A novel fibrosis index comprising a non-cholesterol sterol accurately predicts HCV-related liver cirrhosis.

Directory of Open Access Journals (Sweden)

Magdalena Ydreborg

Full Text Available Diagnosis of liver cirrhosis is essential in the management of chronic hepatitis C virus (HCV infection. Liver biopsy is invasive and thus entails a risk of complications as well as a potential risk of sampling error. Therefore, non-invasive diagnostic tools are preferential. The aim of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive significance for liver fibrosis in 278 patients originally included in a multicenter phase III treatment trial for chronic HCV infection. A stepwise multivariate logistic model selection was performed with liver cirrhosis, defined as Ishak fibrosis stage 5-6, as the outcome variable. A new index, referred to as Nordic Liver Index (NoLI in the paper, was based on the model: Log-odds (predicting cirrhosis = -12.17+ (age × 0.11 + (BMI (kg/m(2 × 0.23 + (D7-lathosterol (μg/100 mg cholesterol×(-0.013 + (Platelet count (x10(9/L × (-0.018 + (Prothrombin-INR × 3.69. The area under the ROC curve (AUROC for prediction of cirrhosis was 0.91 (95% CI 0.86-0.96. The index was validated in a separate cohort of 83 patients and the AUROC for this cohort was similar (0.90; 95% CI: 0.82-0.98. In conclusion, the new index may complement other methods in diagnosing cirrhosis in patients with chronic HCV infection.

Rehabilitation after stroke: predictive power of Barthel Index versus a cognitive and a motor index

DEFF Research Database (Denmark)

Engberg, A; Bentzen, L; Garde, B

1995-01-01

The aim of the present study was to investigate the predictive power of ratings of Barthel Index at Day 40 post stroke, compared with and/or combined with simultaneous ratings from a mobility scale (EG motor index) and a rather simple cognitive test scale (CT50). The parameter to be individually...... predicted was the need for special living facilities and support at discharge from a rehabilitation hospital, as well as six months later; 53 stroke patients with age median 68 years were included in this prospective study. It was shown that a combination of Barthel Index and CT50 had a stronger predictive...

The TyG index may predict the development of cardiovascular events.

Science.gov (United States)

Sánchez-Íñigo, Laura; Navarro-González, David; Fernández-Montero, Alejandro; Pastrana-Delgado, Juan; Martínez, Jose Alfredo

2016-02-01

Cardiovascular disease (CVD) is the worldwide leading cause of morbidity and mortality. An early risk detection of apparently healthy people before CVD onset has clinical relevance in the prevention of cardiovascular events. We evaluated the association between the product of fasting plasma glucose and triglycerides (TyG index) and CVD. A total of 5014 patients of the Vascular Metabolic CUN cohort (VMCUN cohort) were followed up during a median period of 10 years. We used a Cox proportional-hazard ratio with repeated measures to estimate the risk of incidence of CVD across quintiles of the TyG index, calculated as ln[fasting triglycerides (mg/dL) × fasting plasma glucose (mg(dL)/2], and plotted a receiver-operating characteristics (ROC) curve to compare a prediction model fitted on the variables used in the Framingham risk score, a new model containing the Framingham variables with the TyG index, and the risk of coronary heart disease. A higher level of TyG index was significantly associated with an increased risk of developing CVD independent of confounding factors with a value of 2·32 (95% CI: 1·65-3·26) for those in the highest quintile and 1·52 (95% CI: 1·07-2·16) for those in the fourth quintile. The areas under the curve (AUC) of the ROC plots were 0·708 (0·68-0·73) for the Framingham model and 0·719 (0·70-0·74) for the Framingham + TyG index model (P = 0·014). The TyG index, a simple measure reflecting insulin resistance, might be useful to early identify individuals at a high risk of developing a cardiovascular event. © 2015 Stichting European Society for Clinical Investigation Journal Foundation.

Cumulative Mass and NIOSH Variable Lifting Index Method for Risk Assessment: Possible Relations.

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Stucchi, Giulia; Battevi, Natale; Pandolfi, Monica; Galinotti, Luca; Iodice, Simona; Favero, Chiara

2018-02-01

Objective The aim of this study was to explore whether the Variable Lifting Index (VLI) can be corrected for cumulative mass and thus test its efficacy in predicting the risk of low-back pain (LBP). Background A validation study of the VLI method was published in this journal reporting promising results. Although several studies highlighted a positive correlation between cumulative load and LBP, cumulative mass has never been considered in any of the studies investigating the relationship between manual material handling and LBP. Method Both VLI and cumulative mass were calculated for 2,374 exposed subjects using a systematic approach. Due to high variability of cumulative mass values, a stratification within VLI categories was employed. Dummy variables (1-4) were assigned to each class and used as a multiplier factor for the VLI, resulting in a new index (VLI_CMM). Data on LBP were collected by occupational physicians at the study sites. Logistic regression was used to estimate the risk of acute LBP within levels of risk exposure when compared with a control group formed by 1,028 unexposed subjects. Results Data showed greatly variable values of cumulative mass across all VLI classes. The potential effect of cumulative mass on damage emerged as not significant ( p value = .6526). Conclusion When comparing VLI_CMM with raw VLI, the former failed to prove itself as a better predictor of LBP risk. Application To recognize cumulative mass as a modifier, especially for lumbar degenerative spine diseases, authors of future studies should investigate potential association between the VLI and other damage variables.

Variable discrete ordinates method for radiation transfer in plane-parallel semi-transparent media with variable refractive index

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Sarvari, S. M. Hosseini

2017-09-01

The traditional form of discrete ordinates method is applied to solve the radiative transfer equation in plane-parallel semi-transparent media with variable refractive index through using the variable discrete ordinate directions and the concept of refracted radiative intensity. The refractive index are taken as constant in each control volume, such that the direction cosines of radiative rays remain non-variant through each control volume, and then, the directions of discrete ordinates are changed locally by passing each control volume, according to the Snell's law of refraction. The results are compared by the previous studies in this field. Despite simplicity, the results show that the variable discrete ordinate method has a good accuracy in solving the radiative transfer equation in the semi-transparent media with arbitrary distribution of refractive index.

Pulmonary edema predictive scoring index (PEPSI), a new index to predict risk of reperfusion pulmonary edema and improvement of hemodynamics in percutaneous transluminal pulmonary angioplasty.

Science.gov (United States)

Inami, Takumi; Kataoka, Masaharu; Shimura, Nobuhiko; Ishiguro, Haruhisa; Yanagisawa, Ryoji; Taguchi, Hiroki; Fukuda, Keiichi; Yoshino, Hideaki; Satoh, Toru

2013-07-01

This study sought to identify useful predictors for hemodynamic improvement and risk of reperfusion pulmonary edema (RPE), a major complication of this procedure. Percutaneous transluminal pulmonary angioplasty (PTPA) has been reported to be effective for the treatment of chronic thromboembolic pulmonary hypertension (CTEPH). PTPA has not been widespread because RPE has not been well predicted. We included 140 consecutive procedures in 54 patients with CTEPH. The flow appearance of the target vessels was graded into 4 groups (Pulmonary Flow Grade), and we proposed PEPSI (Pulmonary Edema Predictive Scoring Index) = (sum total change of Pulmonary Flow Grade scores) × (baseline pulmonary vascular resistance). Correlations between occurrence of RPE and 11 variables, including hemodynamic parameters, number of target vessels, and PEPSI, were analyzed. Hemodynamic parameters significantly improved after median observation period of 6.4 months, and the sum total changes in Pulmonary Flow Grade scores were significantly correlated with the improvement in hemodynamics. Multivariate analysis revealed that PEPSI was the strongest factor correlated with the occurrence of RPE (p PEPSI to be a useful marker of the risk of RPE (cutoff value 35.4, negative predictive value 92.3%). Pulmonary Flow Grade score is useful in determining therapeutic efficacy, and PEPSI is highly supportive to reduce the risk of RPE after PTPA. Using these 2 indexes, PTPA could become a safe and common therapeutic strategy for CTEPH. Copyright © 2013 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Predictive Variable Gain Iterative Learning Control for PMSM

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Huimin Xu

2015-01-01

Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.

Uncertainty in wave energy resource assessment. Part 2: Variability and predictability

International Nuclear Information System (INIS)

Mackay, Edward B.L.; Bahaj, AbuBakr S.; Challenor, Peter G.

2010-01-01

The uncertainty in estimates of the energy yield from a wave energy converter (WEC) is considered. The study is presented in two articles. The first article considered the accuracy of the historic data and the second article, presented here, considers the uncertainty which arises from variability in the wave climate. Mean wave conditions exhibit high levels of interannual variability. Moreover, many previous studies have demonstrated longer-term decadal changes in wave climate. The effect of interannual and climatic changes in wave climate on the predictability of long-term mean WEC power is examined for an area off the north coast of Scotland. In this location anomalies in mean WEC power are strongly correlated with the North Atlantic Oscillation (NAO) index. This link enables the results of many previous studies on the variability of the NAO and its sensitivity to climate change to be applied to WEC power levels. It is shown that the variability in 5, 10 and 20 year mean power levels is greater than if annual power anomalies were uncorrelated noise. It is also shown that the change in wave climate from anthropogenic climate change over the life time of a wave farm is likely to be small in comparison to the natural level of variability. Finally, it is shown that despite the uncertainty related to variability in the wave climate, improvements in the accuracy of historic data will improve the accuracy of predictions of future WEC yield. (author)

Discriminative ability of commonly used indices to predict adverse outcomes after poster lumbar fusion: a comparison of demographics, ASA, the modified Charlson Comorbidity Index, and the modified Frailty Index.

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Ondeck, Nathaniel T; Bohl, Daniel D; Bovonratwet, Patawut; McLynn, Ryan P; Cui, Jonathan J; Shultz, Blake N; Lukasiewicz, Adam M; Grauer, Jonathan N

2018-01-01

As research tools, the American Society of Anesthesiologists (ASA) physical status classification system, the modified Charlson Comorbidity Index (mCCI), and the modified Frailty Index (mFI) have been associated with complications following spine procedures. However, with respect to clinical use for various adverse outcomes, no known study has compared the predictive performance of these indices specifically following posterior lumbar fusion (PLF). This study aimed to compare the discriminative ability of ASA, mCCI, and mFI, as well as demographic factors including age, body mass index, and gender for perioperative adverse outcomes following PLF. A retrospective review of prospectively collected data was performed. Patients undergoing elective PLF with or without interbody fusion were extracted from the 2011-2014 American College of Surgeons National Surgical Quality Improvement Program (NSQIP). Perioperative adverse outcome variables assessed included the occurrence of minor adverse events, severe adverse events, infectious adverse events, any adverse event, extended length of hospital stay, and discharge to higher-level care. Patient comorbidity indices and characteristics were delineated and assessed for discriminative ability in predicting perioperative adverse outcomes using an area under the curve analysis from the receiver operating characteristics curves. In total, 16,495 patients were identified who met the inclusion criteria. The most predictive comorbidity index was ASA and demographic factor was age. Of these two factors, age had the larger discriminative ability for three out of the six adverse outcomes and ASA was the most predictive for one out of six adverse outcomes. A combination of the most predictive demographic factor and comorbidity index resulted in improvements in discriminative ability over the individual components for five of the six outcome variables. For PLF, easily obtained patient ASA and age have overall similar or better

Prediction of Baseflow Index of Catchments using Machine Learning Algorithms

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Yadav, B.; Hatfield, K.

2017-12-01

We present the results of eight machine learning techniques for predicting the baseflow index (BFI) of ungauged basins using a surrogate of catchment scale climate and physiographic data. The tested algorithms include ordinary least squares, ridge regression, least absolute shrinkage and selection operator (lasso), elasticnet, support vector machine, gradient boosted regression trees, random forests, and extremely randomized trees. Our work seeks to identify the dominant controls of BFI that can be readily obtained from ancillary geospatial databases and remote sensing measurements, such that the developed techniques can be extended to ungauged catchments. More than 800 gauged catchments spanning the continental United States were selected to develop the general methodology. The BFI calculation was based on the baseflow separated from daily streamflow hydrograph using HYSEP filter. The surrogate catchment attributes were compiled from multiple sources including digital elevation model, soil, landuse, climate data, other publicly available ancillary and geospatial data. 80% catchments were used to train the ML algorithms, and the remaining 20% of the catchments were used as an independent test set to measure the generalization performance of fitted models. A k-fold cross-validation using exhaustive grid search was used to fit the hyperparameters of each model. Initial model development was based on 19 independent variables, but after variable selection and feature ranking, we generated revised sparse models of BFI prediction that are based on only six catchment attributes. These key predictive variables selected after the careful evaluation of bias-variance tradeoff include average catchment elevation, slope, fraction of sand, permeability, temperature, and precipitation. The most promising algorithms exceeding an accuracy score (r-square) of 0.7 on test data include support vector machine, gradient boosted regression trees, random forests, and extremely randomized

Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors.

Science.gov (United States)

Zabor, Emily C; Coit, Daniel; Gershenwald, Jeffrey E; McMasters, Kelly M; Michaelson, James S; Stromberg, Arnold J; Panageas, Katherine S

2018-02-22

Prognostic models are increasingly being made available online, where they can be publicly accessed by both patients and clinicians. These online tools are an important resource for patients to better understand their prognosis and for clinicians to make informed decisions about treatment and follow-up. The goal of this analysis was to highlight the possible variability in multiple online prognostic tools in a single disease. To demonstrate the variability in survival predictions across online prognostic tools, we applied a single validation dataset to three online melanoma prognostic tools. Data on melanoma patients treated at Memorial Sloan Kettering Cancer Center between 2000 and 2014 were retrospectively collected. Calibration was assessed using calibration plots and discrimination was assessed using the C-index. In this demonstration project, we found important differences across the three models that led to variability in individual patients' predicted survival across the tools, especially in the lower range of predictions. In a validation test using a single-institution data set, calibration and discrimination varied across the three models. This study underscores the potential variability both within and across online tools, and highlights the importance of using methodological rigor when developing a prognostic model that will be made publicly available online. The results also reinforce that careful development and thoughtful interpretation, including understanding a given tool's limitations, are required in order for online prognostic tools that provide survival predictions to be a useful resource for both patients and clinicians.

Efficacy of the Omega-3 Index in predicting non-alcoholic fatty liver disease in overweight and obese adults: a pilot study.

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Parker, Helen M; O'Connor, Helen T; Keating, Shelley E; Cohn, Jeffrey S; Garg, Manohar L; Caterson, Ian D; George, Jacob; Johnson, Nathan A

2015-09-14

Non-alcoholic fatty liver disease (NAFLD) is an independent predictor of CVD in otherwise healthy individuals. Low n-3 PUFA intake has been associated with the presence of NAFLD; however, the relationship between a biomarker of n-3 status - the Omega-3 Index - and liver fat is yet to be elucidated. A total of eighty overweight adults (fifty-six men) completed the anthropometric and biochemical measurements, including the Omega-3 Index, and underwent proton magnetic resonance spectroscopy assessment of liver fat. Bivariate correlations and multiple regression analyses were performed with reference to prediction of liver fat percentage. The mean Omega-3 Index was high in both NAFLD (intrahepatic lipid concentration≥5·5 %) and non-NAFLD groups. The Omega-3 Index, BMI, waist circumference, glucose, insulin, TAG, high-sensitive C-reactive protein (hsCRP) and alanine aminotransferase (ALT) were positively correlated, and HDL and erythrocyte n-6:n-3 ratio negatively correlated with liver fat concentration. Regression analysis found that simple anthropometric and demographic variables (waist, age) accounted for 31 % of the variance in liver fat and the addition of traditional cardiometabolic blood markers (TAG, HDL, hsCRP and ALT) increased the predictive power to 43 %. The addition of the novel erythrocyte fatty acid variable (Omega-3 Index) to the model only accounted for a further 3 % of the variance (P=0·049). In conclusion, the Omega-3 Index was associated with liver fat concentration but did not improve the overall capacity of demographic, anthropometric and blood markers to predict NAFLD.

Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.

Science.gov (United States)

Chen, Shangying; Zhang, Peng; Liu, Xin; Qin, Chu; Tao, Lin; Zhang, Cheng; Yang, Sheng Yong; Chen, Yu Zong; Chui, Wai Keung

2016-06-01

The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates. Copyright © 2016. Published by Elsevier Inc.

Prediction of County-Level Corn Yields Using an Energy-Crop Growth Index.

Science.gov (United States)

Andresen, Jeffrey A.; Dale, Robert F.; Fletcher, Jerald J.; Preckel, Paul V.

1989-01-01

Weather conditions significantly affect corn yields. while weather remains as the major uncontrolled variable in crop production, an understanding of the influence of weather on yields can aid in early and accurate assessment of the impact of weather and climate on crop yields and allow for timely agricultural extension advisories to help reduce farm management costs and improve marketing, decisions. Based on data for four representative countries in Indiana from 1960 to 1984 (excluding 1970 because of the disastrous southern corn leaf blight), a model was developed to estimate corn (Zea mays L.) yields as a function of several composite soil-crop-weather variables and a technology-trend marker, applied nitrogen fertilizer (N). The model was tested by predicting corn yields for 15 other counties. A daily energy-crop growth (ECG) variable in which different weights were used for the three crop-weather variables which make up the daily ECG-solar radiation intercepted by the canopy, a temperature function, and the ratio of actual to potential evapotranspiration-performed better than when the ECG components were weighted equally. The summation of the weighted daily ECG over a relatively short period (36 days spanning silk) was found to provide the best index for predicting county average corn yield. Numerical estimation results indicate that the ratio of actual to potential evapotranspiration (ET/PET) is much more important than the other two ECG factors in estimating county average corn yield in Indiana.

Disturbance metrics predict a wetland Vegetation Index of Biotic Integrity

Science.gov (United States)

Stapanian, Martin A.; Mack, John; Adams, Jean V.; Gara, Brian; Micacchion, Mick

2013-01-01

Indices of biological integrity of wetlands based on vascular plants (VIBIs) have been developed in many areas in the USA. Knowledge of the best predictors of VIBIs would enable management agencies to make better decisions regarding mitigation site selection and performance monitoring criteria. We use a novel statistical technique to develop predictive models for an established index of wetland vegetation integrity (Ohio VIBI), using as independent variables 20 indices and metrics of habitat quality, wetland disturbance, and buffer area land use from 149 wetlands in Ohio, USA. For emergent and forest wetlands, predictive models explained 61% and 54% of the variability, respectively, in Ohio VIBI scores. In both cases the most important predictor of Ohio VIBI score was a metric that assessed habitat alteration and development in the wetland. Of secondary importance as a predictor was a metric that assessed microtopography, interspersion, and quality of vegetation communities in the wetland. Metrics and indices assessing disturbance and land use of the buffer area were generally poor predictors of Ohio VIBI scores. Our results suggest that vegetation integrity of emergent and forest wetlands could be most directly enhanced by minimizing substrate and habitat disturbance within the wetland. Such efforts could include reducing or eliminating any practices that disturb the soil profile, such as nutrient enrichment from adjacent farm land, mowing, grazing, or cutting or removing woody plants.

Impact of vegetation variability on potential predictability and skill of EC-Earth simulations

Energy Technology Data Exchange (ETDEWEB)

Weiss, Martina; Hurk, Bart van den; Haarsma, Reindert; Hazeleger, Wilco [Royal Netherlands Meteorological Institute (KNMI), De Bilt (Netherlands)

2012-12-15

Climate models often use a simplified and static representation of vegetation characteristics to determine fluxes of energy, momentum and water vapour between surface and lower atmosphere. In order to analyse the impact of short term variability in vegetation phenology, we use remotely-sensed leaf area index and albedo products to examine the role of vegetation in the coupled land-atmosphere system. Perfect model experiments are carried out to determine the impact of realistic temporal variability of vegetation on potential predictability of evaporation and temperature, as well as model skill of EC-Earth simulations. The length of the simulation period is hereby limited by the availability of satellite products to 2000-2010. While a realistic representation of vegetation positively influences the simulation of evaporation and its potential predictability, a positive impact on 2 m temperature is of smaller magnitude, regionally confined and more pronounced in climatically extreme years. (orig.)

The interannual variability of the Haines Index over North America

Science.gov (United States)

Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman; Joseph J. Charney

2013-01-01

The Haines index (HI) is a fire-weather index that is widely used as an indicator of the potential for dry, low-static-stability air in the lower atmosphere to contribute to erratic fire behavior or large fire growth. This study examines the interannual variability of HI over North America and its relationship to indicators of large-scale circulation anomalies. The...

A Body Shape Index and Heart Rate Variability in Healthy Indians with Low Body Mass Index

Directory of Open Access Journals (Sweden)

Sharma Sowmya

2014-01-01

Full Text Available Background. One third of Indian population is said to be suffering from chronic energy deficiency (CED, with increased risk of developing chronic diseases. A new anthropometric measure called A Body Shape Index (ABSI is said to be a better index in predicting risks for premature mortality. ABSI is also in part said to be a surrogate of visceral fat. Objective. The present study aimed to explore the association between indices of HRV (heart rate variability, BMI, WC, and ABSI in healthy Indian males with low BMI (BMI < 18.5 kg/m2 and to compare with normal BMI group (BMI 18.5 to 24.9 kg/m2. Methodology. ABSI and BMI were derived from anthropometric parameters, namely, height, weight, and waist circumference in 178 males aged 18 to 78 years. Subjects were categorized into two groups based on their BMI. Results and Conclusions. Power spectral analysis of HRV demonstrated a significant negative correlation between Log HF (high frequency and ABSI in both low BMI [−24.2 (9.4, P<0.05] and normal BMI group [−23.41 (10.1, P<0.05] even after controlling for age. Thus even with slight increase in BMI among low BMI individuals, there could be a greater risk of cardiovascular morbidity and mortality.

[Evaluation of circulatory state using pulse oximeter: 2. PI (perfusion index) x PVI (pleth variability index)].

Science.gov (United States)

Kaneda, Toru; Suzuki, Toshiyasu

2009-07-01

Pulse oximeter expressed by SpO2 is used for monitoring respiratory state during operation and in ICU. Perfusion index (PI) and pleth variability index (PVI) as new indexes are calculated from pulse oximeter (Masimo SET Radical-7, Masimo Corp., USA, 1998) waveforms. And these indices were used as parameters to evaluate the circulatory state. For PI calculation, the pulsatile infrared signal is indexed against the nonpulsatile infrared signal and expressed as a percentage. It might thus be of future value in assessment of perioperative changes in peripheral perfusion. PVI is a measure of a dynamic change in PI that occurs during complete respiratory cycle. It might be thought that PVI, an index automatically derived from the pulse oximeter waveform analysis, had potentially clinical applications for noninvasive hypovolemia detection and fluid responsiveness monitoring.

Predictive Value of Beat-to-Beat QT Variability Index across the Continuum of Left Ventricular Dysfunction: Competing Risks of Non-cardiac or Cardiovascular Death, and Sudden or Non-Sudden Cardiac Death

Science.gov (United States)

Tereshchenko, Larisa G.; Cygankiewicz, Iwona; McNitt, Scott; Vazquez, Rafael; Bayes-Genis, Antoni; Han, Lichy; Sur, Sanjoli; Couderc, Jean-Philippe; Berger, Ronald D.; de Luna, Antoni Bayes; Zareba, Wojciech

2012-01-01

Background The goal of this study was to determine the predictive value of beat-to-beat QT variability in heart failure (HF) patients across the continuum of left ventricular dysfunction. Methods and Results Beat-to-beat QT variability index (QTVI), heart rate variance (LogHRV), normalized QT variance (QTVN), and coherence between heart rate variability and QT variability have been measured at rest during sinus rhythm in 533 participants of the Muerte Subita en Insuficiencia Cardiaca (MUSIC) HF study (mean age 63.1±11.7; males 70.6%; LVEF >35% in 254 [48%]) and in 181 healthy participants from the Intercity Digital Electrocardiogram Alliance (IDEAL) database. During a median of 3.7 years of follow-up, 116 patients died, 52 from sudden cardiac death (SCD). In multivariate competing risk analyses, the highest QTVI quartile was associated with cardiovascular death [hazard ratio (HR) 1.67(95%CI 1.14-2.47), P=0.009] and in particular with non-sudden cardiac death [HR 2.91(1.69-5.01), P<0.001]. Elevated QTVI separated 97.5% of healthy individuals from subjects at risk for cardiovascular [HR 1.57(1.04-2.35), P=0.031], and non-sudden cardiac death in multivariate competing risk model [HR 2.58(1.13-3.78), P=0.001]. No interaction between QTVI and LVEF was found. QTVI predicted neither non-cardiac death (P=0.546) nor SCD (P=0.945). Decreased heart rate variability (HRV) rather than increased QT variability was the reason for increased QTVI in this study. Conclusions Increased QTVI due to depressed HRV predicts cardiovascular mortality and non-sudden cardiac death, but neither SCD nor excracardiac mortality in HF across the continuum of left ventricular dysfunction. Abnormally augmented QTVI separates 97.5% of healthy individuals from HF patients at risk. PMID:22730411

Reproducibility of the Pleth Variability Index in premature infants

NARCIS (Netherlands)

Den Boogert, W.J. (Wilhelmina J.); H.A. van Elteren (Hugo); T.G. Goos (Tom); I.K.M. Reiss (Irwin); R.C.J. de Jonge (Rogier); V.J. van den Berg (Victor J.)

2017-01-01

textabstractThe aim was to assess the reproducibility of the Pleth Variability Index (PVI), developed for non-invasive monitoring of peripheral perfusion, in preterm neonates below 32 weeks of gestational age. Three PVI measurements were consecutively performed in stable, comfortable preterm

Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model

Directory of Open Access Journals (Sweden)

Ernest Kissi

2018-03-01

Full Text Available Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.

Rehabilitation after stroke: predictive power of Barthel Index versus a cognitive and a motor index

DEFF Research Database (Denmark)

Engberg, A; Bentzen, L; Garde, B

1995-01-01

The aim of the present study was to investigate the predictive power of ratings of Barthel Index at Day 40 post stroke, compared with and/or combined with simultaneous ratings from a mobility scale (EG motor index) and a rather simple cognitive test scale (CT50). The parameter to be individually...

Quantifying human disturbance in watersheds: Variable selection and performance of a GIS-based disturbance index for predicting the biological condition of perennial streams

Science.gov (United States)

Falcone, James A.; Carlisle, Daren M.; Weber, Lisa C.

2010-01-01

Characterizing the relative severity of human disturbance in watersheds is often part of stream assessments and is frequently done with the aid of Geographic Information System (GIS)-derived data. However, the choice of variables and how they are used to quantify disturbance are often subjective. In this study, we developed a number of disturbance indices by testing sets of variables, scoring methods, and weightings of 33 potential disturbance factors derived from readily available GIS data. The indices were calibrated using 770 watersheds located in the western United States for which the severity of disturbance had previously been classified from detailed local data by the United States Environmental Protection Agency (USEPA) Environmental Monitoring and Assessment Program (EMAP). The indices were calibrated by determining which variable or variable combinations and aggregation method best differentiated between least- and most-disturbed sites. Indices composed of several variables performed better than any individual variable, and best results came from a threshold method of scoring using six uncorrelated variables: housing unit density, road density, pesticide application, dam storage, land cover along a mainstem buffer, and distance to nearest canal/pipeline. The final index was validated with 192 withheld watersheds and correctly classified about two-thirds (68%) of least- and most-disturbed sites. These results provide information about the potential for using a disturbance index as a screening tool for a priori ranking of watersheds at a regional/national scale, and which landscape variables and methods of combination may be most helpful in doing so.

Predicting Bond Betas using Macro-Finance Variables

DEFF Research Database (Denmark)

Aslanidis, Nektarios; Christiansen, Charlotte; Cipollini, Andrea

We conduct in-sample and out-of-sample forecasting using the new approach of combining explanatory variables through complete subset regressions (CSR). We predict bond CAPM betas and bond returns conditioning on various macro-fi…nance variables. We explore differences across long-term government ...... bonds, investment grade corporate bonds, and high-yield corporate bonds. The CSR method performs well in predicting bond betas, especially in-sample, and, mainly high-yield bond betas when the focus is out-of-sample. Bond returns are less predictable than bond betas....

Reproducibility of the Pleth Variability Index in premature infants

NARCIS (Netherlands)

Den Boogert, Wilhelmina J.; Van Elteren, Hugo A.; Goos, T.G.; Reiss, Irwin K.M.; De Jonge, Rogier C.J.; van Den Berg, Victor J.

2017-01-01

The aim was to assess the reproducibility of the Pleth Variability Index (PVI), developed for non-invasive monitoring of peripheral perfusion, in preterm neonates below 32 weeks of gestational age. Three PVI measurements were consecutively performed in stable, comfortable preterm neonates in the

VARIABILITY OF THE THERMAL CONTINENTALITY INDEX IN CENTRAL EUROPE

Directory of Open Access Journals (Sweden)

CIARANEK1 DOMINIKA

2014-03-01

Full Text Available The paper presents the spatial and temporal variability of thermal continentality in Central Europe. Gorczyński’s and Johansson-Ringleb’s formulae were used to derive the continentality index. The study also looked at the annual patterns of air temperature amplitude (A, a component of both of these formulae, and D; the difference between the average temperatures of autumn (Sep.-Nov. and spring (Mar.-May. Records of six weather stations representing the climate of Central Europe were included in the study covering the period 1775-2012 (Potsdam, Drezden, Prague, Vienna, Krakow, Debrecen. The highest continentality index was found in Debrecen and the lowest in Potsdam. The continentality index fluctuated with time with two pronounced dips at the turn of the 19th century and in the second half of the 20th century. The highest continentality index values were recorded during the 1930s and 1940s.

Prediction of Mortality with A Body Shape Index in Young Asians: Comparison with Body Mass Index and Waist Circumference.

Science.gov (United States)

Lee, Da-Young; Lee, Mi-Yeon; Sung, Ki-Chul

2018-06-01

This paper investigated the impact of A Body Shape Index (ABSI) on the risk of all-cause mortality compared with the impact of waist circumference (WC) and body mass index (BMI). This paper reviewed data of 213,569 Korean adults who participated in health checkups between 2002 and 2012 at Kangbuk Samsung Hospital in Seoul, Korea. A multivariate Cox proportional hazard analysis was performed on the BMI, WC, and ABSI z score continuous variables as well as quintiles. During 1,168,668.7 person-years, 1,107 deaths occurred. As continuous variables, a significant positive relationship with the risk of all-cause death was found only in ABSI z scores after adjustment for age, sex, current smoking, alcohol consumption, regular exercise, presence of diabetes or hypertension, and history of cardiovascular diseases. In Cox analysis of quintiles, quintile 5 of the ABSI z score showed significantly increased hazard ratios (HRs) for mortality risk (HR [95% CI] was 1.32 [1.05-1.66]), whereas the risk for all-cause mortality, on the other hand, decreased in quintiles 3 through 5 of BMI and WC compared with their first quintiles after adjusting for several confounders. This study showed that the predictive value of ABSI for mortality risk was strong for a sample of young Asian participants and that its usefulness was better than BMI or WC. © 2018 The Obesity Society.

The PAPAS index: a novel index for the prediction of hepatitis C-related fibrosis.

Science.gov (United States)

Ozel, Banu D; Poyrazoğlu, Orhan K; Karaman, Ahmet; Karaman, Hatice; Altinkaya, Engin; Sevinç, Eylem; Zararsiz, Gökmen

2015-08-01

Several noninvasive tests have been developed to determine the degree of hepatic fibrosis in patients with chronic hepatitis C (CHC) without performing liver biopsy. This study aimed to determine the performance of the PAPAS (Platelet/Age/Phosphatase/AFP/AST) index in patients with CHC for the prediction of significant fibrosis and cirrhosis and to compare it with other noninvasive tests. To date, no study has evaluated the application of the PAPAS index in CHC-associated liver fibrosis. This retrospective study included 137 consecutive patients with CHC who had undergone a percutaneous liver biopsy before treatment. The aspartate aminotransferase/platelet ratio (APRI), aspartate aminotransferase/alanine transaminase ratio (AAR), age-platelet index (API), FIB4, cirrhosis discriminate score (CDS), the Göteborg University cirrhosis index (GUCI), and PAPAS were calculated and compared with the diagnostic accuracies of all fibrosis indices between the groups F0-F2 (no-mild fibrosis) versus F3-F6 (significant fibrosis) and F0-F4 (no cirrhosis) versus F5-F6 (cirrhosis). To predict significant fibrosis, the area under curve (95% confidence interval) for FIB4 was 0.727 followed by GUCI (0.721), PAPAS≈APRI≈CDS (0.716), and API (0.68). To predict cirrhosis, the area under curve (95% confidence interval) for FIB4 was calculated to be 0.735, followed by GUCI (0.723), PAPAS≈APRI≈CDS≈(0.71), and API (0.66). No statistically significant difference was observed among these predictors to exclude both significant fibrosis and cirrhosis (P>0.05). The diagnostic capability of the PAPAS index has moderate efficiency and was not superior to other fibrosis markers for the identification of fibrosis in CHC patients. There is a need for more comprehensive prospective studies to help determine the diagnostic value of PAPAS for liver fibrosis.

On Use of the Variable Zagreb vM2 Index in QSPR: Boiling Points of Benzenoid Hydrocarbons

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Albin Jurić

2004-12-01

Full Text Available The variable Zagreb vM2 index is introduced and applied to the structure-boiling point modeling of benzenoid hydrocarbons. The linear model obtained (thestandard error of estimate for the fit model Sfit=6.8 oC is much better than thecorresponding model based on the original Zagreb M2 index (Sfit=16.4 oC. Surprisingly,the model based on the variable vertex-connectivity index (Sfit=6.8 oC is comparable tothe model based on vM2 index. A comparative study with models based on the vertex-connectivity index, edge-connectivity index and several distance indices favours modelsbased on the variable Zagreb vM2 index and variable vertex-connectivity index.However, the multivariate regression with two-, three- and four-descriptors givesimproved models, the best being the model with four-descriptors (but vM2 index is notamong them with Sfit=5 oC, though the four-descriptor model contaning vM2 index isonly slightly inferior (Sfit=5.3 oC.

Association between different measurements of blood pressure variability by ABP monitoring and ankle-brachial index.

Science.gov (United States)

Wittke, Estefânia; Fuchs, Sandra C; Fuchs, Flávio D; Moreira, Leila B; Ferlin, Elton; Cichelero, Fábio T; Moreira, Carolina M; Neyeloff, Jeruza; Moreira, Marina B; Gus, Miguel

2010-11-05

Blood pressure (BP) variability has been associated with cardiovascular outcomes, but there is no consensus about the more effective method to measure it by ambulatory blood pressure monitoring (ABPM). We evaluated the association between three different methods to estimate BP variability by ABPM and the ankle brachial index (ABI). In a cross-sectional study of patients with hypertension, BP variability was estimated by the time rate index (the first derivative of SBP over time), standard deviation (SD) of 24-hour SBP; and coefficient of variability of 24-hour SBP. ABI was measured with a doppler probe. The sample included 425 patients with a mean age of 57 ± 12 years, being 69.2% women, 26.1% current smokers and 22.1% diabetics. Abnormal ABI (≤ 0.90 or ≥ 1.40) was present in 58 patients. The time rate index was 0.516 ± 0.146 mmHg/min in patients with abnormal ABI versus 0.476 ± 0.124 mmHg/min in patients with normal ABI (P = 0.007). In a logistic regression model the time rate index was associated with ABI, regardless of age (OR = 6.9, 95% CI = 1.1- 42.1; P = 0.04). In a multiple linear regression model, adjusting for age, SBP and diabetes, the time rate index was strongly associated with ABI (P < 0.01). None of the other indexes of BP variability were associated with ABI in univariate and multivariate analyses. Time rate index is a sensible method to measure BP variability by ABPM. Its performance for risk stratification of patients with hypertension should be explored in longitudinal studies.

Prediction of monthly average global solar radiation based on statistical distribution of clearness index

International Nuclear Information System (INIS)

Ayodele, T.R.; Ogunjuyigbe, A.S.O.

2015-01-01

In this paper, probability distribution of clearness index is proposed for the prediction of global solar radiation. First, the clearness index is obtained from the past data of global solar radiation, then, the parameters of the appropriate distribution that best fit the clearness index are determined. The global solar radiation is thereafter predicted from the clearness index using inverse transformation of the cumulative distribution function. To validate the proposed method, eight years global solar radiation data (2000–2007) of Ibadan, Nigeria are used to determine the parameters of appropriate probability distribution for clearness index. The calculated parameters are then used to predict the future monthly average global solar radiation for the following year (2008). The predicted values are compared with the measured values using four statistical tests: the Root Mean Square Error (RMSE), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and the coefficient of determination (R"2). The proposed method is also compared to the existing regression models. The results show that logistic distribution provides the best fit for clearness index of Ibadan and the proposed method is effective in predicting the monthly average global solar radiation with overall RMSE of 0.383 MJ/m"2/day, MAE of 0.295 MJ/m"2/day, MAPE of 2% and R"2 of 0.967. - Highlights: • Distribution of clearnes index is proposed for prediction of global solar radiation. • The clearness index is obtained from the past data of global solar radiation. • The parameters of distribution that best fit the clearness index are determined. • Solar radiation is predicted from the clearness index using inverse transformation. • The method is effective in predicting the monthly average global solar radiation.

The Bird Community Resilience Index: a novel remote sensing-based biodiversity variable for quantifying ecological integrity

Science.gov (United States)

Michel, N. L.; Wilsey, C.; Burkhalter, C.; Trusty, B.; Langham, G.

2017-12-01

Scalable indicators of biodiversity change are critical to reporting overall progress towards national and global targets for biodiversity conservation (e.g. Aichi Targets) and sustainable development (SDGs). These essential biodiversity variables capitalize on new remote sensing technologies and growth of community science participation. Here we present a novel biodiversity metric quantifying resilience of bird communities and, by extension, of their associated ecological communities. This metric adds breadth to the community composition class of essential biodiversity variables that track trends in condition and vulnerability of ecological communities. We developed this index for use with North American grassland birds, a guild that has experienced stronger population declines than any other avian guild, in order to evaluate gains from the implementation of best management practices on private lands. The Bird Community Resilience Index was designed to incorporate the full suite of species-specific responses to management actions, and be flexible enough to work across broad climatic, land cover, and bird community gradients (i.e., grasslands from northern Mexico through Canada). The Bird Community Resilience Index consists of four components: density estimates of grassland and arid land birds; weighting based on conservation need; a functional diversity metric to incorporate resiliency of bird communities and their ecosystems; and a standardized scoring system to control for interannual variation caused by extrinsic factors (e.g., climate). We present an analysis of bird community resilience across ranches in the Northern Great Plains region of the United States. As predicted, Bird Community Resilience was higher in lands implementing best management practices than elsewhere. While developed for grassland birds, this metric holds great potential for use as an Essential Biodiversity Variable for community composition in a variety of habitat.

Utility of Childhood Glucose Homeostasis Variables in Predicting Adult Diabetes and Related Cardiometabolic Risk Factors

OpenAIRE

Nguyen, Quoc Manh; Srinivasan, Sathanur R.; Xu, Ji-Hua; Chen, Wei; Kieltyka, Lyn; Berenson, Gerald S.

2009-01-01

OBJECTIVE This study examines the usefulness of childhood glucose homeostasis variables (glucose, insulin, and insulin resistance index [homeostasis model assessment of insulin resistance {HOMA-IR}]) in predicting pre-diabetes and type 2 diabetes and related cardiometabolic risk factors in adulthood. RESEARCH DESIGN AND METHODS This retrospective cohort study consisted of normoglycemic (n = 1,058), pre-diabetic (n = 37), and type 2 diabetic (n = 25) adults aged 19–39 years who were followed o...

Building and verifying a severity prediction model of acute pancreatitis (AP) based on BISAP, MEWS and routine test indexes.

Science.gov (United States)

Ye, Jiang-Feng; Zhao, Yu-Xin; Ju, Jian; Wang, Wei

2017-10-01

To discuss the value of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Modified Early Warning Score (MEWS), serum Ca2+, similarly hereinafter, and red cell distribution width (RDW) for predicting the severity grade of acute pancreatitis and to develop and verify a more accurate scoring system to predict the severity of AP. In 302 patients with AP, we calculated BISAP and MEWS scores and conducted regression analyses on the relationships of BISAP scoring, RDW, MEWS, and serum Ca2+ with the severity of AP using single-factor logistics. The variables with statistical significance in the single-factor logistic regression were used in a multi-factor logistic regression model; forward stepwise regression was used to screen variables and build a multi-factor prediction model. A receiver operating characteristic curve (ROC curve) was constructed, and the significance of multi- and single-factor prediction models in predicting the severity of AP using the area under the ROC curve (AUC) was evaluated. The internal validity of the model was verified through bootstrapping. Among 302 patients with AP, 209 had mild acute pancreatitis (MAP) and 93 had severe acute pancreatitis (SAP). According to single-factor logistic regression analysis, we found that BISAP, MEWS and serum Ca2+ are prediction indexes of the severity of AP (P-value0.05). The multi-factor logistic regression analysis showed that BISAP and serum Ca2+ are independent prediction indexes of AP severity (P-value0.05); BISAP is negatively related to serum Ca2+ (r=-0.330, P-valuemodel is as follows: ln()=7.306+1.151*BISAP-4.516*serum Ca2+. The predictive ability of each model for SAP follows the order of the combined BISAP and serum Ca2+ prediction model>Ca2+>BISAP. There is no statistical significance for the predictive ability of BISAP and serum Ca2+ (P-value>0.05); however, there is remarkable statistical significance for the predictive ability using the newly built prediction model as well as BISAP

Idiopathic Pulmonary Fibrosis: Gender-Age-Physiology Index Stage for Predicting Future Lung Function Decline.

Science.gov (United States)

Salisbury, Margaret L; Xia, Meng; Zhou, Yueren; Murray, Susan; Tayob, Nabihah; Brown, Kevin K; Wells, Athol U; Schmidt, Shelley L; Martinez, Fernando J; Flaherty, Kevin R

2016-02-01

Idiopathic pulmonary fibrosis is a progressive lung disease with variable course. The Gender-Age-Physiology (GAP) Index and staging system uses clinical variables to stage mortality risk. It is unknown whether clinical staging predicts future decline in pulmonary function. We assessed whether the GAP stage predicts future pulmonary function decline and whether interval pulmonary function change predicts mortality after accounting for stage. Patients with idiopathic pulmonary fibrosis (N = 657) were identified retrospectively at three tertiary referral centers, and baseline GAP stages were assessed. Mixed models were used to describe average trajectories of FVC and diffusing capacity of the lung for carbon monoxide (Dlco). Multivariable Cox proportional hazards models were used to assess whether declines in pulmonary function ≥ 10% in 6 months predict mortality after accounting for GAP stage. Over a 2-year period, GAP stage was not associated with differences in yearly lung function decline. After accounting for stage, a 10% decrease in FVC or Dlco over 6 months independently predicted death or transplantation (FVC hazard ratio, 1.37; Dlco hazard ratio, 1.30; both, P ≤ .03). Patients with GAP stage 2 with declining pulmonary function experienced a survival profile similar to patients with GAP stage 3, with 1-year event-free survival of 59.3% (95% CI, 49.4-67.8) vs 56.9% (95% CI, 42.2-69.1). Baseline GAP stage predicted death or lung transplantation but not the rate of future pulmonary function decline. After accounting for GAP stage, a decline of ≥ 10% over 6 months independently predicted death or lung transplantation. Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Air pollution forecast in cities by an air pollution index highly correlated with meteorological variables

International Nuclear Information System (INIS)

Cogliani, E.

2001-01-01

There are many different air pollution indexes which represent the global urban air pollution situation. The daily index studied here is also highly correlated with meteorological variables and this index is capable of identifying those variables that significantly affect the air pollution. The index is connected with attention levels of NO 2 , CO and O 3 concentrations. The attention levels are fixed by a law proposed by the Italian Ministries of Health and Environment. The relation of that index with some meteorological variables is analysed by the linear multiple partial correlation statistical method. Florence, Milan and Vicence were selected to show the correlation among the air pollution index and the daily thermic excursion, the previous day's air pollution index and the wind speed. During the January-March period the correlation coefficient reaches 0.85 at Milan. The deterministic methods of forecasting air pollution concentrations show very high evaluation errors and are applied on limited areas around the observation stations, as opposed to the whole urban areas. The global air pollution, instead of the concentrations at specific observation stations, allows the evaluation of the level of the sanitary risk regarding the whole urban population. (Author)

A predictive model for pressure ulcer outcome: the Wound Healing Index.

Science.gov (United States)

Horn, Susan D; Barrett, Ryan S; Fife, Caroline E; Thomson, Brett

2015-12-01

The purpose of this learning activity is to provide information regarding the creation of a risk-stratification system to predict the likelihood of the healing of body and heel pressure ulcers (PrUs). This continuing education activity is intended for physicians and nurses with an interest in skin and wound care. After participating in this educational activity, the participant should be better able to:1. Explain the need for a PrU risk stratification tool.2. Describe the purpose and methodology of the study.3. Delineate the results of the study and development of the Wound Healing Index. : To create a validated system to predict the healing likelihood of patients with body and heel pressure ulcers (PrUs), incorporating only patient- and wound-specific variables. The US Wound Registry data were examined retrospectively and assigned a clear outcome (healed, amputated, and so on). Significant variables were identified with bivariate analyses. Multivariable logistic regression models were created based on significant factors (P wound clinics in 24 states : A total of 7973 body PrUs and 2350 heel PrUs were eligible for analysis. Not applicable : Healed PrU MAIN RESULTS:: Because of missing data elements, the logistic regression development model included 6640 body PrUs, of which 4300 healed (64.8%), and the 10% validation sample included 709 PrUs, of which 477 healed (67.3%). For heel PrUs, the logistic regression development model included 1909 heel PrUs, of which 1240 healed (65.0%), and the 10% validation sample included 203 PrUs, of which 133 healed (65.5%). Variables significantly predicting healing were PrU size, PrU age, number of concurrent wounds of any etiology, PrU Stage III or IV, evidence of bioburden/infection, patient age, being nonambulatory, having renal transplant, paralysis, malnutrition, and/or patient hospitalization for any reason. Body and heel PrU Wound Healing Indices are comprehensive, user-friendly, and validated predictive models for

The dynamic relationship between Bursa Malaysia composite index and macroeconomic variables

Science.gov (United States)

Ismail, Mohd Tahir; Rose, Farid Zamani Che; Rahman, Rosmanjawati Abd.

2017-08-01

This study investigates and analyzes the long run and short run relationships between Bursa Malaysia Composite index (KLCI) and nine macroeconomic variables in a VAR/VECM framework. After regression analysis seven out the nine macroeconomic variables are chosen for further analysis. The use of Johansen-Juselius Cointegration and Vector Error Correction Model (VECM) technique indicate that there are long run relationships between the seven macroeconomic variables and KLCI. Meanwhile, Granger causality test shows that bidirectional relationship between KLCI and oil price. Furthermore, after 12 months the shock on KLCI are explained by innovations of the seven macroeconomic variables. This indicate the close relationship between macroeconomic variables and KLCI.

Variable importance and prediction methods for longitudinal problems with missing variables.

Directory of Open Access Journals (Sweden)

Iván Díaz

Full Text Available We present prediction and variable importance (VIM methods for longitudinal data sets containing continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patients, a field in which current medical practice involves rules of thumb and scoring methods that only use a few variables and ignore the dynamic and high-dimensional nature of trauma recovery. Well-principled prediction and VIM methods can provide a tool to make care decisions informed by the high-dimensional patient's physiological and clinical history. Our VIM parameters are analogous to slope coefficients in adjusted regressions, but are not dependent on a specific statistical model, nor require a certain functional form of the prediction regression to be estimated. In addition, they can be causally interpreted under causal and statistical assumptions as the expected outcome under time-specific clinical interventions, related to changes in the mean of the outcome if each individual experiences a specified change in the variable (keeping other variables in the model fixed. Better yet, the targeted MLE used is doubly robust and locally efficient. Because the proposed VIM does not constrain the prediction model fit, we use a very flexible ensemble learner (the SuperLearner, which returns a linear combination of a list of user-given algorithms. Not only is such a prediction algorithm intuitive appealing, it has theoretical justification as being asymptotically equivalent to the oracle selector. The results of the analysis show effects whose size and significance would have been not been found using a parametric approach (such as stepwise regression or LASSO. In addition, the procedure is even more compelling as the predictor on which it is based showed significant improvements in cross-validated fit, for instance area under the curve (AUC for a receiver-operator curve (ROC. Thus, given that 1 our VIM

Association between different measurements of blood pressure variability by ABP monitoring and ankle-brachial index

Directory of Open Access Journals (Sweden)

Moreira Leila B

2010-11-01

Full Text Available Abstract Background Blood pressure (BP variability has been associated with cardiovascular outcomes, but there is no consensus about the more effective method to measure it by ambulatory blood pressure monitoring (ABPM. We evaluated the association between three different methods to estimate BP variability by ABPM and the ankle brachial index (ABI. Methods and Results In a cross-sectional study of patients with hypertension, BP variability was estimated by the time rate index (the first derivative of SBP over time, standard deviation (SD of 24-hour SBP; and coefficient of variability of 24-hour SBP. ABI was measured with a doppler probe. The sample included 425 patients with a mean age of 57 ± 12 years, being 69.2% women, 26.1% current smokers and 22.1% diabetics. Abnormal ABI (≤ 0.90 or ≥ 1.40 was present in 58 patients. The time rate index was 0.516 ± 0.146 mmHg/min in patients with abnormal ABI versus 0.476 ± 0.124 mmHg/min in patients with normal ABI (P = 0.007. In a logistic regression model the time rate index was associated with ABI, regardless of age (OR = 6.9, 95% CI = 1.1- 42.1; P = 0.04. In a multiple linear regression model, adjusting for age, SBP and diabetes, the time rate index was strongly associated with ABI (P Conclusion Time rate index is a sensible method to measure BP variability by ABPM. Its performance for risk stratification of patients with hypertension should be explored in longitudinal studies.

Effectively Indexing Uncertain Moving Objects for Predictive Queries

DEFF Research Database (Denmark)

Zhang, Meihui; Chen, Su; Jensen, Christian Søndergaard

2009-01-01

in more complex and stochastic ways. This paper investigates the possibility of a marriage between moving-object indexing and probabilistic object modelling. Given the distributions of the current locations and velocities of moving objects, we devise an efficient inference method for the prediction...

Prediction of low birth weight: the placental T2* estimated by MRI versus the uterine artery pulsatility index

DEFF Research Database (Denmark)

Sinding, Marianne Munk; Peters, David Alberg; Frøkjær, Jens Brøndum

(MRI) variable T2* reflects the placental oxygenation and thereby placental function. Therefore, we aimed to evaluate the performance of placental T2* in the prediction of low birth weight using the uterine artery (UtA) pulsatility index (PI) as gold standard. Methods: The study population......CONTROL ID: 2516296 ABSTRACT FINAL ID: P22.05 TITLE: Prediction of low birth weight: the placental T2* estimated by MRI versus the uterine artery pulsatility index AUTHORS (FIRST NAME, LAST NAME): Marianne Sinding1, David Peters2, Jens B. Frøkjær3, 4, Ole B. Christiansen1, 4, Astrid Petersen5...... had an EFW T2* was measured by MRI at 1.5T. A gradient recalled echo MRI sequence with readout at 16 echo times was used, and the placental T2* value was obtained by fitting the signal intensity as a function of the echo times...

The Economic Value of Predicting Stock Index Returns and Volatility

NARCIS (Netherlands)

Marquering, W.; Verbeek, M.J.C.M.

2000-01-01

In this paper, we analyze the economic value of predicting index returns as well as volatility. On the basis of fairly simple linear models, estimated recursively, we produce genuine out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data from 1954-1998, we

Construction of prediction intervals for Palmer Drought Severity Index using bootstrap

Science.gov (United States)

Beyaztas, Ufuk; Bickici Arikan, Bugrayhan; Beyaztas, Beste Hamiye; Kahya, Ercan

2018-04-01

In this study, we propose an approach based on the residual-based bootstrap method to obtain valid prediction intervals using monthly, short-term (three-months) and mid-term (six-months) drought observations. The effects of North Atlantic and Arctic Oscillation indexes on the constructed prediction intervals are also examined. Performance of the proposed approach is evaluated for the Palmer Drought Severity Index (PDSI) obtained from Konya closed basin located in Central Anatolia, Turkey. The finite sample properties of the proposed method are further illustrated by an extensive simulation study. Our results revealed that the proposed approach is capable of producing valid prediction intervals for future PDSI values.

Fecal Calprotectin is an Accurate Tool and Correlated to Seo Index in Prediction of Relapse in Iranian Patients With Ulcerative Colitis.

Science.gov (United States)

Hosseini, Seyed Vahid; Jafari, Peyman; Taghavi, Seyed Alireza; Safarpour, Ali Reza; Rezaianzadeh, Abbas; Moini, Maryam; Mehrabi, Manoosh

2015-02-01

The natural clinical course of Ulcerative Colitis (UC) is characterized by episodes of relapse and remission. Fecal Calprotectin (FC) is a relatively new marker of intestinal inflammation and is an available, non-expensive tool for predicting relapse of quiescent UC. The Seo colitis activity index is a clinical index for assessment of the severity of UC. The present study aimed to evaluate the accuracy of FC and the Seo colitis activity index and their correlation in prediction of UC exacerbation. In this prospective cohort study, 157 patients with clinical and endoscopic diagnosis of UC selected randomly from 1273 registered patients in Fars province's IBD registry center in Shiraz, Iran, were followed from October 2012 to October 2013 for 12 months or shorter, if they had a relapse. Two patients left the study before completion and one patient had relapse because of discontinuation of drugs. The participants' clinical and serum factors were evaluated every three months. Furthermore, stool samples were collected at the beginning of study and every three months and FC concentration (commercially available enzyme linked immunoassay) and the Seo Index were assessed. Then univariate analysis, multiple variable logistic regression, Receiver Operating Characteristics (ROC) curve analysis, and Pearson's correlation test (r) were used for statistical analysis of data. According to the results, 74 patients (48.1%) relapsed during the follow-up (33 men and 41 women). Mean ± SD of FC was 862.82 ± 655.97 μg/g and 163.19 ± 215.85 μg/g in relapsing and non-relapsing patients, respectively (P Seo index were significant predictors of relapse. ROC curve analysis of FC level and Seo activity index for prediction of relapse demonstrated area under the curve of 0.882 (P Seo index was significant in prediction of relapse (r = 0.63, P Seo activity index in prediction of relapse in the course of quiescent UC in Iranian patients.

Development of an Integrated Moisture Index for predicting species composition

Science.gov (United States)

Louis R. Iverson; Charles T. Scott; Martin E. Dale; Anantha Prasad

1996-01-01

A geographic information system (GIS) approach was used to develop an Integrated Moisture Index (IMI), which was used to predict species composition for Ohio forests. Several landscape features (a slope-aspect shading index, cumulative flow of water downslope, curvature of the landscape, and the water-holding capacity of the soil) were derived from elevation and soils...

Predictive model for the heat capacity of ionic liquids using the mass connectivity index

International Nuclear Information System (INIS)

Valderrama, Jose O.; Martinez, Gwendolyn; Rojas, Roberto E.

2011-01-01

A simple and accurate model to predict the heat capacity of ionic liquids is presented. The proposed model considers variables readily available for ionic liquids and that have important effect on heat capacity, according to the literature information. Additionally a recently defined structural parameter known as mass connectivity index is incorporated into the model. A set of 602 heat capacity data for 146 ionic liquids have been used in the study. The results were compared with experimental data and with values reported by other available estimation methods. Results show that the new simple correlation gives low deviations and can be used with confidence in thermodynamic and engineering calculations.

Macroeconomic Variables, International Islamic Indices, and The Return Volatility in Jakarta Islamic Index

Directory of Open Access Journals (Sweden)

Yoghi Citra Pratama

2018-01-01

Full Text Available According to understand the behavior of Islamic equity markets the primary objective of this research is to analyze the effect of macroeconomic indicators and International Islamic Index on return volatility of Jakarta Islamic Index. The analysis method used in this study is AutoRegressive Conditional Heteroscedastic-Generalized AutoRegressive Conditional Heteroscedastic (ARCH-GARCH. The result of this research showed that all variables, i.e., BI rate, inflation rate, IDR-USD exchange rate, DJIUS index, DJIUK index, FTSJP index and FTSMY index have a simultaneously significant impact on return volatility of JII. While t-test results show that BI rate, IDR-USD exchange rate, DJIUK index and FTSMY index have a substantial effect on return volatility of JII.DOI: 10.15408/aiq.v10i1.5550

Evaluation of the Cerebral State Index in Cats under Isoflurane Anaesthesia: Dose-Effect Relationship and Prediction of Clinical Signs

Directory of Open Access Journals (Sweden)

Joana R. Sousa

2014-01-01

Full Text Available The performance of the cerebral state index (CSI in reflecting different levels of isoflurane anaesthesia was evaluated in ten cats subjected to four end-tidal isoflurane concentrations (EtIso, each maintained for 15 minutes (0.8%, 1.2%, 1.6%, or 2.0% EtIso. The CSI, hemodynamic data, ocular reflexes, and eye position were recorded for each EtIso concentration. Pharmacodynamic analysis of CSI with EtIso was performed, as well as prediction probability analysis with a clinical scale based on the eye reflexes. The CSI values showed great variability. Between all parameters, burst suppression ratio showed the better fitting with the sigmoidal concentration-effect model (R2=0.93 followed by CSI (R2=0.82 and electromyographic activity (R2=0.79. EtIso was the variable with better prediction of the clinical scale of anaesthesia (prediction probability value of 0.94. Although the CSI values decrease with increasing isoflurane concentrations, the huge variability in CSI values may be a strong limitation for its use in cats and it seems to be no better than EtIso as a predictor of clinical signs.

Granger Causality Between The Stock Market Index and Macroeconomic Variables. A Study of Malaysia and Singapore

OpenAIRE

Sircar, Shadee Mosaddek

2009-01-01

This paper investigates the causal relationships that may be present between the stock market index of developing countries and their macroeconomic variables based on the Vector Error Correction Model (VECM) framework. The countries Malaysia and Singapore are chosen for the purpose of this paper, where FTSE KLCI index and the FTSE STI index are used to represent the stock market performances respectively for each country. The four macroeconomic variables analyzed and used in this paper are Co...

Identify the dominant variables to predict stream water temperature

Science.gov (United States)

Chien, H.; Flagler, J.

2016-12-01

Stream water temperature is a critical variable controlling water quality and the health of aquatic ecosystems. Accurate prediction of water temperature and the assessment of the impacts of environmental variables on water temperature variation are critical for water resources management, particularly in the context of water quality and aquatic ecosystem sustainability. The objective of this study is to measure stream water temperature and air temperature and to examine the importance of streamflow on stream water temperature prediction. The measured stream water temperature and air temperature will be used to test two hypotheses: 1) streamflow is a relatively more important factor than air temperature in regulating water temperature, and 2) by combining air temperature and streamflow data stream water temperature can be more accurately estimated. Water and air temperature data loggers are placed at two USGS stream gauge stations #01362357and #01362370, located in the upper Esopus Creek watershed in Phonecia, NY. The ARIMA (autoregressive integrated moving average) time series model is used to analyze the measured water temperature data, identify the dominant environmental variables, and predict the water temperature with identified dominant variable. The preliminary results show that streamflow is not a significant variable in predicting stream water temperature at both USGS gauge stations. Daily mean air temperature is sufficient to predict stream water temperature at this site scale.

Estimating search engine index size variability: a 9-year longitudinal study.

Science.gov (United States)

van den Bosch, Antal; Bogers, Toine; de Kunder, Maurice

One of the determining factors of the quality of Web search engines is the size of their index. In addition to its influence on search result quality, the size of the indexed Web can also tell us something about which parts of the WWW are directly accessible to the everyday user. We propose a novel method of estimating the size of a Web search engine's index by extrapolating from document frequencies of words observed in a large static corpus of Web pages. In addition, we provide a unique longitudinal perspective on the size of Google and Bing's indices over a nine-year period, from March 2006 until January 2015. We find that index size estimates of these two search engines tend to vary dramatically over time, with Google generally possessing a larger index than Bing. This result raises doubts about the reliability of previous one-off estimates of the size of the indexed Web. We find that much, if not all of this variability can be explained by changes in the indexing and ranking infrastructure of Google and Bing. This casts further doubt on whether Web search engines can be used reliably for cross-sectional webometric studies.

Climate Prediction Center (CPC) Madden-Julian Oscillation (MJO) Index

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The Climate Prediction Center (CPC) Madden Julian Oscillation index (MJO) is a dataset that allows evaluation of the strength and phase of the MJO during the dataset...

Prediction of university student’s addictability based on some demographic variables, academic procrastination, and interpersonal variables

Directory of Open Access Journals (Sweden)

Mohammad Ali Tavakoli

2014-02-01

Full Text Available Objectives: This study aimed to predict addictability among the students, based on demographic variables, academic procrastination, and interpersonal variables, and also to study the prevalence of addictability among these students. Method: The participants were 500 students (260 females, 240 males selected through a stratified random sampling among the students in Islamic Azad University Branch Abadan. The participants were assessed through Individual specification inventory, addiction potential scale and Aitken procrastination Inventory. Findings: The findings showed %23/6 of students’ readiness for addiction. Men showed higher addictability than women, but age wasn’t an issue. Also variables such as economic status, age, major, and academic procrastination predicted %13, and among interpersonal variables, the variables of having friends who use drugs and dissociated family predicted %13/2 of the variance in addictability. Conclusion: This study contains applied implications for addiction prevention.

Predicted impact and evaluation of North Carolina's phosphorus indexing tool.

Science.gov (United States)

Johnson, Amy M; Osmond, Deanna L; Hodges, Steven C

2005-01-01

Increased concern about potential losses of phosphorus (P) from agricultural fields receiving animal waste has resulted in the implementation of new state and federal regulations related to nutrient management. In response to strengthened nutrient management standards that require consideration of P, North Carolina has developed a site-specific P indexing system called the Phosphorus Loss Assessment Tool (PLAT) to predict relative amounts of potential P loss from agricultural fields. The purpose of this study was to apply the PLAT index on farms throughout North Carolina in an attempt to predict the percentage and types of farms that will be forced to change management practices due to implementation of new regulations. Sites from all 100 counties were sampled, with the number of samples taken from each county depending on the proportion of the state's agricultural land that occurs in that county. Results showed that approximately 8% of producers in the state will be required to apply animal waste or inorganic fertilizer on a P rather than nitrogen basis, with the percentage increasing for farmers who apply animal waste (approximately 27%). The PLAT index predicted the greatest amounts of P loss from sites in the Coastal Plain region of North Carolina and from sites receiving poultry waste. Loss of dissolved P through surface runoff tended to be greater than other loss pathways and presents an area of concern as no best management practices (BMPs) currently exist for the reduction of in-field dissolved P. The PLAT index predicted the areas in the state that are known to be disproportionately vulnerable to P loss due to histories of high P applications, high densities of animal units, or soil type and landscapes that are most susceptible to P loss.

Seasonal predictions of Fire Weather Index: Paving the way for their operational applicability in Mediterranean Europe

Directory of Open Access Journals (Sweden)

Joaquín Bedia

2018-01-01

Full Text Available Managers of wildfire-prone landscapes in the Euro-Mediterranean region would greatly benefit from fire weather predictions a few months in advance, and particularly from the reliable prediction of extreme fire seasons. However, in some cases model biases prevent from a direct application of these predictions in an operational context. Fire risk management requires precise knowledge of the likely consequences of climate on fire risk, and the interest for decision-makers is focused on multi-variable fire danger indices, calculated through the combination of different model output variables. In this paper we consider whether the skill in dynamical seasonal predictions of one of the most widely applied of such indices (the Canadian Fire Weather Index, FWI is sufficient to inform management decisions, and we examine various methodological aspects regarding the calibration of model outputs prior to its verification and operational applicability. We find that there is significant skill in predicting above average summer FWI in parts of SE Europe at 1 month lead time, but poor skill elsewhere. These results are largely linked to the predictability of relative humidity. Moreover, practical recommendations are given for the use of empirical quantile mapping in probabilistic seasonal FWI forecasts. Furthermore, we show how researchers, fire managers and other stakeholders can take advantage of a new open-source climate service in order to undertake all the necessary steps for data download, post-processing, analysis and verification in a straightforward and fully reproducible manner. Keywords: Climate impact indicators, Quantile mapping, Bias correction, System 4, Fire danger, Seasonal forecasting

A Bimodel Algorithm with Data-Divider to Predict Stock Index

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Zhaoyue Wang

2018-01-01

Full Text Available There is not yet reliable software for stock prediction, because most experts of this area have been trying to predict an exact stock index. Considering that the fluctuation of a stock index usually is no more than 1% in a day, the error between the forecasted and the actual values should be no more than 0.5%. It is too difficult to realize. However, forecasting whether a stock index will rise or fall does not need to be so exact a numerical value. A few scholars noted the fact, but their systems do not yet work very well because different periods of a stock have different inherent laws. So, we should not depend on a single model or a set of parameters to solve the problem. In this paper, we developed a data-divider to divide a set of historical stock data into two parts according to rising period and falling period, training, respectively, two neural networks optimized by a GA. Above all, the data-divider enables us to avoid the most difficult problem, the effect of unexpected news, which could hardly be predicted. Experiments show that the accuracy of our method increases 20% compared to those of traditional methods.

The c-index is not proper for the evaluation of $t$-year predicted risks.

Science.gov (United States)

Blanche, Paul; Kattan, Michael W; Gerds, Thomas A

2018-02-16

We show that the widely used concordance index for time to event outcome is not proper when interest is in predicting a $t$-year risk of an event, for example 10-year mortality. In the situation with a fixed prediction horizon, the concordance index can be higher for a misspecified model than for a correctly specified model. Impropriety happens because the concordance index assesses the order of the event times and not the order of the event status at the prediction horizon. The time-dependent area under the receiver operating characteristic curve does not have this problem and is proper in this context.

Classification and prediction of port variables

Energy Technology Data Exchange (ETDEWEB)

Molina Serrano, B.

2016-07-01

Many variables are included in planning and management of port terminals. They can beeconomic, social, environmental and institutional. Agent needs to know relationshipbetween these variables to modify planning conditions. Use of Bayesian Networks allowsfor classifying, predicting and diagnosing these variables. Bayesian Networks allow forestimating subsequent probability of unknown variables, basing on know variables.In planning level, it means that it is not necessary to know all variables because theirrelationships are known. Agent can know interesting information about how port variablesare connected. It can be interpreted as cause-effect relationship. Bayesian Networks can beused to make optimal decisions by introduction of possible actions and utility of theirresults.In proposed methodology, a data base has been generated with more than 40 port variables.They have been classified in economic, social, environmental and institutional variables, inthe same way that smart port studies in Spanish Port System make. From this data base, anetwork has been generated using a non-cyclic conducted grafo which allows for knowingport variable relationships - parents-children relationships-. Obtained network exhibits thateconomic variables are – in cause-effect terms- cause of rest of variable typologies.Economic variables represent parent role in the most of cases. Moreover, whenenvironmental variables are known, obtained network allows for estimating subsequentprobability of social variables.It has been concluded that Bayesian Networks allow for modeling uncertainty in aprobabilistic way, even when number of variables is high as occurs in planning andmanagement of port terminals. (Author)

Prediction of melanoma metastasis by the Shields index based on lymphatic vessel density

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Metcalfe Chris

2010-05-01

Full Text Available Abstract Background Melanoma usually presents as an initial skin lesion without evidence of metastasis. A significant proportion of patients develop subsequent local, regional or distant metastasis, sometimes many years after the initial lesion was removed. The current most effective staging method to identify early regional metastasis is sentinel lymph node biopsy (SLNB, which is invasive, not without morbidity and, while improving staging, may not improve overall survival. Lymphatic density, Breslow's thickness and the presence or absence of lymphatic invasion combined has been proposed to be a prognostic index of metastasis, by Shields et al in a patient group. Methods Here we undertook a retrospective analysis of 102 malignant melanomas from patients with more than five years follow-up to evaluate the Shields' index and compare with existing indicators. Results The Shields' index accurately predicted outcome in 90% of patients with metastases and 84% without metastases. For these, the Shields index was more predictive than thickness or lymphatic density. Alternate lymphatic measurement (hot spot analysis was also effective when combined into the Shields index in a cohort of 24 patients. Conclusions These results show the Shields index, a non-invasive analysis based on immunohistochemistry of lymphatics surrounding primary lesions that can accurately predict outcome, is a simple, useful prognostic tool in malignant melanoma.

Perfusion index and plethysmographic variability index in patients with interscalene nerve catheters.

Science.gov (United States)

Sebastiani, Anne; Philippi, Larissa; Boehme, Stefan; Closhen, Dorothea; Schmidtmann, Irene; Scherhag, Anton; Markstaller, Klaus; Engelhard, Kristin; Pestel, Gunther

2012-12-01

Interscalene nerve blocks provide adequate analgesia, but there are no objective criteria for early assessment of correct catheter placement. In the present study, pulse oximetry technology was used to evaluate changes in the perfusion index (PI) in both blocked and unblocked arms, and changes in the plethysmographic variability index (PVI) were evaluated once mechanical ventilation was instituted. The PI and PVI values were assessed using a Radical-7™ finger pulse oximetry device (Masimo Corp., Irvine, CA, USA) in both arms of 30 orthopedic patients who received an interscalene catheter at least 25 min before induction of general anesthesia. Data were evaluated at baseline, on application of local anesthetics; five, ten, and 15 min after onset of interscalene nerve blocks; after induction of general anesthesia; before and after a 500 mL colloid fluid challenge; and five minutes thereafter. In the 25 patients with successful blocks, the difference between the PI values in the blocked arm and the PI values in the contralateral arm increased within five minutes of the application of the local anesthetics (P < 0.05) and increased progressively until 15 min. After induction of general anesthesia, the PI increased in the unblocked arm while it remained relatively constant in the blocked arm, thus reducing the difference in the PI. A fluid challenge resulted in a decrease in PVI values in both arms. The perfusion index increases after successful interscalene nerve blockade and may be used as an indicator for successful block placement in awake patients. The PVI values before and after a fluid challenge can be useful to detect changes in preload, and this can be performed in both blocked and unblocked arms.

Index to Predict In-hospital Mortality in Older Adults after Non-traumatic Emergency Department Intubations

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Kei Ouchi

2017-04-01

Full Text Available Introduction: Our goal was to develop and validate an index to predict in-hospital mortality in older adults after non-traumatic emergency department (ED intubations. Methods: We used Vizient administrative data from hospitalizations of 22,374 adults ≥75 years who underwent non-traumatic ED intubation from 2008–2015 at nearly 300 U.S. hospitals to develop and validate an index to predict in-hospital mortality. We randomly selected one half of participants for the development cohort and one half for the validation cohort. Considering 25 potential predictors, we developed a multivariable logistic regression model using least absolute shrinkage and selection operator method to determine factors associated with in-hospital mortality. We calculated risk scores using points derived from the final model’s beta coefficients. To evaluate calibration and discrimination of the final model, we used Hosmer-Lemeshow chi-square test and receiver-operating characteristic analysis and compared mortality by risk groups in the development and validation cohorts. Results: Death during the index hospitalization occurred in 40% of cases. The final model included six variables: history of myocardial infarction, history of cerebrovascular disease, history of metastatic cancer, age, admission diagnosis of sepsis, and admission diagnosis of stroke/ intracranial hemorrhage. Those with low-risk scores (10 had 58% risk of in-hospital mortality. The Hosmer-Lemeshow chi-square of the model was 6.47 (p=0.09, and the c-statistic was 0.62 in the validation cohort. Conclusion: The model may be useful in identifying older adults at high risk of death after ED intubation.

Prediction of Massive Transfusion in Trauma Patients with Shock Index, Modified Shock Index, and Age Shock Index

Directory of Open Access Journals (Sweden)

Cheng-Shyuan Rau

2016-07-01

Full Text Available Objectives: The shock index (SI and its derivations, the modified shock index (MSI and the age shock index (Age SI, have been used to identify trauma patients with unstable hemodynamic status. The aim of this study was to evaluate their use in predicting the requirement for massive transfusion (MT in trauma patients upon arrival at the hospital. Participants: A patient receiving transfusion of 10 or more units of packed red blood cells or whole blood within 24 h of arrival at the emergency department was defined as having received MT. Detailed data of 2490 patients hospitalized for trauma between 1 January 2009, and 31 December 2014, who had received blood transfusion within 24 h of arrival at the emergency department, were retrieved from the Trauma Registry System of a level I regional trauma center. These included 99 patients who received MT and 2391 patients who did not. Patients with incomplete registration data were excluded from the study. The two-sided Fisher exact test or Pearson chi-square test were used to compare categorical data. The unpaired Student t-test was used to analyze normally distributed continuous data, and the Mann-Whitney U-test was used to compare non-normally distributed data. Parameters including systolic blood pressure (SBP, heart rate (HR, hemoglobin level (Hb, base deficit (BD, SI, MSI, and Age SI that could provide cut-off points for predicting the patients’ probability of receiving MT were identified by the development of specific receiver operating characteristic (ROC curves. High accuracy was defined as an area under the curve (AUC of more than 0.9, moderate accuracy was defined as an AUC between 0.9 and 0.7, and low accuracy was defined as an AUC less than 0.7. Results: In addition to a significantly higher Injury Severity Score (ISS and worse outcome, the patients requiring MT presented with a significantly higher HR and lower SBP, Hb, and BD, as well as significantly increased SI, MSI, and Age SI. Among

An analysis of prediction skill of monthly mean climate variability

Energy Technology Data Exchange (ETDEWEB)

Kumar, Arun; Chen, Mingyue; Wang, Wanqiu [Climate Prediction Center, National Centers for Environmental Prediction (CPC/NCEP), Camp Springs, MD (United States)

2011-09-15

In this paper, lead-time and spatial dependence in skill for prediction of monthly mean climate variability is analyzed. The analysis is based on a set of extensive hindcasts from the Climate Forecast System at the National Centers for Environmental Prediction. The skill characteristics of initialized predictions is also compared with the AMIP simulations forced with the observed sea surface temperature (SST) to quantify the role of initial versus boundary conditions in the prediction of monthly means. The analysis is for prediction of monthly mean SST, precipitation, and 200-hPa height. The results show a rapid decay in skill with lead time for the atmospheric variables in the extratropical latitudes. Further, after a lead-time of approximately 30-40 days, the skill of monthly mean prediction is essentially a boundary forced problem, with SST anomalies in the tropical central/eastern Pacific playing a dominant role. Because of the larger contribution from the atmospheric internal variability to monthly time-averages (compared to seasonal averages), skill for monthly mean prediction associated with boundary forcing is also lower. The analysis indicates that the prospects of skillful prediction of monthly means may remain a challenging problem, and may be limited by inherent limits in predictability. (orig.)

[Evaluation of thermal comfort in a student population: predictive value of an integrated index (Fanger's predicted mean value].

Science.gov (United States)

Catenacci, G; Terzi, R; Marcaletti, G; Tringali, S

1989-01-01

Practical applications and predictive values of a thermal comfort index (Fanger's PRV) were verified on a sample school population (1236 subjects) by studying the relationships between thermal sensations (subjective analysis), determined by means of an individual questionnaire, and the values of thermal comfort index (objective analysis) obtained by calculating the PMV index individually in the subjects under study. In homogeneous conditions of metabolic expenditure rate and thermal impedence from clothing, significant differences were found between the two kinds of analyses. At 22 degrees C mean radiant and operative temperature, the PMV values averaged 0 and the percentage of subjects who experienced thermal comfort did not exceed 60%. The high level of subjects who were dissatisfied with their environmental thermal conditions confirms the doubts regarding the use of the PMV index as a predictive indicator of thermal comfort, especially considering that the negative answers were not homogeneous nor attributable to the small thermal fluctuations (less than 0.5 degree C) measured in the classrooms.

Cortical Response Variability as a Developmental Index of Selective Auditory Attention

Science.gov (United States)

Strait, Dana L.; Slater, Jessica; Abecassis, Victor; Kraus, Nina

2014-01-01

Attention induces synchronicity in neuronal firing for the encoding of a given stimulus at the exclusion of others. Recently, we reported decreased variability in scalp-recorded cortical evoked potentials to attended compared with ignored speech in adults. Here we aimed to determine the developmental time course for this neural index of auditory…

AIR POLLUITON INDEX PREDICTION USING MULTIPLE NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Zainal Ahmad

2017-05-01

Full Text Available Air quality monitoring and forecasting tools are necessary for the purpose of taking precautionary measures against air pollution, such as reducing the effect of a predicted air pollution peak on the surrounding population and ecosystem. In this study a single Feed-forward Artificial Neural Network (FANN is shown to be able to predict the Air Pollution Index (API with a Mean Squared Error (MSE and coefficient determination, R2, of 0.1856 and 0.7950 respectively. However, due to the non-robust nature of single FANN, a selective combination of Multiple Neural Networks (MNN is introduced using backward elimination and a forward selection method. The results show that both selective combination methods can improve the robustness and performance of the API prediction with the MSE and R2 of 0.1614 and 0.8210 respectively. This clearly shows that it is possible to reduce the number of networks combined in MNN for API prediction, without losses of any information in terms of the performance of the final API prediction model.

Predictive value of European Scleroderma Group Activity Index in an early scleroderma cohort.

Science.gov (United States)

Nevskaya, Tatiana; Baron, Murray; Pope, Janet E

2017-07-01

To estimate the effect of disease activity, as measured by the European Scleroderma Research Group Activity Index (EScSG-AI), on the risk of subsequent organ damage in a large systemic sclerosis (SSc) cohort. Of 421 SSc patients from the Canadian Scleroderma Research Group database with disease duration of ⩽ 3 years, 197 who had no evidence of end-stage organ damage initially and available 3 year follow-up were included. Disease activity was assessed by the EScSG-AI with two variability measures: the adjusted mean EScSG-AI (the area under the curve of the EScSG-AI over the observation period) and persistently active disease/flare. Outcomes were based on the Medsger severity scale and included accrual of a new severity score (Δ ⩾ 1) overall and within organ systems or reaching a significant level of deterioration in health status. After adjustment for covariates, the adjusted mean EScSG-AI was the most consistent predictor of risk across the study outcomes over 3 years in dcSSc: disease progression defined as Δ ⩾ 1 in any major internal organ, significant decline in forced vital capacity and diffusing capacity of carbon monoxide, severity of visceral disease and HAQ Disability Index worsening. In multivariate analysis, progression of lung disease was predicted solely by adjusted mean EScSG-AI, while the severity of lung disease was predicted the adjusted mean EScSG-AI, older age, modified Rodnan skin score (mRSS) and initial severity. The EScSG-AI was associated with patient- and physician-assessed measures of health status and overpowered the mRSS in predicting disease outcomes. Disease activity burden quantified with the adjusted mean EScSG-AI predicted the risk of deterioration in health status and severe organ involvement in dcSSc. The EScSG-AI is more responsive when done repeatedly and averaged. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email

Fluorescence microscopy point spread function model accounting for aberrations due to refractive index variability within a specimen.

Science.gov (United States)

Ghosh, Sreya; Preza, Chrysanthe

2015-07-01

A three-dimensional (3-D) point spread function (PSF) model for wide-field fluorescence microscopy, suitable for imaging samples with variable refractive index (RI) in multilayered media, is presented. This PSF model is a key component for accurate 3-D image restoration of thick biological samples, such as lung tissue. Microscope- and specimen-derived parameters are combined with a rigorous vectorial formulation to obtain a new PSF model that accounts for additional aberrations due to specimen RI variability. Experimental evaluation and verification of the PSF model was accomplished using images from 175-nm fluorescent beads in a controlled test sample. Fundamental experimental validation of the advantage of using improved PSFs in depth-variant restoration was accomplished by restoring experimental data from beads (6  μm in diameter) mounted in a sample with RI variation. In the investigated study, improvement in restoration accuracy in the range of 18 to 35% was observed when PSFs from the proposed model were used over restoration using PSFs from an existing model. The new PSF model was further validated by showing that its prediction compares to an experimental PSF (determined from 175-nm beads located below a thick rat lung slice) with a 42% improved accuracy over the current PSF model prediction.

Starch digestibility and predicted glycemic index of fried sweet potato cultivars

Directory of Open Access Journals (Sweden)

Amaka Odenigbo

2012-07-01

Full Text Available Background: Sweet potato (Ipomoea batatas L. is a very rich source of starch. There is increased interest in starch digestibility and the prevention and management of metabolic diseases.Objective: The aim of this study was to evaluate the levels of starch fractions and predicted glycemic index of different cultivars of sweet potato. Material and Method: French fries produced from five cultivars of sweet potato (‘Ginseng Red’, ‘Beauregard’, ‘White Travis’, ‘Georgia Jet clone #2010’ and ‘Georgia Jet’ were used. The level of total starch (TS, resistant starch (RS, digestible starch (DS, and starch digestion index starch digestion index in the samples were evaluated. In vitro starch hydrolysis at 30, 90, and 120 min were determined enzymatically for calculation of rapidly digestible starch (RDS, predicted glycemic index (pGI and slowly digestible starch (SDS respectively. Results: The RS content in all samples had an inversely significant correlation with pGI (-0.52; P<0.05 while RDS had positive and significant influence on both pGI (r=0.55; P<0.05 and SDI (r= 0.94; P<0.01. ‘White Travis’ and ‘Ginseng Red’ had higher levels of beneficial starch fractions (RS and SDS with low pGI and starch digestion Index (SDI, despite their higher TS content. Generally, all the cultivars had products with low to moderate GI values. Conclusion: The glycemic index of these food products highlights the health promoting characteristics of sweet potato cultivars.

Model Predictive Control of a Nonlinear System with Known Scheduling Variable

DEFF Research Database (Denmark)

Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

2012-01-01

Model predictive control (MPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Consequently...... the control problem of the nonlinear system is simplied into a quadratic programming. Wind turbine is chosen as the case study and we choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon....

Assessment of the de Hirsch Predictive Index Tests of Reading Failure.

Science.gov (United States)

Askov, Warren; And Others

The predictive validity and the general usability of a battery of 10 tests reported by de Hirsch, Jansky, and Langford, the de Hirsch Predictive Index Tests of reading failure, were examined. The de Hirsch battery was administered to 433 kindergarten children in six public schools. When the pupils entered first grade, the Metropolitan Readiness…

Predicting sun protection behaviors using protection motivation variables.

Science.gov (United States)

Ch'ng, Joanne W M; Glendon, A Ian

2014-04-01

Protection motivation theory components were used to predict sun protection behaviors (SPBs) using four outcome measures: typical reported behaviors, previous reported behaviors, current sunscreen use as determined by interview, and current observed behaviors (clothing worn) to control for common method bias. Sampled from two SE Queensland public beaches during summer, 199 participants aged 18-29 years completed a questionnaire measuring perceived severity, perceived vulnerability, response efficacy, response costs, and protection motivation (PM). Personal perceived risk (similar to threat appraisal) and response likelihood (similar to coping appraisal) were derived from their respective PM components. Protection motivation predicted all four SPB criterion variables. Personal perceived risk and response likelihood predicted protection motivation. Protection motivation completely mediated the effect of response likelihood on all four criterion variables. Alternative models are considered. Strengths and limitations of the study are outlined and suggestions made for future research.

Heart rate variability analysis as an index of emotion regulation processes: interest of the Analgesia Nociception Index (ANI).

Science.gov (United States)

De Jonckheere, J; Rommel, D; Nandrino, J L; Jeanne, M; Logier, R

2012-01-01

Autonomic Nervous System (ANS) variations are strongly influence by emotion regulation processes. Indeed, emotional stimuli are at the origin of an activation of the ANS and the way an individual pass from a state of alert in the case of emotional situation to a state of calm is closely coupled with the ANS flexibility. We have previously described and developed an Analgesia Nociception Index (ANI) for real time pain measurement during surgical procedure under general anesthesia. This index, based on heart rate variability analysis, constitutes a measure of parasympathetic tone and can be used in several other environments. In this paper, we hypothesized that such an index could be used as a tool to investigate the processes of emotional regulation of a human subject. To test this hypothesis, we analyzed ANI's response to a negative emotional stimulus. This analysis showed that the index decreases during the emotion induction phase and returns to its baseline after 2 minutes. This result confirms that ANI could be a good indicator of parasympathetic changes in emotional situation.

Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

Science.gov (United States)

Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

2017-07-01

To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

Prediction of Indian Summer-Monsoon Onset Variability: A Season in Advance.

Science.gov (United States)

Pradhan, Maheswar; Rao, A Suryachandra; Srivastava, Ankur; Dakate, Ashish; Salunke, Kiran; Shameera, K S

2017-10-27

Monsoon onset is an inherent transient phenomenon of Indian Summer Monsoon and it was never envisaged that this transience can be predicted at long lead times. Though onset is precipitous, its variability exhibits strong teleconnections with large scale forcing such as ENSO and IOD and hence may be predictable. Despite of the tremendous skill achieved by the state-of-the-art models in predicting such large scale processes, the prediction of monsoon onset variability by the models is still limited to just 2-3 weeks in advance. Using an objective definition of onset in a global coupled ocean-atmosphere model, it is shown that the skillful prediction of onset variability is feasible under seasonal prediction framework. The better representations/simulations of not only the large scale processes but also the synoptic and intraseasonal features during the evolution of monsoon onset are the comprehensions behind skillful simulation of monsoon onset variability. The changes observed in convection, tropospheric circulation and moisture availability prior to and after the onset are evidenced in model simulations, which resulted in high hit rate of early/delay in monsoon onset in the high resolution model.

Interpreting weightings of the peer assessment rating index and the discrepancy index across contexts on Chinese patients.

Science.gov (United States)

Liu, Siqi; Oh, Heesoo; Chambers, David William; Xu, Tianmin; Baumrind, Sheldon

2018-04-06

Determine optimal weightings of Peer Assessment Rating (PAR) index and Discrepancy Index (DI) for malocclusion severity assessment in Chinese orthodontic patients. Sixty-nine Chinese orthodontists assessed a full set of pre-treatment records from a stratified random sample of 120 subjects gathered from six university orthodontic centres. Using professional judgment as the outcome variable, multiple regression analyses were performed to derive customized weighting systems for the PAR index and DI, for all subjects and each Angle classification subgroup. Professional judgment was consistent, with an Intraclass Correlation Coefficient (ICC) of 0.995. The PAR index or DI can be reliably measured, with ICC = 0.959 and 0.990, respectively. The predictive accuracy of PAR index was greatly improved by the Chinese weighting process (from r = 0.431 to r = 0.788) with almost equal distribution in each Angle classification subgroup. The Chinese-weighted DI showed a higher predictive accuracy, at P = 0.01, compared with the PAR index (r = 0.851 versus r = 0.788). A better performance was found in the Class II group (r = 0.890) when compared to Class I (r = 0.736) and III (r = 0.785) groups. The Chinese-weighted PAR index and DI were capable of predicting 62 per cent and 73 per cent of total variance in the professional judgment of malocclusion severity in Chinese patients. Differential prediction across Angle classifications merits attention since different weighting formulas were found.

Body mass index predicts risk for complications from transtemporal cerebellopontine angle surgery.

Science.gov (United States)

Mantravadi, Avinash V; Leonetti, John P; Burgette, Ryan; Pontikis, George; Marzo, Sam J; Anderson, Douglas

2013-03-01

To determine the relationship between body mass index (BMI) and risk for specific complications from transtemporal cerebellopontine angle (CPA) surgery for nonmalignant disease. Case series with chart review. Tertiary-care academic hospital. Retrospective review of 134 consecutive patients undergoing transtemporal cerebellopontine angle surgery for nonmalignant disease from 2009 to 2011. Data were collected regarding demographics, body mass index, intraoperative details, hospital stay, and complications including cerebrospinal fluid leak, wound complications, and brachial plexopathy. One hundred thirty-four patients were analyzed with a mean preoperative body mass index of 28.58. Statistical analysis demonstrated a significant difference in body mass index between patients with a postoperative cerebrospinal fluid leak and those without (P = .04), as well as a similar significant difference between those experiencing postoperative brachial plexopathy and those with no such complication (P = .03). Logistical regression analysis confirmed that body mass index is significant in predicting both postoperative cerebrospinal fluid leak (P = .004; odds ratio, 1.10) and brachial plexopathy (P = .04; odds ratio, 1.07). Elevated body mass index was not significant in predicting wound complications or increased hospital stay beyond postoperative day 3. Risk of cerebrospinal fluid leak and brachial plexopathy is increased in patients with elevated body mass index undergoing surgery of the cerebellopontine angle. Consideration should be given to preoperative optimization via dietary and lifestyle modifications as well as intraoperative somatosensory evoked potential monitoring of the brachial plexus to decrease these risks.

Predicting nosocomial lower respiratory tract infections by a risk index based system

NARCIS (Netherlands)

Chen, Yong; Shan, Xue; Zhao, Jingya; Han, Xuelin; Tian, Shuguang; Chen, Fangyan; Su, Xueting; Sun, Yansong; Huang, Liuyu; Grundmann, Hajo; Wang, Hongyuan; Han, Li

2017-01-01

Although belonging to one of the most common type of nosocomial infection, there was currently no simple prediction model for lower respiratory tract infections (LRTIs). This study aims to develop a risk index based system for predicting nosocomial LRTIs based on data from a large point-prevalence

Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity.

Science.gov (United States)

Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

2011-10-31

The calibration performance of partial least squares for one response variable (PLS1) can be improved by elimination of uninformative variables. Many methods are based on so-called predictive variable properties, which are functions of various PLS-model parameters, and which may change during the variable reduction process. In these methods variable reduction is made on the variables ranked in descending order for a given variable property. The methods start with full spectrum modelling. Iteratively, until a specified number of remaining variables is reached, the variable with the smallest property value is eliminated; a new PLS model is calculated, followed by a renewed ranking of the variables. The Stepwise Variable Reduction methods using Predictive-Property-Ranked Variables are denoted as SVR-PPRV. In the existing SVR-PPRV methods the PLS model complexity is kept constant during the variable reduction process. In this study, three new SVR-PPRV methods are proposed, in which a possibility for decreasing the PLS model complexity during the variable reduction process is build in. Therefore we denote our methods as PPRVR-CAM methods (Predictive-Property-Ranked Variable Reduction with Complexity Adapted Models). The selective and predictive abilities of the new methods are investigated and tested, using the absolute PLS regression coefficients as predictive property. They were compared with two modifications of existing SVR-PPRV methods (with constant PLS model complexity) and with two reference methods: uninformative variable elimination followed by either a genetic algorithm for PLS (UVE-GA-PLS) or an interval PLS (UVE-iPLS). The performance of the methods is investigated in conjunction with two data sets from near-infrared sources (NIR) and one simulated set. The selective and predictive performances of the variable reduction methods are compared statistically using the Wilcoxon signed rank test. The three newly developed PPRVR-CAM methods were able to retain

The importance of histopathological and clinical variables in predicting the evolution of colon cancer.

Science.gov (United States)

Diculescu, Mircea; Iacob, Răzvan; Iacob, Speranţa; Croitoru, Adina; Becheanu, Gabriel; Popeneciu, Valentin

2002-09-01

It has been a consensus that prognostic factors should always be taken into account before planning treatment in colorectal cancer. A 5 year prospective study was conducted, in order to assess the importance of several histopathological and clinical prognostic variables in the prediction of evolution in colon cancer. Some of the factors included in the analysis are still subject to dispute by different authors. 46 of 53 screened patients qualified to enter the study and underwent a potentially curative resection of the tumor, followed, when necessary, by adjuvant chemotherapy. Univariate and multivariate analyses were carried out in order to identify independent prognostic indicators. The endpoint of the study was considered the recurrence of the tumor or the detection of metastases. 65.2% of the patients had a good evolution during the follow up period. Multivariate survival analysis performed by Cox proportional hazard model identified 3 independent prognostic factors: Dukes stage (p = 0.00002), the grade of differentiation (p = 0.0009) and the weight loss index, representing the weight loss of the patient divided by the number of months when it was actually lost (p = 0.02). Age under 40 years, sex, microscopic aspect of the tumor, tumor location, anemia degree were not identified by our analysis as having prognostic importance. Histopathological factors continue to be the most valuable source of information regarding the possible evolution of patients with colorectal cancer. Individual clinical symptoms or biological parameters such as erytrocyte sedimentation rate or hemoglobin level are of little or no prognostic value. More research is required relating to the impact of a performance status index (which could include also weight loss index) as another reliable prognostic variable.

Predicting long-term streamflow variability in moist eucalypt forests using forest growth models and a sapwood area index

Science.gov (United States)

Jaskierniak, D.; Kuczera, G.; Benyon, R.

2016-04-01

A major challenge in surface hydrology involves predicting streamflow in ungauged catchments with heterogeneous vegetation and spatiotemporally varying evapotranspiration (ET) rates. We present a top-down approach for quantifying the influence of broad-scale changes in forest structure on ET and hence streamflow. Across three catchments between 18 and 100 km2 in size and with regenerating Eucalyptus regnans and E. delegatensis forest, we demonstrate how variation in ET can be mapped in space and over time using LiDAR data and commonly available forest inventory data. The model scales plot-level sapwood area (SA) to the catchment-level using basal area (BA) and tree stocking density (N) estimates in forest growth models. The SA estimates over a 69 year regeneration period are used in a relationship between SA and vegetation induced streamflow loss (L) to predict annual streamflow (Q) with annual rainfall (P) estimates. Without calibrating P, BA, N, SA, and L to Q data, we predict annual Q with R2 between 0.68 and 0.75 and Nash Sutcliffe efficiency (NSE) between 0.44 and 0.48. To remove bias, the model was extended to allow for runoff carry-over into the following year as well as minor correction to rainfall bias, which produced R2 values between 0.72 and 0.79, and NSE between 0.70 and 0.79. The model under-predicts streamflow during drought periods as it lacks representation of ecohydrological processes that reduce L with either reduced growth rates or rainfall interception during drought. Refining the relationship between sapwood thickness and forest inventory variables is likely to further improve results.

Development and validation of an ICD-10-based disability predictive index for patients admitted to hospitals with trauma.

Science.gov (United States)

Wada, Tomoki; Yasunaga, Hideo; Yamana, Hayato; Matsui, Hiroki; Fushimi, Kiyohide; Morimura, Naoto

2018-03-01

There was no established disability predictive measurement for patients with trauma that could be used in administrative claims databases. The aim of the present study was to develop and validate a diagnosis-based disability predictive index for severe physical disability at discharge using the International Classification of Diseases, 10th revision (ICD-10) coding. This retrospective observational study used the Diagnosis Procedure Combination database in Japan. Patients who were admitted to hospitals with trauma and discharged alive from 01 April 2010 to 31 March 2015 were included. Pediatric patients under 15 years old were excluded. Data for patients admitted to hospitals from 01 April 2010 to 31 March 2013 was used for development of a disability predictive index (derivation cohort), while data for patients admitted to hospitals from 01 April 2013 to 31 March 2015 was used for the internal validation (validation cohort). The outcome of interest was severe physical disability defined as the Barthel Index score of predictive index for each patient was defined as the sum of the scores. The predictive performance of the index was validated using the receiver operating characteristic curve analysis in the validation cohort. The derivation cohort included 1,475,158 patients, while the validation cohort included 939,659 patients. Of the 939,659 patients, 235,382 (25.0%) were discharged with severe physical disability. The c-statistics of the disability predictive index was 0.795 (95% confidence interval [CI] 0.794-0.795), while that of a model using the disability predictive index and patient baseline characteristics was 0.856 (95% CI 0.855-0.857). Severe physical disability at discharge may be well predicted with patient age, sex, CCI score, and the diagnosis-based disability predictive index in patients admitted to hospitals with trauma. Copyright © 2018 Elsevier Ltd. All rights reserved.

VizieR Online Data Catalog: AAVSO International Variable Star Index VSX (Watson+, 2006-2014)

Science.gov (United States)

Watson, C.; Henden, A. A.; Price, A.

2018-05-01

This file contains Galactic stars known or suspected to be variable. It lists all stars that have an entry in the AAVSO International Variable Star Index (VSX; http://www.aavso.org/vsx). The database consisted initially of the General Catalogue of Variable Stars (GCVS) and the New Catalogue of Suspected Variables (NSV) and was then supplemented with a large number of variable star catalogues, as well as individual variable star discoveries or variables found in the literature. Effort has also been invested to update the entries with the latest information regarding position, type and period and to remove duplicates. The VSX database is being continually updated and maintained. For historical reasons some objects outside of the Galaxy have been included. (3 data files).

Predictive modeling and reducing cyclic variability in autoignition engines

Science.gov (United States)

Hellstrom, Erik; Stefanopoulou, Anna; Jiang, Li; Larimore, Jacob

2016-08-30

Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.

Estimating Search Engine Index Size Variability

DEFF Research Database (Denmark)

Van den Bosch, Antal; Bogers, Toine; De Kunder, Maurice

2016-01-01

One of the determining factors of the quality of Web search engines is the size of their index. In addition to its influence on search result quality, the size of the indexed Web can also tell us something about which parts of the WWW are directly accessible to the everyday user. We propose a novel...... method of estimating the size of a Web search engine’s index by extrapolating from document frequencies of words observed in a large static corpus of Web pages. In addition, we provide a unique longitudinal perspective on the size of Google and Bing’s indices over a nine-year period, from March 2006...... until January 2015. We find that index size estimates of these two search engines tend to vary dramatically over time, with Google generally possessing a larger index than Bing. This result raises doubts about the reliability of previous one-off estimates of the size of the indexed Web. We find...

Predictability and Variability of Wave and Wind

DEFF Research Database (Denmark)

Chozas, Julia Fernandez; Kofoed, Jens Peter; Sørensen, Hans Christian

This project covers two fields of study: a) Wave energy predictability and electricity markets. b) Variability of the power output of WECs in diversified systems : diversified renewable systems with wave and offshore wind production. See page 2-4 in the report for a executive summery....

Spatial Variability of Soil-Water Storage in the Southern Sierra Critical Zone Observatory: Measurement and Prediction

Science.gov (United States)

Oroza, C.; Bales, R. C.; Zheng, Z.; Glaser, S. D.

2017-12-01

Predicting the spatial distribution of soil moisture in mountain environments is confounded by multiple factors, including complex topography, spatial variably of soil texture, sub-surface flow paths, and snow-soil interactions. While remote-sensing tools such as passive-microwave monitoring can measure spatial variability of soil moisture, they only capture near-surface soil layers. Large-scale sensor networks are increasingly providing soil-moisture measurements at high temporal resolution across a broader range of depths than are accessible from remote sensing. It may be possible to combine these in-situ measurements with high-resolution LIDAR topography and canopy cover to estimate the spatial distribution of soil moisture at high spatial resolution at multiple depths. We study the feasibility of this approach using six years (2009-2014) of daily volumetric water content measurements at 10-, 30-, and 60-cm depths from the Southern Sierra Critical Zone Observatory. A non-parametric, multivariate regression algorithm, Random Forest, was used to predict the spatial distribution of depth-integrated soil-water storage, based on the in-situ measurements and a combination of node attributes (topographic wetness, northness, elevation, soil texture, and location with respect to canopy cover). We observe predictable patterns of predictor accuracy and independent variable ranking during the six-year study period. Predictor accuracy is highest during the snow-cover and early recession periods but declines during the dry period. Soil texture has consistently high feature importance. Other landscape attributes exhibit seasonal trends: northness peaks during the wet-up period, and elevation and topographic-wetness index peak during the recession and dry period, respectively.

Flood Risk Index Assessment in Johor River Basin

International Nuclear Information System (INIS)

Ahmad Shakir Mohd Saudi; Hafizan Juahir; Azman Azid; Fazureen Azaman; Ahmad Shakir Mohd Saudi

2015-01-01

This study is focusing on constructing the flood risk index in the Johor river basin. The application of statistical methods such as factor analysis (FA), statistical process control (SPC) and artificial neural network (ANN) had revealed the most efficient flood risk index. The result in FA was water level has correlation coefficient of 0.738 and the most practicable variable to be used for the warning alert system. The upper control limits (UCL) for the water level in the river basin Johor is 4.423 m and the risk index for the water level has been set by this method consisting of 0-100.The accuracy of prediction has been evaluated by using ANN and the accuracy of the test result was R"2 = 0.96408 with RMSE= 2.5736. The future prediction for UCL in Johor river basin has been predicted and the value was 3.75 m. This model can shows the current and future prediction for flood risk index in the Johor river basin and can help local authorities for flood control and prevention of the state of Johor. (author)

Can bispectral index or auditory evoked potential index predict implicit memory during propofol-induced sedation?

Science.gov (United States)

Wang, Yun; Yue, Yun; Sun, Yong-hai; Wu, An-shi

2006-06-05

Some patients still suffer from implicit memory of intraoperative events under adequate depth of anaesthesia. The elimination of implicit memory should be a necessary aim of clinical general anaesthesia. However, implicit memory cannot be tested during anaesthesia yet. We propose bispectral index (BIS) and auditory evoked potential index (AEPI), as predictors of implicit memory during anaesthesia. Thirty-six patients were equally divided into 3 groups according to the Observer's Assessment of Alertness/Sedation Score: A, level 3; B, level 2; and C, level 1. Every patient was given the first auditory stimulus before sedation. Then every patient received the second auditory stimulus after the target level of sedation had been reached. BIS and AEPI were monitored before and after the second auditory stimulus presentation. Four hours later, the inclusion test and exclusion test were performed on the ward using process dissociation procedure and the scores of implicit memory estimated. In groups A and B but not C, implicit memory estimates were statistically greater than zero (P memory scores in group A did not differ significantly from those in group B (P > 0.05). Implicit memory scores correlated with BIS and AEPI (P AEPI. The 95% cutoff points of BIS and AEPI for predicting implicit memory are 47 and 28, respectively. Implicit memory does not disappear until the depth of sedation increases to level 1 of OAA/S score. Implicit memory scores correlate well with BIS and AEPI during sedation. BIS is a better index for predicting implicit memory than AEPI during propofol induced sedation.

Optimal no-go theorem on hidden-variable predictions of effect expectations

Science.gov (United States)

Blass, Andreas; Gurevich, Yuri

2018-03-01

No-go theorems prove that, under reasonable assumptions, classical hidden-variable theories cannot reproduce the predictions of quantum mechanics. Traditional no-go theorems proved that hidden-variable theories cannot predict correctly the values of observables. Recent expectation no-go theorems prove that hidden-variable theories cannot predict the expectations of observables. We prove the strongest expectation-focused no-go theorem to date. It is optimal in the sense that the natural weakenings of the assumptions and the natural strengthenings of the conclusion make the theorem fail. The literature on expectation no-go theorems strongly suggests that the expectation-focused approach is more general than the value-focused one. We establish that the expectation approach is not more general.

Novel Radiobiological Gamma Index for Evaluation of 3-Dimensional Predicted Dose Distribution

Energy Technology Data Exchange (ETDEWEB)

Sumida, Iori, E-mail: sumida@radonc.med.osaka-u.ac.jp [Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka (Japan); Yamaguchi, Hajime; Kizaki, Hisao; Aboshi, Keiko; Tsujii, Mari; Yoshikawa, Nobuhiko; Yamada, Yuji [Department of Radiation Oncology, NTT West Osaka Hospital, Osaka (Japan); Suzuki, Osamu; Seo, Yuji [Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka (Japan); Isohashi, Fumiaki [Department of Radiation Oncology, NTT West Osaka Hospital, Osaka (Japan); Yoshioka, Yasuo [Department of Radiation Oncology, Osaka University Graduate School of Medicine, Osaka (Japan); Ogawa, Kazuhiko [Department of Radiation Oncology, NTT West Osaka Hospital, Osaka (Japan)

2015-07-15

Purpose: To propose a gamma index-based dose evaluation index that integrates the radiobiological parameters of tumor control (TCP) and normal tissue complication probabilities (NTCP). Methods and Materials: Fifteen prostate and head and neck (H&N) cancer patients received intensity modulated radiation therapy. Before treatment, patient-specific quality assurance was conducted via beam-by-beam analysis, and beam-specific dose error distributions were generated. The predicted 3-dimensional (3D) dose distribution was calculated by back-projection of relative dose error distribution per beam. A 3D gamma analysis of different organs (prostate: clinical [CTV] and planned target volumes [PTV], rectum, bladder, femoral heads; H&N: gross tumor volume [GTV], CTV, spinal cord, brain stem, both parotids) was performed using predicted and planned dose distributions under 2%/2 mm tolerance and physical gamma passing rate was calculated. TCP and NTCP values were calculated for voxels with physical gamma indices (PGI) >1. We propose a new radiobiological gamma index (RGI) to quantify the radiobiological effects of TCP and NTCP and calculate radiobiological gamma passing rates. Results: The mean RGI gamma passing rates for prostate cases were significantly different compared with those of PGI (P<.03–.001). The mean RGI gamma passing rates for H&N cases (except for GTV) were significantly different compared with those of PGI (P<.001). Differences in gamma passing rates between PGI and RGI were due to dose differences between the planned and predicted dose distributions. Radiobiological gamma distribution was visualized to identify areas where the dose was radiobiologically important. Conclusions: RGI was proposed to integrate radiobiological effects into PGI. This index would assist physicians and medical physicists not only in physical evaluations of treatment delivery accuracy, but also in clinical evaluations of predicted dose distribution.

Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

Science.gov (United States)

Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

2015-04-01

Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for

Six-SOMAmer Index Relating to Immune, Protease and Angiogenic Functions Predicts Progression in IPF.

Directory of Open Access Journals (Sweden)

Shanna L Ashley

Full Text Available Biomarkers in easily accessible compartments like peripheral blood that can predict disease progression in idiopathic pulmonary fibrosis (IPF would be clinically useful regarding clinical trial participation or treatment decisions for patients. In this study, we used unbiased proteomics to identify relevant disease progression biomarkers in IPF.Plasma from IPF patients was measured using an 1129 analyte slow off-rate modified aptamer (SOMAmer array, and patient outcomes were followed over the next 80 weeks. Receiver operating characteristic (ROC curves evaluated sensitivity and specificity for levels of each biomarker and estimated area under the curve (AUC when prognostic biomarker thresholds were used to predict disease progression. Both logistic and Cox regression models advised biomarker selection for a composite disease progression index; index biomarkers were weighted via expected progression-free days lost during follow-up with a biomarker on the unfavorable side of the threshold.A six-analyte index, scaled 0 to 11, composed of markers of immune function, proteolysis and angiogenesis [high levels of ficolin-2 (FCN2, cathepsin-S (Cath-S, legumain (LGMN and soluble vascular endothelial growth factor receptor 2 (VEGFsR2, but low levels of inducible T cell costimulator (ICOS or trypsin 3 (TRY3] predicted better progression-free survival in IPF with a ROC AUC of 0.91. An index score ≥ 3 (group ≥ 2 was strongly associated with IPF progression after adjustment for age, gender, smoking status, immunomodulation, forced vital capacity % predicted and diffusing capacity for carbon monoxide % predicted (HR 16.8, 95% CI 2.2-126.7, P = 0.006.This index, derived from the largest proteomic analysis of IPF plasma samples to date, could be useful for clinical decision making in IPF, and the identified analytes suggest biological processes that may promote disease progression.

PDW Index - A Simple Model for the Prediction of Liver Fibrosis in Chronic Viral Hepatitis

International Nuclear Information System (INIS)

Ashraf, S.; Ali, N.

2013-01-01

Objectives: To assess the accuracy of platelets, platelet morphological parameters, mean platelet volume(MPV) and platelet distribution width, (PDW) to diagnose advanced fibrosis. Study Design: Validation study. Place and Duration of Study: Combined Military Hospital, Malir, from Jun 2008 to Jun 2009. Patients and Methods: Simple laboratory tests, aspartate aminotransferase (AST) alanine aminotransferase (ALT) platelet count and platelet morphological parameters were measured in 91 chronic viral hepatitis patients. All patients had liver biopsy performed. A new index, PDW index was derived to detect the opposing effects of liver fibrosis on platelet count, MPV, and PDW. The predictive value of the index for advanced fibrosis (F3-F4) was assessed through descriptive statistics and area under the ROC curves. Results: Two cut-offs were chosen to qualify different stages of fibrosis. A value of > 8.00 predicted advanced fibrosis, F3-F4, with a specificity of 94% and positive predictive value of 78%. A value of < 6.00 ruled out advanced fibrosis with a negative predictive value of 93% and a sensitivity of 82%. The area under the ROC curve for advanced fibrosis was 0.840. PDW Index values outside of these cut-offs correctly classified 60% of patients. Conclusion: A simple index comprising platelet as only parameters have high diagnostic value for the advanced stages of fibrosis. (author)

Prediction of immediate postoperative pain using the analgesia/nociception index: a prospective observational study.

Science.gov (United States)

Boselli, E; Bouvet, L; Bégou, G; Dabouz, R; Davidson, J; Deloste, J-Y; Rahali, N; Zadam, A; Allaouchiche, B

2014-04-01

The analgesia/nociception index (ANI) is derived from heart rate variability, ranging from 0 (maximal nociception) to 100 (maximal analgesia), to reflect the analgesia/nociception balance during general anaesthesia. This should be correlated with immediate postoperative pain in the post-anaesthesia care unit (PACU). The aim of this study was to evaluate the performance of ANI measured at arousal from general anaesthesia to predict immediate postoperative pain on arrival in PACU. Two hundred patients undergoing ear, nose, and throat or lower limb orthopaedic surgery with general anaesthesia using an inhalational agent and remifentanil were included in this prospective observational study. The ANI was measured immediately before tracheal extubation and pain intensity was assessed within 10 min of arrival in PACU using a 0-10 numerical rating scale (NRS). The relationship between ANI and NRS was assessed using linear regression. A receiver-operating characteristic (ROC) curve was used to evaluate the performance of ANI to predict NRS>3. A negative linear relationship was observed between ANI immediately before extubation and NRS on arrival in PACU. Using a threshold of 3 were both 86% with 92% negative predictive value, corresponding to an area under the ROC curve of 0.89. The measurement of ANI immediately before extubation after inhalation-remifentanil anaesthesia was significantly associated with pain intensity on arrival in PACU. The performance of ANI for the prediction of immediate postoperative pain is good and may assist physicians in optimizing acute pain management. ClinicalTrials.gov NCT01796249.

Droughts in Amazonia: Spatiotemporal Variability, Teleconnections, and Seasonal Predictions

Science.gov (United States)

Lima, Carlos H. R.; AghaKouchak, Amir

2017-12-01

Most Amazonia drought studies have focused on rainfall deficits and their impact on river discharges, while the analysis of other important driver variables, such as temperature and soil moisture, has attracted less attention. Here we try to better understand the spatiotemporal dynamics of Amazonia droughts and associated climate teleconnections as characterized by the Palmer Drought Severity Index (PDSI), which integrates information from rainfall deficit, temperature anomalies, and soil moisture capacity. The results reveal that Amazonia droughts are most related to one dominant pattern across the entire region, followed by two seesaw kind of patterns: north-south and east-west. The main two modes are correlated with sea surface temperature (SST) anomalies in the tropical Pacific and Atlantic oceans. The teleconnections associated with global SST are then used to build a seasonal forecast model for PDSI over Amazonia based on predictors obtained from a sparse canonical correlation analysis approach. A unique feature of the presented drought prediction method is using only a few number of predictors to avoid excessive noise in the predictor space. Cross-validated results show correlations between observed and predicted spatial average PDSI up to 0.60 and 0.45 for lead times of 5 and 9 months, respectively. To the best of our knowledge, this is the first study in the region that, based on cross-validation results, leads to appreciable forecast skills for lead times beyond 4 months. This is a step forward in better understanding the dynamics of Amazonia droughts and improving risk assessment and management, through improved drought forecasting.

Association of Body Mass Index with Depression, Anxiety and Suicide-An Instrumental Variable Analysis of the HUNT Study.

Directory of Open Access Journals (Sweden)

Johan Håkon Bjørngaard

Full Text Available While high body mass index is associated with an increased risk of depression and anxiety, cumulative evidence indicates that it is a protective factor for suicide. The associations from conventional observational studies of body mass index with mental health outcomes are likely to be influenced by reverse causality or confounding by ill-health. In the present study, we investigated the associations between offspring body mass index and parental anxiety, depression and suicide in order to avoid problems with reverse causality and confounding by ill-health.We used data from 32,457 mother-offspring and 27,753 father-offspring pairs from the Norwegian HUNT-study. Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale and suicide death from national registers. Associations between offspring and own body mass index and symptoms of anxiety and depression and suicide mortality were estimated using logistic and Cox regression. Causal effect estimates were estimated with a two sample instrument variable approach using offspring body mass index as an instrument for parental body mass index.Both own and offspring body mass index were positively associated with depression, while the results did not indicate any substantial association between body mass index and anxiety. Although precision was low, suicide mortality was inversely associated with own body mass index and the results from the analysis using offspring body mass index supported these results. Adjusted odds ratios per standard deviation body mass index from the instrumental variable analysis were 1.22 (95% CI: 1.05, 1.43 for depression, 1.10 (95% CI: 0.95, 1.27 for anxiety, and the instrumental variable estimated hazard ratios for suicide was 0.69 (95% CI: 0.30, 1.63.The present study's results indicate that suicide mortality is inversely associated with body mass index. We also found support for a positive association between body mass index and depression, but not

Leg pain and psychological variables predict outcome 2-3 years after lumbar fusion surgery.

Science.gov (United States)

Abbott, Allan D; Tyni-Lenné, Raija; Hedlund, Rune

2011-10-01

Prediction studies testing a thorough range of psychological variables in addition to demographic, work-related and clinical variables are lacking in lumbar fusion surgery research. This prospective cohort study aimed at examining predictions of functional disability, back pain and health-related quality of life (HRQOL) 2-3 years after lumbar fusion by regressing nonlinear relations in a multivariate predictive model of pre-surgical variables. Before and 2-3 years after lumbar fusion surgery, patients completed measures investigating demographics, work-related variables, clinical variables, functional self-efficacy, outcome expectancy, fear of movement/(re)injury, mental health and pain coping. Categorical regression with optimal scaling transformation, elastic net regularization and bootstrapping were used to investigate predictor variables and address predictive model validity. The most parsimonious and stable subset of pre-surgical predictor variables explained 41.6, 36.0 and 25.6% of the variance in functional disability, back pain intensity and HRQOL 2-3 years after lumbar fusion. Pre-surgical control over pain significantly predicted functional disability and HRQOL. Pre-surgical catastrophizing and leg pain intensity significantly predicted functional disability and back pain while the pre-surgical straight leg raise significantly predicted back pain. Post-operative psychomotor therapy also significantly predicted functional disability while pre-surgical outcome expectations significantly predicted HRQOL. For the median dichotomised classification of functional disability, back pain intensity and HRQOL levels 2-3 years post-surgery, the discriminative ability of the prediction models was of good quality. The results demonstrate the importance of pre-surgical psychological factors, leg pain intensity, straight leg raise and post-operative psychomotor therapy in the predictions of functional disability, back pain and HRQOL-related outcomes.

Predicting heat stress index in Sasso hens using automatic linear modeling and artificial neural network

Science.gov (United States)

Yakubu, A.; Oluremi, O. I. A.; Ekpo, E. I.

2018-03-01

There is an increasing use of robust analytical algorithms in the prediction of heat stress. The present investigation therefore, was carried out to forecast heat stress index (HSI) in Sasso laying hens. One hundred and sixty seven records on the thermo-physiological parameters of the birds were utilized. They were reared on deep litter and battery cage systems. Data were collected when the birds were 42- and 52-week of age. The independent variables fitted were housing system, age of birds, rectal temperature (RT), pulse rate (PR), and respiratory rate (RR). The response variable was HSI. Data were analyzed using automatic linear modeling (ALM) and artificial neural network (ANN) procedures. The ALM model building method involved Forward Stepwise using the F Statistic criterion. As regards ANN, multilayer perceptron (MLP) with back-propagation network was used. The ANN network was trained with 90% of the data set while 10% were dedicated to testing for model validation. RR and PR were the two parameters of utmost importance in the prediction of HSI. However, the fractional importance of RR was higher than that of PR in both ALM (0.947 versus 0.053) and ANN (0.677 versus 0.274) models. The two models also predicted HSI effectively with high degree of accuracy [r = 0.980, R 2 = 0.961, adjusted R 2 = 0.961, and RMSE = 0.05168 (ALM); r = 0.983, R 2 = 0.966; adjusted R 2 = 0.966, and RMSE = 0.04806 (ANN)]. The present information may be exploited in the development of a heat stress chart based largely on RR. This may aid detection of thermal discomfort in a poultry house under tropical and subtropical conditions.

An application of plot-scale NDVI in predicting carbon dioxide exchange and leaf area index in heterogeneous subarctic tundra

Energy Technology Data Exchange (ETDEWEB)

Dagg, J.; Lafleur, P.

2010-07-01

This paper reported on a study that examined the flow of carbon into and out of tundra ecosystems. It is necessary to accurately predict carbon dioxide (CO{sub 2}) exchange in the Tundra because of the impacts of climate change on carbon stored in permafrost. Understanding the relationships between the normalized difference vegetation index (NDVI) and vegetation and CO{sub 2} exchange may explain how small-scale variation in vegetation community extends to remotely sensed estimates of landscape characteristics. In this study, CO{sub 2} fluxes were measured with a portable chamber in a range of Tundra vegetation communities. Biomass and leaf area were measured with destructive harvest, and NDVI was obtained using a hand-held infrared camera. There was a weak correlation between NDVI and leaf area index in some vegetation communities, but a significant correlation between NDVI and biomass, including mosses. NDVI was found to be strongly related to photosynthetic activity and net CO{sub 2} uptake in all vegetation groups. However, NDVI related to ecosystem respiration only in wet sedge. It was concluded that at plot scale, the ability of NDVI to predict ecosystem properties and CO{sub 2} exchange in heterogeneous Tundra vegetation is variable.

An application of plot-scale NDVI in predicting carbon dioxide exchange and leaf area index in heterogeneous subarctic tundra

International Nuclear Information System (INIS)

Dagg, J.; Lafleur, P.

2010-01-01

This paper reported on a study that examined the flow of carbon into and out of tundra ecosystems. It is necessary to accurately predict carbon dioxide (CO 2 ) exchange in the Tundra because of the impacts of climate change on carbon stored in permafrost. Understanding the relationships between the normalized difference vegetation index (NDVI) and vegetation and CO 2 exchange may explain how small-scale variation in vegetation community extends to remotely sensed estimates of landscape characteristics. In this study, CO 2 fluxes were measured with a portable chamber in a range of Tundra vegetation communities. Biomass and leaf area were measured with destructive harvest, and NDVI was obtained using a hand-held infrared camera. There was a weak correlation between NDVI and leaf area index in some vegetation communities, but a significant correlation between NDVI and biomass, including mosses. NDVI was found to be strongly related to photosynthetic activity and net CO 2 uptake in all vegetation groups. However, NDVI related to ecosystem respiration only in wet sedge. It was concluded that at plot scale, the ability of NDVI to predict ecosystem properties and CO 2 exchange in heterogeneous Tundra vegetation is variable.

Options for refractive index and viscosity matching to study variable density flows

Science.gov (United States)

Clément, Simon A.; Guillemain, Anaïs; McCleney, Amy B.; Bardet, Philippe M.

2018-02-01

Variable density flows are often studied by mixing two miscible aqueous solutions of different densities. To perform optical diagnostics in such environments, the refractive index of the fluids must be matched, which can be achieved by carefully choosing the two solutes and the concentration of the solutions. To separate the effects of buoyancy forces and viscosity variations, it is desirable to match the viscosity of the two solutions in addition to their refractive index. In this manuscript, several pairs of index matched fluids are compared in terms of viscosity matching, monetary cost, and practical use. Two fluid pairs are studied in detail, with two aqueous solutions (binary solutions of water and a salt or alcohol) mixed into a ternary solution. In each case: an aqueous solution of isopropanol mixed with an aqueous solution of sodium chloride (NaCl) and an aqueous solution of glycerol mixed with an aqueous solution of sodium sulfate (Na_2SO_4). The first fluid pair allows reaching high-density differences at low cost, but brings a large difference in dynamic viscosity. The second allows matching dynamic viscosity and refractive index simultaneously, at reasonable cost. For each of these four solutes, the density, kinematic viscosity, and refractive index are measured versus concentration and temperature, as well as wavelength for the refractive index. To investigate non-linear effects when two index-matched, binary solutions are mixed, the ternary solutions formed are also analyzed. Results show that density and refractive index follow a linear variation with concentration. However, the viscosity of the isopropanol and NaCl pair deviates from the linear law and has to be considered. Empirical correlations and their coefficients are given to create index-matched fluids at a chosen temperature and wavelength. Finally, the effectiveness of the refractive index matching is illustrated with particle image velocimetry measurements performed for a buoyant jet in a

Variability of the autoregulation index decreases after removing the effect of the very low frequency band

NARCIS (Netherlands)

Elting, J. W.; Maurits, N. M.; Aries, M. J. H.

Dynamic cerebral autoregulation (dCA) estimates show large between and within subject variability. Sources of variability include low coherence and influence of CO2 in the very low frequency (VLF) band, where dCA is active. This may lead to unreliable transfer function and autoregulation index (ARI)

Quantifying Net Synergy/Redundancy of Spontaneous Variability Regulation via Predictability and Transfer Entropy Decomposition Frameworks.

Science.gov (United States)

Porta, Alberto; Bari, Vlasta; De Maria, Beatrice; Takahashi, Anielle C M; Guzzetti, Stefano; Colombo, Riccardo; Catai, Aparecida M; Raimondi, Ferdinando; Faes, Luca

2017-11-01

Objective: Indexes assessing the balance between redundancy and synergy were hypothesized to be helpful in characterizing cardiovascular control from spontaneous beat-to-beat variations of heart period (HP), systolic arterial pressure (SAP), and respiration (R). Methods: Net redundancy/synergy indexes were derived according to predictability and transfer entropy decomposition strategies via a multivariate linear regression approach. Indexes were tested in two protocols inducing modifications of the cardiovascular regulation via baroreflex loading/unloading (i.e., head-down tilt at -25° and graded head-up tilt at 15°, 30°, 45°, 60°, 75°, and 90°, respectively). The net redundancy/synergy of SAP and R to HP and of HP and R to SAP were estimated over stationary sequences of 256 successive values. Results: We found that: 1) regardless of the target (i.e., HP or SAP) redundancy was prevalent over synergy and this prevalence was independent of type and magnitude of the baroreflex challenge; 2) the prevalence of redundancy disappeared when decoupling inputs from output via a surrogate approach; 3) net redundancy was under autonomic control given that it varied in proportion to the vagal withdrawal during graded head-up tilt; and 4) conclusions held regardless of the decomposition strategy. Conclusion: Net redundancy indexes can monitor changes of cardiovascular control from a perspective completely different from that provided by more traditional univariate and multivariate methods. Significance: Net redundancy measures might provide a practical tool to quantify the reservoir of effective cardiovascular regulatory mechanisms sharing causal influences over a target variable. Objective: Indexes assessing the balance between redundancy and synergy were hypothesized to be helpful in characterizing cardiovascular control from spontaneous beat-to-beat variations of heart period (HP), systolic arterial pressure (SAP), and respiration (R). Methods: Net redundancy

Discrete curved ray-tracing method for radiative transfer in an absorbing-emitting semitransparent slab with variable spatial refractive index

International Nuclear Information System (INIS)

Liu, L.H.

2004-01-01

A discrete curved ray-tracing method is developed to analyze the radiative transfer in one-dimensional absorbing-emitting semitransparent slab with variable spatial refractive index. The curved ray trajectory is locally treated as straight line and the complicated and time-consuming computation of ray trajectory is cut down. A problem of radiative equilibrium with linear variable spatial refractive index is taken as an example to examine the accuracy of the proposed method. The temperature distributions are determined by the proposed method and compared with the data in references, which are obtained by other different methods. The results show that the discrete curved ray-tracing method has a good accuracy in solving the radiative transfer in one-dimensional semitransparent slab with variable spatial refractive index

Importance of the macroeconomic variables for variance prediction: A GARCH-MIDAS approach

DEFF Research Database (Denmark)

Asgharian, Hossein; Hou, Ai Jun; Javed, Farrukh

2013-01-01

This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term compone......This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long...

Using the Speech Transmission Index for predicting non-native speech intelligibility

NARCIS (Netherlands)

Wijngaarden, S.J. van; Bronkhorst, A.W.; Houtgast, T.; Steeneken, H.J.M.

2004-01-01

While the Speech Transmission Index ~STI! is widely applied for prediction of speech intelligibility in room acoustics and telecommunication engineering, it is unclear how to interpret STI values when non-native talkers or listeners are involved. Based on subjectively measured psychometric functions

Hydroclimatic variability and predictability: a survey of recent research

Directory of Open Access Journals (Sweden)

R. D. Koster

2017-07-01

Full Text Available Recent research in large-scale hydroclimatic variability is surveyed, focusing on five topics: (i variability in general, (ii droughts, (iii floods, (iv land–atmosphere coupling, and (v hydroclimatic prediction. Each surveyed topic is supplemented by illustrative examples of recent research, as presented at a 2016 symposium honoring the career of Professor Eric Wood. Taken together, the recent literature and the illustrative examples clearly show that current research into hydroclimatic variability is strong, vibrant, and multifaceted.

A behavioral economic reward index predicts drinking resolutions: moderation revisited and compared with other outcomes.

Science.gov (United States)

Tucker, Jalie A; Roth, David L; Vignolo, Mary J; Westfall, Andrew O

2009-04-01

Data were pooled from 3 studies of recently resolved community-dwelling problem drinkers to determine whether a behavioral economic index of the value of rewards available over different time horizons distinguished among moderation (n = 30), abstinent (n = 95), and unresolved (n = 77) outcomes. Moderation over 1- to 2-year prospective follow-up intervals was hypothesized to involve longer term behavior regulation processes than abstinence or relapse and to be predicted by more balanced preresolution monetary allocations between short-term and longer term objectives (i.e., drinking and saving for the future). Standardized odds ratios (ORs) based on changes in standard deviation units from a multinomial logistic regression indicated that increases on this "Alcohol-Savings Discretionary Expenditure" index predicted higher rates of abstinence (OR = 1.93, p = .004) and relapse (OR = 2.89, p moderation outcomes. The index had incremental utility in predicting moderation in complex models that included other established predictors. The study adds to evidence supporting a behavioral economic analysis of drinking resolutions and shows that a systematic analysis of preresolution spending patterns aids in predicting moderation.

Variable context Markov chains for HIV protease cleavage site prediction.

Science.gov (United States)

Oğul, Hasan

2009-06-01

Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids. It was applied for HIV-1 protease cleavage site prediction problem and shown to outperform existing methods in terms of prediction accuracy on a common dataset. In general, the method is a promising tool for prediction of cleavage sites of all proteases and encouraged to be used for any kind of peptide classification problem as well.

Validation of an image quality index: its correlation with quality control parameters

International Nuclear Information System (INIS)

Cabrejas, M.L.C.; Giannone, C.A.; Arashiro, J.G.; Cabrejas, R.C.

2002-01-01

Objective and Rationale: To validate a new image quality index (the Performance Index: PI) that assesses detectability of simulated lesions with a phantom. This index, presumably must depend markedly on quality control (QC) parameters as tomographic uniformity (Unif), Centre of Rotation (COR) and Spatial resolution (FWHM). The simultaneous effects of the QC parameters may explain much of the variation in the PIs; i.e. they may be predictors of the PI values. Methods: An overall performance phantom containing 3 sections was used. The first uniform section was used to determine tomographic uniformity. From the analysis of the slices corresponding to the second section containing 8 cold cylindrical simulated lesions of different diameters (range 7 mm - 17 mm), the number of true and false positives are determined and from these a new Performance Index (PI) is defined as the ratio between the positive predictive value and the sensitivity (expressed as its complement adding a constant to avoid a singularity). A point source located on the top of the phantom was used to determine the Centre of Rotation and the Spatial Resolution expressed by the FWHM in mm. 40 nuclear medicine labs participate at the survey. Standard multiple regression analysis between the Performance Index, as dependent variable, and FWHM, COR and Unif as independent variables was performed to evaluate the influence of the QC parameters on the PI values. Results: It is shown that resolution and COR are both predictors of the PIs, with statistical significance for the multiple correlation co-efficient R. However the addition of the variable tomographic uniformity to the model, does not improve the prediction of PIs. Moreover, the regression model lacks overall statistical significance. Regression summary for dependent variable Performance Index is presented. Conclusions: We confirm that the new Performance Index (PI), depends on QC parameters as COR and Spatial resolution. Those labs whose PIs are out

Predicting waist circumference from body mass index.

Science.gov (United States)

Bozeman, Samuel R; Hoaglin, David C; Burton, Tanya M; Pashos, Chris L; Ben-Joseph, Rami H; Hollenbeak, Christopher S

2012-08-03

Being overweight or obese increases risk for cardiometabolic disorders. Although both body mass index (BMI) and waist circumference (WC) measure the level of overweight and obesity, WC may be more important because of its closer relationship to total body fat. Because WC is typically not assessed in clinical practice, this study sought to develop and verify a model to predict WC from BMI and demographic data, and to use the predicted WC to assess cardiometabolic risk. Data were obtained from the Third National Health and Nutrition Examination Survey (NHANES) and the Atherosclerosis Risk in Communities Study (ARIC). We developed linear regression models for men and women using NHANES data, fitting waist circumference as a function of BMI. For validation, those regressions were applied to ARIC data, assigning a predicted WC to each individual. We used the predicted WC to assess abdominal obesity and cardiometabolic risk. The model correctly classified 88.4% of NHANES subjects with respect to abdominal obesity. Median differences between actual and predicted WC were -0.07 cm for men and 0.11 cm for women. In ARIC, the model closely estimated the observed WC (median difference: -0.34 cm for men, +3.94 cm for women), correctly classifying 86.1% of ARIC subjects with respect to abdominal obesity and 91.5% to 99.5% as to cardiometabolic risk.The model is generalizable to Caucasian and African-American adult populations because it was constructed from data on a large, population-based sample of men and women in the United States, and then validated in a population with a larger representation of African-Americans. The model accurately estimates WC and identifies cardiometabolic risk. It should be useful for health care practitioners and public health officials who wish to identify individuals and populations at risk for cardiometabolic disease when WC data are unavailable.

Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity.

Directory of Open Access Journals (Sweden)

Lian Leng Low

Full Text Available To reduce readmissions, it may be cost-effective to consider risk stratification, with targeting intervention programs to patients at high risk of readmissions. In this study, we aimed to derive and validate a prediction model including several novel markers of hospitalization severity, and compare the model with the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, Emergency department visits in past 6 months, an established risk stratification tool.This was a retrospective cohort study of all patients ≥ 21 years of age, who were admitted to a tertiary hospital in Singapore from January 1, 2013 through May 31, 2015. Data were extracted from the hospital's electronic health records. The outcome was defined as unplanned readmissions within 30 days of discharge from the index hospitalization. Candidate predictive variables were broadly grouped into five categories: Patient demographics, social determinants of health, past healthcare utilization, medical comorbidities, and markers of hospitalization severity. Multivariable logistic regression was used to predict the outcome, and receiver operating characteristic analysis was performed to compare our model with the LACE index.74,102 cases were enrolled for analysis. Of these, 11,492 patient cases (15.5% were readmitted within 30 days of discharge. A total of fifteen predictive variables were strongly associated with the risk of 30-day readmissions, including number of emergency department visits in the past 6 months, Charlson Comorbidity Index, markers of hospitalization severity such as 'requiring inpatient dialysis during index admission, and 'treatment with intravenous furosemide 40 milligrams or more' during index admission. Our predictive model outperformed the LACE index by achieving larger area under the curve values: 0.78 (95% confidence interval [CI]: 0.77-0.79 versus 0.70 (95% CI: 0.69-0.71.Several factors are important for the risk of 30-day readmissions

Estimating the reliability of glycemic index values and potential sources of methodological and biological variability.

Science.gov (United States)

Matthan, Nirupa R; Ausman, Lynne M; Meng, Huicui; Tighiouart, Hocine; Lichtenstein, Alice H

2016-10-01

The utility of glycemic index (GI) values for chronic disease risk management remains controversial. Although absolute GI value determinations for individual foods have been shown to vary significantly in individuals with diabetes, there is a dearth of data on the reliability of GI value determinations and potential sources of variability among healthy adults. We examined the intra- and inter-individual variability in glycemic response to a single food challenge and methodologic and biological factors that potentially mediate this response. The GI value for white bread was determined by using standardized methodology in 63 volunteers free from chronic disease and recruited to differ by sex, age (18-85 y), and body mass index [BMI (in kg/m 2 ): 20-35]. Volunteers randomly underwent 3 sets of food challenges involving glucose (reference) and white bread (test food), both providing 50 g available carbohydrates. Serum glucose and insulin were monitored for 5 h postingestion, and GI values were calculated by using different area under the curve (AUC) methods. Biochemical variables were measured by using standard assays and body composition by dual-energy X-ray absorptiometry. The mean ± SD GI value for white bread was 62 ± 15 when calculated by using the recommended method. Mean intra- and interindividual CVs were 20% and 25%, respectively. Increasing sample size, replication of reference and test foods, and length of blood sampling, as well as AUC calculation method, did not improve the CVs. Among the biological factors assessed, insulin index and glycated hemoglobin values explained 15% and 16% of the variability in mean GI value for white bread, respectively. These data indicate that there is substantial variability in individual responses to GI value determinations, demonstrating that it is unlikely to be a good approach to guiding food choices. Additionally, even in healthy individuals, glycemic status significantly contributes to the variability in GI value

Days on radiosensitivity: individual variability and predictive tests

International Nuclear Information System (INIS)

2008-01-01

The radiosensitivity is a part of usual clinical observations. It is already included in the therapy protocols. however, some questions stay on its individual variability and on the difficulty to evaluate it. The point will be stocked on its origin and its usefulness in predictive medicine. Through examples on the use of predictive tests and ethical and legal questions that they raise, concrete cases will be presented by specialists such radio biologists, geneticists, immunologists, jurists and occupational physicians. (N.C.)

Prediction of Bispectral Index during Target-controlled Infusion of Propofol and Remifentanil: A Deep Learning Approach.

Science.gov (United States)

Lee, Hyung-Chul; Ryu, Ho-Geol; Chung, Eun-Jin; Jung, Chul-Woo

2018-03-01

The discrepancy between predicted effect-site concentration and measured bispectral index is problematic during intravenous anesthesia with target-controlled infusion of propofol and remifentanil. We hypothesized that bispectral index during total intravenous anesthesia would be more accurately predicted by a deep learning approach. Long short-term memory and the feed-forward neural network were sequenced to simulate the pharmacokinetic and pharmacodynamic parts of an empirical model, respectively, to predict intraoperative bispectral index during combined use of propofol and remifentanil. Inputs of long short-term memory were infusion histories of propofol and remifentanil, which were retrieved from target-controlled infusion pumps for 1,800 s at 10-s intervals. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the bispectral index. The performance of bispectral index prediction was compared between the deep learning model and previously reported response surface model. The model hyperparameters comprised 8 memory cells in the long short-term memory layer and 16 nodes in the hidden layer of the feed-forward network. The model training and testing were performed with separate data sets of 131 and 100 cases. The concordance correlation coefficient (95% CI) were 0.561 (0.560 to 0.562) in the deep learning model, which was significantly larger than that in the response surface model (0.265 [0.263 to 0.266], P deep learning model-predicted bispectral index during target-controlled infusion of propofol and remifentanil more accurately compared to the traditional model. The deep learning approach in anesthetic pharmacology seems promising because of its excellent performance and extensibility.

Improved predictability of droughts over southern Africa using the standardized precipitation evapotranspiration index and ENSO

Science.gov (United States)

Manatsa, Desmond; Mushore, Terrence; Lenouo, Andre

2017-01-01

The provision of timely and reliable climate information on which to base management decisions remains a critical component in drought planning for southern Africa. In this observational study, we have not only proposed a forecasting scheme which caters for timeliness and reliability but improved relevance of the climate information by using a novel drought index called the standardised precipitation evapotranspiration index (SPEI), instead of the traditional precipitation only based index, the standardised precipitation index (SPI). The SPEI which includes temperature and other climatic factors in its construction has a more robust connection to ENSO than the SPI. Consequently, the developed ENSO-SPEI prediction scheme can provide quantitative information about the spatial extent and severity of predicted drought conditions in a way that reflects more closely the level of risk in the global warming context of the sub region. However, it is established that the ENSO significant regional impact is restricted only to the period December-March, implying a revisit to the traditional ENSO-based forecast scheme which essentially divides the rainfall season into the two periods, October to December and January to March. Although the prediction of ENSO events has increased with the refinement of numerical models, this work has demonstrated that the prediction of drought impacts related to ENSO is also a reality based only on observations. A large temporal lag is observed between the development of ENSO phenomena (typically in May of the previous year) and the identification of regional SPEI defined drought conditions. It has been shown that using the Southern Africa Regional Climate Outlook Forum's (SARCOF) traditional 3-month averaged Nino 3.4 SST index (June to August) as a predictor does not have an added advantage over using only the May SST index values. In this regard, the extended lead time and improved skill demonstrated in this study could immensely benefit

Empirical modelling to predict the refractive index of human blood

Science.gov (United States)

Yahya, M.; Saghir, M. Z.

2016-02-01

Optical techniques used for the measurement of the optical properties of blood are of great interest in clinical diagnostics. Blood analysis is a routine procedure used in medical diagnostics to confirm a patient’s condition. Measuring the optical properties of blood is difficult due to the non-homogenous nature of the blood itself. In addition, there is a lot of variation in the refractive indices reported in the literature. These are the reasons that motivated the researchers to develop a mathematical model that can be used to predict the refractive index of human blood as a function of concentration, temperature and wavelength. The experimental measurements were conducted on mimicking phantom hemoglobin samples using the Abbemat Refractometer. The results analysis revealed a linear relationship between the refractive index and concentration as well as temperature, and a non-linear relationship between refractive index and wavelength. These results are in agreement with those found in the literature. In addition, a new formula was developed based on empirical modelling which suggests that temperature and wavelength coefficients be added to the Barer formula. The verification of this correlation confirmed its ability to determine refractive index and/or blood hematocrit values with appropriate clinical accuracy.

Higher resting heart rate variability predicts skill in expressing some emotions.

Science.gov (United States)

Tuck, Natalie L; Grant, Rosemary C I; Sollers, John J; Booth, Roger J; Consedine, Nathan S

2016-12-01

Vagally mediated heart rate variability (vmHRV) is a measure of cardiac vagal tone, and is widely viewed as a physiological index of the capacity to regulate emotions. However, studies have not directly tested whether vmHRV is associated with the ability to facially express emotions. In extending prior work, the current report tested links between resting vmHRV and the objectively assessed ability to facially express emotions, hypothesizing that higher vmHRV would predict greater expressive skill. Eighty healthy women completed self-reported measures, before attending a laboratory session in which vmHRV and the ability to express six emotions in the face were assessed. A repeated measures analysis of variance revealed a marginal main effect for vmHRV on skill overall; individuals with higher resting vmHRV were only better able to deliberately facially express anger and interest. Findings suggest that differences in resting vmHRV are associated with the objectively assessed ability to facially express some, but not all, emotions, with potential implications for health and well-being. © 2016 Society for Psychophysiological Research.

The estimation of soil parameters using observations on crop biophysical variables and the crop model STICS improve the predictions of agro environmental variables.

Science.gov (United States)

Varella, H.-V.

2009-04-01

Dynamic crop models are very useful to predict the behavior of crops in their environment and are widely used in a lot of agro-environmental work. These models have many parameters and their spatial application require a good knowledge of these parameters, especially of the soil parameters. These parameters can be estimated from soil analysis at different points but this is very costly and requires a lot of experimental work. Nevertheless, observations on crops provided by new techniques like remote sensing or yield monitoring, is a possibility for estimating soil parameters through the inversion of crop models. In this work, the STICS crop model is studied for the wheat and the sugar beet and it includes more than 200 parameters. After a previous work based on a large experimental database for calibrate parameters related to the characteristics of the crop, a global sensitivity analysis of the observed variables (leaf area index LAI and absorbed nitrogen QN provided by remote sensing data, and yield at harvest provided by yield monitoring) to the soil parameters is made, in order to determine which of them have to be estimated. This study was made in different climatic and agronomic conditions and it reveals that 7 soil parameters (4 related to the water and 3 related to the nitrogen) have a clearly influence on the variance of the observed variables and have to be therefore estimated. For estimating these 7 soil parameters, a Bayesian data assimilation method is chosen (because of available prior information on these parameters) named Importance Sampling by using observations, on wheat and sugar beet crop, of LAI and QN at various dates and yield at harvest acquired on different climatic and agronomic conditions. The quality of parameter estimation is then determined by comparing the result of parameter estimation with only prior information and the result with the posterior information provided by the Bayesian data assimilation method. The result of the

APPLICATILITY OF THE VISCERAL ADIPOSITY INDEX (VAI) IN THE PREDICTION OF THE COMPONENTS OF THE METABOLIC SYNDROME IN ELDERLY.

Science.gov (United States)

Goldani, Heloisa; Adami, Fernanda Scherer; Antunes, Maria Terezinha; Rosa, Luis Henrique; Fassina, Patrícia; Quevedo Grave, Magali Terezinha; Morelo Dal Bosco, Simone

2015-10-01

The nutritional assessment may detect a state of malnutrition, overweight and cardiometabolic risk in the elderly. Easy to apply instruments enable the identification of risk factors for cardiovascular diseases (CVD). to analyze the applicability of Visceral Adiposity Index (VAI) in the prediction of MS components in the elderly. cross-sectional study with 221 elderly at a mean age of 70.65 ± 7.34 years; 53.4% female and 46.4% male. Weight, height, waist circumference (WC), fasting glucose, triglycerides (TG), total cholesterol (TC), HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), and blood pressure (BP), data was obtained, as well as information about lifestyle. There were calculated the Body Mass Index (BMI), the Waist-Hip Ratio (WHR), and the VAI. The adiposity measures were compared with the components of MS, and for the VAI there was determined the capability of predicting the occurrence of MS components. by analyzing the association among the biochemical and pressoric variables and MS components with the anthropometric indicators of obesity, there was a direct and significant correlation of the BMI, the weight and the VAI with blood glucose, HDL and TG (p. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Analyst-to-Analyst Variability in Simulation-Based Prediction

Energy Technology Data Exchange (ETDEWEB)

Glickman, Matthew R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Romero, Vicente J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-02-01

This report describes findings from the culminating experiment of the LDRD project entitled, "Analyst-to-Analyst Variability in Simulation-Based Prediction". For this experiment, volunteer participants solving a given test problem in engineering and statistics were interviewed at different points in their solution process. These interviews are used to trace differing solutions to differing solution processes, and differing processes to differences in reasoning, assumptions, and judgments. The issue that the experiment was designed to illuminate -- our paucity of understanding of the ways in which humans themselves have an impact on predictions derived from complex computational simulations -- is a challenging and open one. Although solution of the test problem by analyst participants in this experiment has taken much more time than originally anticipated, and is continuing past the end of this LDRD, this project has provided a rare opportunity to explore analyst-to-analyst variability in significant depth, from which we derive evidence-based insights to guide further explorations in this important area.

Dynamic Algorithm for LQGPC Predictive Control

DEFF Research Database (Denmark)

Hangstrup, M.; Ordys, A.W.; Grimble, M.J.

1998-01-01

In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated into the control......In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated...... into the controller design and the solution is derived using the method of Lagrange multipliers. It is shown how well-known GPC controller can be obtained as a special case of the LQGPC controller design. The important advantage of using the LQGPC framework for designing predictive, e.g. GPS is that LQGPC enables...

Predicting travel time variability for cost-benefit analysis

NARCIS (Netherlands)

Peer, S.; Koopmans, C.; Verhoef, E.T.

2010-01-01

Unreliable travel times cause substantial costs to travelers. Nevertheless, they are not taken into account in many cost-benefit-analyses (CBA), or only in very rough ways. This paper aims at providing simple rules on how variability can be predicted, based on travel time data from Dutch highways.

Accuracy of Body Mass Index Versus Lean Mass Index for Prediction of Sarcopenia in Older Women.

Science.gov (United States)

Benton, M J; Silva-Smith, A L

2018-01-01

We compared accuracy of body mass index (BMI) versus lean mass index (LMI) to predict sarcopenia in 58 community-dwelling women (74.1±0.9 years). Lean mass was measured with multi-frequency bioelectrical impedance analysis, and strength was measured with Arm Curl test, Chair Stand test, and handgrip dynamometry. Sarcopenia was defined as low LMI. When categorized by BMI, normal women had less absolute lean mass (37.6±1.0 vs. 42.6±0.9 kg; Plean mass (14.1±0.2 vs. 16.1±0.2 kg/m2; Plean mass (44.0±0.7 vs. 35.7±0.7 kg; Plean mass (16.2±0.2 vs. 13.8±0.2 kg/m2; Plean mass and strength. For clinical assessment, calculation of LMI rather than BMI is appropriate.

Empirical modelling to predict the refractive index of human blood

International Nuclear Information System (INIS)

Yahya, M; Saghir, M Z

2016-01-01

Optical techniques used for the measurement of the optical properties of blood are of great interest in clinical diagnostics. Blood analysis is a routine procedure used in medical diagnostics to confirm a patient’s condition. Measuring the optical properties of blood is difficult due to the non-homogenous nature of the blood itself. In addition, there is a lot of variation in the refractive indices reported in the literature. These are the reasons that motivated the researchers to develop a mathematical model that can be used to predict the refractive index of human blood as a function of concentration, temperature and wavelength. The experimental measurements were conducted on mimicking phantom hemoglobin samples using the Abbemat Refractometer. The results analysis revealed a linear relationship between the refractive index and concentration as well as temperature, and a non-linear relationship between refractive index and wavelength. These results are in agreement with those found in the literature. In addition, a new formula was developed based on empirical modelling which suggests that temperature and wavelength coefficients be added to the Barer formula. The verification of this correlation confirmed its ability to determine refractive index and/or blood hematocrit values with appropriate clinical accuracy. (paper)

A Behavioral Economic Reward Index Predicts Drinking Resolutions: Moderation Re-visited and Compared with Other Outcomes

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Tucker, Jalie A.; Roth, David L.; Vignolo, Mary J.; Westfall, Andrew O.

2014-01-01

Data were pooled from three studies of recently resolved community-dwelling problem drinkers to determine whether a behavioral economic index of the value of rewards available over different time horizons distinguished among moderation (n = 30), abstinent (n = 95), and unresolved (n = 77) outcomes. Moderation over 1-2 year prospective follow-up intervals was hypothesized to involve longer term behavior regulation processes compared to abstinence or relapse and to be predicted by more balanced pre-resolution monetary allocations between short- and longer-term objectives (i.e., drinking and saving for the future). Standardized odds ratios (OR) based on changes in standard deviation units from a multinomial logistic regression indicated that increases on this “Alcohol-Savings Discretionary Expenditure” index predicted higher rates of both abstinence (OR = 1.93, p = .004) and relapse (OR = 2.89, p moderation outcomes. The index had incremental utility in predicting moderation in complex models that included other established predictors. The study adds to evidence supporting a behavioral economic analysis of drinking resolutions and shows that a systematic analysis of pre-resolution spending patterns aids in predicting moderation. PMID:19309182

Predicting waist circumference from body mass index

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Bozeman Samuel R

2012-08-01

Full Text Available Abstract Background Being overweight or obese increases risk for cardiometabolic disorders. Although both body mass index (BMI and waist circumference (WC measure the level of overweight and obesity, WC may be more important because of its closer relationship to total body fat. Because WC is typically not assessed in clinical practice, this study sought to develop and verify a model to predict WC from BMI and demographic data, and to use the predicted WC to assess cardiometabolic risk. Methods Data were obtained from the Third National Health and Nutrition Examination Survey (NHANES and the Atherosclerosis Risk in Communities Study (ARIC. We developed linear regression models for men and women using NHANES data, fitting waist circumference as a function of BMI. For validation, those regressions were applied to ARIC data, assigning a predicted WC to each individual. We used the predicted WC to assess abdominal obesity and cardiometabolic risk. Results The model correctly classified 88.4% of NHANES subjects with respect to abdominal obesity. Median differences between actual and predicted WC were − 0.07 cm for men and 0.11 cm for women. In ARIC, the model closely estimated the observed WC (median difference: − 0.34 cm for men, +3.94 cm for women, correctly classifying 86.1% of ARIC subjects with respect to abdominal obesity and 91.5% to 99.5% as to cardiometabolic risk. The model is generalizable to Caucasian and African-American adult populations because it was constructed from data on a large, population-based sample of men and women in the United States, and then validated in a population with a larger representation of African-Americans. Conclusions The model accurately estimates WC and identifies cardiometabolic risk. It should be useful for health care practitioners and public health officials who wish to identify individuals and populations at risk for cardiometabolic disease when WC data are unavailable.

Refractive Index Imaging of Cells with Variable-Angle Near-Total Internal Reflection (TIR) Microscopy.

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Bohannon, Kevin P; Holz, Ronald W; Axelrod, Daniel

2017-10-01

The refractive index in the interior of single cells affects the evanescent field depth in quantitative studies using total internal reflection (TIR) fluorescence, but often that index is not well known. We here present method to measure and spatially map the absolute index of refraction in a microscopic sample, by imaging a collimated light beam reflected from the substrate/buffer/cell interference at variable angles of incidence. Above the TIR critical angle (which is a strong function of refractive index), the reflection is 100%, but in the immediate sub-critical angle zone, the reflection intensity is a very strong ascending function of incidence angle. By analyzing the angular position of that edge at each location in the field of view, the local refractive index can be estimated. In addition, by analyzing the steepness of the edge, the distance-to-substrate can be determined. We apply the technique to liquid calibration samples, silica beads, cultured Chinese hamster ovary cells, and primary culture chromaffin cells. The optical technique suffers from decremented lateral resolution, scattering, and interference artifacts. However, it still provides reasonable results for both refractive index (~1.38) and for distance-to-substrate (~150 nm) for the cells, as well as a lateral resolution to about 1 µm.

Vigorous physical activity predicts higher heart rate variability among younger adults.

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May, Richard; McBerty, Victoria; Zaky, Adam; Gianotti, Melino

2017-06-14

Baseline heart rate variability (HRV) is linked to prospective cardiovascular health. We tested intensity and duration of weekly physical activity as predictors of heart rate variability in young adults. Time and frequency domain indices of HRV were calculated based on 5-min resting electrocardiograms collected from 82 undergraduate students. Hours per week of both moderate and vigorous activity were estimated using the International Physical Activity Questionnaire. In regression analyses, hours of vigorous physical activity, but not moderate activity, significantly predicted greater time domain and frequency domain indices of heart rate variability. Adjusted for weekly frequency, greater daily duration of vigorous activity failed to predict HRV indices. Future studies should test direct measurements of vigorous activity patterns as predictors of autonomic function in young adulthood.

Probabilistic approaches to accounting for data variability in the practical application of bioavailability in predicting aquatic risks from metals.

Science.gov (United States)

Ciffroy, Philippe; Charlatchka, Rayna; Ferreira, Daniel; Marang, Laura

2013-07-01

The biotic ligand model (BLM) theoretically enables the derivation of environmental quality standards that are based on true bioavailable fractions of metals. Several physicochemical variables (especially pH, major cations, dissolved organic carbon, and dissolved metal concentrations) must, however, be assigned to run the BLM, but they are highly variable in time and space in natural systems. This article describes probabilistic approaches for integrating such variability during the derivation of risk indexes. To describe each variable using a probability density function (PDF), several methods were combined to 1) treat censored data (i.e., data below the limit of detection), 2) incorporate the uncertainty of the solid-to-liquid partitioning of metals, and 3) detect outliers. From a probabilistic perspective, 2 alternative approaches that are based on log-normal and Γ distributions were tested to estimate the probability of the predicted environmental concentration (PEC) exceeding the predicted non-effect concentration (PNEC), i.e., p(PEC/PNEC>1). The probabilistic approach was tested on 4 real-case studies based on Cu-related data collected from stations on the Loire and Moselle rivers. The approach described in this article is based on BLM tools that are freely available for end-users (i.e., the Bio-Met software) and on accessible statistical data treatments. This approach could be used by stakeholders who are involved in risk assessments of metals for improving site-specific studies. Copyright © 2013 SETAC.

Review of some advances of the literature about predictive variables concerning subjective well-being

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Gloria Cajiao

2013-06-01

Full Text Available This review of scientific literature presents some tendencies, conceptual advances, empirical findings and tests that measure the predictive variables of subjective well-being. It was done through the search in bibliographical database like ProQuest, PsycArticles, Psyctest, OVID SP, books and Thesis. Two types of predictive variables were recognized- internal and external to the individual-. Both of them influence the achievement of the subjective well-being. Besides, the studies and conceptualization about Subjetive well-being and some of the Predictive Variables were analyzed in the conclusion.

Inverse radiation problem of temperature distribution in one-dimensional isotropically scattering participating slab with variable refractive index

International Nuclear Information System (INIS)

Namjoo, A.; Sarvari, S.M. Hosseini; Behzadmehr, A.; Mansouri, S.H.

2009-01-01

In this paper, an inverse analysis is performed for estimation of source term distribution from the measured exit radiation intensities at the boundary surfaces in a one-dimensional absorbing, emitting and isotropically scattering medium between two parallel plates with variable refractive index. The variation of refractive index is assumed to be linear. The radiative transfer equation is solved by the constant quadrature discrete ordinate method. The inverse problem is formulated as an optimization problem for minimizing an objective function which is expressed as the sum of square deviations between measured and estimated exit radiation intensities at boundary surfaces. The conjugate gradient method is used to solve the inverse problem through an iterative procedure. The effects of various variables on source estimation are investigated such as type of source function, errors in the measured data and system parameters, gradient of refractive index across the medium, optical thickness, single scattering albedo and boundary emissivities. The results show that in the case of noisy input data, variation of system parameters may affect the inverse solution, especially at high error values in the measured data. The error in measured data plays more important role than the error in radiative system parameters except the refractive index distribution; however the accuracy of source estimation is very sensitive toward error in refractive index distribution. Therefore, refractive index distribution and measured exit intensities should be measured accurately with a limited error bound, in order to have an accurate estimation of source term in a graded index medium.

Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables

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Mortensen, Eric; Wu, Shu; Notaro, Michael; Vavrus, Stephen; Montgomery, Rob; De Piérola, José; Sánchez, Carlos; Block, Paul

2018-01-01

Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January-March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet-dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought.

Scaling prediction errors to reward variability benefits error-driven learning in humans.

Science.gov (United States)

Diederen, Kelly M J; Schultz, Wolfram

2015-09-01

Effective error-driven learning requires individuals to adapt learning to environmental reward variability. The adaptive mechanism may involve decays in learning rate across subsequent trials, as shown previously, and rescaling of reward prediction errors. The present study investigated the influence of prediction error scaling and, in particular, the consequences for learning performance. Participants explicitly predicted reward magnitudes that were drawn from different probability distributions with specific standard deviations. By fitting the data with reinforcement learning models, we found scaling of prediction errors, in addition to the learning rate decay shown previously. Importantly, the prediction error scaling was closely related to learning performance, defined as accuracy in predicting the mean of reward distributions, across individual participants. In addition, participants who scaled prediction errors relative to standard deviation also presented with more similar performance for different standard deviations, indicating that increases in standard deviation did not substantially decrease "adapters'" accuracy in predicting the means of reward distributions. However, exaggerated scaling beyond the standard deviation resulted in impaired performance. Thus efficient adaptation makes learning more robust to changing variability. Copyright © 2015 the American Physiological Society.

Modeling Chronic Toxicity: A Comparison of Experimental Variability With (QSAR/Read-Across Predictions

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Christoph Helma

2018-04-01

Full Text Available This study compares the accuracy of (QSAR/read-across predictions with the experimental variability of chronic lowest-observed-adverse-effect levels (LOAELs from in vivo experiments. We could demonstrate that predictions of the lazy structure-activity relationships (lazar algorithm within the applicability domain of the training data have the same variability as the experimental training data. Predictions with a lower similarity threshold (i.e., a larger distance from the applicability domain are also significantly better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended.

Can biomechanical variables predict improvement in crouch gait?

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Hicks, Jennifer L.; Delp, Scott L.; Schwartz, Michael H.

2011-01-01

Many patients respond positively to treatments for crouch gait, yet surgical outcomes are inconsistent and unpredictable. In this study, we developed a multivariable regression model to determine if biomechanical variables and other subject characteristics measured during a physical exam and gait analysis can predict which subjects with crouch gait will demonstrate improved knee kinematics on a follow-up gait analysis. We formulated the model and tested its performance by retrospectively analyzing 353 limbs of subjects who walked with crouch gait. The regression model was able to predict which subjects would demonstrate ‘improved’ and ‘unimproved’ knee kinematics with over 70% accuracy, and was able to explain approximately 49% of the variance in subjects’ change in knee flexion between gait analyses. We found that improvement in stance phase knee flexion was positively associated with three variables that were drawn from knowledge about the biomechanical contributors to crouch gait: i) adequate hamstrings lengths and velocities, possibly achieved via hamstrings lengthening surgery, ii) normal tibial torsion, possibly achieved via tibial derotation osteotomy, and iii) sufficient muscle strength. PMID:21616666

Bayesian data fusion for spatial prediction of categorical variables in environmental sciences

Science.gov (United States)

Gengler, Sarah; Bogaert, Patrick

2014-12-01

First developed to predict continuous variables, Bayesian Maximum Entropy (BME) has become a complete framework in the context of space-time prediction since it has been extended to predict categorical variables and mixed random fields. This method proposes solutions to combine several sources of data whatever the nature of the information. However, the various attempts that were made for adapting the BME methodology to categorical variables and mixed random fields faced some limitations, as a high computational burden. The main objective of this paper is to overcome this limitation by generalizing the Bayesian Data Fusion (BDF) theoretical framework to categorical variables, which is somehow a simplification of the BME method through the convenient conditional independence hypothesis. The BDF methodology for categorical variables is first described and then applied to a practical case study: the estimation of soil drainage classes using a soil map and point observations in the sandy area of Flanders around the city of Mechelen (Belgium). The BDF approach is compared to BME along with more classical approaches, as Indicator CoKringing (ICK) and logistic regression. Estimators are compared using various indicators, namely the Percentage of Correctly Classified locations (PCC) and the Average Highest Probability (AHP). Although BDF methodology for categorical variables is somehow a simplification of BME approach, both methods lead to similar results and have strong advantages compared to ICK and logistic regression.

Bayesian data fusion for spatial prediction of categorical variables in environmental sciences

International Nuclear Information System (INIS)

Gengler, Sarah; Bogaert, Patrick

2014-01-01

First developed to predict continuous variables, Bayesian Maximum Entropy (BME) has become a complete framework in the context of space-time prediction since it has been extended to predict categorical variables and mixed random fields. This method proposes solutions to combine several sources of data whatever the nature of the information. However, the various attempts that were made for adapting the BME methodology to categorical variables and mixed random fields faced some limitations, as a high computational burden. The main objective of this paper is to overcome this limitation by generalizing the Bayesian Data Fusion (BDF) theoretical framework to categorical variables, which is somehow a simplification of the BME method through the convenient conditional independence hypothesis. The BDF methodology for categorical variables is first described and then applied to a practical case study: the estimation of soil drainage classes using a soil map and point observations in the sandy area of Flanders around the city of Mechelen (Belgium). The BDF approach is compared to BME along with more classical approaches, as Indicator CoKringing (ICK) and logistic regression. Estimators are compared using various indicators, namely the Percentage of Correctly Classified locations (PCC) and the Average Highest Probability (AHP). Although BDF methodology for categorical variables is somehow a simplification of BME approach, both methods lead to similar results and have strong advantages compared to ICK and logistic regression

Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

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B. M. Brentan

2017-01-01

Full Text Available Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA and machine learning powerful algorithms such as Self-Organizing Maps (SOMs and Random Forest (RF. We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.

Genome-wide prediction of traits with different genetic architecture through efficient variable selection.

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Wimmer, Valentin; Lehermeier, Christina; Albrecht, Theresa; Auinger, Hans-Jürgen; Wang, Yu; Schön, Chris-Carolin

2013-10-01

In genome-based prediction there is considerable uncertainty about the statistical model and method required to maximize prediction accuracy. For traits influenced by a small number of quantitative trait loci (QTL), predictions are expected to benefit from methods performing variable selection [e.g., BayesB or the least absolute shrinkage and selection operator (LASSO)] compared to methods distributing effects across the genome [ridge regression best linear unbiased prediction (RR-BLUP)]. We investigate the assumptions underlying successful variable selection by combining computer simulations with large-scale experimental data sets from rice (Oryza sativa L.), wheat (Triticum aestivum L.), and Arabidopsis thaliana (L.). We demonstrate that variable selection can be successful when the number of phenotyped individuals is much larger than the number of causal mutations contributing to the trait. We show that the sample size required for efficient variable selection increases dramatically with decreasing trait heritabilities and increasing extent of linkage disequilibrium (LD). We contrast and discuss contradictory results from simulation and experimental studies with respect to superiority of variable selection methods over RR-BLUP. Our results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice, assumed to be influenced by a few major QTL. We extend our conclusions to the analysis of whole-genome sequence data and infer upper bounds for the number of causal mutations which can be identified by LASSO. Our results have major impact on the choice of statistical method needed to make credible inferences about genetic architecture and prediction accuracy of complex traits.

An index predictive of cognitive outcome in retired professional American Football players with a history of sports concussion.

Science.gov (United States)

Wright, Mathew J; Woo, Ellen; Birath, J Brandon; Siders, Craig A; Kelly, Daniel F; Wang, Christina; Swerdloff, Ronald; Romero, Elizabeth; Kernan, Claudia; Cantu, Robert C; Guskiewicz, Kevin

2016-01-01

Various concussion characteristics and personal factors are associated with cognitive recovery in athletes. We developed an index based on concussion frequency, severity, and timeframe, as well as cognitive reserve (CR), and we assessed its predictive power regarding cognitive ability in retired professional football players. Data from 40 retired professional American football players were used in the current study. On average, participants had been retired from football for 20 years. Current neuropsychological performances, indicators of CR, concussion history, and play data were used to create an index for predicting cognitive outcome. The sample displayed a range of concussions, concussion severities, seasons played, CR, and cognitive ability. Many of the participants demonstrated cognitive deficits. The index strongly predicted global cognitive ability (R(2) = .31). The index also predicted the number of areas of neuropsychological deficit, which varied as a function of the deficit classification system used (Heaton: R(2) = .15; Wechsler: R(2) = .28). The current study demonstrated that a unique combination of CR, sports concussion, and game-related data can predict cognitive outcomes in participants who had been retired from professional American football for an average of 20 years. Such indices may prove to be useful for clinical decision making and research.

The predictive content of CBOE crude oil volatility index

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Chen, Hongtao; Liu, Li; Li, Xiaolei

2018-02-01

Volatility forecasting is an important issue in the area of econophysics. The information content of implied volatility for financial return volatility has been well documented in the literature but very few studies focus on oil volatility. In this paper, we show that the CBOE crude oil volatility index (OVX) has predictive ability for spot volatility of WTI and Brent oil returns, from both in-sample and out-of-sample perspectives. Including OVX-based implied volatility in GARCH-type volatility models can improve forecasting accuracy most of time. The predictability from OVX to spot volatility is also found for longer forecasting horizons of 5 days and 20 days. The simple GARCH(1,1) and fractionally integrated GARCH with OVX performs significantly better than the other OVX models and all 6 univariate GARCH-type models without OVX. Robustness test results suggest that OVX provides different information from as short-term interest rate.

Prediction of clamp-derived insulin sensitivity from the oral glucose insulin sensitivity index

DEFF Research Database (Denmark)

Tura, Andrea; Chemello, Gaetano; Szendroedi, Julia

2018-01-01

that underwent both a clamp and an OGTT or meal test, thereby allowing calculation of both the M value and OGIS. The population was divided into a training and a validation cohort (n = 359 and n = 154, respectively). After a stepwise selection approach, the best model for M value prediction was applied......AIMS/HYPOTHESIS: The euglycaemic-hyperinsulinaemic clamp is the gold-standard method for measuring insulin sensitivity, but is less suitable for large clinical trials. Thus, several indices have been developed for evaluating insulin sensitivity from the oral glucose tolerance test (OGTT). However......, most of them yield values different from those obtained by the clamp method. The aim of this study was to develop a new index to predict clamp-derived insulin sensitivity (M value) from the OGTT-derived oral glucose insulin sensitivity index (OGIS). METHODS: We analysed datasets of people...

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection.

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Sepehri, Bakhtyar; Omidikia, Nematollah; Kompany-Zareh, Mohsen; Ghavami, Raouf

2018-01-01

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Validation of three noninvasive laboratory variables to predict significant fibrosis and cirrhosis in patients with chronic hepatitis C in Saudi Arabia

International Nuclear Information System (INIS)

Ado, Ayman A.; Al-Swat, Khalid; Azzam, N.; Al-Faleh, Faleh; Ahmed, S.

2007-01-01

We tested the clinical utility of the platelet count, aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio, and the AST to platelet ratio index (APRI) score in predicting the presence or absence of advanced fibrosis and cirrhosis in patients with chronic hepatitis C in Saudi Arabia. Liver biopsy procedures performed on chronic hepatitis C patients in our gastroenterology unit at King Khalid University Hospital were traced form records between 1998 to 2003. The hospital computer database was then accessed and detailed laboratory parameters obtained. By plotting receiver operating characteristic curves (ROC), three selected models (platelet count, AST/ALT ratio and the APRI score) were compared in terms of the best variable to predict significant fibrosis. Two hundred and forty-six patients with hepatitis C were included in this analysis. Overall, 26% of patients had advanced fibrosis. When comparing the three above mentioned prediction models, APRI score was the one associated with the highest area under the curve (AUC) = 0.812 (95%Cl, 0.756-0.868) on the ROC curves, compared to the platelet count and AST/ALT ratio, which yielded an AUC of 0.783 (0.711-0.855) and 0.716 (0.642-0.789), respectively. The APRI score seemed to be the best predictive variable for the presence or absence of advanced fibrosis in Saudi hepatitis C patients. (author)

New considerations on variability of creep rupture data and life prediction

International Nuclear Information System (INIS)

Kim, Seon Jin; Jeong, Won Taek; Kong, Yu Sik

2009-01-01

This paper deals with the variability analysis of short term creep rupture test data based on the previous creep rupture tests and the possibility of the creep life prediction. From creep tests performed by constant uniaxial stresses at 600, 650 and 700 .deg. C elevated temperature, in order to investigate the variability of short-term creep rupture data, the creep curves were analyzed for normalized creep strain divided by initial strain. There are some variability in thee creep rupture data. And, the difference between general creep curves and normalized creep curves were obtained. The effects of the creep rupture time and state steady creep rate on the Weibull distribution parameters were investigated. There were good relation between normal Weibull parameters and normalized Weibull parameters. Finally, the predicted creep life were compared with the Monkman-Grant model.

New Considerations on Variability of Creep Rupture Data and Life Prediction

International Nuclear Information System (INIS)

Jung, Won Taek; Kong, Yu Sik; Kim, Seon Jin

2009-01-01

This paper deals with the variability analysis of short term creep rupture test data based on the previous creep rupture tests and the possibility of the creep life prediction. From creep tests performed by constant uniaxial stresses at 600, 650 and 700 .deg. C elevated temperature, in order to investigate the variability of short-term creep rupture data, the creep curves were analyzed for normalized creep strain divided by initial strain. There are some variability in the creep rupture data. And, the difference between general creep curves and normalized creep curves were obtained. The effects of the creep rupture time (RT) and steady state creep rate (SSCR) on the Weibull distribution parameters were investigated. There were good relation between normal Weibull parameters and normalized Weibull parameters. Finally, the predicted creep life were compared with the Monkman-Grant model

Resting heart rate variability predicts safety learning and fear extinction in an interoceptive fear conditioning paradigm.

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Meike Pappens

Full Text Available This study aimed to investigate whether interindividual differences in autonomic inhibitory control predict safety learning and fear extinction in an interoceptive fear conditioning paradigm. Data from a previously reported study (N = 40 were extended (N = 17 and re-analyzed to test whether healthy participants' resting heart rate variability (HRV - a proxy of cardiac vagal tone - predicts learning performance. The conditioned stimulus (CS was a slight sensation of breathlessness induced by a flow resistor, the unconditioned stimulus (US was an aversive short-lasting suffocation experience induced by a complete occlusion of the breathing circuitry. During acquisition, the paired group received 6 paired CS-US presentations; the control group received 6 explicitly unpaired CS-US presentations. In the extinction phase, both groups were exposed to 6 CS-only presentations. Measures included startle blink EMG, skin conductance responses (SCR and US-expectancy ratings. Resting HRV significantly predicted the startle blink EMG learning curves both during acquisition and extinction. In the unpaired group, higher levels of HRV at rest predicted safety learning to the CS during acquisition. In the paired group, higher levels of HRV were associated with better extinction. Our findings suggest that the strength or integrity of prefrontal inhibitory mechanisms involved in safety- and extinction learning can be indexed by HRV at rest.

Exploration of Machine Learning Approaches to Predict Pavement Performance

Science.gov (United States)

2018-03-23

Machine learning (ML) techniques were used to model and predict pavement condition index (PCI) for various pavement types using a variety of input variables. The primary objective of this research was to develop and assess PCI predictive models for t...

Prediction of Cerebral Hyperperfusion Syndrome with Velocity Blood Pressure Index

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Zhi-Chao Lai

2015-01-01

Full Text Available Background: Cerebral hyperperfusion syndrome is an important complication of carotid endarterectomy (CEA. An >100% increase in middle cerebral artery velocity (MCAV after CEA is used to predict the cerebral hyperperfusion syndrome (CHS development, but the accuracy is limited. The increase in blood pressure (BP after surgery is a risk factor of CHS, but no study uses it to predict CHS. This study was to create a more precise parameter for prediction of CHS by combined the increase of MCAV and BP after CEA. Methods: Systolic MCAV measured by transcranial Doppler and systematic BP were recorded preoperatively; 30 min postoperatively. The new parameter velocity BP index (VBI was calculated from the postoperative increase ratios of MCAV and BP. The prediction powers of VBI and the increase ratio of MCAV (velocity ratio [VR] were compared for predicting CHS occurrence. Results: Totally, 6/185 cases suffered CHS. The best-fit cut-off point of 2.0 for VBI was identified, which had 83.3% sensitivity, 98.3% specificity, 62.5% positive predictive value and 99.4% negative predictive value for CHS development. This result is significantly better than VR (33.3%, 97.2%, 28.6% and 97.8%. The area under the curve (AUC of receiver operating characteristic: AUC VBI = 0.981, 95% confidence interval [CI] 0.949-0.995; AUC VR = 0.935, 95% CI 0.890-0.966, P = 0.02. Conclusions: The new parameter VBI can more accurately predict patients at risk of CHS after CEA. This observation needs to be validated by larger studies.

Prediction of Cerebral Hyperperfusion Syndrome with Velocity Blood Pressure Index.

Science.gov (United States)

Lai, Zhi-Chao; Liu, Bao; Chen, Yu; Ni, Leng; Liu, Chang-Wei

2015-06-20

Cerebral hyperperfusion syndrome is an important complication of carotid endarterectomy (CEA). An >100% increase in middle cerebral artery velocity (MCAV) after CEA is used to predict the cerebral hyperperfusion syndrome (CHS) development, but the accuracy is limited. The increase in blood pressure (BP) after surgery is a risk factor of CHS, but no study uses it to predict CHS. This study was to create a more precise parameter for prediction of CHS by combined the increase of MCAV and BP after CEA. Systolic MCAV measured by transcranial Doppler and systematic BP were recorded preoperatively; 30 min postoperatively. The new parameter velocity BP index (VBI) was calculated from the postoperative increase ratios of MCAV and BP. The prediction powers of VBI and the increase ratio of MCAV (velocity ratio [VR]) were compared for predicting CHS occurrence. Totally, 6/185 cases suffered CHS. The best-fit cut-off point of 2.0 for VBI was identified, which had 83.3% sensitivity, 98.3% specificity, 62.5% positive predictive value and 99.4% negative predictive value for CHS development. This result is significantly better than VR (33.3%, 97.2%, 28.6% and 97.8%). The area under the curve (AUC) of receiver operating characteristic: AUC(VBI) = 0.981, 95% confidence interval [CI] 0.949-0.995; AUC(VR) = 0.935, 95% CI 0.890-0.966, P = 0.02. The new parameter VBI can more accurately predict patients at risk of CHS after CEA. This observation needs to be validated by larger studies.

Combining biological and psychosocial baseline variables did not improve prediction of outcome of a very-low-energy diet in a clinic referral population.

Science.gov (United States)

Sumithran, P; Purcell, K; Kuyruk, S; Proietto, J; Prendergast, L A

2018-02-01

Consistent, strong predictors of obesity treatment outcomes have not been identified. It has been suggested that broadening the range of predictor variables examined may be valuable. We explored methods to predict outcomes of a very-low-energy diet (VLED)-based programme in a clinically comparable setting, using a wide array of pre-intervention biological and psychosocial participant data. A total of 61 women and 39 men (mean ± standard deviation [SD] body mass index: 39.8 ± 7.3 kg/m 2 ) underwent an 8-week VLED and 12-month follow-up. At baseline, participants underwent a blood test and assessment of psychological, social and behavioural factors previously associated with treatment outcomes. Logistic regression, linear discriminant analysis, decision trees and random forests were used to model outcomes from baseline variables. Of the 100 participants, 88 completed the VLED and 42 attended the Week 60 visit. Overall prediction rates for weight loss of ≥10% at weeks 8 and 60, and attrition at Week 60, using combined data were between 77.8 and 87.6% for logistic regression, and lower for other methods. When logistic regression analyses included only baseline demographic and anthropometric variables, prediction rates were 76.2-86.1%. In this population, considering a wide range of biological and psychosocial data did not improve outcome prediction compared to simply-obtained baseline characteristics. © 2017 World Obesity Federation.

Different minimally important clinical difference (MCID) scores lead to different clinical prediction rules for the Oswestry disability index for the same sample of patients.

Science.gov (United States)

Schwind, Julie; Learman, Kenneth; O'Halloran, Bryan; Showalter, Christopher; Cook, Chad

2013-05-01

Minimal clinically important difference (MCID) scores for outcome measures are frequently used evidence-based guides to gage meaningful changes. There are numerous outcome instruments used for analyzing pain, disability, and dysfunction of the low back; perhaps the most common of these is the Oswestry disability index (ODI). A single agreed-upon MCID score for the ODI has yet to be established. What is also unknown is whether selected baseline variables will be universal predictors regardless of the MCID used for a particular outcome measure. To explore the relationship between predictive models and the MCID cutpoint on the ODI. Data were collected from 16 outpatient physical therapy clinics in 10 states. Secondary database analysis using backward stepwise deletion logistic regression of data from a randomized controlled trial (RCT) to create prognostic clinical prediction rules (CPR). One hundred and forty-nine patients with low back pain (LBP) were enrolled in the RCT. All were treated with manual therapy, with a majority also receiving spine-strengthening exercises. The resultant predictive models were dependent upon the MCID used and baseline sample characteristics. All CPR were statistically significant (P < 001). All six MCID cutpoints used resulted in completely different significant predictor variables with no predictor significant across all models. The primary limitations include sub-optimal sample size and study design. There is extreme variability among predictive models created using different MCIDs on the ODI within the same patient population. Our findings highlight the instability of predictive modeling, as these models are significantly affected by population baseline characteristics along with the MCID used. Clinicians must be aware of the fragility of CPR prior to applying each in clinical practice.

The Doppler echocardiographic myocardial performance index predicts left-ventricular dilation and cardiac death after myocardial infarction

DEFF Research Database (Denmark)

Møller, J E; Søndergaard, E; Poulsen, S H

2001-01-01

To investigate the value of the Doppler-derived myocardial performance index to predict early left-ventricular (LV) dilation and cardiac death after a first acute myocardial infarction (AMI), Doppler echocardiography was performed within 24 h of hospital admission, on day 5, 1 and 3 months after...... AMI in 125 consecutive patients. The index measured on day 1 correlated well with the change in end-diastolic volume index observed from day 1 to 3 months following AMI (r = 0.66, p 0.0001). One-year survival in patients with Doppler index index > or = 0......, we conclude that the Doppler echocardiographic myocardial performance index is a predictor of LV dilation and cardiac death after a first AMI....

The prostate health index PHI predicts oncological outcome and biochemical recurrence after radical prostatectomy - analysis in 437 patients.

Science.gov (United States)

Maxeiner, Andreas; Kilic, Ergin; Matalon, Julia; Friedersdorff, Frank; Miller, Kurt; Jung, Klaus; Stephan, Carsten; Busch, Jonas

2017-10-03

The purpose of this study was to investigate the Prostate-Health-Index (PHI) for pathological outcome prediction following radical prostatectomy and also for biochemical recurrence prediction in comparison to established parameters such as Gleason-score, pathological tumor stage, resection status (R0/1) and prostate-specific antigen (PSA). Out of a cohort of 460 cases with preoperative PHI-measurements (World Health Organization calibration: Beckman Coulter Access-2-Immunoassay) between 2001 and 2014, 437 patients with complete follow up data were included. From these 437 patients, 87 (19.9%) developed a biochemical recurrence. Patient characteristics were compared by using chi-square test. Predictors were analyzed by multivariate adjusted logistic and Cox regression. The median follow up for a biochemical recurrence was 65 (range 3-161) months. PHI, PSA, [-2]proPSA, PHI- and PSA-density performed as significant variables (p PHI, PSA, %fPSA, [-2]proPSA, PHI- and PSA-density significantly discriminated between stages PHI. In biochemical recurrence prediction PHI, PSA, [-2]proPSA, PHI- and PSA-density were the strongest predictors. In conclusion, due to heterogeneity of time spans to biochemical recurrence, longer follow up periods are crucial. This study with a median follow up of more than 5 years, confirmed a clinical value for PHI as an independent biomarker essential for biochemical recurrence prediction.

Relative Contributions of Socio-Cultural Variables to the Prediction ...

African Journals Online (AJOL)

Erah

the Prediction of Maternal Mortality in Edo South. Senatorial ... variables across the two locations (rural and urban) was early marriage/early child bearing (R2 = 0.200;. F = 401.40 ... severe bleeding, infections, obstructed or prolonged .... Analytical System (SAS) mode. Descriptive .... incontinence of urine and faeces due to.

THE EFFECT OF MACROECONOMIC VARIABLES ON BANKING STOCK PRICE INDEX IN INDONESIA STOCK EXCHANGE

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Laduna R.

2018-01-01

Full Text Available Stock price index can be regarded as a barometer in the measuremet of a nation’s economic condition, besides it can also be used in conducting statistical analysis on the current market. Stock is the proof of one’s share in a company in the form of securities issued by the listed go-public companies. This study was conducted to measure the effect of macroeconomic variables such as inflation, interest rate, and exchange rate on banking stock price index in Indonesia stock exchange or Bursa Efek Indonesia (BEI. The results of study show that inflation and exchange rate posively influence the stock price index. The positive effect of the exchange rate shows that issuers who were positively affected by Rupiah (IDR depreciation appear to be the most dominant group. Meanwhile, the interest rate or Suku Bunga (SBI has a negative effect. Lower interest rate stimulates higher investments and better economic activities which increase the stock price.

Predicting farm-level animal populations using environmental and socioeconomic variables.

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van Andel, Mary; Jewell, Christopher; McKenzie, Joanna; Hollings, Tracey; Robinson, Andrew; Burgman, Mark; Bingham, Paul; Carpenter, Tim

2017-09-15

Accurate information on the geographic distribution of domestic animal populations helps biosecurity authorities to efficiently prepare for and rapidly eradicate exotic diseases, such as Foot and Mouth Disease (FMD). Developing and maintaining sufficiently high-quality data resources is expensive and time consuming. Statistical modelling of population density and distribution has only begun to be applied to farm animal populations, although it is commonly used in wildlife ecology. We developed zero-inflated Poisson regression models in a Bayesian framework using environmental and socioeconomic variables to predict the counts of livestock units (LSUs) and of cattle on spatially referenced farm polygons in a commercially available New Zealand farm database, Agribase. Farm-level counts of cattle and of LSUs varied considerably by region, because of the heterogeneous farming landscape in New Zealand. The amount of high quality pasture per farm was significantly associated with the presence of both cattle and LSUs. Internal model validation (predictive performance) showed that the models were able to predict the count of the animal population on groups of farms that were located in randomly selected 3km zones with a high level of accuracy. Predicting cattle or LSU counts on individual farms was less accurate. Predicted counts were statistically significantly more variable for farms that were contract grazing dry stock, such as replacement dairy heifers and dairy cattle not currently producing milk, compared with other farm types. This analysis presents a way to predict numbers of LSUs and cattle for farms using environmental and socio-economic data. The technique has the potential to be extrapolated to predicting other pastoral based livestock species. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Predictive performance of the visceral adiposity index for a visceral adiposity-related risk: Type 2 Diabetes

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Azizi Fereidoun

2011-05-01

Full Text Available Abstract Background Visceral adiposity index (VAI has recently been developed based on waist circumference, body mass index (BMI, triglycerides (TGs, and high-density lipoprotein cholesterol (HDL-C. We examined predictive performances for incident diabetes of the VAI per se and as compared to the metabolic syndrome (MetS and waist-to-height-ratio (WHtR. Methods Participants free of diabetes at baseline with at least one follow-up examination (5,964 were included for the current study. Weibull regression models were developed for interval-censored survival data. Absolute and relative integrated discriminatory improvement index (IDI and cut-point-based and cut-point-free net reclassification improvement index (NRI were used as measures of predictive ability for incident diabetes added by VAI, as compared to the MetS and WHtR. Results The annual incidence rate of diabetes was 0.85 per 1000 person. Mean VAI was 3.06 (95%CIs 2.99-3.13. Diabetes risk factors levels increased in stepwise fashion across VAI quintiles. Risk gradient between the highest and lowest quintile of VAI was 4.5 (95%CIs 3.0-6.9. VAI significantly improved predictive ability of the MetS. The relative IDI and cut-point free NRI for predictive ability added to MetS by VAI were 30.3% (95%CIs 18.8-41.8% and 30.7% (95%CIs 20.8-40.7%, respectively. WHtR, outperformed VAI with cut-point-free NRI of 24.6% (95%CIs 14.1-35.2%. Conclusions In conclusion, although VAI could be a prognostic tool for incident diabetes events, gathering information on its components (WC, BMI, TGs, and HDL-C is unlikely to improve the prediction ability beyond what could be achieved by the simply assessable and commonly available information on WHtR.

Use of social adaptability index to explain self-care and diabetes outcomes.

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Campbell, Jennifer A; Walker, Rebekah J; Smalls, Brittany L; Egede, Leonard E

2017-06-20

To examine whether the social adaptability index (SAI) alone or components of the index provide a better explanatory model for self-care and diabetes outcomes. Six hundred fifteen patients were recruited from two primary care settings. A series of multiple linear regression models were run to assess (1) associations between the SAI and diabetes self-care/outcomes, and (2) associations between individual SAI indicator variables and diabetes self-care/outcomes. Separate models were run for each self-care behavior and outcome. Two models were run for each dependent variable to compare associations with the SAI and components of the index. The SAI has a significant association with the mental component of quality of life (0.23, p < 0.01). In adjusted analyses, the SAI score did not have a significant association with any of the self-care behaviors. Individual components from the index had significant associations between self-care and multiple SAI indicator variables. Significant associations also exist between outcomes and the individual SAI indicators for education and employment. In this population, the SAI has low explanatory power and few significant associations with diabetes self-care/outcomes. While the use of a composite index to predict outcomes within a diabetes population would have high utility, particularly for clinical settings, this SAI lacks statistical and clinical significance in a representative diabetes population. Based on these results, the index does not provide a good model fit and masks the relationship of individual components to diabetes self-care and outcomes. These findings suggest that five items alone are not adequate to explain or predict outcomes for patients with type 2 diabetes.

Predicting Jakarta composite index using hybrid of fuzzy time series and support vector regression models

Science.gov (United States)

Febrian Umbara, Rian; Tarwidi, Dede; Budi Setiawan, Erwin

2018-03-01

The paper discusses the prediction of Jakarta Composite Index (JCI) in Indonesia Stock Exchange. The study is based on JCI historical data for 1286 days to predict the value of JCI one day ahead. This paper proposes predictions done in two stages., The first stage using Fuzzy Time Series (FTS) to predict values of ten technical indicators, and the second stage using Support Vector Regression (SVR) to predict the value of JCI one day ahead, resulting in a hybrid prediction model FTS-SVR. The performance of this combined prediction model is compared with the performance of the single stage prediction model using SVR only. Ten technical indicators are used as input for each model.

An efficient link prediction index for complex military organization

Science.gov (United States)

Fan, Changjun; Liu, Zhong; Lu, Xin; Xiu, Baoxin; Chen, Qing

2017-03-01

Quality of information is crucial for decision-makers to judge the battlefield situations and design the best operation plans, however, real intelligence data are often incomplete and noisy, where missing links prediction methods and spurious links identification algorithms can be applied, if modeling the complex military organization as the complex network where nodes represent functional units and edges denote communication links. Traditional link prediction methods usually work well on homogeneous networks, but few for the heterogeneous ones. And the military network is a typical heterogeneous network, where there are different types of nodes and edges. In this paper, we proposed a combined link prediction index considering both the nodes' types effects and nodes' structural similarities, and demonstrated that it is remarkably superior to all the 25 existing similarity-based methods both in predicting missing links and identifying spurious links in a real military network data; we also investigated the algorithms' robustness under noisy environment, and found the mistaken information is more misleading than incomplete information in military areas, which is different from that in recommendation systems, and our method maintained the best performance under the condition of small noise. Since the real military network intelligence must be carefully checked at first due to its significance, and link prediction methods are just adopted to purify the network with the left latent noise, the method proposed here is applicable in real situations. In the end, as the FINC-E model, here used to describe the complex military organizations, is also suitable to many other social organizations, such as criminal networks, business organizations, etc., thus our method has its prospects in these areas for many tasks, like detecting the underground relationships between terrorists, predicting the potential business markets for decision-makers, and so on.

US Climate Variability and Predictability Project

Energy Technology Data Exchange (ETDEWEB)

Patterson, Mike [University Corporation for Atmospheric Research (UCAR), Boulder, CO (United States)

2017-11-14

The US CLIVAR Project Office administers the US CLIVAR Program with its mission to advance understanding and prediction of climate variability and change across timescales with an emphasis on the role of the ocean and its interaction with other elements of the Earth system. The Project Office promotes and facilitates scientific collaboration within the US and international climate and Earth science communities, addressing priority topics from subseasonal to centennial climate variability and change; the global energy imbalance; the ocean’s role in climate, water, and carbon cycles; climate and weather extremes; and polar climate changes. This project provides essential one-year support of the Project Office, enabling the participation of US scientists in the meetings of the US CLIVAR bodies that guide scientific planning and implementation, including the scientific steering committee that establishes program goals and evaluates progress of activities to address them, the science team of funded investigators studying the ocean overturning circulation in the Atlantic, and two working groups tackling the priority research topics of Arctic change influence on midlatitude climate and weather extremes and the decadal-scale widening of the tropical belt.

Variables that predict academic procrastination behavior in prospective primary school teachers

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Asuman Seda SARACALOĞLU

2016-04-01

Full Text Available This study aimed to examine the variables predicting academic procrastination behavior of prospective primary school teachers and is conducted using the correlational survey model. The study group is composed of 294 undergraduate students studying primary school teaching programs in faculties of education at Adnan Menderes, Pamukkale, and Muğla Sıtkı Koçman Universities in Turkey. The data collection instruments used were the Procrastination Assessment Scale Students (PASS, Academic Self-Efficacy Scale (ASES, and Academic Motivation Scale (AMS. While analyzing the gathered data, descriptive analysis techniques were utilized. Moreover, while analyzing the data, power of variables namely reasons of academic procrastination, academic motivation, and academic efficacy to predict prospective primary school teachers’ academic procrastination tendencies were tested. For that purpose, stepwise regression analysis was employed. It was found that nearly half of the prospective primary school teachers displayed no academic procrastination behavior. Participants’ reasons for procrastination were fear of failure, laziness, taking risks, and rebellion against control. An average level significant correlation was found between participants’ academic procrastination and other variables. As a result, it was identified that prospective primary school teachers had less academic procrastination than reported in literature and laziness, fear of failure, academic motivation predicted academic procrastination.

Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain Gain

DEFF Research Database (Denmark)

Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik

2012-01-01

Robust model predictive control (RMPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Because...... of the special structure of the problem, uncertainty is only in the B matrix (gain) of the state space model. Therefore by taking advantage of this structure, we formulate a tractable minimax optimization problem to solve robust model predictive control problem. Wind turbine is chosen as the case study and we...... choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon....

Interobserver variability of sonography for prediction of placenta accreta.

Science.gov (United States)

Bowman, Zachary S; Eller, Alexandra G; Kennedy, Anne M; Richards, Douglas S; Winter, Thomas C; Woodward, Paula J; Silver, Robert M

2014-12-01

The sensitivity of sonography to predict accreta has been reported as higher than 90%. However, most studies are from single expert investigators. Our objective was to analyze interobserver variability of sonography for prediction of placenta accreta. Patients with previa with and without accreta were ascertained, and images with placental views were collected, deidentified, and placed in random sequence. Three radiologists and 3 maternal-fetal medicine specialists interpreted each study for the presence of accreta and specific findings reported to be associated with its diagnosis. Investigator-specific sensitivity, specificity, and accuracy were calculated. κ statistics were used to assess variability between individuals and types of investigators. A total of 229 sonographic studies from 55 patients with accreta and 56 control patients were examined. Accuracy ranged from 55.9% to 76.4%. Of imaging studies yielding diagnoses, sensitivity ranged from 53.4% to 74.4%, and specificity ranged from 70.8% to 94.8%. Overall interobserver agreement was moderate (mean κ ± SD = 0.47 ± 0.12). κ values between pairs of investigators ranged from 0.32 (fair agreement) to 0.73 (substantial agreement). Average individual agreement ranged from fair (κ = 0.35) to moderate (κ = 0.53). Blinded from clinical data, sonography has significant interobserver variability for the diagnosis of placenta accreta. © 2013 by the American Institute of Ultrasound in Medicine.

A comparative study on approximate entropy measure and poincaré plot indexes of minimum foot clearance variability in the elderly during walking

Directory of Open Access Journals (Sweden)

Begg Rezaul K

2008-02-01

Full Text Available Abstract Background Trip-related falls which is a major problem in the elderly population, might be linked to declines in the balance control function due to ageing. Minimum foot clearance (MFC which provides a more sensitive measure of the motor function of the locomotor system, has been identified as a potential gait parameter associated with trip-related falls in older population. This paper proposes nonlinear indexes (approximate entropy (ApEn and Poincaré plot indexes of MFC variability and investigates the relationship of MFC with derived indexes of elderly gait patterns. The main aim is to find MFC variability indexes that well correlate with balance impairments. Methods MFC data during treadmill walking for 14 healthy elderly and 10 elderly participants with balance problems and a history of falls (falls risk were analysed using a PEAK-2D motion analysis system. ApEn and Poincaré plot indexes of all MFC data sets were calculated and compared. Results Significant relationships of mean MFC with Poincaré plot indexes (SD1, SD2 and ApEn (r = 0.70, p Conclusion Results have implication for quantifying gait dynamics in normal and pathological conditions, thus could be useful for the early diagnosis of at-risk gait. Further research should provide important information on whether falls prevention intervention can improve the gait performance of falls risk elderly by monitoring the change in MFC variability indexes.

Dynamic Variables Fail to Predict Fluid Responsiveness in an Animal Model With Pericardial Effusion.

Science.gov (United States)

Broch, Ole; Renner, Jochen; Meybohm, Patrick; Albrecht, Martin; Höcker, Jan; Haneya, Assad; Steinfath, Markus; Bein, Berthold; Gruenewald, Matthias

2016-10-01

The reliability of dynamic and volumetric variables of fluid responsiveness in the presence of pericardial effusion is still elusive. The aim of the present study was to investigate their predictive power in a porcine model with hemodynamic relevant pericardial effusion. A single-center animal investigation. Twelve German domestic pigs. Pigs were studied before and during pericardial effusion. Instrumentation included a pulmonary artery catheter and a transpulmonary thermodilution catheter in the femoral artery. Hemodynamic variables like cardiac output (COPAC) and stroke volume (SVPAC) derived from pulmonary artery catheter, global end-diastolic volume (GEDV), stroke volume variation (SVV), and pulse-pressure variation (PPV) were obtained. At baseline, SVV, PPV, GEDV, COPAC, and SVPAC reliably predicted fluid responsiveness (area under the curve 0.81 [p = 0.02], 0.82 [p = 0.02], 0.74 [p = 0.07], 0.74 [p = 0.07], 0.82 [p = 0.02]). After establishment of pericardial effusion the predictive power of dynamic variables was impaired and only COPAC and SVPAC and GEDV allowed significant prediction of fluid responsiveness (area under the curve 0.77 [p = 0.04], 0.76 [p = 0.05], 0.83 [p = 0.01]) with clinically relevant changes in threshold values. In this porcine model, hemodynamic relevant pericardial effusion abolished the ability of dynamic variables to predict fluid responsiveness. COPAC, SVPAC, and GEDV enabled prediction, but their threshold values were significantly changed. Copyright © 2016 Elsevier Inc. All rights reserved.

Fatty liver index and hepatic steatosis index for prediction of non-alcoholic fatty liver disease in type 1 diabetes.

Science.gov (United States)

Sviklāne, Laura; Olmane, Evija; Dzērve, Zane; Kupčs, Kārlis; Pīrāgs, Valdis; Sokolovska, Jeļizaveta

2018-01-01

Little is known about the diagnostic value of hepatic steatosis index (HSI) and fatty liver index (FLI), as well as their link to metabolic syndrome in type 1 diabetes mellitus. We have screened the effectiveness of FLI and HSI in an observational pilot study of 40 patients with type 1 diabetes. FLI and HSI were calculated for 201 patients with type 1 diabetes. Forty patients with FLI/HSI values corresponding to different risk of liver steatosis were invited for liver magnetic resonance study. In-phase/opposed-phase technique of magnetic resonance was used. Accuracy of indices was assessed from the area under the receiver operating characteristic curve. Twelve (30.0%) patients had liver steatosis. For FLI, sensitivity was 90%; specificity, 74%; positive likelihood ratio, 3.46; negative likelihood ratio, 0.14; positive predictive value, 0.64; and negative predictive value, 0.93. For HSI, sensitivity was 86%; specificity, 66%; positive likelihood ratio, 1.95; negative likelihood ratio, 0.21; positive predictive value, 0.50; and negative predictive value, 0.92. Area under the receiver operating characteristic curve for FLI was 0.86 (95% confidence interval [0.72; 0.99]); for HSI 0.75 [0.58; 0.91]. Liver fat correlated with liver enzymes, waist circumference, triglycerides, and C-reactive protein. FLI correlated with C-reactive protein, liver enzymes, and blood pressure. HSI correlated with waist circumference and C-reactive protein. FLI ≥ 60 and HSI ≥ 36 were significantly associated with metabolic syndrome and nephropathy. The tested indices, especially FLI, can serve as surrogate markers for liver fat content and metabolic syndrome in type 1 diabetes. © 2017 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data.

Science.gov (United States)

Lee, Kyung Sang; Lee, Hyewon; Myung, Woojae; Song, Gil-Young; Lee, Kihwang; Kim, Ho; Carroll, Bernard J; Kim, Doh Kwan

2018-04-01

Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week. The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period. Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables. These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events.

Predictor variables for a half marathon race time in recreational male runners.

Science.gov (United States)

Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Lepers, Romuald; Rosemann, Thomas

2011-01-01

The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the 'Half Marathon Basel' completed the race distance within (mean and standard deviation, SD) 103.9 (16.5) min, running at a speed of 12.7 (1.9) km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56), suprailiacal (r = 0.36) and medial calf skin fold (r = 0.53) were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = -0.54) were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150) and speed in running during training (P = 0.0045) were related to race time. Race time in a half marathon might be partially predicted by the following equation (r(2) = 0.44): Race time (min) = 72.91 + 3.045 * (body mass index, kg/m(2)) -3.884 * (speed in running during training, km/h) for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable.

Do Assault-Related Variables Predict Response to Cognitive Behavioral Treatment for PTSD?

Science.gov (United States)

Hembree, Elizabeth A.; Street, Gordon P.; Riggs, David S.; Foa, Edna B.

2004-01-01

This study examined the hypothesis that variables such as history of prior trauma, assault severity, and type of assault, previously found to be associated with natural recovery, would also predict treatment outcome. Trauma-related variables were examined as predictors of posttreatment posttraumatic stress disorder (PTSD) severity in a sample of…

Predicting pavement condition index using international roughness index in Washington DC.

Science.gov (United States)

2014-09-01

A number of pavement condition indices are used to conduct pavement management assessments, two of which are the : International Roughness Index (IRI) and Pavement Condition Index (PCI). The IRI is typically measured using specialized : equipment tha...

Learning modulation of odor representations: new findings from Arc-indexed networks

Directory of Open Access Journals (Sweden)

Qi eYuan

2014-12-01

Full Text Available We first review our understanding of odor representations in rodent olfactory bulb and anterior piriform cortex. We then consider learning-induced representation changes. Finally we describe the perspective on network representations gained from examining Arc-indexed odor networks of awake rats. Arc-indexed networks are sparse and distributed, consistent with current views. However Arc provides representations of repeated odors. Arc-indexed repeated odor representations are quite variable. Sparse representations are assumed to be compact and reliable memory codes. Arc suggests this is not necessarily the case. The variability seen is consistent with electrophysiology in awake animals and may reflect top down-cortical modulation of context. Arc-indexing shows that distinct odors share larger than predicted neuron pools. These may be low-threshold neuronal subsets.Learning’s effect on Arc-indexed representations is to increase the stable or overlapping component of rewarded odor representations. This component can decrease for similar odors when their discrimination is rewarded. The learning effects seen are supported by electrophysiology, but mechanisms remain to be elucidated.

Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index.

Directory of Open Access Journals (Sweden)

Ping Wang

2011-02-01

Full Text Available Weight regain after weight loss is common. In the Diogenes dietary intervention study, high protein and low glycemic index (GI diet improved weight maintenance.To identify blood predictors for weight change after weight loss following the dietary intervention within the Diogenes study.Blood samples were collected at baseline and after 8-week low caloric diet-induced weight loss from 48 women who continued to lose weight and 48 women who regained weight during subsequent 6-month dietary intervention period with 4 diets varying in protein and GI levels. Thirty-one proteins and 3 steroid hormones were measured.Angiotensin I converting enzyme (ACE was the most important predictor. Its greater reduction during the 8-week weight loss was related to continued weight loss during the subsequent 6 months, identified by both Logistic Regression and Random Forests analyses. The prediction power of ACE was influenced by immunoproteins, particularly fibrinogen. Leptin, luteinizing hormone and some immunoproteins showed interactions with dietary protein level, while interleukin 8 showed interaction with GI level on the prediction of weight maintenance. A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.A selected panel of blood proteins/steroids can predict the weight change after weight loss. ACE may play an important role in weight maintenance. The interactions of blood factors with dietary components are important for personalized dietary advice after weight loss.ClinicalTrials.gov NCT00390637.

Emotionally Excited Eyeblink-Rate Variability Predicts an Experience of Transportation into the Narrative World

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Ryota eNomura

2015-04-01

Full Text Available Collective spectator communications such as oral presentations, movies, and storytelling performances are ubiquitous in human culture. This study investigated the effects of past viewing experiences and differences in expressive performance on an audience’s transportive experience into a created world of a storytelling performance. In the experiment, 60 participants (mean age = 34.12 yrs., SD = 13.18 yrs., range 18–63 yrs. were assigned to watch one of two videotaped performances that were played (1 in an orthodox way for frequent viewers and (2 in a modified way aimed at easier comprehension for first-time viewers. Eyeblink synchronization among participants was quantified by employing distance-based measurements of spike trains, Dspike and Dinterval (Victor & Purpura, 1997. The results indicated that even non-familiar participants’ eyeblinks were synchronized as the story progressed and that the effect of the viewing experience on transportation was weak. Rather, the results of a multiple regression analysis demonstrated that the degrees of transportation could be predicted by a retrospectively reported humor experience and higher real-time variability (i.e., logarithmic transformed standard deviation of inter blink intervals during a performance viewing. The results are discussed from the viewpoint in which the extent of eyeblink synchronization and eyeblink-rate variability acts as an index of the inner experience of audience members.

Global variability in angina pectoris and its association with body mass index and poverty.

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Liu, Longjian; Ma, Jixiang; Yin, Xiaoyan; Kelepouris, Ellie; Eisen, Howard J

2011-03-01

In the absence of a previous global comparison, we examined the variability in the prevalence of angina across 52 countries and its association with body weight and the poverty index using data from the World Health Organization-World Health Survey. The participants with angina were defined as those who had positive results using a Rose angina questionnaire and/or self-report of a physician diagnosis of angina. The body mass index (BMI) was determined as the weight in kilograms divided by the square of the height in meters. The poverty index (a standard score of socioeconomic status for a given country) was extracted from the United Nations' statistics. The associations of angina with the BMI and poverty index were analyzed cross-sectionally using univariate and multivariate analyses. The results showed that the total participants (n = 210,787) had an average age of 40.64 years. The prevalence of angina ranged from 2.44% in Tunisia to 23.89% in Chad. Those participants with a BMI of poverty status was considered. A tendency was seen for underweight status and a poverty index >14.65% to be associated with the risk of having angina, although these associations were not statistically significant in the multilevel models. In conclusion, significant variations were found in the anginal rates across 52 countries worldwide. An increased BMI was significantly associated with the odds of having angina. Published by Elsevier Inc.

Novel immunological and nutritional-based prognostic index for gastric cancer.

Science.gov (United States)

Sun, Kai-Yu; Xu, Jian-Bo; Chen, Shu-Ling; Yuan, Yu-Jie; Wu, Hui; Peng, Jian-Jun; Chen, Chuang-Qi; Guo, Pi; Hao, Yuan-Tao; He, Yu-Long

2015-05-21

To assess the prognostic significance of immunological and nutritional-based indices, including the prognostic nutritional index (PNI), neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio in gastric cancer. We retrospectively reviewed 632 gastric cancer patients who underwent gastrectomy between 1998 and 2008. Areas under the receiver operating characteristic curve were calculated to compare the predictive ability of the indices, together with estimating the sensitivity, specificity and agreement rate. Univariate and multivariate analyses were performed to identify risk factors for overall survival (OS). Propensity score analysis was performed to adjust variables to control for selection bias. Each index could predict OS in gastric cancer patients in univariate analysis, but only PNI had independent prognostic significance in multivariate analysis before and after adjustment with propensity scoring (hazard ratio, 1.668; 95% confidence interval: 1.368-2.035). In subgroup analysis, a low PNI predicted a significantly shorter OS in patients with stage II-III disease (P = 0.019, P gastric cancer. Canton score can be a novel preoperative prognostic index in gastric cancer.

Wetland habitat disturbance best predicts metrics of an amphibian index of biotic integrity

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Stapanian, Martin A.; Micacchion, Mick; Adams, Jean V.

2015-01-01

Regression and classification trees were used to identify the best predictors of the five component metrics of the Ohio Amphibian Index of Biotic Integrity (AmphIBI) in 54 wetlands in Ohio, USA. Of the 17 wetland- and surrounding landscape-scale variables considered, the best predictor for all AmphIBI metrics was habitat alteration and development within the wetland. The results were qualitatively similar to the best predictors for a wetland vegetation index of biotic integrity, suggesting that similar management practices (e.g., reducing or eliminating nutrient enrichment from agriculture, mowing, grazing, logging, and removing down woody debris) within the boundaries of the wetland can be applied to effectively increase the quality of wetland vegetation and amphibian communities.

Extremes of shock index predicts death in trauma patients

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Stephen R Odom

2016-01-01

Full Text Available Context: We noted a bimodal relationship between mortality and shock index (SI, the ratio of heart rate to systolic blood pressure. Aims: To determine if extremes of SI can predict mortality in trauma patients. Settings and Designs: Retrospective evaluation of adult trauma patients at a tertiary care center from 2000 to 2012 in the United States. Materials and Methods: We examined the SI in trauma patients and determined the adjusted mortality for patients with and without head injuries. Statistical Analysis Used: Descriptive statistics and multivariable logistic regression. Results: SI values demonstrated a U-shaped relationship with mortality. Compared with patients with a SI between 0.5 and 0.7, patients with a SI of 1.3 had an odds ratio of death of 3.1. (95% CI 1.6–5.9. Elevated SI is associated with increased mortality in patients with isolated torso injuries, and is associated with death at both low and high values in patients with head injury. Conclusion: Our data indicate a bimodal relationship between SI and mortality in head injured patients that persists after correction for various co-factors. The distribution of mortality is different between head injured patients and patients without head injuries. Elevated SI predicts death in all trauma patients, but low SI values only predict death in head injured patients.

Methodology for formulating predictions of stress corrosion cracking life

International Nuclear Information System (INIS)

Yamauchi, Kiyoshi; Hattori, Shigeo; Shindo, Takenori; Kuniya, Jiro

1994-01-01

This paper presents a methodology for formulating predictions to evaluate the stress corrosion cracking (SCC) potential of each light-water reactor component, where an index is introduced as a life index or F index. The index denotes the SCC time ratio of a given SCC system to be evaluated against a reference SCC system. The life index is expressed by the products of several subdivided life indexes, which correspond to each SCC influencing factor. Each subdivided life index is constructed as a function containing the influencing factor variable, obtained by analyzing experimental SCC life data. The methodology was termed the subdivided factor method. Application of the life index to SCC life data and field data showed that it was effective for evaluating the SCC potential, i.e. the SCC life. Accordingly, the proposed methodology can potentially describe a phenomenon expressed by a function which consists of the variables of several influencing factors whether there are formulae which unite as a physical model or not. ((orig.))

Variability and predictability of decadal mean temperature and precipitation over China in the CCSM4 last millennium simulation

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Ying, Kairan; Frederiksen, Carsten S.; Zheng, Xiaogu; Lou, Jiale; Zhao, Tianbao

2018-02-01

The modes of variability that arise from the slow-decadal (potentially predictable) and intra-decadal (unpredictable) components of decadal mean temperature and precipitation over China are examined, in a 1000 year (850-1850 AD) experiment using the CCSM4 model. Solar variations, volcanic aerosols, orbital forcing, land use, and greenhouse gas concentrations provide the main forcing and boundary conditions. The analysis is done using a decadal variance decomposition method that identifies sources of potential decadal predictability and uncertainty. The average potential decadal predictabilities (ratio of slow-to-total decadal variance) are 0.62 and 0.37 for the temperature and rainfall over China, respectively, indicating that the (multi-)decadal variations of temperature are dominated by slow-decadal variability, while precipitation is dominated by unpredictable decadal noise. Possible sources of decadal predictability for the two leading predictable modes of temperature are the external radiative forcing, and the combined effects of slow-decadal variability of the Arctic oscillation (AO) and the Pacific decadal oscillation (PDO), respectively. Combined AO and PDO slow-decadal variability is associated also with the leading predictable mode of precipitation. External radiative forcing as well as the slow-decadal variability of PDO are associated with the second predictable rainfall mode; the slow-decadal variability of Atlantic multi-decadal oscillation (AMO) is associated with the third predictable precipitation mode. The dominant unpredictable decadal modes are associated with intra-decadal/inter-annual phenomena. In particular, the El Niño-Southern Oscillation and the intra-decadal variability of the AMO, PDO and AO are the most important sources of prediction uncertainty.

Identification of cognitive and non-cognitive predictive variables related to attrition in baccalaureate nursing education programs in Mississippi

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Hayes, Catherine

2005-07-01

This study sought to identify a variable or variables predictive of attrition among baccalaureate nursing students. The study was quantitative in design and multivariate correlational statistics and discriminant statistical analysis were used to identify a model for prediction of attrition. The analysis then weighted variables according to their predictive value to determine the most parsimonious model with the greatest predictive value. Three public university nursing education programs in Mississippi offering a Bachelors Degree in Nursing were selected for the study. The population consisted of students accepted and enrolled in these three programs for the years 2001 and 2002 and graduating in the years 2003 and 2004 (N = 195). The categorical dependent variable was attrition (includes academic failure or withdrawal) from the program of nursing education. The ten independent variables selected for the study and considered to have possible predictive value were: Grade Point Average for Pre-requisite Course Work; ACT Composite Score, ACT Reading Subscore, and ACT Mathematics Subscore; Letter Grades in the Courses: Anatomy & Physiology and Lab I, Algebra I, English I (101), Chemistry & Lab I, and Microbiology & Lab I; and Number of Institutions Attended (Universities, Colleges, Junior Colleges or Community Colleges). Descriptive analysis was performed and the means of each of the ten independent variables was compared for students who attrited and those who were retained in the population. The discriminant statistical analysis performed created a matrix using the ten variable model that was able to correctly predicted attrition in the study's population in 77.6% of the cases. Variables were then combined and recombined to produce the most efficient and parsimonious model for prediction. A six variable model resulted which weighted each variable according to predictive value: GPA for Prerequisite Coursework, ACT Composite, English I, Chemistry & Lab I, Microbiology

Interannual Variability, Global Teleconnection, and Potential Predictability Associated with the Asian Summer Monsoon

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Lau, K. M.; Kim, K. M.; Li, J. Y.

2001-01-01

In this Chapter, aspects of global teleconnections associated with the interannual variability of the Asian summer monsoon (ASM) are discussed. The basic differences in the basic dynamics of the South Asian Monsoon and the East Asian monsoon, and their implications on global linkages are discussed. Two teleconnection modes linking ASM variability to summertime precipitation over the continental North America were identified. These modes link regional circulation and precipitation anomalies over East Asia and continental North America, via coupled atmosphere-ocean variations over the North Pacific. The first mode has a large zonally symmetrical component and appears to be associated with subtropical jetstream variability and the second mode with Rossby wave dispersion. Both modes possess strong sea surface temperature (SST) expressions in the North Pacific. Results show that the two teleconnection modes may have its origin in intrinsic modes of sea surface temperature variability in the extratropical oceans, which are forced in part by atmospheric variability and in part by air-sea interaction. The potential predictability of the ASM associated with SST variability in different ocean basins is explored using a new canonical ensemble correlation prediction scheme. It is found that SST anomalies in tropical Pacific, i.e., El Nino, is the most dominant forcing for the ASM, especially over the maritime continent and eastern Australia. SST anomalies in the India Ocean may trump the influence from El Nino in western Australia and western maritime continent. Both El Nino, and North Pacific SSTs contribute to monsoon precipitation anomalies over Japan, southern Korea, northern and central China. By optimizing SST variability signals from the world ocean basins using CEC, the overall predictability of ASM can be substantially improved.

Starch digestibility and predicted glycemic index in the bread fortified with pomelo (Citrus maxima) fruit segments.

Science.gov (United States)

Reshmi, S K; Sudha, M L; Shashirekha, M N

2017-12-15

The aim of this study was to evaluate the starch digestibility and predicted glycemic index in breads incorporated with pomelo fruit (Citrus maxima) segments. Volume of the white and brown breads supplemented with pomelo fresh segments increased, while the crumb firmness decreased. Bread with 20% fresh and 5% dry pomelo segments were sensorily acceptable. Bioactive components such as phenolics, flavonoids, naringin and carotenoids were retained to a greater extent in bread containing dry pomelo segments. The pomelo incorporated bread had higher levels of resistant starch fractions (3.87-10.96%) with low predicted glycemic index (62.97-53.13%), despite their higher total starch (69.87-75.47%) content compared to control bread. Thus pomelo segments in the product formulations lowered the glycemic index probably by inhibiting carbohydrate hydrolyzing enzyme activity which could be attributed to naringin. Hence fortified bread prepared from pomelo fruit segment is recommended to gain nutritional value and to decrease the risk of diabetes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effects of physical exercice over corporal fat predictor indexes: corporal mass index, waist-hip proportion and cutaneous folds

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Albertino de Oliveira Filho

2008-06-01

Full Text Available The aim of this study was to verify alterations in corporal fat amount prediction indexes as a consequence of physical exercise, in assiduous individuals of programs offered in academies in the city of Maringá, state of Paraná, Brazil. The sample consisted of 68 subjects who practiced swimming, water aerobics, gymnastics or muscular exercice, being 38 women (age 29±6 years and 30 men (age 28±8 years. The data was collected during the year of 2000. According to the results, both groups showed significant decrease of the variables related to corporal fat prediction (fat percentage, corporal mass index, waist-hip proportion and significant increase in the thin corporal mass, independent of the exercise modality, allowing the conclusion that, besides aesthetic effects, physical exercises precticed with regularity and continuity act positively on aspects related to the individual's life quality, bringing him/her closer to ideal health standards.

Intra and interobserver variability of renal allograft ultrasound volume and resistive index measurements

International Nuclear Information System (INIS)

Mancini, Marcello; Liuzzi, Raffaele; Daniele, Stefania; Raffio, Teresa; Salvatore, Marco; Sabbatini, Massimo; Cianciaruso, Bruno; Ferrara, Liberato Aldo

2005-01-01

Purpose: Aim of the presents study was to evaluate the repeatability and reproducibility of the Doppler Resistive Index (R.I.) and the Ultrasound renal volume measurement in renal transplants. Materials and methods: Twenty -six consecutive patients (18 men, 8 women) mean age of 42,8±12,4 years (M±SD)(range 22-65 years) were studied twice by each of two trained sonographers using a color Doppler ultrasound scanner. Twelve of them had a normal allograft function (defined as stable serum creatinine levels ≤123,76 μmol/L), whilst the remaining 14 had decreased allograft function (serum creatinine 132.6-265.2 μmol/L). Results were given as mean of 6 measurements performed at upper, middle and lower pole of the kidney. Intra- and interobserver variability was assessed by the repeatability coefficient and coefficient of variation (CV). Results: Regarding Resistive Index measurement, repeatability coefficient was between 0.04 and 0.06 and the coefficient of variation was [it

PREDICTING THE BOILING POINT OF PCDD/Fs BY THE QSPR METHOD BASED ON THE MOLECULAR DISTANCE-EDGE VECTOR INDEX

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Long Jiao

2015-05-01

Full Text Available The quantitative structure property relationship (QSPR for the boiling point (Tb of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs was investigated. The molecular distance-edge vector (MDEV index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR and artificial neural network (ANN, respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.

Use of the Charlson Combined Comorbidity Index To Predict Postradiotherapy Quality of Life for Prostate Cancer Patients

International Nuclear Information System (INIS)

Wahlgren, Thomas; Levitt, Seymour; Kowalski, Jan; Nilsson, Sten; Brandberg, Yvonne

2011-01-01

Purpose: To determine the impact of pretreatment comorbidity on late health-related quality of life (HRQoL) scores after patients have undergone combined radiotherapy for prostate cancer, including high-dose rate brachytherapy boost and hormonal deprivation therapy. Methods and Materials: Results from the European Organization for Research and Treatment of Cancer QLQ-C30 questionnaire survey of 158 patients 5 years or more after completion of therapy were used from consecutively accrued subjects treated with curative radiotherapy at our institution, with no signs of disease at the time of questionnaire completion. HRQoL scores were compared with the Charlson combined comorbidity index (CCI), using analysis of covariance and multivariate regression models together with pretreatment factors including tumor stage, tumor grade, pretreatment prostate-specific antigen level, neoadjuvant hormonal treatment, diabetes status, cardiovascular status, and age and Charlson score as separate variables or the composite CCI. Results: An inverse correlation between the two HRQoL domains, long-term global health (QL) and physical function (PF) scores, and the CCI score was observed, indicating an impact of comorbidity in these function areas. Selected pretreatment factors poorly explained the variation in functional HRQoL in the multivariate models; however, a statistically significant impact was found for the CCI (with QL and PF scores) and the presence of diabetes (with QL and emotional function). Cognitive function and social function were not statistically significantly predicted by any of the pretreatment factors. Conclusions: The CCI proved to be valid in this context, but it seems useful mainly in predicting long-term QL and PF scores. Of the other variables investigated, diabetes had more impact than cardiovascular morbidity on HRQoL outcomes in prostate cancer.

Use of the Charlson Combined Comorbidity Index To Predict Postradiotherapy Quality of Life for Prostate Cancer Patients

Energy Technology Data Exchange (ETDEWEB)

Wahlgren, Thomas, E-mail: thomas.wahlgren@telia.com [Department of Oncology-Pathology, Karolinska University Hospital and Institutet, Stockholm (Sweden); Levitt, Seymour [Department of Oncology-Pathology, Karolinska University Hospital and Institutet, Stockholm (Sweden); Department of Therapeutic Radiology, University of Minnesota, Minneapolis, Minnesota (United States); Kowalski, Jan [Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm (Sweden); Nilsson, Sten; Brandberg, Yvonne [Department of Oncology-Pathology, Karolinska University Hospital and Institutet, Stockholm (Sweden)

2011-11-15

Purpose: To determine the impact of pretreatment comorbidity on late health-related quality of life (HRQoL) scores after patients have undergone combined radiotherapy for prostate cancer, including high-dose rate brachytherapy boost and hormonal deprivation therapy. Methods and Materials: Results from the European Organization for Research and Treatment of Cancer QLQ-C30 questionnaire survey of 158 patients 5 years or more after completion of therapy were used from consecutively accrued subjects treated with curative radiotherapy at our institution, with no signs of disease at the time of questionnaire completion. HRQoL scores were compared with the Charlson combined comorbidity index (CCI), using analysis of covariance and multivariate regression models together with pretreatment factors including tumor stage, tumor grade, pretreatment prostate-specific antigen level, neoadjuvant hormonal treatment, diabetes status, cardiovascular status, and age and Charlson score as separate variables or the composite CCI. Results: An inverse correlation between the two HRQoL domains, long-term global health (QL) and physical function (PF) scores, and the CCI score was observed, indicating an impact of comorbidity in these function areas. Selected pretreatment factors poorly explained the variation in functional HRQoL in the multivariate models; however, a statistically significant impact was found for the CCI (with QL and PF scores) and the presence of diabetes (with QL and emotional function). Cognitive function and social function were not statistically significantly predicted by any of the pretreatment factors. Conclusions: The CCI proved to be valid in this context, but it seems useful mainly in predicting long-term QL and PF scores. Of the other variables investigated, diabetes had more impact than cardiovascular morbidity on HRQoL outcomes in prostate cancer.

Prediction of SYM-H index during large storms by NARX neural network from IMF and solar wind data

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L. Cai

2010-02-01

Full Text Available Similar to the Dst index, the SYM-H index may also serve as an indicator of magnetic storm intensity, but having distinct advantage of higher time-resolution. In this study the NARX neural network has been used for the first time to predict SYM-H index from solar wind (SW and IMF parameters. In total 73 time intervals of great storm events with IMF/SW data available from ACE satellite during 1998 to 2006 are used to establish the ANN model. Out of them, 67 are used to train the network and the other 6 samples for test. Additionally, the NARX prediction model is also validated using IMF/SW data from WIND satellite for 7 great storms during 1995–1997 and 2005, as well as for the July 2000 Bastille day storm and November 2001 superstorm using Geotail and OMNI data at 1 AU, respectively. Five interplanetary parameters of IMF B_z, B_y and total B components along with proton density and velocity of solar wind are used as the original external inputs of the neural network to predict the SYM-H index about one hour ahead. For the 6 test storms registered by ACE including two super-storms of min. SYM-H<−200 nT, the correlation coefficient between observed and NARX network predicted SYM-H is 0.95 as a whole, even as high as 0.95 and 0.98 with average relative variance of 13.2% and 7.4%, respectively, for the two super-storms. The prediction for the 7 storms with WIND data is also satisfactory, showing averaged correlation coefficient about 0.91 and RMSE of 14.2 nT. The newly developed NARX model shows much better capability than Elman network for SYM-H prediction, which can partly be attributed to a key feedback to the input layer from the output neuron with a suitable length (about 120 min. This feedback means that nearly real information of the ring current status is effectively directed to take part in the prediction of SYM-H index by ANN. The proper history length of the output-feedback may mainly reflect

Prediction of SYM-H index during large storms by NARX neural network from IMF and solar wind data

Directory of Open Access Journals (Sweden)

L. Cai

2010-02-01

Full Text Available Similar to the Dst index, the SYM-H index may also serve as an indicator of magnetic storm intensity, but having distinct advantage of higher time-resolution. In this study the NARX neural network has been used for the first time to predict SYM-H index from solar wind (SW and IMF parameters. In total 73 time intervals of great storm events with IMF/SW data available from ACE satellite during 1998 to 2006 are used to establish the ANN model. Out of them, 67 are used to train the network and the other 6 samples for test. Additionally, the NARX prediction model is also validated using IMF/SW data from WIND satellite for 7 great storms during 1995–1997 and 2005, as well as for the July 2000 Bastille day storm and November 2001 superstorm using Geotail and OMNI data at 1 AU, respectively. Five interplanetary parameters of IMF Bz, By and total B components along with proton density and velocity of solar wind are used as the original external inputs of the neural network to predict the SYM-H index about one hour ahead. For the 6 test storms registered by ACE including two super-storms of min. SYM-H<−200 nT, the correlation coefficient between observed and NARX network predicted SYM-H is 0.95 as a whole, even as high as 0.95 and 0.98 with average relative variance of 13.2% and 7.4%, respectively, for the two super-storms. The prediction for the 7 storms with WIND data is also satisfactory, showing averaged correlation coefficient about 0.91 and RMSE of 14.2 nT. The newly developed NARX model shows much better capability than Elman network for SYM-H prediction, which can partly be attributed to a key feedback to the input layer from the output neuron with a suitable length (about 120 min. This feedback means that nearly real information of the ring current status is effectively directed to take part in the prediction of SYM-H index by ANN. The proper history length of the output-feedback may mainly reflect on average the characteristic time of ring

Development of a risk index for the prediction of chronic post-surgical pain.

Science.gov (United States)

Althaus, A; Hinrichs-Rocker, A; Chapman, R; Arránz Becker, O; Lefering, R; Simanski, C; Weber, F; Moser, K-H; Joppich, R; Trojan, S; Gutzeit, N; Neugebauer, E

2012-07-01

The incidence of chronic post-surgical pain (CPSP) after various common operations is 10% to 50%. Identification of patients at risk of developing chronic pain, and the management and prevention of CPSP remains inadequate. The aim of this study was to develop an easily applicable risk index for the detection of high-risk patients that takes into account the multifactorial aetiology of CPSP. A comprehensive item pool was derived from a systematic literature search. Items that turned out significant in bivariate analyses were then analysed multivariately, using logistic regression analyses. The items that yielded significant predictors in the multivariate analyses were compiled into an index. The cut-off score for a high risk of developing CPSP with an optimal trade-off between sensitivity and specificity was identified. The data of 150 patients who underwent different types of surgery were included in the analyses. Six months after surgery, 43.3% of the patients reported CPSP. Five predictors multivariately contributed to the prediction of CPSP: capacity overload, preoperative pain in the operating field, other chronic preoperative pain, post-surgical acute pain and co-morbid stress symptoms. These results suggest that several easily assessable preoperative and perioperative patient characteristics can predict a patient's risk of developing CPSP. The risk index may help caregivers to tailor individual pain management and to assist high-risk patients with pain coping. © 2011 European Federation of International Association for the Study of Pain Chapters.

Mastication effects on the glycaemic index: impact on variability and practical implications.

Science.gov (United States)

Ranawana, V; Leow, M K-S; Henry, C J K

2014-01-01

Glycaemic variability challenges the accuracy and use of the glycaemic index (GI). The purpose of the current study was to determine the role of mastication on GI. Using a randomized, controlled, crossover, non-blind design, 15 healthy young subjects returned on 5 separate days for three glucose and two rice test sessions. At the rice sessions, subjects chewed each mouthful either 15 or 30 times. Rice chewed 15 times produced a total glycaemic response (GR; 155 mmol min/l), peak GR (2.4 mmol/l) and GI (68) significantly lower than when chewed for longer (30 times) (184 mmol min/l, 2.8 mmol/l and 88, respectively). The study shows that the GI of rice is affected by the degree of mastication. Chewing 15 times compared with 30 times significantly attenuates the GI, suggesting that mastication may potentially contribute to the glycaemic variability of rice. While future work must establish the extent and limits to which mastication affects glycaemia, it could also explore the potential of using mastication to reduce the glycaemic load of rice.

Modelling the predictive performance of credit scoring

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Shi-Wei Shen

2013-07-01

Research purpose: The purpose of this empirical paper was to examine the predictive performance of credit scoring systems in Taiwan. Motivation for the study: Corporate lending remains a major business line for financial institutions. However, in light of the recent global financial crises, it has become extremely important for financial institutions to implement rigorous means of assessing clients seeking access to credit facilities. Research design, approach and method: Using a data sample of 10 349 observations drawn between 1992 and 2010, logistic regression models were utilised to examine the predictive performance of credit scoring systems. Main findings: A test of Goodness of fit demonstrated that credit scoring models that incorporated the Taiwan Corporate Credit Risk Index (TCRI, micro- and also macroeconomic variables possessed greater predictive power. This suggests that macroeconomic variables do have explanatory power for default credit risk. Practical/managerial implications: The originality in the study was that three models were developed to predict corporate firms’ defaults based on different microeconomic and macroeconomic factors such as the TCRI, asset growth rates, stock index and gross domestic product. Contribution/value-add: The study utilises different goodness of fits and receiver operator characteristics during the examination of the robustness of the predictive power of these factors.

The prediction of the incidence rate of upper limb musculoskeletal disorders, with CTD risk index method on potters of Meybod city

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Reza Khani Jazani

2012-02-01

Full Text Available Background: The objective of this study was to predict the incidence of musculoskeletal disorders in potters of Meybod city by performing CTD risk index method.Materials and Method: This is a descriptive cross-sectional study. Target society was all workers in pottery workshops which were located in the Meybod. Information related to musculoskeletal disorders was obtained by the Nordic questionnaire and we used CTD risk index method to predict the incidence of musculoskeletal disorders.Results: We observed in this study that 59.3% of the potters had symptoms of musculoskeletal disorders in at least in one of their upper extremities. Also significant differences between mean CTD risk index on potters with and without symptoms of the upper limb musculoskeletal disorders, respectively (p=0.038.Conclusion: CTD risk index method can be as a suitable method for predicting the incidence of musculoskeletal disorders used in the potters

A simplified donor risk index for predicting outcome after deceased donor kidney transplantation.

Science.gov (United States)

Watson, Christopher J E; Johnson, Rachel J; Birch, Rhiannon; Collett, Dave; Bradley, J Andrew

2012-02-15

We sought to determine the deceased donor factors associated with outcome after kidney transplantation and to develop a clinically applicable Kidney Donor Risk Index. Data from the UK Transplant Registry on 7620 adult recipients of adult deceased donor kidney transplants between 2000 and 2007 inclusive were analyzed. Donor factors potentially influencing transplant outcome were investigated using Cox regression, adjusting for significant recipient and transplant factors. A United Kingdom Kidney Donor Risk Index was derived from the model and validated. Donor age was the most significant factor predicting poor transplant outcome (hazard ratio for 18-39 and 60+ years relative to 40-59 years was 0.78 and 1.49, respectively, Pinformed consent.

Making oneself predictable: Reduced temporal variability facilitates joint action coordination

DEFF Research Database (Denmark)

Vesper, Cordula; van der Wel, Robrecht; Knoblich, Günther

2011-01-01

Performing joint actions often requires precise temporal coordination of individual actions. The present study investigated how people coordinate their actions at discrete points in time when continuous or rhythmic information about others’ actions is not available. In particular, we tested...... the hypothesis that making oneself predictable is used as a coordination strategy. Pairs of participants were instructed to coordinate key presses in a two-choice reaction time task, either responding in synchrony (Experiments 1 and 2) or in close temporal succession (Experiment 3). Across all experiments, we...... found that coactors reduced the variability of their actions in the joint context compared with the same task performed individually. Correlation analyses indicated that the less variable the actions were, the better was interpersonal coordination. The relation between reduced variability and improved...

Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables.

Science.gov (United States)

Jordan, Pascal; Shedden-Mora, Meike C; Löwe, Bernd

To obtain predictors of suicidal ideation, which can also be used for an indirect assessment of suicidal ideation (SI). To create a classifier for SI based on variables of the Patient Health Questionnaire (PHQ) and sociodemographic variables, and to obtain an upper bound on the best possible performance of a predictor based on those variables. From a consecutive sample of 9025 primary care patients, 6805 eligible patients (60% female; mean age = 51.5 years) participated. Advanced methods of machine learning were used to derive the prediction equation. Various classifiers were applied and the area under the curve (AUC) was computed as a performance measure. Classifiers based on methods of machine learning outperformed ordinary regression methods and achieved AUCs around 0.87. The key variables in the prediction equation comprised four items - namely feelings of depression/hopelessness, low self-esteem, worrying, and severe sleep disturbances. The generalized anxiety disorder scale (GAD-7) and the somatic symptom subscale (PHQ-15) did not enhance prediction substantially. In predicting suicidal ideation researchers should refrain from using ordinary regression tools. The relevant information is primarily captured by the depression subscale and should be incorporated in a nonlinear model. For clinical practice, a classification tree using only four items of the whole PHQ may be advocated. Copyright © 2018 Elsevier Inc. All rights reserved.

Frailty Index Predicts All-Cause Mortality for Middle-Aged and Older Taiwanese: Implications for Active-Aging Programs.

Science.gov (United States)

Lin, Shu-Yu; Lee, Wei-Ju; Chou, Ming-Yueh; Peng, Li-Ning; Chiou, Shu-Ti; Chen, Liang-Kung

2016-01-01

Frailty Index, defined as an individual's accumulated proportion of listed health-related deficits, is a well-established metric used to assess the health status of old adults; however, it has not yet been developed in Taiwan, and its local related structure factors remain unclear. The objectives were to construct a Taiwan Frailty Index to predict mortality risk, and to explore the structure of its factors. Analytic data on 1,284 participants aged 53 and older were excerpted from the Social Environment and Biomarkers of Aging Study (2006), in Taiwan. A consensus workgroup of geriatricians selected 159 items according to the standard procedure for creating a Frailty Index. Cox proportional hazard modeling was used to explore the association between the Taiwan Frailty Index and mortality. Exploratory factor analysis was used to identify structure factors and produce a shorter version-the Taiwan Frailty Index Short-Form. During an average follow-up of 4.3 ± 0.8 years, 140 (11%) subjects died. Compared to those in the lowest Taiwan Frailty Index tertile ( 0.23) had significantly higher risk of death (Hazard ratio: 3.2; 95% CI 1.9-5.4). Thirty-five items of five structure factors identified by exploratory factor analysis, included: physical activities, life satisfaction and financial status, health status, cognitive function, and stresses. Area under the receiver operating characteristic curves (C-statistics) of the Taiwan Frailty Index and its Short-Form were 0.80 and 0.78, respectively, with no statistically significant difference between them. Although both the Taiwan Frailty Index and Short-Form were associated with mortality, the Short-Form, which had similar accuracy in predicting mortality as the full Taiwan Frailty Index, would be more expedient in clinical practice and community settings to target frailty screening and intervention.

MEDEX 2015: Heart Rate Variability Predicts Development of Acute Mountain Sickness.

Science.gov (United States)

Sutherland, Angus; Freer, Joseph; Evans, Laura; Dolci, Alberto; Crotti, Matteo; Macdonald, Jamie Hugo

2017-09-01

Sutherland, Angus, Joseph Freer, Laura Evans, Alberto Dolci, Matteo Crotti, and Jamie Hugo Macdonald. MEDEX 2015: Heart rate variability predicts development of acute mountain sickness. High Alt Med Biol. 18: 199-208, 2017. Acute mountain sickness (AMS) develops when the body fails to acclimatize to atmospheric changes at altitude. Preascent prediction of susceptibility to AMS would be a useful tool to prevent subsequent harm. Changes to peripheral oxygen saturation (SpO 2 ) on hypoxic exposure have previously been shown to be of poor predictive value. Heart rate variability (HRV) has shown promise in the early prediction of AMS, but its use pre-expedition has not previously been investigated. We aimed to determine whether pre- and intraexpedition HRV assessment could predict susceptibility to AMS at high altitude with better diagnostic accuracy than SpO 2 . Forty-four healthy volunteers undertook an expedition in the Nepali Himalaya to >5000 m. SpO 2 and HRV parameters were recorded at rest in normoxia and in a normobaric hypoxic chamber before the expedition. On the expedition HRV parameters and SpO 2 were collected again at 3841 m. A daily Lake Louise Score was obtained to assess AMS symptomology. Low frequency/high frequency (LF/HF) ratio in normoxia (cutpoint ≤2.28 a.u.) and LF following 15 minutes of exposure to normobaric hypoxia had moderate (area under the curve ≥0.8) diagnostic accuracy. LF/HF ratio in normoxia had the highest sensitivity (85%) and specificity (88%) for predicting AMS on subsequent ascent to altitude. In contrast, pre-expedition SpO 2 measurements had poor (area under the curve <0.7) diagnostic accuracy and inferior sensitivity and specificity. Pre-ascent measurement of HRV in normoxia was found to be of better diagnostic accuracy for AMS prediction than all measures of HRV in hypoxia, and better than peripheral oxygen saturation monitoring.

Analysis of variability and predictability challenges of wind and solar power

NARCIS (Netherlands)

Haan, de J.E.S.; Virag, A.; Kling, W.L.

2013-01-01

In power systems, reserves are essential to ensure system security, certainly when challenges of predictability (inaccurate forecast) and variability (imperfect correlation of renewable generation and system load) are causing power imbalances. Different techniques can be used to size and allocate

Index for Predicting Insurance Claims from Wind Storms with an Application in France.

Science.gov (United States)

Mornet, Alexandre; Opitz, Thomas; Luzi, Michel; Loisel, Stéphane

2015-11-01

For insurance companies, wind storms represent a main source of volatility, leading to potentially huge aggregated claim amounts. In this article, we compare different constructions of a storm index allowing us to assess the economic impact of storms on an insurance portfolio by exploiting information from historical wind speed data. Contrary to historical insurance portfolio data, meteorological variables show fewer nonstationarities between years and are easily available with long observation records; hence, they represent a valuable source of additional information for insurers if the relation between observations of claims and wind speeds can be revealed. Since standard correlation measures between raw wind speeds and insurance claims are weak, a storm index focusing on high wind speeds can afford better information. A storm index approach has been applied to yearly aggregated claim amounts in Germany with promising results. Using historical meteorological and insurance data, we assess the consistency of the proposed index constructions with respect to various parameters and weights. Moreover, we are able to place the major insurance events since 1998 on a broader horizon beyond 40 years. Our approach provides a meteorological justification for calculating the return periods of extreme-storm-related insurance events whose magnitude has rarely been reached. © 2015 Society for Risk Analysis.

24-Hour Blood Pressure Variability Assessed by Average Real Variability: A Systematic Review and Meta-Analysis.

Science.gov (United States)

Mena, Luis J; Felix, Vanessa G; Melgarejo, Jesus D; Maestre, Gladys E

2017-10-19

Although 24-hour blood pressure (BP) variability (BPV) is predictive of cardiovascular outcomes independent of absolute BP levels, it is not regularly assessed in clinical practice. One possible limitation to routine BPV assessment is the lack of standardized methods for accurately estimating 24-hour BPV. We conducted a systematic review to assess the predictive power of reported BPV indexes to address appropriate quantification of 24-hour BPV, including the average real variability (ARV) index. Studies chosen for review were those that presented data for 24-hour BPV in adults from meta-analysis, longitudinal or cross-sectional design, and examined BPV in terms of the following issues: (1) methods used to calculate and evaluate ARV; (2) assessment of 24-hour BPV determined using noninvasive ambulatory BP monitoring; (3) multivariate analysis adjusted for covariates, including some measure of BP; (4) association of 24-hour BPV with subclinical organ damage; and (5) the predictive value of 24-hour BPV on target organ damage and rate of cardiovascular events. Of the 19 assessed studies, 17 reported significant associations between high ARV and the presence and progression of subclinical organ damage, as well as the incidence of hard end points, such as cardiovascular events. In all these cases, ARV remained a significant independent predictor ( P <0.05) after adjustment for BP and other clinical factors. In addition, increased ARV in systolic BP was associated with risk of all cardiovascular events (hazard ratio, 1.18; 95% confidence interval, 1.09-1.27). Only 2 cross-sectional studies did not find that high ARV was a significant risk factor. Current evidence suggests that ARV index adds significant prognostic information to 24-hour ambulatory BP monitoring and is a useful approach for studying the clinical value of BPV. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Accuracy of shock index versus ABC score to predict need for massive transfusion in trauma patients.

Science.gov (United States)

Schroll, Rebecca; Swift, David; Tatum, Danielle; Couch, Stuart; Heaney, Jiselle B; Llado-Farrulla, Monica; Zucker, Shana; Gill, Frances; Brown, Griffin; Buffin, Nicholas; Duchesne, Juan

2018-01-01

Various scoring systems have been developed to predict need for massive transfusion in traumatically injured patients. Assessments of Blood Consumption (ABC) score and Shock Index (SI) have been shown to be reliable predictors for Massive Transfusion Protocol (MTP) activation. However, no study has directly compared these two scoring systems to determine which is a better predictor for MTP activation. The primary objective was to determine whether ABC or SI better predicted the need for MTP in adult trauma patients with severe hemorrhage. This was a retrospective cohort study which included all injured patients who were trauma activations between January 1, 2009 and December 31, 2013 at an urban Level I trauma center. Patients ABC and SI were calculated for each patient. MTP was defined as need for >10 units PRBC transfusion within 24h of emergency department arrival. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were used to evaluate scoring systems' ability to predict effective MTP utilization. A total of 645 patients had complete data for analysis. Shock Index ≥1 had sensitivity of 67.7% (95% CI 49.5%-82.6%) and specificity of 81.3% (95% CI 78.0%-84.3%) for predicting MTP, and ABC score ≥2 had sensitivity of 47.0% (95% CI 29.8%-64.9%) and specificity of 89.8% (95% CI 87.2%-92.1%). AUROC analyses showed SI to be the strongest predictor followed by ABC score with AUROC values of 0.83 and 0.74, respectively. SI had a significantly greater sensitivity (P=0.035), but a significantly weaker specificity (PABC score. ABC score and Shock Index can both be used to predict need for massive transfusion in trauma patients, however SI is more sensitive and requires less technical skill than ABC score. Copyright © 2017 Elsevier Ltd. All rights reserved.

Relationship between depression with FEV1 percent predicted and BODE index in chronic obstructive pulmonary disease

Science.gov (United States)

Gunawan, H.; Hanum, H.; Abidin, A.; Hanida, W.

2018-03-01

WHO reported more than 3 million people die from COPD in 2012 and are expected to rank third after cardiovascular and cancer diseases in the future. Recent studies reported the prevalence of depression in COPD patients was higher than in control group. So, it’s important for clinicians to understand the relationship of depression symptoms with clinical aspects of COPD. For determining the association of depression symptoms with lung function and BODE index in patients with stable COPD, a cross-sectional study was in 98 stable COPD outpatients from January to June 2017. Data were analyzed using Independent t-test, Mann-Whitney test, and Spearman’s rank correlation. COPD patients with depression had higher mMRC scores, and lower FEV1 percent predicted, and then 6-Minutes Walk Test compared to those without depression. There was a moderate strength of correlation (r=-0.43) between depression symptoms and FEV1 percent predicted, and strong correlation (r=0.614) between depression symptoms and BODE index. It indicates that BODE index is more accurate to describe symptoms of depression in COPD patients.

It's the People, Stupid: The Role of Personality and Situational Variable in Predicting Decisionmaker Behavior

National Research Council Canada - National Science Library

Sticha, Paul J; Buede, Dennis M; Rees, Richard L

2006-01-01

.... to identity assumptions and determinant variables, and to quantify each variable's relative contribution to the prediction, producing a graphical representation of the analysis with explicit levels of uncertainty...

Analysis and Prediction of Micromilling Stability with Variable Tool Geometry

Directory of Open Access Journals (Sweden)

Ziyang Cao

2014-11-01

Full Text Available Micromilling can fabricate miniaturized components using micro-end mill at high rotational speeds. The analysis of machining stability in micromilling plays an important role in characterizing the cutting process, estimating the tool life, and optimizing the process. A numerical analysis and experimental method are presented to investigate the chatter stability in micro-end milling process with variable milling tool geometry. The schematic model of micromilling process is constructed and the calculation formula to predict cutting force and displacements is derived. This is followed by a detailed numerical analysis on micromilling forces between helical ball and square end mills through time domain and frequency domain method and the results are compared. Furthermore, a detailed time domain simulation for micro end milling with straight teeth and helical teeth end mill is conducted based on the machine-tool system frequency response function obtained through modal experiment. The forces and displacements are predicted and the simulation result between variable cutter geometry is deeply compared. The simulation results have important significance for the actual milling process.

Chaos game representation of the D st index and prediction of geomagnetic storm events

International Nuclear Information System (INIS)

Yu, Z.G.; Anh, V.V.; Wanliss, J.A.; Watson, S.M.

2007-01-01

This paper proposes a two-dimensional chaos game representation (CGR) for the D st index. The CGR provides an effective method to characterize the multifractality of the D st time series. The probability measure of this representation is then modeled as a recurrent iterated function system in fractal theory, which leads to an algorithm for prediction of a storm event. We present an analysis and modeling of the D st time series over the period 1963-2003. The numerical results obtained indicate that the method is useful in predicting storm events one day ahead

Central venous pressure and shock index predict lack of hemodynamic response to volume expansion in septic shock: a prospective, observational study.

Science.gov (United States)

Lanspa, Michael J; Brown, Samuel M; Hirshberg, Eliotte L; Jones, Jason P; Grissom, Colin K

2012-12-01

Volume expansion is a common therapeutic intervention in septic shock, although patient response to the intervention is difficult to predict. Central venous pressure (CVP) and shock index have been used independently to guide volume expansion, although their use is questionable. We hypothesize that a combination of these measurements will be useful. In a prospective, observational study, patients with early septic shock received 10-mL/kg volume expansion at their treating physician's discretion after brief initial resuscitation in the emergency department. Central venous pressure and shock index were measured before volume expansion interventions. Cardiac index was measured immediately before and after the volume expansion using transthoracic echocardiography. Hemodynamic response was defined as an increase in a cardiac index of 15% or greater. Thirty-four volume expansions were observed in 25 patients. A CVP of 8 mm Hg or greater and a shock index of 1 beat min(-1) mm Hg(-1) or less individually had a good negative predictive value (83% and 88%, respectively). Of 34 volume expansions, the combination of both a high CVP and a low shock index was extremely unlikely to elicit hemodynamic response (negative predictive value, 93%; P = .02). Volume expansion in patients with early septic shock with a CVP of 8 mm Hg or greater and a shock index of 1 beat min(-1) mm Hg(-1) or less is unlikely to lead to an increase in cardiac index. Copyright © 2012 Elsevier Inc. All rights reserved.

A Novel Fibrosis Index Comprising a Non-Cholesterol Sterol Accurately Predicts HCV-Related Liver Cirrhosis

DEFF Research Database (Denmark)

Ydreborg, Magdalena; Lisovskaja, Vera; Lagging, Martin

2014-01-01

of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive...

New social adaptability index predicts overall mortality.

Science.gov (United States)

Goldfarb-Rumyantzev, Alexander; Barenbaum, Anna; Rodrigue, James; Rout, Preeti; Isaacs, Ross; Mukamal, Kenneth

2011-08-01

Definitions of underprivileged status based on race, gender and geographic location are neither sensitive nor specific; instead we proposed and validated a composite index of social adaptability (SAI). Index of social adaptability was calculated based on employment, education, income, marital status, and substance abuse, each factor contributing from 0 to 3 points. Index of social adaptability was validated in NHANES-3 by association with all-cause and cause-specific mortality. Weighted analysis of 19,593 subjects demonstrated mean SAI of 8.29 (95% CI 8.17-8.40). Index of social adaptability was higher in Whites, followed by Mexican-Americans and then the African-American population (ANOVA, p adaptability with a strong association with mortality, which can be used to identify underprivileged populations at risk of death.

THE RELATIVE IMPORTANCE OF FINANCIAL RATIOS AND NONFINANCIAL VARIABLES IN PREDICTING OF INSOLVENCY

Directory of Open Access Journals (Sweden)

Ivica Pervan

2013-02-01

Full Text Available One of the most important decisions in every bank is approving loans to firms, which is based on evaluated credit risk and collateral. Namely, it is necessary to evaluate the risk that client will be unable to repay the obligations according to the contract. After Beaver's (1967 and Altman's (1968 seminal papers many authors extended the initial research by changing the methodology, samples, countries, etc. But majority of business failure papers as predictors use financial ratios, while in the real life banks combine financial and nonfinancial variables. In order to test predictive power of nonfinancial variables authors in the paper compare two insolvency prediction models. The first model that used financial rations resulted with classification accuracy of 82.8%, while the combined model with financial and nonfinancial variables resulted with classification accuracy of 88.1%.

Properties of power series of analytic in a bidisc functions of bounded $\\mathbf{L}$-index in joint variables

Directory of Open Access Journals (Sweden)

A. I. Bandura

2017-07-01

Full Text Available We generalized some criteria of boundedness of $\\mathbf{L}$-index in joint variables for analytic in a bidisc functions, where $\\mathbf{L}(z=(l_1(z_1,z_2,$ $l_{2}(z_1,z_2,$ $l_j:\\mathbb{D}^2\\to \\mathbb{R}_+$ is a continuous function, $j\\in\\{1,2\\},$ $\\mathbb{D}^2$ is a bidisc $\\{(z_1,z_2\\in\\mathbb{C}^2: |z_1|<1,|z_2|<1\\}.$ The propositions describe a behaviour of power series expansion on a skeleton of a bidisc. We estimated power series expansion by a dominating homogeneous polynomial with the degree that does not exceed some number depending only from radii of bidisc. Replacing universal quantifier by existential quantifier for radii of bidisc, we also proved sufficient conditions of boundedness of $\\mathbf{L}$-index in joint variables for analytic functions which are weaker than necessary conditions.

Fatigue life prediction of rotor blade composites: Validation of constant amplitude formulations with variable amplitude experiments

International Nuclear Information System (INIS)

Westphal, T; Nijssen, R P L

2014-01-01

The effect of Constant Life Diagram (CLD) formulation on the fatigue life prediction under variable amplitude (VA) loading was investigated based on variable amplitude tests using three different load spectra representative for wind turbine loading. Next to the Wisper and WisperX spectra, the recently developed NewWisper2 spectrum was used. Based on these variable amplitude fatigue results the prediction accuracy of 4 CLD formulations is investigated. In the study a piecewise linear CLD based on the S-N curves for 9 load ratios compares favourably in terms of prediction accuracy and conservativeness. For the specific laminate used in this study Boerstra's Multislope model provides a good alternative at reduced test effort

Fatigue life prediction of rotor blade composites: Validation of constant amplitude formulations with variable amplitude experiments

Science.gov (United States)

Westphal, T.; Nijssen, R. P. L.

2014-12-01

The effect of Constant Life Diagram (CLD) formulation on the fatigue life prediction under variable amplitude (VA) loading was investigated based on variable amplitude tests using three different load spectra representative for wind turbine loading. Next to the Wisper and WisperX spectra, the recently developed NewWisper2 spectrum was used. Based on these variable amplitude fatigue results the prediction accuracy of 4 CLD formulations is investigated. In the study a piecewise linear CLD based on the S-N curves for 9 load ratios compares favourably in terms of prediction accuracy and conservativeness. For the specific laminate used in this study Boerstra's Multislope model provides a good alternative at reduced test effort.

A multilateral modelling of Youth Soccer Performance Index (YSPI)

Science.gov (United States)

Bisyri Husin Musawi Maliki, Ahmad; Razali Abdullah, Mohamad; Juahir, Hafizan; Abdullah, Farhana; Ain Shahirah Abdullah, Nurul; Muazu Musa, Rabiu; Musliha Mat-Rasid, Siti; Adnan, Aleesha; Azura Kosni, Norlaila; Muhamad, Wan Siti Amalina Wan; Afiqah Mohamad Nasir, Nur

2018-04-01

This study aims to identify the most dominant factors that influencing performance of soccer player and to predict group performance for soccer players. A total of 184 of youth soccer players from Malaysia sport school and six soccer academy encompasses as respondence of the study. Exploratory factor analysis (EFA) and Confirmatory factor analysis (CFA) were computed to identify the most dominant factors whereas reducing the initial 26 parameters with recommended >0.5 of factor loading. Meanwhile, prediction of the soccer performance was predicted by regression model. CFA revealed that sit and reach, vertical jump, VO2max, age, weight, height, sitting height, calf circumference (cc), medial upper arm circumference (muac), maturation, bicep, triceps, subscapular, suprailiac, 5M, 10M, and 20M speed were the most dominant factors. Further index analysis forming Youth Soccer Performance Index (YSPI) resulting by categorizing three groups namely, high, moderate, and low. The regression model for this study was significant set as p < 0.001 and R2 is 0.8222 which explained that the model contributed a total of 82% prediction ability to predict the whole set of the variables. The significant parameters in contributing prediction of YSPI are discussed. As a conclusion, the precision of the prediction models by integrating a multilateral factor reflecting for predicting potential soccer player and hopefully can create a competitive soccer games.

How long the singular value decomposed entropy predicts the stock market? - Evidence from the Dow Jones Industrial Average Index

Science.gov (United States)

Gu, Rongbao; Shao, Yanmin

2016-07-01

In this paper, a new concept of multi-scales singular value decomposition entropy based on DCCA cross correlation analysis is proposed and its predictive power for the Dow Jones Industrial Average Index is studied. Using Granger causality analysis with different time scales, it is found that, the singular value decomposition entropy has predictive power for the Dow Jones Industrial Average Index for period less than one month, but not for more than one month. This shows how long the singular value decomposition entropy predicts the stock market that extends Caraiani's result obtained in Caraiani (2014). On the other hand, the result also shows an essential characteristic of stock market as a chaotic dynamic system.

A note on the conditional density estimate in single functional index model

OpenAIRE

2010-01-01

Abstract In this paper, we consider estimation of the conditional density of a scalar response variable Y given a Hilbertian random variable X when the observations are linked with a single-index structure. We establish the pointwise and the uniform almost complete convergence (with the rate) of the kernel estimate of this model. As an application, we show how our result can be applied in the prediction problem via the conditional mode estimate. Finally, the estimation of the funct...

A GIS-derived integrated moisture index to predict forest composition and productivity of Ohio forests (U.S.A.)

Science.gov (United States)

Louis R. Iverson; Martin E. Dale; Charles T. Scott; Anantha Prasad; Anantha Prasad

1997-01-01

A geographic information system (GIS) approach was used in conjunction with forest-plot data to develop an integrated moisture index (IMI), which was then used to predict forest productivity (site index) and species composition for forests in Ohio. In this region, typical of eastern hardwoods across the Midwest and southern Appalachians, topographic aspect and position...

[Reliability of the PROFUND index to predict 4-year mortality in polypathological patients].

Science.gov (United States)

Díez-Manglano, Jesús; Del Corral Beamonte, Esther; Ramos Ibáñez, Rosa; Lambán Aranda, María Pilar; Toyas Miazza, Carla; Rodero Roldán, María Del Mar; Ortiz Domingo, Concepción; Munilla López, Eulalia; de Escalante Yangüela, Begoña

2016-09-16

To determine the usefullness of the PROFUND index to assess the risk of global death after 4 years in polypathological patients. Multicenter prospective cohort (Internal Medicine and Geriatrics) study. Polypathological patients admitted between March 1st and June 30th 2011 were included. For each patient, data concerning age, sex, living at home or in a nursing residence, polypathology categories, Charlson, Barthel and Lawton-Brody indexes, Pfeiffer questionnaire, socio-familial Gijon scale, delirium, number of drugs, hemoglobin and creatinine values were gathered, and the PROFUND index was calculated. The follow-up lasted 4 years. We included 441 patients, 324 from Internal Medicine and 117 from Geriatrics, with a mean age of 80.9 (8.7) years. Of them, 245 (55.6%) were women. Heart (62.7%), neurological (41.4%) and respiratory (37.3%) diseases were the most frequent. Geriatrics inpatients were older and more dependants and presented greater cognitive deterioration. After 4 years, 335 (76%) patients died. Mortality was associated with age, dyspnoea, Barthel index<60, delirium, advanced neoplasia and≥4 admissions in the last year. The area under the curve of the PROFUND index was 0.748, 95% CI 0.689-0.806, P<.001 in Internal Medicine and 0.517, 95% CI 0.369-0.666, P=.818 in Geriatrics patients, respectively. The PROFUND index is a reliable tool for predicting long-term global mortality in polypathological patients from Internal Medicine but not from Geriatrics departments. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

Science.gov (United States)

Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Van Den Hurk, Bart; Doblas Reyes, Francisco; Boussetta, Souhail; Balsamo, Gianpaolo; Miller, Paul A.

2017-08-01

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (twentieth century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2 m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over

Does the arrival index predict physiological stress reactivity in children.

Science.gov (United States)

de Veld, Danielle M J; Riksen-Walraven, J Marianne; de Weerth, Carolina

2014-09-01

Knowledge about children's stress reactivity and its correlates is mostly based on one stress task, making it hard to assess the generalizability of the results. The development of an additional stress paradigm for children, that also limits stress exposure and test time, could greatly advance this field of research. Research in adults may provide a starting point for the development of such an additional stress paradigm, as changes in salivary cortisol and alpha-amylase (sAA) over a 1-h pre-stress period in the laboratory correlated strongly with subsequent reactivity to stress task (Balodis et al., 2010, Psychoneuroendocrinology 35:1363-73). The present study examined whether such strong correlations could be replicated in 9- to 11-year-old children. Cortisol and sAA samples were collected from 158 children (83 girls) during a 2.5-h visit to the laboratory. This visit included a 1-h pre-stress period in which children performed some non-stressful tasks and relaxed before taking part in a psychosocial stress task (TSST-C). A higher cortisol arrival index was significantly and weakly correlated with a higher AUCg but unrelated to cortisol reactivity to the stressor. A higher sAA arrival index was significantly and moderately related to lower stress reactivity and to a lower AUCi. Children's personality and emotion regulation variables were unrelated to the cortisol and sAA arrival indices. The results of this study do not provide a basis for the development of an additional stress paradigm for children. Further replications in children and adults are needed to clarify the potential meaning of an arrival index.

Fall Risk Index predicts functional decline regardless of fall experiences among community-dwelling elderly.

Science.gov (United States)

Ishimoto, Yasuko; Wada, Taizo; Kasahara, Yoriko; Kimura, Yumi; Fukutomi, Eriko; Chen, Wenling; Hirosaki, Mayumi; Nakatsuka, Masahiro; Fujisawa, Michiko; Sakamoto, Ryota; Ishine, Masayuki; Okumiya, Kiyohito; Otsuka, Kuniaki; Matsubayashi, Kozo

2012-10-01

The 21-item Fall Risk Index (FRI-21) has been used to detect elderly persons at risk for falls. The aim of this longitudinal study was to evaluate the FRI-21 as a predictor of decline in basic activities of daily living (BADL) among Japanese community-dwelling elderly persons independent of fall risk. The study population consisted of 518 elderly participants aged 65 years and older who were BADL independent at baseline in Tosa, Japan. We examined risk factors for BADL decline from 2008 to 2009 by multiple logistic regression analysis on the FRI-21 and other functional status measures in all participants. We carried out the same analysis in selected participants who had no experience of falls to remove the effect of falls. A total of 45 of 518 participants showed decline in BADL within 1 year. Multivariate logistic regression analysis showed that age (odds ratio [OR] 1.13, 95% confidence interval [CI] 1.05-1.20), FRI-21 ≥ 10 (OR 3.81, 95% CI 1.49-9.27), intellectual activity dependence (OR 3.25, 95% CI 1.42-7.44) and history of osteoarthropathy (OR 3.17, 95% CI 1.40-7.21) were significant independent risk factors for BADL decline within 1 year. FRI-21 ≥ 10 and intellectual activity dependence (≤ 3) remained significant predictors, even in selected non-fallers. FRI-21 ≥ 10 and intellectual activity dependence were significant predictive factors of BADL decline, regardless of fall experience, after adjustment for confounding variables. The FRI-21 is a brief, useful tool not only for predicting falls, but also future decline in functional ability in community-dwelling elderly persons. © 2012 Japan Geriatrics Society.

Predictive Value of Triglyceride Glucose Index for the Risk of Incident Diabetes: A 4-Year Retrospective Longitudinal Study

OpenAIRE

Lee, Da Young; Lee, Eun Seo; Kim, Ji Hyun; Park, Se Eun; Park, Cheol-Young; Oh, Ki-Won; Park, Sung-Woo; Rhee, Eun-Jung; Lee, Won-Young

2016-01-01

The Triglyceride Glucose Index (TyG index) is considered a surrogate marker of insulin resistance. The aim of this study is to investigate whether the TyG index has a predictive role in identifying individuals with a high risk of incident diabetes and to compare it with other indicators of metabolic health. A total 2900 non-diabetic adults who attended five consecutive annual health check-ups at Kangbuk Samsung Hospital was divided into four subgroups using three methods: (1) baseline TyG ind...

Examining impulse-variability in overarm throwing.

Science.gov (United States)

Urbin, M A; Stodden, David; Boros, Rhonda; Shannon, David

2012-01-01

The purpose of this study was to examine variability in overarm throwing velocity and spatial output error at various percentages of maximum to test the prediction of an inverted-U function as predicted by impulse-variability theory and a speed-accuracy trade-off as predicted by Fitts' Law Thirty subjects (16 skilled, 14 unskilled) were instructed to throw a tennis ball at seven percentages of their maximum velocity (40-100%) in random order (9 trials per condition) at a target 30 feet away. Throwing velocity was measured with a radar gun and interpreted as an index of overall systemic power output. Within-subject throwing velocity variability was examined using within-subjects repeated-measures ANOVAs (7 repeated conditions) with built-in polynomial contrasts. Spatial error was analyzed using mixed model regression. Results indicated a quadratic fit with variability in throwing velocity increasing from 40% up to 60%, where it peaked, and then decreasing at each subsequent interval to maximum (p < .001, η2 = .555). There was no linear relationship between speed and accuracy. Overall, these data support the notion of an inverted-U function in overarm throwing velocity variability as both skilled and unskilled subjects approach maximum effort. However, these data do not support the notion of a speed-accuracy trade-off. The consistent demonstration of an inverted-U function associated with systemic power output variability indicates an enhanced capability to regulate aspects of force production and relative timing between segments as individuals approach maximum effort, even in a complex ballistic skill.

Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico

Directory of Open Access Journals (Sweden)

D. J. Vega-Nieva

2018-04-01

Full Text Available Understanding the linkage between accumulated fuel dryness and temporal fire occurrence risk is key for improving decision-making in forest fire management, especially under growing conditions of vegetation stress associated with climate change. This study addresses the development of models to predict the number of 10-day observed Moderate-Resolution Imaging Spectroradiometer (MODIS active fire hotspots—expressed as a Fire Hotspot Density index (FHD—from an Accumulated Fuel Dryness Index (AcFDI, for 17 main vegetation types and regions in Mexico, for the period 2011–2015. The AcFDI was calculated by applying vegetation-specific thresholds for fire occurrence to a satellite-based fuel dryness index (FDI, which was developed after the structure of the Fire Potential Index (FPI. Linear and non-linear models were tested for the prediction of FHD from FDI and AcFDI. Non-linear quantile regression models gave the best results for predicting FHD using AcFDI, together with auto-regression from previously observed hotspot density values. The predictions of 10-day observed FHD values were reasonably good with R2 values of 0.5 to 0.7 suggesting the potential to be used as an operational tool for predicting the expected number of fire hotspots by vegetation type and region in Mexico. The presented modeling strategy could be replicated for any fire danger index in any region, based on information from MODIS or other remote sensors.

Predictive Value of Triglyceride Glucose Index for the Risk of Incident Diabetes: A 4-Year Retrospective Longitudinal Study.

Science.gov (United States)

Lee, Da Young; Lee, Eun Seo; Kim, Ji Hyun; Park, Se Eun; Park, Cheol-Young; Oh, Ki-Won; Park, Sung-Woo; Rhee, Eun-Jung; Lee, Won-Young

The Triglyceride Glucose Index (TyG index) is considered a surrogate marker of insulin resistance. The aim of this study is to investigate whether the TyG index has a predictive role in identifying individuals with a high risk of incident diabetes and to compare it with other indicators of metabolic health. A total 2900 non-diabetic adults who attended five consecutive annual health check-ups at Kangbuk Samsung Hospital was divided into four subgroups using three methods: (1) baseline TyG index; (2) obesity status (body mass index ≥25 kg/m2) and cutoff value of TyG index; (3) obesity status and metabolic health, defined as having fewer than two of the five components of high blood pressure, fasting blood glucose, triglyceride, low high-density lipoprotein cholesterol, and highest decile of homeostasis model assessment-insulin resistance. The development of diabetes was assessed annually using self-questionnaire, fasting glucose, and glycated hemoglobin. We compared the risk of incident diabetes using multivariate Cox analysis. During 11623 person-years there were 101 case of incident diabetes. Subjects with high TyG index had a high risk of diabetes. For TyG index quartiles, hazard ratios (HRs) of quartiles 3 and 4 were 4.06 (p = 0.033) and 5.65 (p = 0.006) respectively. When the subjects were divided by obesity status and cutoff value of TyG index of 8.8, the subgroups with TyG index ≥ 8.8 regardless of obesity had a significantly high risk for diabetes (HR 2.40 [p = 0.024] and 2.25 [p = 0.048]). For obesity status and metabolic health, the two metabolically unhealthy subgroups regardless of obesity had a significantly high risk for diabetes (HRs 2.54 [p = 0.024] and 2.73 [p = 0.021]). In conclusion, the TyG index measured at a single time point may be an indicator of the risk for incident diabetes. The predictive value of the TyG index was comparable to that of metabolic health.

Transvaginal cervical length and amniotic fluid index: can it predict delivery latency following preterm premature rupture of membranes?

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Mehra, Suwan; Amon, Erol; Hopkins, Sarah; Gavard, Jeffrey A; Shyken, Jaye

2015-03-01

We sought to determine whether transvaginal cervical length (TVCL), amniotic fluid index (AFI), or a combination of both can predict delivery latency within 7 days in women presenting with preterm premature rupture of membranes (PPROM). This was a prospective observational study of TVCL measurements in 106 singleton pregnancies with PPROM between 23-33 weeks. Delivery latency was defined as the period (in days) from the initial TVCL after PPROM to delivery of the infant, with our primary outcome being delivery within 7 days of TVCL. The independent predictability of significant characteristics for delivery within 7 days was determined using multiple logistic regression. Sensitivity, specificity, and predictive values were used to examine whether the presence of a short TVCL, AFI, or a combination of both affected the risk of delivery within 7 days. Delivery within 7 days occurred in 51/106 (48%) of pregnancies. Median duration (interquartile range) from PPROM to delivery and TVCL to delivery was 8 days (4.0-16.0) and 8 days (3.0-15.0), respectively. Using multiple regression TVCL as a continuous variable (odds ratio, 0.65; 95% confidence interval, 0.44-0.97; P 7 days for TVCL >2 cm alone was 61%. This predictive value changed when analyzed in conjunction with an AFI ≤5 cm and >5 cm at 42% and 89%, respectively. A shorter TVCL and an AFI ≤5 cm independently predict delivery within 7 days in women presenting with PPROM. The combination of an AFI >5 cm and TVCL >2 cm greatly improved the potential to remain undelivered at 7 days following cervical length assessment. These findings may be helpful for counseling and optimizing maternal and neonatal care in women with PPROM. Copyright © 2015 Elsevier Inc. All rights reserved.

GIS Based Distributed Runoff Predictions in Variable Source Area Watersheds Employing the SCS-Curve Number

Science.gov (United States)

Steenhuis, T. S.; Mendoza, G.; Lyon, S. W.; Gerard Marchant, P.; Walter, M. T.; Schneiderman, E.

2003-04-01

Because the traditional Soil Conservation Service Curve Number (SCS-CN) approach continues to be ubiquitously used in GIS-BASED water quality models, new application methods are needed that are consistent with variable source area (VSA) hydrological processes in the landscape. We developed within an integrated GIS modeling environment a distributed approach for applying the traditional SCS-CN equation to watersheds where VSA hydrology is a dominant process. Spatial representation of hydrologic processes is important for watershed planning because restricting potentially polluting activities from runoff source areas is fundamental to controlling non-point source pollution. The methodology presented here uses the traditional SCS-CN method to predict runoff volume and spatial extent of saturated areas and uses a topographic index to distribute runoff source areas through watersheds. The resulting distributed CN-VSA method was incorporated in an existing GWLF water quality model and applied to sub-watersheds of the Delaware basin in the Catskill Mountains region of New York State. We found that the distributed CN-VSA approach provided a physically-based method that gives realistic results for watersheds with VSA hydrology.

Predictive coding of dynamical variables in balanced spiking networks.

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Boerlin, Martin; Machens, Christian K; Denève, Sophie

2013-01-01

Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.

Changes in the Oswestry Disability Index that predict improvement after lumbar fusion.

Science.gov (United States)

Djurasovic, Mladen; Glassman, Steven D; Dimar, John R; Crawford, Charles H; Bratcher, Kelly R; Carreon, Leah Y

2012-11-01

Clinical studies use both disease-specific and generic health outcomes measures. Disease-specific measures focus on health domains most relevant to the clinical population, while generic measures assess overall health-related quality of life. There is little information about which domains of the Oswestry Disability Index (ODI) are most important in determining improvement in overall health-related quality of life, as measured by the 36-Item Short Form Health Survey (SF-36), after lumbar spinal fusion. The objective of the study is to determine which clinical elements assessed by the ODI most influence improvement of overall health-related quality of life. A single tertiary spine center database was used to identify patients undergoing lumbar fusion for standard degenerative indications. Patients with complete preoperative and 2-year outcomes measures were included. Pearson correlation was used to assess the relationship between improvement in each item of the ODI with improvement in the SF-36 physical component summary (PCS) score, as well as achievement of the SF-36 PCS minimum clinically important difference (MCID). Multivariate regression modeling was used to examine which items of the ODI best predicted achievement for the SF-36 PCS MCID. The effect size and standardized response mean were calculated for each of the items of the ODI. A total of 1104 patients met inclusion criteria (674 female and 430 male patients). The mean age at surgery was 57 years. All items of the ODI showed significant correlations with the change in SF-36 PCS score and achievement of MCID for the SF-36 PCS, but only pain intensity, walking, and social life had r values > 0.4 reflecting moderate correlation. These 3 variables were also the dimensions that were independent predictors of the SF-36 PCS, and they were the only dimensions that had effect sizes and standardized response means that were moderate to large. Of the health dimensions measured by the ODI, pain intensity, walking

Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market

International Nuclear Information System (INIS)

Qiu, Mingyue; Song, Yu; Akagi, Fumio

2016-01-01

Accurate prediction of stock market returns is a very challenging task because of the highly nonlinear nature of the financial time series. In this study, we apply an artificial neural network (ANN) that can map any nonlinear function without a prior assumption to predict the return of the Japanese Nikkei 225 index. (1) To improve the effectiveness of prediction algorithms, we propose a new set of input variables for ANN models. (2) To verify the prediction ability of the selected input variables, we predict returns for the Nikkei 225 index using the classical back propagation (BP) learning algorithm. (3) Global search techniques, i.e., a genetic algorithm (GA) and simulated annealing (SA), are employed to improve the prediction accuracy of the ANN and overcome the local convergence problem of the BP algorithm. It is observed through empirical experiments that the selected input variables were effective to predict stock market returns. A hybrid approach based on GA and SA improve prediction accuracy significantly and outperform the traditional BP training algorithm.

Are Macro variables good predictors? A prediction based on the number of total medals acquired

Directory of Open Access Journals (Sweden)

Shahram Shafiee

2012-01-01

Full Text Available A large amount of effort is spent on forecasting the outcome of sporting events. Moreover, there are large quantities of data regarding the outcomes of sporting events and the factors which are assumed to contribute to those outcomes. In this paper we tried to predict the success of nations at the Asian Games through macro-economic, political, social and cultural variables. we used the information of variables include urban population, Education Expenditures, Age Structure, GDP Real Growth Rate, GDP Per Capita, Unemployment Rate, Population, Inflation Average, current account balance, life expectancy at birth and Merchandise Trade for all of the participating countries in Asian Games from 1970 to 2006 in order to build the model and then this model was tested by the information of variables in 2010. The prediction is based on the number of total medals acquired each country. In this research we used WEKA software that is a popular suite of machine learning software written in Java. The value of correlation coefficient between the predicted and original ranks is 90.42%. Neural Network Model, between 28 countries mentioned, predicts their ranks according to the maximum difference between predicted and original ranks of 19 countries (67.85% is 3, the maximum difference between predicted and original ranks of 8 countries (28.57% is between 4 to 6 and the difference between predicted and original ranks of 1 countries (3.57% is more than 6.

Variables Predicting Foreign Language Reading Comprehension and Vocabulary Acquisition in a Linear Hypermedia Environment

Science.gov (United States)

Akbulut, Yavuz

2007-01-01

Factors predicting vocabulary learning and reading comprehension of advanced language learners of English in a linear multimedia text were investigated in the current study. Predictor variables of interest were multimedia type, reading proficiency, learning styles, topic interest and background knowledge about the topic. The outcome variables of…

The Predictive Value of the Foot Posture Index on Dynamic Function

DEFF Research Database (Denmark)

Mølgaard, Carsten Møller; Olesen Gammelgaard, Christian; Nielsen, R. G.

2008-01-01

Keenan et. al. identified the six-item version of the Foot Posture Index (FPI) as a valid, simple and clinically useful tool. The model combines measures of the standing foot posture in multiple planes and anatomical segments. It provides an alternative to existing static clinical measures when...... dynamic measures are not feasible. Redmond et. al. found the model able to predict 41% of the variation in the complex rotation of the ankle joint, representing inversion/eversion, during midstance of walking. To our knowledge no studies have been published on the relationship between FPI and the movement...

Functional region prediction with a set of appropriate homologous sequences-an index for sequence selection by integrating structure and sequence information with spatial statistics

Science.gov (United States)

2012-01-01

Background The detection of conserved residue clusters on a protein structure is one of the effective strategies for the prediction of functional protein regions. Various methods, such as Evolutionary Trace, have been developed based on this strategy. In such approaches, the conserved residues are identified through comparisons of homologous amino acid sequences. Therefore, the selection of homologous sequences is a critical step. It is empirically known that a certain degree of sequence divergence in the set of homologous sequences is required for the identification of conserved residues. However, the development of a method to select homologous sequences appropriate for the identification of conserved residues has not been sufficiently addressed. An objective and general method to select appropriate homologous sequences is desired for the efficient prediction of functional regions. Results We have developed a novel index to select the sequences appropriate for the identification of conserved residues, and implemented the index within our method to predict the functional regions of a protein. The implementation of the index improved the performance of the functional region prediction. The index represents the degree of conserved residue clustering on the tertiary structure of the protein. For this purpose, the structure and sequence information were integrated within the index by the application of spatial statistics. Spatial statistics is a field of statistics in which not only the attributes but also the geometrical coordinates of the data are considered simultaneously. Higher degrees of clustering generate larger index scores. We adopted the set of homologous sequences with the highest index score, under the assumption that the best prediction accuracy is obtained when the degree of clustering is the maximum. The set of sequences selected by the index led to higher functional region prediction performance than the sets of sequences selected by other sequence

Variability, Predictability, and Race Factors Affecting Performance in Elite Biathlon.

Science.gov (United States)

Skattebo, Øyvind; Losnegard, Thomas

2018-03-01

To investigate variability, predictability, and smallest worthwhile performance enhancement in elite biathlon sprint events. In addition, the effects of race factors on performance were assessed. Data from 2005 to 2015 including >10,000 and >1000 observations for each sex for all athletes and annual top-10 athletes, respectively, were included. Generalized linear mixed models were constructed based on total race time, skiing time, shooting time, and proportions of targets hit. Within-athlete race-to-race variability was expressed as coefficient of variation of performance times and standard deviation (SD) in proportion units (%) of targets hit. The models were adjusted for random and fixed effects of subject identity, season, event identity, and race factors. The within-athlete variability was independent of sex and performance standard of athletes: 2.5-3.2% for total race time, 1.5-1.8% for skiing time, and 11-15% for shooting times. The SD of the proportion of hits was ∼10% in both shootings combined (meaning ±1 hit in 10 shots). The predictability in total race time was very high to extremely high for all athletes (ICC .78-.84) but trivial for top-10 athletes (ICC .05). Race times during World Championships and Olympics were ∼2-3% faster than in World Cups. Moreover, race time increased by ∼2% per 1000 m of altitude, by ∼5% per 1% of gradient, by 1-2% per 1 m/s of wind speed, and by ∼2-4% on soft vs hard tracks. Researchers and practitioners should focus on strategies that improve biathletes' performance by at least 0.8-0.9%, corresponding to the smallest worthwhile enhancement (0.3 × within-athlete variability).

Predictor Variables for Marathon Race Time in Recreational Female Runners

OpenAIRE

Schmid, Wiebke; Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Rüst, Christoph Alexander; Rosemann, Thomas; Lepers, Romuald

2012-01-01

Purpose We intended to determine predictor variables of anthropometry and training for marathon race time in recreational female runners in order to predict marathon race time for future novice female runners. Methods Anthropometric characteristics such as body mass, body height, body mass index, circumferences of limbs, thicknesses of skin-folds and body fat as well as training variables such as volume and speed in running training were related to marathon race time using bi- and multi-varia...

Epileptic Seizure Prediction Using a New Similarity Index for Chaotic Signals

Science.gov (United States)

Niknazar, Hamid; Nasrabadi, Ali Motie

Epileptic seizures are generated by abnormal activity of neurons. The prediction of epileptic seizures is an important issue in the field of neurology, since it may improve the quality of life of patients suffering from drug resistant epilepsy. In this study a new similarity index based on symbolic dynamic techniques which can be used for extracting behavior of chaotic time series is presented. Using Freiburg EEG dataset, it is found that the method is able to detect the behavioral changes of the neural activity prior to epileptic seizures, so it can be used for prediction of epileptic seizure. A sensitivity of 63.75% with 0.33 false positive rate (FPR) in all 21 patients and sensitivity of 96.66% with 0.33 FPR in eight patients were achieved using the proposed method. Moreover, the method was evaluated by applying on Logistic and Tent map with different parameters to demonstrate its robustness and ability in determining similarity between two time series with the same chaotic characterization.

A study on the abundance of quartz in thermal coals of India and its relation to abrasion index: Development of predictive model for abrasion

Energy Technology Data Exchange (ETDEWEB)

Bandopadhyay, A.K. [Central Institute of Mining and Fuel Research Digwadih Campus, P.O.-FRI, Dhanbad-828108, Jharkhand (India)

2010-10-01

The quartz content of each of the 61 thermal coals used in power stations in India has been determined using Fourier Transform Infra-Red (FTIR) Spectroscopy. It has been observed that quartz is abundant in the thermal coals and its proportion varies from 5 to 20% by wt. The abrasion index (AI), a measure of abrasion caused by coals, has been determined for each coal according to the procedure laid down in Indian Standard IS: 9949-1986. The data generated on abrasion together with ash and quartz percentages of the coals studied have been subjected to regression and correlation analysis. Positive correlations have been found between AI and quartz content and between AI and ash yield, but the correlation between AI and ash (A) and quartz (Q) percentages has been observed to be the most significant (R{sup 2} = 0.86). The linear regression model AI = 1.00A + 1.35Q thus developed has the ability to predict AI of the thermal coals within {+-} 10 mg/kg at 95.5% confidence level. Results of application of the model to predicting abrasion of a limited number of foreign coals with different origins have been found to be encouraging. Integration of other variables like the size and the shape of the abrading particles along with other physical properties of coal, like the bulk density and the grindability, with the model, in addition to the variables already considered, has been suggested for improved prediction. (author)

PREDICTION OF OUTCOME USING THE MANNHEIM PERITONITIS INDEX IN CASES OF PERITONITIS

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Sanjeev

2015-08-01

Full Text Available BACKGROUND: Peritonitis still presents an extremely common & dreaded problem in emergency surgery. Despite aggressive surgical techniques, the prognosis of peritonitis and intra - abdominal sepsis is very poor, especially when multiple organ failure develops. Therefore early objective & reliable classification of the severity of peritonitis and intra - abdominal sepsis is needed not only to predict prognosis & to select patients for these aggressive surgical techniques but also to evaluate & compare the results of different treatment regimens. So, in this prospective st udy of 60 cases of peritonitis, the reliability of the Mannheim peritonitis index is assessed & its predictive power evaluated. MATERIALS & METHOD S : This prospective study was carried out in the department of surgery, GMCH, Udaipur from June 2014 to June 2 015 after taking the permission from institutional ethics committee. Patients from both sexes of various age groups having peritonitis of varied aetiology & who had undergone laparotomy were taken. A detailed history, thorough clinical examination & necess ary investigations were performed in e ach case according to planned p r o forma. After resuscitation laparotomy was done & operative findings were noted carefully and a proper note on the progress of each patient was maintained and any complications encounter ed were noted. So, early classification of patients presenting with peritonitis by means of objective scoring system was done to select patients for aggressive surgery & overall morbidity & mortality were analyzed. RESULTS: Total 60 patients of peritonitis were examined and common causes were peptic (61.6%, typhoid (21.6% and appendicular (8.3 %. Most common age group was found to be 21 to 50 years and male to female ratio was 4:1. Peritonitis was more common in patients involved in hard work and chronic Be di smokers (61.6%. About 46% of patients who presented for treatment within 48 hours of onset of illness

Low-Frequency Temporal Variability in Mira and Semiregular Variables

Science.gov (United States)

Templeton, Matthew R.; Karovska, M.; Waagen, E. O.

2012-01-01

We investigate low-frequency variability in a large sample of Mira and semiregular variables with long-term visual light curves from the AAVSO International Database. Our aim is to determine whether we can detect and measure long-timescale variable phenomena in these stars, for example photometric variations that might be associated with supergranular convection. We analyzed the long-term light curves of 522 variable stars of the Mira and SRa, b, c, and d classes. We calculated their low-frequency time-series spectra to characterize rednoise with the power density spectrum index, and then correlate this index with other observable characteristics such as spectral type and primary pulsation period. In our initial analysis of the sample, we see that the semiregular variables have a much broader range of spectral index than the Mira types, with the SRb subtype having the broadest range. Among Mira variables we see that the M- and S-type Miras have similarly wide ranges of index, while the C-types have the narrowest with generally shallower slopes. There is also a trend of steeper slope with larger amplitude, but at a given amplitude, a wide range of slopes are seen. The ultimate goal of the project is to identify stars with strong intrinsic red noise components as possible targets for resolved surface imaging with interferometry.

Using lexical variables to predict picture-naming errors in jargon aphasia

Directory of Open Access Journals (Sweden)

Catherine Godbold

2015-04-01

Full Text Available Introduction Individuals with jargon aphasia produce fluent output which often comprises high proportions of non-word errors (e.g., maf for dog. Research has been devoted to identifying the underlying mechanisms behind such output. Some accounts posit a reduced flow of spreading activation between levels in the lexical network (e.g., Robson et al., 2003. If activation level differences across the lexical network are a cause of non-word outputs, we would predict improved performance when target items reflect an increased flow of activation between levels (e.g. more frequently-used words are often represented by higher resting levels of activation. This research investigates the effect of lexical properties of targets (e.g., frequency, imageability on accuracy, error type (real word vs. non-word and target-error overlap of non-word errors in a picture naming task by individuals with jargon aphasia. Method Participants were 17 individuals with Wernicke’s aphasia, who produced a high proportion of non-word errors (>20% of errors on the Philadelphia Naming Test (PNT; Roach et al., 1996. The data were retrieved from the Moss Aphasic Psycholinguistic Database Project (MAPPD, Mirman et al., 2010. We used a series of mixed models to test whether lexical variables predicted accuracy, error type (real word vs. non-word and target-error overlap for the PNT data. As lexical variables tend to be highly correlated, we performed a principal components analysis to reduce the variables into five components representing variables associated with phonology (length, phonotactic probability, neighbourhood density and neighbourhood frequency, semantics (imageability and concreteness, usage (frequency and age-of-acquisition, name agreement and visual complexity. Results and Discussion Table 1 shows the components that made a significant contribution to each model. Individuals with jargon aphasia produced more correct responses and fewer non-word errors relative to

The seasonal predictability of blocking frequency in two seasonal prediction systems (CMCC, Met-Office) and the associated representation of low-frequency variability.

Science.gov (United States)

Athanasiadis, Panos; Gualdi, Silvio; Scaife, Adam A.; Bellucci, Alessio; Hermanson, Leon; MacLachlan, Craig; Arribas, Alberto; Materia, Stefano; Borelli, Andrea

2014-05-01

Low-frequency variability is a fundamental component of the atmospheric circulation. Extratropical teleconnections, the occurrence of blocking and the slow modulation of the jet streams and storm tracks are all different aspects of low-frequency variability. Part of the latter is attributed to the chaotic nature of the atmosphere and is inherently unpredictable. On the other hand, primarily as a response to boundary forcings, tropospheric low-frequency variability includes components that are potentially predictable. Seasonal forecasting faces the difficult task of predicting these components. Particularly referring to the extratropics, the current generation of seasonal forecasting systems seem to be approaching this target by realistically initializing most components of the climate system, using higher resolution and utilizing large ensemble sizes. Two seasonal prediction systems (Met-Office GloSea and CMCC-SPS-v1.5) are analyzed in terms of their representation of different aspects of extratropical low-frequency variability. The current operational Met-Office system achieves unprecedented high scores in predicting the winter-mean phase of the North Atlantic Oscillation (NAO, corr. 0.74 at 500 hPa) and the Pacific-N. American pattern (PNA, corr. 0.82). The CMCC system, considering its small ensemble size and course resolution, also achieves good scores (0.42 for NAO, 0.51 for PNA). Despite these positive features, both models suffer from biases in low-frequency variance, particularly in the N. Atlantic. Consequently, it is found that their intrinsic variability patterns (sectoral EOFs) differ significantly from the observed, and the known teleconnections are underrepresented. Regarding the representation of N. hemisphere blocking, after bias correction both systems exhibit a realistic climatology of blocking frequency. In this assessment, instantaneous blocking and large-scale persistent blocking events are identified using daily geopotential height fields at

Evaluation of renal resistive index in cirrhotic patients for predicting the hepatirenal syndrome

Energy Technology Data Exchange (ETDEWEB)

Baek, Seung Yon; Kim, Hyae young; Yi, Sun Young [Ewha WoMans Univ. Mokdong Hospital, Seoul (Korea, Republic of)

1996-04-01

To evaluate the usefulness of renal resistive index(RI) in patients with liver cirrhosis as an indicator for predicting hepatorenal syndrome. Renal RIs of thirty cirrhotic patients were analyzed using the gray-scale and Doppler ultrasonograms. As a control group, eight normal subjects were included. Renal RIs were measured at three sites of interlobar or arcuate arteries of both kidneys. The patients were divided into three groups (A, B, or C) according to the Child-Turcotte-Pugh classification and their serum BUN and creatinine levels were compared. We determined whether RIs of normal controls differed from those of cirrhotic patients or whether RIs of cirrhotic patients correlated with the Child-Turcotte-Pugh classification or BUN and creatinine levels. Mean RIs(0.63 {+-}0.33) of normal subjects were statistically different from those(0.67 {+-} 0.05) of cirrhotic patients(P=0.009). RIs of group A(n=6), B(n=9) and C(n=15) were 0.65 {+-} 0.03, 0.65 {+-} 0.04 and 0.70 {+-} 0.04, respectively. The ANOVA test revealed statistically significant differences between the three groups(F ratio=4.472, P=0.021). RIs did not correlate with BUN or creatinine levels. RI could be used as an index for predicting hepatorenal syndrome before the renal function becomes impaired.

Predictive-property-ranked variable reduction in partial least squares modelling with final complexity adapted models: comparison of properties for ranking.

Science.gov (United States)

Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

2013-01-14

The calibration performance of partial least squares regression for one response (PLS1) can be improved by eliminating uninformative variables. Many variable-reduction methods are based on so-called predictor-variable properties or predictive properties, which are functions of various PLS-model parameters, and which may change during the steps of the variable-reduction process. Recently, a new predictive-property-ranked variable reduction method with final complexity adapted models, denoted as PPRVR-FCAM or simply FCAM, was introduced. It is a backward variable elimination method applied on the predictive-property-ranked variables. The variable number is first reduced, with constant PLS1 model complexity A, until A variables remain, followed by a further decrease in PLS complexity, allowing the final selection of small numbers of variables. In this study for three data sets the utility and effectiveness of six individual and nine combined predictor-variable properties are investigated, when used in the FCAM method. The individual properties include the absolute value of the PLS1 regression coefficient (REG), the significance of the PLS1 regression coefficient (SIG), the norm of the loading weight (NLW) vector, the variable importance in the projection (VIP), the selectivity ratio (SR), and the squared correlation coefficient of a predictor variable with the response y (COR). The selective and predictive performances of the models resulting from the use of these properties are statistically compared using the one-tailed Wilcoxon signed rank test. The results indicate that the models, resulting from variable reduction with the FCAM method, using individual or combined properties, have similar or better predictive abilities than the full spectrum models. After mean-centring of the data, REG and SIG, provide low numbers of informative variables, with a meaning relevant to the response, and lower than the other individual properties, while the predictive abilities are

The role of socio-cognitive variables in predicting learning satisfaction in smart schools

Directory of Open Access Journals (Sweden)

Mohammad Reza Firoozi

2017-03-01

Full Text Available The present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through multi-stage cluster sampling, was 383 primary school students studying in smart schools in Shiraz. The instruments were the Computer Self-Efficiency Questionnaire developed by Torkzadeh (2003, Performance Expectation Questionnaire developed by Compeau and Higgins (1995, System Functionality and Content Feature Questionnaire developed by Pituch and Lee (2006, Interaction Questionnaire developed by Johnston, Killion and Oomen (2005, Learning Climate Questionnaire developed by Chou` and Liu (2005 and Learning Satisfaction Questionnaire developed by Chou and Liu (2005. In order to determine the possible relationship between variables and to predict the changes in the degree of satisfaction, we made use of correlational procedures and step-wise regression analysis. The results indicated that all the socio-cognitive variables have a positive and significant correlation with learning satisfaction. Out of the socio-cognitive variables in question, Computer Self-Efficiency, Performance Expectation and Learning Climate significantly explained 53% of the variance of learning satisfaction.

The Role of Socio-Cognitive Variables in Predicting Learning Satisfaction in Smart Schools

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Mohammad Reza FIROOZI

2017-03-01

Full Text Available The present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through multi-stage cluster sampling, was 383 primary school students studying in smart schools in Shiraz. The instruments were the Computer Self-Efficiency Questionnaire developed by Torkzadeh (2003, Performance Expectation Questionnaire developed by Compeau and Higgins (1995, System Functionality and Content Feature Questionnaire developed by Pituch and Lee (2006, Interaction Questionnaire developed by Johnston, Killion and Oomen (2005, Learning Climate Questionnaire developed by Chou` and Liu (2005 and Learning Satisfaction Questionnaire developed by Chou and Liu (2005. In order to determine the possible relationship between variables and to predict the changes in the degree of satisfaction, we made use of correlational procedures and step-wise regression analysis. The results indicated that all the socio-cognitive variables have a positive and significant correlation with learning satisfaction. Out of the socio-cognitive variables in question, Computer Self-Efficiency, Performance Expectation and Learning Climate significantly explained 53% of the variance of learning satisfaction.

Multi-scale enhancement of climate prediction over land by improving the model sensitivity to vegetation variability

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Alessandri, A.; Catalano, F.; De Felice, M.; Hurk, B. V. D.; Doblas-Reyes, F. J.; Boussetta, S.; Balsamo, G.; Miller, P. A.

2017-12-01

Here we demonstrate, for the first time, that the implementation of a realistic representation of vegetation in Earth System Models (ESMs) can significantly improve climate simulation and prediction across multiple time-scales. The effective sub-grid vegetation fractional coverage vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the surface resistance to evapotranspiration, albedo, roughness lenght, and soil field capacity. To adequately represent this effect in the EC-Earth ESM, we included an exponential dependence of the vegetation cover on the Leaf Area Index.By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal (2-4 months) and weather (4 days) time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation-cover consistently correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in

Strain dyssynchrony index determined by three-dimensional speckle area tracking can predict response to cardiac resynchronization therapy

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Onishi Tetsuari

2011-04-01

Full Text Available Abstract Background We have previously reported strain dyssynchrony index assessed by two-dimensional speckle tracking strain, and a marker of both dyssynchrony and residual myocardial contractility, can predict response to cardiac resynchronization therapy (CRT. A newly developed three-dimensional (3-D speckle tracking system can quantify endocardial area change ratio (area strain, which coupled with the factors of both longitudinal and circumferential strain, from all 16 standard left ventricular (LV segments using complete 3-D pyramidal datasets. Our objective was to test the hypothesis that strain dyssynchrony index using area tracking (ASDI can quantify dyssynchrony and predict response to CRT. Methods We studied 14 heart failure patients with ejection fraction of 27 ± 7% (all≤35% and QRS duration of 172 ± 30 ms (all≥120 ms who underwent CRT. Echocardiography was performed before and 6-month after CRT. ASDI was calculated as the average difference between peak and end-systolic area strain of LV endocardium obtained from 3-D speckle tracking imaging using 16 segments. Conventional dyssynchrony measures were assessed by interventricular mechanical delay, Yu Index, and two-dimensional radial dyssynchrony by speckle-tracking strain. Response was defined as a ≥15% decrease in LV end-systolic volume 6-month after CRT. Results ASDI ≥ 3.8% was the best predictor of response to CRT with a sensitivity of 78%, specificity of 100% and area under the curve (AUC of 0.93 (p Conclusions ASDI can predict responders and LV reverse remodeling following CRT. This novel index using the 3-D speckle tracking system, which shows circumferential and longitudinal LV dyssynchrony and residual endocardial contractility, may thus have clinical significance for CRT patients.

[Prediction of mathematics achievement: effect of personal, socioeducational and contextual variables].

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Rosário, Pedro; Lourenço, Abílio; Paiva, Olímpia; Rodrigues, Adriana; Valle, Antonio; Tuero-Herrero, Ellián

2012-05-01

Based upon the self-regulated learning theoretical framework this study examined to what extent students' Math school achievement (fifth to ninth graders from compulsory education) can be explained by different cognitive-motivational, social, educational, and contextual variables. A sample of 571 students (10 to 15 year old) enrolled in the study. Findings suggest that Math achievement can be predicted by self-efficacy in Math, school success and self-regulated learning and that these same variables can be explained by other motivational (ej., achievement goals) and contextual variables (school disruption) stressing this way the main importance of self-regulated learning processes and the role context can play in the promotion of school success. The educational implications of the results to the school levels taken are also discussed in the present paper.

Variable classifications of glycemic index determined by glucose meters.

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Lin, Meng-Hsueh Amanda; Wu, Ming-Chang; Lin, Jenshinn

2010-07-01

THE STUDY EVALUATED AND COMPARED THE DIFFERENCES OF GLUCOSE RESPONSES, INCREMENTAL AREA UNDER CURVE (IAUC), GLYCEMIC INDEX (GI) AND THE CLASSIFICATION OF GI VALUES BETWEEN MEASURED BY BIOCHEMICAL ANALYZER (FUJI AUTOMATIC BIOCHEMISTRY ANALYZER (FAA)) AND THREE GLUCOSE METERS: Accue Chek Advantage (AGM), BREEZE 2 (BGM), and Optimum Xceed (OGM). Ten healthy subjects were recruited for the study. The results showed OGM yield highest postprandial glucose responses of 119.6 +/- 1.5, followed by FAA, 118.4 +/- 1.2, BGM, 117.4 +/- 1.4 and AGM, 112.6 +/- 1.3 mg/dl respectively. FAA reached highest mean IAUC of 4156 +/- 208 mg x min/dl, followed by OGM (3835 +/- 270 mg x min/dl), BGM (3730 +/- 241 mg x min/dl) and AGM (3394 +/- 253 mg x min/dl). Among four methods, OGM produced highest mean GI value than FAA (87 +/- 5) than FAA, followed by BGM and AGM (77 +/- 1, 68 +/- 4 and 63 +/- 5, pOGM are more variable methods to determine IAUC, GI and rank GI value of food than FAA. The present result does not necessarily apply to other glucose meters. The performance of glucose meter to determine GI value of food should be evaluated and calibrated before use.

Experimental comparison between speech transmission index, rapid speech transmission index, and speech intelligibility index.

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Larm, Petra; Hongisto, Valtteri

2006-02-01

During the acoustical design of, e.g., auditoria or open-plan offices, it is important to know how speech can be perceived in various parts of the room. Different objective methods have been developed to measure and predict speech intelligibility, and these have been extensively used in various spaces. In this study, two such methods were compared, the speech transmission index (STI) and the speech intelligibility index (SII). Also the simplification of the STI, the room acoustics speech transmission index (RASTI), was considered. These quantities are all based on determining an apparent speech-to-noise ratio on selected frequency bands and summing them using a specific weighting. For comparison, some data were needed on the possible differences of these methods resulting from the calculation scheme and also measuring equipment. Their prediction accuracy was also of interest. Measurements were made in a laboratory having adjustable noise level and absorption, and in a real auditorium. It was found that the measurement equipment, especially the selection of the loudspeaker, can greatly affect the accuracy of the results. The prediction accuracy of the RASTI was found acceptable, if the input values for the prediction are accurately known, even though the studied space was not ideally diffuse.

Using a Budyko Derived Index to Evaluate the Internal Hydrological Variability of Catchments in Complex Terrain

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Dominguez, M.

2017-12-01

Headwater catchments in complex terrain typically exhibit significant variations in microclimatic conditions across slopes. This microclimatic variability in turn, modifies land surface properties presumably altering the hydrologic dynamics of these catchments. The extent to which differences in microclimate and land cover dictate the partition of water and energy fluxes within a catchment is still poorly understood. In this study, we attempt to do an assessment of the effects of aspect, elevation and latitude (which are the principal factors that define microclimate conditions) on the hydrologic behavior of the hillslopes within catchments with complex terrain. Using a distributed hydrologic model on a number of catchments at different latitudes, where data is available for calibration and validation, we estimate the different components of the water balance to obtain the aridity index (AI = PET/P) and the evaporative index (EI = AET/P) of each slope for a number of years. We use Budyko's curve as a framework to characterize the inter-annual variability in the hydrologic response of the hillslopes in the studied catchments, developing a hydrologic sensitivity index (HSi) based on the relative change in Budyko's curve components (HSi=ΔAI/ΔEI). With this method, when the HSi values of a given hillslope are larger than 1 the hydrologic behavior of that part of the catchment is considered sensitive to changes in climatic conditions, while values approaching 0 would indicate the opposite. We use this approach as a diagnostic tool to discern the effect of aspect, elevation, and latitude on the hydrologic regime of the slopes in complex terrain catchments and to try to explain observed patterns of land cover conditions on these types of catchments.

The Effectiveness of Monetary Policy Towards Stock Index Case Study : Jakarta Islamic Index 2006-2014

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Lak lak Nashat el Hasanah

2016-06-01

Full Text Available Fluctuation in economy situation is an important indicator for investor decision making. The investor actions are base on the minimum risk while having maximum profit. One of it is observing the condition of macro variables within monetary policy. This research aims to analyze the impact of inflation, money supply, exchange rate, and birate towards stock of jakarta islamic Index. The type data used is times series periode 2006-2014. Multiple linier regression with chow test and dummy variable approach to compare and to know the behavior of each independent variables. The result shows partially that birate and exchange rate negatively impact Jakarta Islamic Index before global monetary crisis in 2008, while inflation and money supply not that significantly impact. After global monetary crisis in 2008, partially, birate variable and money supply significantly giving positive influence to Jakarta Islamic Index, while at same time exchange rate and inflation are not significantly influencial. Simultaneously, inflation, money supply, exchange rate, and birate influence Jakarta islamic Index.

Can transient elastography, Fib-4, Forns Index, and Lok Score predict esophageal varices in HCV-related cirrhotic patients?

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Hassan, Eman M; Omran, Dalia A; El Beshlawey, Mohamad L; Abdo, Mahmoud; El Askary, Ahmad

2014-02-01

Gastroesophageal varices are present in approximately 50% of patients with liver cirrhosis. The aim of this study was to evaluate liver stiffness measurement (LSM), Fib-4, Forns Index and Lok Score as noninvasive predictors of esophageal varices (EV). This prospective study included 65 patients with HCV-related liver cirrhosis. All patients underwent routine laboratory tests, transient elastograhy (TE) and esophagogastroduodenoscopy. FIB-4, Forns Index and Lok Score were calculated. The diagnostic performances of these methods were assessed using sensitivity, specificity, positive predictive value, negative predictive value, accuracy and receiver operating characteristic curves. All predictors (LSM, FIB-4, Forns Index and Lok Score) demonstrated statistically significant correlation with the presence and the grade of EV. TE could diagnose EV at a cutoff value of 18.2kPa. Fib-4, Forns Index, and Lok Score could diagnose EV at cutoff values of 2.8, 6.61 and 0.63, respectively. For prediction of large varices (grade 2, 3), LSM showed the highest accuracy (80%) with a cutoff of 22.4kPa and AUROC of 0.801. Its sensitivity was 84%, specificity 72%, PPV 84% and NPV 72%. The diagnostic accuracies of FIB-4, Forns Index and Lok Score were 70%, 70% and76%, respectively, at cutoffs of 3.3, 6.9 and 0.7, respectively. For diagnosis of large esophageal varices, adding TE to each of the other diagnostic indices (serum fibrosis scores) increased their sensitivities with little decrease in their specificities. Moreover, this combination decreased the LR- in all tests. Noninvasive predictors can restrict endoscopic screening. This is very important as non invasiveness is now a major goal in hepatology. Copyright © 2013 Elsevier España, S.L. and AEEH y AEG. All rights reserved.

The Effect of Consumer Expectation Index, Economic Condition Index and Crude Oil Price on Indonesian Government Bond Yield

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Benny Budiawan Tjandrasa

2017-06-01

Full Text Available Governments sell bonds to finance their budget. The investors willing to buy government bonds because of the yield they will get, but on the other hand if government bond yields is  too high it would burden the state in paying the interest due. Various studies have been done to find the variables that affect government bond yield significantly, such as exchange rate, inflation rate, interest rate, and oil price. This study found two more variables namely consumer expectations index and the economic conditions index to complement the variables that have been discovered. Those two variables are used as a proxy of economic stability of a country, the increase of those variables represent the increase of economic stability and will reduce the level of risk and lowering the yield that investors demand. This research use descriptive method and explanatory study with secondary data using multivariate regression equation model. The results shown consumer expectation index and economic condition index have significant effect on Indonesian Government Bond yield. To keep consumer expectation index and economic condition index increase government should give a positive signal and a sense of security to investor.

Shock index as a mortality predictor in patients with acute polytrauma

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Kevin Fernando Montoya; Jos Daniel Charry; Juan Sebastin Calle-Toro; Luis Ramiro Niez; Gustavo Poveda

2015-01-01

Objective: To evaluate whether the shock index (SI), given by the formula SI = heart rate /systolic blood pressure (HR / SBP), is useful for predicting mortality at 24 h in trauma patients admitted to the emergency department of a university hospital in Colombia. Methods: A database of trauma patients admitted between January 2013 and December 2013 was constructed; the result according to the shock index was determined, generating a dichotomous variable with two groups: Group A (SI 0.9). Univariate analysis was performed. Results: A total of 666 patients were analyzed, 83.3% (555) had SI 0.9. The mean age for Groups A and B was 32.4 and 35.4 respectively. The average injury severity score for both groups was 9.6 and 17.6 respectively. Mortality at 24 h after injury for both groups was 3.1% (P = 0.032) and 59.5% (P = 0.027) respectively. Conclusions: An initial shock index greater than 0.9 implies a worse prognosis 24 h after injury. The shock index predicts mortality in multiple trauma patients in the emergency department, and is also a quick and applicable in all hospital.

Waist-Hip Ratio Surrogate Is More Predictive Than Body Mass Index of Wound Complications After Pelvic and Acetabulum Surgery.

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Jaeblon, Todd; Perry, Kevin J; Kufera, Joseph A

2018-04-01

To determine whether a novel surrogate of waist-hip ratio (WHR) is more predictive of wound complications after pelvis or acetabulum stabilization than body mass index (BMI) and describe the method of measuring a WHR proxy (WHRp). Retrospective review. One Level 1 Trauma Center. One hundred sixty-one patients after operative repair of pelvis and acetabulum fractures. Operative stabilization of a pelvic ring injury or acetabular fracture. Infection (pin, superficial, and deep) and wound healing complication. We retrospectively reviewed 161 subjects after operative repair of pelvic and acetabular fractures. Primary outcome was any wound complication. BMI was acquired from medical records. WHRp was derived from anteroposterior and lateral computed tomography scout images. BMI and WHRp results were analyzed as continuous and categorical variables. BMI was grouped into high-risk categories of ≥30 and ≥40. WHRp data were grouped utilizing the WHO's high-risk profile for females (>0.85) and males (>0.90). An alternative optimal WHR was also assessed. Covariate analysis included demographic data, Injury Severity Score, mechanism, tobacco use, presence and types of open approach, injury type, associated injuries and comorbidities, failure of fixation, and thromboembolism. The mean follow-up was 15.9 months. Twenty-four (15%) patients developed wound complications. Increasing BMI (P < 0.007) and WHRp (P < 0.001) as continuous variables and female sex (P < 0.009) were associated with wound complications. Applying unadjusted continuous data to a receiver operating characteristic curve revealed a greater area under the curve for WHRp than for BMI (P < 0.001). The optimal predictive WHRp was ≥1.0 (P < 0.001, odds ratio 43.11). The receiver operating characteristic curve from adjusted data demonstrated a greater area under the curve for WHRp ≥1.0 (0.93) compared with BMI ≥30 (0.78) or ≥40 (0.75) and WHO WHRp (0.82). Computed tomography generated WHRp demonstrated

Effects of hypocaloric diets with different glycemic indexes on endothelial function and glycemic variability in overweight and in obese adult patients at increased cardiovascular risk.

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Buscemi, Silvio; Cosentino, Loretta; Rosafio, Giuseppe; Morgana, Manuela; Mattina, Alessandro; Sprini, Delia; Verga, Salvatore; Rini, Giovam Battista

2013-06-01

The role of glycemic index of the diet in glucose control and cardiovascular prevention is still not clear. The aim of this study was to determine the effects of hypocaloric diets with different glycemic indexes and glycemic loads on endothelial function and glycemic variability in nondiabetic participants at increased cardiovascular risk. Forty nondiabetic obese participants were randomly assigned to a three-month treatment with either a low glycemic index (LGI; n=19) or high glycemic index (HGI; n=21) hypocaloric diet with similar macronutrient and fiber content. Endothelial function was measured as flow-mediated dilatation (FMD) of the brachial artery before and after dieting. In addition, 48-h continuous subcutaneous glucose monitoring was done before and after dieting in a subgroup of 24 participants. The amount of weight loss after dieting was similar in both groups. The glycemic index of the diet significantly influenced the FMD (Pdiet, and -0.9±3.6% after the HGI diet (Pdiet on results was observed. The glycemic index of the diet significantly influenced the 48-h glycemic variability measured as coefficient of variability (CV%; Pdiet (from 23.5 to 20.0%) and increased after the HGI diet (from 23.6 to 26.6%). The change in percentage of FMD was inversely correlated with the change in the 48-h glycemic CV% (r=-0.45; Phypocaloric diet in nondiabetic obese persons. ISRCTN56834511. Copyright © 2012 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing

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Di Biagio, Claudia; Formenti, Paola; Balkanski, Yves; Caponi, Lorenzo; Cazaunau, Mathieu; Pangui, Edouard; Journet, Emilie; Nowak, Sophie; Caquineau, Sandrine; Andreae, Meinrat O.; Kandler, Konrad; Saeed, Thuraya; Piketh, Stuart; Seibert, David; Williams, Earle; Doussin, Jean-François

2017-02-01

Modeling the interaction of dust with long-wave (LW) radiation is still a challenge because of the scarcity of information on the complex refractive index of dust from different source regions. In particular, little is known about the variability of the refractive index as a function of the dust mineralogical composition, which depends on the specific emission source, and its size distribution, which is modified during transport. As a consequence, to date, climate models and remote sensing retrievals generally use a spatially invariant and time-constant value for the dust LW refractive index. In this paper, the variability of the mineral dust LW refractive index as a function of its mineralogical composition and size distribution is explored by in situ measurements in a large smog chamber. Mineral dust aerosols were generated from 19 natural soils from 8 regions: northern Africa, the Sahel, eastern Africa and the Middle East, eastern Asia, North and South America, southern Africa, and Australia. Soil samples were selected from a total of 137 available samples in order to represent the diversity of sources from arid and semi-arid areas worldwide and to account for the heterogeneity of the soil composition at the global scale. Aerosol samples generated from soils were re-suspended in the chamber, where their LW extinction spectra (3-15 µm), size distribution, and mineralogical composition were measured. The generated aerosol exhibits a realistic size distribution and mineralogy, including both the sub- and super-micron fractions, and represents in typical atmospheric proportions the main LW-active minerals, such as clays, quartz, and calcite. The complex refractive index of the aerosol is obtained by an optical inversion based upon the measured extinction spectrum and size distribution. Results from the present study show that the imaginary LW refractive index (k) of dust varies greatly both in magnitude and spectral shape from sample to sample, reflecting the

Biometeorological and autoregressive indices for predicting olive pollen intensity.

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Oteros, J; García-Mozo, H; Hervás, C; Galán, C

2013-03-01

This paper reports on modelling to predict airborne olive pollen season severity, expressed as a pollen index (PI), in Córdoba province (southern Spain) several weeks prior to the pollen season start. Using a 29-year database (1982-2010), a multivariate regression model based on five indices-the index-based model-was built to enhance the efficacy of prediction models. Four of the indices used were biometeorological indices: thermal index, pre-flowering hydric index, dormancy hydric index and summer index; the fifth was an autoregressive cyclicity index based on pollen data from previous years. The extreme weather events characteristic of the Mediterranean climate were also taken into account by applying different adjustment criteria. The results obtained with this model were compared with those yielded by a traditional meteorological-based model built using multivariate regression analysis of simple meteorological-related variables. The performance of the models (confidence intervals, significance levels and standard errors) was compared, and they were also validated using the bootstrap method. The index-based model built on biometeorological and cyclicity indices was found to perform better for olive pollen forecasting purposes than the traditional meteorological-based model.

Spatial Variability of Physical Soil Quality Index of an Agricultural Field

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Sheikh M. Fazle Rabbi

2014-01-01

Full Text Available A field investigation was carried out to evaluate the spatial variability of physical indicators of soil quality of an agricultural field and to construct a physical soil quality index (SQIP map. Surface soil samples were collected using 10  m×10 m grid from an Inceptisol on Ganges Tidal Floodplain of Bangladesh. Five physical soil quality indicators, soil texture, bulk density, porosity, saturated hydraulic conductivity (KS, and aggregate stability (measured as mean weight diameter, MWD were determined. The spatial structures of sand, clay, and KS were moderate but the structure was strong for silt, bulk density, porosity, and MWD. Each of the physical soil quality indicators was transformed into 0 and 1 using threshold criteria which are required for crop production. The transformed indicators were the combined into SQIP. The kriged SQIP map showed that the agricultural field studied could be divided into two parts having “good physical quality” and “poor physical soil quality.”

Screening for Sleep Apnoea in Mild Cognitive Impairment: The Utility of the Multivariable Apnoea Prediction Index

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Georgina Wilson

2014-01-01

Full Text Available Purpose. Mild cognitive impairment (MCI is considered an “at risk” state for dementia and efforts are needed to target modifiable risk factors, of which Obstructive sleep apnoea (OSA is one. This study aims to evaluate the predictive utility of the multivariate apnoea prediction index (MAPI, a patient self-report survey, to assess OSA in MCI. Methods. Thirty-seven participants with MCI and 37 age-matched controls completed the MAPI and underwent polysomnography (PSG. Correlations were used to compare the MAPI and PSG measures including oxygen desaturation index and apnoea-hypopnoea index (AHI. Receiver-operating characteristics (ROC curve analyses were performed using various cut-off scores for apnoea severity. Results. In controls, there was a significant moderate correlation between higher MAPI scores and more severe apnoea (AHI: r=0.47, P=0.017. However, this relationship was not significant in the MCI sample. ROC curve analysis indicated much lower area under the curve (AUC in the MCI sample compared to the controls across all AHI severity cut-off scores. Conclusions. In older people, the MAPI moderately correlates with AHI severity but only in those who are cognitively intact. Development of further screening tools is required in order to accurately screen for OSA in MCI.

Predicting Spatial Distribution of Key Honeybee Pests in Kenya Using Remotely Sensed and Bioclimatic Variables: Key Honeybee Pests Distribution Models

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David M. Makori

2017-02-01

Full Text Available Bee keeping is indispensable to global food production. It is an alternate income source, especially in rural underdeveloped African settlements, and an important forest conservation incentive. However, dwindling honeybee colonies around the world are attributed to pests and diseases whose spatial distribution and influences are not well established. In this study, we used remotely sensed data to improve the reliability of pest ecological niche (EN models to attain reliable pest distribution maps. Occurrence data on four pests (Aethina tumida, Galleria mellonella, Oplostomus haroldi and Varroa destructor were collected from apiaries within four main agro-ecological regions responsible for over 80% of Kenya’s bee keeping. Africlim bioclimatic and derived normalized difference vegetation index (NDVI variables were used to model their ecological niches using Maximum Entropy (MaxEnt. Combined precipitation variables had a high positive logit influence on all remotely sensed and biotic models’ performance. Remotely sensed vegetation variables had a substantial effect on the model, contributing up to 40.8% for G. mellonella and regions with high rainfall seasonality were predicted to be high-risk areas. Projections (to 2055 indicated that, with the current climate change trend, these regions will experience increased honeybee pest risk. We conclude that honeybee pests could be modelled using bioclimatic data and remotely sensed variables in MaxEnt. Although the bioclimatic data were most relevant in all model results, incorporating vegetation seasonality variables to improve mapping the ‘actual’ habitat of key honeybee pests and to identify risk and containment zones needs to be further investigated.

Random forest variable selection in spatial malaria transmission modelling in Mpumalanga Province, South Africa

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Thandi Kapwata

2016-11-01

Full Text Available Malaria is an environmentally driven disease. In order to quantify the spatial variability of malaria transmission, it is imperative to understand the interactions between environmental variables and malaria epidemiology at a micro-geographic level using a novel statistical approach. The random forest (RF statistical learning method, a relatively new variable-importance ranking method, measures the variable importance of potentially influential parameters through the percent increase of the mean squared error. As this value increases, so does the relative importance of the associated variable. The principal aim of this study was to create predictive malaria maps generated using the selected variables based on the RF algorithm in the Ehlanzeni District of Mpumalanga Province, South Africa. From the seven environmental variables used [temperature, lag temperature, rainfall, lag rainfall, humidity, altitude, and the normalized difference vegetation index (NDVI], altitude was identified as the most influential predictor variable due its high selection frequency. It was selected as the top predictor for 4 out of 12 months of the year, followed by NDVI, temperature and lag rainfall, which were each selected twice. The combination of climatic variables that produced the highest prediction accuracy was altitude, NDVI, and temperature. This suggests that these three variables have high predictive capabilities in relation to malaria transmission. Furthermore, it is anticipated that the predictive maps generated from predictions made by the RF algorithm could be used to monitor the progression of malaria and assist in intervention and prevention efforts with respect to malaria.

Asymptotically Constant-Risk Predictive Densities When the Distributions of Data and Target Variables Are Different

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Keisuke Yano

2014-05-01

Full Text Available We investigate the asymptotic construction of constant-risk Bayesian predictive densities under the Kullback–Leibler risk when the distributions of data and target variables are different and have a common unknown parameter. It is known that the Kullback–Leibler risk is asymptotically equal to a trace of the product of two matrices: the inverse of the Fisher information matrix for the data and the Fisher information matrix for the target variables. We assume that the trace has a unique maximum point with respect to the parameter. We construct asymptotically constant-risk Bayesian predictive densities using a prior depending on the sample size. Further, we apply the theory to the subminimax estimator problem and the prediction based on the binary regression model.

Prediction of default probability in banking industry using CAMELS index: A case study of Iranian banks

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Mohammad Khodaei Valahzaghard

2013-04-01

Full Text Available This study examines the relationship between CAMELS index and default probability among 20 Iranian banks. The proposed study gathers the necessary information from their financial statements over the period 2005-2011. The study uses logistic regression along with Pearson correlation analysis to consider the relationship between default probability and six independent variables including capital adequacy, asset quality, management quality, earning quality, liquidity quality and sensitivity of market risk. The results indicate that there were no meaningful relationship between default probability and three independent variables including capital adequacy, asset quality and sensitivity of market risk. However, the results of our statistical tests support such relationship between default probability and three other variables including management quality, earning quality and liquidity quality.

Predicting Eight Grade Students' Equation Solving Performances via Concepts of Variable and Equality

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Ertekin, Erhan

2017-01-01

This study focused on how two algebraic concepts- equality and variable- predicted 8th grade students' equation solving performance. In this study, predictive design as a correlational research design was used. Randomly selected 407 eight-grade students who were from the central districts of a city in the central region of Turkey participated in…

Local Geomagnetic Indices and the Prediction of Auroral Power

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Newell, P. T.; Gjerloev, J. W.

2014-12-01

As the number of magnetometer stations and data processing power increases, just how auroral power relates to geomagnetic observations becomes a quantitatively more tractable question. This paper compares Polar UVI auroral power observations during 1997 with a variety of geomagnetic indices. Local time (LT) versions of the SuperMAG auroral electojet (SME) are introduced and examined, along with the corresponding upper and lower envelopes (SMU and SML). Also, the East-West component, BE, is investigated. We also consider whether using any of the local indices is actually better at predicting local auroral power than a single global index. Each index is separated into 24 LT indices based on a sliding 3-h MLT window. The ability to predict - or better reconstruct - auroral power varies greatly with LT, peaking at 1900 MLT, where about 75% of the variance (r2) can be predicted at 1-min cadence. The aurora is fairly predictable from 1700 MLT - 0400 MLT, roughly the region in which substorms occur. Auroral power is poorly predicted from auroral electrojet indices from 0500 MLT - 1500 MLT, with the minima at 1000-1300 MLT. In the region of high predictability, the local variable which works best is BE, in contrast to long-standing expectations. However using global SME is better than any local variable. Auroral power is best predicted by combining global SME with a local index: BE from 1500-0200 MLT, and either SMU or SML from 0300-1400 MLT. In the region of the diffuse aurora, it is better to use a 30 min average than the cotemporaneous 1-min SME value, while from 1500-0200 MLT the cotemporaneous 1-min SME works best, suggesting a more direct physical relationship with the auroral circuit. These results suggest a significant role for discrete auroral currents closing locally with Pedersen currents.

Conventional heart rate variability analysis of ambulatory electrocardiographic recordings fails to predict imminent ventricular fibrillation

Science.gov (United States)

Vybiral, T.; Glaeser, D. H.; Goldberger, A. L.; Rigney, D. R.; Hess, K. R.; Mietus, J.; Skinner, J. E.; Francis, M.; Pratt, C. M.

1993-01-01

OBJECTIVES. The purpose of this report was to study heart rate variability in Holter recordings of patients who experienced ventricular fibrillation during the recording. BACKGROUND. Decreased heart rate variability is recognized as a long-term predictor of overall and arrhythmic death after myocardial infarction. It was therefore postulated that heart rate variability would be lowest when measured immediately before ventricular fibrillation. METHODS. Conventional indexes of heart rate variability were calculated from Holter recordings of 24 patients with structural heart disease who had ventricular fibrillation during monitoring. The control group consisted of 19 patients with coronary artery disease, of comparable age and left ventricular ejection fraction, who had nonsustained ventricular tachycardia but no ventricular fibrillation. RESULTS. Heart rate variability did not differ between the two groups, and no consistent trends in heart rate variability were observed before ventricular fibrillation occurred. CONCLUSIONS. Although conventional heart rate variability is an independent long-term predictor of adverse outcome after myocardial infarction, its clinical utility as a short-term predictor of life-threatening arrhythmias remains to be elucidated.

Evaluation of renal resistive index in cirrhotic patients for predicting the hepatirenal syndrome

International Nuclear Information System (INIS)

Baek, Seung Yon; Kim, Hyae young; Yi, Sun Young

1996-01-01

To evaluate the usefulness of renal resistive index(RI) in patients with liver cirrhosis as an indicator for predicting hepatorenal syndrome. Renal RIs of thirty cirrhotic patients were analyzed using the gray-scale and Doppler ultrasonograms. As a control group, eight normal subjects were included. Renal RIs were measured at three sites of interlobar or arcuate arteries of both kidneys. The patients were divided into three groups (A, B, or C) according to the Child-Turcotte-Pugh classification and their serum BUN and creatinine levels were compared. We determined whether RIs of normal controls differed from those of cirrhotic patients or whether RIs of cirrhotic patients correlated with the Child-Turcotte-Pugh classification or BUN and creatinine levels. Mean RIs(0.63 ±0.33) of normal subjects were statistically different from those(0.67 ± 0.05) of cirrhotic patients(P=0.009). RIs of group A(n=6), B(n=9) and C(n=15) were 0.65 ± 0.03, 0.65 ± 0.04 and 0.70 ± 0.04, respectively. The ANOVA test revealed statistically significant differences between the three groups(F ratio=4.472, P=0.021). RIs did not correlate with BUN or creatinine levels. RI could be used as an index for predicting hepatorenal syndrome before the renal function becomes impaired

The predictive effect of inflammatory markers and lipid accumulation product index on clinical symptoms associated with polycystic ovary syndrome in nonobese adolescents and younger aged women.

Science.gov (United States)

Tola, Esra Nur; Yalcin, Serenat Eris; Dugan, Nadiye

2017-07-01

The aim of our study is to analyse the inflammatory markers and lipid accumulation product (LAP) index in nonobese adolescents and younger aged women with polycystic ovary syndrome (PCOS) compared with age and body mass index (BMI)-matched healthy controls and to determine whether the investigated parameters are potential markers for the etiopathogenesis of PCOS. We also aim to determine whether these inflammatory markers are predictive for developing some clinical implications, such as cardiovascular disease (CVD) and insulin resistance (IR), associated with PCOS. A total of 34 adolescents and younger aged females with PCOS, and 33 age and BMI-matched healthy controls were recruited for our study. All participants were nonobese (BMIpredictive effect of investigated inflammatory markers and LAP index on CVD risk among PCOS patients after adjustment for abdominal obesity. We also found a positive predictive effect of WBC and a negative predictive effect of lymphocytes on IR in PCOS patients after adjustment for abdominal obesity. We did not find any predictor effect of NEO on IR, but it was a positive predictive marker for an elevated HOMA-IR index. Elevated NEO, CRP levels and LAP index could have potential roles in the etiopathogenesis of PCOS in nonobese adolescents and younger aged females,NEO could be a predictive marker for elevated HOMA-IR index, and WBC and lymphocytes could be predictive for the development of IR among nonobese adolescents and younger aged females with PCOS. Copyright © 2017 Elsevier B.V. All rights reserved.

Nuclear Division Index may Predict Neoplastic Colorectal Lesions.

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Ionescu, Mirela E; Ciocirlan, Mihai; Becheanu, Gabriel; Nicolaie, Tudor; Ditescu, Cristina; Teiusanu, Adriana G; Gologan, Serban I; Arbanas, Tudor; Diculescu, Mircea M

2011-07-01

Colorectal cancer (CRC) develops by accumulation of multiple genetic damages leading to genetic instability that can be evaluated by cytogenetic methods. In the current study we used Cytokinesis-Blocked Micronucleus Assay (CBMN) technique to assess the behavior of Nuclear Division Index(NDI) in peripheral lymphocytes of patients with CRC and polyps versus patients with normal colonoscopy. Blood samples were collected from patients after informed consent. By CBMN technique we assessed the proportion of mono-nucleated, bi-nucleated, tri-nucleated and tetra-nucleated cells/500 cells, to calculate NDI. Data were statistically analyzed using the SPSS 11.0 package. 45 patients were available for analysis, 23 men and 22 women, with a mean age of 58.7±13.5. 17 had normal colonoscopy, 17 colonic polyps and 11 CRC. The mean NDI values were significantly smaller for patients with CRC or polyps than in patients with normal colonoscopy (1.57 vs 1.73, p=0.013). The difference persisted for patients with neoplastic lesions (adenomas and carcinomas) when compared with patients with normal colonoscopy or non neoplastic (hyperplastic) polyps (1.56 vs.1.71, p=0.018). The NDI cut-off value to predict the presence of adenomas or carcinomas was equal to 1.55 with a 54.2% sensitivity and 81% specificity of lower values (p=0.019). The NDI cut off value to predict the presence of advanced adenomas or cancer was 1.525 for a sensitivity of 56.3% and a specificity of 82.8% (p=0.048). NDI may be useful in screening strategies for colorectal cancer as simple, noninvasive, inexpensive cytogenetic biomarker.

Neural computing thermal comfort index for HVAC systems

International Nuclear Information System (INIS)

Atthajariyakul, S.; Leephakpreeda, T.

2005-01-01

The primary purpose of a heating, ventilating and air conditioning (HVAC) system within a building is to make occupants comfortable. Without real time determination of human thermal comfort, it is not feasible for the HVAC system to yield controlled conditions of the air for human comfort all the time. This paper presents a practical approach to determine human thermal comfort quantitatively via neural computing. The neural network model allows real time determination of the thermal comfort index, where it is not practical to compute the conventional predicted mean vote (PMV) index itself in real time. The feed forward neural network model is proposed as an explicit function of the relation of the PMV index to accessible variables, i.e. the air temperature, wet bulb temperature, globe temperature, air velocity, clothing insulation and human activity. An experiment in an air conditioned office room was done to demonstrate the effectiveness of the proposed methodology. The results show good agreement between the thermal comfort index calculated from the neural network model in real time and those calculated from the conventional PMV model

Global warming influence on climatic variables and thermal comfort index in Paraíba state, Brazil

OpenAIRE

Silva, Gustavo de Assis; Instituto Agronômico de Pernambuco; Souza, Bonifácio Benicio de; Universidade Federal Campina Grande; Silva, Elisângela Maria Nunes da; UFCG

2015-01-01

The increase in the concentration of greenhouse gases originated from burning fossil fuels, along with breeding, been appointed as the main causes of global climate change resulting from global warming in earth's atmosphere. These changes can cause serious impacts on the lives and livestock production mainly in tropical regions. Therefore, the aim with this work was to evaluate the effect of global warming on the climatological variables, thermal comfort index and animal production in the sta...

Ratio index variables or ANCOVA? Fisher's cats revisited.

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Tu, Yu-Kang; Law, Graham R; Ellison, George T H; Gilthorpe, Mark S

2010-01-01

Over 60 years ago Ronald Fisher demonstrated a number of potential pitfalls with statistical analyses using ratio variables. Nonetheless, these pitfalls are largely overlooked in contemporary clinical and epidemiological research, which routinely uses ratio variables in statistical analyses. This article aims to demonstrate how very different findings can be generated as a result of less than perfect correlations among the data used to generate ratio variables. These imperfect correlations result from measurement error and random biological variation. While the former can often be reduced by improvements in measurement, random biological variation is difficult to estimate and eliminate in observational studies. Moreover, wherever the underlying biological relationships among epidemiological variables are unclear, and hence the choice of statistical model is also unclear, the different findings generated by different analytical strategies can lead to contradictory conclusions. Caution is therefore required when interpreting analyses of ratio variables whenever the underlying biological relationships among the variables involved are unspecified or unclear. (c) 2009 John Wiley & Sons, Ltd.

A Risk Prediction Index for Advanced Colorectal Neoplasia at Screening Colonoscopy.

Science.gov (United States)

Schroy, Paul C; Wong, John B; O'Brien, Michael J; Chen, Clara A; Griffith, John L

2015-07-01

Eliciting patient preferences within the context of shared decision making has been advocated for colorectal cancer screening. Risk stratification for advanced colorectal neoplasia (ACN) might facilitate more effective shared decision making when selecting an appropriate screening option. Our objective was to develop and validate a clinical index for estimating the probability of ACN at screening colonoscopy. We conducted a cross-sectional analysis of 3,543 asymptomatic, mostly average-risk patients 50-79 years of age undergoing screening colonoscopy at two urban safety net hospitals. Predictors of ACN were identified using multiple logistic regression. Model performance was internally validated using bootstrapping methods. The final index consisted of five independent predictors of risk (age, smoking, alcohol intake, height, and a combined sex/race/ethnicity variable). Smoking was the strongest predictor (net reclassification improvement (NRI), 8.4%) and height the weakest (NRI, 1.5%). Using a simplified weighted scoring system based on 0.5 increments of the adjusted odds ratio, the risk of ACN ranged from 3.2% (95% confidence interval (CI), 2.6-3.9) for the low-risk group (score ≤2) to 8.6% (95% CI, 7.4-9.7) for the intermediate/high-risk group (score 3-11). The model had moderate to good overall discrimination (C-statistic, 0.69; 95% CI, 0.66-0.72) and good calibration (P=0.73-0.93). A simple 5-item risk index based on readily available clinical data accurately stratifies average-risk patients into low- and intermediate/high-risk categories for ACN at screening colonoscopy. Uptake into clinical practice could facilitate more effective shared decision-making for CRC screening, particularly in situations where patient and provider test preferences differ.

Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network

NARCIS (Netherlands)

Park, Y.S.; Verdonschot, P.F.M.; Chon, T.S.; Lek, S.

2003-01-01

A counterpropagation neural network (CPN) was applied to predict species richness (SR) and Shannon diversity index (SH) of benthic macroinvertebrate communities using 34 environmental variables. The data were collected at 664 sites at 23 different water types such as springs, streams, rivers,

Hub Status and Indexation of Container Ports

Directory of Open Access Journals (Sweden)

Yong-An Park

2015-06-01

This study develops two sub-indexes of port classification and capacity, and combines cases of these two sub-indexes into various types in order to find a proper port hub index. The paper demonstrates how different types of port hub index are useful measurements for evaluating outputs and inputs of container ports. In a case analysis we show that the indexes of period variables and lagged variables have more explanatory power with regard to changes of port throughputs and high correlation with inputs.

Predicting Calcium Values for Gastrointestinal Bleeding Patients in Intensive Care Unit Using Clinical Variables and Fuzzy Modeling

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G Khalili-Zadeh-Mahani

2016-07-01

Full Text Available Introduction: Reducing unnecessary laboratory tests is an essential issue in the Intensive Care Unit. One solution for this issue is to predict the value of a laboratory test to specify the necessity of ordering the tests. The aim of this paper was to propose a clinical decision support system for predicting laboratory tests values. Calcium laboratory tests of three categories of patients, including upper and lower gastrointestinal bleeding, and unspecified hemorrhage of gastrointestinal tract, have been selected as the case studies for this research. Method: In this research, the data have been collected from MIMIC-II database. For predicting calcium laboratory values, a Fuzzy Takagi-Sugeno model is used and the input variables of the model are heart rate and previous value of calcium laboratory test. Results: The results showed that the values of calcium laboratory test for the understudy patients were predictable with an acceptable accuracy. In average, the mean absolute errors of the system for the three categories of the patients are 0.27, 0.29, and 0.28, respectively. Conclusion: In this research, using fuzzy modeling and two variables of heart rate and previous calcium laboratory values, a clinical decision support system was proposed for predicting laboratory values of three categories of patients with gastrointestinal bleeding. Using these two clinical values as input variables, the obtained results were acceptable and showed the capability of the proposed system in predicting calcium laboratory values. For achieving better results, the impact of more input variables should be studied. Since, the proposed system predicts the laboratory values instead of just predicting the necessity of the laboratory tests; it was more generalized than previous studies. So, the proposed method let the specialists make the decision depending on the condition of each patient.

Removing batch effects for prediction problems with frozen surrogate variable analysis

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Hilary S. Parker

2014-09-01

Full Text Available Batch effects are responsible for the failure of promising genomic prognostic signatures, major ambiguities in published genomic results, and retractions of widely-publicized findings. Batch effect corrections have been developed to remove these artifacts, but they are designed to be used in population studies. But genomic technologies are beginning to be used in clinical applications where samples are analyzed one at a time for diagnostic, prognostic, and predictive applications. There are currently no batch correction methods that have been developed specifically for prediction. In this paper, we propose an new method called frozen surrogate variable analysis (fSVA that borrows strength from a training set for individual sample batch correction. We show that fSVA improves prediction accuracy in simulations and in public genomic studies. fSVA is available as part of the sva Bioconductor package.

Prediction Center (CPC) Polar Eurasia Teleconnection Pattern Index

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Monthly tabulated index of the Polar-Eurasia teleconnection pattern. The data spans the period 1950 to present. The index is derived from a rotated principal...

Climate Prediction Center (CPC) Scandinavia Teleconnection Pattern Index

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Monthly tabulated index of the Scandinavia teleconnection pattern. The data spans the period 1950 to present. The index is derived from a rotated principal component...

The Biodiversity Informatics Potential Index

Science.gov (United States)

2011-01-01

Background Biodiversity informatics is a relatively new discipline extending computer science in the context of biodiversity data, and its development to date has not been uniform throughout the world. Digitizing effort and capacity building are costly, and ways should be found to prioritize them rationally. The proposed 'Biodiversity Informatics Potential (BIP) Index' seeks to fulfill such a prioritization role. We propose that the potential for biodiversity informatics be assessed through three concepts: (a) the intrinsic biodiversity potential (the biological richness or ecological diversity) of a country; (b) the capacity of the country to generate biodiversity data records; and (c) the availability of technical infrastructure in a country for managing and publishing such records. Methods Broadly, the techniques used to construct the BIP Index were rank correlation, multiple regression analysis, principal components analysis and optimization by linear programming. We built the BIP Index by finding a parsimonious set of country-level human, economic and environmental variables that best predicted the availability of primary biodiversity data accessible through the Global Biodiversity Information Facility (GBIF) network, and constructing an optimized model with these variables. The model was then applied to all countries for which sufficient data existed, to obtain a score for each country. Countries were ranked according to that score. Results Many of the current GBIF participants ranked highly in the BIP Index, although some of them seemed not to have realized their biodiversity informatics potential. The BIP Index attributed low ranking to most non-participant countries; however, a few of them scored highly, suggesting that these would be high-return new participants if encouraged to contribute towards the GBIF mission of free and open access to biodiversity data. Conclusions The BIP Index could potentially help in (a) identifying countries most likely to

Development of a wound healing index for patients with chronic wounds.

Science.gov (United States)

Horn, Susan D; Fife, Caroline E; Smout, Randall J; Barrett, Ryan S; Thomson, Brett

2013-01-01

Randomized controlled trials in wound care generalize poorly because they exclude patients with significant comorbid conditions. Research using real-world wound care patients is hindered by lack of validated methods to stratify patients according to severity of underlying illnesses. We developed a comprehensive stratification system for patients with wounds that predicts healing likelihood. Complete medical record data on 50,967 wounds from the United States Wound Registry were assigned a clear outcome (healed, amputated, etc.). Factors known to be associated with healing were evaluated using logistic regression models. Significant variables (p healing for each wound type. Some variables predicted significantly in nearly all models: wound size, wound age, number of wounds, evidence of bioburden, tissue type exposed (Wagner grade or stage), being nonambulatory, and requiring hospitalization during the course of care. Variables significant in some models included renal failure, renal transplant, malnutrition, autoimmune disease, and cardiovascular disease. All models validated well when applied to the holdout sample. The "Wound Healing Index" can validly predict likelihood of wound healing among real-world patients and can facilitate comparative effectiveness research to identify patients needing advanced therapeutics. © 2013 by the Wound Healing Society.

Índice de previsão de produção de leite para vacas Jersey Index for predicting milk production in Jersey cows

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Luiz A. Laloni

2004-08-01

Full Text Available No Brasil, o uso de vários modelos de criação intensiva e semi-extensiva desfavorece a adoção generalizada de métodos de manejo do gado bovino, principalmente do gado leiteiro. Mesmo assim, a produção leiteira pode ser melhorada a partir do uso de tecnologias que possam garantir o manejo adequado do rebanho. O objetivo deste trabalho foi desenvolver um índice de previsão de produção de leite para vacas Jersey em lactação, de genética de alta produtividade, em regime semi-estabulado, nas condições tropicais. Para a obtenção do índice, consideraram-se a temperatura e a umidade relativa do ambiente e a velocidade do ar, assim como valores de precipitação pluviométrica, temperatura do solo do pasto e a radiação solar como agentes estressores, os quais podem alterar a produção de leite. O experimento considerou dois tratamentos: A - as vacas permaneceram em sala de espera guarnecida com chuveiro e ventiladores, por um período 30 min antes da ordenha; B - as vacas não tiveram acesso a essa sala de espera (controle. Fora do período de ordenha, as vacas tiveram acesso ao pasto. Observou-se que as diferenças de médias de produção entre os tratamentos não foram estatisticamente significativas. Foram procedidas as análises para efeito de elaboração do modelo e chegou-se a um modelo factível, considerando a relação entre produção e a precipitação, assim como a temperatura máxima do solo do pasto.In Brazil the adoption of several models of cattle confinement leads to special conditions for management methods in dairy production, which can be improved by the use of technology that assures better herd management. Indexes relating environmental variables to production are applied for the prediction of milk production. The values of temperature and relative humidity, rain index, solar radiation and pasture soil temperature are generally considered potential stress agents for cows. The objective of this research was

Understanding interannual, decadal level variability in paralytic shellfish poisoning toxicity in the Gulf of Maine: The HAB Index

Science.gov (United States)

Anderson, Donald M.; Couture, Darcie A.; Kleindinst, Judith L.; Keafer, Bruce A.; McGillicuddy, Dennis J., Jr.; Martin, Jennifer L.; Richlen, Mindy L.; Hickey, J. Michael; Solow, Andrew R.

2014-05-01

A major goal in harmful algal bloom (HAB) research has been to identify mechanisms underlying interannual variability in bloom magnitude and impact. Here the focus is on variability in Alexandrium fundyense blooms and paralytic shellfish poisoning (PSP) toxicity in Maine, USA, over 34 years (1978-2011). The Maine coastline was divided into two regions - eastern and western Maine, and within those two regions, three measures of PSP toxicity (the percent of stations showing detectable toxicity over the year, the cumulative amount of toxicity per station measured in all shellfish (mussel) samples during that year, and the duration of measurable toxicity) were examined for each year in the time series. These metrics were combined into a simple HAB Index that provides a single measure of annual toxin severity across each region. The three toxin metrics, as well as the HAB Index that integrates them, reveal significant variability in overall toxicity between individual years as well as long-term, decadal patterns or regimes. Based on different conceptual models of the system, we considered three trend formulations to characterize the long-term patterns in the Index - a three-phase (mean-shift) model, a linear two-phase model, and a pulse-decline model. The first represents a “regime shift” or multiple equilibria formulation as might occur with alternating periods of sustained high and low cyst abundance or favorable and unfavorable growth conditions, the second depicts a scenario of more gradual transitions in cyst abundance or growth conditions of vegetative cells, and the third characterizes a ”sawtooth” pattern in which upward shifts in toxicity are associated with major cyst recruitment events, followed by a gradual but continuous decline until the next pulse. The fitted models were compared using both residual sum of squares and Akaike's Information Criterion. There were some differences between model fits, but none consistently gave a better fit than the

Application of Intelligent Dynamic Bayesian Network with Wavelet Analysis for Probabilistic Prediction of Storm Track Intensity Index

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Ming Li

2018-06-01

Full Text Available The effective prediction of storm track (ST is greatly beneficial for analyzing the development and anomalies of mid-latitude weather systems. For the non-stationarity, nonlinearity, and uncertainty of ST intensity index (STII, a new probabilistic prediction model was proposed based on dynamic Bayesian network (DBN and wavelet analysis (WA. We introduced probability theory and graph theory for the first time to quantitatively describe the nonlinear relationship and uncertain interaction of the ST system. Then a casual prediction network (i.e., DBN was constructed through wavelet decomposition, structural learning, parameter learning, and probabilistic inference, which was used for expression of relation among predictors and probabilistic prediction of STII. The intensity prediction of the North Pacific ST with data from 1961–2010 showed that the new model was able to give more comprehensive prediction information and higher prediction accuracy and had strong generalization ability and good stability.

Predictor variables for a half marathon race time in recreational male runners

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Rüst CA

2011-08-01

Full Text Available Christoph Alexander Rüst1, Beat Knechtle1,2, Patrizia Knechtle2, Ursula Barandun1, Romuald Lepers3, Thomas Rosemann11Institute of General Practice and Health Services Research, University of Zurich, Zurich, Switzerland; 2Gesundheitszentrum St Gallen, St Gallen, Switzerland; 3INSERM U887, University of Burgundy, Faculty of Sport Sciences, Dijon, FranceAbstract: The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the ‘Half Marathon Basel’ completed the race distance within (mean and standard deviation, SD 103.9 (16.5 min, running at a speed of 12.7 (1.9 km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56, suprailiacal (r = 0.36 and medial calf skin fold (r = 0.53 were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = –0.54 were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150 and speed in running during training (P = 0.0045 were related to race time. Race time in a half marathon might be partially predicted by the following equation (r2 = 0.44: Race time (min = 72.91 + 3.045 * (body mass index, kg/m2 –3.884 * (speed in running during training, km/h for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable.Keywords: anthropometry, body fat, skin-folds, training, endurance

Climate Prediction Center Southern Oscillation Index

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National Oceanic and Atmospheric Administration, Department of Commerce — This is one of the CPC?s Monthly Atmospheric and Sea Surface Temperature (SST)Indices. It contains Southern Oscillation Index which is standardized sea level...

[Frail-VIG index: Design and evaluation of a new frailty index based on the Comprehensive Geriatric Assessment].

Science.gov (United States)

Amblàs-Novellas, Jordi; Martori, Joan Carles; Molist Brunet, Núria; Oller, Ramon; Gómez-Batiste, Xavier; Espaulella Panicot, Joan

Frailty is closely linked to health results. Frailty indexes (FI) and the Comprehensive Geriatric Assessment (CGA) are multidimensional tools. FI serve to quantitatively measure frailty levels. They have shown to have an excellent correlation with mortality. However, they are infrequently used in clinical practice. Given the need for new, more concise, and pragmatic FI, a new FI is proposed based on a CGA (Frail-VIG Index). A prospective, observational, longitudinal study was conducted, with cohort follow up at 12 months or death. Participants were patients admitted in the Geriatric Unit of the University Hospital of Vic (Barcelona, Spain) during 2014. Contrast of hypothesis log-rank for survival curves according to Frail-VIG index, and analysis of ROC curves were performed to assess prognostic capacity. A total of 590 patients were included (mean age=86.39). Mortality rate at 12 months was 46.4%. The comparative analysis showed statistically significant differences (P<.05) for almost all variables included in the Frail-VIG index. Survival curves also show significant differences (X 2 =445, P<.001) for the different Frail-VIG index scores. The area under the ROC curve at 12 months was 0.9 (0.88-0.92). An administration time of the Index is estimated at less than 10minutes. Results endorse the Frail-VIG index as a simple (as for contents), rapid (for administration time) tool, with discriminative (for situational diagnosis) and predictive capacity (high correlation with mortality). Copyright © 2016 SEGG. Publicado por Elsevier España, S.L.U. All rights reserved.

Protein construct storage: Bayesian variable selection and prediction with mixtures.

Science.gov (United States)

Clyde, M A; Parmigiani, G

1998-07-01

Determining optimal conditions for protein storage while maintaining a high level of protein activity is an important question in pharmaceutical research. A designed experiment based on a space-filling design was conducted to understand the effects of factors affecting protein storage and to establish optimal storage conditions. Different model-selection strategies to identify important factors may lead to very different answers about optimal conditions. Uncertainty about which factors are important, or model uncertainty, can be a critical issue in decision-making. We use Bayesian variable selection methods for linear models to identify important variables in the protein storage data, while accounting for model uncertainty. We also use the Bayesian framework to build predictions based on a large family of models, rather than an individual model, and to evaluate the probability that certain candidate storage conditions are optimal.

Prediction of polycystic ovarian syndrome based on ultrasound findings and clinical parameters.

Science.gov (United States)

Moschos, Elysia; Twickler, Diane M

2015-03-01

To determine the accuracy of sonographic-diagnosed polycystic ovaries and clinical parameters in predicting polycystic ovarian syndrome. Medical records and ultrasounds of 151 women with sonographically diagnosed polycystic ovaries were reviewed. Sonographic criteria for polycystic ovaries were based on 2003 Rotterdam European Society of Human Reproduction and Embryology/American Society for Reproductive Medicine guidelines: at least one ovary with 12 or more follicles measuring 2-9 mm and/or increased ovarian volume >10 cm(3) . Clinical variables of age, gravidity, ethnicity, body mass index, and sonographic indication were collected. One hundred thirty-five patients had final outcomes (presence/absence of polycystic ovarian syndrome). Polycystic ovarian syndrome was diagnosed if a patient had at least one other of the following two criteria: oligo/chronic anovulation and/or clinical/biochemical hyperandrogenism. A logistic regression model was constructed using stepwise selection to identify variables significantly associated with polycystic ovarian syndrome (p polycystic ovaries and 115 (89.8%) had polycystic ovarian syndrome (p = .009). Lower gravidity, abnormal bleeding, and body mass index >33 were significant in predicting polycystic ovarian syndrome (receiver operating characteristics curve, c = 0.86). Pain decreased the likelihood of polycystic ovarian syndrome. Polycystic ovaries on ultrasound were sensitive in predicting polycystic ovarian syndrome. Ultrasound, combined with clinical parameters, can be used to generate a predictive index for polycystic ovarian syndrome. © 2014 Wiley Periodicals, Inc.

A New Index of Democracy

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Jesús M. de Miguel

2014-01-01

Full Text Available The present paper analyses and revises the latest Democracy Index published by the Economist Intelligence Unit in the United Kingdom. We analyze the changes produced in the index from 2006 to 2011, as well as in the five basic factors that constitute the index: electoral process and pluralism; civil liberties; the functioning of government; political participation; and political culture. The analysis of these factors ?measured by sixty variables? has made it possible to develop a new index, based on the data from 167 countries, and calculate a revised ranking. Countries have been classified into four types: democracies, flawed democracies, mixed systems, and authoritarian/totalitarian regimes. The new index permits a better understanding of the impact of the crisis through variables such as economic growth, human development, quality of life, corruption, and violence.

Development of the statistical ARIMA model: an application for predicting the upcoming of MJO index

Science.gov (United States)

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

2017-10-01

This study is mainly concerned in development one of the most important equatorial atmospheric phenomena that we call as the Madden Julian Oscillation (MJO) which having strong impacts to the extreme rainfall anomalies over the Indonesian Maritime Continent (IMC). In this study, we focused to the big floods over Jakarta and surrounded area that suspecting caused by the impacts of MJO. We concentrated to develop the MJO index using the statistical model that we call as Box-Jenkis (ARIMA) ini 1996, 2002, and 2007, respectively. They are the RMM (Real Multivariate MJO) index as represented by RMM1 and RMM2, respectively. There are some steps to develop that model, starting from identification of data, estimated, determined model, before finally we applied that model for investigation some big floods that occurred at Jakarta in 1996, 2002, and 2007 respectively. We found the best of estimated model for the RMM1 and RMM2 prediction is ARIMA (2,1,2). Detailed steps how that model can be extracted and applying to predict the rainfall anomalies over Jakarta for 3 to 6 months later is discussed at this paper.

Independent variable complexity for regional regression of the flow duration curve in ungauged basins

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Fouad, Geoffrey; Skupin, André; Hope, Allen

2016-04-01

The flow duration curve (FDC) is one of the most widely used tools to quantify streamflow. Its percentile flows are often required for water resource applications, but these values must be predicted for ungauged basins with insufficient or no streamflow data. Regional regression is a commonly used approach for predicting percentile flows that involves identifying hydrologic regions and calibrating regression models to each region. The independent variables used to describe the physiographic and climatic setting of the basins are a critical component of regional regression, yet few studies have investigated their effect on resulting predictions. In this study, the complexity of the independent variables needed for regional regression is investigated. Different levels of variable complexity are applied for a regional regression consisting of 918 basins in the US. Both the hydrologic regions and regression models are determined according to the different sets of variables, and the accuracy of resulting predictions is assessed. The different sets of variables include (1) a simple set of three variables strongly tied to the FDC (mean annual precipitation, potential evapotranspiration, and baseflow index), (2) a traditional set of variables describing the average physiographic and climatic conditions of the basins, and (3) a more complex set of variables extending the traditional variables to include statistics describing the distribution of physiographic data and temporal components of climatic data. The latter set of variables is not typically used in regional regression, and is evaluated for its potential to predict percentile flows. The simplest set of only three variables performed similarly to the other more complex sets of variables. Traditional variables used to describe climate, topography, and soil offered little more to the predictions, and the experimental set of variables describing the distribution of basin data in more detail did not improve predictions

Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks.

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Rudi, Gabrielle; Bailly, Jean-Stéphane; Vinatier, Fabrice

2018-01-01

To optimize ecosystem services provided by agricultural drainage networks (ditches) in headwater catchments, we need to manage the spatial distribution of plant species living in these networks. Geomorphological variables have been shown to be important predictors of plant distribution in other ecosystems because they control the water regime, the sediment deposition rates and the sun exposure in the ditches. Whether such variables may be used to predict plant distribution in agricultural drainage networks is unknown. We collected presence and absence data for 10 herbaceous plant species in a subset of a network of drainage ditches (35 km long) within a Mediterranean agricultural catchment. We simulated their spatial distribution with GLM and Maxent model using geomorphological variables and distance to natural lands and roads. Models were validated using k-fold cross-validation. We then compared the mean Area Under the Curve (AUC) values obtained for each model and other metrics issued from the confusion matrices between observed and predicted variables. Based on the results of all metrics, the models were efficient at predicting the distribution of seven species out of ten, confirming the relevance of geomorphological variables and distance to natural lands and roads to explain the occurrence of plant species in this Mediterranean catchment. In particular, the importance of the landscape geomorphological variables, ie the importance of the geomorphological features encompassing a broad environment around the ditch, has been highlighted. This suggests that agro-ecological measures for managing ecosystem services provided by ditch plants should focus on the control of the hydrological and sedimentological connectivity at the catchment scale. For example, the density of the ditch network could be modified or the spatial distribution of vegetative filter strips used for sediment trapping could be optimized. In addition, the vegetative filter strips could constitute

TST, as a polysomnographic variable, is superior to the apnea hypopnea index for evaluating intermittent hypoxia in severe obstructive sleep apnea.

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Zhang, Xiao-Bin; Zen, Hui-Qing; Lin, Qi-Chang; Chen, Gong-Ping; Chen, Li-Da; Chen, Hua

2014-10-01

The polysomnography (PSG) index of the apnea hypopnea index (AHI) is considered the 'gold standard' for stratifying the severity of obstructive sleep apnea (OSA). However, AHI cannot reflect the true characteristic of chronic intermittent hypoxia (CIH), which may trigger systemic inflammation in some OSA patients. High-sensitivity C-reactive protein (hsCRP) is considered a biomarker of systemic inflammation in OSA patients. The aim of the present study was to evaluate the relationship between PSG variables and hsCRP in men with severe OSA. Men with severe OSA (AHI ≥ 30 events/h) diagnosed by PSG were enrolled. AHI and body mass index were matched between a high hsCRP group (hsCRP ≥ 3.0 mg/L) and a low hsCRP group. A blood sample was taken for serum hsCRP analysis. Multiple regression analysis was performed to assess independent predictors of high hsCRP. One hundred and fifty-two subjects were enrolled in the study (76 in each group). Mean serum hsCRP was 3.76 ± 2.13 mg/L. The mean percentage of total sleep time spent with SaO2 hypoxia variables.

External validation of the endometriosis fertility index (EFI) staging system for predicting non-ART pregnancy after endometriosis surgery.

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Tomassetti, C; Geysenbergh, B; Meuleman, C; Timmerman, D; Fieuws, S; D'Hooghe, T

2013-05-01

. Subjects were censored when they were lost to follow-up, had subsequent surgery for endometriosis, started ovarian suppression or underwent ART. As K-M estimates might overestimate the actual event rate, cumulative incidence estimates treating ART as competing event were also calculated. Cox regression analysis was used to assess the performance of EFI and constituting variables. Performance of the score (prediction, discrimination) was quantified with the following methods: mean squared error of prediction (Brier score), areas under the receiver-operating curve and global concordance index C(τ). There was a highly significant relationship between the EFI and the time to non-ART pregnancy (cumulative overall pregnancy rate, P = 0.0004), with the K-M estimate of cumulative overall pregnancy rate at 12 months after surgery equal to 45.5% [95% confidence interval (CI) 39.47-49.87]-ranging from 16.67% (95% CI 5.01-47.65) for EFI scores 0-3, to 62.55% (95% CI 55.18-69.94) for EFI scores 9-10. For each increase of 1 point in the EFI score, the relative risk of becoming pregnant increased by 31% (95% CI 16-47%; i.e. hazard ratio 1.31). The 'least function score'-which assesses the tubal/ovarian function at conclusion of surgery-was found to be the most important contributor to the total EFI score among all the other variables (age, duration of infertility, prior pregnancy, AFS endometriosis lesion and total score). The EFI score had a moderate performance in the prediction of the pregnancy rate. Indeed, the decrease in prediction error was rather small, as shown by the decrease in Brier score from 0.213 to 0.198, and low estimates for R² (13%) and C(τ) (0.629). As the EFI was validated externally in our own European population after initial testing by Adamson and Pasta (Endometriosis fertility index: the new, validated endometriosis staging system. Fertil Steril 2010;94:1609-1615) in an American population, it appears that the EFI can be used clinically to counsel infertile

Perceived exertion is as effective as the perceptual strain index in predicting physiological strain when wearing personal protective clothing.

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Borg, David N; Costello, Joseph T; Bach, Aaron J; Stewart, Ian B

2017-02-01

The perceptual strain index (PeSI) has been shown to overcome the limitations associated with the assessment of the physiological strain index (PSI), primarily the need to obtain a core body temperature measurement. The PeSI uses the subjective scales of thermal sensation and perceived exertion (RPE) to provide surrogate measures of core temperature and heart rate, respectively. Unfortunately, thermal sensation has shown large variability in providing an estimation of core body temperature. Therefore, the primary aim of this study was to determine if thermal comfort improved the ability of the PeSI to predict the PSI during exertional-heat stress. Eighteen healthy males (age: 23.5years; body mass: 79.4kg; maximal aerobic capacity: 57.2ml·kg -1 ·min -1 ) wore four different chemical/biological protective garments while walking on treadmill at a low (temperatures 21, 30 or 37°C. Trials were terminated when heart rate exceeded 90% of maximum, when core body temperature reached 39°C, at 120min or due to volitional fatigue. Core body temperature, heart rate, thermal sensation, thermal comfort and RPE were recorded at 15min intervals and at termination. Multiple statistical methods were used to determine the most accurate perceptual predictor. Significant moderate relationships were observed between the PeSI (r=0.74; pestimate physiological strain during exertional-heat stress under these work conditions. Copyright © 2016 Elsevier Inc. All rights reserved.

Analysis of Margin Index as a Method for Predicting Residual Disease After Breast-Conserving Surgery in a European Cancer Center.

LENUS (Irish Health Repository)

Bolger, Jarlath C

2011-06-03

INTRODUCTION: Breast-conserving surgery (BCS), followed by appropriate adjuvant therapies is established as a standard treatment option for women with early-stage invasive breast cancers. A number of factors have been shown to correlate with local and regional disease recurrence. Although margin status is a strong predictor of disease recurrence, consensus is yet to be established on the optimum margin necessary. Margenthaler et al. recently proposed the use of a "margin index," combining tumor size and margin status as a predictor of residual disease after BCS. We applied this new predictive tool to a population of patients with primary breast cancer who presented to a symptomatic breast unit to determine its suitability in predicting those who require reexcision surgery. METHODS: Retrospective analysis of our breast cancer database from January 1, 2000 to June 30, 2010 was performed, including all patients who underwent BCS. Of 531 patients who underwent BCS, 27.1% (144\\/531) required further reexcision procedures, and 55 were eligible for inclusion in the study. Margin index was calculated as: margin index = closest margin (mm)\\/tumor size (mm) × 100, with index >5 considered optimum. RESULTS: Of the 55 patients included, 31% (17\\/55) had residual disease. Fisher\\'s exact test showed margin index not to be a significant predictor of residual disease on reexcision specimen (P = 0.57). Of note, a significantly higher proportion of our patients presented with T2\\/3 tumors (60% vs. 38%). CONCLUSIONS: Although an apparently elegant tool for predicting residual disease after BCS, we have shown that it is not applicable to a symptomatic breast unit in Ireland.

Analysis of margin index as a method for predicting residual disease after breast-conserving surgery in a European cancer center.

LENUS (Irish Health Repository)

Bolger, Jarlath C

2012-02-01

INTRODUCTION: Breast-conserving surgery (BCS), followed by appropriate adjuvant therapies is established as a standard treatment option for women with early-stage invasive breast cancers. A number of factors have been shown to correlate with local and regional disease recurrence. Although margin status is a strong predictor of disease recurrence, consensus is yet to be established on the optimum margin necessary. Margenthaler et al. recently proposed the use of a "margin index," combining tumor size and margin status as a predictor of residual disease after BCS. We applied this new predictive tool to a population of patients with primary breast cancer who presented to a symptomatic breast unit to determine its suitability in predicting those who require reexcision surgery. METHODS: Retrospective analysis of our breast cancer database from January 1, 2000 to June 30, 2010 was performed, including all patients who underwent BCS. Of 531 patients who underwent BCS, 27.1% (144\\/531) required further reexcision procedures, and 55 were eligible for inclusion in the study. Margin index was calculated as: margin index = closest margin (mm)\\/tumor size (mm) x 100, with index >5 considered optimum. RESULTS: Of the 55 patients included, 31% (17\\/55) had residual disease. Fisher\\'s exact test showed margin index not to be a significant predictor of residual disease on reexcision specimen (P = 0.57). Of note, a significantly higher proportion of our patients presented with T2\\/3 tumors (60% vs. 38%). CONCLUSIONS: Although an apparently elegant tool for predicting residual disease after BCS, we have shown that it is not applicable to a symptomatic breast unit in Ireland.

Intraindividual variability in reaction time predicts cognitive outcomes 5 years later.

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Bielak, Allison A M; Hultsch, David F; Strauss, Esther; Macdonald, Stuart W S; Hunter, Michael A

2010-11-01

Building on results suggesting that intraindividual variability in reaction time (inconsistency) is highly sensitive to even subtle changes in cognitive ability, this study addressed the capacity of inconsistency to predict change in cognitive status (i.e., cognitive impairment, no dementia [CIND] classification) and attrition 5 years later. Two hundred twelve community-dwelling older adults, initially aged 64-92 years, remained in the study after 5 years. Inconsistency was calculated from baseline reaction time performance. Participants were assigned to groups on the basis of their fluctuations in CIND classification over time. Logistic and Cox regressions were used. Baseline inconsistency significantly distinguished among those who remained or transitioned into CIND over the 5 years and those who were consistently intact (e.g., stable intact vs. stable CIND, Wald (1) = 7.91, p < .01, Exp(β) = 1.49). Average level of inconsistency over time was also predictive of study attrition, for example, Wald (1) = 11.31, p < .01, Exp(β) = 1.24. For both outcomes, greater inconsistency was associated with a greater likelihood of being in a maladaptive group 5 years later. Variability based on moderately cognitively challenging tasks appeared to be particularly sensitive to longitudinal changes in cognitive ability. Mean rate of responding was a comparable predictor of change in most instances, but individuals were at greater relative risk of being in a maladaptive outcome group if they were more inconsistent rather than if they were slower in responding. Implications for the potential utility of intraindividual variability in reaction time as an early marker of cognitive decline are discussed. (c) 2010 APA, all rights reserved

Dissociating variability and effort as determinants of coordination.

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Ian O'Sullivan

2009-04-01

Full Text Available When coordinating movements, the nervous system often has to decide how to distribute work across a number of redundant effectors. Here, we show that humans solve this problem by trying to minimize both the variability of motor output and the effort involved. In previous studies that investigated the temporal shape of movements, these two selective pressures, despite having very different theoretical implications, could not be distinguished; because noise in the motor system increases with the motor commands, minimization of effort or variability leads to very similar predictions. When multiple effectors with different noise and effort characteristics have to be combined, however, these two cost terms can be dissociated. Here, we measure the importance of variability and effort in coordination by studying how humans share force production between two fingers. To capture variability, we identified the coefficient of variation of the index and little fingers. For effort, we used the sum of squared forces and the sum of squared forces normalized by the maximum strength of each effector. These terms were then used to predict the optimal force distribution for a task in which participants had to produce a target total force of 4-16 N, by pressing onto two isometric transducers using different combinations of fingers. By comparing the predicted distribution across fingers to the actual distribution chosen by participants, we were able to estimate the relative importance of variability and effort of 1:7, with the unnormalized effort being most important. Our results indicate that the nervous system uses multi-effector redundancy to minimize both the variability of the produced output and effort, although effort costs clearly outweighed variability costs.

Biographical and demographical variables as moderators in the prediction of turnover intentions

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Janine du Plooy

2013-04-01

Full Text Available Orientation: The aim of the study was to explore the possible moderation effects of biographical and demographical variables on a prediction model of turnover intention (TI. Research purpose: The main purpose of the study was to determine how biographical and demographical variables have an impact on predictors of turnover intentions. Motivation for the study: Twenty-first century organisations face significant challenges in the management of talent and human capital. One in particular is voluntary employee turnover and the lack of appropriate business models to track this process. Research design, approach, and method: A secondary data analysis (SDA was performed in a quantitative research tradition on the cross-sectional survey sample (n = 2429. Data were collected from a large South African Information and Communication Technologies (ICT sector company (N = 23 134. Main findings: The results of the study confirmed significant moderation effects regarding race, age, and marital status in the prediction equations of TIs. Practical and managerial implications: Practical implications of the study suggested increased understanding of workforce diversity effects within the human resource (HR value chain, with resultant evidence-based, employee retention strategies and interventions. Issues concerning talent management could also be addressed. Contribution and value-add: The study described in this article took Industrial/Organisational (I/O psychological concepts and linked them in unique combinations to establish better predictive validity of a more comprehensive turnover intentions model.

Heat and Humidity in the City: Neighborhood Heat Index Variability in a Mid-Sized City in the Southeastern United States.

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Hass, Alisa L; Ellis, Kelsey N; Reyes Mason, Lisa; Hathaway, Jon M; Howe, David A

2016-01-11

Daily weather conditions for an entire city are usually represented by a single weather station, often located at a nearby airport. This resolution of atmospheric data fails to recognize the microscale climatic variability associated with land use decisions across and within urban neighborhoods. This study uses heat index, a measure of the combined effects of temperature and humidity, to assess the variability of heat exposure from ten weather stations across four urban neighborhoods and two control locations (downtown and in a nearby nature center) in Knoxville, Tennessee, USA. Results suggest that trees may negate a portion of excess urban heat, but are also associated with greater humidity. As a result, the heat index of locations with more trees is significantly higher than downtown and areas with fewer trees. Trees may also reduce heat stress by shading individuals from incoming radiation, though this is not considered in this study. Greater amounts of impervious surfaces correspond with reduced evapotranspiration and greater runoff, in terms of overall mass balance, leading to a higher temperature, but lower relative humidity. Heat index and relative humidity were found to significantly vary between locations with different tree cover and neighborhood characteristics for the full study time period as well as for the top 10% of heat index days. This work demonstrates the need for high-resolution climate data and the use of additional measures beyond temperature to understand urban neighborhood exposure to extreme heat, and expresses the importance of considering vulnerability differences among residents when analyzing neighborhood-scale impacts.

Heat and Humidity in the City: Neighborhood Heat Index Variability in a Mid-Sized City in the Southeastern United States

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Alisa L. Hass

2016-01-01

Full Text Available Daily weather conditions for an entire city are usually represented by a single weather station, often located at a nearby airport. This resolution of atmospheric data fails to recognize the microscale climatic variability associated with land use decisions across and within urban neighborhoods. This study uses heat index, a measure of the combined effects of temperature and humidity, to assess the variability of heat exposure from ten weather stations across four urban neighborhoods and two control locations (downtown and in a nearby nature center in Knoxville, Tennessee, USA. Results suggest that trees may negate a portion of excess urban heat, but are also associated with greater humidity. As a result, the heat index of locations with more trees is significantly higher than downtown and areas with fewer trees. Trees may also reduce heat stress by shading individuals from incoming radiation, though this is not considered in this study. Greater amounts of impervious surfaces correspond with reduced evapotranspiration and greater runoff, in terms of overall mass balance, leading to a higher temperature, but lower relative humidity. Heat index and relative humidity were found to significantly vary between locations with different tree cover and neighborhood characteristics for the full study time period as well as for the top 10% of heat index days. This work demonstrates the need for high-resolution climate data and the use of additional measures beyond temperature to understand urban neighborhood exposure to extreme heat, and expresses the importance of considering vulnerability differences among residents when analyzing neighborhood-scale impacts.

Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China.

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Du, Zhicheng; Xu, Lin; Zhang, Wangjian; Zhang, Dingmei; Yu, Shicheng; Hao, Yuantao

2017-10-06

Hand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD. Ecological study. Information on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011-2014. Analyses were conducted at aggregate level and no confidential information was involved. A seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI. A high correlation between HFMD incidence and BDI ( r =0.794, pengine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Who will have Sustainable Employment After a Back Injury? The Development of a Clinical Prediction Model in a Cohort of Injured Workers.

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Shearer, Heather M; Côté, Pierre; Boyle, Eleanor; Hayden, Jill A; Frank, John; Johnson, William G

2017-09-01

Purpose Our objective was to develop a clinical prediction model to identify workers with sustainable employment following an episode of work-related low back pain (LBP). Methods We used data from a cohort study of injured workers with incident LBP claims in the USA to predict employment patterns 1 and 6 months following a workers' compensation claim. We developed three sequential models to determine the contribution of three domains of variables: (1) basic demographic/clinical variables; (2) health-related variables; and (3) work-related factors. Multivariable logistic regression was used to develop the predictive models. We constructed receiver operator curves and used the c-index to measure predictive accuracy. Results Seventy-nine percent and 77 % of workers had sustainable employment at 1 and 6 months, respectively. Sustainable employment at 1 month was predicted by initial back pain intensity, mental health-related quality of life, claim litigation and employer type (c-index = 0.77). At 6 months, sustainable employment was predicted by physical and mental health-related quality of life, claim litigation and employer type (c-index = 0.77). Adding health-related and work-related variables to models improved predictive accuracy by 8.5 and 10 % at 1 and 6 months respectively. Conclusion We developed clinically-relevant models to predict sustainable employment in injured workers who made a workers' compensation claim for LBP. Inquiring about back pain intensity, physical and mental health-related quality of life, claim litigation and employer type may be beneficial in developing programs of care. Our models need to be validated in other populations.

Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables.

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Roelen, Corné; Thorsen, Sannie; Heymans, Martijn; Twisk, Jos; Bültmann, Ute; Bjørner, Jakob

2018-01-01

The purpose of this study is to develop and validate a prediction model for identifying employees at increased risk of long-term sickness absence (LTSA), by using variables commonly measured in occupational health surveys. Based on the literature, 15 predictor variables were retrieved from the DAnish National working Environment Survey (DANES) and included in a model predicting incident LTSA (≥4 consecutive weeks) during 1-year follow-up in a sample of 4000 DANES participants. The 15-predictor model was reduced by backward stepwise statistical techniques and then validated in a sample of 2524 DANES participants, not included in the development sample. Identification of employees at increased LTSA risk was investigated by receiver operating characteristic (ROC) analysis; the area-under-the-ROC-curve (AUC) reflected discrimination between employees with and without LTSA during follow-up. The 15-predictor model was reduced to a 9-predictor model including age, gender, education, self-rated health, mental health, prior LTSA, work ability, emotional job demands, and recognition by the management. Discrimination by the 9-predictor model was significant (AUC = 0.68; 95% CI 0.61-0.76), but not practically useful. A prediction model based on occupational health survey variables identified employees with an increased LTSA risk, but should be further developed into a practically useful tool to predict the risk of LTSA in the general working population. Implications for rehabilitation Long-term sickness absence risk predictions would enable healthcare providers to refer high-risk employees to rehabilitation programs aimed at preventing or reducing work disability. A prediction model based on health survey variables discriminates between employees at high and low risk of long-term sickness absence, but discrimination was not practically useful. Health survey variables provide insufficient information to determine long-term sickness absence risk profiles. There is a need for

Evaluation of the predictive indices for candidemia in an adult intensive care unit

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Gilberto Gambero Gaspar

2015-02-01

Full Text Available INTRODUCTION: To evaluate predictive indices for candidemia in an adult intensive care unit (ICU and to propose a new index. METHODS: A prospective cohort study was conducted between January 2011 and December 2012. This study was performed in an ICU in a tertiary care hospital at a public university and included 114 patients staying in the adult ICU for at least 48 hours. The association of patient variables with candidemia was analyzed. RESULTS: There were 18 (15.8% proven cases of candidemia and 96 (84.2% cases without candidemia. Univariate analysis revealed the following risk factors: parenteral nutrition, severe sepsis, surgical procedure, dialysis, pancreatitis, acute renal failure, and an APACHE II score higher than 20. For the Candida score index, the odds ratio was 8.50 (95% CI, 2.57 to 28.09; the sensitivity, specificity, positive predictive value, and negative predictive value were 0.78, 0.71, 0.33, and 0.94, respectively. With respect to the clinical predictor index, the odds ratio was 9.45 (95%CI, 2.06 to 43.39; the sensitivity, specificity, positive predictive value, and negative predictive value were 0.89, 0.54, 0.27, and 0.96, respectively. The proposed candidemia index cutoff was 8.5; the sensitivity, specificity, positive predictive value, and negative predictive value were 0.77, 0.70, 0.33, and 0.94, respectively. CONCLUSIONS: The Candida score and clinical predictor index excluded candidemia satisfactorily. The effectiveness of the candidemia index was comparable to that of the Candida score.

Joint spatiotemporal variability of global sea surface temperatures and global Palmer drought severity index values

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Apipattanavis, S.; McCabe, G.J.; Rajagopalan, B.; Gangopadhyay, S.

2009-01-01

Dominant modes of individual and joint variability in global sea surface temperatures (SST) and global Palmer drought severity index (PDSI) values for the twentieth century are identified through a multivariate frequency domain singular value decomposition. This analysis indicates that a secular trend and variability related to the El Niño–Southern Oscillation (ENSO) are the dominant modes of variance shared among the global datasets. For the SST data the secular trend corresponds to a positive trend in Indian Ocean and South Atlantic SSTs, and a negative trend in North Pacific and North Atlantic SSTs. The ENSO reconstruction shows a strong signal in the tropical Pacific, North Pacific, and Indian Ocean regions. For the PDSI data, the secular trend reconstruction shows high amplitudes over central Africa including the Sahel, whereas the regions with strong ENSO amplitudes in PDSI are the southwestern and northwestern United States, South Africa, northeastern Brazil, central Africa, the Indian subcontinent, and Australia. An additional significant frequency, multidecadal variability, is identified for the Northern Hemisphere. This multidecadal frequency appears to be related to the Atlantic multidecadal oscillation (AMO). The multidecadal frequency is statistically significant in the Northern Hemisphere SST data, but is statistically nonsignificant in the PDSI data.

Prediction of BP Reactivity to Talking Using Hybrid Soft Computing Approaches

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Gurmanik Kaur

2014-01-01

Full Text Available High blood pressure (BP is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI, and arm circumference (AC were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA was fused with artificial neural network (ANN, adaptive neurofuzzy inference system (ANFIS, and least square-support vector machine (LS-SVM model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (R2, root mean square error (RMSE, and mean absolute percentage error (MAPE revealed that PCA based LS-SVM (PCA-LS-SVM model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.

Prediction of BP reactivity to talking using hybrid soft computing approaches.

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Kaur, Gurmanik; Arora, Ajat Shatru; Jain, Vijender Kumar

2014-01-01

High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (R (2)), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.

Bayesian spatial prediction of the site index in the study of the Missouri Ozark Forest Ecosystem Project

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Xiaoqian Sun; Zhuoqiong He; John Kabrick

2008-01-01

This paper presents a Bayesian spatial method for analysing the site index data from the Missouri Ozark Forest Ecosystem Project (MOFEP). Based on ecological background and availability, we select three variables, the aspect class, the soil depth and the land type association as covariates for analysis. To allow great flexibility of the smoothness of the random field,...

Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

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Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

2015-06-01

To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

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Choi, Soo Beom; Choi, Joon Yul; Park, Jee Soo; Kim, Deok Won

2016-07-01

In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.

Prediction of half-marathon race time in recreational female and male runners

OpenAIRE

Knechtle, Beat; Barandun, Ursula; Knechtle, Patrizia; Zingg, Matthias A; Rosemann, Thomas; Rüst, Christoph A

2014-01-01

Half-marathon running is of high popularity. Recent studies tried to find predictor variables for half-marathon race time for recreational female and male runners and to present equations to predict race time. The actual equations included running speed during training for both women and men as training variable but midaxillary skinfold for women and body mass index for men as anthropometric variable. An actual study found that percent body fat and running speed during training sessions were ...

Prediction of diffuse solar irradiance using machine learning and multivariable regression

International Nuclear Information System (INIS)

Lou, Siwei; Li, Danny H.W.; Lam, Joseph C.; Chan, Wilco W.H.

2016-01-01

Highlights: • 54.9% of the annual global irradiance is composed by its diffuse part in HK. • Hourly diffuse irradiance was predicted by accessible variables. • The importance of variable in prediction was assessed by machine learning. • Simple prediction equations were developed with the knowledge of variable importance. - Abstract: The paper studies the horizontal global, direct-beam and sky-diffuse solar irradiance data measured in Hong Kong from 2008 to 2013. A machine learning algorithm was employed to predict the horizontal sky-diffuse irradiance and conduct sensitivity analysis for the meteorological variables. Apart from the clearness index (horizontal global/extra atmospheric solar irradiance), we found that predictors including solar altitude, air temperature, cloud cover and visibility are also important in predicting the diffuse component. The mean absolute error (MAE) of the logistic regression using the aforementioned predictors was less than 21.5 W/m"2 and 30 W/m"2 for Hong Kong and Denver, USA, respectively. With the systematic recording of the five variables for more than 35 years, the proposed model would be appropriate to estimate of long-term diffuse solar radiation, study climate change and develope typical meteorological year in Hong Kong and places with similar climates.

Construction of possible integrated predictive index based on EGFR and ANXA3 polymorphisms for chemotherapy response in fluoropyrimidine-treated Japanese gastric cancer patients using a bioinformatic method

International Nuclear Information System (INIS)

Takahashi, Hiro; Kaniwa, Nahoko; Saito, Yoshiro; Sai, Kimie; Hamaguchi, Tetsuya; Shirao, Kuniaki; Shimada, Yasuhiro; Matsumura, Yasuhiro; Ohtsu, Atsushi; Yoshino, Takayuki; Doi, Toshihiko; Takahashi, Anna; Odaka, Yoko; Okuyama, Misuzu; Sawada, Jun-ichi; Sakamoto, Hiromi; Yoshida, Teruhiko

2015-01-01

Variability in drug response between individual patients is a serious concern in medicine. To identify single-nucleotide polymorphisms (SNPs) related to drug response variability, many genome-wide association studies have been conducted. We previously applied a knowledge-based bioinformatic approach to a pharmacogenomics study in which 119 fluoropyrimidine-treated gastric cancer patients were genotyped at 109,365 SNPs using the Illumina Human-1 BeadChip. We identified the SNP rs2293347 in the human epidermal growth factor receptor (EGFR) gene as a novel genetic factor related to chemotherapeutic response. In the present study, we reanalyzed these hypothesis-free genomic data using extended knowledge. We identified rs2867461 in annexin A3 (ANXA3) gene as another candidate. Using logistic regression, we confirmed that the performance of the rs2867461 + rs2293347 model was superior to those of the single factor models. Furthermore, we propose a novel integrated predictive index (iEA) based on these two polymorphisms in EGFR and ANXA3. The p value for iEA was 1.47 × 10 −8 by Fisher’s exact test. Recent studies showed that the mutations in EGFR is associated with high expression of dihydropyrimidine dehydrogenase, which is an inactivating and rate-limiting enzyme for fluoropyrimidine, and suggested that the combination of chemotherapy with fluoropyrimidine and EGFR-targeting agents is effective against EGFR-overexpressing gastric tumors, while ANXA3 overexpression confers resistance to tyrosine kinase inhibitors targeting the EGFR pathway. These results suggest that the iEA index or a combination of polymorphisms in EGFR and ANXA3 may serve as predictive factors of drug response, and therefore could be useful for optimal selection of chemotherapy regimens. The online version of this article (doi:10.1186/s12885-015-1721-z) contains supplementary material, which is available to authorized users

On the predictability of land surface fluxes from meteorological variables

Science.gov (United States)

Haughton, Ned; Abramowitz, Gab; Pitman, Andy J.

2018-01-01

Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

Regional regression models of percentile flows for the contiguous United States: Expert versus data-driven independent variable selection

Directory of Open Access Journals (Sweden)

Geoffrey Fouad

2018-06-01

New hydrological insights for the region: A set of three variables selected based on an expert assessment of factors that influence percentile flows performed similarly to larger sets of variables selected using a data-driven method. Expert assessment variables included mean annual precipitation, potential evapotranspiration, and baseflow index. Larger sets of up to 37 variables contributed little, if any, additional predictive information. Variables used to describe the distribution of basin data (e.g. standard deviation were not useful, and average values were sufficient to characterize physical and climatic basin conditions. Effectiveness of the expert assessment variables may be due to the high degree of multicollinearity (i.e. cross-correlation among additional variables. A tool is provided in the Supplementary material to predict percentile flows based on the three expert assessment variables. Future work should develop new variables with a strong understanding of the processes related to percentile flows.

A new daily dividend-adjusted index for the Danish stock market, 1985-2002: Construction, statistical properties, and return predictability

DEFF Research Database (Denmark)

Belter, Klaus; Engsted, Tom; Tanggaard, Carsten

2005-01-01

is given. In the second part of the paper we analyze the time-series properties of daily, weekly, and monthly returns, and we present evidence on predictability of multi-period returns. We also compare stock returns with the returns on long-term bonds and short-term money market instruments (that is......We present a new dividend-adjusted blue chip index for the Danish stock market covering the period 1985-2002. In contrast to other indices on the Danish stock market, the index is calculated on a daily basis. In the first part of the paper a detailed description of the construction of the index...

Disentangling the effects of genetic, prenatal and parenting influences on children's cortisol variability.

Science.gov (United States)

Marceau, Kristine; Ram, Nilam; Neiderhiser, Jenae M; Laurent, Heidemarie K; Shaw, Daniel S; Fisher, Phil; Natsuaki, Misaki N; Leve, Leslie D

2013-11-01

Developmental plasticity models hypothesize the role of genetic and prenatal environmental influences on the development of the hypothalamic-pituitary-adrenal (HPA) axis and highlight that genes and the prenatal environment may moderate early postnatal environmental influences on HPA functioning. This article examines the interplay of genetic, prenatal and parenting influences across the first 4.5 years of life on a novel index of children's cortisol variability. Repeated measures data were obtained from 134 adoption-linked families, adopted children and both their adoptive parents and birth mothers, who participated in a longitudinal, prospective US domestic adoption study. Genetic and prenatal influences moderated associations between inconsistency in overreactive parenting from child age 9 months to 4.5 years and children's cortisol variability at 4.5 years differently for mothers and fathers. Among children whose birth mothers had high morning cortisol, adoptive fathers' inconsistent overreactive parenting predicted higher cortisol variability, whereas among children with low birth mother morning cortisol adoptive fathers' inconsistent overreactive parenting predicted lower cortisol variability. Among children who experienced high levels of prenatal risk, adoptive mothers' inconsistent overreactive parenting predicted lower cortisol variability and adoptive fathers' inconsistent overreactive parenting predicted higher cortisol variability, whereas among children who experienced low levels of prenatal risk there were no associations between inconsistent overreactive parenting and children's cortisol variability. Findings supported developmental plasticity models and uncovered novel developmental, gene × environment and prenatal × environment influences on children's cortisol functioning.

Walkability Index

Science.gov (United States)

The Walkability Index dataset characterizes every Census 2010 block group in the U.S. based on its relative walkability. Walkability depends upon characteristics of the built environment that influence the likelihood of walking being used as a mode of travel. The Walkability Index is based on the EPA's previous data product, the Smart Location Database (SLD). Block group data from the SLD was the only input into the Walkability Index, and consisted of four variables from the SLD weighted in a formula to create the new Walkability Index. This dataset shares the SLD's block group boundary definitions from Census 2010. The methodology describing the process of creating the Walkability Index can be found in the documents located at ftp://newftp.epa.gov/EPADataCommons/OP/WalkabilityIndex.zip. You can also learn more about the Smart Location Database at https://edg.epa.gov/data/Public/OP/Smart_Location_DB_v02b.zip.

Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability.

Directory of Open Access Journals (Sweden)

Shaowei Sang

Full Text Available Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF, a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue's control and prevention purpose.Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8% imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags.Imported DF cases and mosquito

The predictive value of the baseline Oswestry Disability Index in lumbar disc arthroplasty.

Science.gov (United States)

Deutsch, Harel

2010-06-01

The goal of the study was to determine patient factors predictive of good outcome after lumbar disc arthroplasty. Specifically, the paper examines the relationship of the preoperative Oswestry Disability Index (ODI) to patient outcome at 1 year. The study is a retrospective review of 20 patients undergoing a 1-level lumbar disc arthroplasty at the author's institution between 2004 and 2008. All data were collected prospectively. Data included the ODI, visual analog scale scores, and patient demographics. All patients underwent a 1-level disc arthroplasty at L4-5 or L5-S1. The patients were divided into 2 groups based on their baseline ODI. Patients with an ODI between 38 and 59 demonstrated better outcomes with lumbar disc arthroplasty. Only 1 (20%) of 5 patients with a baseline ODI higher than 60 reported a good outcome. In contrast, 13 (87%) of 15 patients with an ODI between 38 and 59 showed a good outcome (p = 0.03). The negative predictive value of using ODI > 60 is 60% in patients who are determined to be candidates for lumbar arthroplasty. Lumbar arthroplasty is very effective in some patients. Other patients do not improve after surgery. The baseline ODI results are predictive of outcome in patients selected for lumbar disc arthroplasty. A baseline ODI > 60 is predictive of poor outcome. A high ODI may be indicative of psychosocial overlay.

Variability in Cadence During Forced Cycling Predicts Motor Improvement in Individuals With Parkinson’s Disease

Science.gov (United States)

Ridgel, Angela L.; Abdar, Hassan Mohammadi; Alberts, Jay L.; Discenzo, Fred M.; Loparo, Kenneth A.

2014-01-01

Variability in severity and progression of Parkinson’s disease symptoms makes it challenging to design therapy interventions that provide maximal benefit. Previous studies showed that forced cycling, at greater pedaling rates, results in greater improvements in motor function than voluntary cycling. The precise mechanism for differences in function following exercise is unknown. We examined the complexity of biomechanical and physiological features of forced and voluntary cycling and correlated these features to improvements in motor function as measured by the Unified Parkinson’s Disease Rating Scale (UPDRS). Heart rate, cadence, and power were analyzed using entropy signal processing techniques. Pattern variability in heart rate and power were greater in the voluntary group when compared to forced group. In contrast, variability in cadence was higher during forced cycling. UPDRS Motor III scores predicted from the pattern variability data were highly correlated to measured scores in the forced group. This study shows how time series analysis methods of biomechanical and physiological parameters of exercise can be used to predict improvements in motor function. This knowledge will be important in the development of optimal exercise-based rehabilitation programs for Parkinson’s disease. PMID:23144045

Portfolio theory of optimal isometric force production: Variability predictions and nonequilibrium fluctuation dissipation theorem

Science.gov (United States)

Frank, T. D.; Patanarapeelert, K.; Beek, P. J.

2008-05-01

We derive a fundamental relationship between the mean and the variability of isometric force. The relationship arises from an optimal collection of active motor units such that the force variability assumes a minimum (optimal isometric force). The relationship is shown to be independent of the explicit motor unit properties and of the dynamical features of isometric force production. A constant coefficient of variation in the asymptotic regime and a nonequilibrium fluctuation-dissipation theorem for optimal isometric force are predicted.

Portfolio theory of optimal isometric force production: Variability predictions and nonequilibrium fluctuation-dissipation theorem

International Nuclear Information System (INIS)

Frank, T.D.; Patanarapeelert, K.; Beek, P.J.

2008-01-01

We derive a fundamental relationship between the mean and the variability of isometric force. The relationship arises from an optimal collection of active motor units such that the force variability assumes a minimum (optimal isometric force). The relationship is shown to be independent of the explicit motor unit properties and of the dynamical features of isometric force production. A constant coefficient of variation in the asymptotic regime and a nonequilibrium fluctuation-dissipation theorem for optimal isometric force are predicted

Comparison of the significance of the RENAL, PADUA, and C-index nephrometric scales for the prediction of the complexity of laparoscopic nephrectomy

Directory of Open Access Journals (Sweden)

Yu. G. Alyaev

2018-01-01

Full Text Available Objective: to compare the predictive value of RENAL, PADUA, C-index nephrometry score systems according to projection of complexity  of operative measure in terms of warm ischaemic time, extent of blood loss and rate of postoperative complications.Materials and methods. Information for the research was collected from 314 patients with localized kidney cancer, who had laparoscopic partial nephrectomy from January 2012 to May 2017. In 210 (66.8 % cases, in addition to the routine examinations, 3D modelling and operative measure planning were carried out. The average tumor volume of the patients was equal to 62.5 ± 33.5 mm3. All patients before  the operation were estimated the complexity of operative measure on the nephrometry score systems: PADUA, RENAL, C-index. The average sum of balls according to scale RENAL – 7.56 ± 1.12, on scale PADUA – 7.98 ± 1.55, on scale C-index – 2.76 ± 1.14. Then in retrospect by the method of logistic regression analysis was determined predictive value of RENAL, PADUA, C-index nephrometry score systems for prediction of warm ischaemic time, duration of operative measure, extent of intraoperative blood loss and possibility of rate of postoperative complications.Results. In 265 (84.4 cases transperitoneal approach was perfomed and in 49 (15.6 % cases it was retroperitoneal approach. The average time of laparoscopic partial nephrectomy is 140.15 ± 55.8 min, the average time of ischaemic warm is 13.35 ± 7,65 min. The average extent of blood loss during the laparoscopic partial nephrectomy is 291.95 ± 196.5 ml. Intraoperative complications were found in 8 (2.54 % cases. Postoperative complications were estimated according to the Clavien–Dindo classification of surgical complications and were found in 31  (9.9 % cases, among them 12 (3.8 % patients had surgical complications. The index of the RENAL nephrometry scoring system had the highest predictive value in the multivariant analysis for warm ischaemic

Predicting national suicide numbers with social media data.

Science.gov (United States)

Won, Hong-Hee; Myung, Woojae; Song, Gil-Young; Lee, Won-Hee; Kim, Jong-Won; Carroll, Bernard J; Kim, Doh Kwan

2013-01-01

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors - consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.

Predicting National Suicide Numbers with Social Media Data

Science.gov (United States)

Won, Hong-Hee; Song, Gil-Young; Lee, Won-Hee; Kim, Jong-Won; Carroll, Bernard J.

2013-01-01

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors – consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention. PMID:23630615

Predicting national suicide numbers with social media data.

Directory of Open Access Journals (Sweden)

Hong-Hee Won

Full Text Available Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010. Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors - consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009 was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.

Rock index properties for geoengineering in the Paradox Basin

International Nuclear Information System (INIS)

O'Rourke, J.E.; Rey, P.H.; Alviti, E.; Capps, C.C.

1986-02-01

Previous researchers have investigated the use of a number of rapid index tests that can be used on core samples, or in situ, to determine rock properties needed for geoengineering design, or to predict construction performance in these rock types. Selected research is reviewed, and the correlations of index tests with laboratory tests of rock properties found by the earlier investigators are discussed. The selection and testing of rock core samples from the Gibson Dome No. 1 borehole in Paradox Basin are described. The samples consist primarily of non-salt rock above salt cycle 6, but include some samples of anhydrite and salt cycle 6. The index tests included the point load test, Schmidt hammer rebound test, and abrasion hardness test. Statistical methods were used to analyze the correlations of index test data with laboratory test data of rock properties for the same core. Complete statistical results and computer-generated graphics are presented; these results are discussed in relation to the work of earlier investigations for index testing of similar rock types. Generally, fair to good correlations were obtained for predicting unconfined compressive strength and Young's modulus for sandstone and siltstone, while poorer correlations were found for limestone. This may be due to the large variability of limestone properties compared to the small number of samples. Overall, the use of index tests to assess rock properties at Paradox Basin appears to be practial for some conceptual and preliminary design needs, and the technique should prove useful at any salt repository site. However, it is likely that specific correlations should be demonstrated separately for each site, and the data base for establishing the correlations should probably include at least several hundred data points for each type

High impact polystyrene (HIPS). Predicting its molecular, morphological and mechanical properties

International Nuclear Information System (INIS)

Luciani, C; Estenoz, D; Morales, G; Meira, G

2004-01-01

A mathematical model that is able to predict the molecular and morphological structure of high impact polystyrene (HIPS) and the Fluidity Index (MFI) is presented. The model is divided into two parts: a) the estimation of the material's molecular and morphological properties, simulating the polymerization process in discontinuous mass based on the recipe and the conditions of synthesis; and b) the prediction of rheological variables (viscosity at low deformation speeds and relaxation time), and of the MFI, based on average molecular and morphological variables. The model also combines with empirical correlations proposed in the literature [1] in order to estimate impact strength (IS). The predictions for a), b) and impact strength were co-validated by independent data and resulted in a good fit (CW)

Independent Prognostic Value of Stroke Volume Index in Patients With Immunoglobulin Light Chain Amyloidosis.

Science.gov (United States)

2018-05-01

Heart involvement is the most important prognostic determinant in AL amyloidosis patients. Echocardiography is a cornerstone for the diagnosis and provides important prognostic information. We studied 754 patients with AL amyloidosis who underwent echocardiographic assessment at the Mayo Clinic, including a Doppler-derived measurement of stroke volume (SV) within 30 days of their diagnosis to explore the prognostic role of echocardiographic variables in the context of a well-established soluble cardiac biomarker staging system. Reproducibility of SV, myocardial contraction fraction, and left ventricular strain was assessed in a separate, yet comparable, study cohort of 150 patients from the Pavia Amyloidosis Center. The echocardiographic measures most predictive for overall survival were SV index <33 mL/min, myocardial contraction fraction <34%, and cardiac index <2.4 L/min/m 2 with respective hazard ratios (95% confidence intervals) of 2.95 (2.37-3.66), 2.36 (1.96-2.85), and 2.32 (1.91-2.80). For the subset that had left ventricular strain performed, the prognostic cut point was -14% (hazard ratios, 2.70; 95% confidence intervals, 1.84-3.96). Each parameter was independent of systolic blood pressure, Mayo staging system (NT-proBNP [N-terminal pro-B-type natriuretic peptide] and troponin), and ejection fraction on multivariable analysis. Simple predictive models for survival, including biomarker staging along with SV index or left ventricular strain, were generated. SV index prognostic performance was similar to left ventricular strain in predicting survival in AL amyloidosis, independently of biomarker staging. Because SV index is routinely calculated and widely available, it could serve as the preferred echocardiographic measure to predict outcomes in AL amyloidosis patients. © 2018 American Heart Association, Inc.

Limited accuracy of the hyperbaric index, ambulatory blood pressure and sphygmomanometry measurements in predicting gestational hypertension and preeclampsia

NARCIS (Netherlands)

Vollebregt, Karlijn Corien; Gisolf, Janneke; Guelen, Ilja; Boer, Kees; van Montfrans, Gert; Wolf, Hans

2010-01-01

Objective The aim of this study was to validate the hyperbaric index (HBI) for first trimester prediction of preeclampsia and gestational hypertension. Methods Participants were low-risk and high-risk nulliparous women and high-risk multiparous women, and were recruited between April 2004 and June

A combination of compositional index and genetic algorithm for predicting transmembrane helical segments.

Directory of Open Access Journals (Sweden)

Nazar Zaki

Full Text Available Transmembrane helix (TMH topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method.The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm.

ENSO-Based Index Insurance: Approach and Peru Flood Risk Management Application

Science.gov (United States)

Khalil, A. F.; Kwon, H.; Lall, U.; Miranda, M. J.; Skees, J. R.

2006-12-01

Index insurance has recently been advocated as a useful risk transfer tool for disaster management situations where rapid fiscal relief is desirable, and where estimating insured losses may be difficult, time consuming, or subject to manipulation and falsification. For climate related hazards, a rainfall or temperature index may be proposed. However, rainfall may be highly spatially variable relative to the gauge network, and in many locations data are inadequate to develop an index due to short time-series and the spatial dispersion of stations. In such cases, it may be helpful to consider a climate proxy index as a regional rainfall index. This is particularly useful if a long record is available for the climate index through an independent source and it is well correlated with the regional rainfall hazard. Here, ENSO related climate indices are explored for use as a proxy to extreme rainfall in one of the departments of Peru -- Piura. The ENSO index insurance product may be purchased by banks or microfinance institutions (MFIs) to aid agricultural damage relief in Peru. Crop losses in the region are highly correlated with floods, but are difficult to assess directly. Beyond agriculture, many other sectors suffer as well. Basic infrastructure is destroyed during the most severe events. This disrupts trade for many micro-enterprises. The reliability and quality of the local rainfall data is variable. Averaging the financial risk across the region is desirable. Some issues with the implementation of the proxy ENSO index are identified and discussed. Specifically, we explore (a) the reliability of the index at different levels of probability of exceedance of maximum seasonal rainfall; (b) the potential for clustering of payoffs; (c) the potential that the index could be predicted with some lead time prior to the flood season; and (d) evidence for climate change or non-stationarity in the flood exceedance probability from the long ENSO record. Finally, prospects for

Waist Circumference, Body Mass Index, and Other Measures of Adiposity in Predicting Cardiovascular Disease Risk Factors among Peruvian Adults

OpenAIRE

Knowles, K. M.; Paiva, L. L.; Sanchez, S. E.; Revilla, L.; Lopez, T.; Yasuda, M. B.; Yanez, N. D.; Gelaye, B.; Williams, M. A.

2011-01-01

Objectives. To examine the extent to which measures of adiposity can be used to predict selected components of metabolic syndrome (MetS) and elevated C-reactive protein (CRP). Methods. A total of 1,518 Peruvian adults were included in this study. Waist circumference (WC), body mass index (BMI), waist-hip ratio (WHR), waist-height ratio (WHtR), and visceral adiposity index (VAI) were examined. The prevalence of each MetS component was determined according to tertiles of each anthropometric mea...

Remote detection of water stress conditions via a diurnal photochemical reflectance index (PRI) improves yield prediction in rainfed wheat

Science.gov (United States)

Magney, T. S.; Vierling, L. A.; Eitel, J.

2014-12-01

Employing remotely sensed techniques to quantify the existence and magnitude of midday photosynthetic downregulation using the photochemical reflectance index (PRI) may reveal new information about plant responses to abiotic stressors in space and time. However, the interpretation and application of the PRI can be confounded because of its sensitivity to several variables changing at the diurnal (e.g., irradiation, shadow fraction) and seasonal (e.g., leaf area, chlorophyll and carotene pigment concentrations, irradiation) time scales. We explored different techniques to correct the PRI for variations in canopy structure and relative chlorophyll content (ChlR) using highly temporally resolved (frequency = five minutes) in-situ radiometric measurements of PRI and the Normalized Difference Vegetation Index (NDVI) over eight soft white spring wheat (Triticum aestivum L.)field plots under varying nitrogen and soil water conditions over two seasons. Our results suggest that the influence of seasonal variation in canopy ChlR and LAI on the diurnally measured PRI (PRIdiurnal) can be minimized using simple correction techniques, therefore improving the strength of PRI as a tool to quantify abiotic stressors such as daily changes in soil volumetric water content (SVWC), and vapor pressure deficit (VPD). PRIdiurnal responded strongly to available nitrogen, and linearly tracked seasonal changes in SVWC, VPD, and stomatal conductance (gc). Utilizing the PRI as an indicator of stress, yield predictions significantly over greenness indices such as the NDVI. This study provides insight towards the future interpretation and scaling of PRI to quantify rapid changes in photosynthesis, and as an indicator of plant stress.

Prediction of higher cost of antiretroviral therapy (ART) according to clinical complexity. A validated clinical index.

Science.gov (United States)

Velasco, Cesar; Pérez, Inaki; Podzamczer, Daniel; Llibre, Josep Maria; Domingo, Pere; González-García, Juan; Puig, Inma; Ayala, Pilar; Martín, Mayte; Trilla, Antoni; Lázaro, Pablo; Gatell, Josep Maria

2016-03-01

The financing of antiretroviral therapy (ART) is generally determined by the cost incurred in the previous year, the number of patients on treatment, and the evidence-based recommendations, but not the clinical characteristics of the population. To establish a score relating the cost of ART and patient clinical complexity in order to understand the costing differences between hospitals in the region that could be explained by the clinical complexity of their population. Retrospective analysis of patients receiving ART in a tertiary hospital between 2009 and 2011. Factors potentially associated with a higher cost of ART were assessed by bivariate and multivariate analysis. Two predictive models of "high-cost" were developed. The normalized estimated (adjusted for the complexity scores) costs were calculated and compared with the normalized real costs. In the Hospital Index, 631 (16.8%) of the 3758 patients receiving ART were responsible for a "high-cost" subgroup, defined as the highest 25% of spending on ART. Baseline variables that were significant predictors of high cost in the Clinic-B model in the multivariate analysis were: route of transmission of HIV, AIDS criteria, Spanish nationality, year of initiation of ART, CD4+ lymphocyte count nadir, and number of hospital admissions. The Clinic-B score ranged from 0 to 13, and the mean value (5.97) was lower than the overall mean value of the four hospitals (6.16). The clinical complexity of the HIV patient influences the cost of ART. The Clinic-B and Clinic-BF scores predicted patients with high cost of ART and could be used to compare and allocate costs corrected for the patient clinical complexity. Copyright © 2015 Elsevier España, S.L.U. y Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

Potential Predictability and Prediction Skill for Southern Peru Summertime Rainfall

Science.gov (United States)

WU, S.; Notaro, M.; Vavrus, S. J.; Mortensen, E.; Block, P. J.; Montgomery, R. J.; De Pierola, J. N.; Sanchez, C.

2016-12-01

The central Andes receive over 50% of annual climatological rainfall during the short period of January-March. This summertime rainfall exhibits strong interannual and decadal variability, including severe drought events that incur devastating societal impacts and cause agricultural communities and mining facilities to compete for limited water resources. An improved seasonal prediction skill of summertime rainfall would aid in water resource planning and allocation across the water-limited southern Peru. While various underlying mechanisms have been proposed by past studies for the drivers of interannual variability in summertime rainfall across southern Peru, such as the El Niño-Southern Oscillation (ENSO), Madden Julian Oscillation (MJO), and extratropical forcings, operational forecasts continue to be largely based on rudimentary ENSO-based indices, such as NINO3.4, justifying further exploration of predictive skill. In order to bridge this gap between the understanding of driving mechanisms and the operational forecast, we performed systematic studies on the predictability and prediction skill of southern Peru summertime rainfall by constructing statistical forecast models using best available weather station and reanalysis datasets. At first, by assuming the first two empirical orthogonal functions (EOFs) of summertime rainfall are predictable, the potential predictability skill was evaluated for southern Peru. Then, we constructed a simple regression model, based on the time series of tropical Pacific sea-surface temperatures (SSTs), and a more advanced Linear Inverse Model (LIM), based on the EOFs of tropical ocean SSTs and large-scale atmosphere variables from reanalysis. Our results show that the LIM model consistently outperforms the more rudimentary regression models on the forecast skill of domain averaged precipitation index and individual station indices. The improvement of forecast correlation skill ranges from 10% to over 200% for different

Leaf area index from litter collection: impact of specific leaf area variability within a beech stand

Energy Technology Data Exchange (ETDEWEB)

Bouriaud, O. [Inst. National de la Recherche Agronomique, Centre de Recherches Forestieres de Nancy, Champenoux (France); Soudani, K. [Univ. Paris-Sud XI, Dept. d' Ecophysiologie Vegetale, Lab. Ecologie Systematique et Evolution, Orsay Cedex (France); Breda, N. [Inst. National de la Recherche Agronomique, Centre de Recherches Forestieres de Nancy, Champenoux (France)

2003-06-01

Litter fall collection is a direct method widely used to estimate leaf area index (LAI) in broad-leaved forest stands. Indirect measurements using radiation transmittance and gap fraction theory are often compared and calibrated against litter fall, which is considered as a reference method, but few studies address the question of litter specific leaf area (SLA) measurement and variability. SLA (leaf area per unit of dry weight, m{sup 2}{center_dot}g{sup -1}) is used to convert dry leaf litter biomass (g .m{sup -}2) into leaf area per ground unit area (m{sup 2}{center_dot}m{sup -2}). We paid special attention to this parameter in two young beech stands (dense and thinned) in northeastern France. The variability of both canopy (closure, LAI) and site conditions (soil properties, vegetation) was investigated as potential contributing factors to beech SLA variability. A systematic description of soil and floristic composition was performed and three types of soil were identified. Ellenberg's indicator values were averaged for each plot to assess nitrogen soil content. SLA of beech litter was measured three times during the fall in 23 plots in the stands (40 ha). Litter was collected bimonthly in square-shaped traps (0.5 m{sup 2}) and dried. Before drying, 30 leaves per plot and for each date were sampled, and leaf length, width, and area were measured with the help of a LI-COR areameter. SLA was calculated as the ratio of cumulated leaf area to total dry weight of the 30 leaves. Leaves characteristics per plot were averaged for the three dates of litter collection. Plant area index (PAI), estimated using the LAI-2000 plant canopy analyser and considering only the upper three rings, ranged from 2.9 to 8.1. Specific leaf area of beech litter was also highly different from one plot to the other, ranging from 150 to 320 cm{sup 2}{center_dot}g{sup -1}. Nevertheless, no relationship was found between SLA and stand canopy closure or PAI On the contrary, a significant

Leaf area index from litter collection: impact of specific leaf area variability within a beech stand

International Nuclear Information System (INIS)

Bouriaud, O.; Soudani, K.; Breda, N.

2003-01-01

Litter fall collection is a direct method widely used to estimate leaf area index (LAI) in broad-leaved forest stands. Indirect measurements using radiation transmittance and gap fraction theory are often compared and calibrated against litter fall, which is considered as a reference method, but few studies address the question of litter specific leaf area (SLA) measurement and variability. SLA (leaf area per unit of dry weight, m 2 ·g -1 ) is used to convert dry leaf litter biomass (g .m - 2) into leaf area per ground unit area (m 2 ·m -2 ). We paid special attention to this parameter in two young beech stands (dense and thinned) in northeastern France. The variability of both canopy (closure, LAI) and site conditions (soil properties, vegetation) was investigated as potential contributing factors to beech SLA variability. A systematic description of soil and floristic composition was performed and three types of soil were identified. Ellenberg's indicator values were averaged for each plot to assess nitrogen soil content. SLA of beech litter was measured three times during the fall in 23 plots in the stands (40 ha). Litter was collected bimonthly in square-shaped traps (0.5 m 2 ) and dried. Before drying, 30 leaves per plot and for each date were sampled, and leaf length, width, and area were measured with the help of a LI-COR areameter. SLA was calculated as the ratio of cumulated leaf area to total dry weight of the 30 leaves. Leaves characteristics per plot were averaged for the three dates of litter collection. Plant area index (PAI), estimated using the LAI-2000 plant canopy analyser and considering only the upper three rings, ranged from 2.9 to 8.1. Specific leaf area of beech litter was also highly different from one plot to the other, ranging from 150 to 320 cm 2 ·g -1 . Nevertheless, no relationship was found between SLA and stand canopy closure or PAI On the contrary, a significant relationship between SLA and soil properties was observed. Both SLA

Prediction Center (CPC) Tropical/ Northern Hemisphere Teleconnection Pattern Index

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Monthly tabulated index of the Tropical/ Northern Hemisphere teleconnection pattern. The data spans the period 1950 to present. The index is derived from a rotated...

Climate Prediction Center (CPC) Pacific Transition Teleconnection Pattern Index

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Monthly tabulated index of the Pacific Transition teleconnection pattern. The data spans the period 1950 to present. The index is derived from a rotated principal...

Climate Prediction Center (CPC) East Atlantic Teleconnection Pattern Index

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Monthly tabulated index of the East Atlantic Teleconnection pattern. The data spans the period 1950 to present. The index is derived from a rotated principal...

FRELIB, Failure Reliability Index Calculation

International Nuclear Information System (INIS)

Parkinson, D.B.; Oestergaard, C.

1984-01-01

1 - Description of problem or function: Calculation of the reliability index given the failure boundary. A linearization point (design point) is found on the failure boundary for a stationary reliability index (min) and a stationary failure probability density function along the failure boundary, provided that the basic variables are normally distributed. 2 - Method of solution: Iteration along the failure boundary which must be specified - together with its partial derivatives with respect to the basic variables - by the user in a subroutine FSUR. 3 - Restrictions on the complexity of the problem: No distribution information included (first-order-second-moment-method). 20 basic variables (could be extended)

Modelling and prediction of pig iron variables in the blast furnace

Energy Technology Data Exchange (ETDEWEB)

Saxen, H.; Laaksonen, M.; Waller, M. [Aabo Akademi, Turku (Finland). Heat Engineering Lab.

1996-12-31

The blast furnace, where pig iron for steelmaking is produced, is an extremely complicated process, with heat and mass transfer and chemical reactions between several phases. Very few direct measurements on the internal state are available in the operation of the process. A main problem in on-line analysis and modelling is that the state of the furnace may undergo spontaneous changes, which alter the dynamic behaviour of the process. Moreover, large internal disturbances frequently occur, which affect the product quality. The work in this research project focuses on a central problem in the control of the blast furnace process, i.e., short-term prediction of pig iron variables. The problem is of considerable importance for fuel economy, product quality, and for an optimal decision making in integrated steel plants. The operation of the blast furnace aims at producing a product (hot metal) with variables maintained on a stable level (close to their setpoints) without waste of expensive fuel (metallurgical coke). The hot metal temperature and composition affect the downstream (steelmaking) processes, so fluctuations in the pig iron quality must be `corrected` in the steel plant. The goal is to develop a system which predicts the evolution of the hot metal variables (temperature, chemical composition) during the next few taps, and that can be used for decision-making in the operation of the blast furnace. Because of the complicated behaviour of the process, it is considered important to include both deterministic and stochastic components in the modelling: Mathematical models, which on the basis of measurements describe the physical state of the process, and statistical (black-box) models will be combined in the system. Moreover, different models will be applied in different domains in order to capture structural changes in the dynamics of the process SULA 2 Research Programme; 17 refs.

Modelling and prediction of pig iron variables in the blast furnace

Energy Technology Data Exchange (ETDEWEB)

Saxen, H; Laaksonen, M; Waller, M [Aabo Akademi, Turku (Finland). Heat Engineering Lab.

1997-12-31

The blast furnace, where pig iron for steelmaking is produced, is an extremely complicated process, with heat and mass transfer and chemical reactions between several phases. Very few direct measurements on the internal state are available in the operation of the process. A main problem in on-line analysis and modelling is that the state of the furnace may undergo spontaneous changes, which alter the dynamic behaviour of the process. Moreover, large internal disturbances frequently occur, which affect the product quality. The work in this research project focuses on a central problem in the control of the blast furnace process, i.e., short-term prediction of pig iron variables. The problem is of considerable importance for fuel economy, product quality, and for an optimal decision making in integrated steel plants. The operation of the blast furnace aims at producing a product (hot metal) with variables maintained on a stable level (close to their setpoints) without waste of expensive fuel (metallurgical coke). The hot metal temperature and composition affect the downstream (steelmaking) processes, so fluctuations in the pig iron quality must be `corrected` in the steel plant. The goal is to develop a system which predicts the evolution of the hot metal variables (temperature, chemical composition) during the next few taps, and that can be used for decision-making in the operation of the blast furnace. Because of the complicated behaviour of the process, it is considered important to include both deterministic and stochastic components in the modelling: Mathematical models, which on the basis of measurements describe the physical state of the process, and statistical (black-box) models will be combined in the system. Moreover, different models will be applied in different domains in order to capture structural changes in the dynamics of the process SULA 2 Research Programme; 17 refs.

Climate Prediction Center (CPC) West Pacific Teleconnection Pattern Index

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Monthly tabulated index of the West Pacific (WP) teleconnection pattern. The data spans the period 1950 to present. The index is derived from a rotated principal...

Cephalometric variables predicting the long-term success or failure of combined rapid maxillary expansion and facial mask therapy.

Science.gov (United States)

Baccetti, Tiziano; Franchi, Lorenzo; McNamara, James A

2004-07-01

The aim of this study was to select a model of cephalometric variables to predict the results of early treatment of Class III malocclusion with rapid maxillary expansion and facemask therapy followed by comprehensive treatment with fixed appliances. Lateral cephalograms of 42 patients (20 boys, 22 girls) with Class III malocclusion were analyzed at the start of treatment (mean age 8 years 6 months +/- 2 years, at stage I in cervical vertebral maturation). All patients were reevaluated after a mean period of 6 years 6 months (at stage IV or V in cervical vertebral maturation) that included active treatment plus retention. At this time, the sample was divided into 2 groups according to occlusal criteria: a successful group (30 patients) and an unsuccessful group (12 patients). Discriminant analysis was applied to select pretreatment predictive variables of long-term treatment outcome. Stepwise variable selection of the cephalometric measurements at the first observation identified 3 predictive variables. Orthopedic treatment of Class III malocclusion might be unfavorable over the long term when a patient's pretreatment cephalometric records exhibit a long mandibular ramus (ie, increased posterior facial height), an acute cranial base angle, and a steep mandibular plane angle. On the basis of the equation generated by the multivariate statistical method, the outcome of interceptive orthopedic treatment for each new patient with Class III malocclusion can be predicted with a probability error of 16.7%.

Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

Science.gov (United States)

Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui

2017-07-15

New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier

The pharmacist Aggregate Demand Index to explain changing pharmacist demand over a ten-year period.

Science.gov (United States)

Knapp, Katherine K; Shah, Bijal M; Barnett, Mitchell J

2010-12-15

To describe Aggregate Demand Index (ADI) trends from 1999-2010; to compare ADI time trends to concurrent data for US unemployment levels, US entry-level pharmacy graduates, and US retail prescription growth rate; and to determine which variables were significant predictors of ADI. Annual ADI data (dependent variable) were analyzed against annual unemployment rates, annual number of pharmacy graduates, and annual prescription growth rate (independent variables). ADI data trended toward lower demand levels for pharmacists since late 2006, paralleling the US economic downturn. National ADI data were most highly correlated with unemployment (p demand. Predictable increases in future graduates and other factors support revisiting the modeling process as new data accumulate.

Ferritin and body mass index predict cardiac dysfunction in female adolescents with anorexia of the restrictive type.

Science.gov (United States)

Docx, Martine K F; Weyler, Joost; Simons, Annik; Ramet, José; Mertens, Luc

2015-08-01

Decreased left ventricular mass index in anorexia nervosa is amply reported. The aim of this study is to identify non-burdensome predictors of reduced left yentricular mass/height (cLVM) in a cohort of adolescent restrictive anorexic girls. This is a retrospective study of all anorexic girls of the restrictive type referred to our tertiary eating disorder unit between September 2002 and December 2012, for somatic assessment of weig ht loss. All subjects fulfilled DMS-IV criteria, without a family history of cardiac or cardiovascular diseases. In all, 283 restrictive anorexic girls (age: 14.63 +/- 1.65 y; body mass index: 15.72 +/- 1.81 kg/m2) were included. Ferritin and body mass index were independent, statistically significant predictors of the corrected left ventricular mass (P anorexia nervosa of the restrictive type. Two factors predicted decreased cLVM in our population: ferritin and BMI.

Days on radiosensitivity: individual variability and predictive tests; Radiosensibilite: variabilite individuelle et tests predictifs

Energy Technology Data Exchange (ETDEWEB)

NONE

2008-07-01

The radiosensitivity is a part of usual clinical observations. It is already included in the therapy protocols. however, some questions stay on its individual variability and on the difficulty to evaluate it. The point will be stocked on its origin and its usefulness in predictive medicine. Through examples on the use of predictive tests and ethical and legal questions that they raise, concrete cases will be presented by specialists such radio biologists, geneticists, immunologists, jurists and occupational physicians. (N.C.)

Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China

Science.gov (United States)

Du, Zhicheng; Xu, Lin; Zhang, Wangjian; Zhang, Dingmei; Yu, Shicheng; Hao, Yuantao

2017-01-01

Objectives Hand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD. Design Ecological study. Setting and participants Information on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011–2014. Analyses were conducted at aggregate level and no confidential information was involved. Outcome measures A seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI. Results A high correlation between HFMD incidence and BDI (r=0.794, pmodel. Compared with the model based on surveillance data only, the ARIMAX model including BDI reached the best goodness-of-fit with an Akaike information criterion (AIC) value of −345.332, whereas the model including both BDI and temperature had the most accurate prediction in terms of the mean absolute percentage error (MAPE) of 101.745%. Conclusions An ARIMAX model incorporating search engine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings. PMID:28988169

Prediction of Currency Volume Issued in Taiwan Using a Hybrid Artificial Neural Network and Multiple Regression Approach

Directory of Open Access Journals (Sweden)

Yuehjen E. Shao

2013-01-01

Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.

Role of bedside index for severity of acute pancreatitis (bisap score in predicting outcome in acute pancreatitis

Directory of Open Access Journals (Sweden)

Shahnawaz Bashir Bhat

2015-12-01

Full Text Available Objective: To investigate the role of Bedside index for severity of acute pancreatitis (BISAP score in predicting the outcome of acute pancreatitis. Methods: This single hospital based prospective study included fifty patients of acute pancreatitis admitted within 48 hours of onset of symptoms, who were divided into two groups according to admission BISAP score. BISAP score 3 (severe acute pancreatitis. The ability of BISAP score to predict mortality, morbidity and hospital stay in acute pancreatitis patients was analyzed. Results: A BISAP score of >3 was associated with increased risk of development of transient organ failure, persistent organ failure and pancreatic necrosis (Statistically significant. Mortality in group with BISAP and #8805;3 was 23.5% (4 patients which was statistically higher than group with BISAP score and #706;3 (0 patients (p=0.019.The mean duration of hospital stay of patients in group with BISAP score < 3 was 7.58 +/- 4.04 days and in group with BISAP score and #8805;3 was 15.35 +/- 1.66.(p=0.02. Conclusion: Bedside index for severity in acute pancreatitis (BISAP score, at admission is an excellent score in predicting the mortality, morbidity and hospital stay and hence management protocol in patients admitted with acute pancreatitis. [J Contemp Med 2015; 5(4.000: 215-220

Physical activity, body mass index and heart rate variability-based stress and recovery in 16 275 Finnish employees: a cross-sectional study

Directory of Open Access Journals (Sweden)

Tiina Föhr

2016-08-01

Full Text Available Abstract Background Physical inactivity, overweight, and work-related stress are major concerns today. Psychological stress causes physiological responses such as reduced heart rate variability (HRV, owing to attenuated parasympathetic and/or increased sympathetic activity in cardiac autonomic control. This study’s purpose was to investigate the relationships between physical activity (PA, body mass index (BMI, and HRV-based stress and recovery on workdays, among Finnish employees. Methods The participants in this cross-sectional study were 16 275 individuals (6863 men and 9412 women; age 18–65 years; BMI 18.5–40.0 kg/m2. Assessments of stress, recovery and PA were based on HRV data from beat-to-beat R-R interval recording (mainly over 3 days. The validated HRV-derived variables took into account the dynamics and individuality of HRV. Stress percentage (the proportion of stress reactions, workday and working hours, and stress balance (ratio between recovery and stress reactions, sleep describe the amount of physiological stress and recovery, respectively. Variables describing the intensity (i.e. magnitude of recognized reactions of physiological stress and recovery were stress index (workday and recovery index (sleep, respectively. Moderate to vigorous PA was measured and participants divided into the following groups, based on calculated weekly PA: inactive (0 min, low (0 300 min. BMI was calculated from self-reported weight and height. Linear models were employed in the main analyses. Results High PA was associated with lower stress percentages (during workdays and working hours and stress balance. Higher BMI was associated with higher stress index, and lower stress balance and recovery index. These results were similar for men and women (P < 0.001 for all. Conclusion Independent of age and sex, high PA was associated with a lower amount of stress on workdays. Additionally, lower BMI was associated with better recovery during

Evaluation of the Thompson articular index

NARCIS (Netherlands)

van den Brink, H. R.; van der Heide, A.; Jacobs, J. W.; van der Veen, M. J.; Bijlsma, J. W.

1993-01-01

Three articular indices for measuring disease activity are compared. In a cross sectional study the Thompson articular index (a modified Lansbury index) correlated better with laboratory variables than the Ritchie articular index or a swollen joint score (Thompson 0.74-0.77; Ritchie 0.57-0.58;

A novel clinical index for the assessment of RVD in acute pulmonary embolism: Blood pressure index.

Science.gov (United States)

Ates, Hale; Ates, Ihsan; Kundi, Harun; Arikan, Mehmet Fettah; Yilmaz, Fatma Meric

2017-10-01

This study aims to investigate the role of the blood pressure index (BPI), which is a new index that we developed, in detection of right ventricular dysfunction (RVD) in acute pulmonary embolism (APE). A total of 539 patients, (253 males and 286 females), diagnosed with APE using computer tomography pulmonary angiography were included in the study. The BPI was obtained by dividing systolic blood pressure (SBP) by diastolic blood pressure (DBP). Mean DBP (75±11mmHg vs 63±15mmHg; p<0.001, respectively) was found to be higher in RVD patients compared to those without RVD, whereas BPI (1.5±0.1 vs 1.9±0.2; p<0.001, respectively) was lower. Examining the performance of BPI in prediction of RVD using receiver operating characteristic curve analysis (area under curve±SE=0.975±0.006; p<0.001), it was found that BPI could predict RVD with very high sensitivity (92.8%) and specificity (100%) and had a positive predictive value of 100% and a negative predictive value of 42.1%. According to the analysis, the highest youden index for the optimal prediction value was found to be 0.478 and the BPI≤1.4 was found to predict mortality 68.6% sensitivity and 80.8% specificity (Area under curve±SE=0.777±0.051; p<0.001). We found that BPI was an index with high positive predictive value and low negative predictive value in detection of RVD. Copyright © 2017 Elsevier Inc. All rights reserved.

Nonlinear Conte-Zbilut-Federici (CZF Method of Computing LF/HF Ratio: A More Reliable Index of Changes in Heart Rate Variability

Directory of Open Access Journals (Sweden)

Vernon Bond Jr

2016-09-01

Full Text Available Objectives: Acupuncture treatments are safe and effective for a wide variety of diseases involving autonomic dysregulation. Heart rate variability (HRV is a noninvasive method for assessing sympathovagal balance. The low frequency/high frequency (LF/HF spectral power ratio is an index of sympathovagal influence on heart rate and of cardi

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Directory of Open Access Journals (Sweden)

Braz Calderano Filho

2014-12-01

Full Text Available Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+ sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13. Excluding the variable profile curvature (set 12, overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Assessing the potential of random forest method for estimating solar radiation using air pollution index

International Nuclear Information System (INIS)

Sun, Huaiwei; Gui, Dongwei; Yan, Baowei; Liu, Yi; Liao, Weihong; Zhu, Yan; Lu, Chengwei; Zhao, Na

2016-01-01

Highlights: • Models based on random forests for daily solar radiation estimation are proposed. • Three sites within different air pollution index conditions are considered. • Performance of random forests is better than that of empirical methodologies. • Special attention is given to the use of air pollution index. • The potential of air pollution index is assessed by random forest models. - Abstract: Simulations of solar radiation have become increasingly common in recent years because of the rapid global development and deployment of solar energy technologies. The effect of air pollution on solar radiation is well known. However, few studies have attempting to evaluate the potential of the air pollution index in estimating solar radiation. In this study, meteorological data, solar radiation, and air pollution index data from three sites having different air pollution index conditions are used to develop random forest models. We propose different random forest models with and without considering air pollution index data, and then compare their respective performance with that of empirical methodologies. In addition, a variable importance approach based on random forest is applied in order to assess input variables. The results show that the performance of random forest models with air pollution index data is better than that of the empirical methodologies, generating 9.1–17.0% lower values of root-mean-square error in a fitted period and 2.0–17.4% lower values of root-mean-square error in a predicted period. Both the comparative results of different random forest models and variance importance indicate that applying air pollution index data is improves estimation of solar radiation. Also, although the air pollution index values varied largely from season to season, the random forest models appear more robust performances in different seasons than different models. The findings can act as a guide in selecting used variables to estimate daily solar

Predictive validity of the GOSLON Yardstick index in patients with unilateral cleft lip and palate: A systematic review.

Directory of Open Access Journals (Sweden)

Cindy Buj-Acosta

Full Text Available Among the various indices developed for measuring the results of treatment in patients born with unilateral cleft lip and palate (UCLP, the GOSLON Yardstick index is the most widely used to assess the efficacy of treatment and treatment outcomes, which in UCLP cases are closely linked to jaw growth. The aim of this study was to conduct a systematic review to validate the predictability of growth using the GOSLON Yardstick in patients born with UCLP. A systematic literature review was conducted in four Internet databases: Medline, Cochrane Library, Scopus and Embase, complemented by a manual search and a further search in the databases of the leading journals that focus on this topic. An electronic search was also conducted among grey literature. The search identified a total of 131 articles. Duplicated articles were excluded and after reading titles and abstracts, any articles not related to the research objective were excluded, leaving a total of 21 texts. After reading the complete text, only three articles fulfilled the inclusion criteria. The results showed a predictive validity of between 42.2% and 64.7%, which points to a lack of evidence in the literature for the predictive validity of the GOSLON Yardstick index used in children born with UCLP.

Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.

Science.gov (United States)

Pekkala, Timo; Hall, Anette; Lötjönen, Jyrki; Mattila, Jussi; Soininen, Hilkka; Ngandu, Tiia; Laatikainen, Tiina; Kivipelto, Miia; Solomon, Alina

2017-01-01

This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.

Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks.

Science.gov (United States)

León-Roque, Noemí; Abderrahim, Mohamed; Nuñez-Alejos, Luis; Arribas, Silvia M; Condezo-Hoyos, Luis

2016-12-01

Several procedures are currently used to assess fermentation index (FI) of cocoa beans (Theobroma cacao L.) for quality control. However, all of them present several drawbacks. The aim of the present work was to develop and validate a simple image based quantitative procedure, using color measurement and artificial neural network (ANNs). ANN models based on color measurements were tested to predict fermentation index (FI) of fermented cocoa beans. The RGB values were measured from surface and center region of fermented beans in images obtained by camera and desktop scanner. The FI was defined as the ratio of total free amino acids in fermented versus non-fermented samples. The ANN model that included RGB color measurement of fermented cocoa surface and R/G ratio in cocoa bean of alkaline extracts was able to predict FI with no statistical difference compared with the experimental values. Performance of the ANN model was evaluated by the coefficient of determination, Bland-Altman plot and Passing-Bablok regression analyses. Moreover, in fermented beans, total sugar content and titratable acidity showed a similar pattern to the total free amino acid predicted through the color based ANN model. The results of the present work demonstrate that the proposed ANN model can be adopted as a low-cost and in situ procedure to predict FI in fermented cocoa beans through apps developed for mobile device. Copyright © 2016 Elsevier B.V. All rights reserved.

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

Recent and Past Musical Activity Predicts Cognitive Aging Variability: Direct Comparison with Leisure Activities

Directory of Open Access Journals (Sweden)

Brenda eHanna-Pladdy

2012-07-01

Full Text Available Studies evaluating the impact of modifiable lifestyle factors on cognition offer potential insights into sources of cognitive aging variability. Recently, we reported an association between extent of musical instrumental practice throughout the life span (greater than 10 years on preserved cognitive functioning in advanced age . These findings raise the question of whether there are training-induced brain changes in musicians that can transfer to nonmusical cognitive abilities to allow for compensation of age-related cognitive declines. However, because of the relationship between engagement in lifestyle activities and preserved cognition, it remains unclear whether these findings are specifically driven by musical training or the types of individuals likely to engage in greater activities in general. The current study examined the type of leisure activity (musical versus other as well as the timing of engagement (age of acquisition, past versus recent in predictive models of successful cognitive aging. Seventy age and education matched older musicians (> 10 years and nonmusicians (ages 59-80 were evaluated on neuropsychological tests and life-style activities (AAP. Partition analyses were conducted on significant cognitive measures to explain performance variance in musicians. Musicians scored higher on tests of phonemic fluency, verbal immediate recall, judgment of line orientation (JLO, and Letter Number Sequencing (LNS, but not the AAP. The first partition analysis revealed education best predicted JLO in musicians, followed by recent musical engagement which offset low education. In the second partition analysis, early age of musical acquisition (< 9 years predicted enhanced LNS in musicians, while analyses for AAP, verbal recall and fluency were not predictive. Recent and past musical activity, but not leisure activity, predicted variability across verbal and visuospatial domains in aging. Early musical acquisition predicted auditory

A variable capacitance based modeling and power capability predicting method for ultracapacitor

Science.gov (United States)

Liu, Chang; Wang, Yujie; Chen, Zonghai; Ling, Qiang

2018-01-01

Methods of accurate modeling and power capability predicting for ultracapacitors are of great significance in management and application of lithium-ion battery/ultracapacitor hybrid energy storage system. To overcome the simulation error coming from constant capacitance model, an improved ultracapacitor model based on variable capacitance is proposed, where the main capacitance varies with voltage according to a piecewise linear function. A novel state-of-charge calculation approach is developed accordingly. After that, a multi-constraint power capability prediction is developed for ultracapacitor, in which a Kalman-filter-based state observer is designed for tracking ultracapacitor's real-time behavior. Finally, experimental results verify the proposed methods. The accuracy of the proposed model is verified by terminal voltage simulating results under different temperatures, and the effectiveness of the designed observer is proved by various test conditions. Additionally, the power capability prediction results of different time scales and temperatures are compared, to study their effects on ultracapacitor's power capability.

Drivers and potential predictability of summer time North Atlantic polar front jet variability