from flow-duration curves available for seven gauging stations located within the study area, for which three different suitability indices (i.e. ψ10, ψ50 and ψ90 are evaluated. The results of this preliminary analysis are encouraging showing Nash-Sutcliffe efficiencies equal to 0.52, 0.65, and 0.69, respectively.

  10. Regional prediction of basin-scale brown trout habitat suitability

    Science.gov (United States)

    Ceola, S.; Pugliese, A.

    2014-09-01

    In this study we propose a novel method for the estimation of ecological indices describing the habitat suitability of brown trout (Salmo trutta). Traditional hydrological tools are coupled with an innovative regional geostatistical technique, aiming at the prediction of the brown trout habitat suitability index where partial or totally ungauged conditions occur. Several methods for the assessment of ecological indices are already proposed in the scientific literature, but the possibility of exploiting a geostatistical prediction model, such as Topological Kriging, has never been investigated before. In order to develop a regional habitat suitability model we use the habitat suitability curve, obtained from measured data of brown trout adult individuals collected in several river basins across the USA. The Top-kriging prediction model is then employed to assess the spatial correlation between upstream and downstream habitat suitability indices. The study area is the Metauro River basin, located in the central part of Italy (Marche region), for which both water depth and streamflow data were collected. The present analysis focuses on discharge values corresponding to the 0.1-, 0.5-, 0.9-empirical quantiles derived from flow-duration curves available for seven gauging stations located within the study area, for which three different suitability indices (i.e. ψ10, ψ50 and ψ90) are evaluated. The results of this preliminary analysis are encouraging showing Nash-Sutcliffe efficiencies equal to 0.52, 0.65, and 0.69, respectively.

  11. Alzheimer's Disease: Genes, pathogenesis and risk prediction

    NARCIS (Netherlands)

    K. Sleegers (Kristel); C.M. van Duijn (Cornelia)

    2001-01-01

    textabstractWith the aging of western society the contribution to morbidity of diseases of the elderly, such as dementia, will increase exponentially. Thorough preventative and curative strategies are needed to constrain the increasing prevalence of these disabling diseases. Better understanding of

  12. Perceived Vulnerability to Disease Predicts Environmental Attitudes

    Science.gov (United States)

    Prokop, Pavol; Kubiatko, Milan

    2014-01-01

    Investigating predictors of environmental attitudes may bring valuable benefits in terms of improving public awareness about biodiversity degradation and increased pro-environmental behaviour. Here we used an evolutionary approach to study environmental attitudes based on disease-threat model. We hypothesized that people vulnerable to diseases may…

  13. Body region dissatisfaction predicts attention to body regions on other women.

    Science.gov (United States)

    Lykins, Amy D; Ferris, Tamara; Graham, Cynthia A

    2014-09-01

    The proliferation of "idealized" (i.e., very thin and attractive) women in the media has contributed to increasing rates of body dissatisfaction among women. However, it remains relatively unknown how women attend to these images: does dissatisfaction predict greater or lesser attention to these body regions on others? Fifty healthy women (mean age=21.8 years) viewed images of idealized and plus-size models; an eye-tracker recorded visual attention. Participants also completed measures of satisfaction for specific body regions, which were then used as predictors of visual attention to these regions on models. Consistent with an avoidance-type process, lower levels of satisfaction with the two regions of greatest reported concern (mid, lower torso) predicted less attention to these regions; greater satisfaction predicted more attention to these regions. While this visual attention bias may aid in preserving self-esteem when viewing idealized others, it may preclude the opportunity for comparisons that could improve self-esteem. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Predictive modeling of coral disease distribution within a reef system.

    Directory of Open Access Journals (Sweden)

    Gareth J Williams

    2010-02-01

    Full Text Available Diseases often display complex and distinct associations with their environment due to differences in etiology, modes of transmission between hosts, and the shifting balance between pathogen virulence and host resistance. Statistical modeling has been underutilized in coral disease research to explore the spatial patterns that result from this triad of interactions. We tested the hypotheses that: 1 coral diseases show distinct associations with multiple environmental factors, 2 incorporating interactions (synergistic collinearities among environmental variables is important when predicting coral disease spatial patterns, and 3 modeling overall coral disease prevalence (the prevalence of multiple diseases as a single proportion value will increase predictive error relative to modeling the same diseases independently. Four coral diseases: Porites growth anomalies (PorGA, Porites tissue loss (PorTL, Porites trematodiasis (PorTrem, and Montipora white syndrome (MWS, and their interactions with 17 predictor variables were modeled using boosted regression trees (BRT within a reef system in Hawaii. Each disease showed distinct associations with the predictors. Environmental predictors showing the strongest overall associations with the coral diseases were both biotic and abiotic. PorGA was optimally predicted by a negative association with turbidity, PorTL and MWS by declines in butterflyfish and juvenile parrotfish abundance respectively, and PorTrem by a modal relationship with Porites host cover. Incorporating interactions among predictor variables contributed to the predictive power of our models, particularly for PorTrem. Combining diseases (using overall disease prevalence as the model response, led to an average six-fold increase in cross-validation predictive deviance over modeling the diseases individually. We therefore recommend coral diseases to be modeled separately, unless known to have etiologies that respond in a similar manner to

  15. Lipid measures and cardiovascular disease prediction

    NARCIS (Netherlands)

    van Wijk, D.F.; Stroes, E.S.G.; Kastelein, J.J.P.

    2009-01-01

    Traditional lipid measures are the cornerstone of risk assessment and treatment goals in cardiovascular prevention. Whereas the association between total, LDL-, HDL-cholesterol and cardiovascular disease risk has been generally acknowledged, the rather poor capacity to distinguish between patients

  16. Complex Regional Pain Syndrome: An inflammatory disease

    NARCIS (Netherlands)

    M. Dirckx (Maaike)

    2015-01-01

    markdownabstractThe pathophysiology of Complex Regional Pain Syndrome (CRPS) is complex and still not completely understood. In addition to a convincing role of inflammation, there are a number of arguments why an involvement of the immune system has been suggested in the pathophysiology of CRPS.

  17. 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.

  18. Development of a regional ensemble prediction method for probabilistic weather prediction

    International Nuclear Information System (INIS)

    Nohara, Daisuke; Tamura, Hidetoshi; Hirakuchi, Hiromaru

    2015-01-01

    A regional ensemble prediction method has been developed to provide probabilistic weather prediction using a numerical weather prediction model. To obtain consistent perturbations with the synoptic weather pattern, both of initial and lateral boundary perturbations were given by differences between control and ensemble member of the Japan Meteorological Agency (JMA)'s operational one-week ensemble forecast. The method provides a multiple ensemble member with a horizontal resolution of 15 km for 48-hour based on a downscaling of the JMA's operational global forecast accompanied with the perturbations. The ensemble prediction was examined in the case of heavy snow fall event in Kanto area on January 14, 2013. The results showed that the predictions represent different features of high-resolution spatiotemporal distribution of precipitation affected by intensity and location of extra-tropical cyclone in each ensemble member. Although the ensemble prediction has model bias of mean values and variances in some variables such as wind speed and solar radiation, the ensemble prediction has a potential to append a probabilistic information to a deterministic prediction. (author)

  19. Regional Disease Vector Ecology Profile Central Europe

    Science.gov (United States)

    2001-04-01

    burrows, poultry houses, masonry cracks, rock crevices, leaf litter, or moist tree holes. Eggs hatch in one to two weeks, and larvae develop in warm, moist...contact with livestock and exposure to locally butchered animals should be avoided. An inactivated mouse-brain vaccine against CCHF has been used in...pests before they are occupied. Cimex lectularius is common in poultry houses in many parts of the region and should be avoided by military personnel

  20. Advancing Environmental Prediction Capabilities for the Polar Regions and Beyond during The Year of Polar Prediction

    Science.gov (United States)

    Werner, Kirstin; Goessling, Helge; Hoke, Winfried; Kirchhoff, Katharina; Jung, Thomas

    2017-04-01

    Environmental changes in polar regions open up new opportunities for economic and societal operations such as vessel traffic related to scientific, fishery and tourism activities, and in the case of the Arctic also enhanced resource development. The availability of current and accurate weather and environmental information and forecasts will therefore play an increasingly important role in aiding risk reduction and safety management around the poles. The Year of Polar Prediction (YOPP) has been established by the World Meteorological Organization's World Weather Research Programme as the key activity of the ten-year Polar Prediction Project (PPP; see more on www.polarprediction.net). YOPP is an internationally coordinated initiative to significantly advance our environmental prediction capabilities for the polar regions and beyond, supporting improved weather and climate services. Scheduled to take place from mid-2017 to mid-2019, the YOPP core phase covers an extended period of intensive observing, modelling, prediction, verification, user-engagement and education activities in the Arctic and Antarctic, on a wide range of time scales from hours to seasons. The Year of Polar Prediction will entail periods of enhanced observational and modelling campaigns in both polar regions. With the purpose to close the gaps in the conventional polar observing systems in regions where the observation network is sparse, routine observations will be enhanced during Special Observing Periods for an extended period of time (several weeks) during YOPP. This will allow carrying out subsequent forecasting system experiments aimed at optimizing observing systems in the polar regions and providing insight into the impact of better polar observations on forecast skills in lower latitudes. With various activities and the involvement of a wide range of stakeholders, YOPP will contribute to the knowledge base needed to managing the opportunities and risks that come with polar climate change.

  1. Expected packing density allows prediction of both amyloidogenic and disordered regions in protein chains

    Energy Technology Data Exchange (ETDEWEB)

    Galzitskaya, Oxana V; Garbuzynskiy, Sergiy O; Lobanov, Michail Yu [Institute of Protein Research, Russian Academy of Sciences, 142290, Pushchino, Moscow Region (Russian Federation)

    2007-07-18

    The determination of factors that influence conformational changes in proteins is very important for the identification of potentially amyloidogenic and disordered regions in polypeptide chains. In our work we introduce a new parameter, mean packing density, to detect both amyloidogenic and disordered regions in a protein sequence. It has been shown that regions with strong expected packing density are responsible for amyloid formation. Our predictions are consistent with known disease-related amyloidogenic regions for 9 of 12 amyloid-forming proteins and peptides in which the positions of amyloidogenic regions have been revealed experimentally. Our findings support the concept that the mechanism of formation of amyloid fibrils is similar for different peptides and proteins. Moreover, we have demonstrated that regions with weak expected packing density are responsible for the appearance of disordered regions. Our method has been tested on datasets of globular proteins and long disordered protein segments, and it shows improved performance over other widely used methods. Thus, we demonstrate that the expected packing density is a useful value for predicting both disordered and amyloidogenic regions of a protein based on sequence alone. Our results are important for understanding the structural characteristics of protein folding and misfolding.

  2. Accurate Prediction of Coronary Artery Disease Using Bioinformatics Algorithms

    Directory of Open Access Journals (Sweden)

    Hajar Shafiee

    2016-06-01

    Full Text Available Background and Objectives: Cardiovascular disease is one of the main causes of death in developed and Third World countries. According to the statement of the World Health Organization, it is predicted that death due to heart disease will rise to 23 million by 2030. According to the latest statistics reported by Iran’s Minister of health, 3.39% of all deaths are attributed to cardiovascular diseases and 19.5% are related to myocardial infarction. The aim of this study was to predict coronary artery disease using data mining algorithms. Methods: In this study, various bioinformatics algorithms, such as decision trees, neural networks, support vector machines, clustering, etc., were used to predict coronary heart disease. The data used in this study was taken from several valid databases (including 14 data. Results: In this research, data mining techniques can be effectively used to diagnose different diseases, including coronary artery disease. Also, for the first time, a prediction system based on support vector machine with the best possible accuracy was introduced. Conclusion: The results showed that among the features, thallium scan variable is the most important feature in the diagnosis of heart disease. Designation of machine prediction models, such as support vector machine learning algorithm can differentiate between sick and healthy individuals with 100% accuracy.

  3. Neuropsychiatric symptoms predict hypometabolism in preclinical Alzheimer disease.

    Science.gov (United States)

    Ng, Kok Pin; Pascoal, Tharick A; Mathotaarachchi, Sulantha; Chung, Chang-Oh; Benedet, Andréa L; Shin, Monica; Kang, Min Su; Li, Xiaofeng; Ba, Maowen; Kandiah, Nagaendran; Rosa-Neto, Pedro; Gauthier, Serge

    2017-05-09

    To identify regional brain metabolic dysfunctions associated with neuropsychiatric symptoms (NPS) in preclinical Alzheimer disease (AD). We stratified 115 cognitively normal individuals into preclinical AD (both amyloid and tau pathologies present), asymptomatic at risk for AD (either amyloid or tau pathology present), or healthy controls (no amyloid or tau pathology present) using [ 18 F]florbetapir PET and CSF phosphorylated tau biomarkers. Regression and voxel-based regression models evaluated the relationships between baseline NPS measured by the Neuropsychiatric Inventory (NPI) and baseline and 2-year change in metabolism measured by [ 18 F]fluorodeoxyglucose (FDG) PET. Individuals with preclinical AD with higher NPI scores had higher [ 18 F]FDG uptake in the posterior cingulate cortex (PCC), ventromedial prefrontal cortex, and right anterior insula at baseline. High NPI scores predicted subsequent hypometabolism in the PCC over 2 years only in individuals with preclinical AD. Sleep/nighttime behavior disorders and irritability and lability were the components of the NPI that drove this metabolic dysfunction. The magnitude of NPS in preclinical cases, driven by sleep behavior and irritability domains, is linked to transitory metabolic dysfunctions within limbic networks vulnerable to the AD process and predicts subsequent PCC hypometabolism. These findings support an emerging conceptual framework in which NPS constitute an early clinical manifestation of AD pathophysiology. Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

  4. Change in avian abundance predicted from regional forest inventory data

    Science.gov (United States)

    Twedt, Daniel J.; Tirpak, John M.; Jones-Farrand, D. Todd; Thompson, Frank R.; Uihlein, William B.; Fitzgerald, Jane A.

    2010-01-01

    An inability to predict population response to future habitat projections is a shortcoming in bird conservation planning. We sought to predict avian response to projections of future forest conditions that were developed from nationwide forest surveys within the Forest Inventory and Analysis (FIA) program. To accomplish this, we evaluated the historical relationship between silvicolous bird populations and FIA-derived forest conditions within 25 ecoregions that comprise the southeastern United States. We aggregated forest area by forest ownership, forest type, and tree size-class categories in county-based ecoregions for 5 time periods spanning 1963-2008. We assessed the relationship of forest data with contemporaneous indices of abundance for 24 silvicolous bird species that were obtained from Breeding Bird Surveys. Relationships between bird abundance and forest inventory data for 18 species were deemed sufficient as predictive models. We used these empirically derived relationships between regional forest conditions and bird populations to predict relative changes in abundance of these species within ecoregions that are anticipated to coincide with projected changes in forest variables through 2040. Predicted abundances of these 18 species are expected to remain relatively stable in over a quarter (27%) of the ecoregions. However, change in forest area and redistribution of forest types will likely result in changed abundance of some species within many ecosystems. For example, abundances of 11 species, including pine warbler (Dendroica pinus), brown-headed nuthatch (Sitta pusilla), and chuckwills- widow (Caprimulgus carolinensis), are projected to increase within more ecoregions than ecoregions where they will decrease. For 6 other species, such as blue-winged warbler (Vermivora pinus), Carolina wren (Thryothorus ludovicianus), and indigo bunting (Passerina cyanea), we projected abundances will decrease within more ecoregions than ecoregions where they will

  5. Improved apparatus for predictive diagnosis of rotator cuff disease

    Science.gov (United States)

    Pillai, Anup; Hall, Brittany N.; Thigpen, Charles A.; Kwartowitz, David M.

    2014-03-01

    Rotator cuff disease impacts over 50% of the population over 60, with reports of incidence being as high as 90% within this population, causing pain and possible loss of function. The rotator cuff is composed of muscles and tendons that work in tandem to support the shoulder. Heavy use of these muscles can lead to rotator cuff tear, with the most common causes is age-related degeneration or sport injuries, both being a function of overuse. Tears ranges in severity from partial thickness tear to total rupture. Diagnostic techniques are based on physical assessment, detailed patient history, and medical imaging; primarily X-ray, MRI and ultrasonography are the chosen modalities for assessment. The final treatment technique and imaging modality; however, is chosen by the clinician is at their discretion. Ultrasound has been shown to have good accuracy for identification and measurement of full-thickness and partial-thickness rotator cuff tears. In this study, we report on the progress and improvement of our method of transduction and analysis of in situ measurement of rotator cuff biomechanics. We have improved the ability of the clinician to apply a uniform force to the underlying musculotendentious tissues while simultaneously obtaining the ultrasound image. This measurement protocol combined with region of interest (ROI) based image processing will help in developing a predictive diagnostic model for treatment of rotator cuff disease and help the clinicians choose the best treatment technique.

  6. Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

    KAUST Repository

    Hou, Siqing

    2018-05-21

    In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix completion (NIMC), in disease-gene prediction. Comparing to the state-of-the-art inductive matrix completion method, using neural networks allows us to learn latent features from non-linear functions of input features. Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help increase the performance of predicting disease-gene associations. We compare the proposed method with other state-of-the-art methods for pre- dicting associated genes for diseases from the Online Mendelian Inheritance in Man (OMIM) database. Results show that both new features and the proposed NIMC model can improve the chance of recovering an unknown associated gene in the top 100 predicted genes. Best results are obtained by using both the new features and the new model. Results also show the proposed method does better in predicting associated genes for newly discovered diseases.

  7. Predicting redox conditions in groundwater at a regional scale

    Science.gov (United States)

    Tesoriero, Anthony J.; Terziotti, Silvia; Abrams, Daniel B.

    2015-01-01

    Defining the oxic-suboxic interface is often critical for determining pathways for nitrate transport in groundwater and to streams at the local scale. Defining this interface on a regional scale is complicated by the spatial variability of reaction rates. The probability of oxic groundwater in the Chesapeake Bay watershed was predicted by relating dissolved O2 concentrations in groundwater samples to indicators of residence time and/or electron donor availability using logistic regression. Variables that describe surficial geology, position in the flow system, and soil drainage were important predictors of oxic water. The probability of encountering oxic groundwater at a 30 m depth and the depth to the bottom of the oxic layer were predicted for the Chesapeake Bay watershed. The influence of depth to the bottom of the oxic layer on stream nitrate concentrations and time lags (i.e., time period between land application of nitrogen and its effect on streams) are illustrated using model simulations for hypothetical basins. Regional maps of the probability of oxic groundwater should prove useful as indicators of groundwater susceptibility and stream susceptibility to contaminant sources derived from groundwater.

  8. The UKC2 regional coupled environmental prediction system

    Science.gov (United States)

    Lewis, Huw W.; Castillo Sanchez, Juan Manuel; Graham, Jennifer; Saulter, Andrew; Bornemann, Jorge; Arnold, Alex; Fallmann, Joachim; Harris, Chris; Pearson, David; Ramsdale, Steven; Martínez-de la Torre, Alberto; Bricheno, Lucy; Blyth, Eleanor; Bell, Victoria A.; Davies, Helen; Marthews, Toby R.; O'Neill, Clare; Rumbold, Heather; O'Dea, Enda; Brereton, Ashley; Guihou, Karen; Hines, Adrian; Butenschon, Momme; Dadson, Simon J.; Palmer, Tamzin; Holt, Jason; Reynard, Nick; Best, Martin; Edwards, John; Siddorn, John

    2018-01-01

    It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere-land-ocean-wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean waves (WAVEWATCH III). These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period highlight the potential

  9. The UKC2 regional coupled environmental prediction system

    Directory of Open Access Journals (Sweden)

    H. W. Lewis

    2018-01-01

    Full Text Available It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere–land–ocean–wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model, land surface with river routing (JULES, shelf-sea ocean (NEMO and ocean waves (WAVEWATCH III. These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period

  10. Poisson Mixture Regression Models for Heart Disease Prediction

    Science.gov (United States)

    Erol, Hamza

    2016-01-01

    Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

  11. CT in diagnosis of thoracolumbar region diseases

    International Nuclear Information System (INIS)

    Dimitrov, I.; Karadjova, M.

    2003-01-01

    The lumbalgia caused by affected thoracolumbar transition (Th 11 -L 2 ) imitates the clinical symptomatic of disc lesions in the lower lumbar segments. The syndrome is presented by a pain projected in the area of the three branchings of the spinal nerves, coming from thoracolumbar segments. The aim of this study is to determine the pathological processes, causing the clinical symptoms of this syndrome, using computer tomography. 51 patients are studied with clinically proved thoracolumbar transition syndrome: 14 men and 37 women. CT slices of 96 vertebral segments are made. Two patient are scanned at Th 11 -Th 12 and L 1 -L 2 . Only Th 12 -L 1 scans are made on 10 patients and 42 are made on two neighbouring segments (41 of them on Th 11 -Th 12 and Th 12 -L 1 and one on Th 11 -L 1 and L 1 -L 2 ). An asymmetry (facet tropism) has been found at 59 levels, 21 if them are with spondiloarthrosis. Spondiloarthrosis has been found in 24 segments - 21 of them with osteochondrosis, one with disc prolapse, and 2 with disc protrusion. It is also found osteoporotic changes osteolysis in multiple myeloma, metastasis etc. During the 3 level examination no evidence for either of the mentioned changes is obtained. The CT slices of two neighbouring segments showed an unexpected change from thoracic to lumbar type of the intervertebral joints in 34 patients. The results from this study support the hypothesis about joints origin of the clinical symptoms of the thoracolumbar transition and demonstrate the importance of the computer tomography as a diagnostic method in this disease

  12. Network-based prediction and knowledge mining of disease genes.

    Science.gov (United States)

    Carson, Matthew B; Lu, Hui

    2015-01-01

    In recent years, high-throughput protein interaction identification methods have generated a large amount of data. When combined with the results from other in vivo and in vitro experiments, a complex set of relationships between biological molecules emerges. The growing popularity of network analysis and data mining has allowed researchers to recognize indirect connections between these molecules. Due to the interdependent nature of network entities, evaluating proteins in this context can reveal relationships that may not otherwise be evident. We examined the human protein interaction network as it relates to human illness using the Disease Ontology. After calculating several topological metrics, we trained an alternating decision tree (ADTree) classifier to identify disease-associated proteins. Using a bootstrapping method, we created a tree to highlight conserved characteristics shared by many of these proteins. Subsequently, we reviewed a set of non-disease-associated proteins that were misclassified by the algorithm with high confidence and searched for evidence of a disease relationship. Our classifier was able to predict disease-related genes with 79% area under the receiver operating characteristic (ROC) curve (AUC), which indicates the tradeoff between sensitivity and specificity and is a good predictor of how a classifier will perform on future data sets. We found that a combination of several network characteristics including degree centrality, disease neighbor ratio, eccentricity, and neighborhood connectivity help to distinguish between disease- and non-disease-related proteins. Furthermore, the ADTree allowed us to understand which combinations of strongly predictive attributes contributed most to protein-disease classification. In our post-processing evaluation, we found several examples of potential novel disease-related proteins and corresponding literature evidence. In addition, we showed that first- and second-order neighbors in the PPI network

  13. Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators.

    Directory of Open Access Journals (Sweden)

    Linda Valeri

    Full Text Available The recent Ebola virus disease (EVD outbreak in West Africa has spread wider than any previous human EVD epidemic. While individual-level risk factors that contribute to the spread of EVD have been studied, the population-level attributes of subnational regions associated with outbreak severity have not yet been considered.To investigate the area-level predictors of EVD dynamics, we integrated time series data on cumulative reported cases of EVD from the World Health Organization and covariate data from the Demographic and Health Surveys. We first estimated the early growth rates of epidemics in each second-level administrative district (ADM2 in Guinea, Sierra Leone and Liberia using exponential, logistic and polynomial growth models. We then evaluated how these growth rates, as well as epidemic size within ADM2s, were ecologically associated with several demographic and socio-economic characteristics of the ADM2, using bivariate correlations and multivariable regression models.The polynomial growth model appeared to best fit the ADM2 epidemic curves, displaying the lowest residual standard error. Each outcome was associated with various regional characteristics in bivariate models, however in stepwise multivariable models only mean education levels were consistently associated with a worse local epidemic.By combining two common methods-estimation of epidemic parameters using mathematical models, and estimation of associations using ecological regression models-we identified some factors predicting rapid and severe EVD epidemics in West African subnational regions. While care should be taken interpreting such results as anything more than correlational, we suggest that our approach of using data sources that were publicly available in advance of the epidemic or in real-time provides an analytic framework that may assist countries in understanding the dynamics of future outbreaks as they occur.

  14. Disease-associated mutations disrupt functionally important regions of intrinsic protein disorder.

    Directory of Open Access Journals (Sweden)

    Vladimir Vacic

    Full Text Available The effects of disease mutations on protein structure and function have been extensively investigated, and many predictors of the functional impact of single amino acid substitutions are publicly available. The majority of these predictors are based on protein structure and evolutionary conservation, following the assumption that disease mutations predominantly affect folded and conserved protein regions. However, the prevalence of the intrinsically disordered proteins (IDPs and regions (IDRs in the human proteome together with their lack of fixed structure and low sequence conservation raise a question about the impact of disease mutations in IDRs. Here, we investigate annotated missense disease mutations and show that 21.7% of them are located within such intrinsically disordered regions. We further demonstrate that 20% of disease mutations in IDRs cause local disorder-to-order transitions, which represents a 1.7-2.7 fold increase compared to annotated polymorphisms and neutral evolutionary substitutions, respectively. Secondary structure predictions show elevated rates of transition from helices and strands into loops and vice versa in the disease mutations dataset. Disease disorder-to-order mutations also influence predicted molecular recognition features (MoRFs more often than the control mutations. The repertoire of disorder-to-order transition mutations is limited, with five most frequent mutations (R→W, R→C, E→K, R→H, R→Q collectively accounting for 44% of all deleterious disorder-to-order transitions. As a proof of concept, we performed accelerated molecular dynamics simulations on a deleterious disorder-to-order transition mutation of tumor protein p63 and, in agreement with our predictions, observed an increased α-helical propensity of the region harboring the mutation. Our findings highlight the importance of mutations in IDRs and refine the traditional structure-centric view of disease mutations. The results of this study

  15. 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%.

  16. Inductive matrix completion for predicting gene-disease associations.

    Science.gov (United States)

    Natarajan, Nagarajan; Dhillon, Inderjit S

    2014-06-15

    Most existing methods for predicting causal disease genes rely on specific type of evidence, and are therefore limited in terms of applicability. More often than not, the type of evidence available for diseases varies-for example, we may know linked genes, keywords associated with the disease obtained by mining text, or co-occurrence of disease symptoms in patients. Similarly, the type of evidence available for genes varies-for example, specific microarray probes convey information only for certain sets of genes. In this article, we apply a novel matrix-completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for diseases and genes to learn latent factors that explain the observed gene-disease associations. We construct features from different biological sources such as microarray expression data and disease-related textual data. A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix-completion approaches and network-based inference methods that are transductive. Comparison with state-of-the-art methods on diseases from the Online Mendelian Inheritance in Man (OMIM) database shows that the proposed approach is substantially better-it has close to one-in-four chance of recovering a true association in the top 100 predictions, compared to the recently proposed Catapult method (second best) that has bigdata.ices.utexas.edu/project/gene-disease. © The Author 2014. Published by Oxford University Press.

  17. Limitations of regional myocardial thallium clearance for identification of disease in individual coronary arteries

    International Nuclear Information System (INIS)

    Becker, L.C.; Rogers, W.J. Jr.; Links, J.M.; Corn, C.

    1989-01-01

    The purpose of this study was to critically evaluate the usefulness of postexercise regional myocardial thallium-201 clearance for identifying disease in individual coronary arteries. Exercise and redistribution planar imaging studies were performed in 114 subjects, including 19 normal volunteers and 95 patients undergoing cardiac catheterization (70 with and 25 without greater than or equal to 50% narrowing in one or more coronary arteries). Thallium clearance was measured from predefined myocardial regions corresponding to the left anterior descending, left circumflex and right coronary arteries and was expressed as the percent decrease in activity at 4 h, assuming monoexponential clearance. In regions perfused by a normal or insignificantly diseased coronary artery, mean 4 h clearance was 58.9 +/- 9.4% for normal volunteers, 43.1 +/- 15.5% for catheterized patients without coronary artery disease and 36.3 +/- 24.9% for catheterized patients with coronary artery disease (p less than 0.001 patients with coronary artery disease versus normal volunteers). Clearance from normal regions was significantly associated with two measures of exercise performance: percent of predicted maximal heart rate achieved (r = 0.49) and exercise duration (r = 0.35). In regions perfused by a stenotic coronary artery, mean clearance was lower (31.1 +/- 19.8%) but was not significantly different from that in normal regions in the same patients. Clearance from diseased regions was also associated with maximal exercise heart rate (r = 0.28) and exercise duration (r = 0.41), but not with percent coronary artery stenosis (r = 0.02). After taking exercise performance into account, the number of diseased vessels or the presence or absence of disease in a given vessel had little influence on regional thallium clearance

  18. Regional cerebral blood flow in SPECT pattern in Parkinson's disease

    International Nuclear Information System (INIS)

    Lenart-Jankowska, D.; Junik, R.; Sowinski, J.; Gembicki, M.; Wender, M.

    1997-01-01

    The purpose of our work was to compare the regional cerebral blood flow (rCBF) in SPECT examination in Parkinson's disease with (17 cases) and without (7 cases) dementia and in various clinical stages of the disease. The patients underwent SPECT examination 5-40 min after intravenous application of HMPAO (Ceretec, Amersham) with 740 Mbq (20 mCi) pertechnate 99m Tc. SPECT was performed with a Siemens Diacam single-head rotating gamma camera coupled to a high resolution collimator and Icon computer system provided by the manufacturer. The results were defined in relative values of ROI in relation to cerebellum. Patients with Parkinson's disease showed hypoperfusion in cerebral lobes and in deep cerebral structures including the basal ganglia. Regional perfusion deficit in SPECT was seen with and without associated dementia and already in early stage of the disease. Parkinson's disease is provoked by the lesions of dopaminergic neurons of the central nervous system leading to domination of extrapyramidal symptoms. There are many indications that also the neurotransmitters associated with cognitive functions as acetylcholine demonstrate some abnormalities. However, only in some cases of Parkinson's disease dementia is the dominating symptom. Our results of regional cerebral blood flow testify that in Parkinson's disease the dysfunction of the central nervous system is more diffuse than has previously been suggested. (author)

  19. Nucleus basalis of Meynert degeneration precedes and predicts cognitive impairment in Parkinson's disease.

    Science.gov (United States)

    Schulz, Jonathan; Pagano, Gennaro; Fernández Bonfante, Juan Alberto; Wilson, Heather; Politis, Marios

    2018-05-01

    Currently, no reliable predictors of cognitive impairment in Parkinson's disease exist. We hypothesized that microstructural changes at grey matter T1-weighted MRI and diffusion tensor imaging in the cholinergic system nuclei and associated limbic pathways underlie cognitive impairment in Parkinson's disease. We performed a cross-sectional comparison between patients with Parkinson's disease with and without cognitive impairment. We also performed a longitudinal 36-month follow-up study of cognitively intact Parkinson's disease patients, comparing patients who remained cognitively intact to those who developed cognitive impairment. Patients with Parkinson's disease with cognitive impairment showed lower grey matter volume and increased mean diffusivity in the nucleus basalis of Meynert, compared to patients with Parkinson's disease without cognitive impairment. These results were confirmed both with region of interest and voxel-based analyses, and after partial volume correction. Lower grey matter volume and increased mean diffusivity in the nucleus basalis of Meynert was predictive for developing cognitive impairment in cognitively intact patients with Parkinson's disease, independent of other clinical and non-clinical markers of the disease. Structural and microstructural alterations in entorhinal cortex, amygdala, hippocampus, insula, and thalamus were not predictive for developing cognitive impairment in Parkinson's disease. Our findings provide evidence that degeneration of the nucleus basalis of Meynert precedes and predicts the onset of cognitive impairment, and might be used in a clinical setting as a reliable biomarker to stratify patients at higher risk of cognitive decline.

  20. Plasma proteins predict conversion to dementia from prodromal disease.

    Science.gov (United States)

    Hye, Abdul; Riddoch-Contreras, Joanna; Baird, Alison L; Ashton, Nicholas J; Bazenet, Chantal; Leung, Rufina; Westman, Eric; Simmons, Andrew; Dobson, Richard; Sattlecker, Martina; Lupton, Michelle; Lunnon, Katie; Keohane, Aoife; Ward, Malcolm; Pike, Ian; Zucht, Hans Dieter; Pepin, Danielle; Zheng, Wei; Tunnicliffe, Alan; Richardson, Jill; Gauthier, Serge; Soininen, Hilkka; Kłoszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon

    2014-11-01

    The study aimed to validate previously discovered plasma biomarkers associated with AD, using a design based on imaging measures as surrogate for disease severity and assess their prognostic value in predicting conversion to dementia. Three multicenter cohorts of cognitively healthy elderly, mild cognitive impairment (MCI), and AD participants with standardized clinical assessments and structural neuroimaging measures were used. Twenty-six candidate proteins were quantified in 1148 subjects using multiplex (xMAP) assays. Sixteen proteins correlated with disease severity and cognitive decline. Strongest associations were in the MCI group with a panel of 10 proteins predicting progression to AD (accuracy 87%, sensitivity 85%, and specificity 88%). We have identified 10 plasma proteins strongly associated with disease severity and disease progression. Such markers may be useful for patient selection for clinical trials and assessment of patients with predisease subjective memory complaints. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  1. Review article. Predicting disease onset in clinically healthy people

    Directory of Open Access Journals (Sweden)

    Zeliger . Harold I.

    2016-06-01

    Full Text Available Virtually all human disease is induced by oxidative stress. Oxidative stress, which is caused by toxic environmental exposure, the presence of disease, lifestyle choices, stress, chronic inflammation or combinations of these, is responsible for most disease. Oxidative stress from all sources is additive and it is the total oxidative stress from all sources that induces the onset of most disease. Oxidative stress leads to lipid peroxidation, which in turn produces Malondialdehyde. Serum malondialdehyde level is an additive parameter resulting from all sources of oxidative stress and, therefore, is a reliable indicator of total oxidative stress which can be used to predict the onset of disease in clinically asymptomatic individuals and to suggest the need for treatment that can prevent much human disease.

  2. Quantitative analysis of contrast-enhanced ultrasonography of the bowel wall can predict disease activity in inflammatory bowel disease

    Energy Technology Data Exchange (ETDEWEB)

    Romanini, Laura, E-mail: laura.romanini@libero.it [Department of Radiology, Spedali Civili di Brescia, P.le Spedali Civili, 1, 25123 Brescia (Italy); Passamonti, Matteo, E-mail: matteopassamonti@gmail.com [Department of Radiology-AO Provincia di Lodi, Via Fissiraga, 15, 26900 Lodi (Italy); Navarria, Mario, E-mail: navarria.mario@tiscali.it [Department of Radiology-ASL Vallecamonica-Sebino, Via Manzoni 142, 25040 Esine, BS (Italy); Lanzarotto, Francesco, E-mail: francesco.lanzarotto@spedalicivili.brescia.it [Department of Gastroenterology, Spedali Civili di Brescia, P.le Spedali Civili, 1, 25123 Brescia (Italy); Villanacci, Vincenzo, E-mail: villanac@alice.it [Department of Pathology, Spedali Civili di Brescia, P.le Spedali Civili, 1, 25123 Brescia (Italy); Grazioli, Luigi, E-mail: radiologia1@spedalicivili.brescia.it [Department of Radiology, Spedali Civili di Brescia, P.le Spedali Civili, 1, 25123 Brescia (Italy); Calliada, Fabrizio, E-mail: fabrizio.calliada@gmail.com [Department of Radiology, University of Pavia, Viale Camillo Golgi 19, 27100 Pavia (Italy); Maroldi, Roberto, E-mail: rmaroldi@gmail.com [Department of Radiology, University of Brescia, P.le Spedali Civili, 1, 25123 Brescia (Italy)

    2014-08-15

    Purpose: To evaluate the accuracy of quantitative analysis of bowel wall enhancement in inflammatory bowel disease (IBD) with contrast enhanced ultrasound (CEUS) by comparing the results with vascular density in a biopsy sample from the same area of the intestinal tract, and to determine the usefulness of this analysis for the prediction of disease activity. Materials and methods: This prospective study was approved by our institute's ethics committee and all patients gave written informed consent. We enrolled 33 consecutive adult patients undergoing colonoscopy and biopsy for IBD. All patients underwent CEUS and the results were quantitatively analyzed. Vessel count per high-power field on biopsy specimens was compared with colonoscopy, baseline ultrasonography, and CEUS findings, and with analysis of peak intensity, time to peak, regional blood volume, mean transit time, and regional blood flow. Results in patients with high and low vascular density were compared using Fisher's test, t-test, Pearson's correlation test, and receiver operating characteristic curve (ROC) analysis. Cutoff values were determined using ROC analysis, and sensitivity and specificity were calculated. Results: High vascular density (>265 vessels per field) on histological examination was significantly correlated with active disease on colonoscopy, baseline ultrasonography, and CEUS (p < .0001). Quantitative analysis showed a higher enhancement peak, a shorter time to peak enhancement, a higher regional blood flow and regional blood volume in patients with high vascular density than in those with low vascular density. Cutoff values to distinguish between active and inactive disease were identified for peak enhancement (>40.5%), and regional blood flow (>54.8 ml/min). Conclusion: Quantitative analysis of CEUS data correlates with disease activity as determined by vascular density. Quantitative parameters of CEUS can be used to predict active disease with high sensitivity and

  3. Quantitative analysis of contrast-enhanced ultrasonography of the bowel wall can predict disease activity in inflammatory bowel disease

    International Nuclear Information System (INIS)

    Romanini, Laura; Passamonti, Matteo; Navarria, Mario; Lanzarotto, Francesco; Villanacci, Vincenzo; Grazioli, Luigi; Calliada, Fabrizio; Maroldi, Roberto

    2014-01-01

    Purpose: To evaluate the accuracy of quantitative analysis of bowel wall enhancement in inflammatory bowel disease (IBD) with contrast enhanced ultrasound (CEUS) by comparing the results with vascular density in a biopsy sample from the same area of the intestinal tract, and to determine the usefulness of this analysis for the prediction of disease activity. Materials and methods: This prospective study was approved by our institute's ethics committee and all patients gave written informed consent. We enrolled 33 consecutive adult patients undergoing colonoscopy and biopsy for IBD. All patients underwent CEUS and the results were quantitatively analyzed. Vessel count per high-power field on biopsy specimens was compared with colonoscopy, baseline ultrasonography, and CEUS findings, and with analysis of peak intensity, time to peak, regional blood volume, mean transit time, and regional blood flow. Results in patients with high and low vascular density were compared using Fisher's test, t-test, Pearson's correlation test, and receiver operating characteristic curve (ROC) analysis. Cutoff values were determined using ROC analysis, and sensitivity and specificity were calculated. Results: High vascular density (>265 vessels per field) on histological examination was significantly correlated with active disease on colonoscopy, baseline ultrasonography, and CEUS (p < .0001). Quantitative analysis showed a higher enhancement peak, a shorter time to peak enhancement, a higher regional blood flow and regional blood volume in patients with high vascular density than in those with low vascular density. Cutoff values to distinguish between active and inactive disease were identified for peak enhancement (>40.5%), and regional blood flow (>54.8 ml/min). Conclusion: Quantitative analysis of CEUS data correlates with disease activity as determined by vascular density. Quantitative parameters of CEUS can be used to predict active disease with high sensitivity and

  4. Regional hippocampal volumes and development predict learning and memory.

    Science.gov (United States)

    Tamnes, Christian K; Walhovd, Kristine B; Engvig, Andreas; Grydeland, Håkon; Krogsrud, Stine K; Østby, Ylva; Holland, Dominic; Dale, Anders M; Fjell, Anders M

    2014-01-01

    The hippocampus is an anatomically and functionally heterogeneous structure, but longitudinal studies of its regional development are scarce and it is not known whether protracted maturation of the hippocampus in adolescence is related to memory development. First, we investigated hippocampal subfield development using 170 longitudinally acquired brain magnetic resonance imaging scans from 85 participants aged 8-21 years. Hippocampal subfield volumes were estimated by the use of automated segmentation of 7 subfields, including the cornu ammonis (CA) sectors and the dentate gyrus (DG), while longitudinal subfield volumetric change was quantified using a nonlinear registration procedure. Second, associations between subfield volumes and change and verbal learning/memory across multiple retention intervals (5 min, 30 min and 1 week) were tested. It was hypothesized that short and intermediate memory would be more closely related to CA2-3/CA4-DG and extended, remote memory to CA1. Change rates were significantly different across hippocampal subfields, but nearly all subfields showed significant volume decreases over time throughout adolescence. Several subfield volumes were larger in the right hemisphere and in males, while for change rates there were no hemisphere or sex differences. Partly in support of the hypotheses, greater volume of CA1 and CA2-3 was related to recall and retention after an extended delay, while longitudinal reduction of CA2-3 and CA4-DG was related to learning. This suggests continued regional development of the hippocampus across adolescence and that volume and volume change in specific subfields differentially predict verbal learning and memory over different retention intervals, but future high-resolution studies are called for. © 2014 S. Karger AG, Basel.

  5. Using Earth Observations to Understand and Predict Infectious Diseases

    Science.gov (United States)

    Soebiyanto, Radina P.; Kiang, Richard

    2015-01-01

    This presentation discusses the processes from data collection and processing to analysis involved in unraveling patterns between disease outbreaks and the surrounding environment and meteorological conditions. We used these patterns to estimate when and where disease outbreaks will occur. As a case study, we will present our work on assessing the relationship between meteorological conditions and influenza in Central America. Our work represents the discovery, prescriptive and predictive aspects of data analytics.

  6. HAMDA: Hybrid Approach for MiRNA-Disease Association prediction.

    Science.gov (United States)

    Chen, Xing; Niu, Ya-Wei; Wang, Guang-Hui; Yan, Gui-Ying

    2017-12-01

    For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA also achieved good performance with AUC of 0.8965 ± 0.0012 in 5-fold cross validation. Additionally, we conducted case studies about three important human cancers for performance evaluation of HAMDA. As a result, 90% (Lymphoma), 86% (Prostate Cancer) and 92% (Kidney Cancer) of top 50 predicted miRNAs were confirmed by recent experiment literature, which showed the reliable prediction ability of HAMDA. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Predictive factors of thyroid cancer in patients with Graves' disease.

    Science.gov (United States)

    Ren, Meng; Wu, Mu Chao; Shang, Chang Zhen; Wang, Xiao Yi; Zhang, Jing Lu; Cheng, Hua; Xu, Ming Tong; Yan, Li

    2014-01-01

    The best preoperative examination in Graves' disease with thyroid cancer still remains uncertain. The objectives of the present study were to investigate the prevalence of thyroid cancer in Graves' disease patients, and to identify the predictive factors and ultrasonographic features of thyroid cancer that may aid the preoperative diagnosis in Graves' disease. This retrospective study included 423 patients with Graves' disease who underwent surgical treatment from 2002 to 2012 at our institution. The clinical features and ultrasonographic findings of thyroid nodules were recorded. The diagnosis of thyroid cancer was determined according to the pathological results. Thyroid cancer was discovered in 58 of the 423 (13.7 %) surgically treated Graves' disease patients; 46 of those 58 patients had thyroid nodules, and the other 12 patients were diagnosed with incidentally discovered thyroid carcinomas without thyroid nodules. Among the 58 patients with thyroid cancer, papillary microcarcinomas were discovered in 50 patients, and multifocality and lymph node involvement were detected in the other 8 patients. Multivariate regression analysis showed younger age was the only significant factor predictive of metastatic thyroid cancer. Ultrasonographic findings of calcification and intranodular blood flow in thyroid nodules indicate that they are more likely to harbor thyroid cancers. Because the influencing factor of metastatic thyroid cancers in Graves' disease is young age, every suspicious nodule in Graves' disease patients should be evaluated and treated carefully, especially in younger patients because of the potential for metastasis.

  8. Predicting the effect of prevention of ischaemic heart disease

    DEFF Research Database (Denmark)

    Brønnum-Hansen, Henrik

    2002-01-01

    Priority setting in public health policy must be based on information on the effectiveness of alternative preventive and therapeutic interventions. The purpose of this study is to predict the effect on mortality from ischaemic heart disease (IHD) in Denmark of reduced exposure to the risk factors...... hypertension, hypercholesterolaemia, cigarette smoking, and physical inactivity....

  9. Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data.

    Directory of Open Access Journals (Sweden)

    Anna Gerasimova

    Full Text Available Genome-wide association studies (GWASs identify single nucleotide polymorphisms (SNPs that are enriched in individuals suffering from a given disease. Most disease-associated SNPs fall into non-coding regions, so that it is not straightforward to infer phenotype or function; moreover, many SNPs are in tight genetic linkage, so that a SNP identified as associated with a particular disease may not itself be causal, but rather signify the presence of a linked SNP that is functionally relevant to disease pathogenesis. Here, we present an analysis method that takes advantage of the recent rapid accumulation of epigenomics data to address these problems for some SNPs. Using asthma as a prototypic example; we show that non-coding disease-associated SNPs are enriched in genomic regions that function as regulators of transcription, such as enhancers and promoters. Identifying enhancers based on the presence of the histone modification marks such as H3K4me1 in different cell types, we show that the location of enhancers is highly cell-type specific. We use these findings to predict which SNPs are likely to be directly contributing to disease based on their presence in regulatory regions, and in which cell types their effect is expected to be detectable. Moreover, we can also predict which cell types contribute to a disease based on overlap of the disease-associated SNPs with the locations of enhancers present in a given cell type. Finally, we suggest that it will be possible to re-analyze GWAS studies with much higher power by limiting the SNPs considered to those in coding or regulatory regions of cell types relevant to a given disease.

  10. The Potential of Tropospheric Gradients for Regional Precipitation Prediction

    Science.gov (United States)

    Boisits, Janina; Möller, Gregor; Wittmann, Christoph; Weber, Robert

    2017-04-01

    Changes of temperature and humidity in the neutral atmosphere cause variations in tropospheric path delays and tropospheric gradients. By estimating zenith wet delays (ZWD) and gradients using a GNSS reference station network the obtained time series provide information about spatial and temporal variations of water vapour in the atmosphere. Thus, GNSS-based tropospheric parameters can contribute to the forecast of regional precipitation events. In a recently finalized master thesis at TU Wien the potential of tropospheric gradients for weather prediction was investigated. Therefore, ZWD and gradient time series at selected GNSS reference stations were compared to precipitation data over a period of six months (April to September 2014). The selected GNSS stations form two test areas within Austria. All required meteorological data was provided by the Central Institution for Meteorology and Geodynamics (ZAMG). Two characteristics in ZWD and gradient time series can be anticipated in case of an approaching weather front. First, an induced asymmetry in tropospheric delays results in both, an increased magnitude of the gradient and in gradients pointing towards the weather front. Second, an increase in ZWD reflects the increased water vapour concentration right before a precipitation event. To investigate these characteristics exemplary test events were processed. On the one hand, the sequence of the anticipated increase in ZWD at each GNSS station obtained by cross correlation of the time series indicates the direction of the approaching weather front. On the other hand, the corresponding peak in gradient time series allows the deduction of the direction of movement as well. To verify the results precipitation data from ZAMG was used. It can be deduced, that tropospheric gradients show high potential for predicting precipitation events. While ZWD time series rather indicate the orientation of the air mass boundary, gradients rather indicate the direction of movement

  11. Field guide to diseases & insects of the Rocky Mountain Region

    Science.gov (United States)

    Forest Health Protection. Rocky Mountain Region

    2010-01-01

    This field guide is a forest management tool for field identification of biotic and abiotic agents that damage native trees in Colorado, Kansas, Nebraska, South Dakota, and Wyoming, which constitute the USDA Forest Service's Rocky Mountain Region. The guide focuses only on tree diseases and forest insects that have significant economic, ecological, and/ or...

  12. 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

  13. Intergenic disease-associated regions are abundant in novel transcripts.

    Science.gov (United States)

    Bartonicek, N; Clark, M B; Quek, X C; Torpy, J R; Pritchard, A L; Maag, J L V; Gloss, B S; Crawford, J; Taft, R J; Hayward, N K; Montgomery, G W; Mattick, J S; Mercer, T R; Dinger, M E

    2017-12-28

    Genotyping of large populations through genome-wide association studies (GWAS) has successfully identified many genomic variants associated with traits or disease risk. Unexpectedly, a large proportion of GWAS single nucleotide polymorphisms (SNPs) and associated haplotype blocks are in intronic and intergenic regions, hindering their functional evaluation. While some of these risk-susceptibility regions encompass cis-regulatory sites, their transcriptional potential has never been systematically explored. To detect rare tissue-specific expression, we employed the transcript-enrichment method CaptureSeq on 21 human tissues to identify 1775 multi-exonic transcripts from 561 intronic and intergenic haploblocks associated with 392 traits and diseases, covering 73.9 Mb (2.2%) of the human genome. We show that a large proportion (85%) of disease-associated haploblocks express novel multi-exonic non-coding transcripts that are tissue-specific and enriched for GWAS SNPs as well as epigenetic markers of active transcription and enhancer activity. Similarly, we captured transcriptomes from 13 melanomas, targeting nine melanoma-associated haploblocks, and characterized 31 novel melanoma-specific transcripts that include fusion proteins, novel exons and non-coding RNAs, one-third of which showed allelically imbalanced expression. This resource of previously unreported transcripts in disease-associated regions ( http://gwas-captureseq.dingerlab.org ) should provide an important starting point for the translational community in search of novel biomarkers, disease mechanisms, and drug targets.

  14. Can we Predict Disease Course with Clinical Factors?

    Science.gov (United States)

    Vegh, Zsuzsanna; Kurti, Zsuzsanna; Golovics, Petra A; Lakatos, Peter L

    2018-01-01

    The disease phenotype at diagnosis and the disease course of Crohn's disease (CD) and ulcerative colitis (UC) show remarkable heterogeneity across patients. This review aims to summarize the currently available evidence on clinical and some environmental predictive factors, which clinicians should evaluate in the everyday practice together with other laboratory and imaging data to prevent disease progression, enable a more personalized therapy, and avoid negative disease outcomes. In recent population-based epidemiological and referral cohort studies, the evolution of disease phenotype of CD and UC varied significantly. Most CD and severe UC patients still require hospitalization or surgery/colectomy during follow-up. A change in the natural history of inflammatory bowel diseases (IBD) with improved outcomes in parallel with tailored positioning of aggressive immunomodulator and biological therapy has been suspected. According to the currently available literature, it is of major importance to refer IBD cases at risk for adverse disease outcomes as early during the disease course as possible. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  15. Hourly Wind Speed Interval Prediction in Arid Regions

    Science.gov (United States)

    Chaouch, M.; Ouarda, T.

    2013-12-01

    The long and extended warm and dry summers, the low rate of rain and humidity are the main factors that explain the increase of electricity consumption in hot arid regions. In such regions, the ventilating and air-conditioning installations, that are typically the most energy-intensive among energy consumption activities, are essential for securing healthy, safe and suitable indoor thermal conditions for building occupants and stored materials. The use of renewable energy resources such as solar and wind represents one of the most relevant solutions to overcome the increase of the electricity demand challenge. In the recent years, wind energy is gaining more importance among the researchers worldwide. Wind energy is intermittent in nature and hence the power system scheduling and dynamic control of wind turbine requires an estimate of wind energy. Accurate forecast of wind speed is a challenging task for the wind energy research field. In fact, due to the large variability of wind speed caused by the unpredictable and dynamic nature of the earth's atmosphere, there are many fluctuations in wind power production. This inherent variability of wind speed is the main cause of the uncertainty observed in wind power generation. Furthermore, producing wind power forecasts might be obtained indirectly by modeling the wind speed series and then transforming the forecasts through a power curve. Wind speed forecasting techniques have received substantial attention recently and several models have been developed. Basically two main approaches have been proposed in the literature: (1) physical models such as Numerical Weather Forecast and (2) statistical models such as Autoregressive integrated moving average (ARIMA) models, Neural Networks. While the initial focus in the literature has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp events has led to an interest in producing probabilistic forecasts. In short term

  16. Context predicts word order processing in Broca's region.

    Science.gov (United States)

    Kristensen, Line Burholt; Engberg-Pedersen, Elisabeth; Wallentin, Mikkel

    2014-12-01

    The function of the left inferior frontal gyrus (L-IFG) is highly disputed. A number of language processing studies have linked the region to the processing of syntactical structure. Still, there is little agreement when it comes to defining why linguistic structures differ in their effects on the L-IFG. In a number of languages, the processing of object-initial sentences affects the L-IFG more than the processing of subject-initial ones, but frequency and distribution differences may act as confounding variables. Syntactically complex structures (like the object-initial construction in Danish) are often less frequent and only viable in certain contexts. With this confound in mind, the L-IFG activation may be sensitive to other variables than a syntax manipulation on its own. The present fMRI study investigates the effect of a pragmatically appropriate context on the processing of subject-initial and object-initial clauses with the IFG as our ROI. We find that Danish object-initial clauses yield a higher BOLD response in L-IFG, but we also find an interaction between appropriateness of context and word order. This interaction overlaps with traditional syntax areas in the IFG. For object-initial clauses, the effect of an appropriate context is bigger than for subject-initial clauses. This result is supported by an acceptability study that shows that, given appropriate contexts, object-initial clauses are considered more appropriate than subject-initial clauses. The increased L-IFG activation for processing object-initial clauses without a supportive context may be interpreted as reflecting either reinterpretation or the recipients' failure to correctly predict word order from contextual cues.

  17. Massive global ozone loss predicted following regional nuclear conflict

    Science.gov (United States)

    Mills, Michael J.; Toon, Owen B.; Turco, Richard P.; Kinnison, Douglas E.; Garcia, Rolando R.

    2008-01-01

    We use a chemistry-climate model and new estimates of smoke produced by fires in contemporary cities to calculate the impact on stratospheric ozone of a regional nuclear war between developing nuclear states involving 100 Hiroshima-size bombs exploded in cities in the northern subtropics. We find column ozone losses in excess of 20% globally, 25–45% at midlatitudes, and 50–70% at northern high latitudes persisting for 5 years, with substantial losses continuing for 5 additional years. Column ozone amounts remain near or <220 Dobson units at all latitudes even after three years, constituting an extratropical “ozone hole.” The resulting increases in UV radiation could impact the biota significantly, including serious consequences for human health. The primary cause for the dramatic and persistent ozone depletion is heating of the stratosphere by smoke, which strongly absorbs solar radiation. The smoke-laden air rises to the upper stratosphere, where removal mechanisms are slow, so that much of the stratosphere is ultimately heated by the localized smoke injections. Higher stratospheric temperatures accelerate catalytic reaction cycles, particularly those of odd-nitrogen, which destroy ozone. In addition, the strong convection created by rising smoke plumes alters the stratospheric circulation, redistributing ozone and the sources of ozone-depleting gases, including N2O and chlorofluorocarbons. The ozone losses predicted here are significantly greater than previous “nuclear winter/UV spring” calculations, which did not adequately represent stratospheric plume rise. Our results point to previously unrecognized mechanisms for stratospheric ozone depletion. PMID:18391218

  18. The prevalence of diabetic foot disease in the Waikato region.

    Science.gov (United States)

    O'Shea, C; McClintock, J; Lawrenson, R

    2017-07-01

    The aim of this study was to establish the prevalence of diabetic foot disease by utilising the retinal eye screening register in the Waikato region of New Zealand. Understanding both the prevalence and the degree of foot disease across the general diabetes population will help to determine what podiatry services are required for people with diabetes. 2192 people aged 15years and over, who attended the Waikato Regional Diabetes Service mobile retinal photo screening service for the six-month period between May and November 2014, consented to a foot screen including testing for sensation and pedal pulses. A digital image was taken of the dorsal and plantar aspect of each foot for review by a registered Podiatrist. Thirteen percent of the study sample was identified as having a high-risk foot including active foot complications. 65% were categorised as low risk and a further 22% at moderate risk of diabetic foot disease. Factors identified as significant included age, type of diabetes, duration of diabetes, and smoking. These factors placed people at greater risk of diabetic foot disease. A significant number of people with diabetes are at risk of diabetic foot disease. This study has highlighted the need for targeted podiatry services to address diabetic foot disease. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Prediction of treatment response and metastatic disease in soft tissue sarcoma

    Science.gov (United States)

    Farhidzadeh, Hamidreza; Zhou, Mu; Goldgof, Dmitry B.; Hall, Lawrence O.; Raghavan, Meera.; Gatenby, Robert A.

    2014-03-01

    Soft tissue sarcomas (STS) are a heterogenous group of malignant tumors comprised of more than 50 histologic subtypes. Based on spatial variations of the tumor, predictions of the development of necrosis in response to therapy as well as eventual progression to metastatic disease are made. Optimization of treatment, as well as management of therapy-related side effects, may be improved using progression information earlier in the course of therapy. Multimodality pre- and post-gadolinium enhanced magnetic resonance images (MRI) were taken before and after treatment for 30 patients. Regional variations in the tumor bed were measured quantitatively. The voxel values from the tumor region were used as features and a fuzzy clustering algorithm was used to segment the tumor into three spatial regions. The regions were given labels of high, intermediate and low based on the average signal intensity of pixels from the post-contrast T1 modality. These spatially distinct regions were viewed as essential meta-features to predict the response of the tumor to therapy based on necrosis (dead tissue in tumor bed) and metastatic disease (spread of tumor to sites other than primary). The best feature was the difference in the number of pixels in the highest intensity regions of tumors before and after treatment. This enabled prediction of patients with metastatic disease and lack of positive treatment response (i.e. less necrosis). The best accuracy, 73.33%, was achieved by a Support Vector Machine in a leave-one-out cross validation on 30 cases predicting necrosis treatment and metastasis.

  20. Periodontal profile classes predict periodontal disease progression and tooth loss.

    Science.gov (United States)

    Morelli, Thiago; Moss, Kevin L; Preisser, John S; Beck, James D; Divaris, Kimon; Wu, Di; Offenbacher, Steven

    2018-02-01

    Current periodontal disease taxonomies have limited utility for predicting disease progression and tooth loss; in fact, tooth loss itself can undermine precise person-level periodontal disease classifications. To overcome this limitation, the current group recently introduced a novel patient stratification system using latent class analyses of clinical parameters, including patterns of missing teeth. This investigation sought to determine the clinical utility of the Periodontal Profile Classes and Tooth Profile Classes (PPC/TPC) taxonomy for risk assessment, specifically for predicting periodontal disease progression and incident tooth loss. The analytic sample comprised 4,682 adult participants of two prospective cohort studies (Dental Atherosclerosis Risk in Communities Study and Piedmont Dental Study) with information on periodontal disease progression and incident tooth loss. The PPC/TPC taxonomy includes seven distinct PPCs (person-level disease pattern and severity) and seven TPCs (tooth-level disease). Logistic regression modeling was used to estimate relative risks (RR) and 95% confidence intervals (CI) for the association of these latent classes with disease progression and incident tooth loss, adjusting for examination center, race, sex, age, diabetes, and smoking. To obtain personalized outcome propensities, risk estimates associated with each participant's PPC and TPC were combined into person-level composite risk scores (Index of Periodontal Risk [IPR]). Individuals in two PPCs (PPC-G: Severe Disease and PPC-D: Tooth Loss) had the highest tooth loss risk (RR = 3.6; 95% CI = 2.6 to 5.0 and RR = 3.8; 95% CI = 2.9 to 5.1, respectively). PPC-G also had the highest risk for periodontitis progression (RR = 5.7; 95% CI = 2.2 to 14.7). Personalized IPR scores were positively associated with both periodontitis progression and tooth loss. These findings, upon additional validation, suggest that the periodontal/tooth profile classes and the derived

  1. Regional PET/CT after water gastric inflation for evaluating loco-regional disease of gastric cancer

    International Nuclear Information System (INIS)

    Lee, Soo Jin; Lee, Won Woo; Yoon, Hai-Jeon; Lee, Ho-Young; Lee, Kyoung Ho; Kim, Young Hoon; Park, Do Joong; Kim, Hyung-Ho; So, Young

    2013-01-01

    Objective: We aimed to improve diagnostic accuracy of 18 F-fluoro-2-deoxyglucose (FDG) PET/CT for gastric cancer with water gastric inflation. Materials and methods: 44 gastric cancer patients (M:F = 30:14, age ± std = 62.1 ± 14.5y) were enrolled before surgery. Fifty minutes after injection of FDG (0.14 mCi/kg body weight), whole body PET/CT was performed first and then regional PET/CT over gastric area was obtained 80 min post FDG injection after water gastric inflation. Diagnostic accuracies for loco-regional lesions were compared between whole body and regional PET/CT. Results: 48 primary tumors (23 EGC and 25 AGC) and 348 LN stations (61 metastatic and 287 benign) in 44 patients were investigated. Primary tumor sensitivity of whole body PET/CT (50% = 24/48) was significantly improved by regional PET/CT (75% = 36/48, p < 0.005). Sensitivity of whole body PET/CT (24.6% = 15/61) for LN metastasis was also significantly improved by regional PET/CT (36.1% = 22/61, p < 0.01), whereas specificity of whole body PET/CT (99.3% = 285/287) was not compromised by regional PET/CT (98.3% = 282/287, p > 0.05). Higher primary tumor FDG uptake in regional PET/CT indicated shorter progress-free survival (p = 0.0003). Conclusion: Diagnostic accuracy of whole body PET/CT for loco-regional disease of gastric cancer could be significantly improved by regional PET/CT after water gastric inflation and prognosis could be effectively predicted by primary tumor FDG uptake in regional PET/CT

  2. Extended HLA-D region haplotype associated with celiac disease

    International Nuclear Information System (INIS)

    Howell, M.D.; Smith, J.R.; Austin, R.K.; Kelleher, D.; Nepom, G.T.; Volk, B.; Kagnoff, M.F.

    1988-01-01

    Celiac disease has one of the strongest associations with HLA (human leukocyte antigen) class II markers of the known HLA-linked diseases. This association is primarily with the class II serologic specificities HLA-DR3 and -DQw2. The authors previously described a restriction fragment length polymorphism (RFLP) characterized by the presence of a 4.0-kilobase Rsa I fragment derived from an HLA class II β-chain gene, which distinguishes the class II HLA haplotype of celiac disease patients from those of many serologically matched controls. They now report the isolation of this β-chain gene from a bacteriophage genomic library constructed from the DNA of a celiac disease patient. Based on restriction mapping and differential hybridization with class II cDNA and oligonucleotide probes, this gene was identified as one encoding an HLA-DP β-chain. This celiac disease-associated HLA-DP β-chain gene was flanked by HLA-DP α-chain genes and, therefore, was probably in its normal chromosomal location. The HLA-DPα-chain genes of celiac disease patients also were studied by RFLP analysis. Celiac disease is associated with a subset of HLA-DR3, -DQw2 haplotypes characterized by HLA-DP α- and β-chain gene RFLPs. Within the celiac-disease patient population, the joint segregation of these HLA-DP genes with those encoding the serologic specificities HLA-DR3 and -DQw2 indicates: (i) that the class II HLA haplotype associated with celiac disease is extended throughout the entire HLA-D region, and (ii) that celiac-disease susceptibility genes may reside as far centromeric on this haplotype as the HLA-DP subregion

  3. Extended HLA-D region haplotype associated with celiac disease

    Energy Technology Data Exchange (ETDEWEB)

    Howell, M.D.; Smith, J.R.; Austin, R.K.; Kelleher, D.; Nepom, G.T.; Volk, B.; Kagnoff, M.F.

    1988-01-01

    Celiac disease has one of the strongest associations with HLA (human leukocyte antigen) class II markers of the known HLA-linked diseases. This association is primarily with the class II serologic specificities HLA-DR3 and -DQw2. The authors previously described a restriction fragment length polymorphism (RFLP) characterized by the presence of a 4.0-kilobase Rsa I fragment derived from an HLA class II ..beta..-chain gene, which distinguishes the class II HLA haplotype of celiac disease patients from those of many serologically matched controls. They now report the isolation of this ..beta..-chain gene from a bacteriophage genomic library constructed from the DNA of a celiac disease patient. Based on restriction mapping and differential hybridization with class II cDNA and oligonucleotide probes, this gene was identified as one encoding an HLA-DP ..beta..-chain. This celiac disease-associated HLA-DP ..beta..-chain gene was flanked by HLA-DP ..cap alpha..-chain genes and, therefore, was probably in its normal chromosomal location. The HLA-DP..cap alpha..-chain genes of celiac disease patients also were studied by RFLP analysis. Celiac disease is associated with a subset of HLA-DR3, -DQw2 haplotypes characterized by HLA-DP ..cap alpha..- and ..beta..-chain gene RFLPs. Within the celiac-disease patient population, the joint segregation of these HLA-DP genes with those encoding the serologic specificities HLA-DR3 and -DQw2 indicates: (i) that the class II HLA haplotype associated with celiac disease is extended throughout the entire HLA-D region, and (ii) that celiac-disease susceptibility genes may reside as far centromeric on this haplotype as the HLA-DP subregion.

  4. Disease stage, but not sex, predicts depression and psychological distress in Huntington's disease

    DEFF Research Database (Denmark)

    Dale, Maria; Maltby, John; Shimozaki, Steve

    2016-01-01

    OBJECTIVE: Depression and anxiety significantly affect morbidity in Huntington's disease. Mice. models of Huntington's disease have identified sex differences in mood-like behaviours that vary across disease lifespan, but this interaction has not previously been explored in humans with Huntington......'s disease. However, among certain medical populations, evidence of sex differences in mood across various disease stages has been found, reflecting trends among the general population that women tend to experience anxiety and depression 1.5 to 2 times more than men. The current study examined whether...... disease stage and sex, either separately or as an interaction term, predicted anxiety and depression in Huntington's disease. METHODS: A cross-sectional study of REGISTRY data involving 453 Huntington's disease participants from 12 European countries was undertaken using the Hospital Anxiety...

  5. Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes.

    Science.gov (United States)

    Peng, Hui; Lan, Chaowang; Liu, Yuansheng; Liu, Tao; Blumenstein, Michael; Li, Jinyan

    2017-10-03

    Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes.

  6. Psychodynamic theory and counseling in predictive testing for Huntington's disease.

    Science.gov (United States)

    Tassicker, Roslyn J

    2005-04-01

    This paper revisits psychodynamic theory, which can be applied in predictive testing counseling for Huntington's Disease (HD). Psychodynamic theory has developed from the work of Freud and places importance on early parent-child experiences. The nature of these relationships, or attachments are reflected in adult expectations and relationships. Two significant concepts, identification and fear of abandonment, have been developed and expounded by the psychodynamic theorist, Melanie Klein. The processes of identification and fear of abandonment can become evident in predictive testing counseling and are colored by the client's experience of growing up with a parent affected by Huntington's Disease. In reflecting on family-of-origin experiences, clients can also express implied expectations of the future, and future relationships. Case examples are given to illustrate the dynamic processes of identification and fear of abandonment which may present in the clinical setting. Counselor recognition of these processes can illuminate and inform counseling practice.

  7. CSF neurofilament light chain and phosphorylated tau 181 predict disease progression in PSP.

    Science.gov (United States)

    Rojas, Julio C; Bang, Jee; Lobach, Iryna V; Tsai, Richard M; Rabinovici, Gil D; Miller, Bruce L; Boxer, Adam L

    2018-01-23

    To determine the ability of CSF biomarkers to predict disease progression in progressive supranuclear palsy (PSP). We compared the ability of baseline CSF β-amyloid 1-42 , tau, phosphorylated tau 181 (p-tau), and neurofilament light chain (NfL) concentrations, measured by INNO-BIA AlzBio3 or ELISA, to predict 52-week changes in clinical (PSP Rating Scale [PSPRS] and Schwab and England Activities of Daily Living [SEADL]), neuropsychological, and regional brain volumes on MRI using linear mixed effects models controlled for age, sex, and baseline disease severity, and Fisher F density curves to compare effect sizes in 50 patients with PSP. Similar analyses were done using plasma NfL measured by single molecule arrays in 141 patients. Higher CSF NfL concentration predicted more rapid decline (biomarker × time interaction) over 52 weeks in PSPRS ( p = 0.004, false discovery rate-corrected) and SEADL ( p = 0.008), whereas lower baseline CSF p-tau predicted faster decline on PSPRS ( p = 0.004). Higher CSF tau concentrations predicted faster decline by SEADL ( p = 0.004). The CSF NfL/p-tau ratio was superior for predicting change in PSPRS, compared to p-tau ( p = 0.003) or NfL ( p = 0.001) alone. Higher NfL concentrations in CSF or blood were associated with greater superior cerebellar peduncle atrophy (fixed effect, p ≤ 0.029 and 0.008, respectively). Both CSF p-tau and NfL correlate with disease severity and rate of disease progression in PSP. The inverse correlation of p-tau with disease severity suggests a potentially different mechanism of tau pathology in PSP as compared to Alzheimer disease. Copyright © 2017 American Academy of Neurology.

  8. Forest insect and disease conditions, Vancouver forest region, 1987. Annual publication

    Energy Technology Data Exchange (ETDEWEB)

    Humphreys, N; Ferris, R L

    1988-01-01

    The Forest Insect and Disease Survey (FIDS) is a nation-wide network within Forestry Canada with the responsibility of producing an overview of forest pest conditions and their implications; maintaining records and surveys to support quarantine and facilitate predictions; supporting forestry research with records, insect collections and herbaria; providing advice on forest insect and disease conditions; developing and testing survey techniques; and conducting related biological studies. This report outlines the status of forest pest conditions in the Vancouver Forest Region, and forecasts population trends of some potentially damaging pests. Pests are listed by host in order of importance.

  9. Prediction in ungauged basins: approaches for Canada's cold regions

    National Research Council Canada - National Science Library

    Pietroniro, Alain; Pomeroy, John Willard; Spence, Christopher

    2005-01-01

    In March, 2004, Water Survey of Canada and the Canadian Society for Hydrological Sciences co-hosted a workshop in Yellowknife to discuss how to improve our community's abilities to predict streamflow...

  10. Integrating models to predict regional haze from wildland fire.

    Science.gov (United States)

    D. McKenzie; S.M. O' Neill; N. Larkin; R.A. Norheim

    2006-01-01

    Visibility impairment from regional haze is a significant problem throughout the continental United States. A substantial portion of regional haze is produced by smoke from prescribed and wildland fires. Here we describe the integration of four simulation models, an array of GIS raster layers, and a set of algorithms for fire-danger calculations into a modeling...

  11. Risk Matrix for Prediction of Disease Progression in a Referral Cohort of Patients with Crohn's Disease.

    Science.gov (United States)

    Lakatos, Peter L; Sipeki, Nora; Kovacs, Gyorgy; Palyu, Eszter; Norman, Gary L; Shums, Zakera; Golovics, Petra A; Lovasz, Barbara D; Antal-Szalmas, Peter; Papp, Maria

    2015-10-01

    Early identification of patients with Crohn's disease (CD) at risk of subsequent complications is essential for adapting the treatment strategy. We aimed to develop a prediction model including clinical and serological markers for assessing the probability of developing advanced disease in a prospective referral CD cohort. Two hundred and seventy-one consecutive CD patients (42.4% males, median follow-up 108 months) were included and followed up prospectively. Anti-Saccharomyces cerevisiae antibodies (ASCA IgA/IgG) were determined by enzyme-linked immunosorbent assay. The final analysis was limited to patients with inflammatory disease behaviour at diagnosis. The final definition of advanced disease outcome was having intestinal resection or disease behaviour progression. Antibody (ASCA IgA and/or IgG) status, disease location and need for early azathioprine were included in a 3-, 5- and 7-year prediction matrix. The probability of advanced disease after 5 years varied from 6.2 to 55% depending on the combination of predictors. Similar findings were obtained in Kaplan-Meier analysis; the combination of ASCA, location and early use of azathioprine was associated with the probability of developing advanced disease (p < 0.001, log rank test). Our prediction models identified substantial differences in the probability of developing advanced disease in the early disease course of CD. Markers identified in this referral cohort were different from those previously published in a population-based cohort, suggesting that different prediction models should be used in the referral setting. Copyright © 2015 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  12. Insulin Resistance Predicts Medial Temporal Hypermetabolism in Mild Cognitive Impairment Conversion to Alzheimer Disease

    Science.gov (United States)

    Willette, Auriel A.; Modanlo, Nina

    2015-01-01

    Alzheimer disease (AD) is characterized by progressive hypometabolism on [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) scans. Peripheral insulin resistance (IR) increases AD risk. No studies have examined associations between FDG metabolism and IR in mild cognitive impairment (MCI) and AD, as well as MCI conversion to AD. We studied 26 cognitively normal (CN), 194 MCI (39 MCI-progressors, 148 MCI-stable, 2 years after baseline), and 60 AD subjects with baseline FDG-PET from the Alzheimer’s Disease Neuroimaging Initiative. Mean FDG metabolism was derived for AD-vulnerable regions of interest (ROIs), including lateral parietal and posteromedial cortices, medial temporal lobe (MTL), hippocampus, and ventral prefrontal cortices (vPFC), as well as postcentral gyrus and global cerebrum control regions. The homeostasis model assessment of IR (HOMA-IR) was used to measure IR. For AD, higher HOMA-IR predicted lower FDG in all ROIs. For MCI-progressors, higher HOMA-IR predicted higher FDG in the MTL and hippocampus. Control regions showed no associations. Higher HOMA-IR predicted hypermetabolism in MCI-progressors and hypometabolism in AD in medial temporal regions. Future longitudinal studies should examine the pathophysiologic significance of the shift from MTL hyper- to hypometabolism associated with IR. PMID:25576061

  13. 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.

  14. Increased brain-predicted aging in treated HIV disease.

    Science.gov (United States)

    Cole, James H; Underwood, Jonathan; Caan, Matthan W A; De Francesco, Davide; van Zoest, Rosan A; Leech, Robert; Wit, Ferdinand W N M; Portegies, Peter; Geurtsen, Gert J; Schmand, Ben A; Schim van der Loeff, Maarten F; Franceschi, Claudio; Sabin, Caroline A; Majoie, Charles B L M; Winston, Alan; Reiss, Peter; Sharp, David J

    2017-04-04

    To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters. A large sample of virologically suppressed HIV-positive adults (n = 162, age 45-82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18-90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age - chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out. HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (-0.87 ± 8.40 years; b = 3.48, p brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures. Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging. Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

  15. Orchid Classification Disease Identification And Healthiness Prediction System

    Directory of Open Access Journals (Sweden)

    K. W. V Sanjaya

    2015-03-01

    Full Text Available Abstract Floriculture has become one of Sri Lankas major foreign exchange ventures and it has grown substantially during the last few years. Currently we can find three major types of growers in floriculture. They are Large Commercial Ventures Middle Level growers and Village Level growers. Both Middle Level and Village level growers usually go for low cost cultivation with minimum advanced techniques sticking to conventional methods. Orchid cultivation is more pleasurable and profitable than any other floriculture ventures. As the orchid cultivation is so pleasurable we can introduce another group of growers who cultivate orchid in their home gardens for making their home gardens beautiful. But the problem is that most of these growers may not have the knowledge to identify the specie of the plants as there are a number of similar looking plants which are in different species. And also they may not have the knowledge about the orchid diseases. Because of that they may not be able to get the maximum outcome from their cultivations. So the aim of our project is to address the above mentioned issues by introducing a system which can identify orchid species amp diseases and predict the healthiness of the orchid plants. The only input to this system is an image of an orchid leaf and the system will provide the orchid specie name diseases if there any healthiness of the orchid plant and suggestions to overcome the issues associated with the orchid plant as the output. We identify the orchid species and diseases by extracting the features of orchid plant leaf in the input image using image processing technics and with the use of data mining technics we predict the healthiness of the orchid plant. So this system will be a great help for the people who love to grow orchids but dont have knowledge about the orchid species and diseases. And also they will be able to find the healthiness of their orchid plants.

  16. Regional decadal predictions of coupled climate-human systems

    Science.gov (United States)

    Curchitser, E. N.; Lawrence, P.; Felder, F.; Large, W.; Bacmeister, J. T.; Andrews, C.; Kopp, R. E.

    2016-12-01

    We present results from a project to develop a framework for investigating the interactions between human activity and the climate system using state-of-the-art multi-scale, climate and economic models. The model is applied to the highly industrialized and urbanized coastal region of the northeast US with an emphasis on New Jersey. The framework is developed around the NCAR Community Earth System Model (CESM). The CESM model capabilities are augmented with enhanced resolution of the atmosphere (25 km), land surface (I km) and ocean models (7 km) in our region of interest. To the climate model, we couple human activity models for the utility sector and a 300-equation econometric model with sectorial details of an input-output model for the New Jersey economy. We will present results to date showing the potential impact of climate change on electricity markets on its consequences on economic activity in the region.

  17. Classification and prediction of river network ephemerality and its relevance for waterborne disease epidemiology

    Science.gov (United States)

    Perez-Saez, Javier; Mande, Theophile; Larsen, Joshua; Ceperley, Natalie; Rinaldo, Andrea

    2017-12-01

    The transmission of waterborne diseases hinges on the interactions between hydrology and ecology of hosts, vectors and parasites, with the long-term absence of water constituting a strict lower bound. However, the link between spatio-temporal patterns of hydrological ephemerality and waterborne disease transmission is poorly understood and difficult to account for. The use of limited biophysical and hydroclimate information from otherwise data scarce regions is therefore needed to characterize, classify, and predict river network ephemerality in a spatially explicit framework. Here, we develop a novel large-scale ephemerality classification and prediction methodology based on monthly discharge data, water and energy availability, and remote-sensing measures of vegetation, that is relevant to epidemiology, and maintains a mechanistic link to catchment hydrologic processes. Specifically, with reference to the context of Burkina Faso in sub-Saharan Africa, we extract a relevant set of catchment covariates that include the aridity index, annual runoff estimation using the Budyko framework, and hysteretical relations between precipitation and vegetation. Five ephemerality classes, from permanent to strongly ephemeral, are defined from the duration of 0-flow periods that also accounts for the sensitivity of river discharge to the long-lasting drought of the 70's-80's in West Africa. Using such classes, a gradient-boosted tree-based prediction yielded three distinct geographic regions of ephemerality. Importantly, we observe a strong epidemiological association between our predictions of hydrologic ephemerality and the known spatial patterns of schistosomiasis, an endemic parasitic waterborne disease in which infection occurs with human-water contact, and requires aquatic snails as an intermediate host. The general nature of our approach and its relevance for predicting the hydrologic controls on schistosomiasis occurrence provides a pathway for the explicit inclusion of

  18. Predictive gene testing for Huntington disease and other neurodegenerative disorders.

    Science.gov (United States)

    Wedderburn, S; Panegyres, P K; Andrew, S; Goldblatt, J; Liebeck, T; McGrath, F; Wiltshire, M; Pestell, C; Lee, J; Beilby, J

    2013-12-01

    Controversies exist around predictive testing (PT) programmes in neurodegenerative disorders. This study sets out to answer the following questions relating to Huntington disease (HD) and other neurodegenerative disorders: differences between these patients in their PT journeys, why and when individuals withdraw from PT, and decision-making processes regarding reproductive genetic testing. A case series analysis of patients having PT from the multidisciplinary Western Australian centre for PT over the past 20 years was performed using internationally recognised guidelines for predictive gene testing in neurodegenerative disorders. Of 740 at-risk patients, 518 applied for PT: 466 at risk of HD, 52 at risk of other neurodegenerative disorders - spinocerebellar ataxias, hereditary prion disease and familial Alzheimer disease. Thirteen percent withdrew from PT - 80.32% of withdrawals occurred during counselling stages. Major withdrawal reasons related to timing in the patients' lives or unknown as the patient did not disclose the reason. Thirty-eight HD individuals had reproductive genetic testing: 34 initiated prenatal testing (of which eight withdrew from the process) and four initiated pre-implantation genetic diagnosis. There was no recorded or other evidence of major psychological reactions or suicides during PT. People withdrew from PT in relation to life stages and reasons that are unknown. Our findings emphasise the importance of: (i) adherence to internationally recommended guidelines for PT; (ii) the role of the multidisciplinary team in risk minimisation; and (iii) patient selection. © 2013 The Authors; Internal Medicine Journal © 2013 Royal Australasian College of Physicians.

  19. 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.

  20. Saturated properties prediction in critical region by a quartic ...

    African Journals Online (AJOL)

    A diverse substance library containing extensive PVT data for 77 pure components was used to critically evaluate the performance of a quartic equation of state and other four famous cubic equations of state in critical region. The quartic EOS studied in this work was found to significantly superior to the others in both vapor ...

  1. New Ground Motion Prediction Models for Caucasus Region

    Science.gov (United States)

    Jorjiashvili, N.

    2012-12-01

    The Caucasus is a region of numerous natural hazards and ensuing disasters. Analysis of the losses due to past disasters indicates the those most catastrophic in the region have historically been due to strong earthquakes. Estimation of expected ground motion is a fundamental earthquake hazard assessment. The most commonly used parameter for attenuation relation is peak ground acceleration because this parameter gives useful information for Seismic Hazard Assessment. Because of this, many peak ground acceleration attenuation relations have been developed by different authors. Besides, a few attenuation relations were developed for Caucasus region: Ambraseys et al. (1996,2005) which were based on entire European region and they were not focused locally on Caucasus Region; Smit et.al. (2000) that was based on a small amount of acceleration data that really is not enough. Since 2003 construction of Georgian Digital Seismic Network has started with the help of number of International organizations, Projects and Private companies. The works conducted involved scientific as well as organizational activities: Resolving technical problems concerning communication and data transmission. Thus, today we have a possibility to get real time data and make scientific research based on digital seismic data. Generally, ground motion and damage are influenced by the magnitude of the earthquake, the distance from the seismic source to site, the local ground conditions and the characteristics of buildings. Estimation of expected ground motion is a fundamental earthquake hazard assessment. This is the reason why this topic is emphasized in this study. In this study new GMP models are obtained based on new data from Georgian seismic network and also from neighboring countries. Estimation of models are obtained by classical, statistical way, regression analysis. Also site ground conditions are considered because the same earthquake recorded at the same distance may cause different damage

  2. Regional differences in prediction models of lung function in Germany

    Directory of Open Access Journals (Sweden)

    Schäper Christoph

    2010-04-01

    Full Text Available Abstract Background Little is known about the influencing potential of specific characteristics on lung function in different populations. The aim of this analysis was to determine whether lung function determinants differ between subpopulations within Germany and whether prediction equations developed for one subpopulation are also adequate for another subpopulation. Methods Within three studies (KORA C, SHIP-I, ECRHS-I in different areas of Germany 4059 adults performed lung function tests. The available data consisted of forced expiratory volume in one second, forced vital capacity and peak expiratory flow rate. For each study multivariate regression models were developed to predict lung function and Bland-Altman plots were established to evaluate the agreement between predicted and measured values. Results The final regression equations for FEV1 and FVC showed adjusted r-square values between 0.65 and 0.75, and for PEF they were between 0.46 and 0.61. In all studies gender, age, height and pack-years were significant determinants, each with a similar effect size. Regarding other predictors there were some, although not statistically significant, differences between the studies. Bland-Altman plots indicated that the regression models for each individual study adequately predict medium (i.e. normal but not extremely high or low lung function values in the whole study population. Conclusions Simple models with gender, age and height explain a substantial part of lung function variance whereas further determinants add less than 5% to the total explained r-squared, at least for FEV1 and FVC. Thus, for different adult subpopulations of Germany one simple model for each lung function measures is still sufficient.

  3. Predicting the flammable region reach of propane vapor clouds

    OpenAIRE

    Vílchez Sánchez, Juan Antonio; Villafañe, Diana; Casal Fàbrega, Joaquim

    2014-01-01

    Liquified gas fuels are widely used around the world, and the growth of LNG and LPG consumption continues to increase. However, using these fuels can lead to accidents if they are released to the environment. Consequently, the challenge to control and predict such hazards has become an objective in emergency planning and risk analysis. In a previous article the “Dispersion Safety Factor” (DSF) was proposed, defined as the ratio between the distance at which the lower flammability limit concen...

  4. Improvement of Disease Prediction and Modeling through the Use of Meteorological Ensembles: Human Plague in Uganda

    Science.gov (United States)

    Moore, Sean M.; Monaghan, Andrew; Griffith, Kevin S.; Apangu, Titus; Mead, Paul S.; Eisen, Rebecca J.

    2012-01-01

    Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection) in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February) and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases. PMID:23024750

  5. Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.

    Directory of Open Access Journals (Sweden)

    Sean M Moore

    Full Text Available Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases.

  6. Abnormal ankle brachial indices may predict cardiovascular disease among diabetic patients without known heart disease.

    Science.gov (United States)

    Fine, Jeffrey J; Hopkins, Christie B; Hall, Patrick Ax

    2005-07-01

    Cardiovascular disease remains the primary cause of diabetes-associated morbidity and mortality. Previous studies have failed to provide accurate, inexpensive, screening techniques to detect cardiovascular disease in diabetics. Ankle brachial indices (ABI) testing may be an effective screening technique for diabetics. The aim of this 100-subject clinical study was to determine cardiovascular disease prevalence, via perfusion stress testing, in diabetic patients having abnormal ABI (<0.90) and without known heart disease who were referred to the South Carolina Heart Center, Columbia, SC for nuclear perfusion stress testing. Study data were analyzed using frequency and descriptive statistics and 2-sample T-testing. Mean subject age was 62+/-11 years, ABI 0.76+/-13, and ejection fraction 60+/-12%. Perfusion stress testing detected 49 abnormal electrocardiograms, 36 subjects with coronary ischemia, 20 with diminished left ventricular function, and 26 subjects having significant thinning of the myocardium. There were 71 subjects who tested positive for at least one form of cardiovascular disease. The sole predictive variable reaching significance for the presence of cardiovascular disease was an ABI score <0.90 (p< or =0.0001). Cardiovascular disease may be predicted among diabetic patients via ABI scores and confirmed by nuclear perfusion testing.

  7. Stargardt disease: towards developing a model to predict phenotype.

    Science.gov (United States)

    Heathfield, Laura; Lacerda, Miguel; Nossek, Christel; Roberts, Lisa; Ramesar, Rajkumar S

    2013-10-01

    Stargardt disease is an ABCA4-associated retinopathy, which generally follows an autosomal recessive inheritance pattern and is a frequent cause of macular degeneration in childhood. ABCA4 displays significant allelic heterogeneity whereby different mutations can cause retinal diseases with varying severity and age of onset. A genotype-phenotype model has been proposed linking ABCA4 mutations, purported ABCA4 functional protein activity and severity of disease, as measured by degree of visual loss and the age of onset. It has, however, been difficult to verify this model statistically in observational studies, as the number of individuals sharing any particular mutation combination is typically low. Seven founder mutations have been identified in a large number of Caucasian Afrikaner patients in South Africa, making it possible to test the genotype-phenotype model. A generalised linear model was developed to predict and assess the relative pathogenic contribution of the seven mutations to the age of onset of Stargardt disease. It is shown that the pathogenicity of an individual mutation can differ significantly depending on the genetic context in which it occurs. The results reported here may be used to identify suitable candidates for inclusion in clinical trials, as well as guide the genetic counselling of affected individuals and families.

  8. Predictive factors for intraoperative excessive bleeding in Graves' disease.

    Science.gov (United States)

    Yamanouchi, Kosho; Minami, Shigeki; Hayashida, Naomi; Sakimura, Chika; Kuroki, Tamotsu; Eguchi, Susumu

    2015-01-01

    In Graves' disease, because a thyroid tends to have extreme vascularity, the amount of intraoperative blood loss (AIOBL) becomes significant in some cases. We sought to elucidate the predictive factors of the AIOBL. A total of 197 patients underwent thyroidectomy for Graves' disease between 2002 and 2012. We evaluated clinical factors that would be potentially related to AIOBL retrospectively. The median period between disease onset and surgery was 16 months (range: 1-480 months). Conventional surgery was performed in 125 patients, whereas video-assisted surgery was performed in 72 patients. Subtotal and near-total/total thyroidectomies were performed in 137 patients and 60 patients, respectively. The median weight of the thyroid was 45 g (range: 7.3-480.0 g). Univariate analysis revealed that the strongest correlation of AIOBL was noted with the weight of thyroid (p Graves' disease, and preparation for blood transfusion should be considered in cases where thyroids weigh more than 200 g. Copyright © 2014. Published by Elsevier Taiwan.

  9. Predicting the number and sizes of IBD regions among family members and evaluating the family size requirement for linkage studies.

    Science.gov (United States)

    Yang, Wanling; Wang, Zhanyong; Wang, Lusheng; Sham, Pak-Chung; Huang, Peng; Lau, Yu Lung

    2008-12-01

    With genotyping of high-density single nucleotide polymorphisms (SNPs) replacing that of microsatellite markers in linkage studies, it becomes possible to accurately determine the genomic regions shared identity by descent (IBD) by family members. In addition to evaluating the likelihood of linkage for a region with the underlining disease (the LOD score approach), an appropriate question to ask is what would be the expected number and sizes of IBD regions among the affecteds, as there could be more than one region reaching the maximum achievable LOD score for a given family. Here, we introduce a computer program to allow the prediction of the total number of IBD regions among family members and their sizes. Reversely, it can be used to predict the portion of the genome that can be excluded from consideration according to the family size and user-defined inheritance mode and penetrance. Such information has implications on the feasibility of conducting linkage analysis on a given family of certain size and structure or on a few small families when interfamily homogeneity can be assumed. It can also help determine the most relevant members to be genotyped for such a study. Simulation results showed that the IBD regions containing true mutations are usually larger than regions IBD due to random chance. We have made use of this feature in our program to allow evaluation of the identified IBD regions based on Bayesian probability calculation and simulation results.

  10. Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data.

    Science.gov (United States)

    Reddy, Bhargava K; Delen, Dursun; Agrawal, Rupesh K

    2018-01-01

    Crohn's disease is among the chronic inflammatory bowel diseases that impact the gastrointestinal tract. Understanding and predicting the severity of inflammation in real-time settings is critical to disease management. Extant literature has primarily focused on studies that are conducted in clinical trial settings to investigate the impact of a drug treatment on the remission status of the disease. This research proposes an analytics methodology where three different types of prediction models are developed to predict and to explain the severity of inflammation in patients diagnosed with Crohn's disease. The results show that machine-learning-based analytic methods such as gradient boosting machines can predict the inflammation severity with a very high accuracy (area under the curve = 92.82%), followed by regularized regression and logistic regression. According to the findings, a combination of baseline laboratory parameters, patient demographic characteristics, and disease location are among the strongest predictors of inflammation severity in Crohn's disease patients.

  11. A hydroclimatological approach to predicting regional landslide probability using Landlab

    Science.gov (United States)

    Strauch, Ronda; Istanbulluoglu, Erkan; Nudurupati, Sai Siddhartha; Bandaragoda, Christina; Gasparini, Nicole M.; Tucker, Gregory E.

    2018-02-01

    We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km2. The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at mid-elevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

  12. A hydroclimatological approach to predicting regional landslide probability using Landlab

    Directory of Open Access Journals (Sweden)

    R. Strauch

    2018-02-01

    Full Text Available We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km2. The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at mid-elevations (1400 to 2400 m, and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

  13. Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

    International Nuclear Information System (INIS)

    Jairam, Pushpa M.; Jong, Pim A. de; Mali, Willem P.T.M.; Isgum, Ivana; Graaf, Yolanda van der

    2015-01-01

    Certain pulmonary diseases are associated with cardiovascular disease (CVD). Therefore we investigated the incremental predictive value of pulmonary, mediastinal and pleural features over cardiovascular imaging findings. A total of 10,410 patients underwent diagnostic chest CT for non-cardiovascular indications. Using a case-cohort approach, we visually graded CTs from the cases and from an approximately 10 % random sample of the baseline cohort (n = 1,203) for cardiovascular, pulmonary, mediastinal and pleural findings. The incremental value of pulmonary disease-related CT findings above cardiovascular imaging findings in cardiovascular event risk prediction was quantified by comparing discrimination and reclassification. During a mean follow-up of 3.7 years (max. 7.0 years), 1,148 CVD events (cases) were identified. Addition of pulmonary, mediastinal and pleural features to a cardiovascular imaging findings-based prediction model led to marginal improvement of discrimination (increase in c-index from 0.72 (95 % CI 0.71-0.74) to 0.74 (95 % CI 0.72-0.75)) and reclassification measures (net reclassification index 6.5 % (p < 0.01)). Pulmonary, mediastinal and pleural features have limited predictive value in the identification of subjects at high risk of CVD events beyond cardiovascular findings on diagnostic chest CT scans. (orig.)

  14. Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

    Energy Technology Data Exchange (ETDEWEB)

    Jairam, Pushpa M. [University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht (Netherlands); University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Jong, Pim A. de; Mali, Willem P.T.M. [University Medical Center Utrecht, Department of Radiology, Utrecht (Netherlands); Isgum, Ivana [University Medical Center Utrecht, Image Sciences Institute, Utrecht (Netherlands); Graaf, Yolanda van der [University Medical Center Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht (Netherlands); Collaboration: PROVIDI study-group

    2015-06-01

    Certain pulmonary diseases are associated with cardiovascular disease (CVD). Therefore we investigated the incremental predictive value of pulmonary, mediastinal and pleural features over cardiovascular imaging findings. A total of 10,410 patients underwent diagnostic chest CT for non-cardiovascular indications. Using a case-cohort approach, we visually graded CTs from the cases and from an approximately 10 % random sample of the baseline cohort (n = 1,203) for cardiovascular, pulmonary, mediastinal and pleural findings. The incremental value of pulmonary disease-related CT findings above cardiovascular imaging findings in cardiovascular event risk prediction was quantified by comparing discrimination and reclassification. During a mean follow-up of 3.7 years (max. 7.0 years), 1,148 CVD events (cases) were identified. Addition of pulmonary, mediastinal and pleural features to a cardiovascular imaging findings-based prediction model led to marginal improvement of discrimination (increase in c-index from 0.72 (95 % CI 0.71-0.74) to 0.74 (95 % CI 0.72-0.75)) and reclassification measures (net reclassification index 6.5 % (p < 0.01)). Pulmonary, mediastinal and pleural features have limited predictive value in the identification of subjects at high risk of CVD events beyond cardiovascular findings on diagnostic chest CT scans. (orig.)

  15. 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.

  16. Bioluminescence in vivo imaging of autoimmune encephalomyelitis predicts disease

    Directory of Open Access Journals (Sweden)

    Steinman Lawrence

    2008-02-01

    Full Text Available Abstract Background Experimental autoimmune encephalomyelitis is a widely used animal model to understand not only multiple sclerosis but also basic principles of immunity. The disease is scored typically by observing signs of paralysis, which do not always correspond with pathological changes. Methods Experimental autoimmune encephalomyelitis was induced in transgenic mice expressing an injury responsive luciferase reporter in astrocytes (GFAP-luc. Bioluminescence in the brain and spinal cord was measured non-invasively in living mice. Mice were sacrificed at different time points to evaluate clinical and pathological changes. The correlation between bioluminescence and clinical and pathological EAE was statistically analyzed by Pearson correlation analysis. Results Bioluminescence from the brain and spinal cord correlates strongly with severity of clinical disease and a number of pathological changes in the brain in EAE. Bioluminescence at early time points also predicts severity of disease. Conclusion These results highlight the potential use of bioluminescence imaging to monitor neuroinflammation for rapid drug screening and immunological studies in EAE and suggest that similar approaches could be applied to other animal models of autoimmune and inflammatory disorders.

  17. Prediction of population with Alzheimer's disease in the European Union using a system dynamics model.

    Science.gov (United States)

    Tomaskova, Hana; Kuhnova, Jitka; Cimler, Richard; Dolezal, Ondrej; Kuca, Kamil

    2016-01-01

    Alzheimer's disease (AD) is a slowly progressing neurodegenerative brain disease with irreversible brain effects; it is the most common cause of dementia. With increasing age, the probability of suffering from AD increases. In this research, population growth of the European Union (EU) until the year 2080 and the number of patients with AD are modeled. The aim of this research is to predict the spread of AD in the EU population until year 2080 using a computer simulation. For the simulation of the EU population and the occurrence of AD in this population, a system dynamics modeling approach has been used. System dynamics is a useful and effective method for the investigation of complex social systems. Over the past decades, its applicability has been demonstrated in a wide variety of applications. In this research, this method has been used to investigate the growth of the EU population and predict the number of patients with AD. The model has been calibrated on the population prediction data created by Eurostat. Based on data from Eurostat, the EU population until year 2080 has been modeled. In 2013, the population of the EU was 508 million and the number of patients with AD was 7.5 million. Based on the prediction, in 2040, the population of the EU will be 524 million and the number of patients with AD will be 13.1 million. By the year 2080, the EU population will be 520 million and the number of patients with AD will be 13.7 million. System dynamics modeling approach has been used for the prediction of the number of patients with AD in the EU population till the year 2080. These results can be used to determine the economic burden of the treatment of these patients. With different input data, the simulation can be used also for the different regions as well as for different noncontagious disease predictions.

  18. Predicting the effects of dietary manipulation in chronic renal disease

    International Nuclear Information System (INIS)

    El Nahas, A.M.; Brady, S.A.; Masters-Thomas, A.; Wilkinson, V.; Hilson, A.J.W.; Moorhead, J.F.

    1984-01-01

    It has been suggested that the progressive fall in renal function in some patients with CRF is due to hyperfusion of the remnant nephrons in response to the relatively high protein diet of modern life. The authors attempted to assess this and to see what was the shortest time in which any effect could be demonstrated. In the first phase, 39 patients with CRF had their renal function followed for 6 months on their normal diet and 6 months on a low-protein diet (LPD). The patients on LPD all showed an improvement in the rate of fall of renal function. This was marked in patients with mainly tubular disease, and poor in those with glomerular and vascular disease. In the second phase, 11 of these patients (and 1 other) were started on a high protein diet (HPD) for two weeks, and then switched back to a LPD for 2 weeks. There was no change in GFR during this period, but there were marked changes in ERPF, which correlated well with the changes in renal function in the first phase (r = 0.76, rho < 0.01); 4/4 patients with tubular disease showed a rise in ERPF on HPD and a fall on LPD, while only 4/8 with glomerular or vascular disease responded. In the third phase, they assessed the effect of a single high-protein meal in normal volunteers. This showed that there are major changes in hemodynamics following a meal, such that it is not possible to make any statement about renal function using the single-shot methods. The authors conclude that a 2-week period of HPD followed by LPD allows prediction of the possible beneficial response to diet in CRF; that this is best monitored by ERPF; and that a single meal may invalidate renal function measurement

  19. Two Kilometer Coastal Ocean Current Predictions, Region 9, 2014, US EPA Region 9

    Data.gov (United States)

    U.S. Environmental Protection Agency — This data is derived from the NetCDF files that come from http://hfrnet.ucsd.edu/. EPA Region 9 has developed a series of python scripts to download the data hourly,...

  20. Six Kilometer Coastal Ocean Current Predictions, Region 9, 2014, US EPA Region 9

    Data.gov (United States)

    U.S. Environmental Protection Agency — This data is derived from the NetCDF files that come from http://hfrnet.ucsd.edu/. EPA Region 9 has developed a series of python scripts to download the data hourly,...

  1. Predicted Habitat Suitability for Montipora Corals in the Au'au Channel Region

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This raster denotes predicted habitat suitability for Montipora in the Au'au Channel region. Maximum Entropy (MaxEnt) modeling software was used to create this...

  2. Predicted Habitat Suitability for Porites in the Au'au Channel Region

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This raster denotes predicted habitat suitability for Porites in the Au'au Channel region. Maximum Entropy (MaxEnt) modeling software was used to create this...

  3. Predicted Habitat Suitability for Leptoseris Corals in the Au'au Channel Region

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This raster denotes predicted habitat suitability for Leptoseris in the Au'au Channel region. Maximum Entropy (MaxEnt) modeling software was used to create this...

  4. Predicted Habitat Suitability for Leptoseris in the Au'au Channel Region

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This raster denotes predicted habitat suitability for Leptoseris in the Au'au Channel region. Maximum Entropy (MaxEnt) modeling software was used to create this...

  5. Predicted Habitat Suitability for All Mesophotic Corals in the Au'au Channel Region

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This raster denotes predicted habitat suitability for all mesophotic corals in the Au'au Channel region. Maximum Entropy (MaxEnt) modeling software was used to...

  6. A retrospective study and predictive modelling of Newcastle Disease trends among rural poultry of eastern Zambia.

    Science.gov (United States)

    Mubamba, C; Ramsay, G; Abolnik, C; Dautu, G; Gummow, B

    2016-10-01

    Newcastle Disease (ND) is a highly infectious disease of poultry that seriously impacts on food security and livelihoods of livestock farmers and communities in tropical regions of the world. ND is a constant problem in the eastern province of Zambia which has more than 740 000 rural poultry. Very few studies give a situational analysis of the disease that can be used for disease control planning in the region. With this background in mind, a retrospective epidemiological study was conducted using Newcastle Disease data submitted to the eastern province headquarters for the period from 1989 to 2014. The study found that Newcastle Disease cases in eastern Zambia followed a seasonal and cyclic pattern with peaks in the hot dry season (Overall Seasonal Index 1.1) as well as cycles every three years with an estimated provincial incidence range of 0.16 to 1.7% per year. Annual trends were compared with major intervention policies implemented by the Zambian government, which often received donor support from the international community during the study period. Aid delivered through government programmes appeared to have no major impact on ND trends between 1989 and 2014 and reasons for this are discussed. There were apparent spatial shifts in districts with outbreaks over time which could be as a result of veterinary interventions chasing outbreaks rather than implementing uniform control. Data was also fitted to a predictive time series model for ND which could be used to plan for future ND control. Time series modelling showed an increasing trend in ND annual incidence over 25 years if existing interventions continue. A different approach to controlling the disease is needed if this trend is to be halted. Conversely, the positive trend may be a function of improved reporting by farmers as a result of more awareness of the disease. Copyright © 2016 Elsevier B.V. All rights reserved.

  7. Evaluating Alzheimer's disease progression using rate of regional hippocampal atrophy.

    Directory of Open Access Journals (Sweden)

    Edit Frankó

    Full Text Available Alzheimer's disease (AD is characterized by neurofibrillary tangle and neuropil thread deposition, which ultimately results in neuronal loss. A large number of magnetic resonance imaging studies have reported a smaller hippocampus in AD patients as compared to healthy elderlies. Even though this difference is often interpreted as atrophy, it is only an indirect measurement. A more direct way of measuring the atrophy is to use repeated MRIs within the same individual. Even though several groups have used this appropriate approach, the pattern of hippocampal atrophy still remains unclear and difficult to relate to underlying pathophysiology. Here, in this longitudinal study, we aimed to map hippocampal atrophy rates in patients with AD, mild cognitive impairment (MCI and elderly controls. Data consisted of two MRI scans for each subject. The symmetric deformation field between the first and the second MRI was computed and mapped onto the three-dimensional hippocampal surface. The pattern of atrophy rate was similar in all three groups, but the rate was significantly higher in patients with AD than in control subjects. We also found higher atrophy rates in progressive MCI patients as compared to stable MCI, particularly in the antero-lateral portion of the right hippocampus. Importantly, the regions showing the highest atrophy rate correspond to those that were described to have the highest burden of tau deposition. Our results show that local hippocampal atrophy rate is a reliable biomarker of disease stage and progression and could also be considered as a method to objectively evaluate treatment effects.

  8. A surface hydrology model for regional vector borne disease models

    Science.gov (United States)

    Tompkins, Adrian; Asare, Ernest; Bomblies, Arne; Amekudzi, Leonard

    2016-04-01

    Small, sun-lit temporary pools that form during the rainy season are important breeding sites for many key mosquito vectors responsible for the transmission of malaria and other diseases. The representation of this surface hydrology in mathematical disease models is challenging, due to their small-scale, dependence on the terrain and the difficulty of setting soil parameters. Here we introduce a model that represents the temporal evolution of the aggregate statistics of breeding sites in a single pond fractional coverage parameter. The model is based on a simple, geometrical assumption concerning the terrain, and accounts for the processes of surface runoff, pond overflow, infiltration and evaporation. Soil moisture, soil properties and large-scale terrain slope are accounted for using a calibration parameter that sets the equivalent catchment fraction. The model is calibrated and then evaluated using in situ pond measurements in Ghana and ultra-high (10m) resolution explicit simulations for a village in Niger. Despite the model's simplicity, it is shown to reproduce the variability and mean of the pond aggregate water coverage well for both locations and validation techniques. Example malaria simulations for Uganda will be shown using this new scheme with a generic calibration setting, evaluated using district malaria case data. Possible methods for implementing regional calibration will be briefly discussed.

  9. Relationship between regional severity of emphysema and coronary heart disease

    International Nuclear Information System (INIS)

    Nagao, Michinobu; Sakai, Shinya; Yasuhara, Yoshifumi; Ikezoe, Junpei; Murase, Kenya; Ichiki, Taku

    2000-01-01

    We analyzed the relationship between regional severity of emphysema, which was evaluated by three-dimensional fractal analysis (3D-FA) of Technegas SPECT images, and coronary heart disease (CHD). For 22 patients with emphysema who underwent Technegas SPECT, we followed up CHD events. The follow-up period was 5.4±0.5 (mean ±SD) years. We defined the upper-lung fractal dimension (U-FD) and lower-lung fractal dimension (L-FD) obtained with 3D-FA of Technegas SPECT images as the regional severity of emphysema. FD became greater with the progression of emphysematous change. During the follow-up period, CHD events occurred in 6 (27%) of the 22 patients. The ratio of U-FD to L-FD for patients with CHD events (0.87±0.22) was significantly smaller than for patients without CHD events (1.52±0.38) (p=0.0015). These findings suggest that severer emphysema in the lower lung indicates a higher risk of CHD than that in the upper lung. (author)

  10. [Diarrheal disease in the region of Marrakech, Morocco].

    Science.gov (United States)

    Bourrous, M; Elmjati, H; Amine, M; El Omari, J; Bouskraoui, M

    2010-04-01

    Diarrhea is the second cause of child morbidity and mortality in Morocco after acute respiratory infection. Each child suffers from 4 to 8 episodes of diarrhea per year. The purpose of this study was to evaluate the knowledge as well as diagnostic and therapeutic practices of general practitioners regarding children presenting with diarrhea. Study was based on an epidemiologic survey using a written questionnaire completed by general practitioners in state-run hospitals in the Marrakesh (Tensift El Haouz) region. The anonymous questionnaire containing items on the epidemiological, clinical, laboratory, and therapeutic aspects was distributed in all 5 medical districts in the region. Analysis of reponses concerning therapeutic practices showed heavy reliance on oral rehydration that was prescribed by 98.2% of general practitioners. Dietary analysis was performed by only 24% of practitioners and blood/stool testing was not systematically ordered. Only 3% of practitioners recommended early resumption of feeding. However, data showed excessive use of additional laboratory tests (57.8%) and prescription drugs (48.8%). Overprescription mainly involved antiemetics and anti-diarrheals (77.7%). This study demonstrates an urgent need to develop a strategy to improve the quality of dietary management of diarrhea by general practitioners and rationalize prescription drug use. A continuing medical education program would be useful to increase the awareness of general practitioners and reduce child/infant morbidity and mortality relating to this disease.

  11. Predicting Acute Exacerbations in Chronic Obstructive Pulmonary Disease.

    Science.gov (United States)

    Samp, Jennifer C; Joo, Min J; Schumock, Glen T; Calip, Gregory S; Pickard, A Simon; Lee, Todd A

    2018-03-01

    With increasing health care costs that have outpaced those of other industries, payers of health care are moving from a fee-for-service payment model to one in which reimbursement is tied to outcomes. Chronic obstructive pulmonary disease (COPD) is a disease where this payment model has been implemented by some payers, and COPD exacerbations are a quality metric that is used. Under an outcomes-based payment model, it is important for health systems to be able to identify patients at risk for poor outcomes so that they can target interventions to improve outcomes. To develop and evaluate predictive models that could be used to identify patients at high risk for COPD exacerbations. This study was retrospective and observational and included COPD patients treated with a bronchodilator-based combination therapy. We used health insurance claims data to obtain demographics, enrollment information, comorbidities, medication use, and health care resource utilization for each patient over a 6-month baseline period. Exacerbations were examined over a 6-month outcome period and included inpatient (primary discharge diagnosis for COPD), outpatient, and emergency department (outpatient/emergency department visits with a COPD diagnosis plus an acute prescription for an antibiotic or corticosteroid within 5 days) exacerbations. The cohort was split into training (75%) and validation (25%) sets. Within the training cohort, stepwise logistic regression models were created to evaluate risk of exacerbations based on factors measured during the baseline period. Models were evaluated using sensitivity, specificity, and positive and negative predictive values. The base model included all confounding or effect modifier covariates. Several other models were explored using different sets of observations and variables to determine the best predictive model. There were 478,772 patients included in the analytic sample, of which 40.5% had exacerbations during the outcome period. Patients with

  12. Predicting onset of chronic lung disease using cord blood cytokines.

    Science.gov (United States)

    Takao, Daishi; Ibara, Satoshi; Tokuhisa, Takuya; Ishihara, Chie; Maede, Yoshinobu; Matsui, Takako; Tokumasu, Hironobu; Sato, Kyoko; Hirakawa, Eiji; Kabayama, Chika; Yamamoto, Masakatu

    2014-08-01

    Applicability of cord blood interleukin-6 (IL-6) and interleukin-8 (IL-8) as markers for early prediction of the onset of chronic lung disease (CLD) due to intrauterine infection was investigated in the present study. Eighty very low-birthweight infants with chorioamnionitis were divided into two groups: the CLD group (42 patients) and the non-CLD group (38 patients), according to the presence or absence of CLD, and the clinical background and cord blood IL-6 and IL-8 levels in each group were compared and investigated. The CLD group had significantly longer duration of mechanical ventilation and hospitalization (P CLD group. Using the receiver operating characteristic curves of CLD onset for both IL-6 and IL-8, the cut-off value of IL-6 for predicting onset of CLD was 48.0 pg/mL, and its sensitivity and specificity were 76% and 96%, respectively. The cut-off value for IL-8 was 66.0 pg/mL, and its sensitivity and specificity were 71% and 82%, respectively. The cord blood levels of both IL-6 and IL-8 were significantly higher in the CLD group, indicating that both IL-6 and IL-8 are useful predictors of onset of CLD. © 2014 Japan Pediatric Society.

  13. Pain drawings predict outcome of surgical treatment for degenerative disc disease in the cervical spine.

    Science.gov (United States)

    MacDowall, Anna; Robinson, Yohan; Skeppholm, Martin; Olerud, Claes

    2017-08-01

    Pain drawings have been frequently used in the preoperative evaluation of spine patients. For lumbar conditions comprehensive research has established both the reliability and predictive value, but for the cervical spine most of this knowledge is lacking. The aims of this study were to validate pain drawings for the cervical spine, and to investigate the predictive value for treatment outcome of four different evaluation methods. We carried out a post hoc analysis of a randomized controlled trial, comparing cervical disc replacement to fusion for radiculopathy related to degenerative disc disease. A pain drawing together with Neck Disability Index (NDI) was completed preoperatively, after 2 and 5 years. The inter- and intraobserver reliability of four evaluation methods was tested using κ statistics, and its predictive value investigated by correlation to change in NDI. Included were 151 patients, mean age of 47 years, female/male: 78/73. The interobserver reliability was fair for the modified Ransford and Udén methods, good for the Gatchel method, and very good for the modified Ohnmeiss method. Markings in the shoulder and upper arm region on the pain drawing were positive predictors of outcome after 2 years of follow-up, and markings in the upper arm region remained a positive predictor of outcome even after 5 years of follow-up. Pain drawings were a reliable tool to interpret patients' pain prior to cervical spine surgery and were also to some extent predictive for treatment outcome.

  14. The contribution of educational class in improving accuracy of cardiovascular risk prediction across European regions

    DEFF Research Database (Denmark)

    Ferrario, Marco M; Veronesi, Giovanni; Chambless, Lloyd E

    2014-01-01

    OBJECTIVE: To assess whether educational class, an index of socioeconomic position, improves the accuracy of the SCORE cardiovascular disease (CVD) risk prediction equation. METHODS: In a pooled analysis of 68 455 40-64-year-old men and women, free from coronary heart disease at baseline, from 47...

  15. An integrative approach to predicting the functional effects of small indels in non-coding regions of the human genome.

    Science.gov (United States)

    Ferlaino, Michael; Rogers, Mark F; Shihab, Hashem A; Mort, Matthew; Cooper, David N; Gaunt, Tom R; Campbell, Colin

    2017-10-06

    Small insertions and deletions (indels) have a significant influence in human disease and, in terms of frequency, they are second only to single nucleotide variants as pathogenic mutations. As the majority of mutations associated with complex traits are located outside the exome, it is crucial to investigate the potential pathogenic impact of indels in non-coding regions of the human genome. We present FATHMM-indel, an integrative approach to predict the functional effect, pathogenic or neutral, of indels in non-coding regions of the human genome. Our method exploits various genomic annotations in addition to sequence data. When validated on benchmark data, FATHMM-indel significantly outperforms CADD and GAVIN, state of the art models in assessing the pathogenic impact of non-coding variants. FATHMM-indel is available via a web server at indels.biocompute.org.uk. FATHMM-indel can accurately predict the functional impact and prioritise small indels throughout the whole non-coding genome.

  16. Importance of Foliar Nitrogen Concentration to Predict Forest Productivity in the Mid-Atlantic Region

    Science.gov (United States)

    Yude Pan; John Hom; Jennifer Jenkins; Richard Birdsey

    2004-01-01

    To assess what difference it might make to include spatially defined estimates of foliar nitrogen in the regional application of a forest ecosystem model (PnET-II), we composed model predictions of wood production from extensive ground-based forest inventory analysis data across the Mid-Atlantic region. Spatial variation in foliar N concentration was assigned based on...

  17. Prediction of DHF disease spreading patterns using inverse distances weighted (IDW), ordinary and universal kriging

    Science.gov (United States)

    Prasetiyowati, S. S.; Sibaroni, Y.

    2018-03-01

    Dengue hemorrhagic disease, is a disease caused by the Dengue virus of the Flavivirus genus Flaviviridae family. Indonesia is the country with the highest case of dengue in Southeast Asia. In addition to mosquitoes as vectors and humans as hosts, other environmental and social factors are also the cause of widespread dengue fever. To prevent the occurrence of the epidemic of the disease, fast and accurate action is required. Rapid and accurate action can be taken, if there is appropriate information support on the occurrence of the epidemic. Therefore, a complete and accurate information on the spread pattern of endemic areas is necessary, so that precautions can be done as early as possible. The information on dispersal patterns can be obtained by various methods, which are based on empirical and theoretical considerations. One of the methods used is based on the estimated number of infected patients in a region based on spatial and time. The first step of this research is conducted by predicting the number of DHF patients in 2016 until 2018 based on 2010 to 2015 data using GSTAR (1, 1). In the second phase, the distribution pattern prediction of dengue disease area is conducted. Furthermore, based on the characteristics of DHF epidemic trends, i.e. down, stable or rising, the analysis of distribution patterns of dengue fever distribution areas with IDW and Kriging (ordinary and universal Kriging) were conducted in this study. The difference between IDW and Kriging, is the initial process that underlies the prediction process. Based on the experimental results, it is known that the dispersion pattern of epidemic areas of dengue disease with IDW and Ordinary Kriging is similar in the period of time.

  18. Predicting Alzheimer's disease by classifying 3D-Brain MRI images using SVM and other well-defined classifiers

    International Nuclear Information System (INIS)

    Matoug, S; Abdel-Dayem, A; Passi, K; Gross, W; Alqarni, M

    2012-01-01

    Alzheimer's disease (AD) is the most common form of dementia affecting seniors age 65 and over. When AD is suspected, the diagnosis is usually confirmed with behavioural assessments and cognitive tests, often followed by a brain scan. Advanced medical imaging and pattern recognition techniques are good tools to create a learning database in the first step and to predict the class label of incoming data in order to assess the development of the disease, i.e., the conversion from prodromal stages (mild cognitive impairment) to Alzheimer's disease, which is the most critical brain disease for the senior population. Advanced medical imaging such as the volumetric MRI can detect changes in the size of brain regions due to the loss of the brain tissues. Measuring regions that atrophy during the progress of Alzheimer's disease can help neurologists in detecting and staging the disease. In the present investigation, we present a pseudo-automatic scheme that reads volumetric MRI, extracts the middle slices of the brain region, performs segmentation in order to detect the region of brain's ventricle, generates a feature vector that characterizes this region, creates an SQL database that contains the generated data, and finally classifies the images based on the extracted features. For our results, we have used the MRI data sets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

  19. Deep learning for predicting the monsoon over the homogeneous regions of India

    Science.gov (United States)

    Saha, Moumita; Mitra, Pabitra; Nanjundiah, Ravi S.

    2017-06-01

    Indian monsoon varies in its nature over the geographical regions. Predicting the rainfall not just at the national level, but at the regional level is an important task. In this article, we used a deep neural network, namely, the stacked autoencoder to automatically identify climatic factors that are capable of predicting the rainfall over the homogeneous regions of India. An ensemble regression tree model is used for monsoon prediction using the identified climatic predictors. The proposed model provides forecast of the monsoon at a long lead time which supports the government to implement appropriate policies for the economic growth of the country. The monsoon of the central, north-east, north-west, and south-peninsular India regions are predicted with errors of 4.1%, 5.1%, 5.5%, and 6.4%, respectively. The identified predictors show high skill in predicting the regional monsoon having high variability. The proposed model is observed to be competitive with the state-of-the-art prediction models.

  20. Approaches to predicting potential impacts of climate change on forest disease: an example with Armillaria root disease

    Science.gov (United States)

    Ned B. Klopfenstein; Mee-Sook Kim; John W. Hanna; Bryce A. Richardson; John E. Lundquist

    2009-01-01

    Predicting climate change influences on forest diseases will foster forest management practices that minimize adverse impacts of diseases. Precise locations of accurately identified pathogens and hosts must be documented and spatially referenced to determine which climatic factors influence species distribution. With this information, bioclimatic models can predict the...

  1. Prediction of monthly regional groundwater levels through hybrid soft-computing techniques

    Science.gov (United States)

    Chang, Fi-John; Chang, Li-Chiu; Huang, Chien-Wei; Kao, I.-Feng

    2016-10-01

    Groundwater systems are intrinsically heterogeneous with dynamic temporal-spatial patterns, which cause great difficulty in quantifying their complex processes, while reliable predictions of regional groundwater levels are commonly needed for managing water resources to ensure proper service of water demands within a region. In this study, we proposed a novel and flexible soft-computing technique that could effectively extract the complex high-dimensional input-output patterns of basin-wide groundwater-aquifer systems in an adaptive manner. The soft-computing models combined the Self Organized Map (SOM) and the Nonlinear Autoregressive with Exogenous Inputs (NARX) network for predicting monthly regional groundwater levels based on hydrologic forcing data. The SOM could effectively classify the temporal-spatial patterns of regional groundwater levels, the NARX could accurately predict the mean of regional groundwater levels for adjusting the selected SOM, the Kriging was used to interpolate the predictions of the adjusted SOM into finer grids of locations, and consequently the prediction of a monthly regional groundwater level map could be obtained. The Zhuoshui River basin in Taiwan was the study case, and its monthly data sets collected from 203 groundwater stations, 32 rainfall stations and 6 flow stations during 2000 and 2013 were used for modelling purpose. The results demonstrated that the hybrid SOM-NARX model could reliably and suitably predict monthly basin-wide groundwater levels with high correlations (R2 > 0.9 in both training and testing cases). The proposed methodology presents a milestone in modelling regional environmental issues and offers an insightful and promising way to predict monthly basin-wide groundwater levels, which is beneficial to authorities for sustainable water resources management.

  2. 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)

  3. 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...

  4. Urate predicts rate of clinical decline in Parkinson disease

    Science.gov (United States)

    Ascherio, Alberto; LeWitt, Peter A.; Xu, Kui; Eberly, Shirley; Watts, Arthur; Matson, Wayne R.; Marras, Connie; Kieburtz, Karl; Rudolph, Alice; Bogdanov, Mikhail B.; Schwid, Steven R.; Tennis, Marsha; Tanner, Caroline M.; Beal, M. Flint; Lang, Anthony E.; Oakes, David; Fahn, Stanley; Shoulson, Ira; Schwarzschild, Michael A.

    2009-01-01

    Context The risk of Parkinson disease (PD) and its rate of progression may decline with increasing blood urate, a major antioxidant. Objective To determine whether serum and cerebrospinal fluid (CSF) concentrations of urate predict clinical progression in patients with PD. Design, Setting, and Participants 800 subjects with early PD enrolled in the DATATOP trial. Pre-treatment urate was measured in serum for 774 subjects and in CSF for 713. Main Outcome Measures Treatment-, age- and sex-adjusted hazard ratios (HRs) for clinical disability requiring levodopa therapy, the pre-specified primary endpoint. Results The HR of progressing to endpoint decreased with increasing serum urate (HR for 1 standard deviation increase = 0.82; 95% CI = 0.73 to 0.93). In analyses stratified by α-tocopherol treatment (2,000 IU/day), a decrease in the HR for the primary endpoint was seen only among subjects not treated with α-tocopherol (HR = 0.75; 95% CI = 0.62 to 0.89, versus those treated HR = 0.90; 95% CI = 0.75 to 1.08). Results were similar for the rate of change in the United Parkinson Disease Rating Scale (UPDRS). CSF urate was also inversely related to both the primary endpoint (HR for highest versus lowest quintile = 0.65; 95% CI: 0.54 to 0.96) and to the rate of change in UPDRS. As with serum urate, these associations were present only among subjects not treated with α-tocopherol. Conclusion Higher serum and CSF urate at baseline were associated with slower rates of clinical decline. The findings strengthen the link between urate and PD and the rationale for considering CNS urate elevation as a potential strategy to slow PD progression. PMID:19822770

  5. Forecasting high-priority infectious disease surveillance regions: a socioeconomic model.

    Science.gov (United States)

    Chan, Emily H; Scales, David A; Brewer, Timothy F; Madoff, Lawrence C; Pollack, Marjorie P; Hoen, Anne G; Choden, Tenzin; Brownstein, John S

    2013-02-01

    Few researchers have assessed the relationships between socioeconomic inequality and infectious disease outbreaks at the population level globally. We use a socioeconomic model to forecast national annual rates of infectious disease outbreaks. We constructed a multivariate mixed-effects Poisson model of the number of times a given country was the origin of an outbreak in a given year. The dataset included 389 outbreaks of international concern reported in the World Health Organization's Disease Outbreak News from 1996 to 2008. The initial full model included 9 socioeconomic variables related to education, poverty, population health, urbanization, health infrastructure, gender equality, communication, transportation, and democracy, and 1 composite index. Population, latitude, and elevation were included as potential confounders. The initial model was pared down to a final model by a backwards elimination procedure. The dependent and independent variables were lagged by 2 years to allow for forecasting future rates. Among the socioeconomic variables tested, the final model included child measles immunization rate and telephone line density. The Democratic Republic of Congo, China, and Brazil were predicted to be at the highest risk for outbreaks in 2010, and Colombia and Indonesia were predicted to have the highest percentage of increase in their risk compared to their average over 1996-2008. Understanding socioeconomic factors could help improve the understanding of outbreak risk. The inclusion of the measles immunization variable suggests that there is a fundamental basis in ensuring adequate public health capacity. Increased vigilance and expanding public health capacity should be prioritized in the projected high-risk regions.

  6. Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

    KAUST Repository

    Hou, Siqing

    2018-01-01

    Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help

  7. Accelerating regional atrophy rates in the progression from normal aging to Alzheimer's disease

    Energy Technology Data Exchange (ETDEWEB)

    Sluimer, Jasper D. [VU University Medical Centre, Department of Diagnostic Radiology, Amsterdam (Netherlands); VU University Medical Centre, Alzheimer Centre, Amsterdam (Netherlands); VU University Medical Centre, Image Analysis Centre, Amsterdam (Netherlands); VU University Medical Centre, Department of Diagnostic Radiology and Alzheimer Centre, PO Box 7057, Amsterdam (Netherlands); Flier, Wiesje M. van der; Scheltens, Philip [VU University Medical Centre, Alzheimer Centre, Amsterdam (Netherlands); VU University Medical Centre, Department of Neurology, Amsterdam (Netherlands); Karas, Giorgos B.; Barkhof, Frederik [VU University Medical Centre, Department of Diagnostic Radiology, Amsterdam (Netherlands); VU University Medical Centre, Alzheimer Centre, Amsterdam (Netherlands); VU University Medical Centre, Image Analysis Centre, Amsterdam (Netherlands); Schijndel, Ronald van [VU University Medical Centre, Image Analysis Centre, Amsterdam (Netherlands); VU University Medical Centre, Department of Informatics, Amsterdam (Netherlands); Barnes, Josephine; Boyes, Richard G. [UCL, Institute of Neurology, Dementia Research Centre, London (United Kingdom); Cover, Keith S. [VU University Medical Centre, Department of Physics and Medical Technology, Amsterdam (Netherlands); Olabarriaga, Silvia D. [University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Centre, Amsterdam (Netherlands); Fox, Nick C. [VU University Medical Centre, Department of Neurology, Amsterdam (Netherlands); UCL, Institute of Neurology, Dementia Research Centre, London (United Kingdom); Vrenken, Hugo [VU University Medical Centre, Alzheimer Centre, Amsterdam (Netherlands); VU University Medical Centre, Image Analysis Centre, Amsterdam (Netherlands); VU University Medical Centre, Department of Physics and Medical Technology, Amsterdam (Netherlands)

    2009-12-15

    We investigated progression of atrophy in vivo, in Alzheimer's disease (AD), and mild cognitive impairment (MCI). We included 64 patients with AD, 44 with MCI and 34 controls with serial MRI examinations (interval 1.8 {+-} 0.7 years). A nonlinear registration algorithm (fluid) was used to calculate atrophy rates in six regions: frontal, medial temporal, temporal (extramedial), parietal, occipital lobes and insular cortex. In MCI, the highest atrophy rate was observed in the medial temporal lobe, comparable with AD. AD patients showed even higher atrophy rates in the extramedial temporal lobe. Additionally, atrophy rates in frontal, parietal and occipital lobes were increased. Cox proportional hazard models showed that all regional atrophy rates predicted conversion to AD. Hazard ratios varied between 2.6 (95% confidence interval (CI) = 1.1-6.2) for occipital atrophy and 15.8 (95% CI = 3.5-71.8) for medial temporal lobe atrophy. In conclusion, atrophy spreads through the brain with development of AD. MCI is marked by temporal lobe atrophy. In AD, atrophy rate in the extramedial temporal lobe was even higher. Moreover, atrophy rates also accelerated in parietal, frontal, insular and occipital lobes. Finally, in nondemented elderly, medial temporal lobe atrophy was most predictive of progression to AD, demonstrating the involvement of this region in the development of AD. (orig.)

  8. Accelerating regional atrophy rates in the progression from normal aging to Alzheimer's disease

    International Nuclear Information System (INIS)

    Sluimer, Jasper D.; Flier, Wiesje M. van der; Scheltens, Philip; Karas, Giorgos B.; Barkhof, Frederik; Schijndel, Ronald van; Barnes, Josephine; Boyes, Richard G.; Cover, Keith S.; Olabarriaga, Silvia D.; Fox, Nick C.; Vrenken, Hugo

    2009-01-01

    We investigated progression of atrophy in vivo, in Alzheimer's disease (AD), and mild cognitive impairment (MCI). We included 64 patients with AD, 44 with MCI and 34 controls with serial MRI examinations (interval 1.8 ± 0.7 years). A nonlinear registration algorithm (fluid) was used to calculate atrophy rates in six regions: frontal, medial temporal, temporal (extramedial), parietal, occipital lobes and insular cortex. In MCI, the highest atrophy rate was observed in the medial temporal lobe, comparable with AD. AD patients showed even higher atrophy rates in the extramedial temporal lobe. Additionally, atrophy rates in frontal, parietal and occipital lobes were increased. Cox proportional hazard models showed that all regional atrophy rates predicted conversion to AD. Hazard ratios varied between 2.6 (95% confidence interval (CI) = 1.1-6.2) for occipital atrophy and 15.8 (95% CI = 3.5-71.8) for medial temporal lobe atrophy. In conclusion, atrophy spreads through the brain with development of AD. MCI is marked by temporal lobe atrophy. In AD, atrophy rate in the extramedial temporal lobe was even higher. Moreover, atrophy rates also accelerated in parietal, frontal, insular and occipital lobes. Finally, in nondemented elderly, medial temporal lobe atrophy was most predictive of progression to AD, demonstrating the involvement of this region in the development of AD. (orig.)

  9. Consumer preferences for the predictive genetic test for Alzheimer disease.

    Science.gov (United States)

    Huang, Ming-Yi; Huston, Sally A; Perri, Matthew

    2014-04-01

    The purpose of this study was to assess consumer preferences for predictive genetic testing for Alzheimer disease in the United States. A rating conjoint analysis was conducted using an anonymous online survey distributed by Qualtrics to a general population panel in April 2011 in the United States. The study design included three attributes: Accuracy (40%, 80%, and 100%), Treatment Availability (Cure is available/Drug for symptom relief but no cure), and Anonymity (Anonymous/Not anonymous). A total of 12 scenarios were used to elicit people's preference, assessed by an 11-point scale. The respondents also indicated their highest willingness-to-pay (WTP) for each scenario through open-ended questions. A total of 295 responses were collected over 4 days. The most important attribute for the aggregate model was Accuracy, contributing 64.73% to the preference rating. Treatment Availability and Anonymity contributed 20.72% and 14.59%, respectively, to the preference rating. The median WTP for the highest-rating scenario (Accuracy 100%, a cure is available, test result is anonymous) was $100 (mean = $276). The median WTP for the lowest-rating scenario (40% accuracy, no cure but drugs for symptom relief, not anonymous) was zero (mean = $34). The results of this study highlight attributes people find important when making the hypothetical decision to obtain an AD genetic test. These results should be of interests to policy makers, genetic test developers and health care providers.

  10. Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs

    Directory of Open Access Journals (Sweden)

    Ruan Jishou

    2007-04-01

    Full Text Available Abstract Background Traditionally, it is believed that the native structure of a protein corresponds to a global minimum of its free energy. However, with the growing number of known tertiary (3D protein structures, researchers have discovered that some proteins can alter their structures in response to a change in their surroundings or with the help of other proteins or ligands. Such structural shifts play a crucial role with respect to the protein function. To this end, we propose a machine learning method for the prediction of the flexible/rigid regions of proteins (referred to as FlexRP; the method is based on a novel sequence representation and feature selection. Knowledge of the flexible/rigid regions may provide insights into the protein folding process and the 3D structure prediction. Results The flexible/rigid regions were defined based on a dataset, which includes protein sequences that have multiple experimental structures, and which was previously used to study the structural conservation of proteins. Sequences drawn from this dataset were represented based on feature sets that were proposed in prior research, such as PSI-BLAST profiles, composition vector and binary sequence encoding, and a newly proposed representation based on frequencies of k-spaced amino acid pairs. These representations were processed by feature selection to reduce the dimensionality. Several machine learning methods for the prediction of flexible/rigid regions and two recently proposed methods for the prediction of conformational changes and unstructured regions were compared with the proposed method. The FlexRP method, which applies Logistic Regression and collocation-based representation with 95 features, obtained 79.5% accuracy. The two runner-up methods, which apply the same sequence representation and Support Vector Machines (SVM and Naïve Bayes classifiers, obtained 79.2% and 78.4% accuracy, respectively. The remaining considered methods are

  11. Regional brain morphometry predicts memory rehabilitation outcome after traumatic brain injury

    Directory of Open Access Journals (Sweden)

    Gary E Strangman

    2010-10-01

    Full Text Available Cognitive deficits following traumatic brain injury (TBI commonly include difficulties with memory, attention, and executive dysfunction. These deficits are amenable to cognitive rehabilitation, but optimally selecting rehabilitation programs for individual patients remains a challenge. Recent methods for quantifying regional brain morphometry allow for automated quantification of tissue volumes in numerous distinct brain structures. We hypothesized that such quantitative structural information could help identify individuals more or less likely to benefit from memory rehabilitation. Fifty individuals with TBI of all severities who reported having memory difficulties first underwent structural MRI scanning. They then participated in a 12 session memory rehabilitation program emphasizing internal memory strategies (I-MEMS. Primary outcome measures (HVLT, RBMT were collected at the time of the MRI scan, immediately following therapy, and again at one month post-therapy. Regional brain volumes were used to predict outcome, adjusting for standard predictors (e.g., injury severity, age, education, pretest scores. We identified several brain regions that provided significant predictions of rehabilitation outcome, including the volume of the hippocampus, the lateral prefrontal cortex, the thalamus, and several subregions of the cingulate cortex. The prediction range of regional brain volumes were in some cases nearly equal in magnitude to prediction ranges provided by pretest scores on the outcome variable. We conclude that specific cerebral networks including these regions may contribute to learning during I-MEMS rehabilitation, and suggest that morphometric measures may provide substantial predictive value for rehabilitation outcome in other cognitive interventions as well.

  12. Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network

    International Nuclear Information System (INIS)

    Xu, Bo; Dan, Han-Cheng; Li, Liang

    2017-01-01

    Highlights: • Pavement temperature prediction model is presented with improved BP neural network. • Dynamic and static methods are presented to predict pavement temperature. • Pavement temperature can be excellently predicted in next 3 h. - Abstract: Ice cover on pavement threatens traffic safety, and pavement temperature is the main factor used to determine whether the wet pavement is icy or not. In this paper, a temperature prediction model of the pavement in winter is established by introducing an improved Back Propagation (BP) neural network model. Before the application of the BP neural network model, many efforts were made to eliminate chaos and determine the regularity of temperature on the pavement surface (e.g., analyze the regularity of diurnal and monthly variations of pavement temperature). New dynamic and static prediction methods are presented by improving the algorithms to intelligently overcome the prediction inaccuracy at the change point of daily temperature. Furthermore, some scenarios have been compared for different dates and road sections to verify the reliability of the prediction model. According to the analysis results, the daily pavement temperatures can be accurately predicted for the next 3 h from the time of prediction by combining the dynamic and static prediction methods. The presented method in this paper can provide technical references for temperature prediction of the pavement and the development of an early-warning system for icy pavements in cold regions.

  13. Predicting cognitive decline in Alzheimer's disease: an integrated analysis

    DEFF Research Database (Denmark)

    Lopez, Oscar L; Schwam, Elias; Cummings, Jeffrey

    2010-01-01

    Numerous patient- and disease-related factors increase the risk of rapid cognitive decline in patients with Alzheimer's disease (AD). The ability of pharmacological treatment to attenuate this risk remains undefined.......Numerous patient- and disease-related factors increase the risk of rapid cognitive decline in patients with Alzheimer's disease (AD). The ability of pharmacological treatment to attenuate this risk remains undefined....

  14. Framingham coronary heart disease risk score can be predicted from structural brain images in elderly subjects.

    Directory of Open Access Journals (Sweden)

    Jane Maryam Rondina

    2014-12-01

    Full Text Available Recent literature has presented evidence that cardiovascular risk factors (CVRF play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer’s disease (AD and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies in a sample of healthy elderly individuals. We aim to answer the following questions: Is it possible to decode (i.e., to learn information regarding cardiovascular risk from structural brain images enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: i we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease. ii When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. iii We found important gender differences, and the possible causes of that finding are discussed.

  15. Failed rib region prediction in a human body model during crash events with precrash braking.

    Science.gov (United States)

    Guleyupoglu, B; Koya, B; Barnard, R; Gayzik, F S

    2018-02-28

    The objective of this study is 2-fold. We used a validated human body finite element model to study the predicted chest injury (focusing on rib fracture as a function of element strain) based on varying levels of simulated precrash braking. Furthermore, we compare deterministic and probabilistic methods of rib injury prediction in the computational model. The Global Human Body Models Consortium (GHBMC) M50-O model was gravity settled in the driver position of a generic interior equipped with an advanced 3-point belt and airbag. Twelve cases were investigated with permutations for failure, precrash braking system, and crash severity. The severities used were median (17 kph), severe (34 kph), and New Car Assessment Program (NCAP; 56.4 kph). Cases with failure enabled removed rib cortical bone elements once 1.8% effective plastic strain was exceeded. Alternatively, a probabilistic framework found in the literature was used to predict rib failure. Both the probabilistic and deterministic methods take into consideration location (anterior, lateral, and posterior). The deterministic method is based on a rubric that defines failed rib regions dependent on a threshold for contiguous failed elements. The probabilistic method depends on age-based strain and failure functions. Kinematics between both methods were similar (peak max deviation: ΔX head = 17 mm; ΔZ head = 4 mm; ΔX thorax = 5 mm; ΔZ thorax = 1 mm). Seat belt forces at the time of probabilistic failed region initiation were lower than those at deterministic failed region initiation. The probabilistic method for rib fracture predicted more failed regions in the rib (an analog for fracture) than the deterministic method in all but 1 case where they were equal. The failed region patterns between models are similar; however, there are differences that arise due to stress reduced from element elimination that cause probabilistic failed regions to continue to rise after no deterministic failed region would be

  16. Lipid-related markers and cardiovascular disease prediction

    DEFF Research Database (Denmark)

    Di Angelantonio, Emanuele; Gao, Pei; Pennells, Lisa

    2012-01-01

    The value of assessing various emerging lipid-related markers for prediction of first cardiovascular events is debated.......The value of assessing various emerging lipid-related markers for prediction of first cardiovascular events is debated....

  17. DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields

    Directory of Open Access Journals (Sweden)

    Sheng Wang

    2015-07-01

    Full Text Available Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields, to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors.

  18. Prediction of exacerbation chronic bronchopulmonary diseases in children with influenza

    Directory of Open Access Journals (Sweden)

    O. I. Afanaseva

    2015-01-01

    Full Text Available The objective: To develop a method for predicting exacerbation of chronic illness in children with asthma and cystic fibrosis, patients with influenza, based on the study of the dynamics of cytokines. Materials and methods: Were examined 52 patients with bronchial asthma and 45 children with cystic fibrosis at the age from 1 year to 12 years, located in infectious pulmonary Department at the planned treatment of underlying pathology, in which influenza was in-hospital infection. Control group observations included 40 patients with the flu, without concomitant pulmonary disease. The etiology of viral infection was established by detection of viral RNA in nasopharyngeal swabs by PCR. Among the influenza viruses were identified influenza АH1N1, АH3N2, influenza B, and in 2009–2010 the predominant antigen was the pandemic influenza virus АH1N1pdm09. Determination of the concentration of serum interleukins IL-1β, IL-4, IL-8, IL-10, ТNF-α, IFN-γ was performed in the 1st and 3rd day of hospitalization cytokines by the solid-phase immune-enzyme assay. Analysis of the results performed using statistical package SPSS 17.0 EN for Windows. Results: The flu caused the aggravation associated bronchopulmonary pathology in 2/3 of children, as MV patients, and patients with BA (65,4%-66,7%, respectively. With an increase of the ratio of IL-4 / IFN-γ and IL-10/IFN-γ, at least 5-6 times, influenza can be considered a trigger of exacerbation of chronic bronchopulmonary pathologies that require amplification of the therapy of bronchial asthma and of сystic fibrosis. The growth of prognostic coefficients in 2-3 times allows using for treatment of influenza in these patients only antiviral agents. Conclusion: The study has shown a method for predicting exacerbation of bronchial asthma and cystic fibrosis in children at an early stage of influenza by calculating the ratio of IL-4/IFN-γ and IL-10/IFN-γ in children aged from 1 year to 12 years. 

  19. Saturated properties prediction in critical region by a quartic equation of state

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2011-08-01

    Full Text Available A diverse substance library containing extensive PVT data for 77 pure components was used to critically evaluate the performance of a quartic equation of state and other four famous cubic equations of state in critical region. The quartic EOS studied in this work was found to significantly superior to the others in both vapor pressure prediction and saturated volume prediction in vicinity of critical point.

  20. COLLISION-AVOIDANCE FOR MOBILE ROBOTS USING REGION OF CERTAINTY: A PREDICTIVE APPROACH

    Directory of Open Access Journals (Sweden)

    B. MANUP

    2016-01-01

    Full Text Available In on-line environment, obstacles may exhibit different trajectory. Trajectory analysis of the obstacle is essential in determining its future location. If this analysis is accurate the futuristic region where robot and obstacle collision is likely to occur can be estimated. This enables the mobile robot to take corrective action prior to collision. In this approach, the motion pattern of the obstacle is analysed by taking into account the past co-ordinates traversed by the obstacle. Then the futuristic region where the obstacle is likely to occupy is predicted. This region is termed as region of certainty. Simulation results shows that the approach gives more reliable prediction as many number of sample points representing the past positions travelled by the obstacles are taken into consideration. The algorithm yielded better performance under higher obstacle velocity conditions and the results were compared with distance time transform method.

  1. 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.

  2. 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.

  3. Prediction of neurodegenerative diseases from functional brain imaging data

    NARCIS (Netherlands)

    Mudali, Deborah

    2016-01-01

    Neurodegenerative diseases are a challenge, especially in the developed society where life expectancy is high. Since these diseases progress slowly, they are not easy to diagnose at an early stage. Moreover, they portray similar disease features, which makes them hard to differentiate. In this

  4. Evaluation of performance of seasonal precipitation prediction at regional scale over India

    Science.gov (United States)

    Mohanty, U. C.; Nageswararao, M. M.; Sinha, P.; Nair, A.; Singh, A.; Rai, R. K.; Kar, S. C.; Ramesh, K. J.; Singh, K. K.; Ghosh, K.; Rathore, L. S.; Sharma, R.; Kumar, A.; Dhekale, B. S.; Maurya, R. K. S.; Sahoo, R. K.; Dash, G. P.

    2018-03-01

    The seasonal scale precipitation amount is an important ingredient in planning most of the agricultural practices (such as a type of crops, and showing and harvesting schedules). India being an agroeconomic country, the seasonal scale prediction of precipitation is directly linked to the socioeconomic growth of the nation. At present, seasonal precipitation prediction at regional scale is a challenging task for the scientific community. In the present study, an attempt is made to develop multi-model dynamical-statistical approach for seasonal precipitation prediction at the regional scale (meteorological subdivisions) over India for four prominent seasons which are winter (from December to February; DJF), pre-monsoon (from March to May; MAM), summer monsoon (from June to September; JJAS), and post-monsoon (from October to December; OND). The present prediction approach is referred as extended range forecast system (ERFS). For this purpose, precipitation predictions from ten general circulation models (GCMs) are used along with the India Meteorological Department (IMD) rainfall analysis data from 1982 to 2008 for evaluation of the performance of the GCMs, bias correction of the model results, and development of the ERFS. An extensive evaluation of the performance of the ERFS is carried out with dependent data (1982-2008) as well as independent predictions for the period 2009-2014. In general, the skill of the ERFS is reasonably better and consistent for all the seasons and different regions over India as compared to the GCMs and their simple mean. The GCM products failed to explain the extreme precipitation years, whereas the bias-corrected GCM mean and the ERFS improved the prediction and well represented the extremes in the hindcast period. The peak intensity, as well as regions of maximum precipitation, is better represented by the ERFS than the individual GCMs. The study highlights the improvement of forecast skill of the ERFS over 34 meteorological subdivisions

  5. Prediction of Associations between OMIM Diseases and MicroRNAs by Random Walk on OMIM Disease Similarity Network

    Directory of Open Access Journals (Sweden)

    Hailin Chen

    2013-01-01

    Full Text Available Increasing evidence has revealed that microRNAs (miRNAs play important roles in the development and progression of human diseases. However, efforts made to uncover OMIM disease-miRNA associations are lacking and the majority of diseases in the OMIM database are not associated with any miRNA. Therefore, there is a strong incentive to develop computational methods to detect potential OMIM disease-miRNA associations. In this paper, random walk on OMIM disease similarity network is applied to predict potential OMIM disease-miRNA associations under the assumption that functionally related miRNAs are often associated with phenotypically similar diseases. Our method makes full use of global disease similarity values. We tested our method on 1226 known OMIM disease-miRNA associations in the framework of leave-one-out cross-validation and achieved an area under the ROC curve of 71.42%. Excellent performance enables us to predict a number of new potential OMIM disease-miRNA associations and the newly predicted associations are publicly released to facilitate future studies. Some predicted associations with high ranks were manually checked and were confirmed from the publicly available databases, which was a strong evidence for the practical relevance of our method.

  6. Prediction of lake depth across a 17-state region in the United States

    Science.gov (United States)

    Oliver, Samantha K.; Soranno, Patricia A.; Fergus, C. Emi; Wagner, Tyler; Winslow, Luke A.; Scott, Caren E.; Webster, Katherine E.; Downing, John A.; Stanley, Emily H.

    2016-01-01

    Lake depth is an important characteristic for understanding many lake processes, yet it is unknown for the vast majority of lakes globally. Our objective was to develop a model that predicts lake depth using map-derived metrics of lake and terrestrial geomorphic features. Building on previous models that use local topography to predict lake depth, we hypothesized that regional differences in topography, lake shape, or sedimentation processes could lead to region-specific relationships between lake depth and the mapped features. We therefore used a mixed modeling approach that included region-specific model parameters. We built models using lake and map data from LAGOS, which includes 8164 lakes with maximum depth (Zmax) observations. The model was used to predict depth for all lakes ≥4 ha (n = 42 443) in the study extent. Lake surface area and maximum slope in a 100 m buffer were the best predictors of Zmax. Interactions between surface area and topography occurred at both the local and regional scale; surface area had a larger effect in steep terrain, so large lakes embedded in steep terrain were much deeper than those in flat terrain. Despite a large sample size and inclusion of regional variability, model performance (R2 = 0.29, RMSE = 7.1 m) was similar to other published models. The relative error varied by region, however, highlighting the importance of taking a regional approach to lake depth modeling. Additionally, we provide the largest known collection of observed and predicted lake depth values in the United States.

  7. Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

    Directory of Open Access Journals (Sweden)

    Waqasuddin Khan

    Full Text Available Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58.Next, we trained a bidirectional recurrent neural network (BRNN using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72 showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors.

  8. LANDIS PRO: a landscape model that predicts forest composition and structure changes at regional scales

    Science.gov (United States)

    Wen J. Wang; Hong S. He; Jacob S. Fraser; Frank R. Thompson; Stephen R. Shifley; Martin A. Spetich

    2014-01-01

    LANDIS PRO predicts forest composition and structure changes incorporating species-, stand-, and landscape-scales processes at regional scales. Species-scale processes include tree growth, establishment, and mortality. Stand-scale processes contain density- and size-related resource competition that regulates self-thinning and seedling establishment. Landscapescale...

  9. Adjusting the Stems Regional Forest Growth Model to Improve Local Predictions

    Science.gov (United States)

    W. Brad Smith

    1983-01-01

    A simple procedure using double sampling is described for adjusting growth in the STEMS regional forest growth model to compensate for subregional variations. Predictive accuracy of the STEMS model (a distance-independent, individual tree growth model for Lake States forests) was improved by using this procedure

  10. Machine learning and hurdle models for improving regional predictions of stream water acid neutralizing capacity

    Science.gov (United States)

    Nicholas A. Povak; Paul F. Hessburg; Keith M. Reynolds; Timothy J. Sullivan; Todd C. McDonnell; R. Brion Salter

    2013-01-01

    In many industrialized regions of the world, atmospherically deposited sulfur derived from industrial, nonpoint air pollution sources reduces stream water quality and results in acidic conditions that threaten aquatic resources. Accurate maps of predicted stream water acidity are an essential aid to managers who must identify acid-sensitive streams, potentially...

  11. Regional calibration models for predicting loblolly pine tracheid properties using near-infrared spectroscopy

    Science.gov (United States)

    Mohamad Nabavi; Joseph Dahlen; Laurence Schimleck; Thomas L. Eberhardt; Cristian Montes

    2018-01-01

    This study developed regional calibration models for the prediction of loblolly pine (Pinus taeda) tracheid properties using near-infrared (NIR) spectroscopy. A total of 1842 pith-to-bark radial strips, aged 19–31 years, were acquired from 268 trees from 109 stands across the southeastern USA. Diffuse reflectance NIR spectra were collected at 10-mm...

  12. [Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

    Science.gov (United States)

    Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

    2012-02-01

    In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

  13. Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management.

    Science.gov (United States)

    Erraguntla, Madhav; Zapletal, Josef; Lawley, Mark

    2017-12-01

    The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.

  14. Infectious disease morbidity in the US region bordering Mexico, 1990-1998.

    Science.gov (United States)

    Doyle, T J; Bryan, R T

    2000-11-01

    The United States and Mexico share an international boundary approximately 3000 km long. This border separates 2 nations with great differences in health status. The objective of this study was to assess morbidity due to infectious diseases in the US region bordering Mexico. The incidence between 1990 and 1998 of 22 nationally notifiable infectious diseases was compared between border and nonborder regions. Disease rates, reflected as rate ratios, were higher in the border region for botulism, brucellosis, diphtheria, hepatitis A, measles, mumps, rabies, rubella, salmonellosis, and shigellosis than in either of 2 nonborder comparison regions. These data indicate that incidence rates for a variety of infectious diseases of public health importance are significantly higher in the United States along the Mexican border than in nonborder regions. These results suggest that an inadequate public health infrastructure may contribute to excess morbidity due to infectious diseases in the border region.

  15. C-reactive protein, fibrinogen, and cardiovascular disease prediction

    DEFF Research Database (Denmark)

    Kaptoge, Stephen; Di Angelantonio, Emanuele; Pennells, Lisa

    2012-01-01

    There is debate about the value of assessing levels of C-reactive protein (CRP) and other biomarkers of inflammation for the prediction of first cardiovascular events.......There is debate about the value of assessing levels of C-reactive protein (CRP) and other biomarkers of inflammation for the prediction of first cardiovascular events....

  16. Glycated Hemoglobin Measurement and Prediction of Cardiovascular Disease

    DEFF Research Database (Denmark)

    Di Angelantonio, Emanuele; Gao, Pei; Khan, Hassan

    2014-01-01

    IMPORTANCE: The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain. OBJECTIVE: To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of c...

  17. [Prediction of potential geographic distribution of Lyme disease in Qinghai province with Maximum Entropy model].

    Science.gov (United States)

    Zhang, Lin; Hou, Xuexia; Liu, Huixin; Liu, Wei; Wan, Kanglin; Hao, Qin

    2016-01-01

    To predict the potential geographic distribution of Lyme disease in Qinghai by using Maximum Entropy model (MaxEnt). The sero-diagnosis data of Lyme disease in 6 counties (Huzhu, Zeku, Tongde, Datong, Qilian and Xunhua) and the environmental and anthropogenic data including altitude, human footprint, normalized difference vegetation index (NDVI) and temperature in Qinghai province since 1990 were collected. By using the data of Huzhu Zeku and Tongde, the prediction of potential distribution of Lyme disease in Qinghai was conducted with MaxEnt. The prediction results were compared with the human sero-prevalence of Lyme disease in Datong, Qilian and Xunhua counties in Qinghai. Three hot spots of Lyme disease were predicted in Qinghai, which were all in the east forest areas. Furthermore, the NDVI showed the most important role in the model prediction, followed by human footprint. Datong, Qilian and Xunhua counties were all in eastern Qinghai. Xunhua was in hot spot areaⅡ, Datong was close to the north of hot spot area Ⅲ, while Qilian with lowest sero-prevalence of Lyme disease was not in the hot spot areas. The data were well modeled in MaxEnt (Area Under Curve=0.980). The actual distribution of Lyme disease in Qinghai was in consistent with the results of the model prediction. MaxEnt could be used in predicting the potential distribution patterns of Lyme disease. The distribution of vegetation and the range and intensity of human activity might be related with Lyme disease distribution.

  18. Rainfall prediction of Cimanuk watershed regions with canonical correlation analysis (CCA)

    Science.gov (United States)

    Rustiana, Shailla; Nurani Ruchjana, Budi; Setiawan Abdullah, Atje; Hermawan, Eddy; Berliana Sipayung, Sinta; Gede Nyoman Mindra Jaya, I.; Krismianto

    2017-10-01

    Rainfall prediction in Indonesia is very influential on various development sectors, such as agriculture, fisheries, water resources, industry, and other sectors. The inaccurate predictions can lead to negative effects. Cimanuk watershed is one of the main pillar of water resources in West Java. This watersheds divided into three parts, which is a headwater of Cimanuk sub-watershed, Middle of Cimanuk sub-watershed and downstream of Cimanuk sub- watershed. The flow of this watershed will flow through the Jatigede reservoir and will supply water to the north-coast area in the next few years. So, the reliable model of rainfall prediction is very needed in this watershed. Rainfall prediction conducted with Canonical Correlation Analysis (CCA) method using Climate Predictability Tool (CPT) software. The prediction is every 3months on 2016 (after January) based on Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over West Java. Predictors used in CPT were the monthly data index of Nino3.4, Dipole Mode (DMI), and Monsoon Index (AUSMI-ISMI-WNPMI-WYMI) with initial condition January. The initial condition is chosen by the last data update. While, the predictant were monthly rainfall data CHIRPS region of West Java. The results of prediction rainfall showed by skill map from Pearson Correlation. High correlation of skill map are on MAM (Mar-Apr-May), AMJ (Apr-May-Jun), and JJA (Jun-Jul-Aug) which means the model is reliable to forecast rainfall distribution over Cimanuk watersheds region (over West Java) on those seasons. CCA score over those season prediction mostly over 0.7. The accuracy of the model CPT also indicated by the Relative Operating Characteristic (ROC) curve of the results of Pearson correlation 3 representative point of sub-watershed (Sumedang, Majalengka, and Cirebon), were mostly located in the top line of non-skill, and evidenced by the same of rainfall patterns between observation and forecast. So, the model of CPT with CCA method

  19. Changes in predicted protein disorder tendency may contribute to disease risk

    Directory of Open Access Journals (Sweden)

    Hu Yang

    2011-12-01

    Full Text Available Abstract Background Recent studies suggest that many proteins or regions of proteins lack 3D structure. Defined as intrinsically disordered proteins, these proteins/peptides are functionally important. Recent advances in next generation sequencing technologies enable genome-wide identification of novel nucleotide variations in a specific population or cohort. Results Using the exonic single nucleotide variations (SNVs identified in the 1,000 Genomes Project and distributed by the Genetic Analysis Workshop 17, we systematically analysed the genetic and predicted disorder potential features of the non-synonymous variations. The result of experiments suggests that a significant change in the tendency of a protein region to be structured or disordered caused by SNVs may lead to malfunction of such a protein and contribute to disease risk. Conclusions After validation with functional SNVs on the traits distributed by GAW17, we conclude that it is valuable to consider structure/disorder tendencies while prioritizing and predicting mechanistic effects arising from novel genetic variations.

  20. Stargardt disease: towards developing a model to predict phenotype

    OpenAIRE

    Heathfield, Laura; Lacerda, Miguel; Nossek, Christel; Roberts, Lisa; Ramesar, Rajkumar S

    2013-01-01

    Stargardt disease is an ABCA4-associated retinopathy, which generally follows an autosomal recessive inheritance pattern and is a frequent cause of macular degeneration in childhood. ABCA4 displays significant allelic heterogeneity whereby different mutations can cause retinal diseases with varying severity and age of onset. A genotype–phenotype model has been proposed linking ABCA4 mutations, purported ABCA4 functional protein activity and severity of disease, as measured by degree of visual...

  1. Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

    Science.gov (United States)

    Li, Yifeng; Shi, Wenqiang; Wasserman, Wyeth W

    2018-05-31

    In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

  2. Host persistence or extinction from emerging infectious disease: insights from white-nose syndrome in endemic and invading regions.

    Science.gov (United States)

    Hoyt, Joseph R; Langwig, Kate E; Sun, Keping; Lu, Guanjun; Parise, Katy L; Jiang, Tinglei; Frick, Winifred F; Foster, Jeffrey T; Feng, Jiang; Kilpatrick, A Marm

    2016-03-16

    Predicting species' fates following the introduction of a novel pathogen is a significant and growing problem in conservation. Comparing disease dynamics between introduced and endemic regions can offer insight into which naive hosts will persist or go extinct, with disease acting as a filter on host communities. We examined four hypothesized mechanisms for host-pathogen persistence by comparing host infection patterns and environmental reservoirs for Pseudogymnoascus destructans (the causative agent of white-nose syndrome) in Asia, an endemic region, and North America, where the pathogen has recently invaded. Although colony sizes of bats and hibernacula temperatures were very similar, both infection prevalence and fungal loads were much lower on bats and in the environment in Asia than North America. These results indicate that transmission intensity and pathogen growth are lower in Asia, likely due to higher host resistance to pathogen growth in this endemic region, and not due to host tolerance, lower transmission due to smaller populations, or lower environmentally driven pathogen growth rate. Disease filtering also appears to be favouring initially resistant species in North America. More broadly, determining the mechanisms allowing species persistence in endemic regions can help identify species at greater risk of extinction in introduced regions, and determine the consequences for disease dynamics and host-pathogen coevolution. © 2016 The Author(s).

  3. Prediction of Chronic Kidney Disease Stage 3 by CKD273, a Urinary Proteomic Biomarker

    DEFF Research Database (Denmark)

    Pontillo, Claudia; Zhang, Zhen-Yu; Schanstra, Joost P

    2017-01-01

    Introduction: CKD273 is a urinary biomarker, which in advanced chronic kidney disease predicts further deterioration. We investigated whether CKD273 can also predict a decline of estimated glomerular filtration rate (eGFR) to ... threshold (P = 0.086). Discussion: In conclusion, while accounting for baseline eGFR, albuminuria, and covariables, CKD273 adds to the prediction of stage 3 chronic kidney disease, at which point intervention remains an achievable therapeutic target....

  4. Research on Improved Depth Belief Network-Based Prediction of Cardiovascular Diseases

    Directory of Open Access Journals (Sweden)

    Peng Lu

    2018-01-01

    Full Text Available Quantitative analysis and prediction can help to reduce the risk of cardiovascular disease. Quantitative prediction based on traditional model has low accuracy. The variance of model prediction based on shallow neural network is larger. In this paper, cardiovascular disease prediction model based on improved deep belief network (DBN is proposed. Using the reconstruction error, the network depth is determined independently, and unsupervised training and supervised optimization are combined. It ensures the accuracy of model prediction while guaranteeing stability. Thirty experiments were performed independently on the Statlog (Heart and Heart Disease Database data sets in the UCI database. Experimental results showed that the mean of prediction accuracy was 91.26% and 89.78%, respectively. The variance of prediction accuracy was 5.78 and 4.46, respectively.

  5. Altered protein glycosylation predicts Alzheimer's disease and modulates its pathology in disease model Drosophila.

    Science.gov (United States)

    Frenkel-Pinter, Moran; Stempler, Shiri; Tal-Mazaki, Sharon; Losev, Yelena; Singh-Anand, Avnika; Escobar-Álvarez, Daniela; Lezmy, Jonathan; Gazit, Ehud; Ruppin, Eytan; Segal, Daniel

    2017-08-01

    The pathological hallmarks of Alzheimer's disease (AD) are pathogenic oligomers and fibrils of misfolded amyloidogenic proteins (e.g., β-amyloid and hyper-phosphorylated tau in AD), which cause progressive loss of neurons in the brain and nervous system. Although deviations from normal protein glycosylation have been documented in AD, their role in disease pathology has been barely explored. Here our analysis of available expression data sets indicates that many glycosylation-related genes are differentially expressed in brains of AD patients compared with healthy controls. The robust differences found enabled us to predict the occurrence of AD with remarkable accuracy in a test cohort and identify a set of key genes whose expression determines this classification. We then studied in vivo the effect of reducing expression of homologs of 6 of these genes in transgenic Drosophila overexpressing human tau, a well-established invertebrate AD model. These experiments have led to the identification of glycosylation genes that may augment or ameliorate tauopathy phenotypes. Our results indicate that OstDelta, l(2)not and beta4GalT7 are tauopathy suppressors, whereas pgnat5 and CG33303 are enhancers, of tauopathy. These results suggest that specific alterations in protein glycosylation may play a causal role in AD etiology. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. Advanced Methods for Clinical Outcome Prediction in Acquired Heart Disease

    NARCIS (Netherlands)

    L.C. Battes (Linda)

    2014-01-01

    markdownabstract__Abstract__ Acquired heart disease, which includes conditions such as coronary artery disease (CAD) and heart failure, continues to pose a large impediment on the individuals that suffer from it as well as on society in general. CAD is the leading cause of death in the

  7. Does a carpal tunnel syndrome predict an underlying disease?

    NARCIS (Netherlands)

    M.C. de Rijk (Maarten); F.H. Vermeij (Frederique); M. Suntjens (Maartje); P.A. van Doorn (Pieter)

    2007-01-01

    textabstractCarpal tunnel syndrome (CTS) may be the presenting symptom of an underlying disease such as diabetes mellitus, hypothyroidism or connective tissue disease (CTD). It was investigated whether additional blood tests (glucose level, thyroid-stimulating hormone level and erythrocyte

  8. Software tool for improved prediction of Alzheimer's disease

    DEFF Research Database (Denmark)

    Soininen, Hilkka; Mattila, Jussi; Koikkalainen, Juha

    2012-01-01

    Diagnostic criteria of Alzheimer's disease (AD) emphasize the integration of clinical data and biomarkers. In practice, collection and analysis of patient data vary greatly across different countries and clinics.......Diagnostic criteria of Alzheimer's disease (AD) emphasize the integration of clinical data and biomarkers. In practice, collection and analysis of patient data vary greatly across different countries and clinics....

  9. A noninvasive method for the prediction of fetal hemolytic disease

    Directory of Open Access Journals (Sweden)

    E. N. Kravchenko

    2017-01-01

    Full Text Available Objective: to improve the diagnosis of fetal hemolytic disease.Subjects and methods. A study group consisted of 42 pregnant women whose newborn infants had varying degrees of hemolytic disease. The women were divided into 3 subgroups according to the severity of neonatal hemolytic disease: 1 pregnant women whose neonates were born with severe hemolytic disease (n = 14; 2 those who gave birth to babies with moderate hemolytic disease (n = 11; 3 those who delivered infants with mild hemolytic disease (n = 17. A comparison group included 42 pregnant women whose babies were born without signs of hemolytic disease. Curvesfor blood flow velocity in the middle cerebral artery were analyzed in a fetus of 25 to 39 weeks’ gestation.Results. The peak systolic blood flow velocity was observed in Subgroup 1; however, the indicator did not exceed 1.5 MoM even in severe fetal anemic syndrome. The fetal middle artery blood flow velocity rating scale was divided into 2 zones: 1 the boundary values of peak systolic blood flow velocity from the median to the obtained midscore; 2 the boundary values of peak systolic blood flow velocity of the obtained values of as high as 1.5 MoM.Conclusion. The value of peak systolic blood flow velocity being in Zone 2, or its dynamic changes by transiting to this zone can serve as a prognostic factor in the development of severe fetal hemolytic disease

  10. Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer's disease neuroimaging initiative.

    Science.gov (United States)

    Gomar, Jesus J; Bobes-Bascaran, Maria T; Conejero-Goldberg, Concepcion; Davies, Peter; Goldberg, Terry E

    2011-09-01

    Biomarkers have become increasingly important in understanding neurodegenerative processes associated with Alzheimer disease. Markers include regional brain volumes, cerebrospinal fluid measures of pathological Aβ1-42 and total tau, cognitive measures, and individual risk factors. To determine the discriminative utility of different classes of biomarkers and cognitive markers by examining their ability to predict a change in diagnostic status from mild cognitive impairment to Alzheimer disease. Longitudinal study. We analyzed the Alzheimer's Disease Neuroimaging Initiative database to study patients with mild cognitive impairment who converted to Alzheimer disease (n = 116) and those who did not convert (n = 204) within a 2-year period. We determined the predictive utility of 25 variables from all classes of markers, biomarkers, and risk factors in a series of logistic regression models and effect size analyses. The Alzheimer's Disease Neuroimaging Initiative public database. Primary outcome measures were odds ratios, pseudo- R(2)s, and effect sizes. In comprehensive stepwise logistic regression models that thus included variables from all classes of markers, the following baseline variables predicted conversion within a 2-year period: 2 measures of delayed verbal memory and middle temporal lobe cortical thickness. In an effect size analysis that examined rates of decline, change scores for biomarkers were modest for 2 years, but a change in an everyday functional activities measure (Functional Assessment Questionnaire) was considerably larger. Decline in scores on the Functional Assessment Questionnaire and Trail Making Test, part B, accounted for approximately 50% of the predictive variance in conversion from mild cognitive impairment to Alzheimer disease. Cognitive markers at baseline were more robust predictors of conversion than most biomarkers. Longitudinal analyses suggested that conversion appeared to be driven less by changes in the neurobiologic

  11. Generalizability of the Disease State Index Prediction Model for Identifying Patients Progressing from Mild Cognitive Impairment to Alzheimer's Disease

    NARCIS (Netherlands)

    Hall, A.; Munoz-Ruiz, M.; Mattila, J.; Koikkalainen, J.; Tsolaki, M.; Mecocci, P.; Kloszewska, I.; Vellas, B.; Lovestone, S.; Visser, P.J.; Lotjonen, J.; Soininen, H.

    2015-01-01

    Background: The Disease State Index (DSI) prediction model measures the similarity of patient data to diagnosed stable and progressive mild cognitive impairment (MCI) cases to identify patients who are progressing to Alzheimer's disease. Objectives: We evaluated how well the DSI generalizes across

  12. Approaches to predicting potential impacts of climate change on forest disease: An example with Armillaria root disease

    Science.gov (United States)

    Ned B. Klopfenstein; Mee-Sook Kim; John W. Hanna; Bryce A. Richardson; John E. Lundquist

    2011-01-01

    Climate change will likely have dramatic impacts on forest health because many forest trees could become maladapted to climate. Furthermore, climate change will have additional impacts on forest health through changes in the distribution and severity of forest disease. Methods are needed to predict the influence of climate change on forest disease so that appropriate...

  13. Lake nutrient stoichiometry is less predictable than nutrient concentrations at regional and sub-continental scales.

    Science.gov (United States)

    Collins, Sarah M; Oliver, Samantha K; Lapierre, Jean-Francois; Stanley, Emily H; Jones, John R; Wagner, Tyler; Soranno, Patricia A

    2017-07-01

    Production in many ecosystems is co-limited by multiple elements. While a known suite of drivers associated with nutrient sources, nutrient transport, and internal processing controls concentrations of phosphorus (P) and nitrogen (N) in lakes, much less is known about whether the drivers of single nutrient concentrations can also explain spatial or temporal variation in lake N:P stoichiometry. Predicting stoichiometry might be more complex than predicting concentrations of individual elements because some drivers have similar relationships with N and P, leading to a weak relationship with their ratio. Further, the dominant controls on elemental concentrations likely vary across regions, resulting in context dependent relationships between drivers, lake nutrients and their ratios. Here, we examine whether known drivers of N and P concentrations can explain variation in N:P stoichiometry, and whether explaining variation in stoichiometry differs across regions. We examined drivers of N:P in ~2,700 lakes at a sub-continental scale and two large regions nested within the sub-continental study area that have contrasting ecological context, including differences in the dominant type of land cover (agriculture vs. forest). At the sub-continental scale, lake nutrient concentrations were correlated with nutrient loading and lake internal processing, but stoichiometry was only weakly correlated to drivers of lake nutrients. At the regional scale, drivers that explained variation in nutrients and stoichiometry differed between regions. In the Midwestern U.S. region, dominated by agricultural land use, lake depth and the percentage of row crop agriculture were strong predictors of stoichiometry because only phosphorus was related to lake depth and only nitrogen was related to the percentage of row crop agriculture. In contrast, all drivers were related to N and P in similar ways in the Northeastern U.S. region, leading to weak relationships between drivers and stoichiometry

  14. Predicting geographically distributed adult dental decay in the greater Auckland region of New Zealand.

    Science.gov (United States)

    Rocha, C M; Kruger, E; Whyman, R; Tennant, M

    2014-06-01

    To model the geographic distribution of current (and treated) dental decay on a high-resolution geographic basis for the Auckland region of New Zealand. The application of matrix-based mathematics to modelling adult dental disease-based on known population risk profiles to provide a detailed map of the dental caries distribution for the greater Auckland region. Of the 29 million teeth in adults in the region some 1.2 million (4%) are suffering decay whilst 7.2 million (25%) have previously suffered decay and are now restored. The model provides a high-resolution picture of where the disease burden lies geographically and presents to health planners a method for developing future service plans.

  15. M-ficolin levels reflect disease activity and predict remission in early rheumatoid arthritis

    DEFF Research Database (Denmark)

    Ammitzbøll, Christian Gytz; Thiel, Steffen; Jensenius, Jens Christian

    2013-01-01

    To assess plasma M-ficolin concentrations in disease-modifying antirheumatic drug (DMARD)-naive patients with early rheumatoid arthritis (RA), to investigate the correlation of M-ficolin concentrations with disease activity markers, and to determine the predictive value of M-ficolin with respect...... to the Disease Activity Score in 28 joints (DAS28)....

  16. REVIEW OF HEART DISEASE PREDICTION SYSTEM USING DATA MINING AND HYBRID INTELLIGENT TECHNIQUES

    Directory of Open Access Journals (Sweden)

    R. Chitra

    2013-07-01

    Full Text Available The Healthcare industry generally clinical diagnosis is done mostly by doctor’s expertise and experience. Computer Aided Decision Support System plays a major role in medical field. With the growing research on heart disease predicting system, it has become important to categories the research outcomes and provides readers with an overview of the existing heart disease prediction techniques in each category. Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for medical data. From the study it is observed that Hybrid Intelligent Algorithm improves the accuracy of the heart disease prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are summarized in this paper.

  17. Epidemiology of chronic kidney disease in northern region of Senegal

    African Journals Online (AJOL)

    Introduction: Chronic kidney disease (CKD) is an emerging worldwide epidemic but few data are available in African populations. We aimed to assess prevalence of CKD in adult populations of Saint-Louis (northern Senegal). Methods: In a population-based survey between January and May 2012, we included 1,037 adults ...

  18. Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs.

    Science.gov (United States)

    Le, Duc-Hau; Dao, Lan T M

    2018-05-23

    Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease-lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs. Therefore, prediction performance is highly dependent on how well the similarities can be captured. Previous studies have calculated the similarity between two diseases by mapping exactly each disease to a single Disease Ontology (DO) term, and then use a semantic similarity measure to calculate the similarity between them. However, the problem of this approach is that a disease can be described by more than one DO terms. Until now, there is no annotation database of DO terms for diseases except for genes. In contrast, Human Phenotype Ontology (HPO) is designed to fully annotate human disease phenotypes. Therefore, in this study, we constructed disease similarity networks/matrices using HPO instead of DO. Then, we used these networks/matrices as inputs of two representative machine learning-based and network-based ranking algorithms, that is, regularized least square and heterogeneous graph-based inference, respectively. The results showed that the prediction performance of the two algorithms on HPO-based is better than that on DO-based networks/matrices. In addition, our method can predict 11 novel cancer-associated lncRNAs, which are supported by literature evidence. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. Regional Scale High Resolution δ18O Prediction in Precipitation Using MODIS EVI

    Science.gov (United States)

    Huang, Cho-Ying; Wang, Chung-Ho; Lin, Shou-De; Lo, Yi-Chen; Huang, Bo-Wen; Hatch, Kent A.; Shiu, Hau-Jie; You, Cheng-Feng; Chang, Yuan-Mou; Shen, Sheng-Feng

    2012-01-01

    The natural variation in stable water isotope ratio data, also known as water isoscape, is a spatiotemporal fingerprint and a powerful natural tracer that has been widely applied in disciplines as diverse as hydrology, paleoclimatology, ecology and forensic investigation. Although much effort has been devoted to developing a predictive water isoscape model, it remains a central challenge for scientists to generate high accuracy, fine scale spatiotemporal water isoscape prediction. Here we develop a novel approach of using the MODIS-EVI (the Moderate Resolution Imagining Spectroradiometer-Enhanced Vegetation Index), to predict δ18O in precipitation at the regional scale. Using a structural equation model, we show that the EVI and precipitated δ18O are highly correlated and thus the EVI is a good predictor of precipitated δ18O. We then test the predictability of our EVI-δ18O model and demonstrate that our approach can provide high accuracy with fine spatial (250×250 m) and temporal (16 days) scale δ18O predictions (annual and monthly predictabilities [r] are 0.96 and 0.80, respectively). We conclude the merging of the EVI and δ18O in precipitation can greatly extend the spatial and temporal data availability and thus enhance the applicability for both the EVI and water isoscape. PMID:23029053

  20. C-Reactive Protein, Fibrinogen, and Cardiovascular Disease Prediction

    NARCIS (Netherlands)

    Kaptoge, Stephen; Di Angelantonio, Emanuele; Pennells, Lisa; Wood, Angela M.; White, Ian R.; Gao, Pei; Walker, Matthew; Thompson, Alexander; Sarwar, Nadeem; Caslake, Muriel; Butterworth, Adam S.; Amouyel, Philippe; Assmann, Gerd; Bakker, Stephan J. L.; Barr, Elizabeth L. M.; Barrett-Connor, Elizabeth; Benjamin, Emelia J.; Bjorkelund, Cecilia; Brenner, Hermann; Brunner, Eric; Clarke, Robert; Cooper, Jackie A.; Cremer, Peter; Cushman, Mary; Dagenais, Gilles R.; D'Agostino, Ralph B.; Dankner, Rachel; Davey-Smith, George; Deeg, Dorly; Dekker, Jacqueline M.; Engstrom, Gunnar; Folsom, Aaron R.; Fowkes, F. Gerry R.; Gallacher, John; Gaziano, J. Michael; Giampaoli, Simona; Gillum, Richard F.; Hofman, Albert; Howard, Barbara V.; Ingelsson, Erik; Iso, Hiroyasu; Jorgensen, Torben; Kiechl, Stefan; Kitamura, Akihiko; Kiyohara, Yutaka; Koenig, Wolfgang; Kromhout, Daan; Kuller, Lewis H.; Lawlor, Debbie A.; Meade, Tom W.

    2012-01-01

    Background There is debate about the value of assessing levels of C-reactive protein (CRP) and other biomarkers of inflammation for the prediction of first cardiovascular events. Methods We analyzed data from 52 prospective studies that included 246,669 participants without a history of

  1. Association of grey matter changes with stability and flexibility of prediction in akinetic-rigid Parkinson's disease.

    Science.gov (United States)

    Trempler, Ima; Binder, Ellen; El-Sourani, Nadiya; Schiffler, Patrick; Tenberge, Jan-Gerd; Schiffer, Anne-Marike; Fink, Gereon R; Schubotz, Ricarda I

    2018-06-01

    Parkinson's disease (PD), which is caused by degeneration of dopaminergic neurons in the midbrain, results in a heterogeneous clinical picture including cognitive decline. Since the phasic signal of dopamine neurons is proposed to guide learning by signifying mismatches between subjects' expectations and external events, we here investigated whether akinetic-rigid PD patients without mild cognitive impairment exhibit difficulties in dealing with either relevant (requiring flexibility) or irrelevant (requiring stability) prediction errors. Following our previous study on flexibility and stability in prediction (Trempler et al. J Cogn Neurosci 29(2):298-309, 2017), we then assessed whether deficits would correspond with specific structural alterations in dopaminergic regions as well as in inferior frontal cortex, medial prefrontal cortex, and the hippocampus. Twenty-one healthy controls and twenty-one akinetic-rigid PD patients on and off medication performed a task which required to serially predict upcoming items. Switches between predictable sequences had to be indicated via button press, whereas sequence omissions had to be ignored. Independent of the disease, midbrain volume was related to a general response bias to unexpected events, whereas right putamen volume correlated with the ability to discriminate between relevant and irrelevant prediction errors. However, patients compared with healthy participants showed deficits in stabilisation against irrelevant prediction errors, associated with thickness of right inferior frontal gyrus and left medial prefrontal cortex. Flexible updating due to relevant prediction errors was also affected in patients compared with controls and associated with right hippocampus volume. Dopaminergic medication influenced behavioural performance across, but not within the patients. Our exploratory study warrants further research on deficient prediction error processing and its structural correlates as a core of cognitive symptoms

  2. Prevalence and predictive factors for regional osteopenia in women with anorexia nervosa.

    Science.gov (United States)

    Grinspoon, S; Thomas, E; Pitts, S; Gross, E; Mickley, D; Miller, K; Herzog, D; Klibanski, A

    2000-11-21

    Anorexia nervosa is highly prevalent among young women. To determine prevalence and predictive factors for regional bone loss. Prospective cohort analysis. University hospital. 130 women with anorexia nervosa. Dual-energy x-ray absorptiometry. The prevalence of osteopenia (-1.0 SD >/= T-score > -2.5 SD) and osteoporosis (T-score anorexia nervosa. Weight, but not estrogen use, is a significant predictor of BMD in this population at all skeletal sites.

  3. Quantitative analysis of regional myocardial performance in coronary artery disease

    Science.gov (United States)

    Stewart, D. K.; Dodge, H. T.; Frimer, M.

    1975-01-01

    Findings from a group of subjects with significant coronary artery stenosis are given. A group of controls determined by use of a quantitative method for the study of regional myocardial performance based on the frame-by-frame analysis of biplane left ventricular angiograms are presented. Particular emphasis was placed upon the analysis of wall motion in terms of normalized segment dimensions, timing and velocity of contraction. The results were compared with the method of subjective assessment used clinically.

  4. Predicting domestic and community violence by soldiers living in a conflict region.

    Science.gov (United States)

    Nandi, Corina; Elbert, Thomas; Bambonye, Manassé; Weierstall, Roland; Reichert, Manfred; Zeller, Anja; Crombach, Anselm

    2017-11-01

    Past research revealed war trauma and posttraumatic stress disorder (PTSD) symptoms as potential predictors for domestic and community violence in crisis regions and among soldiers in different armed conflicts. The impact of family violence and other adversities experienced in childhood as well as of a combat-enhanced appeal for aggressive behavior (appetitive aggression) remains to be specified. In the present study, the authors separately predicted violence against children, intimate partner violence and community violence in 381 Burundian soldiers returning from foreign deployment and living in a post- conflict region. Using path analysis, they aimed to disentangle the independent contributions and pathways of the following variables: Exposure to war trauma and childhood familial violence, PTSD and depression symptom severity, and appetitive aggression. Childhood familial violence had an independent effect on all contexts of violence and was the only significant predictor for violence against the soldiers' own children. Intimate partner violence was additionally predicted by depression symptom severity, while community violence was additionally predicted by PTSD symptom severity and appetitive aggression. Besides war-related mental ill-health and appetitive aggression, violent experiences during childhood development must not be overlooked as a factor fueling the cycle of violence in conflict regions. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  5. DEVELOPMENT OF THE SOCIAL TENSION RISK PREDICTING ALGORITHM IN THE POPULATION OF CERTAIN REGIONS OF RUSSIA

    Directory of Open Access Journals (Sweden)

    A. B. Mulik

    2017-01-01

    Full Text Available Aim. The aim of the study was development of approaches to predict the risk of social tension for population of the Russian Federation regions.Methods. Theoretical studies based on the analysis of cartographic material from the National Atlas of Russia. The use of geo-information technologies has provided modeling of environmental load in the territory of certain regions of Russia. Experimental studies were performed using standard methods of psycho-physiological testing involving 336 persons 18-23 years old of both sexes.Results. As a fundamental biologically significant factor of the environment, differentiating the Russian Federation territory to areas with discrete actual physical effects, total solar radiation was determined. The subsequent allocation of model regions (Republic of Crimea, Rostov and Saratov regions based on the principle of minimizing regional differences associated factors of environmental pressure per person. Experimental studies have revealed persistent systemic relationships of phenotypic characteristics and tendency of person to neuropsychic tension. The risk of social tension for the study area population is predicted on the condition of finding more than two thirds of the representatives of sample within the borders of a high level of general non-specific reactivity of an organism.Main conclusions. The expediency of using the northern latitude as an integral index of differentiation of areas on the specifics of the severity of the physical factors of environmental impact on human activity is justified. The possibility of the application for the level of general nonspecific reactivity of an organism as a phenotypic trait marker of social tension risk is identified. An algorithm for predicting the risk of social tension among the population, compactly living in certain territories of the Russian Federation is designed. 

  6. The prevalence of secondary diseases of the HIV patients in the Omsk region: cross sectional study

    OpenAIRE

    Pasechnik, Oksana; Pitsenko, Natalia

    2014-01-01

    The prevalence of secondary diseases of the HIV infected patients who were under observation in medical organizations of Omsk region in 2013 has been studied. 16, 8% of HIV-infected patients had a wide spectrum of secondary diseases, mainly infectious etiology. In the structure of secondary infections the leading position was occupied by tuberculosis (32, 3%), candidiasis (24,4%), bacterial diseases (23,7%). The average risk of tuberculosis diseases is 24 cases for 1000 HIV-infected patients.

  7. Increased brain-predicted aging in treated HIV disease

    NARCIS (Netherlands)

    Cole, James H; Underwood, Jonathan; Caan, Matthan W A; De Francesco, Davide; van Zoest, Rosan A; Leech, Robert; Wit, Ferdinand W N M; Portegies, Peter; Geurtsen, Gert J; Schmand, Ben A; Schim van der Loeff, Maarten F; Franceschi, Claudio; Sabin, Caroline A; Majoie, Charles B L M; Winston, Alan; Reiss, Peter; Sharp, David J; Kalsbeek, A.

    OBJECTIVE: To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters. METHODS: A

  8. Increased brain-predicted aging in treated HIV disease

    NARCIS (Netherlands)

    Cole, James H.; Underwood, Jonathan; Caan, Matthan W. A.; de Francesco, Davide; van Zoest, Rosan A.; Leech, Robert; Wit, Ferdinand W. N. M.; Portegies, Peter; Geurtsen, Gert J.; Schmand, Ben A.; Schim van der Loeff, Maarten F.; Franceschi, Claudio; Sabin, Caroline A.; Majoie, Charles B. L. M.; Winston, Alan; Reiss, Peter; Sharp, David J.; Schouten, J.; Kooij, K. W.; Elsenga, B. C.; Janssen, F. R.; Heidenrijk, M.; Schrijver, J. H. N.; Zikkenheiner, W.; van der Valk, M.; Henderiks, A.; Kootstra, N. A.; Harskamp-Holwerda, A. M.; Maurer, I.; Ruiz, M. M. Mangas; Booiman, T.; Girigorie, A. F.; Villaudy, J.; Frankin, E.; Pasternak, A.; Berkhout, B.; van der Kuyl, T.; Stege, J. A. ter; Twennaar, M. Klein; Su, T.; Siteur-van Rijnstra, E.; Weijer, K.; Bisschop, P. H. L. T.; Kalsbeek, A.; Wezel, M.; Visser, I.; Ruhé , H. G.; Tembo, L.; Stott, M.; Prins, M. [= Maria

    2017-01-01

    To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters. A large sample of

  9. Positive predictive value of serological diagnostic measures in celiac disease

    DEFF Research Database (Denmark)

    Toftedal, Peter; Nielsen, Christian; Madsen, Jonas Trolle

    2010-01-01

    Celiac disease (CD) antibodies, immunoglobulin A (IgA) anti-tissue transglutaminase (anti-tTG), IgA endomysium antibody (EMA), IgA and IgG anti-gliadin antibodies (IgA and IgG AGA) are first-line diagnostic tools used in selecting patients for duodenal biopsy. The goal of this study was to evaluate...

  10. Predictive genetic testing for cardiovascular diseases: Impact on carrier children

    NARCIS (Netherlands)

    Meulenkamp, Tineke M.; Tibben, Aad; Mollema, Eline D.; Van Langen, Irene M.; Wiegman, Albert; De Wert, Guido M.; De Beaufort, Inez D.; Wilde, Arthur A. M.; Smets, Ellen M. A.

    2008-01-01

    We studied the experiences of children identified by family screening who were found to be a mutation carrier for a genetic cardiovascular disease (Long QT Syndrome (LQTS), Hypertrophic Cardiomyopathy (HCM), Familial Hypercholesterolemia (FH)). We addressed the (a) manner in which they perceive

  11. Bushmeat Hunting, Deforestation, and Prediction of Zoonotic Disease

    Science.gov (United States)

    Daszak, Peter; Kilpatrick, A. Marm; Burke, Donald S.

    2005-01-01

    Understanding the emergence of new zoonotic agents requires knowledge of pathogen biodiversity in wildlife, human-wildlife interactions, anthropogenic pressures on wildlife populations, and changes in society and human behavior. We discuss an interdisciplinary approach combining virology, wildlife biology, disease ecology, and anthropology that enables better understanding of how deforestation and associated hunting leads to the emergence of novel zoonotic pathogens. PMID:16485465

  12. 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) ...

  13. Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology

    DEFF Research Database (Denmark)

    Rossin, Elizabeth J.; Hansen, Kasper Lage; Raychaudhuri, Soumya

    2011-01-01

    Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these r......Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed...... in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein-protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more...... that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non...

  14. A regional neural network model for predicting mean daily river water temperature

    Science.gov (United States)

    Wagner, Tyler; DeWeber, Jefferson Tyrell

    2014-01-01

    Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May–October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate

  15. High incidence of diseases endemic to the Amazon region of Brazil, 2001-2006.

    Science.gov (United States)

    Penna, Gerson; Pinto, Luiz Felipe; Soranz, Daniel; Glatt, Ruth

    2009-04-01

    In Brazil, reportable diseases are the responsibility of the Secretariat of Health Surveillance of the Brazilian Federal Ministry of Health. During 2001-2006, to determine incidence and hospitalization rates, we analyzed 5 diseases (malaria, leishmaniasis [cutaneous and visceral], dengue fever, leprosy, and tuberculosis) that are endemic to the Amazon region of Brazil. Data were obtained from 773 municipalities in 3 regions. Although incidence rates of malaria, leishmaniasis, tuberculosis, and leprosy are decreasing, persons in lower socioeconomic classes with insufficient formal education are affected more by these diseases and other health inequalities than are other population groups in the region.

  16. High Incidence of Diseases Endemic to the Amazon Region of Brazil, 2001–2006

    Science.gov (United States)

    Pinto, Luiz Felipe; Soranz, Daniel; Glatt, Ruth

    2009-01-01

    In Brazil, reportable diseases are the responsibility of the Secretariat of Health Surveillance of the Brazilian Federal Ministry of Health. During 2001–2006, to determine incidence and hospitalization rates, we analyzed 5 diseases (malaria, leishmaniasis [cutaneous and visceral], dengue fever, leprosy, and tuberculosis) that are endemic to the Amazon region of Brazil. Data were obtained from 773 municipalities in 3 regions. Although incidence rates of malaria, leishmaniasis, tuberculosis, and leprosy are decreasing, persons in lower socioeconomic classes with insufficient formal education are affected more by these diseases and other health inequalities than are other population groups in the region. PMID:19331758

  17. Regional variation in the predictive validity of self-rated health for mortality

    Directory of Open Access Journals (Sweden)

    Edward R. Berchick

    2017-12-01

    Full Text Available Self-rated health (SRH is a commonly used measure for assessing general health in surveys in the United States. However, individuals from different parts of the United States may vary in how they assess their health. Geographic differences in health care access and in the prevalence of illnesses may make it difficult to discern true regional differences in health when using SRH as a health measure. In this article, we use data from the 1986 and 1989–2006 National Health Interview Survey Linked Mortality Files and estimate Cox regression models to examine whether the relationship between SRH and five-year all-cause mortality differs by Census region. Contrary to hypotheses, there is no evidence of regional variation in the predictive validity of SRH for mortality. At all levels of SRH, and for both non-Hispanic white and non-Hispanic black respondents, SRH is equally and strongly associated with five-year mortality across regions. Our results suggest that differences in SRH across regions are not solely due to differences in how respondents assess their health across regions, but reflect true differences in health. Future research can, therefore, employ this common measure to investigate the geographic patterning of health in the United States.

  18. Frontal white matter hyperintensity predicts lower urinary tract dysfunction in older adults with amnestic mild cognitive impairment and Alzheimer's disease.

    Science.gov (United States)

    Ogama, Noriko; Yoshida, Masaki; Nakai, Toshiharu; Niida, Shumpei; Toba, Kenji; Sakurai, Takashi

    2016-02-01

    Lower urinary tract symptoms often limit activities of daily life and impair quality of life in the elderly. The purpose of the present study was to determine whether regional white matter hyperintensity (WMH) can predict lower urinary tract symptoms in elderly with amnestic mild cognitive impairment or Alzheimer's disease. The participants were 461 patients aged 65-85 years diagnosed with amnestic mild cognitive impairment or Alzheimer's disease. Patients and their caregivers were asked about symptoms of lower urinary tract symptoms (urinary difficulty, frequency and incontinence). Cognition, behavior and psychological symptoms of dementia and medication were evaluated. WMH and brain atrophy were analyzed using an automatic segmentation program. Regional WMH was evaluated in the frontal, parietal, temporal and occipital lobes. Patients with urinary incontinence showed significantly greater volume of WMH. WMH increased with age, especially in the frontal lobe. WMH in the frontal lobe was closely associated with urinary incontinence after adjustment for brain atrophy and classical confounding factors. Frontal WMH was a predictive factor for urinary incontinence in older adults with amnestic mild cognitive impairment or Alzheimer's disease. Urinary incontinence in demented older adults is not an incidental event, and careful insight into regional WMH on brain magnetic resonance imaging might greatly help in diagnosing individuals with a higher risk of urinary incontinence. © 2015 Japan Geriatrics Society.

  19. Low serum leptin predicts mortality in patients with chronic kidney disease stage 5

    DEFF Research Database (Denmark)

    Scholze, Alexandra; Rattensperger, Dirk; Zidek, Walter

    2007-01-01

    Leptin, secreted from adipose tissue, regulates food intake, energy expenditure, and immune function. It is unknown whether leptin predicts mortality in patients with chronic kidney disease stage 5 on hemodialysis therapy....

  20. Cardiovascular disease risk score prediction models for women and its applicability to Asians

    Directory of Open Access Journals (Sweden)

    Goh LGH

    2014-03-01

    Full Text Available Louise GH Goh,1 Satvinder S Dhaliwal,1 Timothy A Welborn,2 Peter L Thompson,2–4 Bruce R Maycock,1 Deborah A Kerr,1 Andy H Lee,1 Dean Bertolatti,1 Karin M Clark,1 Rakhshanda Naheed,1 Ranil Coorey,1 Phillip R Della5 1School of Public Health, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia; 2Sir Charles Gairdner Hospital, Nedlands, Perth, WA, Australia; 3School of Population Health, University of Western Australia, Perth, WA, Australia; 4Harry Perkins Institute for Medical Research, Perth, WA, Australia; 5School of Nursing and Midwifery, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, Australia Purpose: Although elevated cardiovascular disease (CVD risk factors are associated with a higher risk of developing heart conditions across all ethnic groups, variations exist between groups in the distribution and association of risk factors, and also risk levels. This study assessed the 10-year predicted risk in a multiethnic cohort of women and compared the differences in risk between Asian and Caucasian women. Methods: Information on demographics, medical conditions and treatment, smoking behavior, dietary behavior, and exercise patterns were collected. Physical measurements were also taken. The 10-year risk was calculated using the Framingham model, SCORE (Systematic COronary Risk Evaluation risk chart for low risk and high risk regions, the general CVD, and simplified general CVD risk score models in 4,354 females aged 20–69 years with no heart disease, diabetes, or stroke at baseline from the third Australian Risk Factor Prevalence Study. Country of birth was used as a surrogate for ethnicity. Nonparametric statistics were used to compare risk levels between ethnic groups. Results: Asian women generally had lower risk of CVD when compared to Caucasian women. The 10-year predicted risk was, however, similar between Asian and Australian women, for some models. These findings were

  1. Predicting and controlling infectious disease epidemics using temporal networks

    OpenAIRE

    Masuda, Naoki; Holme, Petter

    2013-01-01

    Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended anal...

  2. Poor Response to Periodontal Treatment May Predict Future Cardiovascular Disease.

    Science.gov (United States)

    Holmlund, A; Lampa, E; Lind, L

    2017-07-01

    Periodontal disease has been associated with cardiovascular disease (CVD), but whether the response to the treatment of periodontal disease affects this association has not been investigated in any large prospective study. Periodontal data obtained at baseline and 1 y after treatment were available in 5,297 individuals with remaining teeth who were treated at a specialized clinic for periodontal disease. Poor response to treatment was defined as having >10% sites with probing pocket depth >4 mm deep and bleeding on probing at ≥20% of the sites 1 y after active treatment. Fatal/nonfatal incidence rate of CVD (composite end point of myocardial infarction, stroke, and heart failure) was obtained from the Swedish cause-of-death and hospital discharge registers. Poisson regression analysis was performed to analyze future risk of CVD. During a median follow-up of 16.8 y (89,719 person-years at risk), those individuals who did not respond well to treatment (13.8% of the sample) had an increased incidence of CVD ( n = 870) when compared with responders (23.6 vs. 15.3%, P 4 mm, and number of teeth, the incidence rate ratio for CVD among poor responders was 1.28 (95% CI, 1.07 to 1.53; P = 0.007) as opposed to good responders. The incidence rate ratio among poor responders increased to 1.39 (95% CI, 1.13 to 1.73; P = 0.002) for those with the most remaining teeth. Individuals who did not respond well to periodontal treatment had an increased risk for future CVD, indicating that successful periodontal treatment might influence progression of subclinical CVD.

  3. Improving prediction of Alzheimer’s disease using patterns of cortical thinning and homogenizing images according to disease stage

    DEFF Research Database (Denmark)

    Eskildsen, Simon Fristed; Coupé, Pierrick; García-Lorenzo, Daniel

    Predicting Alzheimer’s disease (AD) in individuals with some symptoms of cognitive decline may have great influence on treatment choice and guide subject selection in trials on disease modifying drugs. Structural MRI has the potential of revealing early signs of neurodegeneration in the human brain...... and may thus aid in predicting and diagnosing AD. Surface-based cortical thickness measurements from T1-weighted MRI have demonstrated high sensitivity to cortical gray matter changes. In this study, we investigated the possibility of using patterns of cortical thickness measurements for predicting AD...... of conversion from MCI to AD can be improved by learning the atrophy patterns that are specific to the different stages of disease progression. This has the potential to guide the further development of imaging biomarkers in AD....

  4. Prediction of Thyroid Disease Using Data Mining Techniques

    Directory of Open Access Journals (Sweden)

    Irina Ioniţă

    2016-08-01

    Full Text Available Recently, thyroid diseases are more and more spread worldwide. In Romania, for example, one of eight women suffer from hypothyroidism, hyperthyroidism or thyroid cancer. Various research studies estimate that about 30% of Romanians are diagnosed with endemic goiter. The factors that affect the thyroid function are: stress, infection, trauma, toxins, low-calorie diet, certain medication etc. It is very important to prevent such diseases rather than cure them, because the majority of treatments consist in long term medication or in chirurgical intervention. The current study refers to the thyroid disease classification in two of the most common thyroid dysfunctions (hyperthyroidism and hypothyroidism among the population. The authors analyzed and compared four classification models: Naive Bayes, Decision Tree, Multilayer Perceptron and Radial Basis Function Network. The results indicate a significant accuracy for all the classification models mentioned above, the best classification rate being that of the Decision Tree model. The data set used to build and to validate the classifier was provided by the UCI machine learning repository and by a website with Romanian data. The framework for building and testing the classification models was KNIME Analytics Platform and Weka, two data mining software.

  5. Using prediction markets of market scoring rule to forecast infectious diseases: a case study in Taiwan.

    Science.gov (United States)

    Tung, Chen-yuan; Chou, Tzu-chuan; Lin, Jih-wen

    2015-08-11

    The Taiwan CDC relied on the historical average number of disease cases or rate (AVG) to depict the trend of epidemic diseases in Taiwan. By comparing the historical average data with prediction markets, we show that the latter have a better prediction capability than the former. Given the volatility of the infectious diseases in Taiwan, historical average is unlikely to be an effective prediction mechanism. We designed and built the Epidemic Prediction Markets (EPM) system based upon the trading mechanism of market scoring rule. By using this system, we aggregated dispersed information from various medical professionals to predict influenza, enterovirus, and dengue fever in Taiwan. EPM was more accurate in 701 out of 1,085 prediction events than the traditional baseline of historical average and the winning ratio of EPM versus AVG was 64.6 % for the target week. For the absolute prediction error of five diseases indicators of three infectious diseases, EPM was more accurate for the target week than AVG except for dengue fever confirmed cases. The winning ratios of EPM versus AVG for the confirmed cases of severe complicated influenza case, the rate of enterovirus infection, and the rate of influenza-like illness in the target week were 69.6 %, 83.9 and 76.0 %, respectively; instead, for the prediction of the confirmed cases of dengue fever and the confirmed cases of severe complicated enterovirus infection, the winning ratios of EPM were all below 50 %. Except confirmed cases of dengue fever, EPM provided accurate, continuous and real-time predictions of four indicators of three infectious diseases for the target week in Taiwan and outperformed the historical average data of infectious diseases.

  6. LDAP: a web server for lncRNA-disease association prediction.

    Science.gov (United States)

    Lan, Wei; Li, Min; Zhao, Kaijie; Liu, Jin; Wu, Fang-Xiang; Pan, Yi; Wang, Jianxin

    2017-02-01

    Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis. Some computational methods have been developed to infer lncRNA-disease associations. However, most of these methods infer lncRNA-disease associations only based on single data resource. In this paper, we propose a new computational method to predict lncRNA-disease associations by integrating multiple biological data resources. Then, we implement this method as a web server for lncRNA-disease association prediction (LDAP). The input of the LDAP server is the lncRNA sequence. The LDAP predicts potential lncRNA-disease associations by using a bagging SVM classifier based on lncRNA similarity and disease similarity. The web server is available at http://bioinformatics.csu.edu.cn/ldap jxwang@mail.csu.edu.cn. Supplementary data are available at Bioinformatics online.

  7. Disease activity in pregnant women with Crohn's disease and birth outcomes: a regional Danish cohort study

    DEFF Research Database (Denmark)

    Nørgård, Bente; Hundborg, Heidi H; Jacobsen, Bent Ascanius

    2007-01-01

    OBJECTIVES: CD is associated with increased risk of adverse birth outcomes, but existing studies have not assessed the impact of disease activity during pregnancy. We examined the impact of disease activity on birth outcomes: LBW, preterm birth, LBW at term, and CAs. METHODS: All births by CD wom...... disease activity). Further research is needed to assess the critical impact of disease activity in larger cohorts of CD women....

  8. Risk factors control in patients with cardiovascular diseases in Ivanovo region: possibilities of a regional registry

    Directory of Open Access Journals (Sweden)

    Belova O.A.

    2016-03-01

    Conclusion ― In primary care units of Ivanovo region in 2015 patients were insufficiently asked about their lifestyle (smoking, physical activity, eating habits, as well as their body weight was measured. If a patient had a risk factor he usually receive a proper advice. For BP, weight and blood lipids the goals were achieved rare.

  9. The influence of active region information on the prediction of solar flares: an empirical model using data mining

    Directory of Open Access Journals (Sweden)

    M. Núñez

    2005-11-01

    Full Text Available Predicting the occurrence of solar flares is a challenge of great importance for many space weather scientists and users. We introduce a data mining approach, called Behavior Pattern Learning (BPL, for automatically discovering correlations between solar flares and active region data, in order to predict the former. The goal of BPL is to predict the interval of time to the next solar flare and provide a confidence value for the associated prediction. The discovered correlations are described in terms of easy-to-read rules. The results indicate that active region dynamics is essential for predicting solar flares.

  10. RandomForest4Life: a Random Forest for predicting ALS disease progression.

    Science.gov (United States)

    Hothorn, Torsten; Jung, Hans H

    2014-09-01

    We describe a method for predicting disease progression in amyotrophic lateral sclerosis (ALS) patients. The method was developed as a submission to the DREAM Phil Bowen ALS Prediction Prize4Life Challenge of summer 2012. Based on repeated patient examinations over a three- month period, we used a random forest algorithm to predict future disease progression. The procedure was set up and internally evaluated using data from 1197 ALS patients. External validation by an expert jury was based on undisclosed information of an additional 625 patients; all patient data were obtained from the PRO-ACT database. In terms of prediction accuracy, the approach described here ranked third best. Our interpretation of the prediction model confirmed previous reports suggesting that past disease progression is a strong predictor of future disease progression measured on the ALS functional rating scale (ALSFRS). We also found that larger variability in initial ALSFRS scores is linked to faster future disease progression. The results reported here furthermore suggested that approaches taking the multidimensionality of the ALSFRS into account promise some potential for improved ALS disease prediction.

  11. Predicting and controlling infectious disease epidemics using temporal networks.

    Science.gov (United States)

    Masuda, Naoki; Holme, Petter

    2013-01-01

    Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments.

  12. Detection of a sleep disorder predicting Parkinson's disease

    DEFF Research Database (Denmark)

    Hansen, Ingeborg H.; Marcussen, Mikkel; Christensen, Julie Anja Engelhard

    2013-01-01

    Idiopathic rapid eye-movement (REM) sleep behavior disorder (iRBD) has been found to be a strong early predictor for later development into Parkinson's disease (PD). iRBD is diagnosed by polysomnography but the manual evaluation is laborious, why the aims of this study are to develop supportive...... methods for detecting iRBD from electroencephalo-graphic (EEG) signals recorded during REM sleep. This method classified subjects from their EEG similarity with the two classes iRBD patients and control subjects. The feature sets used for classifying subjects were based on the relative powers of the EEG...

  13. First case of Mycobacterium heckeshornense cavitary lung disease in the Latin America and Caribbean region

    NARCIS (Netherlands)

    Coitinho, C.; Greif, G.; Ingen, J. van; Laserra, P.; Robello, C.; Rivas, C.

    2016-01-01

    A case of cavitary pulmonary disease caused by Mycobacterium heckeshornense in Uruguay is described. This is the first case reported in the Latin America and Caribbean region, showing that this species is a worldwide opportunistic human pathogen.

  14. Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease

    Directory of Open Access Journals (Sweden)

    Elaheh Moradi

    2017-01-01

    Full Text Available Rey's Auditory Verbal Learning Test (RAVLT is a powerful neuropsychological tool for testing episodic memory, which is widely used for the cognitive assessment in dementia and pre-dementia conditions. Several studies have shown that an impairment in RAVLT scores reflect well the underlying pathology caused by Alzheimer's disease (AD, thus making RAVLT an effective early marker to detect AD in persons with memory complaints. We investigated the association between RAVLT scores (RAVLT Immediate and RAVLT Percent Forgetting and the structural brain atrophy caused by AD. The aim was to comprehensively study to what extent the RAVLT scores are predictable based on structural magnetic resonance imaging (MRI data using machine learning approaches as well as to find the most important brain regions for the estimation of RAVLT scores. For this, we built a predictive model to estimate RAVLT scores from gray matter density via elastic net penalized linear regression model. The proposed approach provided highly significant cross-validated correlation between the estimated and observed RAVLT Immediate (R = 0.50 and RAVLT Percent Forgetting (R = 0.43 in a dataset consisting of 806 AD, mild cognitive impairment (MCI or healthy subjects. In addition, the selected machine learning method provided more accurate estimates of RAVLT scores than the relevance vector regression used earlier for the estimation of RAVLT based on MRI data. The top predictors were medial temporal lobe structures and amygdala for the estimation of RAVLT Immediate and angular gyrus, hippocampus and amygdala for the estimation of RAVLT Percent Forgetting. Further, the conversion of MCI subjects to AD in 3-years could be predicted based on either observed or estimated RAVLT scores with an accuracy comparable to MRI-based biomarkers.

  15. Evaluating the ability of regional models to predict local avian abundance

    Science.gov (United States)

    LeBrun, Jaymi J.; Thogmartin, Wayne E.; Miller, James R.

    2012-01-01

    Spatial modeling over broad scales can potentially direct conservation efforts to areas with high species-specific abundances. We examined the performance of regional models for predicting bird abundance at spatial scales typically addressed in conservation planning. Specifically, we used point count data on wood thrush (Hylocichla mustelina) and blue-winged warbler (Vermivora cyanoptera) from 2 time periods (1995-1998 and 2006-2007) to evaluate the ability of regional models derived via Bayesian hierarchical techniques to predict bird abundance. We developed models for each species within Bird Conservation Region (BCR) 23 in the upper midwestern United States at 800-ha, 8,000-ha, and approximately 80,000-ha scales. We obtained count data from the Breeding Bird Survey and land cover data from the National Land Cover Dataset (1992). We evaluated predictions from the best models, as defined by an information-theoretic criterion, using point count data collected within an ecological subregion of BCR 23 at 131 count stations in the 1990s and again in 2006-2007. Competing (Deviance Information Criteria rs = 0.57; P = 0.14), the survey period that most closely aligned with the time period of data used for regional model construction. Wood thrush models exhibited positive correlations with point count data for all survey areas and years combined (rs = 0.58, P ≤ 0.001). In comparison, blue-winged warbler models performed worse as time increased between the point count surveys and vintage of the model building data (rs = 0.03, P = 0.92 for Iowa and rs = 0.13, P = 0.51 for all areas, 2006-2007), likely related to the ephemeral nature of their preferred early successional habitat. Species abundance and sensitivity to changing habitat conditions seems to be an important factor in determining the predictive ability of regional models. Hierarchical models can be a useful tool for concentrating efforts at the scale of management units and should be one of many tools used by

  16. Probability-based collaborative filtering model for predicting gene–disease associations

    OpenAIRE

    Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

    2017-01-01

    Background Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. Methods We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our mo...

  17. Chronic disease burden predicts food insecurity among older adults.

    Science.gov (United States)

    Jih, Jane; Stijacic-Cenzer, Irena; Seligman, Hilary K; Boscardin, W John; Nguyen, Tung T; Ritchie, Christine S

    2018-06-01

    Increased out-of-pocket health-care expenditures may exert budget pressure on low-income households that leads to food insecurity. The objective of the present study was to examine whether older adults with higher chronic disease burden are at increased risk of food insecurity. Secondary analysis of the 2013 Health and Retirement Study (HRS) Health Care and Nutrition Study (HCNS) linked to the 2012 nationally representative HRS. USA. Respondents of the 2013 HRS HCNS with household incomes insecurity was 27·8 %. Compared with those having 0-1 conditions, respondents with MCC were significantly more likely to report food insecurity, with the adjusted odds ratio for those with 2-4 conditions being 2·12 (95 % CI 1·45, 3·09) and for those with ≥5 conditions being 3·64 (95 % CI 2·47, 5·37). A heavy chronic disease burden likely exerts substantial pressure on the household budgets of older adults, creating an increased risk for food insecurity. Given the high prevalence of food insecurity among older adults, screening those with MCC for food insecurity in the clinical setting may be warranted in order to refer to community food resources.

  18. Hypoalbuminaemia predicts outcome in adult patients with congenital heart disease

    Science.gov (United States)

    Kempny, Aleksander; Diller, Gerhard-Paul; Alonso-Gonzalez, Rafael; Uebing, Anselm; Rafiq, Isma; Li, Wei; Swan, Lorna; Hooper, James; Donovan, Jackie; Wort, Stephen J; Gatzoulis, Michael A; Dimopoulos, Konstantinos

    2015-01-01

    Background In patients with acquired heart failure, hypoalbuminaemia is associated with increased risk of death. The prevalence of hypoproteinaemia and hypoalbuminaemia and their relation to outcome in adult patients with congenital heart disease (ACHD) remains, however, unknown. Methods Data on patients with ACHD who underwent blood testing in our centre within the last 14 years were collected. The relation between laboratory, clinical or demographic parameters at baseline and mortality was assessed using Cox proportional hazards regression analysis. Results A total of 2886 patients with ACHD were included. Mean age was 33.3 years (23.6–44.7) and 50.1% patients were men. Median plasma albumin concentration was 41.0 g/L (38.0–44.0), whereas hypoalbuminaemia (disease complexity, hypoalbuminaemia remained a significant predictor of death. Conclusions Hypoalbuminaemia is common in patients with ACHD and is associated with a threefold increased risk of risk of death. Hypoalbuminaemia, therefore, should be included in risk-stratification algorithms as it may assist management decisions and timing of interventions in the growing ACHD population. PMID:25736048

  19. Investigation of Thyroid Metabolism Diseases in Kütahya Region

    Directory of Open Access Journals (Sweden)

    Mehmet Yakar

    2012-07-01

    Full Text Available Aim: The study was performed on the sera sent for other diagnostic purposes like thyroid function tests (thyroid-stimulating hormone, total triiodothyronine and total thyroxin to the Laboratory of Kütahya Hıfzısıhha Institute. Material and Method: Patients visiting 13 health care centers province and districts of Kütahya province were included in this study. The study popula-tion consisted of 320 patients. Serum levels of cholesterol, trigliserid, HDL-cholesterol, LDL-cholesterol and lipid were measured. Results: The results of our study showed 250 individuals (78.12% to be within normal ranges, 42 (13.12% as hypothyroid, and 28 (8.75% were hyperthyroid. Hypothyroid pa-tients had significantly higher levels of cholesterol, LDL-cholesterol, lipid and thyroid-stimulating hormone levels (p<0.05. While hyperthyroid patients had significantly lower levels of cholesterol, LDL-cholesterol and lipid levels when compared with patients with normal thyroid hormone levels (p<0.05; Thyroxin levels were significantly higher (p<0.05. Discussion: The results of this study showed that the population under study was at risk of goiter diseases.

  20. Research on cardiovascular disease prediction based on distance metric learning

    Science.gov (United States)

    Ni, Zhuang; Liu, Kui; Kang, Guixia

    2018-04-01

    Distance metric learning algorithm has been widely applied to medical diagnosis and exhibited its strengths in classification problems. The k-nearest neighbour (KNN) is an efficient method which treats each feature equally. The large margin nearest neighbour classification (LMNN) improves the accuracy of KNN by learning a global distance metric, which did not consider the locality of data distributions. In this paper, we propose a new distance metric algorithm adopting cosine metric and LMNN named COS-SUBLMNN which takes more care about local feature of data to overcome the shortage of LMNN and improve the classification accuracy. The proposed methodology is verified on CVDs patient vector derived from real-world medical data. The Experimental results show that our method provides higher accuracy than KNN and LMNN did, which demonstrates the effectiveness of the Risk predictive model of CVDs based on COS-SUBLMNN.

  1. Communicable diseases in the Eastern Mediterranean Region: prevention and control 2010-2011.

    Science.gov (United States)

    Haq, Z; Mahjour, J; Khan, W

    2013-10-01

    One-third of all morbidities and mortalities in the Eastern Mediterranean Region are attributed to communicable diseases. A continued situation of war and conflict, and growing political unrest in the Region, coupled with factors such as travel and migration, and insufficient infrastructure and inadequate technical and managerial capacity ofthe programmes are the major challenges. Despite these challenges, the Region continued making progress towards the elimination of specific diseases such as lymphatic filariasis, measles, malaria, schistosomiasis and dracunculiasis during 2010-11. Coverage for vaccine-preventable diseases was enhanced. Preparedness and response to emerging (e.g. dengue fever in Pakistan and Yemen) and re-emerging (e.g. cholera in Sudan) infections was improved. The Region has continued its efforts for controlling tuberculosis and curbing HIV/AIDS. Looking ahead, the Region aims to improve surveillance and response capacities, legislation issues, coordination, bio-risk and bio-security and quality management in the coming years.

  2. Thermal regime of the lithosphere and prediction of seismic hazard in the Caspian region

    International Nuclear Information System (INIS)

    Levin, L.E.; Solodilov, L.N.; Kondorskaya, N.V.; Gasanov, A.G; Panahi, B.M.

    2002-01-01

    Full text : Prediction of seicmicity is one of elements of ecology hazard warning. In this collective research, it is elaborated in three directions : quantitative estimate of regional faults by level of seismic activity; ascertainment of space position of earthquake risk zones, determination of high seismic potential sites for the period of the next 3-5 years. During elaboration of prediction, it takes into account that peculiar feature all over the is determined by relationship of about 90 percent of earthquake hypocenters and released energy of seismic waves with elactic-brittle ayer of the lithosphere. Concetration of earthquakes epicenters is established predominantly in zones of complex structure of elastic-brittle layer where gradient of it thickness is 20-30 km. Directions of hypocenters migration in the plastic-viscous layer reveal a space position of seismic dangerous zones. All this provides a necessity for generalization of data on location of earthquakes epicenters; determination of their magnitudes, space position of regional faults and heat flow with calculation of thermal regime being made for clarification of the lithosphere and elastic-brittle thickness variations separately. General analysis includes a calculation of released seismic wave energy and determination of peculiar features of its distribution in the entire region and also studies of hypocenters migration in the plastic-viscous layer of the litosphere in time.

  3. Turning 18 with congenital heart disease: prediction of infective endocarditis based on a large population

    NARCIS (Netherlands)

    Verheugt, Carianne L.; Uiterwaal, Cuno S. P. M.; van der Velde, Enno T.; Meijboom, Folkert J.; Pieper, Petronella G.; Veen, Gerrit; Stappers, Jan L. M.; Grobbee, Diederick E.; Mulder, Barbara J. M.

    2011-01-01

    The risk of infective endocarditis (IE) in adults with congenital heart disease is known to be increased, yet empirical risk estimates are lacking. We sought to predict the occurrence of IE in patients with congenital heart disease at the transition from childhood into adulthood. We identified

  4. Genome-based prediction of common diseases: Methodological considerations for future research

    NARCIS (Netherlands)

    A.C.J.W. Janssens (Cécile); P. Tikka-Kleemola (Päivi)

    2009-01-01

    textabstractThe translation of emerging genomic knowledge into public health and clinical care is one of the major challenges for the coming decades. At the moment, genome-based prediction of common diseases, such as type 2 diabetes, coronary heart disease and cancer, is still not informative. Our

  5. A Knowledge-Base for a Personalized Infectious Disease Risk Prediction System.

    Science.gov (United States)

    Vinarti, Retno; Hederman, Lucy

    2018-01-01

    We present a knowledge-base to represent collated infectious disease risk (IDR) knowledge. The knowledge is about personal and contextual risk of contracting an infectious disease obtained from declarative sources (e.g. Atlas of Human Infectious Diseases). Automated prediction requires encoding this knowledge in a form that can produce risk probabilities (e.g. Bayesian Network - BN). The knowledge-base presented in this paper feeds an algorithm that can auto-generate the BN. The knowledge from 234 infectious diseases was compiled. From this compilation, we designed an ontology and five rule types for modelling IDR knowledge in general. The evaluation aims to assess whether the knowledge-base structure, and its application to three disease-country contexts, meets the needs of personalized IDR prediction system. From the evaluation results, the knowledge-base conforms to the system's purpose: personalization of infectious disease risk.

  6. Molecular prediction of disease risk and severity in a large Dutch Crohn's disease cohort

    NARCIS (Netherlands)

    Weersma, R.K.; Stokkers, P.C.F.; van Bodegraven, A.A.; van Hogezand, R.A.; Verspaget, H.W.; de Jong, D.J.; van der Woude, C.J.; Oldenburg, B.; Linskens, R.K.; Festen, E.A.M.; van der Steege, G.; Hommes, D.W.; Crusius, J.B.A.; Wijmenga, C.; Nolte, I.M.; Dijkstra, G.

    2009-01-01

    Background: Crohn's disease and ulcerative colitis have a complex genetic background. We assessed the risk for both the development and severity of the disease by combining information from genetic variants associated with inflammatory bowel disease (IBD). Methods: We studied 2804 patients (1684

  7. Predicting population extinction or disease outbreaks with stochastic models

    Directory of Open Access Journals (Sweden)

    Linda J. S. Allen

    2017-01-01

    Full Text Available Models of exponential growth, logistic growth and epidemics are common applications in undergraduate differential equation courses. The corresponding stochastic models are not part of these courses, although when population sizes are small their behaviour is often more realistic and distinctly different from deterministic models. For example, the randomness associated with births and deaths may lead to population extinction even in an exponentially growing population. Some background in continuous-time Markov chains and applications to populations, epidemics and cancer are presented with a goal to introduce this topic into the undergraduate mathematics curriculum that will encourage further investigation into problems on conservation, infectious diseases and cancer therapy. MATLAB programs for graphing sample paths of stochastic models are provided in the Appendix.

  8. Preclinical diagnosis of Alzheimer's disease: Prevention or prediction?

    Directory of Open Access Journals (Sweden)

    Ricardo Nitrini

    Full Text Available Abstract The diagnosis of Alzheimer's disease (AD for cases with dementia may be too late to allow effective treatment. Criteria for diagnosis of preclinical AD suggested by the Alzheimer's Association include the use of molecular and structural biomarkers. Preclinical diagnosis will enable testing of new drugs and forms of treatment toward achieving successful preventive treatment. But what are the advantages for the individual? To know that someone who is cognitively normal is probably going to develop AD's dementia when there is no effective preventive treatment is definitely not good news. A research method whereby volunteers are assigned to receive treatment or placebo without knowing whether they are in the control or at-risk arm of a trial would overcome this potential problem. If these new criteria are used wisely they may represent a relevant milestone in the search for a definitive treatment for AD.

  9. Active surveillance of the aquatic environment for potential prediction, prevention and spread of water borne disease: the cholera paradigm

    Science.gov (United States)

    Huq, A.; Colwell, R.

    2011-12-01

    Based on results of ecological and epidemiological studies, occurrence and spread of certain diseases are more fully understood. Cholera is a major waterborne disease, that is relatively easily treatable and clearly preventable, yet tens of thousands die each year worldwide. A dose dependent disease, the infectious dose can vary from 103-106, depending on health status of the victim. Historically, cholera has been shown to spread from person to person. Furthermore, the disease is caused predominantly via ingestion of contaminated water and most of the outbreaks that have been recorded worldwide originated in a coastal region. Using appropriate detection methods, Vibrio cholerae can be isolated from samples collected from ponds, rivers, estuaries, and coastal waters globally. The populations of V. cholerae may vary in numbers during different seasons of the year. It is important to have a clear understanding of the distribution of the causative agent in the environment as such information can assist public health officials in taking action to prevent outbreaks of cholera. Thus an effective monitoring program is critical, particularly in light of climate change with temperature extremes more likely to be occurring. Based on a predictive model and results of ground truth data, temperature has been found to be a factor in the increase of V. cholerae in the environment. Correlation was observed with occurrence of cholera and both temperature and salinity. More recent research indicates additional factors need to be considered in predicting cholera epidemics, including the hydrology and disease dynamics.

  10. Prediction of multipactor in the iris region of rf deflecting mode cavities

    Directory of Open Access Journals (Sweden)

    G. Burt

    2011-12-01

    Full Text Available Multipactor is a major cause of field limitation in many superconducting rf cavities. Multipacting is a particular issue for deflecting mode cavities as the typical behavior is not well studied, understood, or parametrized. In this paper an approximate analytical model for the prediction of multipactor in the iris region of deflecting mode cavities is developed. This new but simple model yields a clear explanation on the broad range of rf field levels over which the multipactor can occur. The principle multipactors under investigation here are two-point multipactors associated with cyclotron motion in the cavity’s rf magnetic field. The predictions from the model are compared to numerical simulations and good agreement is obtained. The results are also compared to experimental results previously reported by KEK and are also found in good agreement.

  11. Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer.

    Science.gov (United States)

    Cheng, Nai-Ming; Fang, Yu-Hua Dean; Lee, Li-yu; Chang, Joseph Tung-Chieh; Tsan, Din-Li; Ng, Shu-Hang; Wang, Hung-Ming; Liao, Chun-Ta; Yang, Lan-Yan; Hsu, Ching-Han; Yen, Tzu-Chen

    2015-03-01

    The question as to whether the regional textural features extracted from PET images predict prognosis in oropharyngeal squamous cell carcinoma (OPSCC) remains open. In this study, we investigated the prognostic impact of regional heterogeneity in patients with T3/T4 OPSCC. We retrospectively reviewed the records of 88 patients with T3 or T4 OPSCC who had completed primary therapy. Progression-free survival (PFS) and disease-specific survival (DSS) were the main outcome measures. In an exploratory analysis, a standardized uptake value of 2.5 (SUV 2.5) was taken as the cut-off value for the detection of tumour boundaries. A fixed threshold at 42 % of the maximum SUV (SUVmax 42 %) and an adaptive threshold method were then used for validation. Regional textural features were extracted from pretreatment (18)F-FDG PET/CT images using the grey-level run length encoding method and grey-level size zone matrix. The prognostic significance of PET textural features was examined using receiver operating characteristic (ROC) curves and Cox regression analysis. Zone-size nonuniformity (ZSNU) was identified as an independent predictor of PFS and DSS. Its prognostic impact was confirmed using both the SUVmax 42 % and the adaptive threshold segmentation methods. Based on (1) total lesion glycolysis, (2) uniformity (a local scale texture parameter), and (3) ZSNU, we devised a prognostic stratification system that allowed the identification of four distinct risk groups. The model combining the three prognostic parameters showed a higher predictive value than each variable alone. ZSNU is an independent predictor of outcome in patients with advanced T-stage OPSCC, and may improve their prognostic stratification.

  12. Prediction of disease causing non-synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP.

    Directory of Open Access Journals (Sweden)

    Morten Bo Johansen

    Full Text Available We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSNP.

  13. Genome-scale prediction of proteins with long intrinsically disordered regions.

    Science.gov (United States)

    Peng, Zhenling; Mizianty, Marcin J; Kurgan, Lukasz

    2014-01-01

    Proteins with long disordered regions (LDRs), defined as having 30 or more consecutive disordered residues, are abundant in eukaryotes, and these regions are recognized as a distinct class of biologically functional domains. LDRs facilitate various cellular functions and are important for target selection in structural genomics. Motivated by the lack of methods that directly predict proteins with LDRs, we designed Super-fast predictor of proteins with Long Intrinsically DisordERed regions (SLIDER). SLIDER utilizes logistic regression that takes an empirically chosen set of numerical features, which consider selected physicochemical properties of amino acids, sequence complexity, and amino acid composition, as its inputs. Empirical tests show that SLIDER offers competitive predictive performance combined with low computational cost. It outperforms, by at least a modest margin, a comprehensive set of modern disorder predictors (that can indirectly predict LDRs) and is 16 times faster compared to the best currently available disorder predictor. Utilizing our time-efficient predictor, we characterized abundance and functional roles of proteins with LDRs over 110 eukaryotic proteomes. Similar to related studies, we found that eukaryotes have many (on average 30.3%) proteins with LDRs with majority of proteomes having between 25 and 40%, where higher abundance is characteristic to proteomes that have larger proteins. Our first-of-its-kind large-scale functional analysis shows that these proteins are enriched in a number of cellular functions and processes including certain binding events, regulation of catalytic activities, cellular component organization, biogenesis, biological regulation, and some metabolic and developmental processes. A webserver that implements SLIDER is available at http://biomine.ece.ualberta.ca/SLIDER/. Copyright © 2013 Wiley Periodicals, Inc.

  14. 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...

  15. World Health Organization Global Estimates and Regional Comparisons of the Burden of Foodborne Disease in 2010

    NARCIS (Netherlands)

    Havelaar, Arie H|info:eu-repo/dai/nl/072306122; Kirk, Martyn D; Torgerson, Paul R; Gibb, Herman J; Hald, Tine; Lake, Robin J; Praet, Nicolas; Bellinger, David C; de Silva, Nilanthi R; Gargouri, Neyla; Speybroeck, Niko; Cawthorne, Amy; Mathers, Colin; Stein, Claudia; Angulo, Frederick J; Devleesschauwer, Brecht

    2015-01-01

    Illness and death from diseases caused by contaminated food are a constant threat to public health and a significant impediment to socio-economic development worldwide. To measure the global and regional burden of foodborne disease (FBD), the World Health Organization (WHO) established the Foodborne

  16. The ecological condition and diseases malignant new formation at the population of north region Kazakhstan

    International Nuclear Information System (INIS)

    Makishev, A.K.; Rakhimbekov, M.O.; Zhakipbaev, K.A.; Malaniya, Z.Sh.; Zhagiparov, M.K.; Kusainov, K.Z.

    2003-01-01

    Growing diseases malignant formation by many countries of the world, including Kazakhstan, relatedness with deterioration ecological state. Broadly geological researches, development of pits (coal, gold-bearing, and uranium ore) in north region of the Republic renders negative influence on the picture of population health, that bring increasing level of diseases mortality

  17. Control of drug treatment of chronic coronary artery disease: possibilities of a regional registry

    Directory of Open Access Journals (Sweden)

    Rachkova S.A.

    2016-03-01

    Full Text Available The article describes the results of the Register of hypertension, coronary artery disease, chronic heart failure (Register of AH, CAD, HF in the Ivanovo region in 2015. The frequency of prescribing of the main groups of drugs in patients with coronary artery disease was estimated.

  18. Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology

    Science.gov (United States)

    Rossin, Elizabeth J.; Lage, Kasper; Raychaudhuri, Soumya; Xavier, Ramnik J.; Tatar, Diana; Benita, Yair

    2011-01-01

    Genome-wide association studies (GWAS) have defined over 150 genomic regions unequivocally containing variation predisposing to immune-mediated disease. Inferring disease biology from these observations, however, hinges on our ability to discover the molecular processes being perturbed by these risk variants. It has previously been observed that different genes harboring causal mutations for the same Mendelian disease often physically interact. We sought to evaluate the degree to which this is true of genes within strongly associated loci in complex disease. Using sets of loci defined in rheumatoid arthritis (RA) and Crohn's disease (CD) GWAS, we build protein–protein interaction (PPI) networks for genes within associated loci and find abundant physical interactions between protein products of associated genes. We apply multiple permutation approaches to show that these networks are more densely connected than chance expectation. To confirm biological relevance, we show that the components of the networks tend to be expressed in similar tissues relevant to the phenotypes in question, suggesting the network indicates common underlying processes perturbed by risk loci. Furthermore, we show that the RA and CD networks have predictive power by demonstrating that proteins in these networks, not encoded in the confirmed list of disease associated loci, are significantly enriched for association to the phenotypes in question in extended GWAS analysis. Finally, we test our method in 3 non-immune traits to assess its applicability to complex traits in general. We find that genes in loci associated to height and lipid levels assemble into significantly connected networks but did not detect excess connectivity among Type 2 Diabetes (T2D) loci beyond chance. Taken together, our results constitute evidence that, for many of the complex diseases studied here, common genetic associations implicate regions encoding proteins that physically interact in a preferential manner, in

  19. GIMDA: Graphlet interaction-based MiRNA-disease association prediction.

    Science.gov (United States)

    Chen, Xing; Guan, Na-Na; Li, Jian-Qiang; Yan, Gui-Ying

    2018-03-01

    MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures. © 2017 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

  20. 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

  1. Decisions on foot-and-mouth disease control informed by model prediction

    DEFF Research Database (Denmark)

    Hisham Beshara Halasa, Tariq; Willeberg, Preben; Christiansen, Lasse Engbo

    2013-01-01

    of affected herds, epidemic duration, geographical size, and costs. The first fourteen days spatial spread (FFS) was also included to support the prediction. The epidemic data were obtained from a Danish version (DTU-DADS) of the Davis Animal Disease Spread simulation model. The FFI and FFS showed good......The predictive capability of the first fortnight incidence (FFI), which is the number of detected herds within the first 14 days following detection of the disease, of the course of a foot-and-mouth disease (FMD) epidemic and its outcomes were investigated. Epidemic outcomes included the number...... correlations with the epidemic outcomes. The predictive capability of the FFI was high. This indicates that the FFI may take a part in the decision of whether or not to boost FMD control, which might prevent occurrence of a large epidemic in the face of an FMD incursion. The prediction power was improved...

  2. Global, Regional, and National Burden of Rheumatic Heart Disease, 1990-2015.

    Science.gov (United States)

    Watkins, David A; Johnson, Catherine O; Colquhoun, Samantha M; Karthikeyan, Ganesan; Beaton, Andrea; Bukhman, Gene; Forouzanfar, Mohammed H; Longenecker, Christopher T; Mayosi, Bongani M; Mensah, George A; Nascimento, Bruno R; Ribeiro, Antonio L P; Sable, Craig A; Steer, Andrew C; Naghavi, Mohsen; Mokdad, Ali H; Murray, Christopher J L; Vos, Theo; Carapetis, Jonathan R; Roth, Gregory A

    2017-08-24

    Rheumatic heart disease remains an important preventable cause of cardiovascular death and disability, particularly in low-income and middle-income countries. We estimated global, regional, and national trends in the prevalence of and mortality due to rheumatic heart disease as part of the 2015 Global Burden of Disease study. We systematically reviewed data on fatal and nonfatal rheumatic heart disease for the period from 1990 through 2015. Two Global Burden of Disease analytic tools, the Cause of Death Ensemble model and DisMod-MR 2.1, were used to produce estimates of mortality and prevalence, including estimates of uncertainty. We estimated that there were 319,400 (95% uncertainty interval, 297,300 to 337,300) deaths due to rheumatic heart disease in 2015. Global age-standardized mortality due to rheumatic heart disease decreased by 47.8% (95% uncertainty interval, 44.7 to 50.9) from 1990 to 2015, but large differences were observed across regions. In 2015, the highest age-standardized mortality due to and prevalence of rheumatic heart disease were observed in Oceania, South Asia, and central sub-Saharan Africa. We estimated that in 2015 there were 33.4 million (95% uncertainty interval, 29.7 million to 43.1 million) cases of rheumatic heart disease and 10.5 million (95% uncertainty interval, 9.6 million to 11.5 million) disability-adjusted life-years due to rheumatic heart disease globally. We estimated the global disease prevalence of and mortality due to rheumatic heart disease over a 25-year period. The health-related burden of rheumatic heart disease has declined worldwide, but high rates of disease persist in some of the poorest regions in the world. (Funded by the Bill and Melinda Gates Foundation and the Medtronic Foundation.).

  3. Quantitative prediction of shrimp disease incidence via the profiles of gut eukaryotic microbiota.

    Science.gov (United States)

    Xiong, Jinbo; Yu, Weina; Dai, Wenfang; Zhang, Jinjie; Qiu, Qiongfen; Ou, Changrong

    2018-04-01

    One common notion is emerging that gut eukaryotes are commensal or beneficial, rather than detrimental. To date, however, surprisingly few studies have been taken to discern the factors that govern the assembly of gut eukaryotes, despite growing interest in the dysbiosis of gut microbiota-disease relationship. Herein, we firstly explored how the gut eukaryotic microbiotas were assembled over shrimp postlarval to adult stages and a disease progression. The gut eukaryotic communities changed markedly as healthy shrimp aged, and converged toward an adult-microbiota configuration. However, the adult-like stability was distorted by disease exacerbation. A null model untangled that the deterministic processes that governed the gut eukaryotic assembly tended to be more important over healthy shrimp development, whereas this trend was inverted as the disease progressed. After ruling out the baseline of gut eukaryotes over shrimp ages, we identified disease-discriminatory taxa (species level afforded the highest accuracy of prediction) that characteristic of shrimp health status. The profiles of these taxa contributed an overall 92.4% accuracy in predicting shrimp health status. Notably, this model can accurately diagnose the onset of shrimp disease. Interspecies interaction analysis depicted how the disease-discriminatory taxa interacted with one another in sustaining shrimp health. Taken together, our findings offer novel insights into the underlying ecological processes that govern the assembly of gut eukaryotes over shrimp postlarval to adult stages and a disease progression. Intriguingly, the established model can quantitatively and accurately predict the incidences of shrimp disease.

  4. Diseases of livestock in the Pacific Islands region: setting priorities for food animal biosecurity.

    Science.gov (United States)

    Brioudes, Aurélie; Warner, Jeffrey; Hedlefs, Robert; Gummow, Bruce

    2015-03-01

    Most Pacific Island countries and territories (PICTs) have developing economies and face a critical shortage of veterinarians with limited financial resources allocated to their animal disease surveillance programmes. Thus, animal health authorities have to set priorities for better focusing their scarce resources. The main objective of this study was to identify animal diseases perceived to be of importance by decision makers within selected PICTs, at the regional and national levels, to ensure better targeting of animal health resources. A second objective was to investigate whether the targeted surveillance programmes resulting from this rationalized approach would also benefit the local communities engaged in livestock production. A multi-criteria prioritization process was developed, involving local experts, to score and rank 132 animal diseases based on their priority at the regional and national levels for four PICTs: Fiji, Papua New Guinea, Solomon Islands, and Vanuatu, which form part of a regional Food Animal Biosecurity Network. In parallel interviews with farmers and field animal health and production workers were conducted to assess their perception of animal diseases. The list of the top-twenty ranked diseases for the Pacific Islands region shows a mix of endemic zoonotic diseases (such as leptospirosis ranked first; brucellosis third; tuberculosis sixth; and endoparasites and ectoparasites, respectively eleventh and thirteenth) with exotic diseases (such as HPAI ranked second, FMD fifth, and rabies ninth). There were different disease ranking lists for each of the four targeted PICTs, confirming different strategies of disease prevention and control may be required for each country, rather than a regional approach. Interviewed animal health and production workers were unfamiliar with most of the prioritized diseases and a majority acknowledged that they would not be able to recognize clinical signs if outbreaks were to occur in their area

  5. Broca's region and Visual Word Form Area activation differ during a predictive Stroop task

    DEFF Research Database (Denmark)

    Wallentin, Mikkel; Gravholt, Claus Højbjerg; Skakkebæk, Anne

    2015-01-01

    displayed in green or red (incongruent vs congruent colors). One of the colors, however, was presented three times as often as the other, making it possible to study both congruency and frequency effects independently. Auditory stimuli saying “GREEN” or “RED” had the same distribution, making it possible...... to study frequency effects across modalities. We found significant behavioral effects of both incongruency and frequency. A significant effect (p effect of frequency was observed and no interaction. Conjoined effects of incongruency...... and frequency were found in parietal regions as well as in the Visual Word Form Area (VWFA). No interaction between perceptual modality and frequency was found in VWFA suggesting that the region is not strictly visual. These findings speak against a strong version of the prediction error processing hypothesis...

  6. Identifying risk factors of avian infectious diseases at household level in Poyang Lake region, China.

    Science.gov (United States)

    Jiang, Qian; Zhou, Jieting; Jiang, Zhiben; Xu, Bing

    2014-09-01

    Poultry kept in backyard farms are susceptible to acquiring and spreading infectious diseases because of free ranging and poor biosecurity measures. Since some of these diseases are zoonoses, this is also a significant health concern to breeders and their families. Backyard farms are common in rural regions of China. However, there is lack of knowledge of backyard poultry in the country. To obtain first-hand information of backyard poultry and identify risk factors of avian infectious diseases, a cross-sectional study was carried out at household level in rural regions around Poyang Lake. A door-to-door survey was conducted to collect data on husbandry practices, trading practices of backyard farmers, and surrounding environments of backyard farms. Farms were categorized into cases and controls based on their history of poultry death. Data were collected for 137 farms, and the association with occurrence of poultry death event was explored by chi-square tests. Results showed that vaccination implementation was a protective factor (odds ratio OR=0.40, 95% confidence interval CI: 0.20-0.80, p=0.01), while contact with other backyard flocks increased risk (OR=1.72, 95% CI: 0.79-3.74, p=0.16). A concept of "farm connectivity" characterized by the density of particular land-use types in the vicinity of the farm was proposed to characterize the degree of contact between poultry in one household farm and those in other household farms. It was found that housing density in a 20-m buffer zone of the farmhouse was most significantly associated with poultry death occurrence (OR=1.08, 95% CI: 1.02-1.17, p=0.03), and was in agreement with observation of villagers. Binary logistic regression was applied to evaluate the relationship between poultry death event and density of land-use types in all buffer zones. When integrated with vaccination implementation for poultry, prediction accuracy of poultry death event reached 72.0%. Results combining questionnaire survey with

  7. Predicting relapse of Graves' disease following treatment with antithyroid drugs

    Science.gov (United States)

    LIU, LIN; LU, HONGWEN; LIU, YANG; LIU, CHANGSHAN; XUN, CHU

    2016-01-01

    The aim of the present study was to monitor long term antithyroid drug treatments and to identify prognostic factors for Graves' disease (GD). A total of 306 patients with GD who were referred to the Endocrinology Clinic at Weifang People's Hospital (Weifang, China) between August 2005 and June 2009 and treated with methimazole were included in the present study. Following treatment, patients were divided into non-remission, including recurrence and constant treatment subgroups, and remission groups. Various prognosis factors were analyzed and compared, including: Patient age, gender, size of thyroid prior to and following treatment, thyroid hormone levels, disease relapse, hypothyroidism and drug side-effects, and states of thyrotropin suppression were observed at 3, 6 and 12 months post-treatment. Sixty-five patients (21.2%) were male, and 241 patients (78.8%) were female. The mean age was 42±11 years, and the follow-up was 31.5±6.8 months. Following long-term treatment, 141 patients (46%) demonstrated remission of hyperthyroidism with a mean duration of 18.7±1.9 months. The average age at diagnosis was 45.6±10.3 years in the remission group, as compared with 36.4±8.8 years in the non-remission group (t=3.152; P=0.002). Free thyroxine (FT)3 levels were demonstrated to be 25.2±8.9 and 18.7±9.4 pmol/l in the non-remission and remission groups, respectively (t=3.326, P=0.001). The FT3/FT4 ratio and thyrotrophin receptor antibody (TRAb) levels were both significantly higher in the non-remission group (t=3.331, 3.389, P=0.001), as compared with the remission group. Logistic regression analysis demonstrated that elevated thyroid size, FT3/FT4 ratio and TRAb at diagnosis were associated with poor outcomes. The ratio of continued thyrotropin suppression in the recurrent subgroup was significantly increased, as compared with the remission group (P=0.001), as thyroid function reached euthyroid state at 3, 6 and 12 months post-treatment. Patients with GD exhibiting

  8. Network-based ranking methods for prediction of novel disease associated microRNAs.

    Science.gov (United States)

    Le, Duc-Hau

    2015-10-01

    Many studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a "disease module" principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the "disease module" principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction. We constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm. Analyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In

  9. In silico prediction of novel therapeutic targets using gene-disease association data.

    Science.gov (United States)

    Ferrero, Enrico; Dunham, Ian; Sanseau, Philippe

    2017-08-29

    Target identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market. To test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets. We observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature. Our in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target

  10. A Bayesian Belief Network framework to predict SOC stock change: the Veneto region (Italy) case study

    Science.gov (United States)

    Dal Ferro, Nicola; Quinn, Claire Helen; Morari, Francesco

    2017-04-01

    A key challenge for soil scientists is predicting agricultural management scenarios that combine crop productions with high standards of environmental quality. In this context, reversing the soil organic carbon (SOC) decline in croplands is required for maintaining soil fertility and contributing to mitigate GHGs emissions. Bayesian belief networks (BBN) are probabilistic models able to accommodate uncertainty and variability in the predictions of the impacts of management and environmental changes. By linking multiple qualitative and quantitative variables in a cause-and-effect relationships, BBNs can be used as a decision support system at different spatial scales to find best management strategies in the agroecosystems. In this work we built a BBN to model SOC dynamics (0-30 cm layer) in the low-lying plain of Veneto region, north-eastern Italy, and define best practices leading to SOC accumulation and GHGs (CO2-equivalent) emissions reduction. Regional pedo-climatic, land use and management information were combined with experimental and modelled data on soil C dynamics as natural and anthropic key drivers affecting SOC stock change. Moreover, utility nodes were introduced to determine optimal decisions for mitigating GHGs emissions from croplands considering also three different IPCC climate scenarios. The network was finally validated with real field data in terms of SOC stock change. Results showed that the BBN was able to model real SOC stock changes, since validation slightly overestimated SOC reduction (+5%) at the expenses of its accumulation. At regional level, probability distributions showed 50% of SOC loss, while only 17% of accumulation. However, the greatest losses (34%) were associated with low reduction rates (100-500 kg C ha-1 y-1), followed by 33% of stabilized conditions (-100 < SOC < 100 kg ha-1 y-1). Land use management (especially tillage operations and soil cover) played a primary role to affect SOC stock change, while climate conditions

  11. White Matter Volume Predicts Language Development in Congenital Heart Disease.

    Science.gov (United States)

    Rollins, Caitlin K; Asaro, Lisa A; Akhondi-Asl, Alireza; Kussman, Barry D; Rivkin, Michael J; Bellinger, David C; Warfield, Simon K; Wypij, David; Newburger, Jane W; Soul, Janet S

    2017-02-01

    To determine whether brain volume is reduced at 1 year of age and whether these volumes are associated with neurodevelopment in biventricular congenital heart disease (CHD) repaired in infancy. Infants with biventricular CHD (n = 48) underwent brain magnetic resonance imaging (MRI) and neurodevelopmental testing with the Bayley Scales of Infant Development-II and the MacArthur-Bates Communicative Development Inventories at 1 year of age. A multitemplate based probabilistic segmentation algorithm was applied to volumetric MRI data. We compared volumes with those of 13 healthy control infants of comparable ages. In the group with CHD, we measured Spearman correlations between neurodevelopmental outcomes and the residuals from linear regression of the volumes on corrected chronological age at MRI and sex. Compared with controls, infants with CHD had reductions of 54 mL in total brain (P = .009), 40 mL in cerebral white matter (P Development-II scores but did correlate positively with MacArthur-Bates Communicative Development Inventory language development. Infants with biventricular CHD show total brain volume reductions at 1 year of age, driven by differences in cerebral white matter. White matter volume correlates with language development, but not broader developmental indices. These findings suggest that abnormalities in white matter development detected months after corrective heart surgery may contribute to language impairment. ClinicalTrials.gov: NCT00006183. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Short-Range Prediction of Monsoon Precipitation by NCMRWF Regional Unified Model with Explicit Convection

    Science.gov (United States)

    Mamgain, Ashu; Rajagopal, E. N.; Mitra, A. K.; Webster, S.

    2018-03-01

    There are increasing efforts towards the prediction of high-impact weather systems and understanding of related dynamical and physical processes. High-resolution numerical model simulations can be used directly to model the impact at fine-scale details. Improvement in forecast accuracy can help in disaster management planning and execution. National Centre for Medium Range Weather Forecasting (NCMRWF) has implemented high-resolution regional unified modeling system with explicit convection embedded within coarser resolution global model with parameterized convection. The models configurations are based on UK Met Office unified seamless modeling system. Recent land use/land cover data (2012-2013) obtained from Indian Space Research Organisation (ISRO) are also used in model simulations. Results based on short-range forecast of both the global and regional models over India for a month indicate that convection-permitting simulations by the high-resolution regional model is able to reduce the dry bias over southern parts of West Coast and monsoon trough zone with more intense rainfall mainly towards northern parts of monsoon trough zone. Regional model with explicit convection has significantly improved the phase of the diurnal cycle of rainfall as compared to the global model. Results from two monsoon depression cases during study period show substantial improvement in details of rainfall pattern. Many categories in rainfall defined for operational forecast purposes by Indian forecasters are also well represented in case of convection-permitting high-resolution simulations. For the statistics of number of days within a range of rain categories between `No-Rain' and `Heavy Rain', the regional model is outperforming the global model in all the ranges. In the very heavy and extremely heavy categories, the regional simulations show overestimation of rainfall days. Global model with parameterized convection have tendency to overestimate the light rainfall days and

  13. The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer’s disease

    Directory of Open Access Journals (Sweden)

    Olga eVoevodskaya

    2014-10-01

    Full Text Available In neurodegeneration research, normalization of regional volumes by intracranial volume (ICV is important to estimate the extent of disease-driven atrophy. There is little agreement as to whether raw volumes, volume-to-ICV fractions or regional volumes from which the ICV factor has been regressed out should be used for volumetric brain imaging studies. Using multiple regional cortical and subcortical volumetric measures generated by Freesurfer (51 in total, the main aim of this study was to elucidate the implications of these adjustment approaches. Magnetic resonance imaging (MRI data were analyzed from two large cohorts, the population-based PIVUS cohort (N=406, all subjects age 75 and the Alzheimer disease Neuroimaging Initiative (ADNI cohort (N=724. Further, we studied whether the chosen ICV normalization approach influenced the relationship between hippocampus and cognition in the three diagnostic groups of the ADNI cohort (Alzheimer’s disease, mild cognitive impairment and healthy individuals. The ability of raw vs adjusted hippocampal volumes to predict diagnostic status was also assessed. In both cohorts raw volumes correlate positively with ICV, but do not scale directly proportionally with it. The correlation direction is reversed for all volume-to-ICV fractions, except the lateral and third ventricles. Most grey matter fractions are larger in females, while lateral ventricle fractions are greater in males. Residual correction effectively eliminated the correlation between the regional volumes and ICV and removed gender differences. The association between hippocampal volumes and cognition was not altered by ICV normalization. Comparing prediction of diagnostic status using the different approaches, small but significant differences were found. The choice of normalization approach should be carefully considered when designing a volumetric brain imaging study.

  14. Effects of lateral boundary condition resolution and update frequency on regional climate model predictions

    Science.gov (United States)

    Pankatz, Klaus; Kerkweg, Astrid

    2015-04-01

    The work presented is part of the joint project "DecReg" ("Regional decadal predictability") which is in turn part of the project "MiKlip" ("Decadal predictions"), an effort funded by the German Federal Ministry of Education and Research to improve decadal predictions on a global and regional scale. In MiKlip, one big question is if regional climate modeling shows "added value", i.e. to evaluate, if regional climate models (RCM) produce better results than the driving models. However, the scope of this study is to look more closely at the setup specific details of regional climate modeling. As regional models only simulate a small domain, they have to inherit information about the state of the atmosphere at their lateral boundaries from external data sets. There are many unresolved questions concerning the setup of lateral boundary conditions (LBC). External data sets come from global models or from global reanalysis data-sets. A temporal resolution of six hours is common for this kind of data. This is mainly due to the fact, that storage space is a limiting factor, especially for climate simulations. However, theoretically, the coupling frequency could be as high as the time step of the driving model. Meanwhile, it is unclear if a more frequent update of the LBCs has a significant effect on the climate in the domain of the RCM. The first study examines how the RCM reacts to a higher update frequency. The study is based on a 30 year time slice experiment for three update frequencies of the LBC, namely six hours, one hour and six minutes. The evaluation of means, standard deviations and statistics of the climate in the regional domain shows only small deviations, some statistically significant though, of 2m temperature, sea level pressure and precipitation. The second part of the first study assesses parameters linked to cyclone activity, which is affected by the LBC update frequency. Differences in track density and strength are found when comparing the simulations

  15. Application of Genetic Algorithm to Predict Optimal Sowing Region and Timing for Kentucky Bluegrass in China.

    Directory of Open Access Journals (Sweden)

    Erxu Pi

    Full Text Available Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA models, which are automatically parameterized with observed germination data. The seeds of five P. pratensis (Kentucky bluegrass, KB cultivars were germinated under 36 day/night temperature regimes ranging from 5/5 to 40/40 °C with 5 °C increments. Results showed that optimal germination percentages of all five tested KB cultivars were observed under a fluctuating temperature regime of 20/25 °C. Meanwhile, the constant temperature regimes (e.g., 5/5, 10/10, 15/15 °C, etc. suppressed the germination of all five cultivars. Furthermore, the back propagation artificial neural network (BP-ANN algorithm was integrated to optimize temperature-germination response models from these observed germination data. It was found that integrations of GA-BP-ANN (back propagation aided genetic algorithm artificial neural network significantly reduced the Root Mean Square Error (RMSE values from 0.21~0.23 to 0.02~0.09. In an effort to provide a more reliable prediction of optimum sowing time for the tested KB cultivars in various regions in the country, the optimized GA-BP-ANN models were applied to map spatial and temporal germination percentages of blue grass cultivars in China. Our results demonstrate that the GA-BP-ANN model is a convenient and reliable option for constructing thermal-germination response models since it automates model parameterization and has excellent prediction accuracy.

  16. High-permeability region size on perfusion CT predicts hemorrhagic transformation after intravenous thrombolysis in stroke.

    Directory of Open Access Journals (Sweden)

    Josep Puig

    Full Text Available Blood-brain barrier (BBB permeability has been proposed as a predictor of hemorrhagic transformation (HT after tissue plasminogen activator (tPA administration; however, the reliability of perfusion computed tomography (PCT permeability imaging for predicting HT is uncertain. We aimed to determine the performance of high-permeability region size on PCT (HPrs-PCT in predicting HT after intravenous tPA administration in patients with acute stroke.We performed a multimodal CT protocol (non-contrast CT, PCT, CT angiography to prospectively study patients with middle cerebral artery occlusion treated with tPA within 4.5 hours of symptom onset. HT was graded at 24 hours using the European-Australasian Acute Stroke Study II criteria. ROC curves selected optimal volume threshold, and multivariate logistic regression analysis identified predictors of HT.The study included 156 patients (50% male, median age 75.5 years. Thirty-seven (23,7% developed HT [12 (7,7%, parenchymal hematoma type 2 (PH-2]. At admission, patients with HT had lower platelet values, higher NIHSS scores, increased ischemic lesion volumes, larger HPrs-PCT, and poorer collateral status. The negative predictive value of HPrs-PCT at a threshold of 7mL/100g/min was 0.84 for HT and 0.93 for PH-2. The multiple regression analysis selected HPrs-PCT at 7mL/100g/min combined with platelets and baseline NIHSS score as the best model for predicting HT (AUC 0.77. HPrs-PCT at 7mL/100g/min was the only independent predictor of PH-2 (OR 1, AUC 0.68, p = 0.045.HPrs-PCT can help predict HT after tPA, and is particularly useful in identifying patients at low risk of developing HT.

  17. High-permeability region size on perfusion CT predicts hemorrhagic transformation after intravenous thrombolysis in stroke

    Science.gov (United States)

    Puig, Josep; Blasco, Gerard; Daunis-i-Estadella, Pepus; van Eendendburg, Cecile; Carrillo-García, María; Aboud, Carlos; Hernández-Pérez, María; Serena, Joaquín; Biarnés, Carles; Nael, Kambiz; Liebeskind, David S.; Thomalla, Götz; Menon, Bijoy K.; Demchuk, Andrew; Wintermark, Max; Pedraza, Salvador

    2017-01-01

    Objective Blood-brain barrier (BBB) permeability has been proposed as a predictor of hemorrhagic transformation (HT) after tissue plasminogen activator (tPA) administration; however, the reliability of perfusion computed tomography (PCT) permeability imaging for predicting HT is uncertain. We aimed to determine the performance of high-permeability region size on PCT (HPrs-PCT) in predicting HT after intravenous tPA administration in patients with acute stroke. Methods We performed a multimodal CT protocol (non-contrast CT, PCT, CT angiography) to prospectively study patients with middle cerebral artery occlusion treated with tPA within 4.5 hours of symptom onset. HT was graded at 24 hours using the European-Australasian Acute Stroke Study II criteria. ROC curves selected optimal volume threshold, and multivariate logistic regression analysis identified predictors of HT. Results The study included 156 patients (50% male, median age 75.5 years). Thirty-seven (23,7%) developed HT [12 (7,7%), parenchymal hematoma type 2 (PH-2)]. At admission, patients with HT had lower platelet values, higher NIHSS scores, increased ischemic lesion volumes, larger HPrs-PCT, and poorer collateral status. The negative predictive value of HPrs-PCT at a threshold of 7mL/100g/min was 0.84 for HT and 0.93 for PH-2. The multiple regression analysis selected HPrs-PCT at 7mL/100g/min combined with platelets and baseline NIHSS score as the best model for predicting HT (AUC 0.77). HPrs-PCT at 7mL/100g/min was the only independent predictor of PH-2 (OR 1, AUC 0.68, p = 0.045). Conclusions HPrs-PCT can help predict HT after tPA, and is particularly useful in identifying patients at low risk of developing HT. PMID:29182658

  18. Analysis of the regional MiKlip decadal prediction system over Europe: skill, added value of regionalization, and ensemble size dependeny

    Science.gov (United States)

    Reyers, Mark; Moemken, Julia; Pinto, Joaquim; Feldmann, Hendrik; Kottmeier, Christoph; MiKlip Module-C Team

    2017-04-01

    Decadal climate predictions can provide a useful basis for decision making support systems for the public and private sectors. Several generations of decadal hindcasts and predictions have been generated throughout the German research program MiKlip. Together with the global climate predictions computed with MPI-ESM, the regional climate model (RCM) COSMO-CLM is used for regional downscaling by MiKlip Module-C. The RCMs provide climate information on spatial and temporal scales closer to the needs of potential users. In this study, two downscaled hindcast generations are analysed (named b0 and b1). The respective global generations are both initialized by nudging them towards different reanalysis anomaly fields. An ensemble of five starting years (1961, 1971, 1981, 1991, and 2001), each comprising ten ensemble members, is used for both generations in order to quantify the regional decadal prediction skill for precipitation and near-surface temperature and wind speed over Europe. All datasets (including hindcasts, observations, reanalysis, and historical MPI-ESM runs) are pre-processed in an analogue manner by (i) removing the long-term trend and (ii) re-gridding to a common grid. Our analysis shows that there is potential for skillful decadal predictions over Europe in the regional MiKlip ensemble, but the skill is not systematic and depends on the PRUDENCE region and the variable. Further, the differences between the two hindcast generations are mostly small. As we used detrended time series, the predictive skill found in our study can probably attributed to reasonable predictions of anomalies which are associated with the natural climate variability. In a sensitivity study, it is shown that the results may strongly change when the long-term trend is kept in the datasets, as here the skill of predicting the long-term trend (e.g. for temperature) also plays a major role. The regionalization of the global ensemble provides an added value for decadal predictions for

  19. A model to predict multivessel coronary artery disease from the exercise thallium-201 stress test

    International Nuclear Information System (INIS)

    Pollock, S.G.; Abbott, R.D.; Boucher, C.A.; Watson, D.D.; Kaul, S.

    1991-01-01

    The aim of this study was to (1) determine whether nonimaging variables add to the diagnostic information available from exercise thallium-201 images for the detection of multivessel coronary artery disease; and (2) to develop a model based on the exercise thallium-201 stress test to predict the presence of multivessel disease. The study populations included 383 patients referred to the University of Virginia and 325 patients referred to the Massachusetts General Hospital for evaluation of chest pain. All patients underwent both cardiac catheterization and exercise thallium-201 stress testing between 1978 and 1981. In the University of Virginia cohort, at each level of thallium-201 abnormality (no defects, one defect, more than one defect), ST depression and patient age added significantly in the detection of multivessel disease. Logistic regression analysis using data from these patients identified three independent predictors of multivessel disease: initial thallium-201 defects, ST depression, and age. A model was developed to predict multivessel disease based on these variables. As might be expected, the risk of multivessel disease predicted by the model was similar to that actually observed in the University of Virginia population. More importantly, however, the model was accurate in predicting the occurrence of multivessel disease in the unrelated population studied at the Massachusetts General Hospital. It is, therefore, concluded that (1) nonimaging variables (age and exercise-induced ST depression) add independent information to thallium-201 imaging data in the detection of multivessel disease; and (2) a model has been developed based on the exercise thallium-201 stress test that can accurately predict the probability of multivessel disease in other populations

  20. Utilizing Dental Electronic Health Records Data to Predict Risk for Periodontal Disease.

    Science.gov (United States)

    Thyvalikakath, Thankam P; Padman, Rema; Vyawahare, Karnali; Darade, Pratiksha; Paranjape, Rhucha

    2015-01-01

    Periodontal disease is a major cause for tooth loss and adversely affects individuals' oral health and quality of life. Research shows its potential association with systemic diseases like diabetes and cardiovascular disease, and social habits such as smoking. This study explores mining potential risk factors from dental electronic health records to predict and display patients' contextualized risk for periodontal disease. We retrieved relevant risk factors from structured and unstructured data on 2,370 patients who underwent comprehensive oral examinations at the Indiana University School of Dentistry, Indianapolis, IN, USA. Predicting overall risk and displaying relationships between risk factors and their influence on the patient's oral and general health can be a powerful educational and disease management tool for patients and clinicians at the point of care.

  1. Using Decision Trees in Data Mining for Predicting Factors Influencing of Heart Disease

    Directory of Open Access Journals (Sweden)

    Moloud Abdar

    2015-12-01

    Full Text Available Statistics from the World Health Organization (WHO shows that heart disease is one of the leading causes of mortality all over the world. Because of the importance of heart disease, in recent years, many studies have been conducted on this disease using data mining. The main objective of this study is to find a better decision tree algorithm and then use the algorithm for extracting rules in predicting heart disease. Cleveland data, including 303 records are used for this study. These data include 13 features and we have categorized them into five classes. In this paper, C5.0 algorithm with a accuracy value of 85.33% has a better performance compared to the rest of the algorithms used in this study. Considering the rules created by this algorithm, the attributes of Trestbps, Restecg, Thalach, Slope, Oldpeak, and CP were extracted as the most influential causes in predicting heart disease.

  2. Topoclimatic modeling for minimum temperature prediction at a regional scale in the Central Valley of Chile

    International Nuclear Information System (INIS)

    Santibáñez, F.; Morales, L.; Fuente, J. de la; Cellier, P.; Huete, A.

    1997-01-01

    Spring frost may strongly affect fruit production in the Central Valley of Chile. Minimum temperatures are spatially variable owing to topography and soil conditions. A methodology for forecasting minimum temperature at a regional scale in the Central Valley of Chile, integrating spatial variability of temperature under radiative frost conditions, has been developed. It uses simultaneously a model for forecasting minimum temperatures at a reference station using air temperature and humidity measured at 6 pm, and topoclimatic models, based on satellite infra-red imagery (NOAA/AVHRR) and a digital elevation model, to extend the prediction at a regional scale. The methodological developments were integrated in a geographic information system for geo referencing of a meteorological station with satellite imagery and modeled output. This approach proved to be a useful tool for short range (12 h) minimum temperature prediction by generating thermal images over the Central Valley of Chile. It may also be used as a tool for frost risk assessment, in order to adapt production to local climatological conditions. (author)

  3. From field to region yield predictions in response to pedo-climatic variations in Eastern Canada

    Science.gov (United States)

    JÉGO, G.; Pattey, E.; Liu, J.

    2013-12-01

    The increase in global population coupled with new pressures to produce energy and bioproducts from agricultural land requires an increase in crop productivity. However, the influence of climate and soil variations on crop production and environmental performance is not fully understood and accounted for to define more sustainable and economical management strategies. Regional crop modeling can be a great tool for understanding the impact of climate variations on crop production, for planning grain handling and for assessing the impact of agriculture on the environment, but it is often limited by the availability of input data. The STICS ("Simulateur mulTIdisciplinaire pour les Cultures Standard") crop model, developed by INRA (France) is a functional crop model which has a built-in module to optimize several input parameters by minimizing the difference between calculated and measured output variables, such as Leaf Area Index (LAI). STICS crop model was adapted to the short growing season of the Mixedwood Plains Ecozone using field experiments results, to predict biomass and yield of soybean, spring wheat and corn. To minimize the numbers of inference required for regional applications, 'generic' cultivars rather than specific ones have been calibrated in STICS. After the calibration of several model parameters, the root mean square error (RMSE) of yield and biomass predictions ranged from 10% to 30% for the three crops. A bit more scattering was obtained for LAI (20%prediction to climate variations. Using RS data to re-initialize input parameters that are not readily available (e.g. seeding date) is considered an effective way

  4. Collaborative Research: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic

    Energy Technology Data Exchange (ETDEWEB)

    Gutowski, William J. [Iowa State Univ., Ames, IA (United States)

    2017-12-28

    This project developed and applied a regional Arctic System model for enhanced decadal predictions. It built on successful research by four of the current PIs with support from the DOE Climate Change Prediction Program, which has resulted in the development of a fully coupled Regional Arctic Climate Model (RACM) consisting of atmosphere, land-hydrology, ocean and sea ice components. An expanded RACM, a Regional Arctic System Model (RASM), has been set up to include ice sheets, ice caps, mountain glaciers, and dynamic vegetation to allow investigation of coupled physical processes responsible for decadal-scale climate change and variability in the Arctic. RASM can have high spatial resolution (~4-20 times higher than currently practical in global models) to advance modeling of critical processes and determine the need for their explicit representation in Global Earth System Models (GESMs). The pan-Arctic region is a key indicator of the state of global climate through polar amplification. However, a system-level understanding of critical arctic processes and feedbacks needs further development. Rapid climate change has occurred in a number of Arctic System components during the past few decades, including retreat of the perennial sea ice cover, increased surface melting of the Greenland ice sheet, acceleration and thinning of outlet glaciers, reduced snow cover, thawing permafrost, and shifts in vegetation. Such changes could have significant ramifications for global sea level, the ocean thermohaline circulation and heat budget, ecosystems, native communities, natural resource exploration, and commercial transportation. The overarching goal of the RASM project has been to advance understanding of past and present states of arctic climate and to improve seasonal to decadal predictions. To do this the project has focused on variability and long-term change of energy and freshwater flows through the arctic climate system. The three foci of this research are: - Changes

  5. Hyperspectral-based predictive modelling of grapevine water status in the Portuguese Douro wine region

    Science.gov (United States)

    Pôças, Isabel; Gonçalves, João; Costa, Patrícia Malva; Gonçalves, Igor; Pereira, Luís S.; Cunha, Mario

    2017-06-01

    In this study, hyperspectral reflectance (HySR) data derived from a handheld spectroradiometer were used to assess the water status of three grapevine cultivars in two sub-regions of Douro wine region during two consecutive years. A large set of potential predictors derived from the HySR data were considered for modelling/predicting the predawn leaf water potential (Ψpd) through different statistical and machine learning techniques. Three HySR vegetation indices were selected as final predictors for the computation of the models and the in-season time trend was removed from data by using a time predictor. The vegetation indices selected were the Normalized Reflectance Index for the wavelengths 554 nm and 561 nm (NRI554;561), the water index (WI) for the wavelengths 900 nm and 970 nm, and the D1 index which is associated with the rate of reflectance increase in the wavelengths of 706 nm and 730 nm. These vegetation indices covered the green, red edge and the near infrared domains of the electromagnetic spectrum. A large set of state-of-the-art analysis and statistical and machine-learning modelling techniques were tested. Predictive modelling techniques based on generalized boosted model (GBM), bagged multivariate adaptive regression splines (B-MARS), generalized additive model (GAM), and Bayesian regularized neural networks (BRNN) showed the best performance for predicting Ψpd, with an average determination coefficient (R2) ranging between 0.78 and 0.80 and RMSE varying between 0.11 and 0.12 MPa. When cultivar Touriga Nacional was used for training the models and the cultivars Touriga Franca and Tinta Barroca for testing (independent validation), the models performance was good, particularly for GBM (R2 = 0.85; RMSE = 0.09 MPa). Additionally, the comparison of Ψpd observed and predicted showed an equitable dispersion of data from the various cultivars. The results achieved show a good potential of these predictive models based on vegetation indices to support

  6. Global, Regional, and National Burden of Cardiovascular Diseases for 10 Causes, 1990 to 2015

    DEFF Research Database (Denmark)

    Roth, Gregory A; Johnson, Catherine; Abajobir, Amanuel

    2017-01-01

    BACKGROUND: The burden of cardiovascular diseases (CVDs) remains unclear in many regions of the world. OBJECTIVES: The GBD (Global Burden of Disease) 2015 study integrated data on disease incidence, prevalence, and mortality to produce consistent, up-to-date estimates for cardiovascular burden......-income countries. Ischemic heart disease was the leading cause of CVD health lost globally, as well as in each world region, followed by stroke. As SDI increased beyond 0.25, the highest CVD mortality shifted from women to men. CVD mortality decreased sharply for both sexes in countries with an SDI >0...... be used to guide policymakers who are focused on reducing the overall burden of noncommunicable disease and achieving specific global health targets for CVD....

  7. Using a predictive model to evaluate spatiotemporal variability in streamflow permanence across the Pacific Northwest region

    Science.gov (United States)

    Jaeger, K. L.

    2017-12-01

    The U.S. Geological Survey (USGS) has developed the PRObability Of Streamflow PERmanence (PROSPER) model, a GIS-based empirical model that provides predictions of the annual probability of a stream channel having year-round flow (Streamflow permanence probability; SPP) for any unregulated and minimally-impaired stream channel in the Pacific Northwest (Washington, Oregon, Idaho, western Montana). The model provides annual predictions for 2004-2016 at a 30-m spatial resolution based on monthly or annually updated values of climatic conditions, and static physiographic variables associated with the upstream basin. Prediction locations correspond to the channel network consistent with the National Hydrography Dataset stream grid and are publicly available through the USGS StreamStats platform (https://water.usgs.gov/osw/streamstats/). In snowmelt-driven systems, the most informative predictor variable was mean upstream snow water equivalent on May 1, which highlights the influence of late spring snow cover for supporting streamflow in mountain river networks. In non-snowmelt-driven systems, the most informative variable was mean annual precipitation. Streamflow permanence probabilities varied across the study area by geography and from year-to-year. Notably lower SPP corresponded to the climatically drier subregions of the study area. Higher SPP were concentrated in coastal and higher elevation mountain regions. In addition, SPP appeared to trend with average hydroclimatic conditions, which were also geographically coherent. The year-to-year variability lends support for the growing recognition of the spatiotemporal dynamism of streamflow permanence. An analysis of three focus basins located in contrasting geographical and hydroclimatic settings demonstrates differences in the sensitivity of streamflow permanence to antecedent climate conditions as a function of geography. Consequently, results suggest that PROSPER model can be a useful tool to evaluate regions of the

  8. Predicting Climate-sensitive Infectious Diseases: Development of a Federal Science Plan and the Path Forward

    Science.gov (United States)

    Trtanj, J.; Balbus, J. M.; Brown, C.; Shimamoto, M. M.

    2017-12-01

    The transmission and spread of infectious diseases, especially vector-borne diseases, water-borne diseases and zoonosis, are influenced by short and long-term climate factors, in conjunction with numerous other drivers. Public health interventions, including vaccination, vector control programs, and outreach campaigns could be made more effective if the geographic range and timing of increased disease risk could be more accurately targeted, and high risk areas and populations identified. While some progress has been made in predictive modeling for transmission of these diseases using climate and weather data as inputs, they often still start after the first case appears, the skill of those models remains limited, and their use by public health officials infrequent. And further, predictions with lead times of weeks, months or seasons are even rarer, yet the value of acting early holds the potential to save more lives, reduce cost and enhance both economic and national security. Information on high-risk populations and areas for infectious diseases is also potentially useful for the federal defense and intelligence communities as well. The US Global Change Research Program, through its Interagency Group on Climate Change and Human Health (CCHHG), has put together a science plan that pulls together federal scientists and programs working on predictive modeling of climate-sensitive diseases, and draws on academic and other partners. Through a series of webinars and an in-person workshop, the CCHHG has convened key federal and academic stakeholders to assess the current state of science and develop an integrated science plan to identify data and observation systems needs as well as a targeted research agenda for enhancing predictive modeling. This presentation will summarize the findings from this effort and engage AGU members on plans and next steps to improve predictive modeling for infectious diseases.

  9. African horse sickness: The potential for an outbreak in disease-free regions and current disease control and elimination techniques.

    Science.gov (United States)

    Robin, M; Page, P; Archer, D; Baylis, M

    2016-09-01

    African horse sickness (AHS) is an arboviral disease of equids transmitted by Culicoides biting midges. The virus is endemic in parts of sub-Saharan Africa and official AHS disease-free status can be obtained from the World Organization for Animal Health on fulfilment of a number of criteria. AHS is associated with case fatality rates of up to 95%, making an outbreak among naïve horses both a welfare and economic disaster. The worldwide distributions of similar vector-borne diseases (particularly bluetongue disease of ruminants) are changing rapidly, probably due to a combination of globalisation and climate change. There is extensive evidence that the requisite conditions for an AHS epizootic currently exist in disease-free countries. In particular, although the stringent regulations enforced upon competition horses make them extremely unlikely to redistribute the virus, there are great concerns over the effects of illegal equid movement. An outbreak of AHS in a disease free region would have catastrophic effects on equine welfare and industry, particularly for international events such as the Olympic Games. While many regions have contingency plans in place to manage an outbreak of AHS, further research is urgently required if the equine industry is to avoid or effectively contain an AHS epizootic in disease-free regions. This review describes the key aspects of AHS as a global issue and discusses the evidence supporting concerns that an epizootic may occur in AHS free countries, the planned government responses, and the roles and responsibilities of equine veterinarians. © 2016 EVJ Ltd.

  10. Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures

    Science.gov (United States)

    Ye, Zheng; Rae, Charlotte L.; Nombela, Cristina; Ham, Timothy; Rittman, Timothy; Jones, Peter Simon; Rodríguez, Patricia Vázquez; Coyle‐Gilchrist, Ian; Regenthal, Ralf; Altena, Ellemarije; Housden, Charlotte R.; Maxwell, Helen; Sahakian, Barbara J.; Barker, Roger A.; Robbins, Trevor W.

    2016-01-01

    Abstract Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double‐blind randomized three‐way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion‐weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave‐one‐out cross‐validation (LOOCV) to predict patients’ responses in terms of improved stopping efficiency. We identified two optimal models: (1) a “clinical” model that predicted the response of an individual patient with 77–79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion‐weighted imaging scan; and (2) a “mechanistic” model that explained the behavioral response with 85% accuracy for each drug, using drug‐induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinson's disease, the individual patient's response to each drug can be predicted using a pattern of clinical and neuroimaging features. Hum Brain Mapp 37:1026–1037

  11. Morbidity Forecast in Cities: A Study of Urban Air Pollution and Respiratory Diseases in the Metropolitan Region of Curitiba, Brazil.

    Science.gov (United States)

    de Souza, Fabio Teodoro

    2018-05-29

    In the last two decades, urbanization has intensified, and in Brazil, about 90% of the population now lives in urban centers. Atmospheric patterns have changed owing to the high growth rate of cities, with negative consequences for public health. This research aims to elucidate the spatial patterns of air pollution and respiratory diseases. A data-based model to aid local urban management to improve public health policies concerning air pollution is described. An example of data preparation and multivariate analysis with inventories from different cities in the Metropolitan Region of Curitiba was studied. A predictive model with outstanding accuracy in prediction of outbreaks was developed. Preliminary results describe relevant relations among morbidity scales, air pollution levels, and atmospheric seasonal patterns. The knowledge gathered here contributes to the debate on social issues and public policies. Moreover, the results of this smaller scale study can be extended to megacities.

  12. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-05-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  13. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-04-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  14. An ensemble approach to predicting the impact of vaccination on rotavirus disease in Niger.

    Science.gov (United States)

    Park, Jaewoo; Goldstein, Joshua; Haran, Murali; Ferrari, Matthew

    2017-10-13

    Recently developed vaccines provide a new way of controlling rotavirus in sub-Saharan Africa. Models for the transmission dynamics of rotavirus are critical both for estimating current burden from imperfect surveillance and for assessing potential effects of vaccine intervention strategies. We examine rotavirus infection in the Maradi area in southern Niger using hospital surveillance data provided by Epicentre collected over two years. Additionally, a cluster survey of households in the region allows us to estimate the proportion of children with diarrhea who consulted at a health structure. Model fit and future projections are necessarily particular to a given model; thus, where there are competing models for the underlying epidemiology an ensemble approach can account for that uncertainty. We compare our results across several variants of Susceptible-Infectious-Recovered (SIR) compartmental models to quantify the impact of modeling assumptions on our estimates. Model-specific parameters are estimated by Bayesian inference using Markov chain Monte Carlo. We then use Bayesian model averaging to generate ensemble estimates of the current dynamics, including estimates of R 0 , the burden of infection in the region, as well as the impact of vaccination on both the short-term dynamics and the long-term reduction of rotavirus incidence under varying levels of coverage. The ensemble of models predicts that the current burden of severe rotavirus disease is 2.6-3.7% of the population each year and that a 2-dose vaccine schedule achieving 70% coverage could reduce burden by 39-42%. Copyright © 2017. Published by Elsevier Ltd.

  15. Immunogenetic markers of Crohn's disease in adults population of the Moscow region

    Directory of Open Access Journals (Sweden)

    Stavtsev D.S.

    2014-12-01

    Full Text Available Aim: to study immunogenetic markers of predisposition to the development and protection for Crohn's disease in adults population of the Moscow region. Material and methods. The study included 53 samples of peripheral blood of patients with Crohn's disease in the Moscow region. The control group was represented by 1,700 samples of umbilical cord blood is healthy newborns. Revealing HLA antigens at low level performed by SSO method on DynalRELI 48 processor. The results received with ambiguous interpretation was using PCR-SSP method (Ivitrogen. Results. Were found the positive and negative associations of groups of HLA alleles with clinical form, the course of Crohn's disease and response to steroid treatment, in particular revealed that, predisposition to the development for Crohn's disease in women and with sensitivity to steroid treatment in this disease associated allele group C*12, to the characteristic restricting markers such as Crohn's disease include the В 38 and A*11 markers nonrestricting, nonpenetrating noninflammatory type groups are alleles B*56 and C*14 and C*14 is also associated with the risk of Crohn's disease in men, characteristic markers of protection to the development of the disease crown with chronic relapsing and severe clinical course are DQB1*02 and DQB1*03, respectively. Conclusion. These results demonstrate the need for studies of gene polymorphism HLA-system, not only in relation to the disease in general, but in selected patients with clinical groups.

  16. Exploration of machine learning techniques in predicting multiple sclerosis disease course.

    Directory of Open Access Journals (Sweden)

    Yijun Zhao

    Full Text Available To explore the value of machine learning methods for predicting multiple sclerosis disease course.1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening or not (non-worsening at up to five years after baseline visit. Support vector machines (SVM were used to build the classifier, and compared to logistic regression (LR using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up.Baseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%. Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group.SVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.

  17. Reward Prediction Errors in Drug Addiction and Parkinson's Disease: from Neurophysiology to Neuroimaging.

    Science.gov (United States)

    García-García, Isabel; Zeighami, Yashar; Dagher, Alain

    2017-06-01

    Surprises are important sources of learning. Cognitive scientists often refer to surprises as "reward prediction errors," a parameter that captures discrepancies between expectations and actual outcomes. Here, we integrate neurophysiological and functional magnetic resonance imaging (fMRI) results addressing the processing of reward prediction errors and how they might be altered in drug addiction and Parkinson's disease. By increasing phasic dopamine responses, drugs might accentuate prediction error signals, causing increases in fMRI activity in mesolimbic areas in response to drugs. Chronic substance dependence, by contrast, has been linked with compromised dopaminergic function, which might be associated with blunted fMRI responses to pleasant non-drug stimuli in mesocorticolimbic areas. In Parkinson's disease, dopamine replacement therapies seem to induce impairments in learning from negative outcomes. The present review provides a holistic overview of reward prediction errors across different pathologies and might inform future clinical strategies targeting impulsive/compulsive disorders.

  18. Adjusting a cancer mortality-prediction model for disease status-related eligibility criteria

    Directory of Open Access Journals (Sweden)

    Kimmel Marek

    2011-05-01

    Full Text Available Abstract Background Volunteering participants in disease studies tend to be healthier than the general population partially due to specific enrollment criteria. Using modeling to accurately predict outcomes of cohort studies enrolling volunteers requires adjusting for the bias introduced in this way. Here we propose a new method to account for the effect of a specific form of healthy volunteer bias resulting from imposing disease status-related eligibility criteria, on disease-specific mortality, by explicitly modeling the length of the time interval between the moment when the subject becomes ineligible for the study, and the outcome. Methods Using survival time data from 1190 newly diagnosed lung cancer patients at MD Anderson Cancer Center, we model the time from clinical lung cancer diagnosis to death using an exponential distribution to approximate the length of this interval for a study where lung cancer death serves as the outcome. Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET. The effect of the adjustment using the MD Anderson-derived approximation is compared to that based on SEER data. Results Using the adjustment developed in conjunction with our existing lung cancer model, we are able to accurately predict the number of lung cancer deaths observed in the control arm of CARET. Conclusions The resulting adjustment was accurate in predicting the lower rates of disease observed in the early years while still maintaining reasonable prediction ability in the later years of the trial. This method could be used to adjust for, or predict the duration and relative effect of any possible biases related to disease-specific eligibility criteria in modeling studies of volunteer-based cohorts.

  19. Automatic detection of the hippocampal region associated with Alzheimer's disease from microscopic images of mice brain

    Science.gov (United States)

    Albaidhani, Tahseen; Hawkes, Cheryl; Jassim, Sabah; Al-Assam, Hisham

    2016-05-01

    The hippocampus is the region of the brain that is primarily associated with memory and spatial navigation. It is one of the first brain regions to be damaged when a person suffers from Alzheimer's disease. Recent research in this field has focussed on the assessment of damage to different blood vessels within the hippocampal region from a high throughput brain microscopic images. The ultimate aim of our research is the creation of an automatic system to count and classify different blood vessels such as capillaries, veins, and arteries in the hippocampus region. This work should provide biologists with efficient and accurate tools in their investigation of the causes of Alzheimer's disease. Locating the boundary of the Region of Interest in the hippocampus from microscopic images of mice brain is the first essential stage towards developing such a system. This task benefits from the variation in colour channels and texture between the two sides of the hippocampus and the boundary region. Accordingly, the developed initial step of our research to locating the hippocampus edge uses a colour-based segmentation of the brain image followed by Hough transforms on the colour channel that isolate the hippocampus region. The output is then used to split the brain image into two sides of the detected section of the boundary: the inside region and the outside region. Experimental results on a sufficiently number of microscopic images demonstrate the effectiveness of the developed solution.

  20. Polio infrastructure strengthened disease outbreak preparedness and response in the WHO African Region.

    Science.gov (United States)

    Kouadio, Koffi; Okeibunor, Joseph; Nsubuga, Peter; Mihigo, Richard; Mkanda, Pascal

    2016-10-10

    The continuous deployments of polio resources, infrastructures and systems for responding to other disease outbreaks in many African countries has led to a number of lessons considered as best practice that need to be documented for strengthening preparedness and response activities in future outbreaks. We reviewed and documented the influence of polio best practices in outbreak preparedness and response in Angola, Nigeria and Ethiopia. Data from relevant programmes of the WHO African Region were also analyzed to demonstrate clearly the relative contributions of PEI resources and infrastructure to effective disease outbreak preparedness and response. Polio resources including, human, financial, and logistic, tool and strategies have tremendously contributed to responding to diseases outbreaks across the African region. In Angola, Nigeria and Ethiopia, many disease epidemics including Marburg Hemorrhagic fever, Dengue fever, Ebola Virus Diseases (EVD), Measles, Anthrax and Shigella have been controlled using existing polio Eradication Initiatives resources. Polio staffs are usually deployed in occasions to supports outbreak response activities (coordination, surveillance, contact tracing, case investigation, finance, data management, etc.). Polio logistics such vehicles, laboratories were also used in the response activities to other infectious diseases. Many polio tools including micro planning, dashboard, guidelines, SOPs on preparedness and response have also benefited to other epidemic-prone diseases. The Countries' preparedness and response plan to WPV importation as well as the Polio Emergency Operation Center models were successfully used to develop, strengthen and respond to many other diseases outbreak with the implication of partners and the strong leadership and ownership of governments. This review has important implications for WHO/AFRO initiative to strengthening and improving disease outbreak preparedness and responses in the African Region in respect

  1. Crash prediction model for two-lane rural highways in the Ashanti region of Ghana

    Directory of Open Access Journals (Sweden)

    Williams Ackaah

    2011-07-01

    Full Text Available Crash Prediction Models (CPMs have been used elsewhere as a useful tool by road Engineers and Planners. There is however no study on the prediction of road traffic crashes on rural highways in Ghana. The main objective of the study was to develop a prediction model for road traffic crashes occurring on the rural sections of the highways in the Ashanti Region of Ghana. The model was developed for all injury crashes occurring on selected rural highways in the Region over the three (3 year period 2005–2007. Data was collected from 76 rural highway sections and each section varied between 0.8 km and 6.7 km. Data collected for each section comprised injury crash data, traffic flow and speed data, and roadway characteristics and road geometry data. The Generalised Linear Model (GLM with Negative Binomial (NB error structure was used to estimate the model parameters. Two types of models, the ‘core’ model which included key exposure variables only and the ‘full’ model which included a wider range of variables were developed. The results show that traffic flow, highway segment length, junction density, terrain type and presence of a village settlement within road segments were found to be statistically significant explanatory variables (p<0.05 for crash involvement. Adding one junction to a 1 km section of road segment was found to increase injury crashes by 32.0% and sections which had a village settlement within them were found to increase injury crashes by 60.3% compared with segments with no settlements. The model explained 61.2% of the systematic variation in the data. Road and Traffic Engineers and Planners can apply the crash prediction model as a tool in safety improvement works and in the design of safer roads. It is recommended that to improve safety, highways should be designed to by-pass village settlements and that the number of junctions on a highway should be limited to carefully designed ones.

  2. Contrasting predictability of summer monsoon rainfall ISOs over the northeastern and western Himalayan region: an application of Hurst exponent

    Science.gov (United States)

    Mukherjee, Sandipan

    2017-09-01

    Due to heterogeneous nonlinear forcing of complex geomorphological features, predictability of monsoon rainfall 10-90-day intra-seasonal oscillations (ISO) over the complex terrain of northeastern and western Himalayan region (NEH and WH) remained poorly quantified. Using 72 and 61 number of station observations of monsoon rainfall ISOs of NEH and WH, respectively, this study attempts to investigate variation in the regional scale predictability of monsoon rainfall ISOs with respect to changing geomorphological features and monsoon rainfall characteristics. In view of the bimodal nonlinear dynamical structure of monsoon rainfall ISO, the fractal dynamical Hurst exponent-based predictability indices are estimated as an indicator of predictability for station observations of NEH and WH, and relationships with elevations, slopes, aspects, and average numbers of occurrences of long (short) spell of active (break) phases are investigated. Results show 10-90-day ISOs are anti-persistent throughout the IHR, although, predictability of 10-90-day ISOs is higher over the NEH region than WH. Predictabilities of ISOs are found to decrease with increasing elevation and slope for both NEH and WH regions. Predictabilities of ISOs over both regions are also found to increase linearly as the number of occurrences of monsoon rainfall ISO phases (active/break) increases.

  3. Exploration of machine learning techniques in predicting multiple sclerosis disease course

    OpenAIRE

    Zhao, Yijun; Healy, Brian C.; Rotstein, Dalia; Guttmann, Charles R. G.; Bakshi, Rohit; Weiner, Howard L.; Brodley, Carla E.; Chitnis, Tanuja

    2017-01-01

    Objective To explore the value of machine learning methods for predicting multiple sclerosis disease course. Methods 1693 CLIMB study patients were classified as increased EDSS?1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up. Results Baseline data...

  4. A radar-based hydrological model for flash flood prediction in the dry regions of Israel

    Science.gov (United States)

    Ronen, Alon; Peleg, Nadav; Morin, Efrat

    2014-05-01

    Flash floods are floods which follow shortly after rainfall events, and are among the most destructive natural disasters that strike people and infrastructures in humid and arid regions alike. Using a hydrological model for the prediction of flash floods in gauged and ungauged basins can help mitigate the risk and damage they cause. The sparsity of rain gauges in arid regions requires the use of radar measurements in order to get reliable quantitative precipitation estimations (QPE). While many hydrological models use radar data, only a handful do so in dry climate. This research presents a robust radar-based hydro-meteorological model built specifically for dry climate. Using this model we examine the governing factors of flash floods in the arid and semi-arid regions of Israel in particular and in dry regions in general. The hydrological model built is a semi-distributed, physically-based model, which represents the main hydrological processes in the area, namely infiltration, flow routing and transmission losses. Three infiltration functions were examined - Initial & Constant, SCS-CN and Green&Ampt. The parameters for each function were found by calibration based on 53 flood events in three catchments, and validation was performed using 55 flood events in six catchments. QPE were obtained from a C-band weather radar and adjusted using a weighted multiple regression method based on a rain gauge network. Antecedent moisture conditions were calculated using a daily recharge assessment model (DREAM). We found that the SCS-CN infiltration function performed better than the other two, with reasonable agreement between calculated and measured peak discharge. Effects of storm characteristics were studied using synthetic storms from a high resolution weather generator (HiReS-WG), and showed a strong correlation between storm speed, storm direction and rain depth over desert soils to flood volume and peak discharge.

  5. View of God as benevolent and forgiving or punishing and judgmental predicts HIV disease progression.

    Science.gov (United States)

    Ironson, Gail; Stuetzle, Rick; Ironson, Dale; Balbin, Elizabeth; Kremer, Heidemarie; George, Annie; Schneiderman, Neil; Fletcher, Mary Ann

    2011-12-01

    This study assessed the predictive relationship between View of God beliefs and change in CD4-cell and Viral Load (VL) in HIV positive people over an extended period. A diverse sample of HIVseropositive participants (N = 101) undergoing comprehensive psychological assessment and blood draws over the course of 4 years completed the View of God Inventory with subscales measuring Positive View (benevolent/forgiving) and Negative View of God (harsh/judgmental/punishing). Adjusting for initial disease status, age, gender, ethnicity, education, and antiretroviral medication (at every 6-month visit), a Positive View of God predicted significantly slower disease-progression (better preservation of CD4-cells, better control of VL), whereas a Negative View of God predicted faster disease-progression over 4 years. Effect sizes were greater than those previously demonstrated for psychosocial variables known to predict HIV-disease-progression, such as depression and coping. Results remained significant even after adjusting for church attendance and psychosocial variables (health behaviors, mood, and coping). These results provide good initial evidence that spiritual beliefs may predict health outcomes.

  6. Merging economics and epidemiology to improve the prediction and management of infectious disease.

    Science.gov (United States)

    Perrings, Charles; Castillo-Chavez, Carlos; Chowell, Gerardo; Daszak, Peter; Fenichel, Eli P; Finnoff, David; Horan, Richard D; Kilpatrick, A Marm; Kinzig, Ann P; Kuminoff, Nicolai V; Levin, Simon; Morin, Benjamin; Smith, Katherine F; Springborn, Michael

    2014-12-01

    Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as "economic epidemiology" or "epidemiological economics," the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.

  7. Scanning laser Doppler imaging may predict disease progression of localized scleroderma in children and young adults.

    Science.gov (United States)

    Shaw, L J; Shipley, J; Newell, E L; Harris, N; Clinch, J G; Lovell, C R

    2013-07-01

    Localized scleroderma is a rare but potentially disfiguring and disabling condition. Systemic treatment should be started early in those with active disease in key functional and cosmetic sites, but disease activity is difficult to determine clinically. Superficial blood flow has been shown to correlate with disease activity in localized scleroderma. To examine whether superficial blood flow measured by laser Doppler imaging (LDI) has the potential to predict disease progression and therefore select patients for early systemic treatment. A group of 20 individuals had clinical assessment and scanning LDI blood-flow measurements of 32 affected body sites. After a mean follow-up of 8.7 months their clinical outcome was compared with the results of the initial LDI assessment. Eleven out of 15 patients with an assessment of active LDI had progressed clinically, and 16 out of the 17 scans with inactive LDI assessment had not progressed, giving a positive predictive value of 73% and a negative predictive value of 94%. We believe that LDI can be a useful tool in predicting disease progression in localized scleroderma, and it may help clinicians to decide which patients to treat early. © 2013 The Authors BJD © 2013 British Association of Dermatologists.

  8. Linking spring phenology with mechanistic models of host movement to predict disease transmission risk

    Science.gov (United States)

    Merkle, Jerod A.; Cross, Paul C.; Scurlock, Brandon M.; Cole, Eric K.; Courtemanch, Alyson B.; Dewey, Sarah R.; Kauffman, Matthew J.

    2018-01-01

    Disease models typically focus on temporal dynamics of infection, while often neglecting environmental processes that determine host movement. In many systems, however, temporal disease dynamics may be slow compared to the scale at which environmental conditions alter host space-use and accelerate disease transmission.Using a mechanistic movement modelling approach, we made space-use predictions of a mobile host (elk [Cervus Canadensis] carrying the bacterial disease brucellosis) under environmental conditions that change daily and annually (e.g., plant phenology, snow depth), and we used these predictions to infer how spring phenology influences the risk of brucellosis transmission from elk (through aborted foetuses) to livestock in the Greater Yellowstone Ecosystem.Using data from 288 female elk monitored with GPS collars, we fit step selection functions (SSFs) during the spring abortion season and then implemented a master equation approach to translate SSFs into predictions of daily elk distribution for five plausible winter weather scenarios (from a heavy snow, to an extreme winter drought year). We predicted abortion events by combining elk distributions with empirical estimates of daily abortion rates, spatially varying elk seroprevelance and elk population counts.Our results reveal strong spatial variation in disease transmission risk at daily and annual scales that is strongly governed by variation in host movement in response to spring phenology. For example, in comparison with an average snow year, years with early snowmelt are predicted to have 64% of the abortions occurring on feedgrounds shift to occurring on mainly public lands, and to a lesser extent on private lands.Synthesis and applications. Linking mechanistic models of host movement with disease dynamics leads to a novel bridge between movement and disease ecology. Our analysis framework offers new avenues for predicting disease spread, while providing managers tools to proactively mitigate

  9. A predominance of hypertensive heart disease among patients with cardiac disease in buea, a semi-urban setting, South west region of cameroon

    NARCIS (Netherlands)

    Nkoke, Clovis; Makoge, Christelle; Dzudie, Anastase; Mfeukeu, Liliane Kuate; Luchuo, Engelbert Bain; Menanga, Alain; Kingue, Samuel

    2017-01-01

    Objective: The pattern of heart disease is diverse within and among world regions. The little data on the spectrum of heart disease in Cameroon has been so far limited to major cities. We sought to describe the pattern of heart disease in Buea, the South West Region of Cameroon, a semi-urban

  10. Adverse moisture events predict seasonal abundance of Lyme disease vector ticks (Ixodes scapularis)

    Science.gov (United States)

    Berger, Kathryn A.; Ginsberg, Howard S.; Dugas, Katherine D.; Hamel, Lutz H.; Mather, Thomas N.

    2014-01-01

    Background: Lyme borreliosis (LB) is the most commonly reported vector-borne disease in north temperate regions worldwide, affecting an estimated 300,000 people annually in the United States alone. The incidence of LB is correlated with human exposure to its vector, the blacklegged tick (Ixodes scapularis). To date, attempts to model tick encounter risk based on environmental parameters have been equivocal. Previous studies have not considered (1) the differences between relative humidity (RH) in leaf litter and at weather stations, (2) the RH threshold that affects nymphal blacklegged tick survival, and (3) the time required below the threshold to induce mortality. We clarify the association between environmental moisture and tick survival by presenting a significant relationship between the total number of tick adverse moisture events (TAMEs - calculated as microclimatic periods below a RH threshold) and tick abundance each year.Methods: We used a 14-year continuous statewide tick surveillance database and corresponding weather data from Rhode Island (RI), USA, to assess the effects of TAMEs on nymphal populations of I. scapularis. These TAMEs were defined as extended periods of time (>8 h below 82% RH in leaf litter). We fit a sigmoid curve comparing weather station data to those collected by loggers placed in tick habitats to estimate RH experienced by nymphal ticks, and compiled the number of historical TAMEs during the 14-year record.Results: The total number of TAMEs in June of each year was negatively related to total seasonal nymphal tick densities, suggesting that sub-threshold humidity episodes >8 h in duration naturally lowered nymphal blacklegged tick abundance. Furthermore, TAMEs were positively related to the ratio of tick abundance early in the season when compared to late season, suggesting that lower than average tick abundance for a given year resulted from tick mortality and not from other factors.Conclusions: Our results clarify the mechanism

  11. Segmentation of brain parenchymal regions into gray matter and white matter with Alzheimer's disease

    International Nuclear Information System (INIS)

    Tokunaga, Chiaki; Yoshiura, Takashi; Yamashita, Yasuo; Magome, Taiki; Honda, Hiroshi; Arimura, Hidetaka; Toyofuku, Fukai; Ohki, Masafumi

    2010-01-01

    It is very difficult and time consuming for neuroradiologists to estimate the degree of cerebral atrophy based on the volume of cortical regions etc. Our purpose of this study was to develop an automated segmentation of the brain parenchyma into gray and white matter regions with Alzheimer's disease (AD) in three-dimensional (3D) T1-weighted MR images. Our proposed method consisted of extraction of a brain parenchymal region based on a brain model matching and segmentation of the brain parenchyma into gray and white matter regions based on a fuzzy c-means (FCM) algorithm. We applied our proposed method to MR images of the whole brains obtained from 9 cases, including 4 clinically AD cases and 5 control cases. The mean volume percentage of a cortical region (41.7%) to a brain parenchymal region in AD patients was smaller than that (45.2%) in the control subjects (p=0.000462). (author)

  12. Invisalign® treatment in the anterior region: were the predicted tooth movements achieved?

    Science.gov (United States)

    Krieger, Elena; Seiferth, Jörg; Marinello, Ivana; Jung, Britta A; Wriedt, Susanne; Jacobs, Collin; Wehrbein, Heinrich

    2012-09-01

    Based on our previous pilot study, the objective of this extended study was to compare (a) casts to their corresponding digital ClinCheck® models at baseline and (b) the tooth movement achieved at the end of aligner therapy (Invisalign®) to the predicted movement in the anterior region. Pre- and post-treatment casts as well as initial and final ClinChecks® models of 50 patients (15-63 years of age) were analyzed. All patients were treated with Invisalign® (Align Technology, Santa Clara, CA, USA). Evaluated parameters were: upper/lower anterior arch length and intercanine distance, overjet, overbite, dental midline shift, and the irregularity index according to Little. The comparison achieved/predicted tooth movement was tested for equivalence [adjusted 98.57% confidence interval (- 1.00; + 1.00)]. Before treatment the anterior crowding, according to Little, was on average 5.39 mm (minimum 1.50 mm, maximum 14.50 mm) in the upper dentition and 5.96 mm (minimum 2.00 mm, maximum 11.50 mm) in the lower dentition. After treatment the values were reduced to 1.57 mm (minimum 0 mm, maximum 4.5 mm) in the maxilla and 0.82 mm (minimum 0 mm, maximum 2.50 mm) in the mandible. We found slight deviations between pretreatment casts and initialClinCheck® ranging on average from -0.08 mm (SD ± 0.29) for the overjet and up to -0.28 mm (SD ± 0.46) for the upper anterior arch length. The difference between achieved/predicted tooth movements ranged on average from 0.01 mm (SD ± 0.48) for the lower anterior arch length, up to 0.7 mm (SD ± 0.87) for the overbite. All parameters were significantly equivalent except for the overbite (-1.02; -0.39). Performed with aligners (Invisalign®), the resolvement of the partly severe anterior crowding was successfully accomplished. Resolving lower anterior crowding by protrusion of the anterior teeth (i.e., enlargement of the anterior arch length) seems well predictable. The initial ClinCheck® models provided high accuracy compared to the

  13. Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

    Science.gov (United States)

    Sharma, Sanjib; Siddique, Ridwan; Reed, Seann; Ahnert, Peter; Mendoza, Pablo; Mejia, Alfonso

    2018-03-01

    The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1-7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised of the following components: (i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); (ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); (iii) NOAA's Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); (iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; (v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and (vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the US Middle Atlantic region, ranging in size from 381 to 12 362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble streamflow forecasts, particularly in the cool season, but QR outperforms ARX(1,1). The scenarios that implement preprocessing and postprocessing separately tend to perform similarly, although the postprocessing-alone scenario is often more effective. The scenario involving both preprocessing and postprocessing consistently outperforms the other scenarios. In some cases

  14. Ozone phytotoxicity evaluation and prediction of crops production in tropical regions

    Science.gov (United States)

    Mohammed, Nurul Izma; Ramli, Nor Azam; Yahya, Ahmad Shukri

    2013-04-01

    Increasing ozone concentration in the atmosphere can threaten food security due to its effects on crop production. Since the 1980s, ozone has been believed to be the most damaging air pollutant to crops. In Malaysia, there is no index to indicate the reduction of crops due to the exposure of ozone. Therefore, this study aimed to identify the accumulated exposure over a threshold of X ppb (AOTX) indexes in assessing crop reduction in Malaysia. In European countries, crop response to ozone exposure is mostly expressed as AOT40. This study was designed to evaluate and predict crop reduction in tropical regions and in particular, the Malaysian climate, by adopting the AOT40 index method and modifying it based on Malaysian air quality and crop data. Nine AOTX indexes (AOT0, AOT5, AOT10, AOT15, AOT20, AOT25, AOT30, AOT40, and AOT50) were analyzed, crop responses tested and reduction in crops predicted. The results showed that the AOT50 resulted in the highest reduction in crops and the highest R2 value between the AOT50 and the crops reduction from the linear regression analysis. Hence, this study suggests that the AOT50 index is the most suitable index to estimate the potential ozone impact on crops in tropical regions. The result showed that the critical level for AOT50 index if the estimated crop reduction is 5% was 1336 ppb h. Additionally, the results indicated that the AOT40 index in Malaysia gave a minimum percentage of 6% crop reduction; as contrasted with the European guideline of 5% (due to differences in the climate e.g., average amount of sunshine).

  15. Regional cerebral blood flow in Parkinson's disease by [sup 123]I-IMP SPECT

    Energy Technology Data Exchange (ETDEWEB)

    Kitamura, Yoshihiro [Okayama Univ. (Japan). School of Medicine

    1994-06-01

    Regional cerebral blood flow (rCBF) was evaluated in 63 patients with Parkinson's disease (PD) by single-photon emission computed tomography (SPECT) using N-isopropyl-p-[sup 123]I-iodoamphetamine ([sup 123]I-IMP) as a tracer. Evaluation of the SPECT images was performed in accordance with the rCBF quantification method using a microsphere model. Patients in stage IV demonstrated significantly lower rCBF than those in stage II at the frontal, temporal, parietal, occipital regions and in the thalamus and cerebellum. Subjects with mental symptoms demonstrated decreased rCBF in every region in the brain. The present study indicates that clinical exacerbation and manifestation of dementia and other psychiatric symptoms in Parkinson's disease are associated with decreased blood flow in various brain regions. (author).

  16. Urinary endotrophin predicts disease progression in patients with chronic kidney disease

    DEFF Research Database (Denmark)

    Rasmussen, Daniel Guldager Kring; Fenton, Anthony; Jesky, Mark

    2017-01-01

    Renal fibrosis is the central pathogenic process in progression of chronic kidney disease (CKD). Collagen type VI (COL VI) is upregulated in renal fibrosis. Endotrophin is released from COL VI and promotes pleiotropic pro-fibrotic effects. Kidney disease severity varies considerably and accurate...... information regarding CKD progression may improve clinical decisions. We tested the hypothesis that urinary endotrophin derived during COL VI deposition in fibrotic human kidneys is a marker for progression of CKD in the Renal Impairment in Secondary Care (RIISC) cohort, a prospective observational study...... of 499 CKD patients. Endotrophin localised to areas of increased COL VI deposition in fibrotic kidneys but was not present in histologically normal kidneys. The third and fourth quartiles of urinary endotrophin:creatinine ratio (ECR) were independently associated with one-year disease progression after...

  17. Route prediction model of infectious diseases for 2018 Winter Olympics in Korea

    International Nuclear Information System (INIS)

    Kim, Eungyeong; Lee, Seok; Byun, Young Tae; Kim, Jae Hun; Lee, Taikjin; Lee, Hyuk-jae

    2014-01-01

    There are many types of respiratory infectious diseases caused by germs, virus, mycetes and parasites. Researchers recently have tried to develop mathematical models to predict the epidemic of infectious diseases. However, with the development of ground transportation system in modern society, the spread of infectious diseases became faster and more complicated in terms of the speed and the pathways. The route of infectious diseases during Vancouver Olympics was predicted based on the Susceptible-Infectious-Recovered (SIR) model. In this model only the air traffic as an essential factor for the intercity migration of infectious diseases was involved. Here, we propose a multi-city transmission model to predict the infection route during 2018 Winter Olympics in Korea based on the pre-existing SIR model. Various types of transportation system such as a train, a car, a bus, and an airplane for the interpersonal contact in both inter- and intra-city are considered. Simulation is performed with assumptions and scenarios based on realistic factors including demographic, transportation and diseases data in Korea. Finally, we analyze an economic profit and loss caused by the variation of the number of tourists during the Olympics

  18. Route prediction model of infectious diseases for 2018 Winter Olympics in Korea

    Science.gov (United States)

    Kim, Eungyeong; Lee, Seok; Byun, Young Tae; Kim, Jae Hun; Lee, Hyuk-jae; Lee, Taikjin

    2014-03-01

    There are many types of respiratory infectious diseases caused by germs, virus, mycetes and parasites. Researchers recently have tried to develop mathematical models to predict the epidemic of infectious diseases. However, with the development of ground transportation system in modern society, the spread of infectious diseases became faster and more complicated in terms of the speed and the pathways. The route of infectious diseases during Vancouver Olympics was predicted based on the Susceptible-Infectious-Recovered (SIR) model. In this model only the air traffic as an essential factor for the intercity migration of infectious diseases was involved. Here, we propose a multi-city transmission model to predict the infection route during 2018 Winter Olympics in Korea based on the pre-existing SIR model. Various types of transportation system such as a train, a car, a bus, and an airplane for the interpersonal contact in both inter- and intra-city are considered. Simulation is performed with assumptions and scenarios based on realistic factors including demographic, transportation and diseases data in Korea. Finally, we analyze an economic profit and loss caused by the variation of the number of tourists during the Olympics.

  19. A simple method to predict regional fish abundance: an example in the McKenzie River Basin, Oregon

    Science.gov (United States)

    D.J. McGarvey; J.M. Johnston

    2011-01-01

    Regional assessments of fisheries resources are increasingly called for, but tools with which to perform them are limited. We present a simple method that can be used to estimate regional carrying capacity and apply it to the McKenzie River Basin, Oregon. First, we use a macroecological model to predict trout densities within small, medium, and large streams in the...

  20. Triatoma dimidiata infestation in Chagas disease endemic regions of Guatemala: comparison of random and targeted cross-sectional surveys.

    Directory of Open Access Journals (Sweden)

    Raymond J King

    approximately 0.64 in the targeted surveys in both regions. Sensitivity did not differ between surveys, but the positive predictive value was significantly greater in the random surveys. CONCLUSIONS/SIGNIFICANCE: Surprisingly, targeted surveys were not more effective at determining T. dimidiata prevalence or at directing control to high risk villages in comparison to random surveys. We recommend that random surveys should be selected over targeted surveys whenever possible, particularly when the focus is on directing disease control and elimination and when risk factor association has not been evaluated for all regions under investigation.

  1. Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty

    Science.gov (United States)

    Ling, J.; Templeton, J.

    2015-08-01

    Reynolds Averaged Navier Stokes (RANS) models are widely used in industry to predict fluid flows, despite their acknowledged deficiencies. Not only do RANS models often produce inaccurate flow predictions, but there are very limited diagnostics available to assess RANS accuracy for a given flow configuration. If experimental or higher fidelity simulation results are not available for RANS validation, there is no reliable method to evaluate RANS accuracy. This paper explores the potential of utilizing machine learning algorithms to identify regions of high RANS uncertainty. Three different machine learning algorithms were evaluated: support vector machines, Adaboost decision trees, and random forests. The algorithms were trained on a database of canonical flow configurations for which validated direct numerical simulation or large eddy simulation results were available, and were used to classify RANS results on a point-by-point basis as having either high or low uncertainty, based on the breakdown of specific RANS modeling assumptions. Classifiers were developed for three different basic RANS eddy viscosity model assumptions: the isotropy of the eddy viscosity, the linearity of the Boussinesq hypothesis, and the non-negativity of the eddy viscosity. It is shown that these classifiers are able to generalize to flows substantially different from those on which they were trained. Feature selection techniques, model evaluation, and extrapolation detection are discussed in the context of turbulence modeling applications.

  2. Nudging and predictability in regional climate modelling: investigation in a nested quasi-geostrophic model

    Science.gov (United States)

    Omrani, Hiba; Drobinski, Philippe; Dubos, Thomas

    2010-05-01

    In this work, we consider the effect of indiscriminate and spectral nudging on the large and small scales of an idealized model simulation. The model is a two layer quasi-geostrophic model on the beta-plane driven at its boundaries by the « global » version with periodic boundary condition. This setup mimics the configuration used for regional climate modelling. The effect of large-scale nudging is studied by using the "perfect model" approach. Two sets of experiments are performed: (1) the effect of nudging is investigated with a « global » high resolution two layer quasi-geostrophic model driven by a low resolution two layer quasi-geostrophic model. (2) similar simulations are conducted with the two layer quasi-geostrophic Limited Area Model (LAM) where the size of the LAM domain comes into play in addition to the first set of simulations. The study shows that the indiscriminate nudging time that minimizes the error at both the large and small scales is reached for a nudging time close to the predictability time, for spectral nudging, the optimum nudging time should tend to zero since the best large scale dynamics is supposed to be given by the driving large-scale fields are generally given at much lower frequency than the model time step(e,g, 6-hourly analysis) with a basic interpolation between the fields, the optimum nudging time differs from zero, however remaining smaller than the predictability time.

  3. Improving performance of breast cancer risk prediction using a new CAD-based region segmentation scheme

    Science.gov (United States)

    Heidari, Morteza; Zargari Khuzani, Abolfazl; Danala, Gopichandh; Qiu, Yuchen; Zheng, Bin

    2018-02-01

    Objective of this study is to develop and test a new computer-aided detection (CAD) scheme with improved region of interest (ROI) segmentation combined with an image feature extraction framework to improve performance in predicting short-term breast cancer risk. A dataset involving 570 sets of "prior" negative mammography screening cases was retrospectively assembled. In the next sequential "current" screening, 285 cases were positive and 285 cases remained negative. A CAD scheme was applied to all 570 "prior" negative images to stratify cases into the high and low risk case group of having cancer detected in the "current" screening. First, a new ROI segmentation algorithm was used to automatically remove useless area of mammograms. Second, from the matched bilateral craniocaudal view images, a set of 43 image features related to frequency characteristics of ROIs were initially computed from the discrete cosine transform and spatial domain of the images. Third, a support vector machine model based machine learning classifier was used to optimally classify the selected optimal image features to build a CAD-based risk prediction model. The classifier was trained using a leave-one-case-out based cross-validation method. Applying this improved CAD scheme to the testing dataset, an area under ROC curve, AUC = 0.70+/-0.04, which was significantly higher than using the extracting features directly from the dataset without the improved ROI segmentation step (AUC = 0.63+/-0.04). This study demonstrated that the proposed approach could improve accuracy on predicting short-term breast cancer risk, which may play an important role in helping eventually establish an optimal personalized breast cancer paradigm.

  4. Evaluation of the NMC regional ensemble prediction system during the Beijing 2008 Olympic Games

    Science.gov (United States)

    Li, Xiaoli; Tian, Hua; Deng, Guo

    2011-10-01

    Based on the B08RDP (Beijing 2008 Olympic Games Mesoscale Ensemble Prediction Research and Development Project) that was launched by the World Weather Research Programme (WWRP) in 2004, a regional ensemble prediction system (REPS) at a 15-km horizontal resolution was developed at the National Meteorological Center (NMC) of the China Meteorological Administration (CMA). Supplementing to the forecasters' subjective affirmation on the promising performance of the REPS during the 2008 Beijing Olympic Games (BOG), this paper focuses on the objective verification of the REPS for precipitation forecasts during the BOG period. By use of a set of advanced probabilistic verification scores, the value of the REPS compared to the quasi-operational global ensemble prediction system (GEPS) is assessed for a 36-day period (21 July-24 August 2008). The evaluation here involves different aspects of the REPS and GEPS, including their general forecast skills, specific attributes (reliability and resolution), and related economic values. The results indicate that the REPS generally performs significantly better for the short-range precipitation forecasts than the GEPS, and for light to heavy rainfall events, the REPS provides more skillful forecasts for accumulated 6- and 24-h precipitation. By further identifying the performance of the REPS through the attribute-focused measures, it is found that the advantages of the REPS over the GEPS come from better reliability (smaller biases and better dispersion) and increased resolution. Also, evaluation of a decision-making score reveals that a much larger group of users benefits from using the REPS forecasts than using the single model (the control run) forecasts, especially for the heavy rainfall events.

  5. Prediction and Monitoring of Monsoon Intraseasonal Oscillations over Indian Monsoon Region in an Ensemble Prediction System using CFSv2

    Science.gov (United States)

    Borah, Nabanita; Sukumarpillai, Abhilash; Sahai, Atul Kumar; Chattopadhyay, Rajib; Joseph, Susmitha; De, Soumyendu; Nath Goswami, Bhupendra; Kumar, Arun

    2014-05-01

    An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using NCEP Climate Forecast System model version2 at T126 horizontal resolution. The EPS is formulated by producing 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001-2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio becomes unity by about18 days and the predictability error saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are observed even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of amplitude of large scale MISO as well as the initial conditions related to the different phases of MISO. Categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

  6. Measurement and Prediction of Regional Tourism Sustainability: An Analysis of the Yangtze River Economic Zone, China

    Directory of Open Access Journals (Sweden)

    Canmian Liu

    2018-04-01

    Full Text Available In view of sustainable development of tourism, this paper firstly constructs a more comprehensive and scientific index system from the economical/societal/resource-related/environmental aspects of tourism and evaluates the sustainable and comprehensive development level of tourism in 11 provinces and cities of the Yangtze River economic zone by using the weighted TOPSIS (The Technique for Order Preference by Similarity to an Ideal Solution method; secondly, it analyzes the coupling coordination evolution relationship between tourism and the economy/society/resources/environment in different provinces and cities of the Yangtze river economic zone based on the coupling coordination model from the spatio-temporal dimension; and finally, it predicts the coupling coordination degree of the provinces and cities in the region in the next few years by the grey model (1,1 and puts forward some countermeasures and suggestions. According to the study, this method provides an effective reference to the study on the sustainable development of tourism and is very significant for learning the sustainable development of regional tourism and establishing specific and scientific countermeasures for improvement.

  7. Development of in-vehicle noise prediction models for Mumbai Metropolitan Region, India

    Directory of Open Access Journals (Sweden)

    Vishal Konbattulwar

    2016-08-01

    Full Text Available Traffic noise is one of the major sources of noise pollution in metropolitan regions causing various health hazards (e.g., long-term sleep disturbance, increase in blood pressure, physical tension, etc.. In this research, noise prediction models, which can measure the noise level experienced by the commuters while driving or traveling by motorized vehicles in the Mumbai Metropolitan Region, India, were developed. These models were developed by conducting a comprehensive study of various factors (e.g., vehicle speed, traffic volume and road characteristics, etc. affecting the levels of concentration of noise. A widespread data collection was done by conducting road trips of total length of 403.80 km via different modes of transport, such as air-conditioned (A/C car, non A/C car, bus and intermediate public transport (i.e., traditional 3-wheeler autos. Multiple regression analyses were performed to develop a functional relation between equivalent noise levels experienced by passengers while traveling (which was considered as a dependent variable and explanatory variables such as traffic characteristics, vehicle class, vehicle speed, various other location characteristics, etc. Noise levels are generally higher in the vicinity of intersections and signalized junctions. Independent data sets (for each mode of transport were used to validate the developed models. It was noted that maximum differences between observed and estimated values from the model were within the range of ±7.8% of the observed value.

  8. Regional Characterization of the Crust in Metropolitan Areas for Prediction of Strong Ground Motion

    Science.gov (United States)

    Hirata, N.; Sato, H.; Koketsu, K.; Umeda, Y.; Iwata, T.; Kasahara, K.

    2003-12-01

    Introduction: After the 1995 Kobe earthquake, the Japanese government increased its focus and funding of earthquake hazards evaluation, studies of man-made structures integrity, and emergency response planning in the major urban centers. A new agency, the Ministry of Education, Science, Sports and Culture (MEXT) has started a five-year program titled as Special Project for Earthquake Disaster Mitigation in Urban Areas (abbreviated to Dai-dai-toku in Japanese) since 2002. The project includes four programs: I. Regional characterization of the crust in metropolitan areas for prediction of strong ground motion. II. Significant improvement of seismic performance of structure. III. Advanced disaster management system. IV. Investigation of earthquake disaster mitigation research results. We will present the results from the first program conducted in 2002 and 2003. Regional Characterization of the Crust in Metropolitan Areas for Prediction of Strong Ground Motion: A long-term goal is to produce map of reliable estimations of strong ground motion. This requires accurate determination of ground motion response, which includes a source process, an effect of propagation path, and near surface response. The new five-year project was aimed to characterize the "source" and "propagation path" in the Kanto (Tokyo) region and Kinki (Osaka) region. The 1923 Kanto Earthquake is one of the important targets to be addressed in the project. The proximity of the Pacific and Philippine Sea subducting plates requires study of the relationship between earthquakes and regional tectonics. This project focuses on identification and geometry of: 1) Source faults, 2) Subducting plates and mega-thrust faults, 3) Crustal structure, 4) Seismogenic zone, 5) Sedimentary basins, 6) 3D velocity properties We have conducted a series of seismic reflection and refraction experiment in the Kanto region. In 2002 we have completed to deploy seismic profiling lines in the Boso peninsula (112 km) and the

  9. Potential risk of regional disease spread in West Africa through cross-border cattle trade.

    Directory of Open Access Journals (Sweden)

    Anna S Dean

    Full Text Available Transboundary animal movements facilitate the spread of pathogens across large distances. Cross-border cattle trade is of economic and cultural importance in West Africa. This study explores the potential disease risk resulting from large-scale, cross-border cattle trade between Togo, Burkina Faso, Ghana, Benin, and Nigeria for the first time.A questionnaire-based survey of livestock movements of 226 cattle traders was conducted in the 9 biggest cattle markets of northern Togo in February-March 2012. More than half of the traders (53.5% operated in at least one other country. Animal flows were stochastically simulated based on reported movements and the risk of regional disease spread assessed. More than three quarters (79.2%, range: 78.1-80.0% of cattle flowing into the market system originated from other countries. Through the cattle market system of northern Togo, non-neighbouring countries were connected via potential routes for disease spread. Even for diseases with low transmissibility and low prevalence in a given country, there was a high risk of disease introduction into other countries.By stochastically simulating data collected by interviewing cattle traders in northern Togo, this study identifies potential risks for regional disease spread in West Africa through cross-border cattle trade. The findings highlight that surveillance for emerging infectious diseases as well as control activities targeting endemic diseases in West Africa are likely to be ineffective if only conducted at a national level. A regional approach to disease surveillance, prevention and control is essential.

  10. Potential Risk of Regional Disease Spread in West Africa through Cross-Border Cattle Trade

    Science.gov (United States)

    Dean, Anna S.; Fournié, Guillaume; Kulo, Abalo E.; Boukaya, G. Aboudou; Schelling, Esther; Bonfoh, Bassirou

    2013-01-01

    Background Transboundary animal movements facilitate the spread of pathogens across large distances. Cross-border cattle trade is of economic and cultural importance in West Africa. This study explores the potential disease risk resulting from large-scale, cross-border cattle trade between Togo, Burkina Faso, Ghana, Benin, and Nigeria for the first time. Methods and Principal Findings A questionnaire-based survey of livestock movements of 226 cattle traders was conducted in the 9 biggest cattle markets of northern Togo in February-March 2012. More than half of the traders (53.5%) operated in at least one other country. Animal flows were stochastically simulated based on reported movements and the risk of regional disease spread assessed. More than three quarters (79.2%, range: 78.1–80.0%) of cattle flowing into the market system originated from other countries. Through the cattle market system of northern Togo, non-neighbouring countries were connected via potential routes for disease spread. Even for diseases with low transmissibility and low prevalence in a given country, there was a high risk of disease introduction into other countries. Conclusions By stochastically simulating data collected by interviewing cattle traders in northern Togo, this study identifies potential risks for regional disease spread in West Africa through cross-border cattle trade. The findings highlight that surveillance for emerging infectious diseases as well as control activities targeting endemic diseases in West Africa are likely to be ineffective if only conducted at a national level. A regional approach to disease surveillance, prevention and control is essential. PMID:24130721

  11. Towards flash flood prediction in the dry Dead Sea region utilizing radar rainfall information

    Science.gov (United States)

    Morin, E.; Jacoby, Y.; Navon, S.; Bet-Halachmi, E.

    2009-04-01

    Flash-flood warning models can save lives and protect various kinds of infrastructure. In dry climate regions, rainfall is highly variable and can be of high-intensity. Since rain gauge networks in such areas are sparse, rainfall information derived from weather radar systems can provide useful input for flash-flood models. This paper presents a flash-flood warning model utilizing radar rainfall data and applies it to two catchments that drain into the dry Dead Sea region. Radar-based quantitative precipitation estimates (QPEs) were derived using a rain gauge adjustment approach, either on a daily basis (allowing the adjustment factor to change over time, assuming available real-time gauge data) or using a constant factor value (derived from rain gauge data) over the entire period of the analysis. The QPEs served as input for a continuous hydrological model that represents the main hydrological processes in the region, namely infiltration, flow routing and transmission losses. The infiltration function is applied in a distributed mode while the routing and transmission loss functions are applied in a lumped mode. Model parameters were found by calibration based on five years of data for one of the catchments. Validation was performed for a subsequent five-year period for the same catchment and then for an entire ten year record for the second catchment. The probability of detection and false alarm rates for the validation cases were reasonable. Probabilistic flash-flood prediction is presented applying Monte Carlo simulations with an uncertainty range for the QPEs and model parameters. With low probability thresholds, one can maintain more than 70% detection with no more than 30% false alarms. The study demonstrates that a flash-flood-warning model is feasible for catchments in the area studied.

  12. Towards flash-flood prediction in the dry Dead Sea region utilizing radar rainfall information

    Science.gov (United States)

    Morin, Efrat; Jacoby, Yael; Navon, Shilo; Bet-Halachmi, Erez

    2009-07-01

    Flash-flood warning models can save lives and protect various kinds of infrastructure. In dry climate regions, rainfall is highly variable and can be of high-intensity. Since rain gauge networks in such areas are sparse, rainfall information derived from weather radar systems can provide useful input for flash-flood models. This paper presents a flash-flood warning model which utilizes radar rainfall data and applies it to two catchments that drain into the dry Dead Sea region. Radar-based quantitative precipitation estimates (QPEs) were derived using a rain gauge adjustment approach, either on a daily basis (allowing the adjustment factor to change over time, assuming available real-time gauge data) or using a constant factor value (derived from rain gauge data) over the entire period of the analysis. The QPEs served as input for a continuous hydrological model that represents the main hydrological processes in the region, namely infiltration, flow routing and transmission losses. The infiltration function is applied in a distributed mode while the routing and transmission loss functions are applied in a lumped mode. Model parameters were found by calibration based on the 5 years of data for one of the catchments. Validation was performed for a subsequent 5-year period for the same catchment and then for an entire 10-year record for the second catchment. The probability of detection and false alarm rates for the validation cases were reasonable. Probabilistic flash-flood prediction is presented applying Monte Carlo simulations with an uncertainty range for the QPEs and model parameters. With low probability thresholds, one can maintain more than 70% detection with no more than 30% false alarms. The study demonstrates that a flash-flood warning model is feasible for catchments in the area studied.

  13. Use of Readily Accessible Inflammatory Markers to Predict Diabetic Kidney Disease

    Directory of Open Access Journals (Sweden)

    Lauren Winter

    2018-05-01

    Full Text Available Diabetic kidney disease is a common complication of type 1 and type 2 diabetes and is the primary cause of end-stage renal disease in developed countries. Early detection of diabetic kidney disease will facilitate early intervention aimed at reducing the rate of progression to end-stage renal disease. Diabetic kidney disease has been traditionally classified based on the presence of albuminuria. More recently estimated glomerular filtration rate has also been incorporated into the staging of diabetic kidney disease. While albuminuric diabetic kidney disease is well described, the phenotype of non-albuminuric diabetic kidney disease is now widely accepted. An association between markers of inflammation and diabetic kidney disease has previously been demonstrated. Effector molecules of the innate immune system including C-reactive protein, interleukin-6, and tumor necrosis factor-α are increased in patients with diabetic kidney disease. Furthermore, renal infiltration of neutrophils, macrophages, and lymphocytes are observed in renal biopsies of patients with diabetic kidney disease. Similarly high serum neutrophil and low serum lymphocyte counts have been shown to be associated with diabetic kidney disease. The neutrophil–lymphocyte ratio is considered a robust measure of systemic inflammation and is associated with the presence of inflammatory conditions including the metabolic syndrome and insulin resistance. Cross-sectional studies have demonstrated a link between high levels of the above inflammatory biomarkers and diabetic kidney disease. Further longitudinal studies will be required to determine if these readily available inflammatory biomarkers can accurately predict the presence and prognosis of diabetic kidney disease, above and beyond albuminuria, and estimated glomerular filtration rate.

  14. Predicting distribution of Aedes aegypti and Culex pipiens complex, potential vectors of Rift Valley fever virus in relation to disease epidemics in East Africa

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    Clement Nyamunura Mweya

    2013-10-01

    Full Text Available Background: The East African region has experienced several Rift Valley fever (RVF outbreaks since the 1930s. The objective of this study was to identify distributions of potential disease vectors in relation to disease epidemics. Understanding disease vector potential distributions is a major concern for disease transmission dynamics. Methods: Diverse ecological niche modelling techniques have been developed for this purpose: we present a maximum entropy (Maxent approach for estimating distributions of potential RVF vectors in un-sampled areas in East Africa. We modelled the distribution of two species of mosquitoes (Aedes aegypti and Culex pipiens complex responsible for potential maintenance and amplification of the virus, respectively. Predicted distributions of environmentally suitable areas in East Africa were based on the presence-only occurrence data derived from our entomological study in Ngorongoro District in northern Tanzania. Results: Our model predicted potential suitable areas with high success rates of 90.9% for A. aegypti and 91.6% for C. pipiens complex. Model performance was statistically significantly better than random for both species. Most suitable sites for the two vectors were predicted in central and northwestern Tanzania with previous disease epidemics. Other important risk areas include western Lake Victoria, northern parts of Lake Malawi, and the Rift Valley region of Kenya. Conclusion: Findings from this study show distributions of vectors had biological and epidemiological significance in relation to disease outbreak hotspots, and hence provide guidance for the selection of sampling areas for RVF vectors during inter-epidemic periods.

  15. Predicting distribution of Aedes aegypti and Culex pipiens complex, potential vectors of Rift Valley fever virus in relation to disease epidemics in East Africa.

    Science.gov (United States)

    Mweya, Clement Nyamunura; Kimera, Sharadhuli Iddi; Kija, John Bukombe; Mboera, Leonard E G

    2013-01-01

    The East African region has experienced several Rift Valley fever (RVF) outbreaks since the 1930s. The objective of this study was to identify distributions of potential disease vectors in relation to disease epidemics. Understanding disease vector potential distributions is a major concern for disease transmission dynamics. DIVERSE ECOLOGICAL NICHE MODELLING TECHNIQUES HAVE BEEN DEVELOPED FOR THIS PURPOSE: we present a maximum entropy (Maxent) approach for estimating distributions of potential RVF vectors in un-sampled areas in East Africa. We modelled the distribution of two species of mosquitoes (Aedes aegypti and Culex pipiens complex) responsible for potential maintenance and amplification of the virus, respectively. Predicted distributions of environmentally suitable areas in East Africa were based on the presence-only occurrence data derived from our entomological study in Ngorongoro District in northern Tanzania. Our model predicted potential suitable areas with high success rates of 90.9% for A. aegypti and 91.6% for C. pipiens complex. Model performance was statistically significantly better than random for both species. Most suitable sites for the two vectors were predicted in central and northwestern Tanzania with previous disease epidemics. Other important risk areas include western Lake Victoria, northern parts of Lake Malawi, and the Rift Valley region of Kenya. Findings from this study show distributions of vectors had biological and epidemiological significance in relation to disease outbreak hotspots, and hence provide guidance for the selection of sampling areas for RVF vectors during inter-epidemic periods.

  16. Brain region specific mitophagy capacity could contribute to selective neuronal vulnerability in Parkinson's disease

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    Zabel Claus

    2011-09-01

    Full Text Available Abstract Parkinson's disease (PD is histologically well defined by its characteristic degeneration of dopaminergic neurons in the substantia nigra pars compacta. Remarkably, divergent PD-related mutations can generate comparable brain region specific pathologies. This indicates that some intrinsic region-specificity respecting differential neuron vulnerability exists, which codetermines the disease progression. To gain insight into the pathomechanism of PD, we investigated protein expression and protein oxidation patterns of three different brain regions in a PD mouse model, the PINK1 knockout mice (PINK1-KO, in comparison to wild type control mice. The dysfunction of PINK1 presumably affects mitochondrial turnover by disturbing mitochondrial autophagic pathways. The three brain regions investigated are the midbrain, which is the location of substantia nigra; striatum, the major efferent region of substantia nigra; and cerebral cortex, which is more distal to PD pathology. In all three regions, mitochondrial proteins responsible for energy metabolism and membrane potential were significantly altered in the PINK1-KO mice, but with very different region specific accents in terms of up/down-regulations. This suggests that disturbed mitophagy presumably induced by PINK1 knockout has heterogeneous impacts on different brain regions. Specifically, the midbrain tissue seems to be most severely hit by defective mitochondrial turnover, whereas cortex and striatum could compensate for mitophagy nonfunction by feedback stimulation of other catabolic programs. In addition, cerebral cortex tissues showed the mildest level of protein oxidation in both PINK1-KO and wild type mice, indicating either a better oxidative protection or less reactive oxygen species (ROS pressure in this brain region. Ultra-structural histological examination in normal mouse brain revealed higher incidences of mitophagy vacuoles in cerebral cortex than in striatum and substantia

  17. World Health Organization Estimates of the Global and Regional Disease Burden of 11 Foodborne Parasitic Diseases, 2010: A Data Synthesis.

    Directory of Open Access Journals (Sweden)

    Paul R Torgerson

    2015-12-01

    Full Text Available Foodborne diseases are globally important, resulting in considerable morbidity and mortality. Parasitic diseases often result in high burdens of disease in low and middle income countries and are frequently transmitted to humans via contaminated food. This study presents the first estimates of the global and regional human disease burden of 10 helminth diseases and toxoplasmosis that may be attributed to contaminated food.Data were abstracted from 16 systematic reviews or similar studies published between 2010 and 2015; from 5 disease data bases accessed in 2015; and from 79 reports, 73 of which have been published since 2000, 4 published between 1995 and 2000 and 2 published in 1986 and 1981. These included reports from national surveillance systems, journal articles, and national estimates of foodborne diseases. These data were used to estimate the number of infections, sequelae, deaths, and Disability Adjusted Life Years (DALYs, by age and region for 2010. These parasitic diseases, resulted in 48.4 million cases (95% Uncertainty intervals [UI] of 43.4-79.0 million and 59,724 (95% UI 48,017-83,616 deaths annually resulting in 8.78 million (95% UI 7.62-12.51 million DALYs. We estimated that 48% (95% UI 38%-56% of cases of these parasitic diseases were foodborne, resulting in 76% (95% UI 65%-81% of the DALYs attributable to these diseases. Overall, foodborne parasitic disease, excluding enteric protozoa, caused an estimated 23.2 million (95% UI 18.2-38.1 million cases and 45,927 (95% UI 34,763-59,933 deaths annually resulting in an estimated 6.64 million (95% UI 5.61-8.41 million DALYs. Foodborne Ascaris infection (12.3 million cases, 95% UI 8.29-22.0 million and foodborne toxoplasmosis (10.3 million cases, 95% UI 7.40-14.9 million were the most common foodborne parasitic diseases. Human cysticercosis with 2.78 million DALYs (95% UI 2.14-3.61 million, foodborne trematodosis with 2.02 million DALYs (95% UI 1.65-2.48 million and foodborne

  18. Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity.

    Science.gov (United States)

    Li, Guanghui; Luo, Jiawei; Xiao, Qiu; Liang, Cheng; Ding, Pingjian

    2018-05-12

    Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases. Copyright © 2018. Published by Elsevier Inc.

  19. The Value of Fecal Markers in Predicting Relapse in Inflammatory Bowel Diseases

    Directory of Open Access Journals (Sweden)

    Bianca J. Galgut

    2018-01-01

    Full Text Available The inflammatory bowel diseases (IBDs are lifelong chronic illnesses that place an immense burden on patients. The primary aim of therapy is to reduce disease burden and prevent relapse. However, the occurrence of relapses is often unpredictable. Current disease monitoring is primarily by way of clinical indices, with relapses often only recognized once the inflammatory episode is established with subsequent symptoms and gut damage. The window between initial upregulation of the inflammatory response and the recognition of symptoms may provide an opportunity to prevent the relapse and associated morbidity. This review will describe the existing literature surrounding predictive indicators of relapse of IBD with a specific focus on fecal biomarkers. Fecal biomarkers offer promise as a convenient, non-invasive, low cost option for disease monitoring that is predictive of subsequent relapse. To exploit the potential of fecal biomarkers in this role, further research is now required. This research needs to assess multiple fecal markers in context with demographics, disease phenotype, genetics, and intestinal microbiome composition, to build disease behavior models that can provide the clinician with sufficient confidence to intervene and change the long-term disease course.

  20. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction

    DEFF Research Database (Denmark)

    Paige, Ellie; Barrett, Jessica; Pennells, Lisa

    2017-01-01

    The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data...

  1. Amyloid Load in Fat Tissue Reflects Disease Severity and Predicts Survival in Amyloidosis

    NARCIS (Netherlands)

    Van Gameren, Ingrid I.; Hazenberg, Bouke P. C.; Bijzet, Johan; Haagsma, Elizabeth B.; Vellenga, Edo; Posthumus, Marcel D.; Jager, Pieter L.; Van Rijswijk, Martin H.

    Objective. The severity of systemic amyloidosis is thought to be related to the extent of amyloid deposition. We studied whether amyloid load in fat tissue reflects disease severity and predicts survival. Methods. We studied all consecutive patients with systemic amyloidosis seen between January

  2. Brachial plexus magnetic resonance imaging differentiates between inflammatory neuropathies and does not predict disease course

    NARCIS (Netherlands)

    Jongbloed, BA; Bos, Jeroen W; Rutgers, Dirk; van der Pol, WL; van den Berg, Leonard H

    OBJECTIVE: The main objective of this study was to evaluate the correlation between the distribution of brachial plexus magnetic resonance imaging (MRI) abnormalities and clinical weakness, and to evaluate the value of brachial plexus MRI in predicting disease course and response to treatment in

  3. The intestinal stem cell signature identifies colorectal cancer stem cells and predicts disease relapse

    NARCIS (Netherlands)

    Merlos-Suarez, A.; Barriga, F.M.; Jung, P.; Iglesias, M.; Cespedes, M.V.; Rossell, D.; Sevillano, M.; Hernando-Momblona, X.; da Silva-Diz, V.; Munoz, P.; Clevers, H.; Sancho, E.; Mangues, R.; Batlle, E.

    2011-01-01

    A frequent complication in colorectal cancer (CRC) is regeneration of the tumor after therapy. Here, we report that a gene signature specific for adult intestinal stem cells (ISCs) predicts disease relapse in CRC patients. ISCs are marked by high expression of the EphB2 receptor, which becomes

  4. Predicting sexual problems in young adults with an anorectal malformation or Hirschsprung disease

    NARCIS (Netherlands)

    Witvliet, M.J.; Van Gasteren, S.; Van Den Hondel, D.; Hartman, E.E.; Van Heurn, L.W.E.; Van Der Steeg, A.F.W.

    2018-01-01

    AIM. The aim of this study was to examine the prevalence of sexual dysfunction and distress and to assess whether sexual functioning could be predicted by psychosocial factors in childhood and adolescence in patients with an anorectal malformation or Hirschsprung disease. MATERIAL AND METHODS. In

  5. Serum YKL-40 predicts long-term mortality in patients with stable coronary disease

    DEFF Research Database (Denmark)

    Harutyunyan, Marina; Gøtze, Jens P; Winkel, Per

    2013-01-01

    We investigated whether the inflammatory biomarker YKL-40 could improve the long-term prediction of death made by common risk factors plus high-sensitivity C-reactive protein (hs-CRP) and N-terminal-pro-B natriuretic peptide (NT-proBNP) in patients with stable coronary artery disease (CAD)....

  6. Do Work Characteristics Predict Health Deterioration Among Employees with Chronic Diseases?

    NARCIS (Netherlands)

    Wind, A. de; Boot, C.R.L.; Sewdas, R.; Scharn, M.; Heuvel, S.G. van den; Beek, A.J. van der

    2017-01-01

    Purpose In our ageing workforce, the increasing numbers of employees with chronic diseases are encouraged to prolong their working lives. It is important to prevent health deterioration in this vulnerable group. This study aims to investigate whether work characteristics predict health deterioration

  7. Linearized and Kernelized Sparse Multitask Learning for Predicting Cognitive Outcomes in Alzheimer’s Disease

    Directory of Open Access Journals (Sweden)

    Xiaoli Liu

    2018-01-01

    Full Text Available Alzheimer’s disease (AD has been not only the substantial financial burden to the health care system but also the emotional burden to patients and their families. Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer’s disease. Recently, the multitask learning (MTL methods with sparsity-inducing norm (e.g., l2,1-norm have been widely studied to select the discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, these previous works formulate the prediction tasks as a linear regression problem. The major limitation is that they assumed a linear relationship between the MRI features and the cognitive outcomes. Some multikernel-based MTL methods have been proposed and shown better generalization ability due to the nonlinear advantage. We quantify the power of existing linear and nonlinear MTL methods by evaluating their performance on cognitive score prediction of Alzheimer’s disease. Moreover, we extend the traditional l2,1-norm to a more general lql1-norm (q≥1. Experiments on the Alzheimer’s Disease Neuroimaging Initiative database showed that the nonlinear l2,1lq-MKMTL method not only achieved better prediction performance than the state-of-the-art competitive methods but also effectively fused the multimodality data.

  8. Pattern of regional cortical thinning associated with cognitive deterioration in Parkinson's disease.

    Directory of Open Access Journals (Sweden)

    Javier Pagonabarraga

    Full Text Available BACKGROUND: Dementia is a frequent and devastating complication in Parkinson's disease (PD. There is an intensive search for biomarkers that may predict the progression from normal cognition (PD-NC to dementia (PDD in PD. Mild cognitive impairment in PD (PD-MCI seems to represent a transitional state between PD-NC and PDD. Few studies have explored the structural changes that differentiate PD-NC from PD-MCI and PDD patients. OBJECTIVES AND METHODS: We aimed to analyze changes in cortical thickness on 3.0T Magnetic Resonance Imaging (MRI across stages of cognitive decline in a prospective sample of PD-NC (n = 26, PD-MCI (n = 26 and PDD (n = 20 patients, compared to a group of healthy subjects (HC (n = 18. Cortical thickness measurements were made using the automatic software Freesurfer. RESULTS: In a sample of 72 PD patients, a pattern of linear and progressive cortical thinning was observed between cognitive groups in cortical areas functionally specialized in declarative memory (entorhinal cortex, anterior temporal pole, semantic knowledge (parahippocampus, fusiform gyrus, and visuoperceptive integration (banks of the superior temporal sulcus, lingual gyrus, cuneus and precuneus. Positive correlation was observed between confrontation naming and thinning in the fusiform gyrus, parahippocampal gyrus and anterior temporal pole; clock copy with thinning of the precuneus, parahippocampal and lingual gyrus; and delayed memory with thinning of the bilateral anteromedial temporal cortex. CONCLUSIONS: The pattern of regional decreased cortical thickness that relates to cognitive deterioration is present in PD-MCI patients, involving areas that play a central role in the storage of prior experiences, integration of external perceptions, and semantic processing.

  9. The Norrie disease gene maps to a 150 kb region on chromosome Xp11.3.

    Science.gov (United States)

    Sims, K B; Lebo, R V; Benson, G; Shalish, C; Schuback, D; Chen, Z Y; Bruns, G; Craig, I W; Golbus, M S; Breakefield, X O

    1992-05-01

    Norrie disease is a human X-linked recessive disorder of unknown etiology characterized by congenital blindness, sensory neural deafness and mental retardation. This disease gene was previously linked to the DXS7 (L1.28) locus and the MAO genes in band Xp11.3. We report here fine physical mapping of the obligate region containing the Norrie disease gene (NDP) defined by a recombination and by the smallest submicroscopic chromosomal deletion associated with Norrie disease identified to date. Analysis, using in addition two overlapping YAC clones from this region, allowed orientation of the MAOA and MAOB genes in a 5'-3'-3'-5' configuration. A recombination event between a (GT)n polymorphism in intron 2 of the MAOB gene and the NDP locus, in a family previously reported to have a recombination between DXS7 and NDP, delineates a flanking marker telomeric to this disease gene. An anonymous DNA probe, dc12, present in one of the YACs and in a patient with a submicroscopic deletion which includes MAOA and MAOB but not L1.28, serves as a flanking marker centromeric to the disease gene. An Alu-PCR fragment from the right arm of the MAO YAC (YMAO.AluR) is not deleted in this patient and also delineates the centromeric extent of the obligate disease region. The apparent order of these loci is telomere ... DXS7-MAOA-MAOB-NDP-dc12-YMAO.AluR ... centromere. Together these data define the obligate region containing the NDP gene to a chromosomal segment less than 150 kb.

  10. Studies of cerebral atrophy and regional cerebral blood flow in patients with Parkinson's disease

    International Nuclear Information System (INIS)

    Kitamura, Shin

    1983-01-01

    Cerebral atrophy and regional cerebral blood flow (rCBF) of 25 patients with Parkinson's disease were studied. The rCBF was measured with the intra-arterial Xe-133 injection method. The results obtained were as follows: 1) Sixty four % of Parkinson's disease patients showed ventricular dilation, and 76% of Parkinson's disease patients showed cortical atrophy on the CT scan, but we had to allow for the effects of the natural aging process on these results. 2) No correlation was recognized either between cerebral atrophy and the severity of Parkinson's disease, or between cerebral atrophy and the duration of Parkinson's disease. 3) In Parkinson's disease patients, the mean rCBF was lower than that of normal control subjects. The difference was even more remarkable in older patients. Only 40% of Parkinson's disease patients showed hyperfrontal pattern. 4) There was no correlation either between the mean rCBF and the severity of Parkinson's disease, or between the mean rCBF and the duration of Parkinson's disease. There was no significant difference between the mean rCBF of Parkinson's disease patients receiving levodopa and that of untreated patients. 5) The mean rCBF decreased in patients with cerebral atrophy on the CT scan. 6) Parkinson's disease patients with intellectual impairment showed cerebral atrophy and a remarkable decrease of the mean rCBF. 7) The effect of aging on cerebral atrophy on the CT scan had to be allowed for, but judging from the decrease of the mean rCBF, the cerebral cortex is evidently involved in Parkinson's disease. 8) The rCBF decline in Parkinson's disease patients may be related with the diminished cortical metabolic rate due to a remote effect of striatal dysfunction and a disturbance of mesocortical dopaminergic pathways. (J.P.N.)

  11. Studies of cerebral atrophy and regional cerebral blood flow in patients with Parkinson's disease

    Energy Technology Data Exchange (ETDEWEB)

    Kitamura, Shin [Nippon Medical School, Tokyo

    1983-04-01

    Cerebral atrophy and regional cerebral blood flow (rCBF) of 25 patients with Parkinson's disease were studied. The rCBF was measured with the intra-arterial Xe-133 injection method. The results obtained were as follows: 1) Sixty four % of Parkinson's disease patients showed ventricular dilation, and 76% of Parkinson's disease patients showed cortical atrophy on the CT scan, but we had to allow for the effects of the natural aging process on these results. 2) No correlation was recognized either between cerebral atrophy and the severity of Parkinson's disease, or between cerebral atrophy and the duration of Parkinson's disease. 3) In Parkinson's disease patients, the mean rCBF was lower than that of normal control subjects. The difference was even more remarkable in older patients. Only 40% of Parkinson's disease patients showed hyperfrontal pattern. 4) There was no correlation either between the mean rCBF and the severity of Parkinson's disease, or between the mean rCBF and the duration of Parkinson's disease. There was no significant difference between the mean rCBF of Parkinson's disease patients receiving levodopa and that of untreated patients. 5) The mean rCBF decreased in patients with cerebral atrophy on the CT scan. 6) Parkinson's disease patients with intellectual impairment showed cerebral atrophy and a remarkable decrease of the mean rCBF. 7) The effect of aging on cerebral atrophy on the CT scan had to be allowed for, but judging from the decrease of the mean rCBF, the cerebral cortex is evidently involved in Parkinson's disease. 8) The rCBF decline in Parkinson's disease patients may be related with the diminished cortical metabolic rate due to a remote effect of striatal dysfunction and a disturbance of mesocortical dopaminergic pathways.

  12. Brain morphometric analysis predicts decline of intelligence quotient in children with sickle cell disease: A preliminary study.

    Science.gov (United States)

    Chen, Rong; Krejza, Jaroslaw; Arkuszewski, Michal; Zimmerman, Robert A; Herskovits, Edward H; Melhem, Elias R

    2017-03-01

    For children with sickle cell disease (SCD) and at low risk category of stroke, we aim to build a predictive model to differentiate those with decline of intelligence-quotient (IQ) from counterparts without decline, based on structural magnetic-resonance (MR) imaging volumetric analysis. This preliminary prospective cohort study included 25 children with SCD, homozygous for hemoglobin S, with no history of stroke and transcranial Doppler mean velocities below 170cm/s at baseline. We administered the Kaufman Brief Intelligence Test (K-BIT) to each child at yearly intervals for 2-4 years. Each child underwent MR examination within 30 days of the baseline K-BIT evaluation date. We calculated K-BIT change rates, and used rate of change in K-BIT to classify children into two groups: a decline group and a non-decline group. We then generated predictive models to predict K-BIT decline/non-decline based on regional gray-matter (GM) volumes computed from structural MR images. We identified six structures (the left median cingulate gyrus, the right middle occipital gyrus, the left inferior occipital gyrus, the right fusiform gyrus, the right middle temporal gyrus, the right inferior temporal gyrus) that, when assessed for volume at baseline, are jointly predictive of whether a child would suffer subsequent K-BIT decline. Based on these six regional GM volumes and the baseline K-BIT, we built a prognostic model using the K * algorithm. The accuracy, sensitivity and specificity were 0.84, 0.78 and 0.86, respectively. GM volumetric analysis predicts subsequent IQ decline for children with SCD. Copyright © 2017 Medical University of Bialystok. Published by Elsevier B.V. All rights reserved.

  13. DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach.

    Directory of Open Access Journals (Sweden)

    Zhiheng Wang

    Full Text Available The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database.The DisoMCS is available at http://cal.tongji.edu.cn/disorder/.

  14. Real-time shear wave elastography may predict autoimmune thyroid disease.

    Science.gov (United States)

    Vlad, Mihaela; Golu, Ioana; Bota, Simona; Vlad, Adrian; Timar, Bogdan; Timar, Romulus; Sporea, Ioan

    2015-05-01

    To evaluate and compare the values of the elasticity index as measured by shear wave elastography in healthy subjects and in patients with autoimmune thyroid disease, in order to establish if this investigation can predict the occurrence of autoimmune thyroid disease. A total of 104 cases were included in the study group: 91 women (87.5%), out of which 52 (50%) with autoimmune thyroid disease diagnosed by specific tests and 52 (50%) healthy volunteers, matched for age and gender. For all the subjects, three measurements were performed on each thyroid lobe and a mean value was calculated. The data were expressed in kPa. The investigation was performed with an Aixplorer system (SuperSonic Imagine, France), using a linear high-resolution 15-4 MHz transducer. The mean value for the elasticity index was similar in the right and the left thyroid lobes, both in normal subjects and in patients with autoimmune thyroid disease: 19.6 ± 6.6 vs. 19.5 ± 6.8 kPa, p = 0.92, and 26.6 ± 10.0 vs. 25.8 ± 11.7 kPa, p = 0.71, respectively. This parameter was significantly higher in patients with autoimmune thyroid disease than in controls (p < 0.001). For a cut-off value of 22.3 kPa, which resulted in the highest sum of sensitivity and specificity, the elasticity index assessed by shear wave elastography had a sensitivity of 59.6% and a specificity of 76.9% (AUROC = 0.71; p < 0.001) for predicting the presence of autoimmune thyroid disease. Quantitative elasticity index measured by shear wave elastography was significantly higher in autoimmune thyroid disease than in normal thyroid parenchyma and may predict the presence of autoimmune thyroid disease.

  15. Anonymising the Sparse Dataset: A New Privacy Preservation Approach while Predicting Diseases

    Directory of Open Access Journals (Sweden)

    V. Shyamala Susan

    2016-09-01

    Full Text Available Data mining techniques analyze the medical dataset with the intention of enhancing patient’s health and privacy. Most of the existing techniques are properly suited for low dimensional medical dataset. The proposed methodology designs a model for the representation of sparse high dimensional medical dataset with the attitude of protecting the patient’s privacy from an adversary and additionally to predict the disease’s threat degree. In a sparse data set many non-zero values are randomly spread in the entire data space. Hence, the challenge is to cluster the correlated patient’s record to predict the risk degree of the disease earlier than they occur in patients and to keep privacy. The first phase converts the sparse dataset right into a band matrix through the Genetic algorithm along with Cuckoo Search (GCS.This groups the correlated patient’s record together and arranges them close to the diagonal. The next segment dissociates the patient’s disease, which is a sensitive value (SA with the parameters that determine the disease normally Quasi Identifier (QI.Finally, density based clustering technique is used over the underlying data to  create anonymized groups to maintain privacy and to predict the risk level of disease. Empirical assessments on actual health care data corresponding to V.A.Medical Centre heart disease dataset reveal the efficiency of this model pertaining to information loss, utility and privacy.

  16. Hemoglobin and hematocrit levels in the prediction of complicated Crohn's disease behavior--a cohort study.

    Science.gov (United States)

    Rieder, Florian; Paul, Gisela; Schnoy, Elisabeth; Schleder, Stephan; Wolf, Alexandra; Kamm, Florian; Dirmeier, Andrea; Strauch, Ulrike; Obermeier, Florian; Lopez, Rocio; Achkar, Jean-Paul; Rogler, Gerhard; Klebl, Frank

    2014-01-01

    Markers that predict the occurrence of a complicated disease behavior in patients with Crohn's disease (CD) can permit a more aggressive therapeutic regimen for patients at risk. The aim of this cohort study was to test the blood levels of hemoglobin (Hgb) and hematocrit (Hct) for the prediction of complicated CD behavior and CD related surgery in an adult patient population. Blood samples of 62 CD patients of the German Inflammatory Bowel Disease-network "Kompetenznetz CED" were tested for the levels of Hgb and Hct prior to the occurrence of complicated disease behavior or CD related surgery. The relation of these markers and clinical events was studied using Kaplan-Meier survival analysis and adjusted COX-proportional hazard regression models. The median follow-up time was 55.8 months. Of the 62 CD patients without any previous complication or surgery 34% developed a complication and/or underwent CD related surgery. Low Hgb or Hct levels were independent predictors of a shorter time to occurrence of the first complication or CD related surgery. This was true for early as well as late occurring complications. Stable low Hgb or Hct during serial follow-up measurements had a higher frequency of complications compared to patients with a stable normal Hgb or Hct, respectively. Determination of Hgb or Hct in complication and surgery naïve CD patients might serve as an additional tool for the prediction of complicated disease behavior.

  17. A Supervised Learning Process to Validate Online Disease Reports for Use in Predictive Models.

    Science.gov (United States)

    Patching, Helena M M; Hudson, Laurence M; Cooke, Warrick; Garcia, Andres J; Hay, Simon I; Roberts, Mark; Moyes, Catherine L

    2015-12-01

    Pathogen distribution models that predict spatial variation in disease occurrence require data from a large number of geographic locations to generate disease risk maps. Traditionally, this process has used data from public health reporting systems; however, using online reports of new infections could speed up the process dramatically. Data from both public health systems and online sources must be validated before they can be used, but no mechanisms exist to validate data from online media reports. We have developed a supervised learning process to validate geolocated disease outbreak data in a timely manner. The process uses three input features, the data source and two metrics derived from the location of each disease occurrence. The location of disease occurrence provides information on the probability of disease occurrence at that location based on environmental and socioeconomic factors and the distance within or outside the current known disease extent. The process also uses validation scores, generated by disease experts who review a subset of the data, to build a training data set. The aim of the supervised learning process is to generate validation scores that can be used as weights going into the pathogen distribution model. After analyzing the three input features and testing the performance of alternative processes, we selected a cascade of ensembles comprising logistic regressors. Parameter values for the training data subset size, number of predictors, and number of layers in the cascade were tested before the process was deployed. The final configuration was tested using data for two contrasting diseases (dengue and cholera), and 66%-79% of data points were assigned a validation score. The remaining data points are scored by the experts, and the results inform the training data set for the next set of predictors, as well as going to the pathogen distribution model. The new supervised learning process has been implemented within our live site and is

  18. Measurement of global and regional left ventricular performance with isotope technique in coronary heart disease

    International Nuclear Information System (INIS)

    Bostroem, P.-A.; Svensson, M.; Lilja, B.

    1988-01-01

    To evaluate left ventricular function in coronary artery disease, radionuclide measurements of global and regional ejection fraction (EF), regional wall motion and phase analyses of left ventricular contraction were performed by equilibrium technique, using sup(99m)Tc. One group of patients with angina pectoris and one group with myocardial infarction were compared with a control group. All above-mentioned parameters significantly separated the infarction group from the reference group both at rest and during work, while the group of patients with angina pectoris showed disturbances mainly during work, such as impaired ability to increase global and regional ejection fraction and regional wall motion. Adding regional analysis and phase analysis to the global EF determination increases the possibility of studying the left ventricular function. However, this addition has a limited value in detecting impaired left ventricular function compared to the determination of just global EF in patients with angina pectoris and in patients with myocardial infarction. (author)

  19. Prediction of disease and phenotype associations from genome-wide association studies.

    Directory of Open Access Journals (Sweden)

    Stephanie N Lewis

    Full Text Available Genome wide association studies (GWAS have proven useful as a method for identifying genetic variations associated with diseases. In this study, we analyzed GWAS data for 61 diseases and phenotypes to elucidate common associations based on single nucleotide polymorphisms (SNP. The study was an expansion on a previous study on identifying disease associations via data from a single GWAS on seven diseases.Adjustments to the originally reported study included expansion of the SNP dataset using Linkage Disequilibrium (LD and refinement of the four levels of analysis to encompass SNP, SNP block, gene, and pathway level comparisons. A pair-wise comparison between diseases and phenotypes was performed at each level and the Jaccard similarity index was used to measure the degree of association between two diseases/phenotypes. Disease relatedness networks (DRNs were used to visualize our results. We saw predominant relatedness between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis for the first three levels of analysis. Expected relatedness was also seen between lipid- and blood-related traits.The predominant associations between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis can be validated by clinical studies. The diseases have been proposed to share a systemic inflammation phenotype that can result in progression of additional diseases in patients with one of these three diseases. We also noticed unexpected relationships between metabolic and neurological diseases at the pathway comparison level. The less significant relationships found between diseases require a more detailed literature review to determine validity of the predictions. The results from this study serve as a first step towards a better understanding of seemingly unrelated diseases and phenotypes with similar symptoms or modes of treatment.

  20. Learning to predict is spared in mild cognitive impairment due to Alzheimer's disease.

    Science.gov (United States)

    Baker, Rosalind; Bentham, Peter; Kourtzi, Zoe

    2015-10-01

    Learning the statistics of the environment is critical for predicting upcoming events. However, little is known about how we translate previous knowledge about scene regularities to sensory predictions. Here, we ask whether patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD) that are known to have spared implicit but impaired explicit recognition memory are able to learn temporal regularities and predict upcoming events. We tested the ability of MCI-AD patients and age-matched controls to predict the orientation of a test stimulus following exposure to sequences of leftwards or rightwards oriented gratings. Our results demonstrate that exposure to temporal sequences without feedback facilitates the ability to predict an upcoming stimulus in both MCI-AD patients and controls. Further, we show that executive cognitive control may account for individual variability in predictive learning. That is, we observed significant positive correlations of performance in attentional and working memory tasks with post-training performance in the prediction task. Taken together, these results suggest a mediating role of circuits involved in cognitive control (i.e. frontal circuits) that may support the ability for predictive learning in MCI-AD.

  1. Predicting carotid artery disease and plaque instability from cell-derived microparticles.

    Science.gov (United States)

    Wekesa, A L; Cross, K S; O'Donovan, O; Dowdall, J F; O'Brien, O; Doyle, M; Byrne, L; Phelan, J P; Ross, M D; Landers, R; Harrison, M

    2014-11-01

    Cell-derived microparticles (MPs) are small plasma membrane-derived vesicles shed from circulating blood cells and may act as novel biomarkers of vascular disease. We investigated the potential of circulating MPs to predict (a) carotid plaque instability and (b) the presence of advanced carotid disease. This pilot study recruited carotid disease patients (aged 69.3 ± 1.2 years [mean ± SD], 69% male, 90% symptomatic) undergoing endarterectomy (n = 42) and age- and sex-matched controls (n = 73). Plaques were classified as stable (n = 25) or unstable (n = 16) post surgery using immunohistochemistry. Blood samples were analysed for MP subsets and molecular biomarkers. Odds ratios (OR) are expressed per standard deviation biomarker increase. Endothelial MP (EMP) subsets, but not any vascular, inflammatory, or proteolytic molecular biomarker, were higher (p < .05) in the unstable than the stable plaque patients. The area under the receiver operator characteristic curve for CD31(+)41(-) EMP in discriminating an unstable plaque was 0.73 (0.56-0.90, p < .05). CD31(+)41(-) EMP predicted plaque instability (OR = 2.19, 1.08-4.46, p < .05) and remained significant in a multivariable model that included transient ischaemic attack symptom status. Annexin V(+) MP, platelet MP (PMP) subsets, and C-reactive protein were higher (p < .05) in cases than controls. Annexin V(+) MP (OR = 3.15, 1.49-6.68), soluble vascular cell adhesion molecule-1 (OR = 1.64, 1.03-2.59), and previous smoking history (OR = 3.82, 1.38-10.60) independently (p < .05) predicted the presence of carotid disease in a multivariable model. EMP may have utility in predicting plaque instability in carotid patients and annexin V(+) MPs may predict the presence of advanced carotid disease in aging populations, independent of established biomarkers. Copyright © 2014 European Society for Vascular Surgery. Published by Elsevier Ltd. All rights reserved.

  2. Sizable variations in circulatory disease mortality by region and country of birth in six European countries

    DEFF Research Database (Denmark)

    Rafnsson, Snorri B; Bhopal, Raj S; Agyemang, Charles

    2013-01-01

    BACKGROUND: Circulatory disease mortality inequalities by country of birth (COB) have been demonstrated for some EU countries but pan-European analyses are lacking. We examine inequalities in circulatory mortality by geographical region/COB for six EU countries. METHODS: We obtained national deat...

  3. The advantages of pylorus-preserving pancreatoduodenectomy in malignant disease of the pancreas and periampullary region

    NARCIS (Netherlands)

    Klinkenbijl, J. H.; van der Schelling, G. P.; Hop, W. C.; van Pel, R.; Bruining, H. A.; Jeekel, J.

    1992-01-01

    The aim of this study was to establish whether the pylorus-preserving pancreatoduodenectomy (PPPD) is a safe and radical procedure in malignant disease of the head of the pancreas and periampullary region, without increased morbidity and mortality rates compared with the standard Whipple's

  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. Predicting changes in hypertension control using electronic health records from a chronic disease management program

    Science.gov (United States)

    Sun, Jimeng; McNaughton, Candace D; Zhang, Ping; Perer, Adam; Gkoulalas-Divanis, Aris; Denny, Joshua C; Kirby, Jacqueline; Lasko, Thomas; Saip, Alexander; Malin, Bradley A

    2014-01-01

    Objective Common chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control. Method In a cohort of 1294 patients with hypertension enrolled in a chronic disease management program at the Vanderbilt University Medical Center, patients are modeled as an array of features derived from the clinical domain over time, which are distilled into a core set using an information gain criteria regarding their predictive performance. A model for transition point prediction was then computed using a random forest classifier. Results The most predictive features for transitions in hypertension control status included hypertension assessment patterns, comorbid diagnoses, procedures and medication history. The final random forest model achieved a c-statistic of 0.836 (95% CI 0.830 to 0.842) and an accuracy of 0.773 (95% CI 0.766 to 0.780). Conclusions This study achieved accurate prediction of transition points of hypertension control status, an important first step in the long-term goal of developing personalized hypertension management plans. PMID:24045907

  6. Mortality Risk Prediction in Scleroderma-Related Interstitial Lung Disease: The SADL Model.

    Science.gov (United States)

    Morisset, Julie; Vittinghoff, Eric; Elicker, Brett M; Hu, Xiaowen; Le, Stephanie; Ryu, Jay H; Jones, Kirk D; Haemel, Anna; Golden, Jeffrey A; Boin, Francesco; Ley, Brett; Wolters, Paul J; King, Talmadge E; Collard, Harold R; Lee, Joyce S

    2017-11-01

    Interstitial lung disease (ILD) is an important cause of morbidity and mortality in patients with scleroderma (Scl). Risk prediction and prognostication in patients with Scl-ILD are challenging because of heterogeneity in the disease course. We aimed to develop a clinical mortality risk prediction model for Scl-ILD. Patients with Scl-ILD were identified from two ongoing longitudinal cohorts: 135 patients at the University of California, San Francisco (derivation cohort) and 90 patients at the Mayo Clinic (validation cohort). Using these two separate cohorts, a mortality risk prediction model was developed and validated by testing every potential candidate Cox model, each including three or four variables of a possible 19 clinical predictors, for time to death. Model discrimination was assessed using the C-index. Three variables were included in the final risk prediction model (SADL): ever smoking history, age, and diffusing capacity of the lung for carbon monoxide (% predicted). This continuous model had similar performance in the derivation (C-index, 0.88) and validation (C-index, 0.84) cohorts. We created a point scoring system using the combined cohort (C-index, 0.82) and used it to identify a classification with low, moderate, and high mortality risk at 3 years. The SADL model uses simple, readily accessible clinical variables to predict all-cause mortality in Scl-ILD. Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

  7. Predicting changes in hypertension control using electronic health records from a chronic disease management program.

    Science.gov (United States)

    Sun, Jimeng; McNaughton, Candace D; Zhang, Ping; Perer, Adam; Gkoulalas-Divanis, Aris; Denny, Joshua C; Kirby, Jacqueline; Lasko, Thomas; Saip, Alexander; Malin, Bradley A

    2014-01-01

    Common chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control. In a cohort of 1294 patients with hypertension enrolled in a chronic disease management program at the Vanderbilt University Medical Center, patients are modeled as an array of features derived from the clinical domain over time, which are distilled into a core set using an information gain criteria regarding their predictive performance. A model for transition point prediction was then computed using a random forest classifier. The most predictive features for transitions in hypertension control status included hypertension assessment patterns, comorbid diagnoses, procedures and medication history. The final random forest model achieved a c-statistic of 0.836 (95% CI 0.830 to 0.842) and an accuracy of 0.773 (95% CI 0.766 to 0.780). This study achieved accurate prediction of transition points of hypertension control status, an important first step in the long-term goal of developing personalized hypertension management plans.

  8. 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.

  9. 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

  10. An integrative approach to ortholog prediction for disease-focused and other functional studies

    Directory of Open Access Journals (Sweden)

    Perrimon Norbert

    2011-08-01

    Full Text Available Abstract Background Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. Results We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt, for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM and genes in genome-wide association study (GWAS data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist. Conclusions DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.

  11. An integrative approach to ortholog prediction for disease-focused and other functional studies.

    Science.gov (United States)

    Hu, Yanhui; Flockhart, Ian; Vinayagam, Arunachalam; Bergwitz, Clemens; Berger, Bonnie; Perrimon, Norbert; Mohr, Stephanie E

    2011-08-31

    Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist). DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.

  12. A Novel Risk Stratification to Predict Local-Regional Failures in Urothelial Carcinoma of the Bladder After Radical Cystectomy

    International Nuclear Information System (INIS)

    Baumann, Brian C.; Guzzo, Thomas J.; He Jiwei; Keefe, Stephen M.; Tucker, Kai; Bekelman, Justin E.; Hwang, Wei-Ting; Vaughn, David J.; Malkowicz, S. Bruce; Christodouleas, John P.

    2013-01-01

    Purpose: Local-regional failures (LF) following radical cystectomy (RC) plus pelvic lymph node dissection (PLND) with or without chemotherapy for invasive urothelial bladder carcinoma are more common than previously reported. Adjuvant radiation therapy (RT) could reduce LF but currently has no defined role because of previously reported morbidity. Modern techniques with improved normal tissue sparing have rekindled interest in RT. We assessed the risk of LF and determined those factors that predict recurrence to facilitate patient selection for future adjuvant RT trials. Methods and Materials: From 1990-2008, 442 patients with urothelial bladder carcinoma at University of Pennsylvania were prospectively followed after RC plus PLND with or without chemotherapy with routine pelvic computed tomography (CT) or magnetic resonance imaging (MRI). One hundred thirty (29%) patients received chemotherapy. LF was any pelvic failure detected before or within 3 months of distant failure. Competing risk analyses identified factors predicting increased LF risk. Results: On univariate analysis, pathologic stage ≥pT3, <10 nodes removed, positive margins, positive nodes, hydronephrosis, lymphovascular invasion, and mixed histology significantly predicted LF; node density was marginally predictive, but use of chemotherapy, number of positive nodes, type of surgical diversion, age, gender, race, smoking history, and body mass index were not. On multivariate analysis, only stage ≥pT3 and <10 nodes removed were significant independent LF predictors with hazard ratios of 3.17 and 2.37, respectively (P<.01). Analysis identified 3 patient subgroups with significantly different LF risks: low-risk (≤pT2), intermediate-risk (≥pT3 and ≥10 nodes removed), and high-risk (≥pT3 and <10 nodes) with 5-year LF rates of 8%, 23%, and 42%, respectively (P<.01). Conclusions: This series using routine CT and MRI surveillance to detect LF confirms that such failures are relatively common in

  13. A Novel Risk Stratification to Predict Local-Regional Failures in Urothelial Carcinoma of the Bladder After Radical Cystectomy

    Energy Technology Data Exchange (ETDEWEB)

    Baumann, Brian C. [Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Guzzo, Thomas J. [Department of Urology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); He Jiwei [Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Keefe, Stephen M. [Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Tucker, Kai; Bekelman, Justin E. [Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Hwang, Wei-Ting [Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Vaughn, David J. [Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Malkowicz, S. Bruce [Department of Urology, University of Pennsylvania, Philadelphia, Pennsylvania (United States); Christodouleas, John P., E-mail: christojo@uphs.upenn.edu [Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania (United States)

    2013-01-01

    Purpose: Local-regional failures (LF) following radical cystectomy (RC) plus pelvic lymph node dissection (PLND) with or without chemotherapy for invasive urothelial bladder carcinoma are more common than previously reported. Adjuvant radiation therapy (RT) could reduce LF but currently has no defined role because of previously reported morbidity. Modern techniques with improved normal tissue sparing have rekindled interest in RT. We assessed the risk of LF and determined those factors that predict recurrence to facilitate patient selection for future adjuvant RT trials. Methods and Materials: From 1990-2008, 442 patients with urothelial bladder carcinoma at University of Pennsylvania were prospectively followed after RC plus PLND with or without chemotherapy with routine pelvic computed tomography (CT) or magnetic resonance imaging (MRI). One hundred thirty (29%) patients received chemotherapy. LF was any pelvic failure detected before or within 3 months of distant failure. Competing risk analyses identified factors predicting increased LF risk. Results: On univariate analysis, pathologic stage {>=}pT3, <10 nodes removed, positive margins, positive nodes, hydronephrosis, lymphovascular invasion, and mixed histology significantly predicted LF; node density was marginally predictive, but use of chemotherapy, number of positive nodes, type of surgical diversion, age, gender, race, smoking history, and body mass index were not. On multivariate analysis, only stage {>=}pT3 and <10 nodes removed were significant independent LF predictors with hazard ratios of 3.17 and 2.37, respectively (P<.01). Analysis identified 3 patient subgroups with significantly different LF risks: low-risk ({<=}pT2), intermediate-risk ({>=}pT3 and {>=}10 nodes removed), and high-risk ({>=}pT3 and <10 nodes) with 5-year LF rates of 8%, 23%, and 42%, respectively (P<.01). Conclusions: This series using routine CT and MRI surveillance to detect LF confirms that such failures are relatively common

  14. Late-onset Stargardt disease is associated with missense mutations that map outside known functional regions of ABCR (ABCA4).

    Science.gov (United States)

    Yatsenko, A N; Shroyer, N F; Lewis, R A; Lupski, J R

    2001-04-01

    Based on recent studies of the photoreceptor-specific ABC transporter gene ABCR (ABCA4) in Stargardt disease (STGD1) and other retinal dystrophies, we and others have developed a model in which the severity of retinal disease correlates inversely with residual ABCR activity. This model predicts that patients with late-onset STGDI may retain partial ABCR activity attributable to mild missense alleles. To test this hypothesis, we used late-onset STGDI patients (onset: > or =35 years) to provide an in vivo functional analysis of various combinations of mutant alleles. We sequenced directly the entire coding region of ABCR and detected mutations in 33/50 (66%) disease chromosomes, but surprisingly, 11/33 (33%) were truncating alleles. Importantly, all 22 missense mutations were located outside the known functional domains of ABCR (ATP-binding or transmembrane), whereas in our general cohort of STGDI subjects, alterations occurred with equal frequency across the entire protein. We suggest that these missense mutations in regions of unknown function are milder alleles and more susceptible to modifier effects. Thus, we have corroborated a prediction from the model of ABCR pathogenicity that (1) one mutant ABCR allele is always missense in late-onset STGD1 patients, and (2) the age-of-onset is correlated with the amount of ABCR activity of this allele. In addition, we report three new pseudodominant families that now comprise eight of 178 outbred STGD1 families and suggest a carrier frequency of STGD1-associated ABCR mutations of about 4.5% (approximately 1/22).

  15. 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.

  16. [Duration of work absence attributable to non work-related diseases by health regions in catalonia].

    Science.gov (United States)

    Torá Rocamora, Isabel; Martínez Martínez, José Miguel; Delclos Clanchet, Jordi; Jardí Lliberia, Josefina; Alberti Casas, Constança; Serra Pujadas, Consol; Manzanera López, Rafael; Benavides, Fernando G

    2010-01-01

    This study analyze the duration of episodes of work absence due to non work-related diseases in Catalonia by health regions, assuming a homogeneous distribution of durations between health regions. A retrospective cohort study of 811.790 episodes in 2005 and followed to episode closure through July 2007 provided by the Institut Català d'Avaluacions Mèdiques, describing their median duration (MD) in days for each of the seven health regions of Catalonia. The probability of returning to work was plotted according to Wang_Chang survival curves and median durations were then compared using the Barcelona health region as the referent group. Results were extended through stratification by sex. The Camp de Tarragona health region had the shortest MD (5 days), while the episodes in the Alt Pirineu i Aran region had the longest (MD, 13 days). The Barcelona health region had a MD of 7 days as was the case for Cataluña Central. MD in Girona was 8 days, and in Lleida and Terres de l'Ebre it was 9 days. This latter region also had the highest median duration 13 days. The are significant differences in the duration of work absence between the health regions of Catalonia. These differences persisted after adjusting for age, management of episodes and social security system status, in both men and women.

  17. Development of a disease risk prediction model for downy mildew (Peronospora sparsa) in boysenberry.

    Science.gov (United States)

    Kim, Kwang Soo; Beresford, Robert M; Walter, Monika

    2014-01-01

    Downy mildew caused by Peronospora sparsa has resulted in serious production losses in boysenberry (Rubus hybrid), blackberry (Rubus fruticosus), and rose (Rosa sp.) in New Zealand, Mexico, and the United States and the United Kingdom, respectively. Development of a model to predict downy mildew risk would facilitate development and implementation of a disease warning system for efficient fungicide spray application in the crops affected by this disease. Because detailed disease observation data were not available, a two-step approach was applied to develop an empirical risk prediction model for P. sparsa. To identify the weather patterns associated with a high incidence of downy mildew berry infections (dryberry disease) and derive parameters for the empirical model, classification and regression tree (CART) analysis was performed. Then, fuzzy sets were applied to develop a simple model to predict the disease risk based on the parameters derived from the CART analysis. High-risk seasons with a boysenberry downy mildew incidence >10% coincided with months when the number of hours per day with temperature of 15 to 20°C averaged >9.8 over the month and the number of days with rainfall in the month was >38.7%. The Fuzzy Peronospora Sparsa (FPS) model, developed using fuzzy sets, defined relationships among high-risk events, temperature, and rainfall conditions. In a validation study, the FPS model provided correct identification of both seasons with high downy mildew risk for boysenberry, blackberry, and rose and low risk in seasons when no disease was observed. As a result, the FPS model had a significant degree of agreement between predicted and observed risks of downy mildew for those crops (P = 0.002).

  18. Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study.

    Science.gov (United States)

    Betancur, Julian; Commandeur, Frederic; Motlagh, Mahsaw; Sharir, Tali; Einstein, Andrew J; Bokhari, Sabahat; Fish, Mathews B; Ruddy, Terrence D; Kaufmann, Philipp; Sinusas, Albert J; Miller, Edward J; Bateman, Timothy M; Dorbala, Sharmila; Di Carli, Marcelo; Germano, Guido; Otaki, Yuka; Tamarappoo, Balaji K; Dey, Damini; Berman, Daniel S; Slomka, Piotr J

    2018-03-12

    The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD). Deep convolutional neural networks trained with a large multicenter population may provide improved prediction of per-patient and per-vessel coronary artery disease from single-photon emission computed tomography MPI. A total of 1,638 patients (67% men) without known coronary artery disease, undergoing stress 99m Tc-sestamibi or tetrofosmin MPI with new generation solid-state scanners in 9 different sites, with invasive coronary angiography performed within 6 months of MPI, were studied. Obstructive disease was defined as ≥70% narrowing of coronary arteries (≥50% for left main artery). Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. Stress TPD was computed using sex- and camera-specific normal limits. Deep learning was trained using raw and quantitative polar maps and evaluated for prediction of obstructive stenosis in a stratified 10-fold cross-validation procedure. A total of 1,018 (62%) patients and 1,797 of 4,914 (37%) arteries had obstructive disease. Area under the receiver-operating characteristic curve for disease prediction by deep learning was higher than for TPD (per patient: 0.80 vs. 0.78; per vessel: 0.76 vs. 0.73: p deep learning threshold set to the same specificity as TPD, per-patient sensitivity improved from 79.8% (TPD) to 82.3% (deep learning) (p deep learning) (p Deep learning has the potential to improve automatic interpretation of MPI as compared with current clinical methods. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

  19. Trends of major disease outbreaks in the African region, 2003-2007.

    Science.gov (United States)

    Kebede, Senait; Duales, Sambe; Yokouide, Allarangar; Alemu, Wondimagegnehu

    2010-03-01

    Communicable disease outbreaks cause millions of deaths throughout Sub-Saharan Africa each year. Most of the diseases causing epidemics in the region have been nearly eradicated or brought under control in other parts of the world. In recent years, considerable effort has been directed toward public health initiatives and strategies with a potential for significant impact in the fight against infectious diseases. In 1998, the World Health Organization African Regional Office (WHO/AFRO) launched the Integrated Disease Surveillance and Response (IDSR) strategy aimed at mitigating the impact of communicable diseases, including epidemic-prone diseases, through improving surveillance, laboratory confirmation and appropriate and timely public health interventions. Over the past decade, WHO and its partners have been providing technical and financial resources to African countries to strengthen epidemic preparedness and response (EPR) activities. This review examined the major epidemics reported to WHO/AFRO from 2003 to 2007. we conduct a review of documents and reports obtained from WHO/AFRO, WHO inter-country team, and partners and held meeting and discussions with key stakeholders to elicit the experiences of local, regional and international efforts against these epidemics to evaluate the lessons learned and to stimulate discussion on the future course for enhancing EPR. The most commonly reported epidemic outbreaks in Africa include: cholera, dysentery, malaria and hemorrhagic fevers (e.g. Ebola, Rift Valley fever, Crimean-Congo fever and yellow fever). The cyclic meningococcal meningitis outbreak that affects countries along the "meningitis belt" (spanning Sub-Saharan Africa from Senegal and The Gambia to Kenya and Ethiopia) accounts for other major epidemics in the region. The reporting of disease outbreaks to WHO/AFRO has improved since the launch of the IDSR strategy in 1998. Although the epidemic trends for cholera showed a decline in case fatality rate (CFR

  20. Chagas Disease in Ecuador: Evidence for Disease Transmission in an Indigenous Population in the Amazon Region

    Directory of Open Access Journals (Sweden)

    Chico H Martha

    1997-01-01

    Full Text Available Two well-defined synthetic peptides TcD and PEP2 were used in a sero-epidemiological study for the detection of Trypanosoma cruzi infections in an indigenous group in the Amazon region of Ecuador. Of the 18 communities studied along the Río Napo, province of Napo, 15 (83.3% were found to be positive for T. cruzi infection. Of the 1,011 individuals examined 61 (6.03% resulted positive. A prevalence of infection of 4.8% was found in children aged 1-5 years. The prevalence of infection increased with age, with adults 50 years or older showing a maximum prevalence of 18.8%. Autochthonous transmission of T. cruzi is present among this isolated indigenous population

  1. Regional cerebral glucose metabolism in patients with Parkinson's disease with or without dementia

    Energy Technology Data Exchange (ETDEWEB)

    Sasaki, Masayuki; Ichiya, Yuichi; Hosokawa, Shinichi; Otsuka, Makoto; Kuwabara, Yasuo; Fukumura, Toshimitsu; Kato, Motohiro; Goto, Ikuo; Masuda, Kouji [Kyushu Univ., Fukuoka (Japan). Faculty of Medicine

    1992-11-01

    By means of positron emission tomography, the cerebral glucose metabolism in 5 patients with Parkinson's disease with dementia was compared with that in 9 patients without dementia, and that in 5 normal volunteers. The metabolic rates for glucose were measured by placing one hundred regions of interest. In the demented patients, cerebral glucose metabolism was diffusely decreased compared with that of the non-demented patients and the normal controls. The most significant decrease in glucose metabolism was observed in the angular gyrus (49.7% of the normal controls). The glucose metabolism in the cingulate, pre- and postcentral, occipital and subcortical regions was relatively spared (62.1 to 85.5% of the normal controls). In the patients without dementia, the glucose metabolism in each region was not significantly different from that in the normal controls. These results suggest that diffuse glucose hypometabolism in the cerebral cortex may correlate with that of patients with Parkinson's disease with dementia. (author).

  2. World Health Organization Global Estimates and Regional Comparisons of the Burden of Foodborne Disease in 2010

    DEFF Research Database (Denmark)

    Havelaar, Arie H.; Kirk, Martyn D.; Torgerson, Paul R.

    2015-01-01

    parasitic helminths, were highly localised. Thus, the burden of FBD is borne particularly by children under five years old-although they represent only 9% of the global population-and people living in low-income regions of the world. These estimates are conservative, i.e., underestimates rather than......Illness and death from diseases caused by contaminated food are a constant threat to public health and a significant impediment to socio-economic development worldwide. To measure the global and regional burden of foodborne disease (FBD), the World Health Organization (WHO) established...... different burdens of FBD, with the greatest falling on the subregions in Africa, followed by the subregions in South-East Asia and the Eastern Mediterranean D subregion. Some hazards, such as non-typhoidal S. enterica, were important causes of FBD in all regions of the world, whereas others, such as certain...

  3. [Value of sepsis single-disease manage system in predicting mortality in patients with sepsis].

    Science.gov (United States)

    Chen, J; Wang, L H; Ouyang, B; Chen, M Y; Wu, J F; Liu, Y J; Liu, Z M; Guan, X D

    2018-04-03

    Objective: To observe the effect of sepsis single-disease manage system on the improvement of sepsis treatment and the value in predicting mortality in patients with sepsis. Methods: A retrospective study was conducted. Patients with sepsis admitted to the Department of Surgical Intensive Care Unit of Sun Yat-Sen University First Affiliated Hospital from September 22, 2013 to May 5, 2015 were enrolled in this study. Sepsis single-disease manage system (Rui Xin clinical data manage system, China data, China) was used to monitor 25 clinical quality parameters, consisting of timeliness, normalization and outcome parameters. Based on whether these quality parameters could be completed or not, the clinical practice was evaluated by the system. The unachieved quality parameter was defined as suspicious parameters, and these suspicious parameters were used to predict mortality of patients with receiver operating characteristic curve (ROC). Results: A total of 1 220 patients with sepsis were enrolled, included 805 males and 415 females. The mean age was (59±17) years, and acute physiology and chronic health evaluation (APACHE Ⅱ) scores was 19±8. The area under ROC curve of total suspicious numbers for predicting 28-day mortality was 0.70; when the suspicious parameters number was more than 6, the sensitivity was 68.0% and the specificity was 61.0% for predicting 28-day mortality. In addition, the area under ROC curve of outcome suspicious number for predicting 28-day mortality was 0.89; when the suspicious outcome parameters numbers was more than 1, the sensitivity was 88.0% and the specificity was 78.0% for predicting 28-day mortality. Moreover, the area under ROC curve of total suspicious number for predicting 90-day mortality was 0.73; when the total suspicious parameters number was more than 7, the sensitivity was 60.0% and the specificity was 74.0% for predicting 90-day mortality. Finally, the area under ROC curve of outcome suspicious numbers for predicting 90

  4. A Lagrangian particle model to predict the airborne spread of foot-and-mouth disease virus

    Science.gov (United States)

    Mayer, D.; Reiczigel, J.; Rubel, F.

    Airborne spread of bioaerosols in the boundary layer over a complex terrain is simulated using a Lagrangian particle model, and applied to modelling the airborne spread of foot-and-mouth disease (FMD) virus. Two case studies are made with study domains located in a hilly region in the northwest of the Styrian capital Graz, the second largest town in Austria. Mountainous terrain as well as inhomogeneous and time varying meteorological conditions prevent from application of so far used Gaussian dispersion models, while the proposed model can handle these realistically. In the model, trajectories of several thousands of particles are computed and the distribution of virus concentration near the ground is calculated. This allows to assess risk of infection areas with respect to animal species of interest, such as cattle, swine or sheep. Meteorological input data like wind field and other variables necessary to compute turbulence were taken from the new pre-operational version of the non-hydrostatic numerical weather prediction model LMK ( Lokal-Modell-Kürzestfrist) running at the German weather service DWD ( Deutscher Wetterdienst). The LMK model provides meteorological parameters with a spatial resolution of about 2.8 km. To account for the spatial resolution of 400 m used by the Lagrangian particle model, the initial wind field is interpolated upon the finer grid by a mass consistent interpolation method. Case studies depict a significant influence of local wind systems on the spread of virus. Higher virus concentrations at the upwind side of the hills and marginal concentrations in the lee are well observable, as well as canalization effects by valleys. The study demonstrates that the Lagrangian particle model is an appropriate tool for risk assessment of airborne spread of virus by taking into account the realistic orographic and meteorological conditions.

  5. Real-data comparison of data mining methods in prediction of coronary artery disease in Iran

    Directory of Open Access Journals (Sweden)

    Azam Dekamin

    2017-07-01

    Full Text Available Introduction: Cardiovascular diseases are currently of broad prevalence and constitute one of the major causes of mortality in different societies. Angiography is one of the most accurate methods to diagnose heart diseases; it incurs high expenses and comes with side effects. Data mining is intended to enable timely prognosis of diseases with the least expenses possible, making use of the patients’ information. The present study aims to provide replies for the question whether it is possible to predict coronary artery diseases with higher efficiency and fewer errors and identify the factors impacting the disease using data mining techniques. Method: In this study, the data under investigation was collected from a number of 303 persons referring to the heart unit in Shahid Rajaie hospital (Iranian hospital from 2011 to 2013. It included 54 features. Attempts are made to take advantage of a higher number of characteristics which are helpful for diagnosis of diseases. In addition, Information Gain, Gini, and SVM methods were applied to select influential features, and variables with higher weights were chosen for modeling purposes. In the modeling phase, a combination of classification algorithms and ensemble methods was applied to develop a prediction with fewer errors. Rapid Miner Software was adopted to conduct this study. Results: Findings of this research indicated that the suggested model, if weighted by SVM index, had the highest efficiency, i.e. 95.83%. This model, moreover, was able to accurately predict all patients with coronary artery disease in Iran. According to the proposed model and obtained accuracies, weighting with SVM was found to be the most effective filtering method, and age as well as typical and atypical chest pain were identified to be the most effective features of coronary artery disease. (Graph 3 Conclusion: This study can contribute to the diagnosis of influential factors which lead to cardiovascular disease in Iran

  6. Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges

    Directory of Open Access Journals (Sweden)

    Ranjith Gopalakrishnan

    2015-08-01

    Full Text Available Generating accurate and unbiased wall-to-wall canopy height maps from airborne lidar data for large regions is useful to forest scientists and natural resource managers. However, mapping large areas often involves using lidar data from different projects, with varying acquisition parameters. In this work, we address the important question of whether one can accurately model canopy heights over large areas of the Southeastern US using a very heterogeneous dataset of small-footprint, discrete-return airborne lidar data (with 76 separate lidar projects. A unique aspect of this effort is the use of nationally uniform and extensive field data (~1800 forested plots from the Forest Inventory and Analysis (FIA program of the US Forest Service. Preliminary results are quite promising: Over all lidar projects, we observe a good correlation between the 85th percentile of lidar heights and field-measured height (r = 0.85. We construct a linear regression model to predict subplot-level dominant tree heights from distributional lidar metrics (R2 = 0.74, RMSE = 3.0 m, n = 1755. We also identify and quantify the importance of several factors (like heterogeneity of vegetation, point density, the predominance of hardwoods or softwoods, the average height of the forest stand, slope of the plot, and average scan angle of lidar acquisition that influence the efficacy of predicting canopy heights from lidar data. For example, a subset of plots (coefficient of variation of vegetation heights <0.2 significantly reduces the RMSE of our model from 3.0–2.4 m (~20% reduction. We conclude that when all these elements are factored into consideration, combining data from disparate lidar projects does not preclude robust estimation of canopy heights.

  7. MMP-7 is a predictive biomarker of disease progression in patients with idiopathic pulmonary fibrosis

    Directory of Open Access Journals (Sweden)

    Yasmina Bauer

    2017-03-01

    Full Text Available Idiopathic pulmonary fibrosis (IPF is a progressive interstitial lung disease with poor prognosis, which is characterised by destruction of normal lung architecture and excessive deposition of lung extracellular matrix. The heterogeneity of disease progression in patients with IPF poses significant obstacles to patient care and prevents efficient development of novel therapeutic interventions. Blood biomarkers, reflecting pathobiological processes in the lung, could provide objective evidence of the underlying disease. Longitudinally collected serum samples from the Bosentan Use in Interstitial Lung Disease (BUILD-3 trial were used to measure four biomarkers (metalloproteinase-7 (MMP-7, Fas death receptor ligand, osteopontin and procollagen type I C-peptide, to assess their potential prognostic capabilities and to follow changes during disease progression in patients with IPF. In baseline BUILD-3 samples, only MMP-7 showed clearly elevated protein levels compared with samples from healthy controls, and further investigations demonstrated that MMP-7 levels also increased over time. Baseline levels of MMP-7 were able to predict patients who had higher risk of worsening and, notably, baseline levels of MMP-7 could predict changes in FVC as early as month 4. MMP-7 shows potential to be a reliable predictor of lung function decline and disease progression.

  8. Can dental pulp calcification predict the risk of ischemic cardiovascular disease?

    Science.gov (United States)

    Khojastepour, Leila; Bronoosh, Pegah; Khosropanah, Shahdad; Rahimi, Elham

    2013-09-01

    To report the association of pulp calcification with that of cardiovascular disease (CVD) using digital panoramic dental radiographs. Digital panoramic radiographs of patients referred from the angiography department were included if the patient was under 55 years old and had non-restored or minimally restored molars and canines. An oral and maxillofacial radiologist evaluated the images for pulpal calcifications in the selected teeth. The sensitivity, specificity, positive predictive value and negative predictive value of panoramic radiography in predicting CVD were calculated. Out of 122 patients who met the criteria, 68.2% of the patients with CVD had pulp chamber calcifications. Pulp calcification in panoramic radiography had a sensitivity of 68.9% to predict CVD. This study demonstrates that patients with CVD show an increased incidence of pulp calcification compared with healthy patients. The findings suggest that pulp calcification on panoramic radiography may have possibilities for use in CVD screening.

  9. Polio Eradication Initiative: Contribution to improved communicable diseases surveillance in WHO African region.

    Science.gov (United States)

    Mwengee, William; Okeibunor, Joseph; Poy, Alain; Shaba, Keith; Mbulu Kinuani, Leon; Minkoulou, Etienne; Yahaya, Ali; Gaturuku, Peter; Landoh, Dadja Essoya; Nsubuga, Peter; Salla, Mbaye; Mihigo, Richard; Mkanda, Pascal

    2016-10-10

    Since the launch of the Global Polio Eradication Initiative (GPEI) in 1988, there has been a tremendous progress in the reduction of cases of poliomyelitis. The world is on the verge of achieving global polio eradication and in May 2013, the 66th World Health Assembly endorsed the Polio Eradication and Endgame Strategic Plan (PEESP) 2013-2018. The plan provides a timeline for the completion of the GPEI by eliminating all paralytic polio due to both wild and vaccine-related polioviruses. We reviewed how GPEI supported communicable disease surveillance in seven of the eight countries that were documented as part of World Health Organization African Region best practices documentation. Data from WHO African region was also reviewed to analyze the performance of measles cases based surveillance. All 7 countries (100%) which responded had integrated communicable diseases surveillance core functions with AFP surveillance. The difference is on the number of diseases included based on epidemiology of diseases in a particular country. The results showed that the polio eradication infrastructure has supported and improved the implementation of surveillance of other priority communicable diseases under integrated diseases surveillance and response strategy. As we approach polio eradication, polio-eradication initiative staff, financial resources, and infrastructure can be used as one strategy to build IDSR in Africa. As we are now focusing on measles and rubella elimination by the year 2020, other disease-specific programs having similar goals of eradicating and eliminating diseases like malaria, might consider investing in general infectious disease surveillance following the polio example. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  10. Aberrant monocyte responses predict and characterize dengue virus infection in individuals with severe disease.

    Science.gov (United States)

    Yong, Yean K; Tan, Hong Y; Jen, Soe Hui; Shankar, Esaki M; Natkunam, Santha K; Sathar, Jameela; Manikam, Rishya; Sekaran, Shamala D

    2017-05-31

    Currently, several assays can diagnose acute dengue infection. However, none of these assays can predict the severity of the disease. Biomarkers that predicts the likelihood that a dengue patient will develop a severe form of the disease could permit more efficient patient triage and allows better supportive care for the individual in need, especially during dengue outbreaks. We measured 20 plasma markers i.e. IFN-γ, IL-10, granzyme-B, CX3CL1, IP-10, RANTES, CXCL8, CXCL6, VCAM, ICAM, VEGF, HGF, sCD25, IL-18, LBP, sCD14, sCD163, MIF, MCP-1 and MIP-1β in 141 dengue patients in over 230 specimens and correlate the levels of these plasma markers with the development of dengue without warning signs (DWS-), dengue with warning signs (DWS+) and severe dengue (SD). Our results show that the elevation of plasma levels of IL-18 at both febrile and defervescence phase was significantly associated with DWS+ and SD; whilst increase of sCD14 and LBP at febrile phase were associated with severity of dengue disease. By using receiver operating characteristic (ROC) analysis, the IL-18, LBP and sCD14 were significantly predicted the development of more severe form of dengue disease (DWS+/SD) (AUC = 0.768, P dengue disease. Given that the elevation IL-18, LBP and sCD14 among patients with severe form of dengue disease, our findings suggest a pathogenic role for an aberrant inflammasome and monocyte activation in the development of severe form of dengue disease.

  11. Transcription-factor occupancy at HOT regions quantitatively predicts RNA polymerase recruitment in five human cell lines.

    KAUST Repository

    Foley, Joseph W

    2013-10-20

    BACKGROUND: High-occupancy target (HOT) regions are compact genome loci occupied by many different transcription factors (TFs). HOT regions were initially defined in invertebrate model organisms, and we here show that they are a ubiquitous feature of the human gene-regulation landscape. RESULTS: We identified HOT regions by a comprehensive analysis of ChIP-seq data from 96 DNA-associated proteins in 5 human cell lines. Most HOT regions co-localize with RNA polymerase II binding sites, but many are not near the promoters of annotated genes. At HOT promoters, TF occupancy is strongly predictive of transcription preinitiation complex recruitment and moderately predictive of initiating Pol II recruitment, but only weakly predictive of elongating Pol II and RNA transcript abundance. TF occupancy varies quantitatively within human HOT regions; we used this variation to discover novel associations between TFs. The sequence motif associated with any given TF\\'s direct DNA binding is somewhat predictive of its empirical occupancy, but a great deal of occupancy occurs at sites without the TF\\'s motif, implying indirect recruitment by another TF whose motif is present. CONCLUSIONS: Mammalian HOT regions are regulatory hubs that integrate the signals from diverse regulatory pathways to quantitatively tune the promoter for RNA polymerase II recruitment.

  12. Transcription-factor occupancy at HOT regions quantitatively predicts RNA polymerase recruitment in five human cell lines.

    KAUST Repository

    Foley, Joseph W; Sidow, Arend

    2013-01-01

    BACKGROUND: High-occupancy target (HOT) regions are compact genome loci occupied by many different transcription factors (TFs). HOT regions were initially defined in invertebrate model organisms, and we here show that they are a ubiquitous feature of the human gene-regulation landscape. RESULTS: We identified HOT regions by a comprehensive analysis of ChIP-seq data from 96 DNA-associated proteins in 5 human cell lines. Most HOT regions co-localize with RNA polymerase II binding sites, but many are not near the promoters of annotated genes. At HOT promoters, TF occupancy is strongly predictive of transcription preinitiation complex recruitment and moderately predictive of initiating Pol II recruitment, but only weakly predictive of elongating Pol II and RNA transcript abundance. TF occupancy varies quantitatively within human HOT regions; we used this variation to discover novel associations between TFs. The sequence motif associated with any given TF's direct DNA binding is somewhat predictive of its empirical occupancy, but a great deal of occupancy occurs at sites without the TF's motif, implying indirect recruitment by another TF whose motif is present. CONCLUSIONS: Mammalian HOT regions are regulatory hubs that integrate the signals from diverse regulatory pathways to quantitatively tune the promoter for RNA polymerase II recruitment.

  13. The Use of Fluid Mechanics to Predict Regions of Microscopic Thrombus Formation in Pulsatile VADs.

    Science.gov (United States)

    Topper, Stephen R; Navitsky, Michael A; Medvitz, Richard B; Paterson, Eric G; Siedlecki, Christopher A; Slattery, Margaret J; Deutsch, Steven; Rosenberg, Gerson; Manning, Keefe B

    2014-03-01

    We compare the velocity and shear obtained from particle image velocimetry (PIV) and computational fluid dynamics (CFD) in a pulsatile ventricular assist device (VAD) to further test our thrombus predictive methodology using microscopy data from an explanted VAD. To mimic physiological conditions in vitro , a mock circulatory loop is used with a blood analog that matched blood's viscoelastic behavior at 40% hematocrit. Under normal physiologic pressures and for a heart rate of 75 bpm, PIV data is acquired and wall shear maps are produced. The resolution of the PIV shear rate calculations are tested using the CFD and found to be in the same range. A bovine study, using a model of the 50 cc Penn State V-2 VAD, for 30 days at a constant beat rate of 75 beats per minute (bpm) provides the microscopic data whereby after the 30 days, the device is explanted and the sac surface analyzed using scanning electron microscopy (SEM) and, after immunofluorescent labeling for platelets and fibrin, confocal microscopy. Areas are examined based on PIV measurements and CFD, with special attention to low shear regions where platelet and fibrin deposition are most likely to occur. Data collected within the outlet port in a direction normal to the front wall of the VAD shows that some regions experience wall shear rates less than 500 s -1 , which increases the likelihood of platelet and fibrin deposition. Despite only one animal study, correlations between PIV, CFD, and in vivo data show promise. Deposition probability is quantified by the thrombus susceptibility potential, a calculation to correlate low shear and time of shear with deposition.

  14. OPAL: prediction of MoRF regions in intrinsically disordered protein sequences.

    Science.gov (United States)

    Sharma, Ronesh; Raicar, Gaurav; Tsunoda, Tatsuhiko; Patil, Ashwini; Sharma, Alok

    2018-06-01

    Intrinsically disordered proteins lack stable 3-dimensional structure and play a crucial role in performing various biological functions. Key to their biological function are the molecular recognition features (MoRFs) located within long disordered regions. Computationally identifying these MoRFs from disordered protein sequences is a challenging task. In this study, we present a new MoRF predictor, OPAL, to identify MoRFs in disordered protein sequences. OPAL utilizes two independent sources of information computed using different component predictors. The scores are processed and combined using common averaging method. The first score is computed using a component MoRF predictor which utilizes composition and sequence similarity of MoRF and non-MoRF regions to detect MoRFs. The second score is calculated using half-sphere exposure (HSE), solvent accessible surface area (ASA) and backbone angle information of the disordered protein sequence, using information from the amino acid properties of flanks surrounding the MoRFs to distinguish MoRF and non-MoRF residues. OPAL is evaluated using test sets that were previously used to evaluate MoRF predictors, MoRFpred, MoRFchibi and MoRFchibi-web. The results demonstrate that OPAL outperforms all the available MoRF predictors and is the most accurate predictor available for MoRF prediction. It is available at http://www.alok-ai-lab.com/tools/opal/. ashwini@hgc.jp or alok.sharma@griffith.edu.au. Supplementary data are available at Bioinformatics online.

  15. Actual and predicted prevalence of alcohol consumption during pregnancy in the WHO African Region.

    Science.gov (United States)

    Popova, Svetlana; Lange, Shannon; Probst, Charlotte; Shield, Kevin; Kraicer-Melamed, Hannah; Ferreira-Borges, Carina; Rehm, Jürgen

    2016-10-01

    To estimate the prevalence of alcohol consumption and binge drinking during pregnancy among the general population in the World Health Organization (WHO) African Region, by country. First, a comprehensive systematic literature search was performed to identify all published and unpublished studies. Then, several meta-analyses, assuming a random-effects model, were conducted to estimate the prevalence of alcohol consumption and binge drinking during pregnancy among the general population for countries in the WHO African Region with two or more studies available. Lastly, for countries with less than two studies or no known data predictions were obtained using regression modelling. The estimated prevalence of alcohol consumption during pregnancy among the general population ranged from 2.2% (95% confidence interval [CI]: 1.6-2.8%; Equatorial Guinea) to 12.6% (95% CI: 9.9-15.4%; Cameroon) in Central Africa, 3.4% (95% CI: 2.6-4.3%; Seychelles) to 20.5% (95% CI: 16.4-24.7%; Uganda) in Eastern Africa, 5.7% (95% CI: 4.4-7.1%; Botswana) to 14.2% (95% CI: 11.1-17.3%; Namibia) in Southern Africa, 6.6% (95% CI: 5.0-8.3%; Mauritania) to 14.8% (95% CI: 11.6-17.9%; Sierra Leone) in Western Africa, and 4.3% (95% CI: 3.2-5.3%; Algeria) in Northern Africa. The high prevalence of alcohol consumption and binge drinking during pregnancy in some African countries calls for educational campaigns, screening and targeted interventions for women of childbearing age. © 2016 John Wiley & Sons Ltd.

  16. A polynomial based model for cell fate prediction in human diseases.

    Science.gov (United States)

    Ma, Lichun; Zheng, Jie

    2017-12-21

    Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development. In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable. Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases.

  17. SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction

    Directory of Open Access Journals (Sweden)

    Xiaoying Li

    2018-01-01

    Full Text Available Aberrant expression of microRNAs (miRNAs can be applied for the diagnosis, prognosis, and treatment of human diseases. Identifying the relationship between miRNA and human disease is important to further investigate the pathogenesis of human diseases. However, experimental identification of the associations between diseases and miRNAs is time-consuming and expensive. Computational methods are efficient approaches to determine the potential associations between diseases and miRNAs. This paper presents a new computational method based on the SimRank and density-based clustering recommender model for miRNA-disease associations prediction (SRMDAP. The AUC of 0.8838 based on leave-one-out cross-validation and case studies suggested the excellent performance of the SRMDAP in predicting miRNA-disease associations. SRMDAP could also predict diseases without any related miRNAs and miRNAs without any related diseases.

  18. Probability-based collaborative filtering model for predicting gene-disease associations.

    Science.gov (United States)

    Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

    2017-12-28

    Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

  19. Regional and international approaches on prevention and control of animal transboundary and emerging diseases.

    Science.gov (United States)

    Domenech, J; Lubroth, J; Eddi, C; Martin, V; Roger, F

    2006-10-01

    Transboundary animal diseases pose a serious risk to the world animal agriculture and food security and jeopardize international trade. The world has been facing devastating economic losses from major outbreaks of transboundary animal diseases (TADs) such as foot-and-mouth disease, classical swine fever, rinderpest, peste des petits ruminants (PPR), and Rift Valley fever. Lately the highly pathogenic avian influenza (HPAI) due to H5N1 virus, has become an international crisis as all regions around the world can be considered at risk. In the past decades, public health authorities within industrialized countries have been faced with an increasing number of food safety issues. The situation is equally serious in developing countries. The globalization of food (and feed) trade, facilitated by the liberalization of world trade, while offering many benefits and opportunities, also represents new risks. The GF-TADs Global Secretariat has carried out several regional consultations for the identification of priority diseases and best ways for their administration, prevention and control. In the questionnaires carried out and through the consultative process, it was noted that globally, FMD was ranked as the first and foremost priority. Rift Valley fever, and today highly pathogenic avian influenza, are defined as major animal diseases which also affect human health. PPR and CBPP, a disease which is particularly serious in Africa and finally, African swine fever (ASF) and classical swine fever (CSF) are also regionally recognised as top priorities on which the Framework is determined to work. The FAO philosophy--shared by the OIE--embraces the need to prevent and control TADs and emerging diseases at their source, which is most of the time in developing countries. Regional and international approaches have to be followed, and the FAO and OIE GF-TADs initiative provides the appropriate concepts and objectives as well as an organizational framework to link international and

  20. Exploring gene expression signatures for predicting disease free survival after resection of colorectal cancer liver metastases.

    Directory of Open Access Journals (Sweden)

    Nikol Snoeren

    Full Text Available BACKGROUND AND OBJECTIVES: This study was designed to identify and validate gene signatures that can predict disease free survival (DFS in patients undergoing a radical resection for their colorectal liver metastases (CRLM. METHODS: Tumor gene expression profiles were collected from 119 patients undergoing surgery for their CRLM in the Paul Brousse Hospital (France and the University Medical Center Utrecht (The Netherlands. Patients were divided into high and low risk groups. A randomly selected training set was used to find predictive gene signatures. The ability of these gene signatures to predict DFS was tested in an independent validation set comprising the remaining patients. Furthermore, 5 known clinical risk scores were tested in our complete patient cohort. RESULT: No gene signature was found that significantly predicted DFS in the validation set. In contrast, three out of five clinical risk scores were able to predict DFS in our patient cohort. CONCLUSIONS: No gene signature was found that could predict DFS in patients undergoing CRLM resection. Three out of five clinical risk scores were able to predict DFS in our patient cohort. These results emphasize the need for validating risk scores in independent patient groups and suggest improved designs for future studies.

  1. Do Work Characteristics Predict Health Deterioration Among Employees with Chronic Diseases?

    Science.gov (United States)

    de Wind, Astrid; Boot, Cécile R L; Sewdas, Ranu; Scharn, Micky; van den Heuvel, Swenne G; van der Beek, Allard J

    2017-06-29

    Purpose In our ageing workforce, the increasing numbers of employees with chronic diseases are encouraged to prolong their working lives. It is important to prevent health deterioration in this vulnerable group. This study aims to investigate whether work characteristics predict health deterioration over a 3-year period among employees with (1) chronic diseases, and, more specifically, (2) musculoskeletal and psychological disorders. Methods The study population consisted of 5600 employees aged 45-64 years with a chronic disease, who participated in the Dutch Study on Transitions in Employment, Ability and Motivation (STREAM). Information on work characteristics was derived from the baseline questionnaire. Health deterioration was defined as a decrease in general health (SF-12) between baseline and follow-up (1-3 years). Crude and adjusted logistic regression analyses were performed to investigate prediction of health deterioration by work characteristics. Subgroup analyses were performed for employees with musculoskeletal and psychological disorders. Results At follow-up, 19.2% of the employees reported health deterioration (N = 1075). Higher social support of colleagues or supervisor predicted health deterioration in the crude analyses in the total group, and the groups with either musculoskeletal or psychological disorders (ORs 1.11-1.42). This effect was not found anymore in the adjusted analyses. The other work characteristics did not predict health deterioration in any group. Conclusions This study did not support our hypothesis that work characteristics predict health deterioration among employees with chronic diseases. As our study population succeeded continuing employment to 45 years and beyond, it was probably a relatively healthy selection of employees.

  2. Identification of predictive biomarkers of disease state in transition dairy cows.

    Science.gov (United States)

    Hailemariam, D; Mandal, R; Saleem, F; Dunn, S M; Wishart, D S; Ametaj, B N

    2014-05-01

    In dairy cows, periparturient disease states, such as metritis, mastitis, and laminitis, are leading to increasingly significant economic losses for the dairy industry. Treatments for these pathologies are often expensive, ineffective, or not cost-efficient, leading to production losses, high veterinary bills, or early culling of the cows. Early diagnosis or detection of these conditions before they manifest themselves could lower their incidence, level of morbidity, and the associated economic losses. In an effort to identify predictive biomarkers for postpartum or periparturient disease states in dairy cows, we undertook a cross-sectional and longitudinal metabolomics study to look at plasma metabolite levels of dairy cows during the transition period, before and after becoming ill with postpartum diseases. Specifically we employed a targeted quantitative metabolomics approach that uses direct flow injection mass spectrometry to track the metabolite changes in 120 different plasma metabolites. Blood plasma samples were collected from 12 dairy cows at 4 time points during the transition period (-4 and -1 wk before and 1 and 4 wk after parturition). Out of the 12 cows studied, 6 developed multiple periparturient disorders in the postcalving period, whereas the other 6 remained healthy during the entire experimental period. Multivariate data analysis (principal component analysis and partial least squares discriminant analysis) revealed a clear separation between healthy controls and diseased cows at all 4 time points. This analysis allowed us to identify several metabolites most responsible for separating the 2 groups, especially before parturition and the start of any postpartum disease. Three metabolites, carnitine, propionyl carnitine, and lysophosphatidylcholine acyl C14:0, were significantly elevated in diseased cows as compared with healthy controls as early as 4 wk before parturition, whereas 2 metabolites, phosphatidylcholine acyl-alkyl C42:4 and

  3. Prevalence of hepatobiliary dysfunction in a regional group of patients with chronic inflammatory bowel disease

    DEFF Research Database (Denmark)

    Wewer, V; Gluud, C; Schlichting, P

    1991-01-01

    A regional group of outpatients with chronic inflammatory bowel disease (ulcerative colitis, n = 396, and Crohn's disease, n = 125) was biochemically screened to estimate the prevalence of hepatobiliary dysfunction. Among the 396 patients with ulcerative colitis, 69 (17%; 95% confidence limits, 14...... primary sclerosing cholangitis, of whom two were primarily diagnosed; one patient had cholangiocarcinoma also primarily diagnosed; and two patients were found to have alcoholic hepatic damage. Among the 125 patients with Crohn's disease, 38 (30%; 95% confidence limits, 23-38%) had at least 1 abnormal...... the criteria for further evaluation as described above. One patient appeared to have epithelioid granuloma in the liver and one patient had alcoholic liver disease, whereas one patient refused further examination.(ABSTRACT TRUNCATED AT 250 WORDS)...

  4. Regional Longitudinal Deformation Improves Prediction of Ventricular Tachyarrhythmias in Patients With Heart Failure With Reduced Ejection Fraction

    DEFF Research Database (Denmark)

    Biering-Sørensen, Tor; Knappe, Dorit; Pouleur, Anne-Catherine

    2017-01-01

    BACKGROUND: Left ventricular dysfunction is a known predictor of ventricular arrhythmias. We hypothesized that measures of regional longitudinal deformation by speckle-tracking echocardiography predict ventricular tachyarrhythmias and provide incremental prognostic information over clinical...... in the model, only a decreasing myocardial function in the inferior myocardial wall predicted VT/VF (hazard ratio, 1.05 [1.00-1.11]; P=0.039). Only strain obtained from the inferior myocardial wall provided incremental prognostic information for VT/VF over clinical and echocardiographic parameters (C statistic...... 0.71 versus 0.69; P=0.005). CONCLUSIONS: Assessment of regional longitudinal myocardial deformation in the inferior region provided incremental prognostic information over clinical and echocardiographic risk factors in predicting ventricular tachyarrhythmias. CLINICAL TRIAL REGISTRATION: URL: http...

  5. A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data.

    Science.gov (United States)

    Lu, Qiongshi; Hu, Yiming; Sun, Jiehuan; Cheng, Yuwei; Cheung, Kei-Hoi; Zhao, Hongyu

    2015-05-27

    Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://genocanyon.med.yale.edu.

  6. Data-adaptive harmonic analysis and prediction of sea level change in North Atlantic region

    Science.gov (United States)

    Kondrashov, D. A.; Chekroun, M.

    2017-12-01

    This study aims to characterize North Atlantic sea level variability across the temporal and spatial scales. We apply recently developed data-adaptive Harmonic Decomposition (DAH) and Multilayer Stuart-Landau Models (MSLM) stochastic modeling techniques [Chekroun and Kondrashov, 2017] to monthly 1993-2017 dataset of Combined TOPEX/Poseidon, Jason-1 and Jason-2/OSTM altimetry fields over North Atlantic region. The key numerical feature of the DAH relies on the eigendecomposition of a matrix constructed from time-lagged spatial cross-correlations. In particular, eigenmodes form an orthogonal set of oscillating data-adaptive harmonic modes (DAHMs) that come in pairs and in exact phase quadrature for a given temporal frequency. Furthermore, the pairs of data-adaptive harmonic coefficients (DAHCs), obtained by projecting the dataset onto associated DAHMs, can be very efficiently modeled by a universal parametric family of simple nonlinear stochastic models - coupled Stuart-Landau oscillators stacked per frequency, and synchronized across different frequencies by the stochastic forcing. Despite the short record of altimetry dataset, developed DAH-MSLM model provides for skillful prediction of key dynamical and statistical features of sea level variability. References M. D. Chekroun and D. Kondrashov, Data-adaptive harmonic spectra and multilayer Stuart-Landau models. HAL preprint, 2017, https://hal.archives-ouvertes.fr/hal-01537797

  7. Regional climate model downscaling may improve the prediction of alien plant species distributions

    Science.gov (United States)

    Liu, Shuyan; Liang, Xin-Zhong; Gao, Wei; Stohlgren, Thomas J.

    2014-12-01

    Distributions of invasive species are commonly predicted with species distribution models that build upon the statistical relationships between observed species presence data and climate data. We used field observations, climate station data, and Maximum Entropy species distribution models for 13 invasive plant species in the United States, and then compared the models with inputs from a General Circulation Model (hereafter GCM-based models) and a downscaled Regional Climate Model (hereafter, RCM-based models).We also compared species distributions based on either GCM-based or RCM-based models for the present (1990-1999) to the future (2046-2055). RCM-based species distribution models replicated observed distributions remarkably better than GCM-based models for all invasive species under the current climate. This was shown for the presence locations of the species, and by using four common statistical metrics to compare modeled distributions. For two widespread invasive taxa ( Bromus tectorum or cheatgrass, and Tamarix spp. or tamarisk), GCM-based models failed miserably to reproduce observed species distributions. In contrast, RCM-based species distribution models closely matched observations. Future species distributions may be significantly affected by using GCM-based inputs. Because invasive plants species often show high resilience and low rates of local extinction, RCM-based species distribution models may perform better than GCM-based species distribution models for planning containment programs for invasive species.

  8. Brain Activity in Valuation Regions while Thinking about the Future Predicts Individual Discount Rates

    Science.gov (United States)

    Cooper, Nicole; Kim, B. Kyu; Zauberman, Gal

    2013-01-01

    People vary widely in how much they discount delayed rewards, yet little is known about the sources of these differences. Here we demonstrate that neural activity in ventromedial prefrontal cortex (VMPFC) and ventral striatum (VS) when human subjects are asked to merely think about the future—specifically, to judge the subjective length of future time intervals—predicts delay discounting. High discounters showed lower activity for longer time delays, while low discounters showed the opposite pattern. Our results demonstrate that the correlation between VMPFC and VS activity and discounting occurs even in the absence of choices about future rewards, and does not depend on a person explicitly evaluating future outcomes or judging their self-relevance. This suggests a link between discounting and basic processes involved in thinking about the future, such as temporal perception. Our results also suggest that reducing impatience requires not suppression of VMPFC and VS activity altogether, but rather modulation of how these regions respond to the present versus the future. PMID:23926268

  9. Prediction of maximum earthquake intensities for the San Francisco Bay region

    Science.gov (United States)

    Borcherdt, Roger D.; Gibbs, James F.

    1975-01-01

    The intensity data for the California earthquake of April 18, 1906, are strongly dependent on distance from the zone of surface faulting and the geological character of the ground. Considering only those sites (approximately one square city block in size) for which there is good evidence for the degree of ascribed intensity, the empirical relation derived between 1906 intensities and distance perpendicular to the fault for 917 sites underlain by rocks of the Franciscan Formation is: Intensity = 2.69 - 1.90 log (Distance) (km). For sites on other geologic units intensity increments, derived with respect to this empirical relation, correlate strongly with the Average Horizontal Spectral Amplifications (AHSA) determined from 99 three-component recordings of ground motion generated by nuclear explosions in Nevada. The resulting empirical relation is: Intensity Increment = 0.27 +2.70 log (AHSA), and average intensity increments for the various geologic units are -0.29 for granite, 0.19 for Franciscan Formation, 0.64 for the Great Valley Sequence, 0.82 for Santa Clara Formation, 1.34 for alluvium, 2.43 for bay mud. The maximum intensity map predicted from these empirical relations delineates areas in the San Francisco Bay region of potentially high intensity from future earthquakes on either the San Andreas fault or the Hazard fault.

  10. Prediction of maximum earthquake intensities for the San Francisco Bay region

    Energy Technology Data Exchange (ETDEWEB)

    Borcherdt, R.D.; Gibbs, J.F.

    1975-01-01

    The intensity data for the California earthquake of Apr 18, 1906, are strongly dependent on distance from the zone of surface faulting and the geological character of the ground. Considering only those sites (approximately one square city block in size) for which there is good evidence for the degree of ascribed intensity, the empirical relation derived between 1906 intensities and distance perpendicular to the fault for 917 sites underlain by rocks of the Franciscan formation is intensity = 2.69 - 1.90 log (distance) (km). For sites on other geologic units, intensity increments, derived with respect to this empirical relation, correlate strongly with the average horizontal spectral amplifications (AHSA) determined from 99 three-component recordings of ground motion generated by nuclear explosions in Nevada. The resulting empirical relation is intensity increment = 0.27 + 2.70 log (AHSA), and average intensity increments for the various geologic units are -0.29 for granite, 0.19 for Franciscan formation, 0.64 for the Great Valley sequence, 0.82 for Santa Clara formation, 1.34 for alluvium, and 2.43 for bay mud. The maximum intensity map predicted from these empirical relations delineates areas in the San Francisco Bay region of potentially high intensity from future earthquakes on either the San Andreas fault or the Hayward fault.

  11. Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease

    Directory of Open Access Journals (Sweden)

    Qi Lin

    2018-04-01

    Full Text Available Resting-state functional connectivity (rs-FC is a promising neuromarker for cognitive decline in aging population, based on its ability to reveal functional differences associated with cognitive impairment across individuals, and because rs-fMRI may be less taxing for participants than task-based fMRI or neuropsychological tests. Here, we employ an approach that uses rs-FC to predict the Alzheimer's Disease Assessment Scale (11 items; ADAS11 scores, which measure overall cognitive functioning, in novel individuals. We applied this technique, connectome-based predictive modeling, to a heterogeneous sample of 59 subjects from the Alzheimer's Disease Neuroimaging Initiative, including normal aging, mild cognitive impairment, and AD subjects. First, we built linear regression models to predict ADAS11 scores from rs-FC measured with Pearson's r correlation. The positive network model tested with leave-one-out cross validation (LOOCV significantly predicted individual differences in cognitive function from rs-FC. In a second analysis, we considered other functional connectivity features, accordance and discordance, which disentangle the correlation and anticorrelation components of activity timecourses between brain areas. Using partial least square regression and LOOCV, we again built models to successfully predict ADAS11 scores in novel individuals. Our study provides promising evidence that rs-FC can reveal cognitive impairment in an aging population, although more development is needed for clinical application.

  12. Clusterin levels are increased in Alzheimer's disease and influence the regional distribution of Aβ.

    Science.gov (United States)

    Miners, J Scott; Clarke, Polly; Love, Seth

    2017-05-01

    Clusterin, also known as apoJ, is a lipoprotein abundantly expressed within the CNS. It regulates Aβ fibril formation and toxicity and facilitates amyloid-β (Aβ) transport across the blood-brain barrier. Genome-wide association studies have shown variations in the clusterin gene (CLU) to influence the risk of developing sporadic Alzheimer's disease (AD). To explore whether clusterin modulates the regional deposition of Aβ, we measured levels of soluble (NP40-extracted) and insoluble (guanidine-HCl-extracted) clusterin, Aβ40 and Aβ42 by sandwich ELISA in brain regions with a predilection for amyloid pathology-mid-frontal cortex (MF), cingulate cortex (CC), parahippocampal cortex (PH), and regions with little or no pathology-thalamus (TH) and white matter (WM). Clusterin level was highest in regions with plaque pathology (MF, CC, PH and PC), approximately mirroring the regional distribution of Aβ. It was significantly higher in AD than controls, and correlated positively with Aβ42 and insoluble Aβ40. Soluble clusterin level rose significantly with severity of cerebral amyloid angiopathy, and in MF and PC regions was highest in APOE ɛ4 homozygotes. In the TH and WM (areas with little amyloid pathology) clusterin was unaltered in AD and did not correlate with Aβ level. There was a significant positive correlation between the concentration of clusterin and the regional levels of insoluble Aβ42; however, the molar ratio of clusterin : Aβ42 declined with insoluble Aβ42 level in a region-dependent manner, being lowest in regions with predilection for Aβ plaque pathology. Under physiological conditions, clusterin reduces aggregation and promotes clearance of Aβ. Our findings indicate that in AD, clusterin increases, particularly in regions with most abundant Aβ, but because the increase does not match the rising level of Aβ42, the molar ratio of clusterin : Aβ42 in those regions falls, probably contributing to Aβ deposition within the tissue. © 2016

  13. Implications of the cattle trade network in Cameroon for regional disease prevention and control

    Science.gov (United States)

    Motta, Paolo; Porphyre, Thibaud; Handel, Ian; Hamman, Saidou M.; Ngu Ngwa, Victor; Tanya, Vincent; Morgan, Kenton; Christley, Rob; Bronsvoort, Barend M. Dec.

    2017-03-01

    Movement of live animals is a major risk factor for the spread of livestock diseases and zoonotic infections. Understanding contact patterns is key to informing cost-effective surveillance and control strategies. In West and Central Africa some of the most rapid urbanization globally is expected to increase the demand for animal-source foods and the need for safer and more efficient animal production. Livestock trading points represent a strategic contact node in the dissemination of multiple pathogens. From October 2014 to May 2015 official transaction records were collected and a questionnaire-based survey was carried out in cattle markets throughout Western and Central-Northern Cameroon. The data were used to analyse the cattle trade network including a total of 127 livestock markets within Cameroon and five neighboring countries. This study explores for the first time the influence of animal trade on infectious disease spread in the region. The investigations showed that national borders do not present a barrier against pathogen dissemination and that non-neighbouring countries are epidemiologically connected, highlighting the importance of a regional approach to disease surveillance, prevention and control. Furthermore, these findings provide evidence for the benefit of strategic risk-based approaches for disease monitoring, surveillance and control, as well as for communication and training purposes through targeting key regions, highly connected livestock markets and central trading links.

  14. Periodontal disease awareness among pregnant women in the central and eastern regions of Saudi Arabia.

    Science.gov (United States)

    Asa'ad, Farah A; Rahman, Ghousia; Al Mahmoud, Noura; Al Shamasi, Ebtehaj; Al Khuwaileidi, Abrar

    2015-02-01

    The purpose of this study was to assess the knowledge and awareness regarding periodontal disease and its effects on pregnancy among pregnant women in the central and eastern regions of Saudi Arabia. In this cross-sectional survey, self-administered, structured questionnaires were distributed to 300 pregnant women who were chosen randomly from attendees of maternity health care centers in the central and eastern regions of Saudi Arabia. The questions were developed from literature reviews of articles. The questionnaire addressed personal and sociodemographic variables, periodontal health awareness, and knowledge of pregnant women. The questionnaire was translated into Arabic and was pretested during the pilot study on a random sample of 50 pregnant women. Data were analyzed by χ(2) -tests, with the level of significance set at P disease could be prevented through toothbrushing and flossing. Approximately 97% of the respondents knew the negative effect of smoking, while only 12% knew there was a possible relationship between periodontal disease and adverse pregnancy outcomes. The results of this cross-sectional study found that there is limited knowledge and awareness about periodontal disease and its possible effects on pregnancy among pregnant women attending maternal health care centers in the central and eastern regions of Saudi Arabia. © 2013 Wiley Publishing Asia Pty Ltd.

  15. Meningococcal disease in the Asia-Pacific region: Findings and recommendations from the Global Meningococcal Initiative.

    Science.gov (United States)

    Borrow, Ray; Lee, Jin-Soo; Vázquez, Julio A; Enwere, Godwin; Taha, Muhamed-Kheir; Kamiya, Hajime; Kim, Hwang Min; Jo, Dae Sun

    2016-11-21

    The Global Meningococcal Initiative (GMI) is a global expert group that includes scientists, clinicians, and public health officials with a wide range of specialties. The purpose of the Initiative is to promote the global prevention of meningococcal disease (MD) through education, research, and cooperation. The first Asia-Pacific regional meeting was held in November 2014. The GMI reviewed the epidemiology of MD, surveillance, and prevention strategies, and outbreak control practices from participating countries in the Asia-Pacific region.Although, in general, MD is underreported in this region, serogroup A disease is most prominent in low-income countries such as India and the Philippines, while Taiwan, Japan, and Korea reported disease from serogroups C, W, and Y. China has a mixed epidemiology of serogroups A, B, C, and W. Perspectives from countries outside of the region were also provided to provide insight into lessons learnt. Based on the available data and meeting discussions, a number of challenges and data gaps were identified and, as a consequence, several recommendations were formulated: strengthen surveillance; improve diagnosis, typing and case reporting; standardize case definitions; develop guidelines for outbreak management; and promote awareness of MD among healthcare professionals, public health officials, and the general public. Copyright © 2016. Published by Elsevier Ltd.

  16. Solitary pulmonary nodule evaluation in regions endemic for infectious diseases: Do regional variations impact the effectiveness of fluorodeoxyglucose positron emission tomography/computed tomography.

    Science.gov (United States)

    Purandare, N C; Pramesh, C S; Agarwal, J P; Agrawal, A; Shah, S; Prabhash, K; Karimundackal, G; Jiwnani, S; Tandon, S; Rangarajan, V

    2017-01-01

    Fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) has become a preferred imaging modality for the evaluation of solitary pulmonary nodule (SPN), particularly in the developed world. Since FDG can concentrate in infective/inflammatory lesions, the diagnostic utility of FDG-PET can be questioned, particularly in regions endemic for infectious decisions. To evaluate the accuracy of FDG-PET/CT in evaluation of SPNs in a population endemic for infectious disease and to assess if regional variations have an impact on its effectiveness. All patients who underwent an FDG/PET-CT with a clinico-radiological diagnosis of SPN categorized as indeterminate were included. Based on a maximum standardized uptake values (SUVmax) cut-off of 2.5, lesions were classified as benign (2.5) and compared with gold standard histopathology. The diagnostic accuracy of PET-CT to detect malignancy was calculated. On the basis of final histopathology, lesions were grouped as (a) malignant nodules (b) infective/granulomatous nodules with a specific diagnosis and (c) nonspecific inflammatory nodules. The SUVmaxbetween these groups was compared using nonparametric statistical tests. A total of 191 patients (129 males, 62 females) with a median age of 64 years (range: 36-83) were included. Totally, 144 nodules (75.3%) were malignant and 47 were benign (24.7%). Adenocarcinoma (n = 84) was the most common malignancy. Tuberculosis (n = 16) and nonspecific infections (n = 24) were the two most common benign pathologies. There was a significant overlap in the metabolic uptake of malignant (median SUVmax-11.2, range: 3.3-34.6) and tuberculous nodules (median SUVmax-10.3, range: 2.7-22.5) with no statistically difference between their SUVmaxvalues (P = 0.43). The false-positive rate was 65.2% and the false-negative rate was 5.5%. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of FDG-PET/CT for detecting malignancy were 94

  17. What predicts depression in cardiac patients: sociodemographic factors, disease severity or theoretical vulnerabilities?

    Science.gov (United States)

    Doyle, F; McGee, H M; Conroy, R M; Delaney, M

    2011-05-01

    Depression is associated with increased cardiovascular risk in acute coronary syndrome (ACS) patients, but some argue that elevated depression is actually a marker of cardiovascular disease severity. Therefore, disease indices should better predict depression than established theoretical causes of depression (interpersonal life events, reinforcing events, cognitive distortions, type D personality). However, little theory-based research has been conducted in this area. In a cross-sectional design, ACS patients (n = 336) completed questionnaires assessing depression and psychosocial vulnerabilities. Nested logistic regression assessed the relative contribution of demographic or vulnerability factors, or disease indices or vulnerabilities to depression. In multivariate analysis, all vulnerabilities were independent significant predictors of depression (scoring above threshold on any scale, 48%). Demographic variables accounted for vulnerabilities accounting for significantly more (pseudo R² = 0.16, χ²(change) = 150.9, df = 4, p vulnerabilities increased the overall variance explained to 22% (pseudo R² = 0.22, χ² = 58.6, df = 4, p vulnerabilities predicted depression status better than did either demographic or disease indices. The presence of these proximal causes of depression suggests that depression in ACS patients is not simply a result of cardiovascular disease severity.

  18. Long-term predictability of regions and dates of strong earthquakes

    Science.gov (United States)

    Kubyshen, Alexander; Doda, Leonid; Shopin, Sergey

    2016-04-01

    Results on the long-term predictability of strong earthquakes are discussed. It is shown that dates of earthquakes with M>5.5 could be determined in advance of several months before the event. The magnitude and the region of approaching earthquake could be specified in the time-frame of a month before the event. Determination of number of M6+ earthquakes, which are expected to occur during the analyzed year, is performed using the special sequence diagram of seismic activity for the century time frame. Date analysis could be performed with advance of 15-20 years. Data is verified by a monthly sequence diagram of seismic activity. The number of strong earthquakes expected to occur in the analyzed month is determined by several methods having a different prediction horizon. Determination of days of potential earthquakes with M5.5+ is performed using astronomical data. Earthquakes occur on days of oppositions of Solar System planets (arranged in a single line). At that, the strongest earthquakes occur under the location of vector "Sun-Solar System barycenter" in the ecliptic plane. Details of this astronomical multivariate indicator still require further research, but it's practical significant is confirmed by practice. Another one empirical indicator of approaching earthquake M6+ is a synchronous variation of meteorological parameters: abrupt decreasing of minimal daily temperature, increasing of relative humidity, abrupt change of atmospheric pressure (RAMES method). Time difference of predicted and actual date is no more than one day. This indicator is registered 104 days before the earthquake, so it was called as Harmonic 104 or H-104. This fact looks paradoxical, but the works of A. Sytinskiy and V. Bokov on the correlation of global atmospheric circulation and seismic events give a physical basis for this empirical fact. Also, 104 days is a quarter of a Chandler period so this fact gives insight on the correlation between the anomalies of Earth orientation

  19. Predictive value of the transtheoretical model to smoking cessation in hospitalized patients with cardiovascular disease.

    Science.gov (United States)

    Chouinard, Maud-Christine; Robichaud-Ekstrand, Sylvie

    2007-02-01

    Several authors have questioned the transtheoretical model. Determining the predictive value of each cognitive-behavioural element within this model could explain the multiple successes reported in smoking cessation programmes. The purpose of this study was to predict point-prevalent smoking abstinence at 2 and 6 months, using the constructs of the transtheoretical model, when applied to a pooled sample of individuals who were hospitalized for a cardiovascular event. The study follows a predictive correlation design. Recently hospitalized patients (n=168) with cardiovascular disease were pooled from a randomized, controlled trial. Independent variables of the predictive transtheoretical model comprise stages and processes of change, pros and cons to quit smoking (decisional balance), self-efficacy, and social support. These were evaluated at baseline, 2 and 6 months. Compared to smokers, individuals who abstained from smoking at 2 and 6 months were more confident at baseline to remain non-smokers, perceived less pros and cons to continue smoking, utilized less consciousness raising and self-re-evaluation experiential processes of change, and received more positive reinforcement from their social network with regard to their smoke-free behaviour. Self-efficacy and stages of change at baseline were predictive of smoking abstinence after 6 months. Other variables found to be predictive of smoking abstinence at 6 months were an increase in self-efficacy; an increase in positive social support behaviour and a decrease of the pros within the decisional balance. The results partially support the predictive value of the transtheoretical model constructs in smoking cessation for cardiovascular disease patients.

  20. Evaluation of regional cerebral circulation and metabolism in moyamoya disease using positron emission computed tomography

    International Nuclear Information System (INIS)

    Kuwabara, Yasuo

    1986-01-01

    Regional cerebral blood flow, oxygen extraction fraction, metabolic rate of oxygen, blood volume and transit time were evaluated in 11 patients with moyamoya disease and 3 with suspected moyamoya disease using positron emission computed tomography. Eight of them were examined before and after EC-IC bypass surgery. Moyamoya patients were classified into four groups, namely, pediatric bilateral chronic type (over 5 years from onset), pediatric bilateral early type (within 5 years from onset), pediatric unilateral early type and adult type, according to age, duration of disease from onset and angiographic findings. These four groups showed different patterns on PET images; diffusely decreased CBF and CMRO2 in pediatric bilateral chronic type, decreased CBF and increased OEF in the frontal or temporoparietal region in pediatric bilateral early type, diffusely decreased CBF and increased OEF in the unilateral cerebral hemisphere in pediatric unilateral cerebral hemisphere in pediatric unilateral early type, and decreased CBF and CMRO2 in adult type. An increase of rCBV was demonstrated in frontal regions or basal ganglia in all groups, more prominently in pediatric patients. This was thought to be a common finding in moyamoya disease, corresponding to moyamoya vessels. Staging of moyamoya disease by PET was presented and compared to the angiographic staging. They were significantly correlated, and the stage 3 on PET image with decreased CMRO2 corresponded to the stage 3 or 4 on angiography, the most active stage of moyamoya disease. PET revealed increased CBF in the cortical area around EC-IC bypass but no remarkable changes in mean values of rCBF, OEF, CMRO2 and CBV in cerebral hemisphere. Some patients showed decreased rCBV in the basal ganglia. (J.P.N.)

  1. Deficits in Regional Cerebral Blood Flow on Brain SPECT Predict Treatment Resistant Depression.

    Science.gov (United States)

    Amen, Daniel G; Taylor, Derek V; Meysami, Somayeh; Raji, Cyrus A

    2018-03-22

    Depression remains an important risk factor for Alzheimer's disease, yet few neuroimaging biomarkers are available to identify treatment response in depression. To analyze and compare functional perfusion neuroimaging in persons with treatment resistant depression (TRD) compared to those experiencing full remission. A total of 951 subjects from a community psychiatry cohort were scanned with perfusion single photon emission computed tomography (SPECT) of the brain in both resting and task related settings. Of these, 78% experienced either full remission (n = 506) or partial remission (n = 237) and 11% were minimally responsive (n = 103) or non-responsive (11%. n = 106). Severity of depression symptoms were used to define these groups with changes in the Beck Depression Inventory prior to and following treatment. Voxel-based analyses of brain SPECT images from full remission compared to the worsening group was conducted with the statistical parametric mapping software, version 8 (SPM 8). Multiple comparisons were accounted for with a false discovery rate (p <  0.001). Persons with depression that worsened following treatment had reduced cerebral perfusion compared to full remission in the multiple regions including the bilateral frontal lobes, right hippocampus, left precuneus, and cerebellar vermis. Such differences were observed on both resting and concentration SPECT scans. Our findings identify imaging-based biomarkers in persons with depression related to treatment response. These findings have implications in understanding both depression to prognosis and its role as a risk factor for dementia.

  2. Survey of Armillaria spp. in the Oregon East Cascades: Baseline data for predicting climatic influences on Armillaria root disease

    Science.gov (United States)

    J. W. Hanna; A. L. Smith; H. M. Maffei; M.-S. Kim; N. B. Klopfenstein

    2008-01-01

    Root disease pathogens, such as Armillaria solidipes Peck (recently recognized older name for A. ostoyae), will likely have increasing impacts to forest ecosystems as trees undergo stress due to climate change. Before we can predict future impacts of root disease pathogens, we must first develop an ability to predict current distributions of the pathogens (and their...

  3. Cohort study of predictive value of urinary albumin excretion for atherosclerotic vascular disease in patients with insulin dependent diabetes

    DEFF Research Database (Denmark)

    Deckert, T; Yokoyama, H; Mathiesen, E

    1996-01-01

    atherosclerotic vascular disease during follow up of 2457 person year. Elevated urinary albumin excretion was significantly predictive of atherosclerotic vascular disease (hazard ratio 1.06 (95% confidence interval 1.02 to 1.18) per 5 mg increase in 24 hour urinary albumin excretion, P = 0.002). Predictive effect...

  4. Construction of a yeast artifical chromosome contig spanning the spinal muscular atrophy disease gene region

    Energy Technology Data Exchange (ETDEWEB)

    Kleyn, P.W.; Wang, C.H.; Vitale, E.; Pan, J.; Ross, B.M.; Grunn, A.; Palmer, D.A.; Warburton, D.; Brzustowicz, L.M.; Gilliam, T.G. (New York State Psychiatric Institute, NY (United States)); Lien, L.L.; Kunkel, L.M. (Howard Hughes Medical Institute, Boston, MA (United States))

    1993-07-15

    The childhood spinal muscular atrophies (SMAs) are the most common, serious neuromuscular disorders of childhood second to Duchenne muscular dystrophy. A single locus for these disorders has been mapped by recombination events to a region of 0.7 centimorgan (range, 0.1-2.1 centimorgans) between loci D5S435 and MAP1B on chromosome 5q11.2-13.3. By using PCR amplification to screen yeast artificial chromosome (YAC) DNA pools and the PCR-vectorette method to amplify YAC ends, a YAC contig was constructed across the disease gene region. Nine walk steps identified 32 YACs, including a minimum of seven overlapping YAC clones (average size, 460 kb) that span the SMA region. The contig is characterized by a collection of 30 YAC-end sequence tag sites together with seven genetic markers. The entire YAC contig spans a minimum of 3.2 Mb; the SMA locus is confined to roughly half of this region. Microsatellite markers generated along the YAC contig segregate with the SMA locus in all families where the flanking markers (D5S435 and MAP1B) recombine. Construction of a YAC contig across the disease gene region is an essential step in isolation of the SMA-encoding gene. 26 refs., 3 figs., 1 tab.

  5. Measurement of regional calcium accretion in patients treated for Paget's disease and in paraplegics

    International Nuclear Information System (INIS)

    Bergmann, P.; Schoutens, A.; Paternot, J.; Heilporn, E.

    1975-01-01

    The measurement of regional bone accretion of calcium has proved its worth in the study of two localised bone disorders, Paget's disease and the bone complications of medullar lesions. Intraveinous injection of 47 Ca is followed during 6 days of external measurements with the human whole-body counter and daily determinations of the serum specific activity. Assuming that the decay slope of the extra-osseous activity is parallel to that which follows the serum activity between two and six days, the activity fixed on the skeleton is calculated by zones. The experiment covers 15 normal subjects, 10 patients with Paget's disease, 10 paraplegics and 28 case of miscellaneous ailments [fr

  6. Predictive risk factors for chronic low back pain in Parkinson's disease.

    Science.gov (United States)

    Ozturk, Erhan Arif; Kocer, Bilge Gonenli

    2018-01-01

    Although previous studies have reported that the prevalence of low back pain in Parkinson's disease was over 50% and low back pain was often classified as chronic, risk factors of chronic low back pain have not been previously investigated. The aim of this study was to determine the predictive risk factors of chronic low back pain in Parkinson's disease. One hundred and sixty-eight patients with Parkinson's disease and 179 controls were consecutively included in the study. Demographic data of the two groups and disease characteristics of Parkinson's disease patient group were recorded. Low back pain lasting for ≥3 months was evaluated as chronic. Firstly, the bivariate correlations were calculated between chronic low back pain and all possible risk factors. Then, a multivariate regression was used to evaluate the impact of the predictors of chronic low back pain. The frequency of chronic low back pain in Parkinson's disease patients and controls were 48.2% and 26.7%, respectively (p chronic low back pain in Parkinson's disease were general factors including age (odds ratio = 1.053, p = 0.032) and Hospital Anxiety and Depression Scale - Depression subscore (odds ratio = 1.218, p = 0.001), and Parkinson's disease-related factors including rigidity (odds ratio = 5.109, p = 0.002) and posture item scores (odds ratio = 5.019, p = 0.0001). The chronic low back pain affects approximately half of the patients with Parkinson's disease. Prevention of depression or treatment recommendations for managing depression, close monitoring of anti- parkinsonian medication to keep motor symptoms under control, and attempts to prevent, correct or reduce abnormal posture may help reduce the frequency of chronic low back pain in Parkinson's disease. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. A method for detecting IBD regions simultaneously in multiple individuals--with applications to disease genetics

    DEFF Research Database (Denmark)

    Moltke, Ida; Albrechtsen, Anders; Hansen, Thomas V O

    2011-01-01

    genome containing disease-causing variants. However, IBD regions can be difficult to detect, especially in the common case where no pedigree information is available. In particular, all existing non-pedigree based methods can only infer IBD sharing between two individuals. Here, we present a new Markov...... Chain Monte Carlo method for detection of IBD regions, which does not rely on any pedigree information. It is based on a probabilistic model applicable to unphased SNP data. It can take inbreeding, allele frequencies, genotyping errors, and genomic distances into account. And most importantly, it can...

  8. Decisions on control of foot-and-mouth disease informed using model predictions

    DEFF Research Database (Denmark)

    Hisham Beshara Halasa, Tariq; Willeberg, P.; Christiansen, Lasse Engbo

    2013-01-01

    , epidemic duration, geographical size and costs. The first 14 days spatial spread (FFS) was also included to further support the prediction. The epidemic data was obtained from a Danish version (DTU-DADS) of a pre-existing FMD simulation model (Davis Animal Disease Spread – DADS) adapted to model the spread......The decision on whether or not to change the control strategy, such as introducing emergency vaccination, is perhaps one of the most difficult decisions faced by the veterinary authorities during a foot-and-mouth disease (FMD) epidemic. A simple tool that may predict the epidemic outcome...... and consequences would be useful to assist the veterinary authorities in the decision-making process. A previously proposed simple quantitative tool based on the first 14 days outbreaks (FFO) of FMD was used with results from an FMD simulation exercise. Epidemic outcomes included the number of affected herds...

  9. Prediction of remission in Graves' disease after thionamide therapy by technetium-99m early uptake

    International Nuclear Information System (INIS)

    Misaki, Takashi; Dokoh, Shigeharu; Koh, Toshikiyo; Shimbo, Shin-ichiro; Hidaka, Akinari; Iida, Yasuhiro; Kasagi, Kanji; Konishi, Junji.

    1991-01-01

    In the clinical management of Graves' thyrotoxicosis, one of the most important subject is when to stop antithyroid drugs after achieving an euthyroid state. T 3 suppression test and other methods have been used to forecast the outcome after drug cessation, but the results were not always satisfactory. We have attempted to predict remission of Graves' disease by single measurement of early technetium uptake without administration of triiodothyronine. Drugs were discontinued in the seventy-five patients with Graves' disease on maintenance doses of either methimazole or propylthiouracil who showed normalized uptake (4.0% or less). Of 64 patients evaluable after twelve months, 55 (86%) remained euthyroid, 8 relapsed, and 1 became hypothyoid. With its accuracy in prediction of short-term remission comparable or superior to T 3 suppression test, this rapid and simple method seemed suitable for routine use in clinical practice. (author)

  10. Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Jae Kwon Kim

    2017-01-01

    Full Text Available Background. Of the machine learning techniques used in predicting coronary heart disease (CHD, neural network (NN is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC curve of the proposed model (0.749 ± 0.010 was larger than the Framingham risk score (FRS (0.393 ± 0.010. Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

  11. Simulating infectious disease risk based on climatic drivers: from numerical weather prediction to long term climate change scenario

    Science.gov (United States)

    Caminade, C.; Ndione, J. A.; Diallo, M.; MacLeod, D.; Faye, O.; Ba, Y.; Dia, I.; Medlock, J. M.; Leach, S.; McIntyre, K. M.; Baylis, M.; Morse, A. P.

    2012-04-01

    Climate variability is an important component in determining the incidence of a number of diseases with significant health and socioeconomic impacts. In particular, vector born diseases are the most likely to be affected by climate; directly via the development rates and survival of both the pathogen and the vector, and indirectly through changes in the surrounding environmental conditions. Disease risk models of various complexities using different streams of climate forecasts as inputs have been developed within the QWeCI EU and ENHanCE ERA-NET project frameworks. This work will present two application examples, one for Africa and one for Europe. First, we focus on Rift Valley fever over sub-Saharan Africa, a zoonosis that affects domestic animals and humans by causing an acute fever. We show that the Rift Valley fever outbreak that occurred in late 2010 in the northern Sahelian region of Mauritania might have been anticipated ten days in advance using the GFS numerical weather prediction system. Then, an ensemble of regional climate projections is employed to model the climatic suitability of the Asian tiger mosquito for the future over Europe. The Asian tiger mosquito is an invasive species originally from Asia which is able to transmit West Nile and Chikungunya Fever among others. This species has spread worldwide during the last decades, mainly through the shipments of goods from Asia. Different disease models are employed and inter-compared to achieve such a task. Results show that the climatic conditions over southern England, central Western Europe and the Balkans might become more suitable for the mosquito (including the proviso that the mosquito has already been introduced) to establish itself in the future.

  12. Prediction of complicated disease course for children newly diagnosed with Crohn's disease: a multicentre inception cohort study.

    Science.gov (United States)

    Kugathasan, Subra; Denson, Lee A; Walters, Thomas D; Kim, Mi-Ok; Marigorta, Urko M; Schirmer, Melanie; Mondal, Kajari; Liu, Chunyan; Griffiths, Anne; Noe, Joshua D; Crandall, Wallace V; Snapper, Scott; Rabizadeh, Shervin; Rosh, Joel R; Shapiro, Jason M; Guthery, Stephen; Mack, David R; Kellermayer, Richard; Kappelman, Michael D; Steiner, Steven; Moulton, Dedrick E; Keljo, David; Cohen, Stanley; Oliva-Hemker, Maria; Heyman, Melvin B; Otley, Anthony R; Baker, Susan S; Evans, Jonathan S; Kirschner, Barbara S; Patel, Ashish S; Ziring, David; Trapnell, Bruce C; Sylvester, Francisco A; Stephens, Michael C; Baldassano, Robert N; Markowitz, James F; Cho, Judy; Xavier, Ramnik J; Huttenhower, Curtis; Aronow, Bruce J; Gibson, Greg; Hyams, Jeffrey S; Dubinsky, Marla C

    2017-04-29

    Stricturing and penetrating complications account for substantial morbidity and health-care costs in paediatric and adult onset Crohn's disease. Validated models to predict risk for complications are not available, and the effect of treatment on risk is unknown. We did a prospective inception cohort study of paediatric patients with newly diagnosed Crohn's disease at 28 sites in the USA and Canada. Genotypes, antimicrobial serologies, ileal gene expression, and ileal, rectal, and faecal microbiota were assessed. A competing-risk model for disease complications was derived and validated in independent groups. Propensity-score matching tested the effect of anti-tumour necrosis factor α (TNFα) therapy exposure within 90 days of diagnosis on complication risk. Between Nov 1, 2008, and June 30, 2012, we enrolled 913 patients, 78 (9%) of whom experienced Crohn's disease complications. The validated competing-risk model included age, race, disease location, and antimicrobial serologies and provided a sensitivity of 66% (95% CI 51-82) and specificity of 63% (55-71), with a negative predictive value of 95% (94-97). Patients who received early anti-TNFα therapy were less likely to have penetrating complications (hazard ratio [HR] 0·30, 95% CI 0·10-0·89; p=0·0296) but not stricturing complication (1·13, 0·51-2·51; 0·76) than were those who did not receive early anti-TNFα therapy. Ruminococcus was implicated in stricturing complications and Veillonella in penetrating complications. Ileal genes controlling extracellular matrix production were upregulated at diagnosis, and this gene signature was associated with stricturing in the risk model (HR 1·70, 95% CI 1·12-2·57; p=0·0120). When this gene signature was included, the model's specificity improved to 71%. Our findings support the usefulness of risk stratification of paediatric patients with Crohn's disease at diagnosis, and selection of anti-TNFα therapy. Crohn's and Colitis Foundation of America, Cincinnati

  13. Prediction of complicated disease course for children newly diagnosed with Crohn’s disease: a multicentre inception cohort study

    Science.gov (United States)

    Kugathasan, Subra; Denson, Lee A; Walters, Thomas D; Kim, Mi-Ok; Marigorta, Urko M; Schirmer, Melanie; Mondal, Kajari; Liu, Chunyan; Griffiths, Anne; Noe, Joshua D; Crandall, Wallace V; Snapper, Scott; Rabizadeh, Shervin; Rosh, Joel R; Shapiro, Jason M; Guthery, Stephen; Mack, David R; Kellermayer, Richard; Kappelman, Michael D; Steiner, Steven; Moulton, Dedrick E; Keljo, David; Cohen, Stanley; Oliva-Hemker, Maria; Heyman, Melvin B; Otley, Anthony R; Baker, Susan S; Evans, Jonathan S; Kirschner, Barbara S; Patel, Ashish S; Ziring, David; Trapnell, Bruce C; Sylvester, Francisco A; Stephens, Michael C; Baldassano, Robert N; Markowitz, James F; Cho, Judy; Xavier, Ramnik J; Huttenhower, Curtis; Aronow, Bruce J; Gibson, Greg; Hyams, Jeffrey S; Dubinsky, Marla C

    2017-01-01

    Summary Background Stricturing and penetrating complications account for substantial morbidity and health-care costs in paediatric and adult onset Crohn’s disease. Validated models to predict risk for complications are not available, and the effect of treatment on risk is unknown. Methods We did a prospective inception cohort study of paediatric patients with newly diagnosed Crohn’s disease at 28 sites in the USA and Canada. Genotypes, antimicrobial serologies, ileal gene expression, and ileal, rectal, and faecal microbiota were assessed. A competing-risk model for disease complications was derived and validated in independent groups. Propensity-score matching tested the effect of anti-tumour necrosis factor α (TNFα) therapy exposure within 90 days of diagnosis on complication risk. Findings Between Nov 1, 2008, and June 30, 2012, we enrolled 913 patients, 78 (9%) of whom experienced Crohn’s disease complications. The validated competing-risk model included age, race, disease location, and antimicrobial serologies and provided a sensitivity of 66% (95% CI 51–82) and specificity of 63% (55–71), with a negative predictive value of 95% (94–97). Patients who received early anti-TNFα therapy were less likely to have penetrating complications (hazard ratio [HR] 0·30, 95% CI 0·10–0·89; p=0·0296) but not stricturing complication (1·13, 0·51–2·51; 0·76) than were those who did not receive early anti-TNFα therapy. Ruminococcus was implicated in stricturing complications and Veillonella in penetrating complications. Ileal genes controlling extracellular matrix production were upregulated at diagnosis, and this gene signature was associated with stricturing in the risk model (HR 1·70, 95% CI 1·12–2·57; p=0·0120). When this gene signature was included, the model’s specificity improved to 71%. Interpretation Our findings support the usefulness of risk stratification of paediatric patients with Crohn’s disease at diagnosis, and selection of

  14. Machine learning techniques in disease forecasting: a case study on rice blast prediction

    Directory of Open Access Journals (Sweden)

    Kapoor Amar S

    2006-11-01

    Full Text Available Abstract Background Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. Results Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG approach achieved an average correlation coefficient (r of 0.50, which increased to 0.60 and percent mean absolute error (%MAE decreased from 65.42 to 52.24 when back-propagation neural network (BPNN was used. With generalized regression neural network (GRNN, the r increased to 0.70 and %MAE also improved to 46.30, which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding %MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in %MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. Conclusion Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also

  15. Matrix factorization-based data fusion for the prediction of lncRNA-disease associations.

    Science.gov (United States)

    Fu, Guangyuan; Wang, Jun; Domeniconi, Carlotta; Yu, Guoxian

    2018-05-01

    Long non-coding RNAs (lncRNAs) play crucial roles in complex disease diagnosis, prognosis, prevention and treatment, but only a small portion of lncRNA-disease associations have been experimentally verified. Various computational models have been proposed to identify lncRNA-disease associations by integrating heterogeneous data sources. However, existing models generally ignore the intrinsic structure of data sources or treat them as equally relevant, while they may not be. To accurately identify lncRNA-disease associations, we propose a Matrix Factorization based LncRNA-Disease Association prediction model (MFLDA in short). MFLDA decomposes data matrices of heterogeneous data sources into low-rank matrices via matrix tri-factorization to explore and exploit their intrinsic and shared structure. MFLDA can select and integrate the data sources by assigning different weights to them. An iterative solution is further introduced to simultaneously optimize the weights and low-rank matrices. Next, MFLDA uses the optimized low-rank matrices to reconstruct the lncRNA-disease association matrix and thus to identify potential associations. In 5-fold cross validation experiments to identify verified lncRNA-disease associations, MFLDA achieves an area under the receiver operating characteristic curve (AUC) of 0.7408, at least 3% higher than those given by state-of-the-art data fusion based computational models. An empirical study on identifying masked lncRNA-disease associations again shows that MFLDA can identify potential associations more accurately than competing models. A case study on identifying lncRNAs associated with breast, lung and stomach cancers show that 38 out of 45 (84%) associations predicted by MFLDA are supported by recent biomedical literature and further proves the capability of MFLDA in identifying novel lncRNA-disease associations. MFLDA is a general data fusion framework, and as such it can be adopted to predict associations between other biological

  16. Dieback Disease Predictive Model for Sexually and Asexually Propagated Dalbergia Sissoo (Shisham)

    International Nuclear Information System (INIS)

    Ahmad, I.; Siddiqui, M. T.; Nawaz, M. F.; Asif, M.; Atiq, M.; Gul, S.

    2016-01-01

    Dieback disease is a potential threat to Dalbergia sissoo (Shisham) which is a multipurpose tree of the Indian subcontinent. Different factors have been found associated with inciting shisham dieback. Fungal pathogens have been recognized as the major causal organism but changing climate is a main threat to forest dieback. Sexually (seedlings) and asexually (cuttings) propagated shisham were inoculated with the different fungi (Fusarium solani, Botryodiplodia theobromae, Curvularia lunata and Ganoderma lucidum). As environmental factors play critical role in the development of the disease, so the present study was designed to observe the impact of rainfall, temperature, relative humidity and wind velocity on the disease and for the management of disease predictive model was developed. A significant negative correlation was observed between disease and relative humidity both for seedlings (r = - 0.97) and cuttings (r = -0.487), respectively while maximum temperature expressed significant positive correlation with seedlings and cuttings with coefficient of correlation r = 0.734 and r = 0.629, respectively. Path analysis expressed that with one unit increase in rainfall the disease would rise by 7.58 and 15.04 and for maximum temperature it was 2.47 and 5.27 units in seedlings and cuttings, respectively. Multiple regression analysis showed that coefficient of determination (R/sup 2/) value was 0.62 and 0.48 for cuttings and seedlings, respectively. Normed fit index (NFI) and comparative fit index (CFI) values indicate that model is quite a good fit. Similarly comparison of observed and predicted data also validated the model for forecasting the disease. (author)

  17. Factors Predicting Treatment Failure in Patients Treated with Iodine-131 for Graves’ Disease

    International Nuclear Information System (INIS)

    Manohar, Kuruva; Mittal, Bhagwant Rai; Bhoil, Amit; Bhattacharya, Anish; Dutta, Pinaki; Bhansali, Anil

    2013-01-01

    Treatment of Graves' disease with iodine-131 ( 131 I) is well-known; however, all patients do not respond to a single dose of 131 I and may require higher and repeated doses. This study was carried out to identify the factors, which can predict treatment failure to a single dose of 131 I treatment in these patients. Data of 150 patients with Graves' disease treated with 259-370 MBq of 131 I followed-up for at least 1-year were retrospectively analyzed. Logistic regression analysis was used to predict factors which can predict treatment failure, such as age, sex, duration of disease, grade of goiter, duration of treatment with anti-thyroid drugs, mean dosage of anti-thyroid drugs used, 99m Tc-pertechnetate ( 99m TcO 4 - ) uptake at 20 min, dose of 131 I administered, total triiodothyronine and thyroxine levels. Of the 150 patients, 25 patients required retreatment within 1 year of initial treatment with 131 I. Logistic regression analysis revealed that male sex and 99m TcO 4 - uptake were associated with treatment failure. On receiver operating characteristic (ROC) curve analysis, area under the curve (AUC) was significant for 99m TcO 4 - uptake predicting treatment failure (AUC = 0.623; P = 0.039). Optimum cutoff for 99m TcO 4 - uptake was 17.75 with a sensitivity of 68% and specificity of 66% to predict treatment failure. Patients with >17.75% 99m TcO 4 - uptake had odds ratio of 3.14 (P = 0.014) for treatmen