Advanced Control Algorithms for Compensating the Phase Distortion Due to Transport Delay in Human-Machine Systems

NASA Technical Reports Server (NTRS)

Guo, Liwen; Cardullo, Frank M.; Kelly, Lon C.

2007-01-01

The desire to create more complex visual scenes in modern flight simulators outpaces recent increases in processor speed. As a result, simulation transport delay remains a problem. New approaches for compensating the transport delay in a flight simulator have been developed and are presented in this report. The lead/lag filter, the McFarland compensator and the Sobiski/Cardullo state space filter are three prominent compensators. The lead/lag filter provides some phase lead, while introducing significant gain distortion in the same frequency interval. The McFarland predictor can compensate for much longer delay and cause smaller gain error in low frequencies than the lead/lag filter, but the gain distortion beyond the design frequency interval is still significant, and it also causes large spikes in prediction. Though, theoretically, the Sobiski/Cardullo predictor, a state space filter, can compensate the longest delay with the least gain distortion among the three, it has remained in laboratory use due to several limitations. The first novel compensator is an adaptive predictor that makes use of the Kalman filter algorithm in a unique manner. In this manner the predictor can accurately provide the desired amount of prediction, while significantly reducing the large spikes caused by the McFarland predictor. Among several simplified online adaptive predictors, this report illustrates mathematically why the stochastic approximation algorithm achieves the best compensation results. A second novel approach employed a reference aircraft dynamics model to implement a state space predictor on a flight simulator. The practical implementation formed the filter state vector from the operator s control input and the aircraft states. The relationship between the reference model and the compensator performance was investigated in great detail, and the best performing reference model was selected for implementation in the final tests. Theoretical analyses of data from offline simulations with time delay compensation show that both novel predictors effectively suppress the large spikes caused by the McFarland compensator. The phase errors of the three predictors are not significant. The adaptive predictor yields greater gain errors than the McFarland predictor for short delays (96 and 138 ms), but shows smaller errors for long delays (186 and 282 ms). The advantage of the adaptive predictor becomes more obvious for a longer time delay. Conversely, the state space predictor results in substantially smaller gain error than the other two predictors for all four delay cases.

Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts

PubMed Central

Genders, Tessa S S; Steyerberg, Ewout W; Nieman, Koen; Galema, Tjebbe W; Mollet, Nico R; de Feyter, Pim J; Krestin, Gabriel P; Alkadhi, Hatem; Leschka, Sebastian; Desbiolles, Lotus; Meijs, Matthijs F L; Cramer, Maarten J; Knuuti, Juhani; Kajander, Sami; Bogaert, Jan; Goetschalckx, Kaatje; Cademartiri, Filippo; Maffei, Erica; Martini, Chiara; Seitun, Sara; Aldrovandi, Annachiara; Wildermuth, Simon; Stinn, Björn; Fornaro, Jürgen; Feuchtner, Gudrun; De Zordo, Tobias; Auer, Thomas; Plank, Fabian; Friedrich, Guy; Pugliese, Francesca; Petersen, Steffen E; Davies, L Ceri; Schoepf, U Joseph; Rowe, Garrett W; van Mieghem, Carlos A G; van Driessche, Luc; Sinitsyn, Valentin; Gopalan, Deepa; Nikolaou, Konstantin; Bamberg, Fabian; Cury, Ricardo C; Battle, Juan; Maurovich-Horvat, Pál; Bartykowszki, Andrea; Merkely, Bela; Becker, Dávid; Hadamitzky, Martin; Hausleiter, Jörg; Dewey, Marc; Zimmermann, Elke; Laule, Michael

2012-01-01

Objectives To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations. Design Retrospective pooled analysis of individual patient data. Setting 18 hospitals in Europe and the United States. Participants Patients with stable chest pain without evidence for previous coronary artery disease, if they were referred for computed tomography (CT) based coronary angiography or catheter based coronary angiography (indicated as low and high prevalence settings, respectively). Main outcome measures Obstructive coronary artery disease (≥50% diameter stenosis in at least one vessel found on catheter based coronary angiography). Multiple imputation accounted for missing predictors and outcomes, exploiting strong correlation between the two angiography procedures. Predictive models included a basic model (age, sex, symptoms, and setting), clinical model (basic model factors and diabetes, hypertension, dyslipidaemia, and smoking), and extended model (clinical model factors and use of the CT based coronary calcium score). We assessed discrimination (c statistic), calibration, and continuous net reclassification improvement by cross validation for the four largest low prevalence datasets separately and the smaller remaining low prevalence datasets combined. Results We included 5677 patients (3283 men, 2394 women), of whom 1634 had obstructive coronary artery disease found on catheter based coronary angiography. All potential predictors were significantly associated with the presence of disease in univariable and multivariable analyses. The clinical model improved the prediction, compared with the basic model (cross validated c statistic improvement from 0.77 to 0.79, net reclassification improvement 35%); the coronary calcium score in the extended model was a major predictor (0.79 to 0.88, 102%). Calibration for low prevalence datasets was satisfactory. Conclusions Updated prediction models including age, sex, symptoms, and cardiovascular risk factors allow for accurate estimation of the pretest probability of coronary artery disease in low prevalence populations. Addition of coronary calcium scores to the prediction models improves the estimates. PMID:22692650

Evaluating the predictability of PM10 grades in Seoul, Korea using a neural network model based on synoptic patterns.

PubMed

Hur, Sun-Kyong; Oh, Hye-Ryun; Ho, Chang-Hoi; Kim, Jinwon; Song, Chang-Keun; Chang, Lim-Seok; Lee, Jae-Bum

2016-11-01

As of November 2014, the Korean Ministry of Environment (KME) has been forecasting the concentration of particulate matter with diameters ≤ 10 μm (PM 10 ) classified into four grades: low (PM 10  ≤ 30 μg m -3 ), moderate (30 < PM 10  ≤ 80 μg m -3 ), high (80 < PM 10  ≤ 150 μg m -3 ), and very high (PM 10  > 150 μg m -3 ). The KME operational center generates PM 10 forecasts using statistical and chemistry-transport models, but the overall performance and the hit rate for the four PM 10 grades has not previously been evaluated. To provide a statistical reference for the current air quality forecasting system, we have developed a neural network model based on the synoptic patterns of several meteorological fields such as geopotential height, air temperature, relative humidity, and wind. Hindcast of the four PM 10 grades in Seoul, Korea was performed for the cold seasons (October-March) of 2001-2014 when the high and very high PM 10 grades are frequently observed. Because synoptic patterns of the meteorological fields are distinctive for each PM 10 grade, these fields were adopted and quantified as predictors in the form of cosine similarities to train the neural network model. Using these predictors in conjunction with the PM 10 concentration in Seoul from the day before prediction as an additional predictor, an overall hit rate of 69% was achieved; the hit rates for the low, moderate, high, and very high PM 10 grades were 33%, 83%, 45%, and 33%, respectively. Our findings also suggest that the synoptic patterns of meteorological variables are reliable predictors for the identification of the favorable conditions for each PM 10 grade, as well as for the transboundary transport of PM 10 from China. This evaluation of PM 10 predictability can be reliably used as a statistical reference and further, complement to the current air quality forecasting system. Copyright © 2016 Elsevier Ltd. All rights reserved.

Downscaling reanalysis data to high-resolution variables above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Mölg, Thomas; Marzeion, Ben; Kaser, Georg

2010-05-01

Recently initiated observation networks in the Cordillera Blanca provide temporally high-resolution, yet short-term atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly NCEP/NCAR reanalysis data to the local target variables, measured at the tropical glacier Artesonraju (Northern Cordillera Blanca). The approach is particular in the context of ESD for two reasons. First, the observational time series for model calibration are short (only about two years). Second, unlike most ESD studies in climate research, we focus on variables at a high temporal resolution (i.e., six-hourly values). Our target variables are two important drivers in the surface energy balance of tropical glaciers; air temperature and specific humidity. The selection of predictor fields from the reanalysis data is based on regression analyses and climatologic considerations. The ESD modelling procedure includes combined empirical orthogonal function and multiple regression analyses. Principal component screening is based on cross-validation using the Akaike Information Criterion as model selection criterion. Double cross-validation is applied for model evaluation. Potential autocorrelation in the time series is considered by defining the block length in the resampling procedure. Apart from the selection of predictor fields, the modelling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice by using both single- and mixed-field predictors of the variables air temperature (1000 hPa), specific humidity (1000 hPa), and zonal wind speed (500 hPa). The chosen downscaling domain ranges from 80 to 50 degrees west and from 0 to 20 degrees south. Statistical transfer functions are derived individually for different months and times of day (month/hour-models). The forecast skill of the month/hour-models largely depends on month and time of day, ranging from 0 to 0.8, but the mixed-field predictors generally perform better than the single-field predictors. At all time scales, the ESD model shows added value against two simple reference models; (i) the direct use of reanalysis grid point values, and (ii) mean diurnal and seasonal cycles over the calibration period. The ESD model forecast 1960 to 2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation, but is sensitive to the chosen predictor type. So far, we have not assessed the performance of NCEP/NCAR reanalysis data against other reanalysis products. The developed ESD model is computationally cheap and applicable wherever measurements are available for model calibration.

Religiousness as a Predictor of Alcohol Use in High School Students.

ERIC Educational Resources Information Center

Park, Hae-Seong; Bauer, Scott; Oescher, Jeffrey

2001-01-01

Examines the relationship between religiousness and alcohol use of adolescents based on a sample of high school seniors. Results provide support for examining religiousness variables as predictors of alcohol use patterns of adolescents. (Contains 16 references and 4 tables.) (GCP)

The no-show patient in the model family practice unit.

PubMed

Dervin, J V; Stone, D L; Beck, C H

1978-12-01

Appointment breaking by patients causes problems for the physician's office. Patients who neither keep nor cancel their appointments are often referred to as "no shows." Twenty variables were identified as potential predictors of no-show behavior. These predictors were applied to 291 Family Practice Center patients during a one-month study in April 1977. A discriminant function and multiple regression procedure were utilized ascertain the predictability of the selected variables. Predictive accuracy of the variables was 67.4 percent compared to the presently utilized constant predictor technique, which is 73 percent accurate. Modification of appointment schedules based upon utilization of the variables studies as predictors of show/no-show behavior does not appear to be an effective strategy in the Family Practice Center of the Community Hospital of Sonoma County, Santa Rosa, due to the high proportion of patients who do, in fact, show. In clinics with lower show rates, the technique may prove to be an effective strategy.

Real-time validation of the Dst Predictor model

USGS Publications Warehouse

McCollough, James P.; Young, Shawn L.; Rigler, E. Joshua; Simpson, Hal A.

2015-01-01

The Dst Predictor model, which has been running real-time in the Space Weather Analysis and Forecast System (SWAFS), provides 1-hour and 4-hour forecasts of the Dst index. This is useful for awareness of impending geomagnetic activity, as well as driving other real-time models that use Dst as an input. In this report, we examine the performance of this forecast model in detail. When validating indices it should be noted that performance is only with respect to a reference index as they are derived quantities assumed to reflect a state of the magnetosphere that cannot be directly measured. In this case U.S. Geological Survey (USGS) Definitive Dst is the reference index (Section 3). Whether or not the model better reflects the actual activity level is nearly impossible to discern and is outside the scope of this report. We evaluate the performance of the model by computing continuous predictant skill scores against USGS Definitive Dst values as “observations” (Section 4.2). The two sets of data are not well-correlated for both 1-hour and 4-hour forecasts. The Dst Predictor Prediction Efficiency for both the 1- and 4-hour forecasts suggests poor performance versus the climatological mean. However, the skill score against a nowcast persistence model is positive, suggesting value added by the Dst Predictor model. We further examine statistics for storm times (Section 4.3) with similar results: nowcast persistence performs worse than Dst Predictor.  Dst Predictor is superior to the nowcast persistence model for the metric used in this study. We recommend continued use of the DstPredictor model for 1-and4-hour Dst predictions along with active study of other Dst forecast models that do not rely on nowcast inputs (Section 6). The lack of certified requirements makes further recommendations difficult. A study of how the error in Dst translates to error in models and a better understanding of operational needs for magnetic storm warning are needed to determine such requirements. Nowcast persistence is often hard to beat for short term forecasts and specification and Dst Predictor clearly performs well against that standard (with 1-hour and 4-hour skill-scores of 0.233 and 0.485 respectively), although poor in absolute terms (with1-hourand4-hour prediction efficiencies of-64.6and-43.1, respectively).

Clinical course and prognosis of musculoskeletal pain in patients referred for physiotherapy: does pain site matter?

PubMed

de Vos Andersen, Nils-Bo; Kent, Peter; Hjort, Jakob; Christiansen, David Høyrup

2017-03-29

Danish patients with musculoskeletal disorders are commonly referred for primary care physiotherapy treatment but little is known about their general health status, pain diagnoses, clinical course and prognosis. The objectives of this study were to 1) describe the clinical course of patients with musculoskeletal disorders referred to physiotherapy, 2) identify predictors associated with a satisfactory outcome, and 3) determine the influence of the primary pain site diagnosis relative to those predictors. This was a prospective cohort study of patients (n = 2,706) newly referred because of musculoskeletal pain to 30 physiotherapy practices from January 2012 to May 2012. Data were collected via a web-based questionnaire 1-2 days prior to the first physiotherapy consultation and at 6 weeks, 3 and 6 months, from clinical records (including primary musculoskeletal symptom diagnosis based on the ICPC-2 classification system), and from national registry data. The main outcome was the Patient Acceptable Symptom State. Potential predictors were analysed using backwards step-wise selection during longitudinal Generalised Estimating Equation regression modelling. To assess the influence of pain site on these associations, primary pain site diagnosis was added to the model. Of the patients included, 66% were female and the mean age was 48 (SD 15). The percentage of patients reporting their symptoms as acceptable was 32% at 6 weeks, 43% at 3 months and 52% at 6 months. A higher probability of satisfactory outcome was associated with place of residence, being retired, no compensation claim, less frequent pain, shorter duration of pain, lower levels of disability and fear avoidance, better mental health and being a non-smoker. Primary pain site diagnosis had little influence on these associations, and was not predictive of a satisfactory outcome. Only half of the patients rated their symptoms as acceptable at 6 months. Although satisfactory outcome was difficult to predict at an individual patient level, there were a number of prognostic factors that were associated with this outcome. These factors should be considered when developing generic prediction tools to assess the probability of satisfactory outcome in musculoskeletal physiotherapy patients, because the site of pain did not affect that prognostic association.

Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2

NASA Astrophysics Data System (ADS)

Kurkcuoglu, Zeynep; Koukos, Panagiotis I.; Citro, Nevia; Trellet, Mikael E.; Rodrigues, J. P. G. L. M.; Moreira, Irina S.; Roel-Touris, Jorge; Melquiond, Adrien S. J.; Geng, Cunliang; Schaarschmidt, Jörg; Xue, Li C.; Vangone, Anna; Bonvin, A. M. J. J.

2018-01-01

We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall's Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening.

Human mobility prediction from region functions with taxi trajectories.

PubMed

Wang, Minjie; Yang, Su; Sun, Yi; Gao, Jun

2017-01-01

People in cities nowadays suffer from increasingly severe traffic jams due to less awareness of how collective human mobility is affected by urban planning. Besides, understanding how region functions shape human mobility is critical for business planning but remains unsolved so far. This study aims to discover the association between region functions and resulting human mobility. We establish a linear regression model to predict the traffic flows of Beijing based on the input referred to as bag of POIs. By solving the predictor in the sense of sparse representation, we find that the average prediction precision is over 74% and each type of POI contributes differently in the predictor, which accounts for what factors and how such region functions attract people visiting. Based on these findings, predictive human mobility could be taken into account when planning new regions and region functions.

Determinants of Self-Care in Diabetic Patients Based on Health Belief Model

PubMed Central

Dehghani-Tafti, Abbasali; Mahmoodabad, Seyed Saeed Mazloomy; Morowatisharifabad, Mohammad Ali; Ardakani, Mohammad Afkhami; Rezaeipandari, Hassan; Lotfi, Mohammad Hassan

2015-01-01

Introduction: The aim of this study was to determine self-care predictors in diabetic patients based on health belief model. Materials and Methods: The cross-sectional study was conducted on 110 diabetic patients referred to health service centers in Ardakan city, Yazd, Iran. The data was collected by a questionnaire including perceived benefits, barriers, severity, susceptibility, self-efficacy, social support, self-care behaviors and demographic variables. Results: Regularly medicine use (mean= 6.48 times per week) and shoes checking (mean= 1.17 times per week) were reported as the highest and the lowest self-care behaviors respectively. Health belief model constructs including perceived benefits, barriers, severity, susceptibility, self-efficacy and social support predicted 33.5% of the observed variance of self-care behaviors. Perceived susceptibility and self-efficacy had positive effect on self-care behavior; whereas perceived barrier’s has negative effect. Self-efficacy, perceived susceptibility and barriers were most powerful predictor respectively. Conclusion: The findings approved the efficiency of health belief model in prediction of self-care behaviors among diabetic patients. The findings realized the health belief model structure; therefore, it can be used as a framework for designing and implementing educational interventions in diabetes control plans. PMID:26156902

Predictors of quality of life: A quantitative investigation of the stress-coping model in children with asthma

PubMed Central

Peeters, Yvette; Boersma, Sandra N; Koopman, Hendrik M

2008-01-01

Background Aim of this study is to further explore predictors of health related quality of life in children with asthma using factors derived from to the extended stress-coping model. While the stress-coping model has often been used as a frame of reference in studying health related quality of life in chronic illness, few have actually tested the model in children with asthma. Method In this survey study data were obtained by means of self-report questionnaires from seventy-eight children with asthma and their parents. Based on data derived from these questionnaires the constructs of the extended stress-coping model were assessed, using regression analysis and path analysis. Results The results of both regression analysis and path analysis reveal tentative support for the proposed relationships between predictors and health related quality of life in the stress-coping model. Moreover, as indicated in the stress-coping model, HRQoL is only directly predicted by coping. Both coping strategies 'emotional reaction' (significantly) and 'avoidance' are directly related to HRQoL. Conclusion In children with asthma, the extended stress-coping model appears to be a useful theoretical framework for understanding the impact of the illness on their quality of life. Consequently, the factors suggested by this model should be taken into account when designing optimal psychosocial-care interventions. PMID:18366753

Predicting thermal reference conditions for USA streams and rivers

USGS Publications Warehouse

Hill, Ryan A.; Hawkins, Charles P.; Carlisle, Daren M.

2013-01-01

Temperature is a primary driver of the structure and function of stream ecosystems. However, the lack of stream temperature (ST) data for the vast majority of streams and rivers severely compromises our ability to describe patterns of thermal variation among streams, test hypotheses regarding the effects of temperature on macroecological patterns, and assess the effects of altered STs on ecological resources. Our goal was to develop empirical models that could: 1) quantify the effects of stream and watershed alteration (SWA) on STs, and 2) accurately and precisely predict natural (i.e., reference condition) STs in conterminous USA streams and rivers. We modeled 3 ecologically important elements of the thermal regime: mean summer, mean winter, and mean annual ST. To build reference-condition models (RCMs), we used daily mean ST data obtained from several thousand US Geological Survey temperature sites distributed across the conterminous USA and iteratively modeled ST with Random Forests to identify sites in reference condition. We first created a set of dirty models (DMs) that related STs to both natural factors (e.g., climate, watershed area, topography) and measures of SWA, i.e., reservoirs, urbanization, and agriculture. The 3 models performed well (r2 = 0.84–0.94, residual mean square error [RMSE] = 1.2–2.0°C). For each DM, we used partial dependence plots to identify SWA thresholds below which response in ST was minimal. We then used data from only the sites with upstream SWA below these thresholds to build RCMs with only natural factors as predictors (r2 = 0.87–0.95, RMSE = 1.1–1.9°C). Use of only reference-quality sites caused RCMs to suffer modest loss of predictor space and spatial coverage, but this loss was associated with parts of ST response curves that were flat and, therefore, not responsive to further variation in predictor space. We then compared predictions made with the RCMs to predictions made with the DMs with SWA set to 0. For most DMs, setting SWAs to 0 resulted in biased estimates of thermal reference condition.

Human mobility prediction from region functions with taxi trajectories

PubMed Central

Wang, Minjie; Sun, Yi; Gao, Jun

2017-01-01

People in cities nowadays suffer from increasingly severe traffic jams due to less awareness of how collective human mobility is affected by urban planning. Besides, understanding how region functions shape human mobility is critical for business planning but remains unsolved so far. This study aims to discover the association between region functions and resulting human mobility. We establish a linear regression model to predict the traffic flows of Beijing based on the input referred to as bag of POIs. By solving the predictor in the sense of sparse representation, we find that the average prediction precision is over 74% and each type of POI contributes differently in the predictor, which accounts for what factors and how such region functions attract people visiting. Based on these findings, predictive human mobility could be taken into account when planning new regions and region functions. PMID:29190708

Multi-model blending

DOEpatents

Hamann, Hendrik F.; Hwang, Youngdeok; van Kessel, Theodore G.; Khabibrakhmanov, Ildar K.; Muralidhar, Ramachandran

2016-10-18

A method and a system to perform multi-model blending are described. The method includes obtaining one or more sets of predictions of historical conditions, the historical conditions corresponding with a time T that is historical in reference to current time, and the one or more sets of predictions of the historical conditions being output by one or more models. The method also includes obtaining actual historical conditions, the actual historical conditions being measured conditions at the time T, assembling a training data set including designating the two or more set of predictions of historical conditions as predictor variables and the actual historical conditions as response variables, and training a machine learning algorithm based on the training data set. The method further includes obtaining a blended model based on the machine learning algorithm.

Empirical-statistical downscaling of reanalysis data to high-resolution air temperature and specific humidity above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, Marlis; MöLg, Thomas; Marzeion, Ben; Kaser, Georg

2010-06-01

Recently initiated observation networks in the Cordillera Blanca (Peru) provide temporally high-resolution, yet short-term, atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data to air temperature and specific humidity, measured at the tropical glacier Artesonraju (northern Cordillera Blanca). The ESD modeling procedure includes combined empirical orthogonal function and multiple regression analyses and a double cross-validation scheme for model evaluation. Apart from the selection of predictor fields, the modeling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice using both single-field and mixed-field predictors. Statistical transfer functions are derived individually for different months and times of day. The forecast skill largely depends on month and time of day, ranging from 0 to 0.8. The mixed-field predictors perform better than the single-field predictors. The ESD model shows added value, at all time scales, against simpler reference models (e.g., the direct use of reanalysis grid point values). The ESD model forecast 1960-2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation but is sensitive to the chosen predictor type.

Dynamic prediction in functional concurrent regression with an application to child growth.

PubMed

Leroux, Andrew; Xiao, Luo; Crainiceanu, Ciprian; Checkley, William

2018-04-15

In many studies, it is of interest to predict the future trajectory of subjects based on their historical data, referred to as dynamic prediction. Mixed effects models have traditionally been used for dynamic prediction. However, the commonly used random intercept and slope model is often not sufficiently flexible for modeling subject-specific trajectories. In addition, there may be useful exposures/predictors of interest that are measured concurrently with the outcome, complicating dynamic prediction. To address these problems, we propose a dynamic functional concurrent regression model to handle the case where both the functional response and the functional predictors are irregularly measured. Currently, such a model cannot be fit by existing software. We apply the model to dynamically predict children's length conditional on prior length, weight, and baseline covariates. Inference on model parameters and subject-specific trajectories is conducted using the mixed effects representation of the proposed model. An extensive simulation study shows that the dynamic functional regression model provides more accurate estimation and inference than existing methods. Methods are supported by fast, flexible, open source software that uses heavily tested smoothing techniques. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

The consequences of ignoring measurement invariance for path coefficients in structural equation models

PubMed Central

Guenole, Nigel; Brown, Anna

2014-01-01

We report a Monte Carlo study examining the effects of two strategies for handling measurement non-invariance – modeling and ignoring non-invariant items – on structural regression coefficients between latent variables measured with item response theory models for categorical indicators. These strategies were examined across four levels and three types of non-invariance – non-invariant loadings, non-invariant thresholds, and combined non-invariance on loadings and thresholds – in simple, partial, mediated and moderated regression models where the non-invariant latent variable occupied predictor, mediator, and criterion positions in the structural regression models. When non-invariance is ignored in the latent predictor, the focal group regression parameters are biased in the opposite direction to the difference in loadings and thresholds relative to the referent group (i.e., lower loadings and thresholds for the focal group lead to overestimated regression parameters). With criterion non-invariance, the focal group regression parameters are biased in the same direction as the difference in loadings and thresholds relative to the referent group. While unacceptable levels of parameter bias were confined to the focal group, bias occurred at considerably lower levels of ignored non-invariance than was previously recognized in referent and focal groups. PMID:25278911

Predictors of Intentions to Participate in Politics and Actual Political Behaviors in Young Adulthood

ERIC Educational Resources Information Center

Eckstein, Katharina; Noack, Peter; Gniewosz, Burkhard

2013-01-01

Drawing on data from a three-wave longitudinal study, the present research examined predictors of young adults' intentions to participate in politics and their actual political activities while referring to the broader assumptions of the theory of planned behavior. The analyses were based on a sample of university students from the federal state…

Symptoms Associated With Attention Deficit/Hyperactivity Disorder and Autism Spectrum Disorders in School-Aged Children Prenatally Exposed to Substances

PubMed Central

Sandtorv, Lisbeth Beate; Fevang, Silje Katrine Elgen; Nilsen, Sondre Aasen; Bøe, Tormod; Gjestad, Rolf; Haugland, Siren; Elgen, Irene Bircow

2018-01-01

Prenatal exposure to substances may influence a child’s neurodevelopment and impact on subsequent mental health. In a hospital-based population of school-aged children prenatally exposed to opiates and a number of illicit substances (n = 57), we evaluated mental health symptoms associated with attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorders (ASD) using the Swanson, Nolan, and Pelham Questionnaire, revision IV (SNAP-IV) and the Autism Spectrum Screening Questionnaire (ASSQ) and compared the scores to a reference group which comprised children from the population-based Bergen Child Study (n = 171). Prenatally exposed children had significantly higher SNAP-IV scores associated with ADHD symptoms in both areas of inattention and hyperactivity/impulsivity and also reported a higher ASSQ score related to an increased number of symptoms associated with ASD, compared with the reference group. Of tested predictors of mental health outcomes in the exposed group, the intelligence quotient was a strong predictor of most mental health outcomes, and neonatal abstinence syndrome was a predictor of inattention. In conclusion, prenatally exposed children had more mental health symptoms associated with ADHD and ASD, compared with the reference group. PMID:29618930

A stochastic model for optimizing composite predictors based on gene expression profiles.

PubMed

Ramanathan, Murali

2003-07-01

This project was done to develop a mathematical model for optimizing composite predictors based on gene expression profiles from DNA arrays and proteomics. The problem was amenable to a formulation and solution analogous to the portfolio optimization problem in mathematical finance: it requires the optimization of a quadratic function subject to linear constraints. The performance of the approach was compared to that of neighborhood analysis using a data set containing cDNA array-derived gene expression profiles from 14 multiple sclerosis patients receiving intramuscular inteferon-beta1a. The Markowitz portfolio model predicts that the covariance between genes can be exploited to construct an efficient composite. The model predicts that a composite is not needed for maximizing the mean value of a treatment effect: only a single gene is needed, but the usefulness of the effect measure may be compromised by high variability. The model optimized the composite to yield the highest mean for a given level of variability or the least variability for a given mean level. The choices that meet this optimization criteria lie on a curve of composite mean vs. composite variability plot referred to as the "efficient frontier." When a composite is constructed using the model, it outperforms the composite constructed using the neighborhood analysis method. The Markowitz portfolio model may find potential applications in constructing composite biomarkers and in the pharmacogenomic modeling of treatment effects derived from gene expression endpoints.

Forest loss in New England: A projection of recent trends

PubMed Central

Thompson, Jonathan R.; Plisinski, Joshua S.; Olofsson, Pontus; Holden, Christopher E.; Duveneck, Matthew J.

2017-01-01

New England has lost more than 350,000 ha of forest cover since 1985, marking a reversal of a two-hundred-year trend of forest expansion. We a cellular land-cover change model to project a continuation of recent trends (1990–2010) in forest loss across six New England states from 2010 to 2060. Recent trends were estimated using a continuous change detection algorithm applied to twenty years of Landsat images. We addressed three questions: (1) What would be the consequences of a continuation of the recent trends in terms of changes to New England's forest cover mosaic? (2) What social and biophysical attributes are most strongly associated with recent trends in forest loss, and how do these vary geographically? (3) How sensitive are projections of forest loss to the reference period—i.e. how do projections based on the period spanning 1990-to-2000 differ from 2000-to-2010, or from the full period, 1990-to-2010? Over the full reference period, 8201 ha yr-1 and 468 ha yr-1 of forest were lost to low- and high-density development, respectively. Forest loss was concentrated in suburban areas, particularly near Boston. Of the variables considered, 'distance to developed land' was the strongest predictor of forest loss. The next most important predictor varied geographically: 'distance to roads' ranked second in the more developed regions in the south and 'population density' ranked second in the less developed north. The importance and geographical variation in predictor variables were relatively stable between reference periods. In contrast, there was 55% more forest loss during the 1990-to-2000 reference period compared to the 2000-to-2010 period, highlighting the importance of understanding the variation in reference periods when projecting land cover change. The projection of recent trends is an important baseline scenario with implications for the management of forest ecosystems and the services they provide. PMID:29240810

Forest loss in New England: A projection of recent trends.

PubMed

Thompson, Jonathan R; Plisinski, Joshua S; Olofsson, Pontus; Holden, Christopher E; Duveneck, Matthew J

2017-01-01

New England has lost more than 350,000 ha of forest cover since 1985, marking a reversal of a two-hundred-year trend of forest expansion. We a cellular land-cover change model to project a continuation of recent trends (1990-2010) in forest loss across six New England states from 2010 to 2060. Recent trends were estimated using a continuous change detection algorithm applied to twenty years of Landsat images. We addressed three questions: (1) What would be the consequences of a continuation of the recent trends in terms of changes to New England's forest cover mosaic? (2) What social and biophysical attributes are most strongly associated with recent trends in forest loss, and how do these vary geographically? (3) How sensitive are projections of forest loss to the reference period-i.e. how do projections based on the period spanning 1990-to-2000 differ from 2000-to-2010, or from the full period, 1990-to-2010? Over the full reference period, 8201 ha yr-1 and 468 ha yr-1 of forest were lost to low- and high-density development, respectively. Forest loss was concentrated in suburban areas, particularly near Boston. Of the variables considered, 'distance to developed land' was the strongest predictor of forest loss. The next most important predictor varied geographically: 'distance to roads' ranked second in the more developed regions in the south and 'population density' ranked second in the less developed north. The importance and geographical variation in predictor variables were relatively stable between reference periods. In contrast, there was 55% more forest loss during the 1990-to-2000 reference period compared to the 2000-to-2010 period, highlighting the importance of understanding the variation in reference periods when projecting land cover change. The projection of recent trends is an important baseline scenario with implications for the management of forest ecosystems and the services they provide.

Narrative Structure and Emotional References in Parent-Child Reminiscing: Associations with Child Gender, Temperament, and the Quality of Parent-Child Interactions

ERIC Educational Resources Information Center

Bost, Kelly K.; Choi, Eunsil; Wong, Maria S.

2010-01-01

The present research examined child gender, temperament, and the quality of parent-child interactions as predictors of narrative style and references to emotion during mother-child and father-child reminiscing. Although models predicting parents' narrative styles were non-significant, results revealed significant interactions between parental…

A multimodel approach to interannual and seasonal prediction of Danube discharge anomalies

NASA Astrophysics Data System (ADS)

Rimbu, Norel; Ionita, Monica; Patrut, Simona; Dima, Mihai

2010-05-01

Interannual and seasonal predictability of Danube river discharge is investigated using three model types: 1) time series models 2) linear regression models of discharge with large-scale climate mode indices and 3) models based on stable teleconnections. All models are calibrated using discharge and climatic data for the period 1901-1977 and validated for the period 1978-2008 . Various time series models, like autoregressive (AR), moving average (MA), autoregressive and moving average (ARMA) or singular spectrum analysis and autoregressive moving average (SSA+ARMA) models have been calibrated and their skills evaluated. The best results were obtained using SSA+ARMA models. SSA+ARMA models proved to have the highest forecast skill also for other European rivers (Gamiz-Fortis et al. 2008). Multiple linear regression models using large-scale climatic mode indices as predictors have a higher forecast skill than the time series models. The best predictors for Danube discharge are the North Atlantic Oscillation (NAO) and the East Atlantic/Western Russia patterns during winter and spring. Other patterns, like Polar/Eurasian or Tropical Northern Hemisphere (TNH) are good predictors for summer and autumn discharge. Based on stable teleconnection approach (Ionita et al. 2008) we construct prediction models through a combination of sea surface temperature (SST), temperature (T) and precipitation (PP) from the regions where discharge and SST, T and PP variations are stable correlated. Forecast skills of these models are higher than forecast skills of the time series and multiple regression models. The models calibrated and validated in our study can be used for operational prediction of interannual and seasonal Danube discharge anomalies. References Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part I: intearannual predictability. J. Climate, 2484-2501, 2008. Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part II: seasonal predictability. J. Climate, 2503-2518, 2008. Ionita, M., G. Lohmann, and N. Rimbu, Prediction of spring Elbe river discharge based on stable teleconnections with global temperature and precipitation. J. Climate. 6215-6226, 2008.

Temporal patterns of variable relationships in person-oriented research: longitudinal models of configural frequency analysis.

PubMed

von Eye, Alexander; Mun, Eun Young; Bogat, G Anne

2008-03-01

This article reviews the premises of configural frequency analysis (CFA), including methods of choosing significance tests and base models, as well as protecting alpha, and discusses why CFA is a useful approach when conducting longitudinal person-oriented research. CFA operates at the manifest variable level. Longitudinal CFA seeks to identify those temporal patterns that stand out as more frequent (CFA types) or less frequent (CFA antitypes) than expected with reference to a base model. A base model that has been used frequently in CFA applications, prediction CFA, and a new base model, auto-association CFA, are discussed for analysis of cross-classifications of longitudinal data. The former base model takes the associations among predictors and among criteria into account. The latter takes the auto-associations among repeatedly observed variables into account. Application examples of each are given using data from a longitudinal study of domestic violence. It is demonstrated that CFA results are not redundant with results from log-linear modeling or multinomial regression and that, of these approaches, CFA shows particular utility when conducting person-oriented research.

Technique for ranking potential predictor layers for use in remote sensing analysis

Treesearch

Andrew Lister; Mike Hoppus; Rachel Riemann

2004-01-01

Spatial modeling using GIS-based predictor layers often requires that extraneous predictors be culled before conducting analysis. In some cases, using extraneous predictor layers might improve model accuracy but at the expense of increasing complexity and interpretability. In other cases, using extraneous layers can dilute the relationship between predictors and target...

Predictors of primary care referrals to a vascular disease prevention lifestyle program among participants in a cluster randomised trial.

PubMed

Passey, Megan E; Laws, Rachel A; Jayasinghe, Upali W; Fanaian, Mahnaz; McKenzie, Suzanne; Powell-Davies, Gawaine; Lyle, David; Harris, Mark F

2012-08-03

Cardiovascular disease accounts for a large burden of disease, but is amenable to prevention through lifestyle modification. This paper examines patient and practice predictors of referral to a lifestyle modification program (LMP) offered as part of a cluster randomised controlled trial (RCT) of prevention of vascular disease in primary care. Data from the intervention arm of a cluster RCT which recruited 36 practices through two rural and three urban primary care organisations were used. In each practice, 160 eligible high risk patients were invited to participate. Practices were randomly allocated to intervention or control groups. Intervention practice staff were trained in screening, motivational interviewing and counselling and encouraged to refer high risk patients to a LMP involving individual and group sessions. Data include patient surveys; clinical audit; practice survey on capacity for preventive care; referral records from the LMP. Predictors of referral were examined using multi-level logistic regression modelling after adjustment for confounding factors. Of 301 eligible patients, 190 (63.1%) were referred to the LMP. Independent predictors of referral were baseline BMI ≥ 25 (OR 2.87 95%CI:1.10, 7.47), physical inactivity (OR 2.90 95%CI:1.36,6.14), contemplation/preparation/action stage of change for physical activity (OR 2.75 95%CI:1.07, 7.03), rural location (OR 12.50 95%CI:1.43, 109.7) and smaller practice size (1-3 GPs) (OR 16.05 95%CI:2.74, 94.24). Providing a well-structured evidence-based lifestyle intervention, free of charge to patients, with coordination and support for referral processes resulted in over 60% of participating high risk patients being referred for disease prevention. Contrary to expectations, referrals were more frequent from rural and smaller practices suggesting that these practices may be more ready to engage with these programs. ACTRN12607000423415.

A neuroanatomical model of space-based and object-centered processing in spatial neglect.

PubMed

Pedrazzini, Elena; Schnider, Armin; Ptak, Radek

2017-11-01

Visual attention can be deployed in space-based or object-centered reference frames. Right-hemisphere damage may lead to distinct deficits of space- or object-based processing, and such dissociations are thought to underlie the heterogeneous nature of spatial neglect. Previous studies have suggested that object-centered processing deficits (such as in copying, reading or line bisection) result from damage to retro-rolandic regions while impaired spatial exploration reflects damage to more anterior regions. However, this evidence is based on small samples and heterogeneous tasks. Here, we tested a theoretical model of neglect that takes in account the space- and object-based processing and relates them to neuroanatomical predictors. One hundred and one right-hemisphere-damaged patients were examined with classic neuropsychological tests and structural brain imaging. Relations between neglect measures and damage to the temporal-parietal junction, intraparietal cortex, insula and middle frontal gyrus were examined with two structural equation models by assuming that object-centered processing (involved in line bisection and single-word reading) and space-based processing (involved in cancelation tasks) either represented a unique latent variable or two distinct variables. Of these two models the latter had better explanatory power. Damage to the intraparietal sulcus was a significant predictor of object-centered, but not space-based processing, while damage to the temporal-parietal junction predicted space-based, but not object-centered processing. Space-based processing and object-centered processing were strongly intercorrelated, indicating that they rely on similar, albeit partly dissociated processes. These findings indicate that object-centered and space-based deficits in neglect are partly independent and result from superior parietal and inferior parietal damage, respectively.

Remote sensing-based predictors improve distribution models of rare, early successional and boradleaf tree species in Utah

Treesearch

N. E. Zimmermann; T. C. Edwards; G. G. Moisen; T. S. Frescino; J. A. Blackard

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species...

Emergence and predictors of alcohol reference displays on Facebook during the first year of college

PubMed Central

Moreno, Megan A; D’Angelo, Jonathan; Kacvinsky, Lauren E.; Kerr, Bradley; Zhang, Chong; Eickhoff, Jens

2013-01-01

The purpose of this study was to investigate the emergence of displayed alcohol references on Facebook for first-year students from two universities. Graduated high school seniors who were planning to attend one of the two targeted study universities were recruited. Participants’ Facebook profiles were evaluated for displayed alcohol references at baseline and every four weeks throughout the first year of college. Profiles were categorized as Non-Displayers, Alcohol Displayers or Intoxication/Problem Drinking Displayers. Analyses included logistic regression, univariate and multivariate Cox proportional hazard analysis and multi-state Markov modeling. A total of 338 participants were recruited, 56.1% were female, 74.8% were Caucasian, and 58.8% were from University A. At baseline, 68 Facebook profiles (20.1%) included displayed alcohol references. During the first year of college, 135 (39.9%) profiles newly displayed alcohol. In multivariate Cox proportional hazard analysis, university (University B versus A, HR = 0.47, 95% CI: 0.28–0.77, p = 0.003), number of Facebook friends (HR = 1.19, 95% CI: 1.09–1.28, p < 0.001 for every 100 more friends), and average monthly status updates (HR = 1.03, 95% CI: 1.002–1.05, p = 0.033) were identified as independent predictors for new alcohol display. Findings contribute to understanding the patterns and predictors for displayed alcohol references on Facebook. PMID:24415846

Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

PubMed

Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

2015-11-01

This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A reference set of health utilities for long-term survivors of prostate cancer: population-based data from Ontario, Canada.

PubMed

Krahn, Murray D; Bremner, Karen E; Alibhai, Shabbir M H; Ni, Andy; Tomlinson, George; Laporte, Audrey; Naglie, Gary

2013-12-01

To measure quality of life (QOL) and utilities for prostate cancer (PC) patients and determine their predictors. A population-based, community-dwelling, geographically diverse sample of long-term PC survivors in Ontario, Canada, was identified from the Ontario Cancer Registry and contacted through their referring physician. Consenting patients completed questionnaires by mail: Health Utilities Index (HUI 2/3), Patient Oriented Prostate Utility Scale PORPUS-U (utility), PORPUS-P (health profile), Functional Assessment of Cancer Therapy-Prostate (FACT-P), and Prostate Cancer Index (PCI). Clinical data were obtained from chart reviews. Regression models determined the effects of a series of variables on QOL and utility. We received questionnaires and reviewed charts for 585 patients (mean age 72.6, 2-13 years postdiagnosis). Mean utility scores were as follows: PORPUS-U = 0.92, HUI2 = 0.85, and HUI3 = 0.78. Mean health profile scores were as follows: PORPUS-P = 71.7, PCI sexual, urinary, and bowel function = 23.7, 79.1, and 84.6, respectively (0 = worst, 100 = best), and FACT-P = 125.1 (0 = worst, 156 = best). In multiple regression analyses, comorbidity and PCI urinary, sexual, and bowel function were significant predictors of other QOL measures. With all variables, 32-50 % of the variance in utilities was explained. Many variables affect global QOL of PC survivors; only prostate symptoms and comorbidity have independent effects. Our model allows estimation of the effects of multiple factors on utilities. These utilities for long-term outcomes of PC and its treatment are valuable for decision/cost-effectiveness models of PC treatment.

Evaluating the performance of different predictor strategies in regression-based downscaling with a focus on glacierized mountain environments

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Nemec, Johanna

2016-04-01

This study presents first steps towards verifying the hypothesis that uncertainty in global and regional glacier mass simulations can be reduced considerably by reducing the uncertainty in the high-resolution atmospheric input data. To this aim, we systematically explore the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in geo-environmentally and climatologically very distinct settings, all within highly complex topography and in the close proximity to mountain glaciers: (1) the Vernagtbach station in the Northern European Alps (VERNAGT), (2) the Artesonraju measuring site in the tropical South American Andes (ARTESON), and (3) the Brewster measuring site in the Southern Alps of New Zealand (BREWSTER). As the large-scale predictors, ERA interim reanalysis data are used. In the applied downscaling model training and evaluation procedures, particular emphasis is put on appropriately accounting for the pitfalls of limited and/or patchy observation records that are usually the only (if at all) available data from the glacierized mountain sites. Generalized linear models and beta regression are investigated as alternatives to ordinary least squares regression for the non-Gaussian target variables. By analyzing results for the three different sites, five predictands and for different times of the year, we look for systematic improvements in the downscaling models' skill specifically obtained by (i) using predictor data at the optimum scale rather than the minimum scale of the reanalysis data, (ii) identifying the optimum predictor allocation in the vertical, and (iii) considering multiple (variable, level and/or grid point) predictor options combined with state-of-art empirical feature selection tools. First results show that in particular for air temperature, those downscaling models based on direct predictor selection show comparative skill like those models based on multiple predictors. For all other target variables, however, multiple predictor approaches can considerably outperform those models based on single predictors. Including multiple variable types emerges as the most promising predictor option (in particular for wind speed at all sites), even if the same predictor set is used across the different cases.

Predictor characteristics necessary for building a clinically useful risk prediction model: a simulation study.

PubMed

Schummers, Laura; Himes, Katherine P; Bodnar, Lisa M; Hutcheon, Jennifer A

2016-09-21

Compelled by the intuitive appeal of predicting each individual patient's risk of an outcome, there is a growing interest in risk prediction models. While the statistical methods used to build prediction models are increasingly well understood, the literature offers little insight to researchers seeking to gauge a priori whether a prediction model is likely to perform well for their particular research question. The objective of this study was to inform the development of new risk prediction models by evaluating model performance under a wide range of predictor characteristics. Data from all births to overweight or obese women in British Columbia, Canada from 2004 to 2012 (n = 75,225) were used to build a risk prediction model for preeclampsia. The data were then augmented with simulated predictors of the outcome with pre-set prevalence values and univariable odds ratios. We built 120 risk prediction models that included known demographic and clinical predictors, and one, three, or five of the simulated variables. Finally, we evaluated standard model performance criteria (discrimination, risk stratification capacity, calibration, and Nagelkerke's r 2 ) for each model. Findings from our models built with simulated predictors demonstrated the predictor characteristics required for a risk prediction model to adequately discriminate cases from non-cases and to adequately classify patients into clinically distinct risk groups. Several predictor characteristics can yield well performing risk prediction models; however, these characteristics are not typical of predictor-outcome relationships in many population-based or clinical data sets. Novel predictors must be both strongly associated with the outcome and prevalent in the population to be useful for clinical prediction modeling (e.g., one predictor with prevalence ≥20 % and odds ratio ≥8, or 3 predictors with prevalence ≥10 % and odds ratios ≥4). Area under the receiver operating characteristic curve values of >0.8 were necessary to achieve reasonable risk stratification capacity. Our findings provide a guide for researchers to estimate the expected performance of a prediction model before a model has been built based on the characteristics of available predictors.

Comparing species distribution models constructed with different subsets of environmental predictors

USGS Publications Warehouse

Bucklin, David N.; Basille, Mathieu; Benscoter, Allison M.; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.; Speroterra, Carolina; Watling, James I.

2014-01-01

Our results indicate that additional predictors have relatively minor effects on the accuracy of climate-based species distribution models and minor to moderate effects on spatial predictions. We suggest that implementing species distribution models with only climate predictors may provide an effective and efficient approach for initial assessments of environmental suitability.

Reasoning in Reference Games: Individual- vs. Population-Level Probabilistic Modeling

PubMed Central

Franke, Michael; Degen, Judith

2016-01-01

Recent advances in probabilistic pragmatics have achieved considerable success in modeling speakers’ and listeners’ pragmatic reasoning as probabilistic inference. However, these models are usually applied to population-level data, and so implicitly suggest a homogeneous population without individual differences. Here we investigate potential individual differences in Theory-of-Mind related depth of pragmatic reasoning in so-called reference games that require drawing ad hoc Quantity implicatures of varying complexity. We show by Bayesian model comparison that a model that assumes a heterogenous population is a better predictor of our data, especially for comprehension. We discuss the implications for the treatment of individual differences in probabilistic models of language use. PMID:27149675

Proposed Clinical Decision Rules to Diagnose Acute Rhinosinusitis Among Adults in Primary Care.

PubMed

Ebell, Mark H; Hansen, Jens Georg

2017-07-01

To reduce inappropriate antibiotic prescribing, we sought to develop a clinical decision rule for the diagnosis of acute rhinosinusitis and acute bacterial rhinosinusitis. Multivariate analysis and classification and regression tree (CART) analysis were used to develop clinical decision rules for the diagnosis of acute rhinosinusitis, defined using 3 different reference standards (purulent antral puncture fluid or abnormal finding on a computed tomographic (CT) scan; for acute bacterial rhinosinusitis, we used a positive bacterial culture of antral fluid). Signs, symptoms, C-reactive protein (CRP), and reference standard tests were prospectively recorded in 175 Danish patients aged 18 to 65 years seeking care for suspected acute rhinosinusitis. For each reference standard, we developed 2 clinical decision rules: a point score based on a logistic regression model and an algorithm based on a CART model. We identified low-, moderate-, and high-risk groups for acute rhinosinusitis or acute bacterial rhinosinusitis for each clinical decision rule. The point scores each had between 5 and 6 predictors, and an area under the receiver operating characteristic curve (AUROCC) between 0.721 and 0.767. For positive bacterial culture as the reference standard, low-, moderate-, and high-risk groups had a 16%, 49%, and 73% likelihood of acute bacterial rhinosinusitis, respectively. CART models had an AUROCC ranging from 0.783 to 0.827. For positive bacterial culture as the reference standard, low-, moderate-, and high-risk groups had a likelihood of acute bacterial rhinosinusitis of 6%, 31%, and 59% respectively. We have developed a series of clinical decision rules integrating signs, symptoms, and CRP to diagnose acute rhinosinusitis and acute bacterial rhinosinusitis with good accuracy. They now require prospective validation and an assessment of their effect on clinical and process outcomes. © 2017 Annals of Family Medicine, Inc.

A Deep Machine Learning Algorithm to Optimize the Forecast of Atmospherics

NASA Astrophysics Data System (ADS)

Russell, A. M.; Alliss, R. J.; Felton, B. D.

Space-based applications from imaging to optical communications are significantly impacted by the atmosphere. Specifically, the occurrence of clouds and optical turbulence can determine whether a mission is a success or a failure. In the case of space-based imaging applications, clouds produce atmospheric transmission losses that can make it impossible for an electro-optical platform to image its target. Hence, accurate predictions of negative atmospheric effects are a high priority in order to facilitate the efficient scheduling of resources. This study seeks to revolutionize our understanding of and our ability to predict such atmospheric events through the mining of data from a high-resolution Numerical Weather Prediction (NWP) model. Specifically, output from the Weather Research and Forecasting (WRF) model is mined using a Random Forest (RF) ensemble classification and regression approach in order to improve the prediction of low cloud cover over the Haleakala summit of the Hawaiian island of Maui. RF techniques have a number of advantages including the ability to capture non-linear associations between the predictors (in this case physical variables from WRF such as temperature, relative humidity, wind speed and pressure) and the predictand (clouds), which becomes critical when dealing with the complex non-linear occurrence of clouds. In addition, RF techniques are capable of representing complex spatial-temporal dynamics to some extent. Input predictors to the WRF-based RF model are strategically selected based on expert knowledge and a series of sensitivity tests. Ultimately, three types of WRF predictors are chosen: local surface predictors, regional 3D moisture predictors and regional inversion predictors. A suite of RF experiments is performed using these predictors in order to evaluate the performance of the hybrid RF-WRF technique. The RF model is trained and tuned on approximately half of the input dataset and evaluated on the other half. The RF approach is validated using in-situ observations of clouds. All of the hybrid RF-WRF experiments demonstrated here significantly outperform the base WRF local low cloud cover forecasts in terms of the probability of detection and the overall bias. In particular, RF experiments that use only regional three-dimensional moisture predictors from the WRF model produce the highest accuracy when compared to RF experiments that use local surface predictors only or regional inversion predictors only. Furthermore, adding multiple types of WRF predictors and additional WRF predictors to the RF algorithm does not necessarily add more value in the resulting forecasts, indicating that it is better to have a small set of meaningful predictors than to have a vast set of indiscriminately-chosen predictors. This work also reveals that the WRF-based RF approach is highly sensitive to the time period over which the algorithm is trained and evaluated. Future work will focus on developing a similar WRF-based RF model for high cloud prediction and expanding the algorithm to two-dimensions horizontally.

Validation of prediction models: examining temporal and geographic stability of baseline risk and estimated covariate effects

PubMed Central

Austin, Peter C.; van Klaveren, David; Vergouwe, Yvonne; Nieboer, Daan; Lee, Douglas S.; Steyerberg, Ewout W.

2018-01-01

Background Stability in baseline risk and estimated predictor effects both geographically and temporally is a desirable property of clinical prediction models. However, this issue has received little attention in the methodological literature. Our objective was to examine methods for assessing temporal and geographic heterogeneity in baseline risk and predictor effects in prediction models. Methods We studied 14,857 patients hospitalized with heart failure at 90 hospitals in Ontario, Canada, in two time periods. We focussed on geographic and temporal variation in baseline risk (intercept) and predictor effects (regression coefficients) of the EFFECT-HF mortality model for predicting 1-year mortality in patients hospitalized for heart failure. We used random effects logistic regression models for the 14,857 patients. Results The baseline risk of mortality displayed moderate geographic variation, with the hospital-specific probability of 1-year mortality for a reference patient lying between 0.168 and 0.290 for 95% of hospitals. Furthermore, the odds of death were 11% lower in the second period than in the first period. However, we found minimal geographic or temporal variation in predictor effects. Among 11 tests of differences in time for predictor variables, only one had a modestly significant P value (0.03). Conclusions This study illustrates how temporal and geographic heterogeneity of prediction models can be assessed in settings with a large sample of patients from a large number of centers at different time periods. PMID:29350215

Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation.

PubMed

Guelpa, Anina; Bevilacqua, Marta; Marini, Federico; O'Kennedy, Kim; Geladi, Paul; Manley, Marena

2015-04-15

It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predictors of 12-Months Relapse After Withdrawal Treatment in Hospitalized Patients With Chronic Migraine Associated With Medication Overuse: A Longitudinal Observational Study.

PubMed

Raggi, Alberto; Giovannetti, Ambra M; Leonardi, Matilde; Sansone, Emanuela; Schiavolin, Silvia; Curone, Marcella; Grazzi, Licia; Usai, Susanna; D'Amico, Domenico

2017-01-01

Studies addressing relapse rates conflate relapse into chronic migraine (CM) and medication overuse (MO), and the consequent need to repeat withdrawal. We aim to identify 12-months predictors of relapse into CM (based on headaches frequency) separately from occurrence of another structured withdrawal. Hospitalized patients with CM-MO under withdrawal were enrolled. Candidate predictors included demographic, disability, quality of life, depression scores, general self-efficacy, social support, headaches frequency and intensity, class of overused medications, history of withdrawal treatment in the three years prior to enrollment, attendance to emergency room (ER) between enrollment and follow-up, nonattendance to outpatient neurological examinations. Logistic regressions was used to address the significant predictors for the two outcomes. Complete data were available for 177 patients: 60 (33.9%) relapsed into CM, 38 (21.5%) underwent another withdrawal treatment. Recent history of withdrawal treatments, ER admission after discharge and high baseline BDI-II scores were significant predictors in both models. In addition to this, high baseline headache frequency predicted relapse into another withdrawal treatment. Predictors or relapse into CM and of occurrence of another withdrawal by 12-months are somehow similar. It is important to assess presence of recent previous withdrawal treatments and to plan regular follow-up afterwards, in particular for patients with high headache frequency and relevant mood disturbances: in this way, it will be more likely that situations requiring further structured withdrawal treatments can be identified before patients have to refer to ER. © 2016 American Headache Society.

Allometric Scaling and Resource Limitations Model of Total Aboveground Biomass in Forest Stands: Site-scale Test of Model

NASA Astrophysics Data System (ADS)

CHOI, S.; Shi, Y.; Ni, X.; Simard, M.; Myneni, R. B.

2013-12-01

Sparseness in in-situ observations has precluded the spatially explicit and accurate mapping of forest biomass. The need for large-scale maps has raised various approaches implementing conjugations between forest biomass and geospatial predictors such as climate, forest type, soil property, and topography. Despite the improved modeling techniques (e.g., machine learning and spatial statistics), a common limitation is that biophysical mechanisms governing tree growth are neglected in these black-box type models. The absence of a priori knowledge may lead to false interpretation of modeled results or unexplainable shifts in outputs due to the inconsistent training samples or study sites. Here, we present a gray-box approach combining known biophysical processes and geospatial predictors through parametric optimizations (inversion of reference measures). Total aboveground biomass in forest stands is estimated by incorporating the Forest Inventory and Analysis (FIA) and Parameter-elevation Regressions on Independent Slopes Model (PRISM). Two main premises of this research are: (a) The Allometric Scaling and Resource Limitations (ASRL) theory can provide a relationship between tree geometry and local resource availability constrained by environmental conditions; and (b) The zeroth order theory (size-frequency distribution) can expand individual tree allometry into total aboveground biomass at the forest stand level. In addition to the FIA estimates, two reference maps from the National Biomass and Carbon Dataset (NBCD) and U.S. Forest Service (USFS) were produced to evaluate the model. This research focuses on a site-scale test of the biomass model to explore the robustness of predictors, and to potentially improve models using additional geospatial predictors such as climatic variables, vegetation indices, soil properties, and lidar-/radar-derived altimetry products (or existing forest canopy height maps). As results, the optimized ASRL estimates satisfactorily resemble the FIA aboveground biomass in terms of data distribution, overall agreement, and spatial similarity across scales. Uncertainties are quantified (ranged from 0.2 to 0.4) by taking into account the spatial mismatch (FIA plot vs. PRISM grid), heterogeneity (species composition), and an example bias scenario (= 0.2) in the root system extents.

Using Design-Based Latent Growth Curve Modeling with Cluster-Level Predictor to Address Dependency

ERIC Educational Resources Information Center

Wu, Jiun-Yu; Kwok, Oi-Man; Willson, Victor L.

2014-01-01

The authors compared the effects of using the true Multilevel Latent Growth Curve Model (MLGCM) with single-level regular and design-based Latent Growth Curve Models (LGCM) with or without the higher-level predictor on various criterion variables for multilevel longitudinal data. They found that random effect estimates were biased when the…

How to predict a high rate of inappropriateness for upper endoscopy in an endoscopic centre?

PubMed

Buri, L; Bersani, G; Hassan, C; Anti, M; Bianco, M A; Cipolletta, L; Di Giulio, E; Di Matteo, G; Familiari, L; Ficano, L; Loriga, P; Morini, S; Pietropaolo, V; Zambelli, A; Grossi, E; Intraligi, M; Tessari, F; Buscema, M

2010-09-01

Inappropriateness of upper endoscopy (EGD) indication causes decreased diagnostic yield. Our aim of was to identify predictors of appropriateness rate for EGD among endoscopic centres. A post-hoc analysis of two multicentre cross-sectional studies, including 6270 and 8252 patients consecutively referred to EGD in 44 (group A) and 55 (group B) endoscopic Italian centres in 2003 and 2007, respectively, was performed. A multiple forward stepwise regression was applied to group A, and independently validated in group B. A <70% threshold was adopted to define inadequate appropriateness rate clustered by centre. discrete variability of clustered appropriateness rates among the 44 group A centres was observed (median: 77%; range: 41-97%), and a <70% appropriateness rate was detected in 11 (25%). Independent predictors of centre appropriateness rate were: percentage of patients referred by general practitioners (GP), rate of urgent examinations, prevalence of relevant diseases, and academic status. For group B, sensitivity, specificity and area under receiver operating characteristic curve of the model in detecting centres with a <70% appropriateness rate were 54%, 93% and 0.72, respectively. A simple predictive rule, based on rate of patients referred by GPs, rate of urgent examinations, prevalence of relevant diseases and academic status, identified a small subset of centres characterised by a high rate of inappropriateness. These centres may be presumed to obtain the largest benefit from targeted educational programs. Copyright (c) 2010 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Predictors of work-related sensitisation, allergic rhinitis and asthma in early work life.

PubMed

Kellberger, Jessica; Peters-Weist, Astrid S; Heinrich, Sabine; Pfeiffer, Susanne; Vogelberg, Christian; Roller, Diana; Genuneit, Jon; Weinmayr, Gudrun; von Mutius, Erika; Heumann, Christian; Nowak, Dennis; Radon, Katja

2014-09-01

Although work-related asthma and allergies are a huge burden for society, investigation of occupational exposures in early work life using an unexposed reference group is rare. Thus, the present analyses aimed to assess the potential impact of occupational exposure and other risk factors on the prevalence of work-related sensitisation and incidence of allergic rhinitis/asthma using a population-based approach and taking into account an unexposed reference group. In SOLAR (Study on Occupational Allergy Risks) II, German participants of ISAAC (International Study of Asthma and Allergies in Childhood) phase II were followed from childhood (9-11 years) until early adulthood (19-24 years). Data on 1570 participants were available to fit predictive models. Occupational exposure was not statistically significantly associated with disease prevalence/incidence. Sensitisation in childhood, parental asthma, environmental tobacco smoke exposure during puberty, sex and study location were statistically significant predictors of outcome. Our results indicate that occupational exposure is of little relevance for work-related sensitisation prevalence and allergic rhinitis/asthma incidence in early work life, while other risk factors can be used to improve career guidance for adolescents. Further research on the role of a potential healthy hire effect and the impact of longer exposure duration is needed. ©ERS 2014.

Predictors of Outcome in Traumatic Brain Injury: New Insight Using Receiver Operating Curve Indices and Bayesian Network Analysis.

PubMed

Zador, Zsolt; Sperrin, Matthew; King, Andrew T

2016-01-01

Traumatic brain injury remains a global health problem. Understanding the relative importance of outcome predictors helps optimize our treatment strategies by informing assessment protocols, clinical decisions and trial designs. In this study we establish importance ranking for outcome predictors based on receiver operating indices to identify key predictors of outcome and create simple predictive models. We then explore the associations between key outcome predictors using Bayesian networks to gain further insight into predictor importance. We analyzed the corticosteroid randomization after significant head injury (CRASH) trial database of 10008 patients and included patients for whom demographics, injury characteristics, computer tomography (CT) findings and Glasgow Outcome Scale (GCS) were recorded (total of 13 predictors, which would be available to clinicians within a few hours following the injury in 6945 patients). Predictions of clinical outcome (death or severe disability at 6 months) were performed using logistic regression models with 5-fold cross validation. Predictive performance was measured using standardized partial area (pAUC) under the receiver operating curve (ROC) and we used Delong test for comparisons. Variable importance ranking was based on pAUC targeted at specificity (pAUCSP) and sensitivity (pAUCSE) intervals of 90-100%. Probabilistic associations were depicted using Bayesian networks. Complete AUC analysis showed very good predictive power (AUC = 0.8237, 95% CI: 0.8138-0.8336) for the complete model. Specificity focused importance ranking highlighted age, pupillary, motor responses, obliteration of basal cisterns/3rd ventricle and midline shift. Interestingly when targeting model sensitivity, the highest-ranking variables were age, severe extracranial injury, verbal response, hematoma on CT and motor response. Simplified models, which included only these key predictors, had similar performance (pAUCSP = 0.6523, 95% CI: 0.6402-0.6641 and pAUCSE = 0.6332, 95% CI: 0.62-0.6477) compared to the complete models (pAUCSP = 0.6664, 95% CI: 0.6543-0.679, pAUCSE = 0.6436, 95% CI: 0.6289-0.6585, de Long p value 0.1165 and 0.3448 respectively). Bayesian networks showed the predictors that did not feature in the simplified models were associated with those that did. We demonstrate that importance based variable selection allows simplified predictive models to be created while maintaining prediction accuracy. Variable selection targeting specificity confirmed key components of clinical assessment in TBI whereas sensitivity based ranking suggested extracranial injury as one of the important predictors. These results help refine our approach to head injury assessment, decision-making and outcome prediction targeted at model sensitivity and specificity. Bayesian networks proved to be a comprehensive tool for depicting probabilistic associations for key predictors giving insight into why the simplified model has maintained accuracy.

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing.

PubMed

Van Hertem, T; Bahr, C; Schlageter Tello, A; Viazzi, S; Steensels, M; Romanini, C E B; Lokhorst, C; Maltz, E; Halachmi, I; Berckmans, D

2016-09-01

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

Predictors of Teachers' Intention to Refer Students with ADHD to Mental Health Professionals: Comparison of U.S. and South Korea

ERIC Educational Resources Information Center

Lee, Ji-yeon

2014-01-01

A teacher's intention to refer students to mental health professionals is important to the early identification of attention-deficit/hyperactivity disorder (ADHD) and prevention of further problems. The theory of planned behavior (TPB) was used to determine the strongest belief-related predictors of a teacher's intentions to refer students with…

Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine

NASA Astrophysics Data System (ADS)

Xing, Pengwei; Su, Ran; Guo, Fei; Wei, Leyi

2017-04-01

N6-methyladenosine (m6A) refers to methylation of the adenosine nucleotide acid at the nitrogen-6 position. It plays an important role in a series of biological processes, such as splicing events, mRNA exporting, nascent mRNA synthesis, nuclear translocation and translation process. Numerous experiments have been done to successfully characterize m6A sites within sequences since high-resolution mapping of m6A sites was established. However, as the explosive growth of genomic sequences, using experimental methods to identify m6A sites are time-consuming and expensive. Thus, it is highly desirable to develop fast and accurate computational identification methods. In this study, we propose a sequence-based predictor called RAM-NPPS for identifying m6A sites within RNA sequences, in which we present a novel feature representation algorithm based on multi-interval nucleotide pair position specificity, and use support vector machine classifier to construct the prediction model. Comparison results show that our proposed method outperforms the state-of-the-art predictors on three benchmark datasets across the three species, indicating the effectiveness and robustness of our method. Moreover, an online webserver implementing the proposed predictor has been established at http://server.malab.cn/RAM-NPPS/. It is anticipated to be a useful prediction tool to assist biologists to reveal the mechanisms of m6A site functions.

Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data

NASA Astrophysics Data System (ADS)

DeGrandchamp, Joseph B.; Whisenant, Jennifer G.; Arlinghaus, Lori R.; Abramson, V. G.; Yankeelov, Thomas E.; Cárdenas-Rodríguez, Julio

2016-03-01

The pharmacokinetic parameters derived from dynamic contrast enhanced (DCE) MRI have shown promise as biomarkers for tumor response to therapy. However, standard methods of analyzing DCE MRI data (Tofts model) require high temporal resolution, high signal-to-noise ratio (SNR), and the Arterial Input Function (AIF). Such models produce reliable biomarkers of response only when a therapy has a large effect on the parameters. We recently reported a method that solves the limitations, the Linear Reference Region Model (LRRM). Similar to other reference region models, the LRRM needs no AIF. Additionally, the LRRM is more accurate and precise than standard methods at low SNR and slow temporal resolution, suggesting LRRM-derived biomarkers could be better predictors. Here, the LRRM, Non-linear Reference Region Model (NRRM), Linear Tofts model (LTM), and Non-linear Tofts Model (NLTM) were used to estimate the RKtrans between muscle and tumor (or the Ktrans for Tofts) and the tumor kep,TOI for 39 breast cancer patients who received neoadjuvant chemotherapy (NAC). These parameters and the receptor statuses of each patient were used to construct cross-validated predictive models to classify patients as complete pathological responders (pCR) or non-complete pathological responders (non-pCR) to NAC. Model performance was evaluated using area under the ROC curve (AUC). The AUC for receptor status alone was 0.62, while the best performance using predictors from the LRRM, NRRM, LTM, and NLTM were AUCs of 0.79, 0.55, 0.60, and 0.59 respectively. This suggests that the LRRM can be used to predict response to NAC in breast cancer.

Predictors of early person reference development: maternal language input, attachment and neurodevelopmental markers.

PubMed

Lemche, Erwin; Joraschky, Peter; Klann-Delius, Gisela

2013-12-01

In a longitudinal natural language development study in Germany, the acquisition of verbal symbols for present persons, absent persons, inanimate things and the mother-toddler dyad was investigated. Following the notion that verbal referent use is more developed in ostensive contexts, symbolic play situations were coded for verbal person reference by means of noun and pronoun use. Depending on attachment classifications at twelve months of age, effects of attachment classification and maternal language input were studied up to 36 months in four time points. Hierarchical regression analyses revealed that, except for mother absence, maternal verbal referent input rates at 17 and 36 months were stronger predictors for all referent types than any of the attachment organizations, or any other social or biological predictor variable. Attachment effects accounted for up to 9.8% of unique variance proportions in the person reference variables. Perinatal and familial measures predicted person references dependent on reference type. The results of this investigation indicate that mother-reference, self-reference and thing-reference develop in similar quantities measured from the 17-month time point, but are dependent of attachment quality. Copyright © 2013 Elsevier Inc. All rights reserved.

The prognostic value of standardized reference values for speckle-tracking global longitudinal strain in hypertrophic cardiomyopathy.

PubMed

Hartlage, Gregory R; Kim, Jonathan H; Strickland, Patrick T; Cheng, Alan C; Ghasemzadeh, Nima; Pernetz, Maria A; Clements, Stephen D; Williams, B Robinson

2015-03-01

Speckle-tracking left ventricular global longitudinal strain (GLS) assessment may provide substantial prognostic information for hypertrophic cardiomyopathy (HCM) patients. Reference values for GLS have been recently published. We aimed to evaluate the prognostic value of standardized reference values for GLS in HCM patients. An analysis of HCM clinic patients who underwent GLS was performed. GLS was defined as normal (more negative or equal to -16%) and abnormal (less negative than -16%) based on recently published reference values. Patients were followed for a composite of events including heart failure hospitalization, sustained ventricular arrhythmia, and all-cause death. The power of GLS to predict outcomes was assessed relative to traditional clinical and echocardiographic variables present in HCM. 79 HCM patients were followed for a median of 22 months (interquartile range 9-30 months) after imaging. During follow-up, 15 patients (19%) met the primary outcome. Abnormal GLS was the only echocardiographic variable independently predictive of the primary outcome [multivariate Hazard ratio 5.05 (95% confidence interval 1.09-23.4, p = 0.038)]. When combined with traditional clinical variables, abnormal GLS remained independently predictive of the primary outcome [multivariate Hazard ratio 5.31 (95 % confidence interval 1.18-24, p = 0.030)]. In a model including the strongest clinical and echocardiographic predictors of the primary outcome, abnormal GLS demonstrated significant incremental benefit for risk stratification [net reclassification improvement 0.75 (95 % confidence interval 0.21-1.23, p < 0.0001)]. Abnormal GLS is an independent predictor of adverse outcomes in HCM patients. Standardized use of GLS may provide significant incremental value over traditional variables for risk stratification.

A Pre-Screening Questionnaire to Predict Non-24-Hour Sleep-Wake Rhythm Disorder (N24HSWD) among the Blind

PubMed Central

Flynn-Evans, Erin E.; Lockley, Steven W.

2016-01-01

Study Objectives: There is currently no questionnaire-based pre-screening tool available to detect non-24-hour sleep-wake rhythm disorder (N24HSWD) among blind patients. Our goal was to develop such a tool, derived from gold standard, objective hormonal measures of circadian entrainment status, for the detection of N24HSWD among those with visual impairment. Methods: We evaluated the contribution of 40 variables in their ability to predict N24HSWD among 127 blind women, classified using urinary 6-sulfatoxymelatonin period, an objective marker of circadian entrainment status in this population. We subjected the 40 candidate predictors to 1,000 bootstrapped iterations of a logistic regression forward selection model to predict N24HSWD, with model inclusion set at the p < 0.05 level. We removed any predictors that were not selected at least 1% of the time in the 1,000 bootstrapped models and applied a second round of 1,000 bootstrapped logistic regression forward selection models to the remaining 23 candidate predictors. We included all questions that were selected at least 10% of the time in the final model. We subjected the selected predictors to a final logistic regression model to predict N24SWD over 1,000 bootstrapped models to calculate the concordance statistic and adjusted optimism of the final model. We used this information to generate a predictive model and determined the sensitivity and specificity of the model. Finally, we applied the model to a cohort of 1,262 blind women who completed the survey, but did not collect urine samples. Results: The final model consisted of eight questions. The concordance statistic, adjusted for bootstrapping, was 0.85. The positive predictive value was 88%, the negative predictive value was 79%. Applying this model to our larger dataset of women, we found that 61% of those without light perception, and 27% with some degree of light perception, would be referred for further screening for N24HSWD. Conclusions: Our model has predictive utility sufficient to serve as a pre-screening questionnaire for N24HSWD among the blind. Citation: Flynn-Evans EE, Lockley SW. A pre-screening questionnaire to predict non-24-hour sleep-wake rhythm disorder (N24HSWD) among the blind. J Clin Sleep Med 2016;12(5):703–710. PMID:26951421

Clinical prediction models for mortality and functional outcome following ischemic stroke: A systematic review and meta-analysis

PubMed Central

Crayton, Elise; Wolfe, Charles; Douiri, Abdel

2018-01-01

Objective We aim to identify and critically appraise clinical prediction models of mortality and function following ischaemic stroke. Methods Electronic databases, reference lists, citations were searched from inception to September 2015. Studies were selected for inclusion, according to pre-specified criteria and critically appraised by independent, blinded reviewers. The discrimination of the prediction models was measured by the area under the curve receiver operating characteristic curve or c-statistic in random effects meta-analysis. Heterogeneity was measured using I2. Appropriate appraisal tools and reporting guidelines were used in this review. Results 31395 references were screened, of which 109 articles were included in the review. These articles described 66 different predictive risk models. Appraisal identified poor methodological quality and a high risk of bias for most models. However, all models precede the development of reporting guidelines for prediction modelling studies. Generalisability of models could be improved, less than half of the included models have been externally validated(n = 27/66). 152 predictors of mortality and 192 predictors and functional outcome were identified. No studies assessing ability to improve patient outcome (model impact studies) were identified. Conclusions Further external validation and model impact studies to confirm the utility of existing models in supporting decision-making is required. Existing models have much potential. Those wishing to predict stroke outcome are advised to build on previous work, to update and adapt validated models to their specific contexts opposed to designing new ones. PMID:29377923

Defining Chlorophyll-a Reference Conditions in European Lakes

PubMed Central

Alves, Maria Helena; Argillier, Christine; van den Berg, Marcel; Buzzi, Fabio; Hoehn, Eberhard; de Hoyos, Caridad; Karottki, Ivan; Laplace-Treyture, Christophe; Solheim, Anne Lyche; Ortiz-Casas, José; Ott, Ingmar; Phillips, Geoff; Pilke, Ansa; Pádua, João; Remec-Rekar, Spela; Riedmüller, Ursula; Schaumburg, Jochen; Serrano, Maria Luisa; Soszka, Hanna; Tierney, Deirdre; Urbanič, Gorazd; Wolfram, Georg

2010-01-01

The concept of “reference conditions” describes the benchmark against which current conditions are compared when assessing the status of water bodies. In this paper we focus on the establishment of reference conditions for European lakes according to a phytoplankton biomass indicator—the concentration of chlorophyll-a. A mostly spatial approach (selection of existing lakes with no or minor human impact) was used to set the reference conditions for chlorophyll-a values, supplemented by historical data, paleolimnological investigations and modelling. The work resulted in definition of reference conditions and the boundary between “high” and “good” status for 15 main lake types and five ecoregions of Europe: Alpine, Atlantic, Central/Baltic, Mediterranean, and Northern. Additionally, empirical models were developed for estimating site-specific reference chlorophyll-a concentrations from a set of potential predictor variables. The results were recently formulated into the EU legislation, marking the first attempt in international water policy to move from chemical quality standards to ecological quality targets. PMID:20401659

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Marriage Meets the Joneses: Relative Income, Identity, and Marital Status

PubMed Central

Watson, Tara; McLanahan, Sara

2012-01-01

This paper investigates the effect of relative income on marriage. Accounting flexibly for absolute income, the ratio between a man's income and a local reference group median is a strong predictor of marital status, but only for low-income men. Relative income affects marriage even among those living with a partner. A ten percent higher reference group income is associated with a two percent reduction in marriage. We propose an identity model to explain the results. PMID:24639593

Results from the VALUE perfect predictor experiment: process-based evaluation

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Soares, Pedro; Hertig, Elke; Brands, Swen; Huth, Radan; Cardoso, Rita; Kotlarski, Sven; Casado, Maria; Pongracz, Rita; Bartholy, Judit

2016-04-01

Until recently, the evaluation of downscaled climate model simulations has typically been limited to surface climatologies, including long term means, spatial variability and extremes. But these aspects are often, at least partly, tuned in regional climate models to match observed climate. The tuning issue is of course particularly relevant for bias corrected regional climate models. In general, a good performance of a model for these aspects in present climate does therefore not imply a good performance in simulating climate change. It is now widely accepted that, to increase our condidence in climate change simulations, it is necessary to evaluate how climate models simulate relevant underlying processes. In other words, it is important to assess whether downscaling does the right for the right reason. Therefore, VALUE has carried out a broad process-based evaluation study based on its perfect predictor experiment simulations: the downscaling methods are driven by ERA-Interim data over the period 1979-2008, reference observations are given by a network of 85 meteorological stations covering all European climates. More than 30 methods participated in the evaluation. In order to compare statistical and dynamical methods, only variables provided by both types of approaches could be considered. This limited the analysis to conditioning local surface variables on variables from driving processes that are simulated by ERA-Interim. We considered the following types of processes: at the continental scale, we evaluated the performance of downscaling methods for positive and negative North Atlantic Oscillation, Atlantic ridge and blocking situations. At synoptic scales, we considered Lamb weather types for selected European regions such as Scandinavia, the United Kingdom, the Iberian Pensinsula or the Alps. At regional scales we considered phenomena such as the Mistral, the Bora or the Iberian coastal jet. Such process-based evaluation helps to attribute biases in surface variables to underlying processes and ultimately to improve climate models.

The role of ENSO in understanding changes in Colombia's annual malaria burden by region, 1960–2006

PubMed Central

Mantilla, Gilma; Oliveros, Hugo; Barnston, Anthony G

2009-01-01

Background Malaria remains a serious problem in Colombia. The number of malaria cases is governed by multiple climatic and non-climatic factors. Malaria control policies, and climate controls such as rainfall and temperature variations associated with the El Niño/Southern Oscillation (ENSO), have been associated with malaria case numbers. Using historical climate data and annual malaria case number data from 1960 to 2006, statistical models are developed to isolate the effects of climate in each of Colombia's five contrasting geographical regions. Methods Because year to year climate variability associated with ENSO causes interannual variability in malaria case numbers, while changes in population and institutional control policy result in more gradual trends, the chosen predictors in the models are annual indices of the ENSO state (sea surface temperature [SST] in the tropical Pacific Ocean) and time reference indices keyed to two major malaria trends during the study period. Two models were used: a Poisson and a Negative Binomial regression model. Two ENSO indices, two time reference indices, and one dummy variable are chosen as candidate predictors. The analysis was conducted using the five geographical regions to match the similar aggregation used by the National Institute of Health for its official reports. Results The Negative Binomial regression model is found better suited to the malaria cases in Colombia. Both the trend variables and the ENSO measures are significant predictors of malaria case numbers in Colombia as a whole, and in two of the five regions. A one degree Celsius change in SST (indicating a weak to moderate ENSO event) is seen to translate to an approximate 20% increase in malaria cases, holding other variables constant. Conclusion Regional differentiation in the role of ENSO in understanding changes in Colombia's annual malaria burden during 1960–2006 was found, constituting a new approach to use ENSO as a significant predictor of the malaria cases in Colombia. These results naturally point to additional needed work: (1) refining the regional and seasonal dependence of climate on the ENSO state, and of malaria on the climate variables; (2) incorporating ENSO-related climate variability into dynamic malaria models. PMID:19133152

Prediction of Self-Management Behavior among Iranian Women with Type 2 Diabetes: Application of the Theory of Reasoned Action along with Self-Efficacy (ETRA).

PubMed

Didarloo, A R; Shojaeizadeh, D; Gharaaghaji Asl, R; Habibzadeh, H; Niknami, Sh; Pourali, R

2012-02-01

Continuous performing of diabetes self-care behaviors was shown to be an effective strategy to control diabetes and to prevent or reduce its- related complications. This study aimed to investigate predictors of self-care behavior based on the extended theory of reasoned action by self efficacy (ETRA) among women with type 2 diabetes in Iran. A sample of 352 women with type 2 diabetes, referring to a Diabetes Clinic in Khoy, Iran using the nonprobability sampling was enrolled. Appropriate instruments were designed to measure the variables of interest (diabetes knowledge, personal beliefs, subjective norm, self-efficacy and behavioral intention along with self- care behaviors). Reliability and validity of the instruments using Cronbach's alpha coefficients (the values of them were more than 0.70) and a panel of experts were tested. A statistical significant correlation existed between independent constructs of proposed model and modelrelated dependent constructs, as ETRA model along with its related external factors explained 41.5% of variance of intentions and 25.3% of variance of actual behavior. Among constructs of model, self-efficacy was the strongest predictor of intentions among women with type 2 diabetes, as it lonely explained 31.3% of variance of intentions and 11.4% of variance of self-care behavior. The high ability of the extended theory of reasoned action with self-efficacy in forecasting and explaining diabetes mellitus self management can be a base for educational intervention. So to improve diabetes self management behavior and to control the disease, use of educational interventions based on proposed model is suggested.

AI-based (ANN and SVM) statistical downscaling methods for precipitation estimation under climate change scenarios

NASA Astrophysics Data System (ADS)

Mehrvand, Masoud; Baghanam, Aida Hosseini; Razzaghzadeh, Zahra; Nourani, Vahid

2017-04-01

Since statistical downscaling methods are the most largely used models to study hydrologic impact studies under climate change scenarios, nonlinear regression models known as Artificial Intelligence (AI)-based models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been used to spatially downscale the precipitation outputs of Global Climate Models (GCMs). The study has been carried out using GCM and station data over GCM grid points located around the Peace-Tampa Bay watershed weather stations. Before downscaling with AI-based model, correlation coefficient values have been computed between a few selected large-scale predictor variables and local scale predictands to select the most effective predictors. The selected predictors are then assessed considering grid location for the site in question. In order to increase AI-based downscaling model accuracy pre-processing has been developed on precipitation time series. In this way, the precipitation data derived from various GCM data analyzed thoroughly to find the highest value of correlation coefficient between GCM-based historical data and station precipitation data. Both GCM and station precipitation time series have been assessed by comparing mean and variances over specific intervals. Results indicated that there is similar trend between GCM and station precipitation data; however station data has non-stationary time series while GCM data does not. Finally AI-based downscaling model have been applied to several GCMs with selected predictors by targeting local precipitation time series as predictand. The consequences of recent step have been used to produce multiple ensembles of downscaled AI-based models.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

USGS Publications Warehouse

Zimmermann, N.E.; Edwards, T.C.; Moisen, Gretchen G.; Frescino, T.S.; Blackard, J.A.

2007-01-01

1. Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. 2. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. 3. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. 4. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. 5. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. ?? 2007 The Authors.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

PubMed Central

ZIMMERMANN, N E; EDWARDS, T C; MOISEN, G G; FRESCINO, T S; BLACKARD, J A

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. PMID:18642470

Prediction of sensitivity to warfarin based on VKORC1 and CYP2C9 polymorphisms in patients from different places in Colombia.

PubMed

Cifuentes, Ricardo A; Murillo-Rojas, Juan; Avella-Vargas, Esperanza

2016-03-03

In the search to prevent hemorrhages associated with anticoagulant therapy, a major goal is to validate predictors of sensitivity to warfarin. However, previous studies in Colombia that included polymorphisms in the VKORC1 and CYP2C9 genes as predictors reported different algorithm performances to explain dose variations, and did not evaluate the prediction of sensitivity to warfarin.  To determine the accuracy of the pharmacogenetic analysis, which includes the CYP2C9 *2 and *3 and VKORC1 1639G>A polymorphisms in predicting patients' sensitivity to warfarin at the Hospital Militar Central, a reference center for patients born in different parts of Colombia.  Demographic and clinical data were obtained from 130 patients with stable doses of warfarin for more than two months. Next, their genotypes were obtained through a melting curve analysis. After verifying the Hardy-Weinberg equilibrium of the genotypes from the polymorphisms, a statistical analysis was done, which included multivariate and predictive approaches.  A pharmacogenetic model that explained 52.8% of dose variation (p<0.001) was built, which was only 4% above the performance resulting from the same data using the International Warfarin Pharmacogenetics Consortium algorithm. The model predicting the sensitivity achieved an accuracy of 77.8% and included age (p=0.003), polymorphisms *2 and *3 (p=0.002) and polymorphism 1639G>A (p<0.001) as predictors.  These results in a mixed population support the prediction of sensitivity to warfarin based on polymorphisms in VKORC1 and CYP2C9 as a valid approach in Colombian patients.

Impact of correlation of predictors on discrimination of risk models in development and external populations.

PubMed

Kundu, Suman; Mazumdar, Madhu; Ferket, Bart

2017-04-19

The area under the ROC curve (AUC) of risk models is known to be influenced by differences in case-mix and effect size of predictors. The impact of heterogeneity in correlation among predictors has however been under investigated. We sought to evaluate how correlation among predictors affects the AUC in development and external populations. We simulated hypothetical populations using two different methods based on means, standard deviations, and correlation of two continuous predictors. In the first approach, the distribution and correlation of predictors were assumed for the total population. In the second approach, these parameters were modeled conditional on disease status. In both approaches, multivariable logistic regression models were fitted to predict disease risk in individuals. Each risk model developed in a population was validated in the remaining populations to investigate external validity. For both approaches, we observed that the magnitude of the AUC in the development and external populations depends on the correlation among predictors. Lower AUCs were estimated in scenarios of both strong positive and negative correlation, depending on the direction of predictor effects and the simulation method. However, when adjusted effect sizes of predictors were specified in the opposite directions, increasingly negative correlation consistently improved the AUC. AUCs in external validation populations were higher or lower than in the derivation cohort, even in the presence of similar predictor effects. Discrimination of risk prediction models should be assessed in various external populations with different correlation structures to make better inferences about model generalizability.

Validation of extremes within the Perfect-Predictor Experiment of the COST Action VALUE

NASA Astrophysics Data System (ADS)

Hertig, Elke; Maraun, Douglas; Wibig, Joanna; Vrac, Mathieu; Soares, Pedro; Bartholy, Judith; Pongracz, Rita; Mares, Ileana; Gutierrez, Jose Manuel; Casanueva, Ana; Alzbutas, Robertas

2016-04-01

Extreme events are of widespread concern due to their damaging consequences on natural and anthropogenic systems. From science to applications the statistical attributes of rare and infrequent occurrence and low probability become connected with the socio-economic aspect of strong impact. Specific end-user needs regarding information about extreme events depend on the type of application, but as a joining element there is always the request for easily accessible climate change information with a clear description of their uncertainties and limitations. Within the Perfect-Predictor Experiment of the COST Action VALUE extreme indices modelled from a wide range of downscaling methods are compared to reference indices calculated from observational data. The experiment uses reference data from a selection of 86 weather stations representative of the different climates in Europe. Results are presented for temperature and precipitation extremes and include aspects of the marginal distribution as well as spell-length related aspects.

Cognitive Risk and Protective Factors for Suicidal Ideation: A Two Year Longitudinal Study in Adolescence

PubMed Central

Burke, Taylor A.; Connolly, Samantha L.; Hamilton, Jessica L.; Stange, Jonathan P.; Abramson, Lyn Y.; Alloy, Lauren B.

2015-01-01

Adolescence is a developmental period associated with heightened risk for both the onset and escalation of suicidal ideation (SI). Given that SI is a potent predictor of suicidal behavior, it is important to develop models of vulnerability for and protection against SI, particularly among young adolescents. This study examined the relative impact of several cognitive vulnerabilities, as well as protective factors, for SI among young adolescents over a 2-year interval encompassing their transition to mid-adolescence. At baseline, 324 adolescents (M=12.39 years; SD=0.63; 52.5 % female) completed measures of depressive symptoms, self-referent information processing biases, negative inferential style, and responses to negative affect. Further, the adolescents and their mothers were administered a diagnostic interview to assess current and past depressive disorders and SI. Over follow-up, adolescents and their mothers were administered the diagnostic interview every 12 months and adolescents completed a self-report measure inquiring about SI every 6 months to assess interviewer-rated and self-reported SI. Logistic regressions indicated that preferential endorsement of negative adjectives as self-referent (only among girls), rumination in response to negative affect, and a negative inferential style prospectively predicted SI. Additionally, young adolescents’ tendency to respond to negative affect with distraction and problem-solving buffered against their risk for exhibiting SI. When these factors were entered simultaneously, preferential endorsement of negative adjectives as self-referent and the use of distraction and problem-solving skills remained the only significant prospective predictors of SI. No previous studies have examined these variables as predictors of SI, thereby highlighting their potential utility in improving the predictive validity of extant models of suicide risk and resilience. PMID:26597963

Smith predictor based-sliding mode controller for integrating processes with elevated deadtime.

PubMed

Camacho, Oscar; De la Cruz, Francisco

2004-04-01

An approach to control integrating processes with elevated deadtime using a Smith predictor sliding mode controller is presented. A PID sliding surface and an integrating first-order plus deadtime model have been used to synthesize the controller. Since the performance of existing controllers with a Smith predictor decrease in the presence of modeling errors, this paper presents a simple approach to combining the Smith predictor with the sliding mode concept, which is a proven, simple, and robust procedure. The proposed scheme has a set of tuning equations as a function of the characteristic parameters of the model. For implementation of our proposed approach, computer based industrial controllers that execute PID algorithms can be used. The performance and robustness of the proposed controller are compared with the Matausek-Micić scheme for linear systems using simulations.

Severity and duration of diabetic foot ulcer (DFU) before seeking care as predictors of healing time: A retrospective cohort study

PubMed Central

Iversen, Marjolein M.; Igland, Jannicke; Østbye, Truls; Graue, Marit; Skeie, Svein; Wu, Bei; Rokne, Berit

2017-01-01

Objectives To investigate whether A) duration of ulcer before start of treatment in specialist health care, and B) severity of ulcer according to University of Texas classification system (UT) at start of treatment (baseline), are independent predictors of healing time. Methods This retrospective cohort study, based on electronic medical record data, included 105 patients from two outpatient clinics in Western Norway with a new diabetic foot ulcer during 2009–2011. The associations of duration of ulcer and ulcer severity with healing time were assessed using cumulative incidence curves and subdistribution hazard ratio estimated using competing risk regression with adjustment for potential confounders. Results Of the 105 participants, 45.7% achieved ulcer healing, 36.2% underwent amputations, 9.5% died before ulcer healing and 8.5% were lost to follow-up. Patients who were referred to specialist health care by a general practitioner ≥ 52 days after ulcer onset had a 58% (SHR 0.42, CI 0.18–0.98) decreased healing rate compared to patients who were referred earlier, in the adjusted model. High severity (grade 2/3, stage C/D) according to the UT classification system was associated with a decreased healing rate compared to low severity (grade1, stage A/B or grade 2, stage A) with SHR (95% CI) equal to 0.14 (0.05–0.43) after adjustment for referral time and other potential confounders. Conclusion Early detection and referral by both the patient and general practitioner are crucial for optimal foot ulcer healing. Ulcer grade and severity are also important predictors for healing time, and early screening to assess the severity and initiation of prompt treatment is important. PMID:28498862

Carrying capacity for species richness as context for conservation: a case study of North American birds

Treesearch

Andrew J. Hansen; Linda Bowers Phillips; Curtis H. Flather; Jim Robinson-Cox

2011-01-01

We evaluated the leading hypotheses on biophysical factors affecting species richness for Breeding Bird Survey routes from areas with little influence of human activities.We then derived a best model based on information theory, and used this model to extrapolate SK across North America based on the biophysical predictor variables. The predictor variables included the...

A model to explain at-risk/problem gambling among male and female adolescents: gender similarities and differences.

PubMed

Donati, Maria Anna; Chiesi, Francesca; Primi, Caterina

2013-02-01

This study aimed at testing a model in which cognitive, dispositional, and social factors were integrated into a single perspective as predictors of gambling behavior. We also aimed at providing further evidence of gender differences related to adolescent gambling. Participants were 994 Italian adolescents (64% Males; Mean age = 16.57). Hierarchical logistic regressions attested the predictive power of the considered factors on at-risk/problem gambling - measured by administering the South Oaks Gambling Screen-Revised for Adolescents (SOGS-RA) - in both boys and girls. Sensation seeking and superstitious thinking were consistent predictors across gender, while probabilistic reasoning ability, the perception of the economic profitability of gambling, and peer gambling behavior were found to be predictors only among male adolescents, whereas parental gambling behavior had a predictive power in female adolescents. Findings are discussed referring to practical implications for preventive efforts toward adolescents' gambling problems. Copyright © 2012 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Predictors of teachers' intention to refer students with ADHD to mental health professionals: comparison of U.S. and South Korea.

PubMed

Lee, Ji-yeon

2014-12-01

A teacher's intention to refer students to mental health professionals is important to the early identification of attention-deficit/hyperactivity disorder (ADHD) and prevention of further problems. The theory of planned behavior (TPB) was used to determine the strongest belief-related predictors of a teacher's intentions to refer students with ADHD symptoms to a mental health professional in the U.S. and South Korea. Perceived stigma and knowledge of ADHD were additional predictors in examining the role of culture in a teacher's perceptions of the public's stigma toward ADHD and a teachers' knowledge of ADHD. Cross-cultural differences exist. U.S. teachers' (n = 235) intentions to refer were predicted by all TPB variables (i.e., attitudes about referral, beliefs about whether important others would approve of making a referral, and perceived behavioral control in making a referral). However, among South Korean teachers (n = 144), behavioral control and perceived stigma were the only predictors. The results imply the importance of considering the cultural context in understanding a teacher's referral behaviors. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Factors associated with quality of life and caregiver strain amongst frail older adults referred to a community rehabilitation service: implications for service delivery.

PubMed

Comans, Tracy A; Currin, Michelle L; Brauer, Sandra G; Haines, Terry P

2011-01-01

To identify factors contributing to reduced quality of life and increased caregiver strain in an older population referred to a community rehabilitation team and to recommend service delivery models. Analytical cross-sectional study arising from baseline assessments from 107 subjects drawn from a randomised controlled trial of community rehabilitation service delivery models. A community rehabilitation team based in Brisbane, Queensland, Australia. Primary outcome variables include quality of life (EQ-5D & VAS) and Carer Strain Index. Predictor variables include participation in functional activities, history of falls, number of medications, number of co-morbidities, depression, environmental hazards, physical function and nutrition. Association between variables assessed using linear regression. Major factors contributing to reduced quality of life were having reduced participation in daily activities, depression, and having poor vision. Having poor nutrition and no longer driving also contributed to poor quality of life. The major factor contributing to increased caregiver strain was reduced participation in daily activities by the older person. Community rehabilitation services working with older populations must adopt models of care that screen for and address a wide range of factors that contribute to poor quality of life and caregiver strain.

SHORT-TERM SOLAR FLARE PREDICTION USING MULTIRESOLUTION PREDICTORS

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

Yu Daren; Huang Xin; Hu Qinghua

2010-01-20

Multiresolution predictors of solar flares are constructed by a wavelet transform and sequential feature extraction method. Three predictors-the maximum horizontal gradient, the length of neutral line, and the number of singular points-are extracted from Solar and Heliospheric Observatory/Michelson Doppler Imager longitudinal magnetograms. A maximal overlap discrete wavelet transform is used to decompose the sequence of predictors into four frequency bands. In each band, four sequential features-the maximum, the mean, the standard deviation, and the root mean square-are extracted. The multiresolution predictors in the low-frequency band reflect trends in the evolution of newly emerging fluxes. The multiresolution predictors in the high-frequencymore » band reflect the changing rates in emerging flux regions. The variation of emerging fluxes is decoupled by wavelet transform in different frequency bands. The information amount of these multiresolution predictors is evaluated by the information gain ratio. It is found that the multiresolution predictors in the lowest and highest frequency bands contain the most information. Based on these predictors, a C4.5 decision tree algorithm is used to build the short-term solar flare prediction model. It is found that the performance of the short-term solar flare prediction model based on the multiresolution predictors is greatly improved.« less

Onboard guidance system design for reusable launch vehicles in the terminal area energy management phase

NASA Astrophysics Data System (ADS)

Mu, Lingxia; Yu, Xiang; Zhang, Y. M.; Li, Ping; Wang, Xinmin

2018-02-01

A terminal area energy management (TAEM) guidance system for an unpowered reusable launch vehicle (RLV) is proposed in this paper. The mathematical model representing the RLV gliding motion is provided, followed by a transformation of extracting the required dynamics for reference profile generation. Reference longitudinal profiles are conceived based on the capability of maximum dive and maximum glide that a RLV can perform. The trajectory is obtained by iterating the motion equations at each node of altitude, where the angle of attack and the flight-path angle are regarded as regulating variables. An onboard ground-track predictor is constructed to generate the current range-to-go and lateral commands online. Although the longitudinal profile generation requires pre-processing using the RLV aerodynamics, the ground-track prediction can be executed online. This makes the guidance scheme adaptable to abnormal conditions. Finally, the guidance law is designed to track the reference commands. Numerical simulations demonstrate that the proposed guidance scheme is capable of guiding the RLV to the desired touchdown conditions.

Measuring Teacher Effectiveness through Hierarchical Linear Models: Exploring Predictors of Student Achievement and Truancy

ERIC Educational Resources Information Center

Subedi, Bidya Raj; Reese, Nancy; Powell, Randy

2015-01-01

This study explored significant predictors of student's Grade Point Average (GPA) and truancy (days absent), and also determined teacher effectiveness based on proportion of variance explained at teacher level model. We employed a two-level hierarchical linear model (HLM) with student and teacher data at level-1 and level-2 models, respectively.…

A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors

PubMed Central

Sriraam, N.

2012-01-01

Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications. PMID:22489238

A high-performance lossless compression scheme for EEG signals using wavelet transform and neural network predictors.

PubMed

Sriraam, N

2012-01-01

Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications.

Peer Group Self-Identification as a Predictor of Relational and Physical Aggression among High School Students

ERIC Educational Resources Information Center

Pokhrel, Pallav; Sussman, Steven; Black, David; Sun, Ping

2010-01-01

Background: Adolescent peer group self-identification refers to adolescents' affiliation with reputation-based peer groups such as "Goths" or "Jocks." These groups tend to vary on normative characteristics, including the group members' attitudes and behaviors. This article examined whether adolescents' baseline peer group…

Screening for Osteoporosis in Community-Dwelling Adults with Mental Retardation.

ERIC Educational Resources Information Center

Tyler, Carl V., Jr.; Snyder, Clint W.; Zyzanski, Stephen

2000-01-01

Osteoporosis screening of 107 adults, ages 40 to 60, with mental retardation who attended community-based training centers found 21 percent had osteoporosis and 34 percent had osteopenia. The most significant predictor of lower bone mineral densities were Down syndrome, mobility status, and race. (Contains references.) (Author/DB)

Factors predictive of obstructive sleep apnea in patients undergoing pre-operative evaluation for bariatric surgery and referred to a sleep laboratory for polysomnography

PubMed Central

Duarte, Ricardo Luiz de Menezes; Magalhães-da-Silveira, Flavio José

2015-01-01

Objective: To identify the main predictive factors for obtaining a diagnosis of obstructive sleep apnea (OSA) in patients awaiting bariatric surgery. Methods: Retrospective study of consecutive patients undergoing pre-operative evaluation for bariatric surgery and referred for in-laboratory polysomnography. Eight variables were evaluated: sex, age, neck circumference (NC), BMI, Epworth Sleepiness Scale (ESS) score, snoring, observed apnea, and hypertension. We employed ROC curve analysis to determine the best cut-off value for each variable and multiple linear regression to identify independent predictors of OSA severity. Results: We evaluated 1,089 patients, of whom 781 (71.7%) were female. The overall prevalence of OSA-defined as an apnea/hypopnea index (AHI) ≥ 5.0 events/h-was 74.8%. The best cut-off values for NC, BMI, age, and ESS score were 42 cm, 42 kg/m2, 37 years, and 10 points, respectively. All eight variables were found to be independent predictors of a diagnosis of OSA in general, and all but one were found to be independent predictors of a diagnosis of moderate/severe OSA (AHI ≥ 15.0 events/h), the exception being hypertension. We devised a 6-item model, designated the NO-OSAS model (NC, Obesity, Observed apnea, Snoring, Age, and Sex), with a cut-off value of ≥ 3 for identifying high-risk patients. For a diagnosis of moderate/severe OSA, the model showed 70.8% accuracy, 82.8% sensitivity, and 57.9% specificity. Conclusions: In our sample of patients awaiting bariatric surgery, there was a high prevalence of OSA. At a cut-off value of ≥ 3, the proposed 6-item model showed good accuracy for a diagnosis of moderate/severe OSA. PMID:26578136

Reference right atrial dimensions and volume estimation by steady state free precession cardiovascular magnetic resonance

PubMed Central

2013-01-01

Background Cardiovascular magnetic resonance (CMR) steady state free precession (SSFP) cine sequences with high temporal resolution and improved post-processing can accurately measure RA dimensions. We used this technique to define ranges for normal RA volumes and dimensions normalized, when necessary, to the influence of gender, body surface area (BSA) and age, and also to define the best 2D images-derived predictors of RA enlargement. Methods For definition of normal ranges of RA volume we studied 120 healthy subjects (60 men, 60 women; 20 subjects per age decile from 20 to 80 years), after careful exclusion of cardiovascular abnormality. We also studied 120 patients (60 men, 60 women; age range 20 to 80 years) with a clinical indication for CMR in order to define the best 1D and 2D predictors of RA enlargement. Data were generated from SSFP cine CMR, with 3-dimensional modeling, including tracking of the atrioventricular ring motion and time-volume curves analysis. Results In the group of healthy individuals, age influenced RA 2-chamber area and transverse diameter. Gender influenced most absolute RA dimensions and volume. Interestingly, right atrial volumes did not change with age and gender when indexed to body surface area. New CMR normal ranges for RA dimensions were modeled and displayed for clinical use with normalization for BSA and gender and display of parameter variation with age. Finally, the best 2D images-derived independent predictors of RA enlargement were indexed area and indexed longitudinal diameter in the 2-chamber view. Conclusion Reference RA dimensions and predictors of RA enlargement are provided using state-of-the-art CMR techniques. PMID:23566426

Psychosocial predictors of energy underreporting in a large doubly labeled water study.

PubMed

Tooze, Janet A; Subar, Amy F; Thompson, Frances E; Troiano, Richard; Schatzkin, Arthur; Kipnis, Victor

2004-05-01

Underreporting of energy intake is associated with self-reported diet measures and appears to be selective according to personal characteristics. Doubly labeled water is an unbiased reference biomarker for energy intake that may be used to assess underreporting. Our objective was to determine which factors are associated with underreporting of energy intake on food-frequency questionnaires (FFQs) and 24-h dietary recalls (24HRs). The study participants were 484 men and women aged 40-69 y who resided in Montgomery County, MD. Using the doubly labeled water method to measure total energy expenditure, we considered numerous psychosocial, lifestyle, and sociodemographic factors in multiple logistic regression models for prediction of the probability of underreporting on the FFQ and 24HR. In the FFQ models, fear of negative evaluation, weight-loss history, and percentage of energy from fat were the best predictors of underreporting in women (R(2) = 0.09); body mass index, comparison of activity level with that of others of the same sex and age, and eating frequency were the best predictors in men (R(2) = 0.10). In the 24HR models, social desirability, fear of negative evaluation, body mass index, percentage of energy from fat, usual activity, and variability in number of meals per day were the best predictors of underreporting in women (R(2) = 0.22); social desirability, dietary restraint, body mass index, eating frequency, dieting history, and education were the best predictors in men (R(2) = 0.25). Although the final models were significantly related to underreporting on both the FFQ and the 24HR, the amount of variation explained by these models was relatively low, especially for the FFQ.

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Model averaging and muddled multimodel inferences.

PubMed

Cade, Brian S

2015-09-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Model averaging and muddled multimodel inferences

USGS Publications Warehouse

Cade, Brian S.

2015-01-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the tstatistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly

PubMed Central

2013-01-01

Background This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network (BP-ANN) model and to compare the predictive accuracy with the linear regression model by using energy dual X-ray absorptiometry (DXA) as reference method. Methods A total of 88 Taiwanese elderly adults were recruited in this study as subjects. Linear regression equations and BP-ANN prediction equation were developed using impedances and other anthropometrics for predicting the reference FFM measured by DXA (FFMDXA) in 36 male and 26 female Taiwanese elderly adults. The FFM estimated by BIA prediction equations using traditional linear regression model (FFMLR) and BP-ANN model (FFMANN) were compared to the FFMDXA. The measuring results of an additional 26 elderly adults were used to validate than accuracy of the predictive models. Results The results showed the significant predictors were impedance, gender, age, height and weight in developed FFMLR linear model (LR) for predicting FFM (coefficient of determination, r2 = 0.940; standard error of estimate (SEE) = 2.729 kg; root mean square error (RMSE) = 2.571kg, P < 0.001). The above predictors were set as the variables of the input layer by using five neurons in the BP-ANN model (r2 = 0.987 with a SD = 1.192 kg and relatively lower RMSE = 1.183 kg), which had greater (improved) accuracy for estimating FFM when compared with linear model. The results showed a better agreement existed between FFMANN and FFMDXA than that between FFMLR and FFMDXA. Conclusion When compared the performance of developed prediction equations for estimating reference FFMDXA, the linear model has lower r2 with a larger SD in predictive results than that of BP-ANN model, which indicated ANN model is more suitable for estimating FFM. PMID:23388042

Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

NASA Astrophysics Data System (ADS)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.

Bioclimatic predictors for supporting ecological applications in the conterminous United States

USGS Publications Warehouse

O'Donnel, Michael S.; Ignizio, Drew A.

2012-01-01

The U.S. Geological Survey (USGS) has developed climate indices, referred to as bioclimatic predictors, which highlight climate conditions best related to species physiology. A set of 20 bioclimatic predictors were developed as Geographic Information Systems (GIS) continuous raster surfaces for each year between 1895 and 2009. The Parameter-elevation Regression on Independent Slopes Model (PRISM) and down-scaled PRISM data, which included both averaged multi-year and averaged monthly climate summaries, was used to develop these multi-scale bioclimatic predictors. Bioclimatic predictors capture information about annual conditions (annual mean temperature, annual precipitation, annual range in temperature and precipitation), as well as seasonal mean climate conditions and intra-year seasonality (temperature of the coldest and warmest months, precipitation of the wettest and driest quarters). Examining climate over time is useful when quantifying the effects of climate changes on species' distributions for past, current, and forecasted scenarios. These data, which have not been readily available to scientists, can provide biologists and ecologists with relevant and multi-scaled climate data to augment research on the responses of species to changing climate conditions. The relationships established between species demographics and distributions with bioclimatic predictors can inform land managers of climatic effects on species during decisionmaking processes.

Combining climatic and soil properties better predicts covers of Brazilian biomes.

PubMed

Arruda, Daniel M; Fernandes-Filho, Elpídio I; Solar, Ricardo R C; Schaefer, Carlos E G R

2017-04-01

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km 2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Combining climatic and soil properties better predicts covers of Brazilian biomes

NASA Astrophysics Data System (ADS)

Arruda, Daniel M.; Fernandes-Filho, Elpídio I.; Solar, Ricardo R. C.; Schaefer, Carlos E. G. R.

2017-04-01

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Rank-based estimation in the {ell}1-regularized partly linear model for censored outcomes with application to integrated analyses of clinical predictors and gene expression data.

PubMed

Johnson, Brent A

2009-10-01

We consider estimation and variable selection in the partial linear model for censored data. The partial linear model for censored data is a direct extension of the accelerated failure time model, the latter of which is a very important alternative model to the proportional hazards model. We extend rank-based lasso-type estimators to a model that may contain nonlinear effects. Variable selection in such partial linear model has direct application to high-dimensional survival analyses that attempt to adjust for clinical predictors. In the microarray setting, previous methods can adjust for other clinical predictors by assuming that clinical and gene expression data enter the model linearly in the same fashion. Here, we select important variables after adjusting for prognostic clinical variables but the clinical effects are assumed nonlinear. Our estimator is based on stratification and can be extended naturally to account for multiple nonlinear effects. We illustrate the utility of our method through simulation studies and application to the Wisconsin prognostic breast cancer data set.

Predictors of Sustainability of Social Programs

ERIC Educational Resources Information Center

Savaya, Riki; Spiro, Shimon E.

2012-01-01

This article presents the findings of a large scale study that tested a comprehensive model of predictors of three manifestations of sustainability: continuation, institutionalization, and duration. Based on the literature the predictors were arrayed in four groups: variables pertaining to the project, the auspice organization, the community, and…

Predicting the biological condition of streams: Use of geospatial indicators of natural and anthropogenic characteristics of watersheds

USGS Publications Warehouse

Carlisle, D.M.; Falcone, J.; Meador, M.R.

2009-01-01

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments. ?? Springer Science+Business Media B.V. 2008.

Diagnostic Stability of Autism Spectrum Disorder in Toddlers Prospectively Identified in a Community-Based Setting: Behavioural Characteristics and Predictors of Change over Time

ERIC Educational Resources Information Center

Barbaro, Josephine; Dissanayake, Cheryl

2017-01-01

Autism spectrum disorder diagnoses in toddlers have been established as accurate and stable across time in high-risk siblings and clinic-referred samples. Few studies have investigated diagnostic stability in children prospective identified in community-based settings. Furthermore, there is a dearth of evidence on the individual behaviours that…

Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests.

PubMed

Jaime-González, Carlos; Acebes, Pablo; Mateos, Ana; Mezquida, Eduardo T

2017-01-01

LiDAR technology has firmly contributed to strengthen the knowledge of habitat structure-wildlife relationships, though there is an evident bias towards flying vertebrates. To bridge this gap, we investigated and compared the performance of LiDAR and field data to model habitat preferences of wood mouse (Apodemus sylvaticus) in a Mediterranean high mountain pine forest (Pinus sylvestris). We recorded nine field and 13 LiDAR variables that were summarized by means of Principal Component Analyses (PCA). We then analyzed wood mouse's habitat preferences using three different models based on: (i) field PCs predictors, (ii) LiDAR PCs predictors; and (iii) both set of predictors in a combined model, including a variance partitioning analysis. Elevation was also included as a predictor in the three models. Our results indicate that LiDAR derived variables were better predictors than field-based variables. The model combining both data sets slightly improved the predictive power of the model. Field derived variables indicated that wood mouse was positively influenced by the gradient of increasing shrub cover and negatively affected by elevation. Regarding LiDAR data, two LiDAR PCs, i.e. gradients in canopy openness and complexity in forest vertical structure positively influenced wood mouse, although elevation interacted negatively with the complexity in vertical structure, indicating wood mouse's preferences for plots with lower elevations but with complex forest vertical structure. The combined model was similar to the LiDAR-based model and included the gradient of shrub cover measured in the field. Variance partitioning showed that LiDAR-based variables, together with elevation, were the most important predictors and that part of the variation explained by shrub cover was shared. LiDAR derived variables were good surrogates of environmental characteristics explaining habitat preferences by the wood mouse. Our LiDAR metrics represented structural features of the forest patch, such as the presence and cover of shrubs, as well as other characteristics likely including time since perturbation, food availability and predation risk. Our results suggest that LiDAR is a promising technology for further exploring habitat preferences by small mammal communities.

Continuity and Change in, and Child Predictors of, Caregiver Reported Anxiety Symptoms in Young People with Autism Spectrum Disorder: A Follow-Up Study.

PubMed

Teh, Elizabeth J; Chan, Diana Mei-En; Tan, Germaine Ke Jia; Magiati, Iliana

2017-12-01

Little is known about continuity, change and predictors of anxiety in ASD. This follow-up study investigated changes in caregiver-reported anxiety in 54 non-referred youth with ASD after 10-19 months. Earlier child predictors of later anxiety were also examined. Anxiety scores were generally stable. Time 1 ASD repetitive behavior symptoms, but not social/communication symptoms, predicted Time 2 total anxiety scores, over and above child age, gender and adaptive functioning scores, but this predictive relationship was fully mitigated by Time 1 anxiety scores when these were included as a covariate in the regression model. Exploring bi-directionality between autism and anxiety symptomatology, Time 1 anxiety scores did not predict Time 2 ASD symptoms. Preliminary clinical implications and possible future directions are discussed.

Paper-based and web-based intervention modeling experiments identified the same predictors of general practitioners' antibiotic-prescribing behavior.

PubMed

Treweek, Shaun; Bonetti, Debbie; Maclennan, Graeme; Barnett, Karen; Eccles, Martin P; Jones, Claire; Pitts, Nigel B; Ricketts, Ian W; Sullivan, Frank; Weal, Mark; Francis, Jill J

2014-03-01

To evaluate the robustness of the intervention modeling experiment (IME) methodology as a way of developing and testing behavioral change interventions before a full-scale trial by replicating an earlier paper-based IME. Web-based questionnaire and clinical scenario study. General practitioners across Scotland were invited to complete the questionnaire and scenarios, which were then used to identify predictors of antibiotic-prescribing behavior. These predictors were compared with the predictors identified in an earlier paper-based IME and used to develop a new intervention. Two hundred seventy general practitioners completed the questionnaires and scenarios. The constructs that predicted simulated behavior and intention were attitude, perceived behavioral control, risk perception/anticipated consequences, and self-efficacy, which match the targets identified in the earlier paper-based IME. The choice of persuasive communication as an intervention in the earlier IME was also confirmed. Additionally, a new intervention, an action plan, was developed. A web-based IME replicated the findings of an earlier paper-based IME, which provides confidence in the IME methodology. The interventions will now be evaluated in the next stage of the IME, a web-based randomized controlled trial. Copyright © 2014 Elsevier Inc. All rights reserved.

Electrocardiographic repolarization-related variables as predictors of coronary heart disease death in the women's health initiative study.

PubMed

Rautaharju, Pentti M; Zhang, Zhu-Ming; Vitolins, Mara; Perez, Marco; Allison, Matthew A; Greenland, Philip; Soliman, Elsayed Z

2014-07-28

We evaluated 25 repolarization-related ECG variables for the risk of coronary heart disease (CHD) death in 52 994 postmenopausal women from the Women's Health Initiative study. Hazard ratios from Cox regression were computed for subgroups of women with and without cardiovascular disease (CVD). During the average follow-up of 16.9 years, 941 CHD deaths occurred. Based on electrophysiological considerations, 2 sets of ECG variables with low correlations were considered as candidates for independent predictors of CHD death: Set 1, Ѳ(Tp|Tref), the spatial angle between T peak (Tp) and normal T reference (Tref) vectors; Ѳ(Tinit|Tterm), the angle between the initial and terminal T vectors; STJ depression in V6 and rate-adjusted QTp interval (QTpa); and Set 2, TaVR and TV1 amplitudes, heart rate, and QRS duration. Strong independent predictors with over 2-fold increased risk for CHD death in women with and without CVD were Ѳ(Tp|Tref) >42° from Set 1 and TaVR amplitude >-100 μV from Set 2. The risk for these CHD death predictors remained significant after multivariable adjustment for demographic/clinical factors. Other significant predictors for CHD death in fully adjusted risk models were Ѳ(Tinit|Tterm) >30°, TV1 >175 μV, and QRS duration >100 ms. Ѳ(Tp|Tref) angle and TaVR amplitude are associated with CHD mortality in postmenopausal women. The use of these measures to identify high-risk women for further diagnostic evaluation or more intense preventive intervention warrants further study. http://www.clinicaltrials.gov. Unique identifier: NCT00000611. © 2014 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Simple Decision-Analytic Functions of the AUC for Ruling Out a Risk Prediction Model and an Added Predictor.

PubMed

Baker, Stuart G

2018-02-01

When using risk prediction models, an important consideration is weighing performance against the cost (monetary and harms) of ascertaining predictors. The minimum test tradeoff (MTT) for ruling out a model is the minimum number of all-predictor ascertainments per correct prediction to yield a positive overall expected utility. The MTT for ruling out an added predictor is the minimum number of added-predictor ascertainments per correct prediction to yield a positive overall expected utility. An approximation to the MTT for ruling out a model is 1/[P (H(AUC model )], where H(AUC) = AUC - {½ (1-AUC)} ½ , AUC is the area under the receiver operating characteristic (ROC) curve, and P is the probability of the predicted event in the target population. An approximation to the MTT for ruling out an added predictor is 1 /[P {(H(AUC Model:2 ) - H(AUC Model:1 )], where Model 2 includes an added predictor relative to Model 1. The latter approximation requires the Tangent Condition that the true positive rate at the point on the ROC curve with a slope of 1 is larger for Model 2 than Model 1. These approximations are suitable for back-of-the-envelope calculations. For example, in a study predicting the risk of invasive breast cancer, Model 2 adds to the predictors in Model 1 a set of 7 single nucleotide polymorphisms (SNPs). Based on the AUCs and the Tangent Condition, an MTT of 7200 was computed, which indicates that 7200 sets of SNPs are needed for every correct prediction of breast cancer to yield a positive overall expected utility. If ascertaining the SNPs costs $500, this MTT suggests that SNP ascertainment is not likely worthwhile for this risk prediction.

Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks

NASA Astrophysics Data System (ADS)

Lombardo, Luigi; Saia, Sergio; Schillaci, Calogero; Mai, P. Martin; Huser, Raphaël

2018-05-01

Soil Organic Carbon (SOC) estimation is crucial to manage both natural and anthropic ecosystems and has recently been put under the magnifying glass after the Paris agreement 2016 due to its relationship with greenhouse gas. Statistical applications have dominated the SOC stock mapping at regional scale so far. However, the community has hardly ever attempted to implement Quantile Regression (QR) to spatially predict the SOC distribution. In this contribution, we test QR to estimate SOC stock (0-30 $cm$ depth) in the agricultural areas of a highly variable semi-arid region (Sicily, Italy, around 25,000 $km2$) by using topographic and remotely sensed predictors. We also compare the results with those from available SOC stock measurement. The QR models produced robust performances and allowed to recognize dominant effects among the predictors with respect to the considered quantile. This information, currently lacking, suggests that QR can discern predictor influences on SOC stock at specific sub-domains of each predictors. In this work, the predictive map generated at the median shows lower errors than those of the Joint Research Centre and International Soil Reference, and Information Centre benchmarks. The results suggest the use of QR as a comprehensive and effective method to map SOC using legacy data in agro-ecosystems. The R code scripted in this study for QR is included.

Predictors of Outcomes for African Americans in a Rehabilitation State Agency: Implications for National Policy and Practice

ERIC Educational Resources Information Center

Balcazar, Fabricio E.; Oberoi, Ashmeet K.; Suarez-Balcazar, Yolanda; Alvarado, Francisco

2012-01-01

A review of vocational rehabilitation (VR) data from a Midwestern state was conducted to identify predictors of rehabilitation outcomes for African American consumers. The database included 37,404 African Americans who were referred or self-referred over a period of five years. Logistic regression analysis indicated that except for age and…

An Active Learning Approach to Teach Advanced Multi-Predictor Modeling Concepts to Clinicians

ERIC Educational Resources Information Center

Samsa, Gregory P.; Thomas, Laine; Lee, Linda S.; Neal, Edward M.

2012-01-01

Clinicians have characteristics--high scientific maturity, low tolerance for symbol manipulation and programming, limited time outside of class--that limit the effectiveness of traditional methods for teaching multi-predictor modeling. We describe an active-learning based approach that shows particular promise for accommodating these…

Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

PubMed

Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang

2016-09-01

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for different period of year ecologists might focus on.

Crop weather models of corn and soybeans for Agrophysical Units (APU's) in Iowa using monthly meteorological predictors

NASA Technical Reports Server (NTRS)

Leduc, S. (Principal Investigator)

1982-01-01

Models based on multiple regression were developed to estimate corn and soybean yield from weather data for agrophysical units (APU) in Iowa. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for crop reporting districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU's were selected to be more homogeneous with respect crop to production than the CRDs. The APU models are quite similar to the CRD models, similar explained variation and number of predictor variables. The APU models are to be independently evaluated and compared to the previously evaluated CRD models. That comparison should indicate the preferred model area for this application, i.e., APU or CRD.

Prevalence and predictors of thyroid functional abnormalities in newly diagnosed AL amyloidosis.

PubMed

Muchtar, E; Dean, D S; Dispenzieri, A; Dingli, D; Buadi, F K; Lacy, M Q; Hayman, S R; Kapoor, P; Leung, N; Russell, S; Lust, J A; Lin, Yi; Warsame, R; Gonsalves, W; Kourelis, T V; Go, R S; Chakraborty, R; Zeldenrust, S; Kyle, R A; Rajkumar, S Vincent; Kumar, S K; Gertz, M A

2017-06-01

Data on the effect of systemic immunoglobulin light chain amyloidosis (AL amyloidosis) on thyroid function are limited. To assess the prevalence of hypothyroidism in AL amyloidosis patients and determine its predictors. 1142 newly diagnosed AL amyloidosis patients were grouped based on the thyroid-stimulating hormone (TSH) measurement at diagnosis: hypothyroid group (TSH above upper normal reference; >5 mIU L -1 ; n = 217, 19% of study participants) and euthyroid group (n = 925, 81%). Predictors for hypothyroidism were assessed in a binary multivariate model. Survival between groups was compared using the log-rank test and a multivariate analysis. Patients with hypothyroidism were older, more likely to present with renal and hepatic involvement and had a higher light chain burden compared to patients in the euthyroid group. Higher proteinuria in patients with renal involvement and lower albumin in patients with hepatic involvement were associated with hypothyroidism. In a binary logistic regression model, age ≥65 years, female sex, renal involvement, hepatic involvement, kappa light chain restriction and amiodarone use were independently associated with hypothyroidism. Ninety-three per cent of patients in the hypothyroid group with free thyroxine measurement had normal values, consistent with subclinical hypothyroidism. Patients in the hypothyroid group had a shorter survival compared to patients in the euthyroid group (4-year survival 36% vs 43%; P = 0.008), a difference that was maintained in a multivariate analysis. A significant proportion of patients with AL amyloidosis present with hypothyroidism, predominantly subclinical, which carries a survival disadvantage. Routine assessment of TSH in these patients is warranted. © 2017 The Association for the Publication of the Journal of Internal Medicine.

Graph Lasso-Based Test for Evaluating Functional Brain Connectivity in Sickle Cell Disease.

PubMed

Coloigner, Julie; Phlypo, Ronald; Coates, Thomas D; Lepore, Natasha; Wood, John C

2017-09-01

Sickle cell disease (SCD) is a vascular disorder that is often associated with recurrent ischemia-reperfusion injury, anemia, vasculopathy, and strokes. These cerebral injuries are associated with neurological dysfunction, limiting the full developing potential of the patient. However, recent large studies of SCD have demonstrated that cognitive impairment occurs even in the absence of brain abnormalities on conventional magnetic resonance imaging (MRI). These observations support an emerging consensus that brain injury in SCD is diffuse and that conventional neuroimaging often underestimates the extent of injury. In this article, we postulated that alterations in the cerebral connectivity may constitute a sensitive biomarker of SCD severity. Using functional MRI, a connectivity study analyzing the SCD patients individually was performed. First, a robust learning scheme based on graphical lasso model and Fréchet mean was used for estimating a consistent descriptor of healthy brain connectivity. Then, we tested a statistical method that provides an individual index of similarity between this healthy connectivity model and each SCD patient's connectivity matrix. Our results demonstrated that the reference connectivity model was not appropriate to model connectivity for only 4 out of 27 patients. After controlling for the gender, two separate predictors of this individual similarity index were the anemia (p = 0.02) and white matter hyperintensities (WMH) (silent stroke) (p = 0.03), so that patients with low hemoglobin level or with WMH have the least similarity to the reference connectivity model. Further studies are required to determine whether the resting-state connectivity changes reflect pathological changes or compensatory responses to chronic anemia.

Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

NASA Astrophysics Data System (ADS)

Gorbunov, Michael E.; Kirchengast, Gottfried

2018-01-01

A new reference occultation processing system (rOPS) will include a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA) retrieval in the lower troposphere and introduce (1) an empirically estimated boundary layer bias (BLB) model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2) the estimation of (residual) systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors), where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT) amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect) use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The systematic and random uncertainties are propagated from excess phase to bending angle profiles, using a perturbation approach and the wave-optical method recently introduced by Gorbunov and Kirchengast (2015), starting with estimated excess phase uncertainties. The results are encouraging and this uncertainty propagation approach combined with BLB correction enables a robust reduction and quantification of the uncertainties of excess phases and bending angles in the lower troposphere.

A Model of Contextual Motivation in Physical Education: Using Constructs from Self-Determination and Achievement Goal Theories To Predict Physical Activity Intentions.

ERIC Educational Resources Information Center

Standage, Martyn; Duda, Joan L.; Ntoumanis, Nikos

2003-01-01

Examines a study of student motivation in physical education that incorporated constructs from achievement goal and self-determination theories. Self-determined motivation was found to positively predict, whereas amotivation was a negative predictor of leisure-time physical activity intentions. (Contains 86 references and 3 tables.) (GCP)

Improving risk prediction accuracy for new soldiers in the U.S. Army by adding self-report survey data to administrative data.

PubMed

Bernecker, Samantha L; Rosellini, Anthony J; Nock, Matthew K; Chiu, Wai Tat; Gutierrez, Peter M; Hwang, Irving; Joiner, Thomas E; Naifeh, James A; Sampson, Nancy A; Zaslavsky, Alan M; Stein, Murray B; Ursano, Robert J; Kessler, Ronald C

2018-04-03

High rates of mental disorders, suicidality, and interpersonal violence early in the military career have raised interest in implementing preventive interventions with high-risk new enlistees. The Army Study to Assess Risk and Resilience in Servicemembers (STARRS) developed risk-targeting systems for these outcomes based on machine learning methods using administrative data predictors. However, administrative data omit many risk factors, raising the question whether risk targeting could be improved by adding self-report survey data to prediction models. If so, the Army may gain from routinely administering surveys that assess additional risk factors. The STARRS New Soldier Survey was administered to 21,790 Regular Army soldiers who agreed to have survey data linked to administrative records. As reported previously, machine learning models using administrative data as predictors found that small proportions of high-risk soldiers accounted for high proportions of negative outcomes. Other machine learning models using self-report survey data as predictors were developed previously for three of these outcomes: major physical violence and sexual violence perpetration among men and sexual violence victimization among women. Here we examined the extent to which this survey information increases prediction accuracy, over models based solely on administrative data, for those three outcomes. We used discrete-time survival analysis to estimate a series of models predicting first occurrence, assessing how model fit improved and concentration of risk increased when adding the predicted risk score based on survey data to the predicted risk score based on administrative data. The addition of survey data improved prediction significantly for all outcomes. In the most extreme case, the percentage of reported sexual violence victimization among the 5% of female soldiers with highest predicted risk increased from 17.5% using only administrative predictors to 29.4% adding survey predictors, a 67.9% proportional increase in prediction accuracy. Other proportional increases in concentration of risk ranged from 4.8% to 49.5% (median = 26.0%). Data from an ongoing New Soldier Survey could substantially improve accuracy of risk models compared to models based exclusively on administrative predictors. Depending upon the characteristics of interventions used, the increase in targeting accuracy from survey data might offset survey administration costs.

Evaluating physical habitat and water chemistry data from statewide stream monitoring programs to establish least-impacted conditions in Washington State

USGS Publications Warehouse

Wilmoth, Siri K.; Irvine, Kathryn M.; Larson, Chad

2015-01-01

Various GIS-generated land-use predictor variables, physical habitat metrics, and water chemistry variables from 75 reference streams and 351 randomly sampled sites throughout Washington State were evaluated for effectiveness at discriminating reference from random sites within level III ecoregions. A combination of multivariate clustering and ordination techniques were used. We describe average observed conditions for a subset of predictor variables as well as proposing statistical criteria for establishing reference conditions for stream habitat in Washington. Using these criteria, we determined whether any of the random sites met expectations for reference condition and whether any of the established reference sites failed to meet expectations for reference condition. Establishing these criteria will set a benchmark from which future data will be compared.

Neighborhood Sociodemographic Predictors of Serious Emotional Disturbance (SED) in Schools: Demonstrating a Small Area Estimation Method in the National Comorbidity Survey (NCS-A) Adolescent Supplement

PubMed Central

Alegría, Margarita; Kessler, Ronald C.; McLaughlin, Katie A.; Gruber, Michael J.; Sampson, Nancy A.; Zaslavsky, Alan M.

2014-01-01

We evaluate the precision of a model estimating school prevalence of SED using a small area estimation method based on readily-available predictors from area-level census block data and school principal questionnaires. Adolescents at 314 schools participated in the National Comorbidity Supplement, a national survey of DSM-IV disorders among adolescents. A multilevel model indicated that predictors accounted for under half of the variance in school-level SED and even less when considering block-group predictors or principal report alone. While Census measures and principal questionnaires are significant predictors of individual-level SED, associations are too weak to generate precise school-level predictions of SED prevalence. PMID:24740174

Scrutinizing Homophobia: A Model of Perception of Homosexuals in Russia.

PubMed

Gulevich, Olga A; Osin, Evgeny N; Isaenko, Nadezhda A; Brainis, Lilia M

2017-10-10

We aimed to develop and validate a model of associations of perceived threat of homosexuals with lay beliefs about causes of homosexuality, group entitativity of homosexuals, approval of social action strategies targeting homosexuals, and support for their rights using original Russian-language measures. We tested the model in two samples of social network users (n = 1,007) and student respondents (n = 292) using structural equation modeling and path analysis. Attribution of homosexuality to social causes was a positive predictor of perceived threat of homosexuals, whereas biological causes showed an inverse effect. Perceived threat predicted approval of discriminatory strategies targeting homosexuals and lack of support for their rights and fully mediated the effects of causal beliefs on these variables. Group entitativity of homosexuals was a positive predictor of perceived threat and a significant moderator of its effects on support for punishment and medical treatment of homosexuals. We discuss the findings with reference to the Russian social context.

Towards an automatic statistical model for seasonal precipitation prediction and its application to Central and South Asian headwater catchments

NASA Astrophysics Data System (ADS)

Gerlitz, Lars; Gafurov, Abror; Apel, Heiko; Unger-Sayesteh, Katy; Vorogushyn, Sergiy; Merz, Bruno

2016-04-01

Statistical climate forecast applications typically utilize a small set of large scale SST or climate indices, such as ENSO, PDO or AMO as predictor variables. If the predictive skill of these large scale modes is insufficient, specific predictor variables such as customized SST patterns are frequently included. Hence statistically based climate forecast models are either based on a fixed number of climate indices (and thus might not consider important predictor variables) or are highly site specific and barely transferable to other regions. With the aim of developing an operational seasonal forecast model, which is easily transferable to any region in the world, we present a generic data mining approach which automatically selects potential predictors from gridded SST observations and reanalysis derived large scale atmospheric circulation patterns and generates robust statistical relationships with posterior precipitation anomalies for user selected target regions. Potential predictor variables are derived by means of a cellwise correlation analysis of precipitation anomalies with gridded global climate variables under consideration of varying lead times. Significantly correlated grid cells are subsequently aggregated to predictor regions by means of a variability based cluster analysis. Finally for every month and lead time, an individual random forest based forecast model is automatically calibrated and evaluated by means of the preliminary generated predictor variables. The model is exemplarily applied and evaluated for selected headwater catchments in Central and South Asia. Particularly the for winter and spring precipitation (which is associated with westerly disturbances in the entire target domain) the model shows solid results with correlation coefficients up to 0.7, although the variability of precipitation rates is highly underestimated. Likewise for the monsoonal precipitation amounts in the South Asian target areas a certain skill of the model could be detected. The skill of the model for the dry summer season in Central Asia and the transition seasons over South Asia is found to be low. A sensitivity analysis by means on well known climate indices reveals the major large scale controlling mechanisms for the seasonal precipitation climate of each target area. For the Central Asian target areas, both, the El Nino Southern Oscillation and the North Atlantic Oscillation are identified as important controlling factors for precipitation totals during moist spring season. Drought conditions are found to be triggered by a warm ENSO phase in combination with a positive phase of the NAO. For the monsoonal summer precipitation amounts over Southern Asia, the model suggests a distinct negative response to El Nino events.

HiRel: Hybrid Automated Reliability Predictor (HARP) integrated reliability tool system, (version 7.0). Volume 1: HARP introduction and user's guide

NASA Technical Reports Server (NTRS)

Bavuso, Salvatore J.; Rothmann, Elizabeth; Dugan, Joanne Bechta; Trivedi, Kishor S.; Mittal, Nitin; Boyd, Mark A.; Geist, Robert M.; Smotherman, Mark D.

1994-01-01

The Hybrid Automated Reliability Predictor (HARP) integrated Reliability (HiRel) tool system for reliability/availability prediction offers a toolbox of integrated reliability/availability programs that can be used to customize the user's application in a workstation or nonworkstation environment. HiRel consists of interactive graphical input/output programs and four reliability/availability modeling engines that provide analytical and simulative solutions to a wide host of reliable fault-tolerant system architectures and is also applicable to electronic systems in general. The tool system was designed to be compatible with most computing platforms and operating systems, and some programs have been beta tested, within the aerospace community for over 8 years. Volume 1 provides an introduction to the HARP program. Comprehensive information on HARP mathematical models can be found in the references.

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time‐to‐Event Analysis

PubMed Central

Gong, Xiajing; Hu, Meng

2018-01-01

Abstract Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time‐to‐event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high‐dimensional data featured by a large number of predictor variables. Our results showed that ML‐based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high‐dimensional data. The prediction performances of ML‐based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML‐based methods provide a powerful tool for time‐to‐event analysis, with a built‐in capacity for high‐dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. PMID:29536640

An Adaptive Prediction-Based Approach to Lossless Compression of Floating-Point Volume Data.

PubMed

Fout, N; Ma, Kwan-Liu

2012-12-01

In this work, we address the problem of lossless compression of scientific and medical floating-point volume data. We propose two prediction-based compression methods that share a common framework, which consists of a switched prediction scheme wherein the best predictor out of a preset group of linear predictors is selected. Such a scheme is able to adapt to different datasets as well as to varying statistics within the data. The first method, called APE (Adaptive Polynomial Encoder), uses a family of structured interpolating polynomials for prediction, while the second method, which we refer to as ACE (Adaptive Combined Encoder), combines predictors from previous work with the polynomial predictors to yield a more flexible, powerful encoder that is able to effectively decorrelate a wide range of data. In addition, in order to facilitate efficient visualization of compressed data, our scheme provides an option to partition floating-point values in such a way as to provide a progressive representation. We compare our two compressors to existing state-of-the-art lossless floating-point compressors for scientific data, with our data suite including both computer simulations and observational measurements. The results demonstrate that our polynomial predictor, APE, is comparable to previous approaches in terms of speed but achieves better compression rates on average. ACE, our combined predictor, while somewhat slower, is able to achieve the best compression rate on all datasets, with significantly better rates on most of the datasets.

Seasonal precipitation forecasting for the Melbourne region using a Self-Organizing Maps approach

NASA Astrophysics Data System (ADS)

Pidoto, Ross; Wallner, Markus; Haberlandt, Uwe

2017-04-01

The Melbourne region experiences highly variable inter-annual rainfall. For close to a decade during the 2000s, below average rainfall seriously affected the environment, water supplies and agriculture. A seasonal rainfall forecasting model for the Melbourne region based on the novel approach of a Self-Organizing Map has been developed and tested for its prediction performance. Predictor variables at varying lead times were first assessed for inclusion within the model by calculating their importance via Random Forests. Predictor variables tested include the climate indices SOI, DMI and N3.4, in addition to gridded global sea surface temperature data. Five forecasting models were developed: an annual model and four seasonal models, each individually optimized for performance through Pearson's correlation r and the Nash-Sutcliffe Efficiency. The annual model showed a prediction performance of r = 0.54 and NSE = 0.14. The best seasonal model was for spring, with r = 0.61 and NSE = 0.31. Autumn was the worst performing seasonal model. The sea surface temperature data contributed fewer predictor variables compared to climate indices. Most predictor variables were supplied at a minimum lead, however some predictors were found at lead times of up to a year.

The course of major depressive disorder from childhood to young adulthood: Recovery and recurrence in a longitudinal observational study.

PubMed

Kovacs, Maria; Obrosky, Scott; George, Charles

2016-10-01

The episodic nature of major depressive disorder (MDD) in clinically referred adults has been well-characterized, particularly by the NIMH Collaborative Depression Study. Previous work has established that MDD also is episodic prior to adulthood, but no study has yet provided comprehensive information on the actual course of MDD in clinically referred juveniles. Thus, the present investigation sought to characterize recovery, recurrence, and their predictors across multiple episodes of MDD in initially 8- to 13-year-old outpatients (N=102), and to estimate freedom from morbidity ("well-time") across the years. Clinically referred youngsters with MDD were repeatedly assessed in an observational study across two decades (median follow up length: 15 years). Survival analytic techniques served to model recovery from the 1st, 2nd and 3rd lifetime episodes of MDD, the risk of developing the 2nd, 3rd, and 4th episodes, and the effects of traditional psychosocial and clinical predictors of outcomes. "Well-time" across the follow-up and its predictors also were examined. Recovery rates ranged from 96% to 100% across MDD episodes; episode lengths ranged from 6 to 7 months. Up to 72% of those recovered from the first episode of MDD had a further episode; median inter-episode intervals were about 3-5 years. No single demographic, social, or clinical variable, nor treatment, consistently predicted recovery/recurrence. Psychiatric morbidity over time derived mostly from non-affective disorders, which, however, did not alter the course of MDD. The sample was relatively small and power to detect small effects further declined with each MDD episode recurrence. Echoing findings on adults, the course of pediatric-onset MDD in this clinical sample was unequivocally episodic. Traditional course predictors had limited temporal stability, highlighting the need to examine novel predictor variables. The ongoing risk of depression episodes into the second and third decades of life suggests that prevention efforts should start in late childhood. Copyright © 2016 Elsevier B.V. All rights reserved.

Robust small area prediction for counts.

PubMed

Tzavidis, Nikos; Ranalli, M Giovanna; Salvati, Nicola; Dreassi, Emanuela; Chambers, Ray

2015-06-01

A new semiparametric approach to model-based small area prediction for counts is proposed and used for estimating the average number of visits to physicians for Health Districts in Central Italy. The proposed small area predictor can be viewed as an outlier robust alternative to the more commonly used empirical plug-in predictor that is based on a Poisson generalized linear mixed model with Gaussian random effects. Results from the real data application and from a simulation experiment confirm that the proposed small area predictor has good robustness properties and in some cases can be more efficient than alternative small area approaches. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

Predictors of visitors' intention to return to a nature-based recreation area

Treesearch

Jee In Yoon; Gerard Kyle

2010-01-01

This study explored predictors of recreationists' intention to return to Santee Cooper Country (SCC), a popular destination for angling-based tourism in South Carolina. Our hypothesized model indicated that recreationists' experience use history and place satisfaction would positively affect four dimensions of place attachment to SCC. Place attachment was...

Digital current regulator for proportional electro-hydraulic valves with unknown disturbance rejection.

PubMed

Canuto, Enrico; Acuña-Bravo, Wilber; Agostani, Marco; Bonadei, Marco

2014-07-01

Solenoid current regulation is well-known and standard in any proportional electro-hydraulic valve. The goal is to provide a wide-band transfer function from the reference to the measured current, thus making the solenoid a fast and ideal force actuator within the limits of the power supplier. The power supplier is usually a Pulse Width Modulation (PWM) amplifier fixing the voltage bound and the Nyquist frequency of the regulator. Typical analog regulators include three main terms: a feedforward channel, a proportional feedback channel and the electromotive force compensation. The latter compensation may be accomplished by integrative feedback. Here the problem is faced through a model-based design (Embedded Model Control), on the basis of a wide-band embedded model of the solenoid which includes the effect of eddy currents. To this end model parameters must be identified. The embedded model includes a stochastic disturbance dynamics capable of estimating and correcting the electromotive contribution together with parametric uncertainty, variability and state dependence. The embedded model which is fed by the measured current and the supplied voltage becomes a state predictor of the controllable and disturbance dynamics. The control law combines reference generator, state feedback and disturbance rejection to dispatch the PWM amplifier with the appropriate duty cycle. Modeling, identification and control design are outlined together with experimental result. Comparison with an existing analog regulator is also provided. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Prediction of objectively measured physical activity and sedentariness among blue-collar workers using survey questionnaires.

PubMed

Gupta, Nidhi; Heiden, Marina; Mathiassen, Svend Erik; Holtermann, Andreas

2016-05-01

We aimed at developing and evaluating statistical models predicting objectively measured occupational time spent sedentary or in physical activity from self-reported information available in large epidemiological studies and surveys. Two-hundred-and-fourteen blue-collar workers responded to a questionnaire containing information about personal and work related variables, available in most large epidemiological studies and surveys. Workers also wore accelerometers for 1-4 days measuring time spent sedentary and in physical activity, defined as non-sedentary time. Least-squares linear regression models were developed, predicting objectively measured exposures from selected predictors in the questionnaire. A full prediction model based on age, gender, body mass index, job group, self-reported occupational physical activity (OPA), and self-reported occupational sedentary time (OST) explained 63% (R (2)adjusted) of the variance of both objectively measured time spent sedentary and in physical activity since these two exposures were complementary. Single-predictor models based only on self-reported information about either OPA or OST explained 21% and 38%, respectively, of the variance of the objectively measured exposures. Internal validation using bootstrapping suggested that the full and single-predictor models would show almost the same performance in new datasets as in that used for modelling. Both full and single-predictor models based on self-reported information typically available in most large epidemiological studies and surveys were able to predict objectively measured occupational time spent sedentary or in physical activity, with explained variances ranging from 21-63%.

Small area estimation of proportions with different levels of auxiliary data.

PubMed

Chandra, Hukum; Kumar, Sushil; Aditya, Kaustav

2018-03-01

Binary data are often of interest in many small areas of applications. The use of standard small area estimation methods based on linear mixed models becomes problematic for such data. An empirical plug-in predictor (EPP) under a unit-level generalized linear mixed model with logit link function is often used for the estimation of a small area proportion. However, this EPP requires the availability of unit-level population information for auxiliary data that may not be always accessible. As a consequence, in many practical situations, this EPP approach cannot be applied. Based on the level of auxiliary information available, different small area predictors for estimation of proportions are proposed. Analytic and bootstrap approaches to estimating the mean squared error of the proposed small area predictors are also developed. Monte Carlo simulations based on both simulated and real data show that the proposed small area predictors work well for generating the small area estimates of proportions and represent a practical alternative to the above approach. The developed predictor is applied to generate estimates of the proportions of indebted farm households at district-level using debt investment survey data from India. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predictors of patient non-attendance at Improving Access to Psychological Therapy services demonstration sites

PubMed Central

Di Bona, Laura; Saxon, David; Barkham, Michael; Dent-Brown, Kim; Parry, Glenys

2014-01-01

Background Improving Access to Psychological Therapy (IAPT) services have increased the number of people with common mental health disorders receiving psychological therapy in England, but concerns remain about how equitably these services are accessed. Method Using cohort patient data (N=363) collected as part of the independent evaluation of the two demonstration sites, logistic regression was utilised to identify socio-demographic, clinical and service factors predictive of IAPT non-attendance. Results Significant predictors of IAPT first session non-attendance by patients were: lower non-risk score on the Clinical Outcomes in Routine Evaluation-Outcome Measure (CORE-OM); more frequent thoughts of “being better off dead” (derived from the CORE-OM); either a very recent onset of common mental health disorder (1 month or less) or a long term condition (more than 2 years); and site. Limitations The small sample and low response rate are limitations, as the sample may not be representative of all those referred to IAPT services. The predictive power of the logistic regression model is limited and suggests other variables not available in the dataset may also be important predictors. Conclusions The clinical characteristics of risk to self, severity of emotional distress, and illness duration, along with site, were more predictive of IAPT non-attendance than socio-demographic characteristics. Further testing of the relationship between these variables and IAPT non-attendance is recommended. Clinicians should monitor IAPT uptake in those they refer and implement strategies to increase their engagement with services, particularly when referring people presenting with suicidal ideation or more chronic illness. PMID:25194784

Spatial Scaling of Environmental Variables Improves Species-Habitat Models of Fishes in a Small, Sand-Bed Lowland River

PubMed Central

Radinger, Johannes; Wolter, Christian; Kail, Jochem

2015-01-01

Habitat suitability and the distinct mobility of species depict fundamental keys for explaining and understanding the distribution of river fishes. In recent years, comprehensive data on river hydromorphology has been mapped at spatial scales down to 100 m, potentially serving high resolution species-habitat models, e.g., for fish. However, the relative importance of specific hydromorphological and in-stream habitat variables and their spatial scales of influence is poorly understood. Applying boosted regression trees, we developed species-habitat models for 13 fish species in a sand-bed lowland river based on river morphological and in-stream habitat data. First, we calculated mean values for the predictor variables in five distance classes (from the sampling site up to 4000 m up- and downstream) to identify the spatial scale that best predicts the presence of fish species. Second, we compared the suitability of measured variables and assessment scores related to natural reference conditions. Third, we identified variables which best explained the presence of fish species. The mean model quality (AUC = 0.78, area under the receiver operating characteristic curve) significantly increased when information on the habitat conditions up- and downstream of a sampling site (maximum AUC at 2500 m distance class, +0.049) and topological variables (e.g., stream order) were included (AUC = +0.014). Both measured and assessed variables were similarly well suited to predict species’ presence. Stream order variables and measured cross section features (e.g., width, depth, velocity) were best-suited predictors. In addition, measured channel-bed characteristics (e.g., substrate types) and assessed longitudinal channel features (e.g., naturalness of river planform) were also good predictors. These findings demonstrate (i) the applicability of high resolution river morphological and instream-habitat data (measured and assessed variables) to predict fish presence, (ii) the importance of considering habitat at spatial scales larger than the sampling site, and (iii) that the importance of (river morphological) habitat characteristics differs depending on the spatial scale. PMID:26569119

Cognitive ability, academic achievement and academic self-concept: extending the internal/external frame of reference model.

PubMed

Chen, Ssu-Kuang; Hwang, Fang-Ming; Yeh, Yu-Chen; Lin, Sunny S J

2012-06-01

Marsh's internal/external (I/E) frame of reference model depicts the relationship between achievement and self-concept in specific academic domains. Few efforts have been made to examine concurrent relationships among cognitive ability, achievement, and academic self-concept (ASC) within an I/E model framework. To simultaneously examine the influences of domain-specific cognitive ability and grades on domain self-concept in an extended I/E model, including the indirect effect of domain-specific cognitive ability on domain self-concept via grades. Tenth grade respondents (628 male, 452 female) to a national adolescent survey conducted in Taiwan. Respondents completed surveys designed to measure maths and verbal aptitudes. Data on Maths and Chinese class grades and self-concepts were also collected. Statistically significant and positive path coefficients were found between cognitive ability and self-concept in the same domain (direct effect) and between these two constructs via grades (indirect effect). The cross-domain effects of either ability or grades on ASC were negatively significant. Taiwanese 10th graders tend to evaluate their ASCs based on a mix of ability and achievement, with achievement as a mediator exceeding ability as a predictor. In addition, the cross-domain effects suggest that Taiwanese students are likely to view Maths and verbal abilities and achievements as distinctly different. ©2011 The British Psychological Society.

Cognitive Prediction of Reading, Math, and Attention: Shared and Unique Influences

ERIC Educational Resources Information Center

Peterson, Robin L.; Boada, Richard; McGrath, Lauren M.; Willcutt, Erik G.; Olson, Richard K.; Pennington, Bruce F.

2017-01-01

The current study tested a multiple-cognitive predictor model of word reading, math ability, and attention in a community-based sample of twins ages 8 to 16 years (N = 636). The objective was to identify cognitive predictors unique to each skill domain as well as cognitive predictors shared among skills that could help explain their overlap and…

Work stress, role conflict, social support, and psychological burnout among teachers.

PubMed

Burke, R J; Greenglass, E

1993-10-01

This study examined a research model developed to understand psychological burnout among school-based educators. Data were collected from 833 school-based educators using questionnaires completed anonymously. Four groups of predictor variables identified in previous research were considered: individual demographic and situational variables, work stressors, role conflict, and social support. Some support for the model was found. Work stressors were strong predictors of psychological burnout. Individual demographic characteristics, role conflict, and social support had little effect on psychological burnout.

Calibration of Predictor Models Using Multiple Validation Experiments

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This paper presents a framework for calibrating computational models using data from several and possibly dissimilar validation experiments. The offset between model predictions and observations, which might be caused by measurement noise, model-form uncertainty, and numerical error, drives the process by which uncertainty in the models parameters is characterized. The resulting description of uncertainty along with the computational model constitute a predictor model. Two types of predictor models are studied: Interval Predictor Models (IPMs) and Random Predictor Models (RPMs). IPMs use sets to characterize uncertainty, whereas RPMs use random vectors. The propagation of a set through a model makes the response an interval valued function of the state, whereas the propagation of a random vector yields a random process. Optimization-based strategies for calculating both types of predictor models are proposed. Whereas the formulations used to calculate IPMs target solutions leading to the interval value function of minimal spread containing all observations, those for RPMs seek to maximize the models' ability to reproduce the distribution of observations. Regarding RPMs, we choose a structure for the random vector (i.e., the assignment of probability to points in the parameter space) solely dependent on the prediction error. As such, the probabilistic description of uncertainty is not a subjective assignment of belief, nor is it expected to asymptotically converge to a fixed value, but instead it casts the model's ability to reproduce the experimental data. This framework enables evaluating the spread and distribution of the predicted response of target applications depending on the same parameters beyond the validation domain.

Share2Quit: Web-Based Peer-Driven Referrals for Smoking Cessation

PubMed Central

2013-01-01

Background Smoking is the number one preventable cause of death in the United States. Effective Web-assisted tobacco interventions are often underutilized and require new and innovative engagement approaches. Web-based peer-driven chain referrals successfully used outside health care have the potential for increasing the reach of Internet interventions. Objective The objective of our study was to describe the protocol for the development and testing of proactive Web-based chain-referral tools for increasing the access to Decide2Quit.org, a Web-assisted tobacco intervention system. Methods We will build and refine proactive chain-referral tools, including email and Facebook referrals. In addition, we will implement respondent-driven sampling (RDS), a controlled chain-referral sampling technique designed to remove inherent biases in chain referrals and obtain a representative sample. We will begin our chain referrals with an initial recruitment of former and current smokers as seeds (initial participants) who will be trained to refer current smokers from their social network using the developed tools. In turn, these newly referred smokers will also be provided the tools to refer other smokers from their social networks. We will model predictors of referral success using sample weights from the RDS to estimate the success of the system in the targeted population. Results This protocol describes the evaluation of proactive Web-based chain-referral tools, which can be used in tobacco interventions to increase the access to hard-to-reach populations, for promoting smoking cessation. Conclusions Share2Quit represents an innovative advancement by capitalizing on naturally occurring technology trends to recruit smokers to Web-assisted tobacco interventions. PMID:24067329

Ontario family physician readiness to collaborate with community pharmacists on drug therapy management.

PubMed

Pojskic, Nedzad; Mackeigan, Linda; Boon, Heather; Ellison, Philip; Breslin, Curtis

2011-03-01

Empirical evidence suggests that pharmacist-physician collaboration can improve patients' clinical outcomes; however, such collaboration occurs relatively infrequently in the community setting. There has been little research on physicians' perspectives of such collaboration. To ascertain Ontario family physician readiness to collaborate with community pharmacists on drug therapy management. The survey instrument was based on the transtheoretical model of behavior change. It enquired about 3 physician behaviors that represented low-, mid-, and high-level collaboration with pharmacists. The survey was distributed by fax or mail to a random sample of 848 Ontario family physicians and general practitioners, stratified by practice location (urban/rural). The response rate was 36%. Most respondents reported conversing with community pharmacists about a patient's drug therapy management 5 or fewer times per week. Eighty-four percent reported that they regularly took community pharmacists' phone calls, whereas 78% reported that they sometimes sought pharmacists' recommendations regarding their patients' drug therapy. Twenty-eight percent reported that they sometimes referred their patients to community pharmacists for medication reviews, with 44% unaware of such a service. There were no differences in physician readiness to engage in any of the 3 collaborative behaviors in urban versus rural settings. More accurate patient medication lists were perceived as the main advantage (pro) of collaborating with community pharmacists and pharmacists' lack of patient information as the main disadvantage (con). Collectively, perceived pros of collaboration were positive predictors of physician readiness to collaborate on all 3 behaviors, whereas perceived cons were negative predictors for the low- and mid-level behaviors. Female physicians were more likely than males to seek pharmacists' recommendations, whereas more experienced physicians were more likely to refer patients to pharmacists for medication reviews. Overall, Ontario physicians were more engaged in the low- and mid-level collaboration with community pharmacists with respect to drug therapy management. The strongest predictor of physician readiness to collaborate was perceived advantages of collaboration. Copyright © 2011 Elsevier Inc. All rights reserved.

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

USGS Publications Warehouse

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

2011-01-01

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

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

PubMed Central

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

2011-01-01

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

Sex-specific predictors of inpatient rehabilitation outcomes after traumatic brain injury

PubMed Central

Chan, Vincy; Mollayeva, Tatyana; Ottenbacher, Kenneth J.; Colantonio, Angela

2016-01-01

Objective To identify sex-specific predictors of inpatient rehabilitation outcomes among patients with a traumatic brain injury (TBI) from a population based perspective. Design Retrospective cohort study Setting Ontario, Canada Participants Patients in inpatient rehabilitation for a TBI within one year of acute care discharge between 2008/09 and 2011/12 (N=1,730, 70% male, 30% female). Interventions None Main Outcome Measures Inpatient rehabilitation length of stay, total Functional Independence Measure (FIM™) score, and motor and cognitive FIM™ ratings at discharge. Results Sex, as a covariate in multivariable linear regression models, was not a significant predictor of rehabilitation outcomes. While many of the predictors examined were similar across males and females, sex-specific multivariable models identified some predictors of rehabilitation outcome that are specific for males and females; mechanism of injury (p<.0001) was a significant predictor of functional outcome only among females while comorbidities (p<.0001) was a significant predictor for males only. Conclusions Predictors of outcomes after inpatient rehabilitation differed by sex, providing evidence for a sex-specific approach in planning and resource allocation for inpatient rehabilitation services for patients with TBI. PMID:26836952

Sex-Specific Predictors of Inpatient Rehabilitation Outcomes After Traumatic Brain Injury.

PubMed

Chan, Vincy; Mollayeva, Tatyana; Ottenbacher, Kenneth J; Colantonio, Angela

2016-05-01

To identify sex-specific predictors of inpatient rehabilitation outcomes among patients with a traumatic brain injury (TBI) from a population-based perspective. Retrospective cohort study. Inpatient rehabilitation. Patients in inpatient rehabilitation for a TBI within 1 year of acute care discharge between 2008/2009 and 2011/2012 (N=1730, 70% men, 30% women). None. Inpatient rehabilitation length of stay, total FIM score, and motor and cognitive FIM ratings at discharge. Sex, as a covariate in multivariable linear regression models, was not a significant predictor of rehabilitation outcomes. Although many of the predictors examined were similar across men and women, sex-specific multivariable models identified some predictors of rehabilitation outcome that are specific for men and women; mechanism of injury (P<.0001) was a significant predictor of functional outcome only among women, whereas comorbidities (P<.0001) was a significant predictor for men only. Predictors of outcomes after inpatient rehabilitation differed by sex, providing evidence for a sex-specific approach in planning and resource allocation for inpatient rehabilitation services for patients with TBI. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Moderation analysis with missing data in the predictors.

PubMed

Zhang, Qian; Wang, Lijuan

2017-12-01

The most widely used statistical model for conducting moderation analysis is the moderated multiple regression (MMR) model. In MMR modeling, missing data could pose a challenge, mainly because the interaction term is a product of two or more variables and thus is a nonlinear function of the involved variables. In this study, we consider a simple MMR model, where the effect of the focal predictor X on the outcome Y is moderated by a moderator U. The primary interest is to find ways of estimating and testing the moderation effect with the existence of missing data in X. We mainly focus on cases when X is missing completely at random (MCAR) and missing at random (MAR). Three methods are compared: (a) Normal-distribution-based maximum likelihood estimation (NML); (b) Normal-distribution-based multiple imputation (NMI); and (c) Bayesian estimation (BE). Via simulations, we found that NML and NMI could lead to biased estimates of moderation effects under MAR missingness mechanism. The BE method outperformed NMI and NML for MMR modeling with missing data in the focal predictor, missingness depending on the moderator and/or auxiliary variables, and correctly specified distributions for the focal predictor. In addition, more robust BE methods are needed in terms of the distribution mis-specification problem of the focal predictor. An empirical example was used to illustrate the applications of the methods with a simple sensitivity analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Expected satiation alone does not predict actual intake of desserts.

PubMed

Guillocheau, Etienne; Davidenko, Olga; Marsset-Baglieri, Agnès; Darcel, Nicolas; Gaudichon, Claire; Tomé, Daniel; Fromentin, Gilles

2018-04-01

The degree to which consumers expect foods to satisfy hunger, referred to as expected satiation, has been reported to predict food intake. Yet this relationship has not been established precisely, at a quantitative level. We sought to explore this relationship in detail by determining whether expected satiation predicts the actual intake of semi-solid desserts. Two separate experiments were performed: the first used variations of a given food (eight apple purées), while the second involved a panel of different foods within a given category (eight desserts). Both experiments studied the consumption of two products assigned to volunteers based on their individual liking and expected satiation ratings, given ad libitum at the end of a standardised meal. A linear model was used to find predictors of food intake and included expected satiation scores, palatability scores, BMI, age, sex, TFEQ-R, TFEQ-D, water consumption during the meal, reported frequency of eating desserts, and reported frequency of consuming tested products as explanatory variables. Expected satiation was a significant predictor of actual food intake in both experiments (apple purée: F(1,97) = 18.60, P < .001; desserts: F(1,106) = 9.05, P < .01), along with other parameters such as product palatability and the volunteers' age, sex and food restriction (variation explained by the model/expected satiation in the experiments: 57%/23% and 36%/17%, respectively). However, we found a significant gap between expected and actual consumption of desserts, on group and on individual level. Our results confirm the importance of expected satiation as a predictor of subsequent food intake, but highlight the need to study individual consumption behaviour and preferences in order to fully understand the role of expected satiation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.

PubMed

Gong, Xiajing; Hu, Meng; Zhao, Liang

2018-05-01

Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high-dimensional data featured by a large number of predictor variables. Our results showed that ML-based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high-dimensional data. The prediction performances of ML-based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML-based methods provide a powerful tool for time-to-event analysis, with a built-in capacity for high-dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. © 2018 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Room model based Monte Carlo simulation study of the relationship between the airborne dose rate and the surface-deposited radon progeny.

PubMed

Sun, Kainan; Field, R William; Steck, Daniel J

2010-01-01

The quantitative relationships between radon gas concentration, the surface-deposited activities of various radon progeny, the airborne radon progeny dose rate, and various residential environmental factors were investigated through a Monte Carlo simulation study based on the extended Jacobi room model. Airborne dose rates were calculated from the unattached and attached potential alpha-energy concentrations (PAECs) using two dosimetric models. Surface-deposited (218)Po and (214)Po were significantly correlated with radon concentration, PAECs, and airborne dose rate (p-values <0.0001) in both non-smoking and smoking environments. However, in non-smoking environments, the deposited radon progeny were not highly correlated to the attached PAEC. In multiple linear regression analysis, natural logarithm transformation was performed for airborne dose rate as a dependent variable, as well as for radon and deposited (218)Po and (214)Po as predictors. In non-smoking environments, after adjusting for the effect of radon, deposited (214)Po was a significant positive predictor for one dose model (RR 1.46, 95% CI 1.27-1.67), while deposited (218)Po was a negative predictor for the other dose model (RR 0.90, 95% CI 0.83-0.98). In smoking environments, after adjusting for radon and room size, deposited (218)Po was a significant positive predictor for one dose model (RR 1.10, 95% CI 1.02-1.19), while a significant negative predictor for the other model (RR 0.90, 95% CI 0.85-0.95). After adjusting for radon and deposited (218)Po, significant increases of 1.14 (95% CI 1.03-1.27) and 1.13 (95% CI 1.05-1.22) in the mean dose rates were found for large room sizes relative to small room sizes in the different dose models.

An Interactive Tool For Semi-automated Statistical Prediction Using Earth Observations and Models

NASA Astrophysics Data System (ADS)

Zaitchik, B. F.; Berhane, F.; Tadesse, T.

2015-12-01

We developed a semi-automated statistical prediction tool applicable to concurrent analysis or seasonal prediction of any time series variable in any geographic location. The tool was developed using Shiny, JavaScript, HTML and CSS. A user can extract a predictand by drawing a polygon over a region of interest on the provided user interface (global map). The user can select the Climatic Research Unit (CRU) precipitation or Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as predictand. They can also upload their own predictand time series. Predictors can be extracted from sea surface temperature, sea level pressure, winds at different pressure levels, air temperature at various pressure levels, and geopotential height at different pressure levels. By default, reanalysis fields are applied as predictors, but the user can also upload their own predictors, including a wide range of compatible satellite-derived datasets. The package generates correlations of the variables selected with the predictand. The user also has the option to generate composites of the variables based on the predictand. Next, the user can extract predictors by drawing polygons over the regions that show strong correlations (composites). Then, the user can select some or all of the statistical prediction models provided. Provided models include Linear Regression models (GLM, SGLM), Tree-based models (bagging, random forest, boosting), Artificial Neural Network, and other non-linear models such as Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS). Finally, the user can download the analysis steps they used, such as the region they selected, the time period they specified, the predictand and predictors they chose and preprocessing options they used, and the model results in PDF or HTML format. Key words: Semi-automated prediction, Shiny, R, GLM, ANN, RF, GAM, MARS

The use of regression tree analysis for predicting the functional outcome following traumatic spinal cord injury.

PubMed

Facchinello, Yann; Beauséjour, Marie; Richard-Denis, Andreane; Thompson, Cynthia; Mac-Thiong, Jean-Marc

2017-10-25

Predicting the long-term functional outcome following traumatic spinal cord injury is needed to adapt medical strategies and to plan an optimized rehabilitation. This study investigates the use of regression tree for the development of predictive models based on acute clinical and demographic predictors. This prospective study was performed on 172 patients hospitalized following traumatic spinal cord injury. Functional outcome was quantified using the Spinal Cord Independence Measure collected within the first-year post injury. Age, delay prior to surgery and Injury Severity Score were considered as continuous predictors while energy of injury, trauma mechanisms, neurological level of injury, injury severity, occurrence of early spasticity, urinary tract infection, pressure ulcer and pneumonia were coded as categorical inputs. A simplified model was built using only injury severity, neurological level, energy and age as predictor and was compared to a more complex model considering all 11 predictors mentioned above The models built using 4 and 11 predictors were found to explain 51.4% and 62.3% of the variance of the Spinal Cord Independence Measure total score after validation, respectively. The severity of the neurological deficit at admission was found to be the most important predictor. Other important predictors were the Injury Severity Score, age, neurological level and delay prior to surgery. Regression trees offer promising performances for predicting the functional outcome after a traumatic spinal cord injury. It could help to determine the number and type of predictors leading to a prediction model of the functional outcome that can be used clinically in the future.

ProQ3: Improved model quality assessments using Rosetta energy terms

PubMed Central

Uziela, Karolis; Shu, Nanjiang; Wallner, Björn; Elofsson, Arne

2016-01-01

Quality assessment of protein models using no other information than the structure of the model itself has been shown to be useful for structure prediction. Here, we introduce two novel methods, ProQRosFA and ProQRosCen, inspired by the state-of-art method ProQ2, but using a completely different description of a protein model. ProQ2 uses contacts and other features calculated from a model, while the new predictors are based on Rosetta energies: ProQRosFA uses the full-atom energy function that takes into account all atoms, while ProQRosCen uses the coarse-grained centroid energy function. The two new predictors also include residue conservation and terms corresponding to the agreement of a model with predicted secondary structure and surface area, as in ProQ2. We show that the performance of these predictors is on par with ProQ2 and significantly better than all other model quality assessment programs. Furthermore, we show that combining the input features from all three predictors, the resulting predictor ProQ3 performs better than any of the individual methods. ProQ3, ProQRosFA and ProQRosCen are freely available both as a webserver and stand-alone programs at http://proq3.bioinfo.se/. PMID:27698390

Interleukin-6 is an independent predictor of progressive atherosclerosis in the carotid artery: The Tromsø Study.

PubMed

Eltoft, Agnethe; Arntzen, Kjell Arne; Wilsgaard, Tom; Mathiesen, Ellisiv B; Johnsen, Stein Harald

2018-04-01

Novel biomarkers are linked to cardiovascular disease (CVD). The aim of the present study was to investigate the association between 28 blood biomarkers and the formation and progression of carotid plaque. In a nested case control study with 703 participants from the population based Tromsø Study, a large biomarker panel was measured in blood obtained at baseline. Carotid ultrasound was assessed both at baseline and at 6 years of follow-up. Four groups were defined: Group 1: no plaque at baseline or at follow-up (reference group); Group 2: novel plaque at follow-up; Group 3: stable plaque at follow-up; Group 4: progression of plaque at follow-up. By multinomial logistic regression analyses, we assessed the risk of being in the different plaque groups with regard to traditional cardiovascular risk factors and levels of biomarkers at baseline. Adjusted for traditional risk factors, interleukin-6 (IL-6) was an independent predictor of plaque progression (OR 1.44, 95% CI 1.12-1.85 per SD increase in IL-6 level). This result remained significant after inclusion of other novel biomarkers to the model, and when subjects with former CVD were excluded. Neopterin was protective of novel plaque formation (OR 0.73, 95% CI 0.57-0.93). Myeloperoxidase and Caspase-1 were independent predictors of plaque progression, but this effect disappeared when excluding subjects with former CVD. IL-6 is an independent predictor of plaque progression, suggesting that it may be a marker of progressive atherosclerosis in the general population and that its central role in CVD may be related to promotion of plaque growth. Copyright © 2018 Elsevier B.V. All rights reserved.

Accounting for disease modifying therapy in models of clinical progression in multiple sclerosis.

PubMed

Healy, Brian C; Engler, David; Gholipour, Taha; Weiner, Howard; Bakshi, Rohit; Chitnis, Tanuja

2011-04-15

Identifying predictors of clinical progression in patients with relapsing-remitting multiple sclerosis (RRMS) is complicated in the era of disease modifying therapy (DMT) because patients follow many different DMT regimens. To investigate predictors of progression in a treated RRMS sample, a cohort of RRMS patients was prospectively followed in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital (CLIMB). Enrollment criteria were exposure to either interferon-β (IFN-β, n=164) or glatiramer acetate (GA, n=114) for at least 6 months prior to study entry. Baseline demographic and clinical features were used as candidate predictors of longitudinal clinical change on the Expanded Disability Status Scale (EDSS). We compared three approaches to account for DMT effects in statistical modeling. In all approaches, we analyzed all patients together and stratified based on baseline DMT. Model 1 used all available longitudinal EDSS scores, even those after on-study DMT changes. Model 2 used only clinical observations prior to changing DMT. Model 3 used causal statistical models to identify predictors of clinical change. When all patients were considered using Model 1, patients with a motor symptom as the first relapse had significantly larger change in EDSS scores during follow-up (p=0.04); none of the other clinical or demographic variables significantly predicted change. In Models 2 and 3, results were generally unchanged. DMT modeling choice had a modest impact on the variables classified as predictors of EDSS score change. Importantly, however, interpretation of these predictors is dependent upon modeling choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Uncertainties of statistical downscaling from predictor selection: Equifinality and transferability

NASA Astrophysics Data System (ADS)

Fu, Guobin; Charles, Stephen P.; Chiew, Francis H. S.; Ekström, Marie; Potter, Nick J.

2018-05-01

The nonhomogeneous hidden Markov model (NHMM) statistical downscaling model, 38 catchments in southeast Australia and 19 general circulation models (GCMs) were used in this study to demonstrate statistical downscaling uncertainties caused by equifinality to and transferability. That is to say, there could be multiple sets of predictors that give similar daily rainfall simulation results for both calibration and validation periods, but project different amounts (or even directions of change) of rainfall changing in the future. Results indicated that two sets of predictors (Set 1 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and specific humidity at 700 hPa and Set 2 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and dewpoint temperature depression at 850 hPa) as inputs to the NHMM produced satisfactory results of seasonal rainfall in comparison with observations. For example, during the model calibration period, the relative errors across the 38 catchments ranged from 0.48 to 1.76% with a mean value of 1.09% for the predictor Set 1, and from 0.22 to 2.24% with a mean value of 1.16% for the predictor Set 2. However, the changes of future rainfall from NHMM projections based on 19 GCMs produced projections with a different sign for these two different sets of predictors: Set 1 predictors project an increase of future rainfall with magnitudes depending on future time periods and emission scenarios, but Set 2 predictors project a decline of future rainfall. Such divergent projections may present a significant challenge for applications of statistical downscaling as well as climate change impact studies, and could potentially imply caveats in many existing studies in the literature.

Application of Biotic Ligand and Toxic Unit modeling approaches to predict improvements in zooplankton species richness in smelter-damaged lakes near Sudbury, Ontario.

PubMed

Khan, Farhan R; Keller, W Bill; Yan, Norman D; Welsh, Paul G; Wood, Chris M; McGeer, James C

2012-02-07

Using a 30-year record of biological and water chemistry data collected from seven lakes near smelters in Sudbury (Ontario, Canada) we examined the link between reductions of Cu, Ni, and Zn concentrations and zooplankton species richness. The toxicity of the metal mixtures was assessed using an additive Toxic Unit (TU) approach. Four TU models were developed based on total metal concentrations (TM-TU); free ion concentrations (FI-TU); acute LC50s calculated from the Biotic Ligand Model (BLM-TU); and chronic LC50s (acute LC50s adjusted by metal-specific acute-to-chronic ratios, cBLM-TU). All models significantly correlated reductions in metal concentrations to increased zooplankton species richness over time (p < 0.01) with a rank based on r(2) values of cBLM-TU > BLM-TU = FI-TU > TM-TU. Lake-wise comparisons within each model showed that the BLM-TU and cBLM-TU models provided the best description of recovery across all seven lakes. These two models were used to calculate thresholds for chemical and biological recovery using data from reference lakes in the same region. A threshold value of TU = 1 derived from the cBLM-TU provided the most accurate description of recovery. Overall, BLM-based TU models that integrate site-specific water chemistry-derived estimates of toxicity offer a useful predictor of biological recovery.

Predictors of the accuracy of quotation of references in peer-reviewed orthopaedic literature in relation to publications on the scaphoid.

PubMed

Buijze, G A; Weening, A A; Poolman, R W; Bhandari, M; Ring, D

2012-02-01

Using inaccurate quotations can propagate misleading information, which might affect the management of patients. The aim of this study was to determine the predictors of quotation inaccuracy in the peer-reviewed orthopaedic literature related to the scaphoid. We randomly selected 100 papers from ten orthopaedic journals. All references were retrieved in full text when available or otherwise excluded. Two observers independently rated all quotations from the selected papers by comparing the claims made by the authors with the data and expressed opinions of the reference source. A statistical analysis determined which article-related factors were predictors of quotation inaccuracy. The mean total inaccuracy rate of the 3840 verified quotes was 7.6%. There was no correlation between the rate of inaccuracy and the impact factor of the journal. Multivariable analysis identified the journal and the type of study (clinical, biomechanical, methodological, case report or review) as important predictors of the total quotation inaccuracy rate. We concluded that inaccurate quotations in the peer-reviewed orthopaedic literature related to the scaphoid were common and slightly more so for certain journals and certain study types. Authors, reviewers and editorial staff play an important role in reducing this inaccuracy.

Assessment of initial soil moisture conditions for event-based rainfall-runoff modelling

NASA Astrophysics Data System (ADS)

Tramblay, Yves; Bouvier, Christophe; Martin, Claude; Didon-Lescot, Jean-François; Todorovik, Dragana; Domergue, Jean-Marc

2010-06-01

Flash floods are the most destructive natural hazards that occur in the Mediterranean region. Rainfall-runoff models can be very useful for flash flood forecasting and prediction. Event-based models are very popular for operational purposes, but there is a need to reduce the uncertainties related to the initial moisture conditions estimation prior to a flood event. This paper aims to compare several soil moisture indicators: local Time Domain Reflectometry (TDR) measurements of soil moisture, modelled soil moisture through the Interaction-Sol-Biosphère-Atmosphère (ISBA) component of the SIM model (Météo-France), antecedent precipitation and base flow. A modelling approach based on the Soil Conservation Service-Curve Number method (SCS-CN) is used to simulate the flood events in a small headwater catchment in the Cevennes region (France). The model involves two parameters: one for the runoff production, S, and one for the routing component, K. The S parameter can be interpreted as the maximal water retention capacity, and acts as the initial condition of the model, depending on the antecedent moisture conditions. The model was calibrated from a 20-flood sample, and led to a median Nash value of 0.9. The local TDR measurements in the deepest layers of soil (80-140 cm) were found to be the best predictors for the S parameter. TDR measurements averaged over the whole soil profile, outputs of the SIM model, and the logarithm of base flow also proved to be good predictors, whereas antecedent precipitations were found to be less efficient. The good correlations observed between the TDR predictors and the S calibrated values indicate that monitoring soil moisture could help setting the initial conditions for simplified event-based models in small basins.

Empirical seasonal forecasts of the NAO

NASA Astrophysics Data System (ADS)

Sanchezgomez, E.; Ortizbevia, M.

2003-04-01

We present here seasonal forecasts of the North Atlantic Oscillation (NAO) issued from ocean predictors with an empirical procedure. The Singular Values Decomposition (SVD) of the cross-correlation matrix between predictor and predictand fields at the lag used for the forecast lead is at the core of the empirical model. The main predictor field are sea surface temperature anomalies, although sea ice cover anomalies are also used. Forecasts are issued in probabilistic form. The model is an improvement over a previous version (1), where Sea Level Pressure Anomalies were first forecast, and the NAO Index built from this forecast field. Both correlation skill between forecast and observed field, and number of forecasts that hit the correct NAO sign, are used to assess the forecast performance , usually above those values found in the case of forecasts issued assuming persistence. For certain seasons and/or leads, values of the skill are above the .7 usefulness treshold. References (1) SanchezGomez, E. and Ortiz Bevia M., 2002, Estimacion de la evolucion pluviometrica de la Espana Seca atendiendo a diversos pronosticos empiricos de la NAO, in 'El Agua y el Clima', Publicaciones de la AEC, Serie A, N 3, pp 63-73, Palma de Mallorca, Spain

A comparison of dynamical and statistical downscaling methods for regional wave climate projections along French coastlines.

NASA Astrophysics Data System (ADS)

Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Mendez, Fernando

2013-04-01

Wave climate forecasting is a major issue for numerous marine and coastal related activities, such as offshore industries, flooding risks assessment and wave energy resource evaluation, among others. Generally, there are two main ways to predict the impacts of the climate change on the wave climate at regional scale: the dynamical and the statistical downscaling of GCM (Global Climate Model). In this study, both methods have been applied on the French coast (Atlantic , English Channel and North Sea shoreline) under three climate change scenarios (A1B, A2, B1) simulated with the GCM ARPEGE-CLIMAT, from Météo-France (AR4, IPCC). The aim of the work is to characterise the wave climatology of the 21st century and compare the statistical and dynamical methods pointing out advantages and disadvantages of each approach. The statistical downscaling method proposed by the Environmental Hydraulics Institute of Cantabria (Spain) has been applied (Menendez et al., 2011). At a particular location, the sea-state climate (Predictand Y) is defined as a function, Y=f(X), of several atmospheric circulation patterns (Predictor X). Assuming these climate associations between predictor and predictand are stationary, the statistical approach has been used to project the future wave conditions with reference to the GCM. The statistical relations between predictor and predictand have been established over 31 years, from 1979 to 2009. The predictor is built as the 3-days-averaged squared sea level pressure gradient from the hourly CFSR database (Climate Forecast System Reanalysis, http://cfs.ncep.noaa.gov/cfsr/). The predictand has been extracted from the 31-years hindcast sea-state database ANEMOC-2 performed with the 3G spectral wave model TOMAWAC (Benoit et al., 1996), developed at EDF R&D LNHE and Saint-Venant Laboratory for Hydraulics and forced by the CFSR 10m wind field. Significant wave height, peak period and mean wave direction have been extracted with an hourly-resolution at 110 coastal locations along the French coast. The model, based on the BAJ parameterization of the source terms (Bidlot et al, 2007) was calibrated against ten years of GlobWave altimeter observations (2000-2009) and validated through deep and shallow water buoy observations. The dynamical downscaling method has been performed with the same numerical wave model TOMAWAC used for building ANEMOC-2. Forecast simulations are forced by the 10m wind fields of ARPEGE-CLIMAT (A1B, A2, B1) from 2010 to 2100. The model covers the Atlantic Ocean and uses a spatial resolution along the French and European coast of 10 and 20 km respectively. The results of the model are stored with a time resolution of one hour. References: Benoit M., Marcos F., and F. Becq, (1996). Development of a third generation shallow-water wave model with unstructured spatial meshing. Proc. 25th Int. Conf. on Coastal Eng., (ICCE'1996), Orlando (Florida, USA), pp 465-478. Bidlot J-R, Janssen P. and Adballa S., (2007). A revised formulation of ocean wave dissipation and its model impact, technical memorandum ECMWF n°509. Menendez, M., Mendez, F.J., Izaguirre,C., Camus, P., Espejo, A., Canovas, V., Minguez, R., Losada, I.J., Medina, R. (2011). Statistical Downscaling of Multivariate Wave Climate Using a Weather Type Approach, 12th International Workshop on Wave Hindcasting and Forecasting and 3rd Coastal Hazard Symposium, Kona (Hawaii).

Measuring body composition in dogs using multifrequency bioelectrical impedance analysis and dual energy X-ray absorptiometry.

PubMed

Rae, L S; Vankan, D M; Rand, J S; Flickinger, E A; Ward, L C

2016-06-01

Thirty-five healthy, neutered, mixed breed dogs were used to determine the ability of multifrequency bioelectrical impedance analysis (MFBIA) to predict accurately fat-free mass (FFM) in dogs using dual energy X-ray absorptiometry (DXA)-measured FFM as reference. A second aim was to compare MFBIA predictions with morphometric predictions. MFBIA-based predictors provided an accurate measure of FFM, within 1.5% when compared to DXA-derived FFM, in normal weight dogs. FFM estimates were most highly correlated with DXA-measured FFM when the prediction equation included resistance quotient, bodyweight, and body condition score. At the population level, the inclusion of impedance as a predictor variable did not add substantially to the predictive power achieved with morphometric variables alone; in individual dogs, impedance predictors were more valuable than morphometric predictors. These results indicate that, following further validation, MFBIA could provide a useful tool in clinical practice to objectively measure FFM in canine patients and help improve compliance with prevention and treatment programs for obesity in dogs. Copyright © 2016. Published by Elsevier Ltd.

Comparison of potential fecundity models for walleye pollock Gadus chalcogrammus in the Pacific waters off Hokkaido, Japan.

PubMed

Tanaka, H; Hamatsu, T; Mori, K

2017-01-01

Potential fecundity models of walleye or Alaska pollock Gadus chalcogrammus in the Pacific waters off Hokkaido, Japan, were developed. They were compared using a generalized linear model with using either standard body length (L S ) or total body mass (M T ) as a main covariate along with Fulton's condition factor (K) and mean diameter of oocytes (D O ) as additional potential covariates to account for maternal conditions and maturity stage. The results of model selection showed that M T was a better single predictor of potential fecundity (F P ) than L S . The biological importance of K on F P was obscure, because it was statistically significant when used in the predictor with L S (i.e. length-based model), but not significant when used with M T (i.e. mass-based model). Meanwhile, D O was statistically significant in both length and mass-based models, suggesting the importance of downregulation on the number of oocytes with advancing maturation. Among all candidate models, the model with M T and D O in the predictor had the lowest Akaike's information criterion value, suggesting its better predictive power. These newly developed models will improve future comparisons of the potential fecundity within and among stocks by excluding potential biases other than body size. © 2016 The Fisheries Society of the British Isles.

Simulation Study Using a New Type of Sample Variance

NASA Technical Reports Server (NTRS)

Howe, D. A.; Lainson, K. J.

1996-01-01

We evaluate with simulated data a new type of sample variance for the characterization of frequency stability. The new statistic (referred to as TOTALVAR and its square root TOTALDEV) is a better predictor of long-term frequency variations than the present sample Allan deviation. The statistical model uses the assumption that a time series of phase or frequency differences is wrapped (periodic) with overall frequency difference removed. We find that the variability at long averaging times is reduced considerably for the five models of power-law noise commonly encountered with frequency standards and oscillators.

Regression mixture models: Does modeling the covariance between independent variables and latent classes improve the results?

PubMed Central

Lamont, Andrea E.; Vermunt, Jeroen K.; Van Horn, M. Lee

2016-01-01

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we test the effects of violating an implicit assumption often made in these models – i.e., independent variables in the model are not directly related to latent classes. Results indicated that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. Additionally, this study tests whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations, but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a re-analysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted. PMID:26881956

Scoring and staging systems using cox linear regression modeling and recursive partitioning.

PubMed

Lee, J W; Um, S H; Lee, J B; Mun, J; Cho, H

2006-01-01

Scoring and staging systems are used to determine the order and class of data according to predictors. Systems used for medical data, such as the Child-Turcotte-Pugh scoring and staging systems for ordering and classifying patients with liver disease, are often derived strictly from physicians' experience and intuition. We construct objective and data-based scoring/staging systems using statistical methods. We consider Cox linear regression modeling and recursive partitioning techniques for censored survival data. In particular, to obtain a target number of stages we propose cross-validation and amalgamation algorithms. We also propose an algorithm for constructing scoring and staging systems by integrating local Cox linear regression models into recursive partitioning, so that we can retain the merits of both methods such as superior predictive accuracy, ease of use, and detection of interactions between predictors. The staging system construction algorithms are compared by cross-validation evaluation of real data. The data-based cross-validation comparison shows that Cox linear regression modeling is somewhat better than recursive partitioning when there are only continuous predictors, while recursive partitioning is better when there are significant categorical predictors. The proposed local Cox linear recursive partitioning has better predictive accuracy than Cox linear modeling and simple recursive partitioning. This study indicates that integrating local linear modeling into recursive partitioning can significantly improve prediction accuracy in constructing scoring and staging systems.

Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters.

PubMed

Wang, Jing-Jing; Wu, Hai-Feng; Sun, Tao; Li, Xia; Wang, Wei; Tao, Li-Xin; Huo, Da; Lv, Ping-Xin; He, Wen; Guo, Xiu-Hua

2013-01-01

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

Survival Regression Modeling Strategies in CVD Prediction.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Sardarinia, Mahsa; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-04-01

A fundamental part of prevention is prediction. Potential predictors are the sine qua non of prediction models. However, whether incorporating novel predictors to prediction models could be directly translated to added predictive value remains an area of dispute. The difference between the predictive power of a predictive model with (enhanced model) and without (baseline model) a certain predictor is generally regarded as an indicator of the predictive value added by that predictor. Indices such as discrimination and calibration have long been used in this regard. Recently, the use of added predictive value has been suggested while comparing the predictive performances of the predictive models with and without novel biomarkers. User-friendly statistical software capable of implementing novel statistical procedures is conspicuously lacking. This shortcoming has restricted implementation of such novel model assessment methods. We aimed to construct Stata commands to help researchers obtain the aforementioned statistical indices. We have written Stata commands that are intended to help researchers obtain the following. 1, Nam-D'Agostino X 2 goodness of fit test; 2, Cut point-free and cut point-based net reclassification improvement index (NRI), relative absolute integrated discriminatory improvement index (IDI), and survival-based regression analyses. We applied the commands to real data on women participating in the Tehran lipid and glucose study (TLGS) to examine if information relating to a family history of premature cardiovascular disease (CVD), waist circumference, and fasting plasma glucose can improve predictive performance of Framingham's general CVD risk algorithm. The command is adpredsurv for survival models. Herein we have described the Stata package "adpredsurv" for calculation of the Nam-D'Agostino X 2 goodness of fit test as well as cut point-free and cut point-based NRI, relative and absolute IDI, and survival-based regression analyses. We hope this work encourages the use of novel methods in examining predictive capacity of the emerging plethora of novel biomarkers.

Statistical prediction of seasonal discharge in Central Asia for water resources management: development of a generic (pre-)operational modeling tool

NASA Astrophysics Data System (ADS)

Apel, Heiko; Baimaganbetov, Azamat; Kalashnikova, Olga; Gavrilenko, Nadejda; Abdykerimova, Zharkinay; Agalhanova, Marina; Gerlitz, Lars; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Gafurov, Abror

2017-04-01

The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien-Shan and Pamirs. During the summer months the snow and glacier melt dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for a sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydromet services, this study aims at the development of a generic tool for deriving statistical forecast models of seasonal river discharge. The generic model is kept as simple as possible in order to be driven by available hydrological and meteorological data, and be applicable for all catchments with their often limited data availability in the region. As snowmelt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature as recorded by climatological stations in the catchments. These data sets are accompanied by snow cover predictors derived from the operational ModSnow tool, which provides cloud free snow cover data for the selected catchments based on MODIS satellite images. In addition to the meteorological data antecedent streamflow is used as a predictor variable. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to 3 or 4 predictors. A user selectable number of best models according to pre-defined performance criteria is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross validation. Based on the cross validation the predictive uncertainty was quantified for every prediction model. According to the official procedures of the hydromet services forecasts of the mean seasonal discharge of the period April to September are derived every month starting from January until June. The application of the model for several catchments in Central Asia - ranging from small to the largest rivers - for the period 2000-2015 provided skillful forecasts for most catchments already in January. The skill of the prediction increased every month, with R2 values often in the range 0.8 - 0.9 in April just before the prediction period. The forecasts further improve in the following months, most likely due to the integration of spring precipitation, which is not included in the predictors before May, or spring discharge, which contains indicative information for the overall seasonal discharge. In summary, the proposed generic automatic forecast model development tool provides robust predictions for seasonal water availability in Central Asia, which will be tested against the official forecasts in the upcoming years, with the vision of eventual operational implementation.

Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification.

PubMed

Ramírez, J; Górriz, J M; Segovia, F; Chaves, R; Salas-Gonzalez, D; López, M; Alvarez, I; Padilla, P

2010-03-19

This letter shows a computer aided diagnosis (CAD) technique for the early detection of the Alzheimer's disease (AD) by means of single photon emission computed tomography (SPECT) image classification. The proposed method is based on partial least squares (PLS) regression model and a random forest (RF) predictor. The challenge of the curse of dimensionality is addressed by reducing the large dimensionality of the input data by downscaling the SPECT images and extracting score features using PLS. A RF predictor then forms an ensemble of classification and regression tree (CART)-like classifiers being its output determined by a majority vote of the trees in the forest. A baseline principal component analysis (PCA) system is also developed for reference. The experimental results show that the combined PLS-RF system yields a generalization error that converges to a limit when increasing the number of trees in the forest. Thus, the generalization error is reduced when using PLS and depends on the strength of the individual trees in the forest and the correlation between them. Moreover, PLS feature extraction is found to be more effective for extracting discriminative information from the data than PCA yielding peak sensitivity, specificity and accuracy values of 100%, 92.7%, and 96.9%, respectively. Moreover, the proposed CAD system outperformed several other recently developed AD CAD systems. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies.

PubMed

Korvigo, Ilia; Afanasyev, Andrey; Romashchenko, Nikolay; Skoblov, Mikhail

2018-01-01

Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that combine different predictors, such as PolyPhen and SIFT, to integrate more information in a single score. Although many advances have been made in feature design and machine learning algorithms used, the shortage of high-quality reference data along with the bias towards intensively studied in vitro models call for improved generalisation ability in order to further increase classification accuracy and handle records with insufficient data. Since a meta-estimator basically combines different scoring systems with highly complicated nonlinear relationships, we investigated how deep learning (supervised and unsupervised), which is particularly efficient at discovering hierarchies of features, can improve classification performance. While it is believed that one should only use deep learning for high-dimensional input spaces and other models (logistic regression, support vector machines, Bayesian classifiers, etc) for simpler inputs, we still believe that the ability of neural networks to discover intricate structure in highly heterogenous datasets can aid a meta-estimator. We compare the performance with various popular predictors, many of which are recommended by the American College of Medical Genetics and Genomics (ACMG), as well as available deep learning-based predictors. Thanks to hardware acceleration we were able to use a computationally expensive genetic algorithm to stochastically optimise hyper-parameters over many generations. Overfitting was hindered by noise injection and dropout, limiting coadaptation of hidden units. Although we stress that this work was not conceived as a tool comparison, but rather an exploration of the possibilities of deep learning application in ensemble scores, our results show that even relatively simple modern neural networks can significantly improve both prediction accuracy and coverage. We provide open-access to our finest model via the web-site: http://score.generesearch.ru/services/badmut/.

Comparative Effectiveness of Biosimilar, Reference Product and Other Erythropoiesis-Stimulating Agents (ESAs) Still Covered by Patent in Chronic Kidney Disease and Cancer Patients: An Italian Population-Based Study.

PubMed

Ingrasciotta, Ylenia; Giorgianni, Francesco; Marcianò, Ilaria; Bolcato, Jenny; Pirolo, Roberta; Chinellato, Alessandro; Ientile, Valentina; Santoro, Domenico; Genazzani, Armando A; Alibrandi, Angela; Fontana, Andrea; Caputi, Achille P; Trifirò, Gianluca

2016-01-01

Since 2007 biosimilars of erythropoiesis-stimulating agents (ESAs) are available on the Italian market. Very limited post-marketing data exist on the comparative effectiveness of biosimilar and originator ESAs. This population-based study was aimed to compare the effects of biosimilars, reference product and other ESAs still covered by patent on hemoglobinemia in chronic kidney disease (CKD) and cancer patients in a Local Health Unit (LHU) from Northern Italy. A retrospective cohort study was conducted during the years 2009-2014 using data from Treviso LHU administrative database. Incident ESA users (no ESA dispensing within 6 months prior to treatment start, i.e. index date (ID)) with at least one hemoglobin measurement within one month prior to ID (baseline Hb value) and another measurement between 2nd and 3rd month after ID (follow-up Hb value) were identified. The strength of the consumption (as total number of defined daily dose (DDD) dispensed during the follow-up divided by days of follow-up) and the difference between follow-up and baseline Hb values [delta Hb (ΔHb)] were evaluated. Based on Hb changes, ESA users were classified as non-responders (ΔHb≤0 g/dl), responders (0<ΔHb≤2 g/dl), and highly responders (ΔHb>2 g/dl). A multivariate ordinal logistic regression model to identify predictors for responsiveness to treatment was performed. All analyses were stratified by indication for use and type of dispensed ESA at ID. Overall, 1,003 incident ESA users (reference product: 252, 25.1%; other ESAs covered by patent: 303, 30.2%; biosimilars: 448, 44.7%) with CKD or cancer were eligible for the study. No statistically significant difference in the amount of dose dispensed during the follow-up among biosimilars, reference product and other ESAs covered by patent was found in both CKD and cancer. After three months from treatment start, all ESAs increased Hb values on average by 2g/dl. No differences in ΔHb as well as in frequency of non-responders, responders and highly responders among different types of ESAs were observed in both indications of use. Overall, around 15-20% of ESA users were non-responders. Strength of treatment, but no type of dispensed ESAs was found to be predictor of responsiveness to treatment. No difference on the effects on hemoglobinemia among users of either biosimilars or reference product or ESAs covered by patent was observed in a general population from Northern Italy, despite a comparable dispensed dose of the different ESAs during the first three months of treatment.

A multichannel model-based methodology for extubation readiness decision of patients on weaning trials.

PubMed

Casaseca-de-la-Higuera, Pablo; Simmross-Wattenberg, Federico; Martín-Fernández, Marcos; Alberola-López, Carlos

2009-07-01

Discontinuation of mechanical ventilation is a challenging task that involves a number of subtle clinical issues. The gradual removal of the respiratory support (referred to as weaning) should be performed as soon as autonomous respiration can be sustained. However, the prediction rate of successful extubation is still below 25% based on previous studies. Construction of an automatic system that provides information on extubation readiness is thus desirable. Recent works have demonstrated that the breathing pattern variability is a useful extubation readiness indicator, with improving performance when multiple respiratory signals are jointly processed. However, the existing methods for predictor extraction present several drawbacks when length-limited time series are to be processed in heterogeneous groups of patients. In this paper, we propose a model-based methodology for automatic readiness prediction. It is intended to deal with multichannel, nonstationary, short records of the breathing pattern. Results on experimental data yield an 87.27% of successful readiness prediction, which is in line with the best figures reported in the literature. A comparative analysis shows that our methodology overcomes the shortcomings of so far proposed methods when applied to length-limited records on heterogeneous groups of patients.

Selective decrease in central nervous system serotonin turnover in children with dopa-nonresponsive dystonia.

PubMed

Assmann, Birgit; Köhler, Martin; Hoffmann, Georg F; Heales, Simon; Surtees, Robert

2002-07-01

Childhood dystonia that does not respond to treatment with levodopa (dopa-nonresponsive dystonia, DND) has an unclear pathogenesis and is notoriously difficult to treat. To test the hypothesis that there may be abnormalities in serotonin turnover in DND we measured cerebrospinal fluid (CSF) concentrations of homovanillic (HVA) and 5-hydroxyindoleacetic (HIAA) acids, metabolites of dopamine and serotonin, respectively, in 18 children with dystonia not responsive to levodopa. These were combined with a reference population of 85 children with neurologic or metabolic disease known not to affect dopamine or serotonin metabolism. Because of the known natural age-related decrement in HVA and HIAA concentrations, the results were analyzed using multiple regression using age and DND as predictors of CSF HIAA and HVA concentrations. DND was a highly significant predictor of CSF HIAA concentration (p < 0.001) but not of CSF HVA concentration (p = 0.59). After fitting a regression model, the geometric mean ratio of CSF HIAA in DND compared with the reference range was 0.53 whereas that for CSF HVA was 0.95. We also analyzed CSF HIAA/HVA ratios. After fitting a regression model, we found no dependence on age, and the mean of CSF HIAA/HVA in DND was 0.28 whereas that for the reference range was 0.49 (p < 0.001). We conclude that a significant number of children with DND have reduced CNS serotonin turnover. Treatment with drugs that increase serotonin concentration in the synaptic cleft should be considered in this group of patients.

Relations of Anxiety Sensitivity, Control Beliefs, and Maternal Over-Control to Fears in Clinic-Referred Children with Specific Phobia

PubMed Central

Kane, Elisabeth J; Braunstein, Kara; Ollendick, Thomas H.; Muris, Peter

2014-01-01

The relations of fear to anxiety sensitivity, control beliefs, and maternal overprotection were examined in 126 7- to 13-year-old clinically referred children with specific phobias. Results indicated that anxiety sensitivity and control beliefs were significant predictors of children’s fear levels, accounting for approximately 48% of the total variance. Unexpectedly, age, gender, and maternal overprotection did not emerge as significant predictors of fear in the overall sample. In subsequent analyses, anxiety sensitivity was found to be a consistent, significant predictor for both girls and boys, for both younger and older children, and for children with and without an additional anxiety disorder diagnosis. Control beliefs were only a significant predictor for girls, younger children, and children with an additional anxiety diagnosis. Maternal overprotection was not a significant predictor for any group. Children with an additional anxiety disorder diagnosis had higher levels of fear, anxiety sensitivity, and maternal overprotection, as well as lower levels of control beliefs than the non-additional anxiety disorder subgroup. Future directions and clinical implications are explored. PMID:26273182

Relations of Anxiety Sensitivity, Control Beliefs, and Maternal Over-Control to Fears in Clinic-Referred Children with Specific Phobia.

PubMed

Kane, Elisabeth J; Braunstein, Kara; Ollendick, Thomas H; Muris, Peter

2015-07-01

The relations of fear to anxiety sensitivity, control beliefs, and maternal overprotection were examined in 126 7- to 13-year-old clinically referred children with specific phobias. Results indicated that anxiety sensitivity and control beliefs were significant predictors of children's fear levels, accounting for approximately 48% of the total variance. Unexpectedly, age, gender, and maternal overprotection did not emerge as significant predictors of fear in the overall sample. In subsequent analyses, anxiety sensitivity was found to be a consistent, significant predictor for both girls and boys, for both younger and older children, and for children with and without an additional anxiety disorder diagnosis. Control beliefs were only a significant predictor for girls, younger children, and children with an additional anxiety diagnosis. Maternal overprotection was not a significant predictor for any group. Children with an additional anxiety disorder diagnosis had higher levels of fear, anxiety sensitivity, and maternal overprotection, as well as lower levels of control beliefs than the non-additional anxiety disorder subgroup. Future directions and clinical implications are explored.

The impact of clinical conditions and social factors on the psychological distress of cancer patients: an explorative study at a consultation and liaison service in a rural general hospital.

PubMed

Valdes-Stauber, Juan; Vietz, Eva; Kilian, Reinhold

2013-09-20

In recent decades, increasing attention has been paid to the subjective dimension of cancer, especially to psychosocial screening procedures, major psychiatric disorders but also psychological and psychosocial distress, and finally to met needs of oncologic patients. This study aims first to describe cancer patients in a rural hospital attended by a psycho-oncological consultation-liaison team, second to assess predictors for psychological distress in cancer patients, and finally to identify predictors for recommendation of further psychosocial support. The sample (n = 290) comprises a full survey of patients at breast and bowel cancer services (n=209) and patients referred by other medical and surgical services because of psychosocial impairment (n = 81). All patients were assessed by means of the PO-Bado (Psycho-Oncological Basic Documentation) expert rating scale. Assessment of predictors for psychological distress was conducted by multivariate regression models and assessment for predictors for need for outpatient psychosocial support by a logistic regression analysis. All analyses were conducted using STATA 12. Most members of the assessed sample (average age 65, 82% women) were not severely impaired from a functional and psychological point of view. A total of 14% had received psychiatric treatment before. Mood swings, anxiety, grief, and fatigue were the most important distress symptoms. Selectively referred patients vs. full survey patients of cancer centres, as well as bowel vs. breast cancer patients show a higher level of psychological and physical distress. Fatigue, assessed metastases, and functional limitations were the best predictors for psychological burden. Referral mode, gender, age, family problems, fatigue, and previous psychiatric treatment were associated with further need of psychosocial support. Psycho-oncological consultation and liaison services may offer support to patients in an early stage of cancer, especially in cancer centres. Because of selectively referred patients show a higher burden, the use of basic screening instruments could be meaningful. Fatigue, metastases status, and functional limitations may better predict psychological distress than pain, duration of illness, psychosocial conditions or previous psychiatric treatment. More attention has to be paid to outpatient follow-up with older cancer patients, those with family problems, and those suffering from significant fatigue.

The PMDB Protein Model Database

PubMed Central

Castrignanò, Tiziana; De Meo, Paolo D'Onorio; Cozzetto, Domenico; Talamo, Ivano Giuseppe; Tramontano, Anna

2006-01-01

The Protein Model Database (PMDB) is a public resource aimed at storing manually built 3D models of proteins. The database is designed to provide access to models published in the scientific literature, together with validating experimental data. It is a relational database and it currently contains >74 000 models for ∼240 proteins. The system is accessible at and allows predictors to submit models along with related supporting evidence and users to download them through a simple and intuitive interface. Users can navigate in the database and retrieve models referring to the same target protein or to different regions of the same protein. Each model is assigned a unique identifier that allows interested users to directly access the data. PMID:16381873

Assessment of the uncertainty and predictive power of large-scale predictors for nonlinear precipitation downscaling in the European Arctic (Invited)

NASA Astrophysics Data System (ADS)

Sauter, T.

2013-12-01

Despite the extensive research on downscaling methods there is still little consensus about the choice of useful atmospheric predictor variables. Besides the general decision of a proper statistical downscaling model, the selection of an informative predictor set is crucial for the accuracy and stability of the resulting downscaled time series. These requirements must be fullfilled by both the atmospheric variables and the predictor domains in terms of geographical location and spatial extend, to which in general not much attention is paid. However, only a limited number of studies is interested in the predictive capability of the predictor domain size or shape, and the question to what extent variability of neighboring grid points influence local-scale events. In this study we emphasized the spatial relationships between observed daily precipitation and selected number of atmospheric variables for the European Arctic. Several nonlinear regression models are used to link the large-scale predictors obtained from reanalysed Weather Research and Forecast model runs to the local-scale observed precipitation. Inferences on the sources of uncertainty are then drawn from variance based sensitivity measures, which also permit to capture interaction effects between individual predictors. The information is further used to develop more parsimonious downscaling models with only small decreases in accuracy. Individual predictors (without interactions) account for almost 2/3 of the total output variance, while the remaining fraction is solely due to interactions. Neglecting predictor interactions in the screening process will lead to some loss of information. Hence, linear screening methods are insufficient as they neither account for interactions nor for non-additivity as given by many nonlinear prediction algorithms.

Predictors of the number of under-five malnourished children in Bangladesh: application of the generalized poisson regression model

PubMed Central

2013-01-01

Background Malnutrition is one of the principal causes of child mortality in developing countries including Bangladesh. According to our knowledge, most of the available studies, that addressed the issue of malnutrition among under-five children, considered the categorical (dichotomous/polychotomous) outcome variables and applied logistic regression (binary/multinomial) to find their predictors. In this study malnutrition variable (i.e. outcome) is defined as the number of under-five malnourished children in a family, which is a non-negative count variable. The purposes of the study are (i) to demonstrate the applicability of the generalized Poisson regression (GPR) model as an alternative of other statistical methods and (ii) to find some predictors of this outcome variable. Methods The data is extracted from the Bangladesh Demographic and Health Survey (BDHS) 2007. Briefly, this survey employs a nationally representative sample which is based on a two-stage stratified sample of households. A total of 4,460 under-five children is analysed using various statistical techniques namely Chi-square test and GPR model. Results The GPR model (as compared to the standard Poisson regression and negative Binomial regression) is found to be justified to study the above-mentioned outcome variable because of its under-dispersion (variance < mean) property. Our study also identify several significant predictors of the outcome variable namely mother’s education, father’s education, wealth index, sanitation status, source of drinking water, and total number of children ever born to a woman. Conclusions Consistencies of our findings in light of many other studies suggest that the GPR model is an ideal alternative of other statistical models to analyse the number of under-five malnourished children in a family. Strategies based on significant predictors may improve the nutritional status of children in Bangladesh. PMID:23297699

Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses.

PubMed

Griffith, Daniel A; Peres-Neto, Pedro R

2006-10-01

Recently, analytical approaches based on the eigenfunctions of spatial configuration matrices have been proposed in order to consider explicitly spatial predictors. The present study demonstrates the usefulness of eigenfunctions in spatial modeling applied to ecological problems and shows equivalencies of and differences between the two current implementations of this methodology. The two approaches in this category are the distance-based (DB) eigenvector maps proposed by P. Legendre and his colleagues, and spatial filtering based upon geographic connectivity matrices (i.e., topology-based; CB) developed by D. A. Griffith and his colleagues. In both cases, the goal is to create spatial predictors that can be easily incorporated into conventional regression models. One important advantage of these two approaches over any other spatial approach is that they provide a flexible tool that allows the full range of general and generalized linear modeling theory to be applied to ecological and geographical problems in the presence of nonzero spatial autocorrelation.

A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults

PubMed Central

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A.; Aguiló, Antoni

2015-01-01

Background Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Methods Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. Results The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). Conclusions There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF. PMID:25821960

A comparison between multiple regression models and CUN-BAE equation to predict body fat in adults.

PubMed

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A; Aguiló, Antoni

2015-01-01

Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.

Retrieving relevant factors with exploratory SEM and principal-covariate regression: A comparison.

PubMed

Vervloet, Marlies; Van den Noortgate, Wim; Ceulemans, Eva

2018-02-12

Behavioral researchers often linearly regress a criterion on multiple predictors, aiming to gain insight into the relations between the criterion and predictors. Obtaining this insight from the ordinary least squares (OLS) regression solution may be troublesome, because OLS regression weights show only the effect of a predictor on top of the effects of other predictors. Moreover, when the number of predictors grows larger, it becomes likely that the predictors will be highly collinear, which makes the regression weights' estimates unstable (i.e., the "bouncing beta" problem). Among other procedures, dimension-reduction-based methods have been proposed for dealing with these problems. These methods yield insight into the data by reducing the predictors to a smaller number of summarizing variables and regressing the criterion on these summarizing variables. Two promising methods are principal-covariate regression (PCovR) and exploratory structural equation modeling (ESEM). Both simultaneously optimize reduction and prediction, but they are based on different frameworks. The resulting solutions have not yet been compared; it is thus unclear what the strengths and weaknesses are of both methods. In this article, we focus on the extents to which PCovR and ESEM are able to extract the factors that truly underlie the predictor scores and can predict a single criterion. The results of two simulation studies showed that for a typical behavioral dataset, ESEM (using the BIC for model selection) in this regard is successful more often than PCovR. Yet, in 93% of the datasets PCovR performed equally well, and in the case of 48 predictors, 100 observations, and large differences in the strengths of the factors, PCovR even outperformed ESEM.

Neuropsychological predictors of performance-based measures of functional capacity and social skills in individuals with severe mental illness.

PubMed

Mahmood, Zanjbeel; Burton, Cynthia Z; Vella, Lea; Twamley, Elizabeth W

2018-04-13

Neuropsychological abilities may underlie successful performance of everyday functioning and social skills. We aimed to determine the strongest neuropsychological predictors of performance-based functional capacity and social skills performance across the spectrum of severe mental illness (SMI). Unemployed outpatients with SMI (schizophrenia, bipolar disorder, or major depression; n = 151) were administered neuropsychological (expanded MATRICS Consensus Cognitive Battery), functional capacity (UCSD Performance-Based Skills Assessment-Brief; UPSA-B), and social skills (Social Skills Performance Assessment; SSPA) assessments. Bivariate correlations between neuropsychological performance and UPSA-B and SSPA total scores showed that most neuropsychological tests were significantly associated with each performance-based measure. Forward entry stepwise regression analyses were conducted entering education, diagnosis, symptom severity, and neuropsychological performance as predictors of functional capacity and social skills. Diagnosis, working memory, sustained attention, and category and letter fluency emerged as significant predictors of functional capacity, in a model that explained 43% of the variance. Negative symptoms, sustained attention, and letter fluency were significant predictors of social skill performance, in a model explaining 35% of the variance. Functional capacity is positively associated with neuropsychological functioning, but diagnosis remains strongly influential, with mood disorder participants outperforming those with psychosis. Social skill performance appears to be positively associated with sustained attention and verbal fluency regardless of diagnosis; however, negative symptom severity strongly predicts social skills performance. Improving neuropsychological functioning may improve psychosocial functioning in people with SMI. Published by Elsevier Ltd.

Early identification of patients requiring massive transfusion, embolization, or hemostatic surgery for traumatic hemorrhage: a systematic review protocol.

PubMed

Tran, Alexandre; Matar, Maher; Steyerberg, Ewout W; Lampron, Jacinthe; Taljaard, Monica; Vaillancourt, Christian

2017-04-13

Hemorrhage is a major cause of early mortality following a traumatic injury. The progression and consequences of significant blood loss occur quickly as death from hemorrhagic shock or exsanguination often occurs within the first few hours. The mainstay of treatment therefore involves early identification of patients at risk for hemorrhagic shock in order to provide blood products and control of the bleeding source if necessary. The intended scope of this review is to identify and assess combinations of predictors informing therapeutic decision-making for clinicians during the initial trauma assessment. The primary objective of this systematic review is to identify and critically assess any existing multivariable models predicting significant traumatic hemorrhage that requires intervention, defined as a composite outcome comprising massive transfusion, surgery for hemostasis, or angiography with embolization for the purpose of external validation or updating in other study populations. If no suitable existing multivariable models are identified, the secondary objective is to identify candidate predictors to inform the development of a new prediction rule. We will search the EMBASE and MEDLINE databases for all randomized controlled trials and prospective and retrospective cohort studies developing or validating predictors of intervention for traumatic hemorrhage in adult patients 16 years of age or older. Eligible predictors must be available to the clinician during the first hour of trauma resuscitation and may be clinical, lab-based, or imaging-based. Outcomes of interest include the need for surgical intervention, angiographic embolization, or massive transfusion within the first 24 h. Data extraction will be performed independently by two reviewers. Items for extraction will be based on the CHARMS checklist. We will evaluate any existing models for relevance, quality, and the potential for external validation and updating in other populations. Relevance will be described in terms of appropriateness of outcomes and predictors. Quality criteria will include variable selection strategies, adequacy of sample size, handling of missing data, validation techniques, and measures of model performance. This systematic review will describe the availability of multivariable prediction models and summarize evidence regarding predictors that can be used to identify the need for intervention in patients with traumatic hemorrhage. PROSPERO CRD42017054589.

Factors Related to Organizational Turnover Intentions of Louisiana Cooperative Extension Service Agents.

ERIC Educational Resources Information Center

Carter, Carolyn G.; And Others

The relationship between employee turnover intentions and various predictors of turnover are examined in this study based on the theoretical framework of March and Simon's (1958) "decision to participate" model. Specifically, the predictors include desirability of movement (organizational commitment), ease of movement, job satisfaction,…

Development and validation of prediction models for endometrial cancer in postmenopausal bleeding.

PubMed

Wong, Alyssa Sze-Wai; Cheung, Chun Wai; Fung, Linda Wen-Ying; Lao, Terence Tzu-Hsi; Mol, Ben Willem J; Sahota, Daljit Singh

2016-08-01

To develop and assess the accuracy of risk prediction models to diagnose endometrial cancer in women having postmenopausal bleeding (PMB). A retrospective cohort study of 4383 women in a One-stop PMB clinic from a university teaching hospital in Hong Kong. Clinical risk factors, transvaginal ultrasonic measurement of endometrial thickness (ET) and endometrial histology were obtained from consecutive women between 2002 and 2013. Two models to predict risk of endometrial cancer were developed and assessed, one based on patient characteristics alone and a second incorporated ET with patient characteristics. Endometrial histology was used as the reference standard. The split-sample internal validation and bootstrapping technique were adopted. The optimal threshold for prediction of endometrial cancer by the final models was determined using a receiver-operating characteristics (ROC) curve and Youden Index. The diagnostic gain was compared to a reference strategy of measuring ET only by comparing the AUC using the Delong test. Out of 4383 women with PMB, 168 (3.8%) were diagnosed with endometrial cancer. ET alone had an area under curve (AUC) of 0.92 (95% confidence intervals [CIs] 0.89-0.94). In the patient characteristics only model, independent predictors of cancer were age at presentation, age at menopause, body mass index, nulliparity and recurrent vaginal bleeding. The AUC and Youdens Index of the patient characteristic only model were respectively 0.73 (95% CI 0.67-0.80) and 0.72 (Sensitivity=66.5%; Specificity=68.9%; +ve LR=2.14; -ve LR=0.49). ET, age at presentation, nulliparity and recurrent vaginal bleeding were independent predictors in the patient characteristics plus ET model. The AUC and Youdens Index of the patient characteristic plus ET model where respectively 0.92 (95% CI 0.88-0.96) and 0.71 (Sensitivity=82.7%; Specificity=88.3%; +ve LR=6.38; -ve LR=0.2). Comparison of AUC indicated that a history alone model was inferior to a model using ET alone (difference=0.19, 95% CI 0.15-0.24; p<0.0001) and History plus ET (difference=0.19, 95% CI 0.16-0.23, p<0.0001) and history plus ET was similar to that of using ET alone (difference=0.001 95% CI -0.015 to 0.0018, p=0.84). A risk model using only patient characteristics showed fair diagnostic accuracy. Addition of patient characteristics to ET did not improve the diagnostic accuracy as compared to ET alone in our cohort. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Referenceless perceptual fog density prediction model

NASA Astrophysics Data System (ADS)

Choi, Lark Kwon; You, Jaehee; Bovik, Alan C.

2014-02-01

We propose a perceptual fog density prediction model based on natural scene statistics (NSS) and "fog aware" statistical features, which can predict the visibility in a foggy scene from a single image without reference to a corresponding fogless image, without side geographical camera information, without training on human-rated judgments, and without dependency on salient objects such as lane markings or traffic signs. The proposed fog density predictor only makes use of measurable deviations from statistical regularities observed in natural foggy and fog-free images. A fog aware collection of statistical features is derived from a corpus of foggy and fog-free images by using a space domain NSS model and observed characteristics of foggy images such as low contrast, faint color, and shifted intensity. The proposed model not only predicts perceptual fog density for the entire image but also provides a local fog density index for each patch. The predicted fog density of the model correlates well with the measured visibility in a foggy scene as measured by judgments taken in a human subjective study on a large foggy image database. As one application, the proposed model accurately evaluates the performance of defog algorithms designed to enhance the visibility of foggy images.

Software development predictors, error analysis, reliability models and software metric analysis

NASA Technical Reports Server (NTRS)

Basili, Victor

1983-01-01

The use of dynamic characteristics as predictors for software development was studied. It was found that there are some significant factors that could be useful as predictors. From a study on software errors and complexity, it was shown that meaningful results can be obtained which allow insight into software traits and the environment in which it is developed. Reliability models were studied. The research included the field of program testing because the validity of some reliability models depends on the answers to some unanswered questions about testing. In studying software metrics, data collected from seven software engineering laboratory (FORTRAN) projects were examined and three effort reporting accuracy checks were applied to demonstrate the need to validate a data base. Results are discussed.

A Concept-Wide Association Study of Clinical Notes to Discover New Predictors of Kidney Failure.

PubMed

Singh, Karandeep; Betensky, Rebecca A; Wright, Adam; Curhan, Gary C; Bates, David W; Waikar, Sushrut S

2016-12-07

Identifying predictors of kidney disease progression is critical toward the development of strategies to prevent kidney failure. Clinical notes provide a unique opportunity for big data approaches to identify novel risk factors for disease. We used natural language processing tools to extract concepts from the preceding year's clinical notes among patients newly referred to a tertiary care center's outpatient nephrology clinics and retrospectively evaluated these concepts as predictors for the subsequent development of ESRD using proportional subdistribution hazards (competing risk) regression. The primary outcome was time to ESRD, accounting for a competing risk of death. We identified predictors from univariate and multivariate (adjusting for Tangri linear predictor) models using a 5% threshold for false discovery rate (q value <0.05). We included all patients seen by an adult outpatient nephrologist between January 1, 2004 and June 18, 2014 and excluded patients seen only by transplant nephrology, with preexisting ESRD, with fewer than five clinical notes, with no follow-up, or with no baseline creatinine values. Among the 4013 patients selected in the final study cohort, we identified 960 concepts in the unadjusted analysis and 885 concepts in the adjusted analysis. Novel predictors identified included high-dose ascorbic acid (adjusted hazard ratio, 5.48; 95% confidence interval, 2.80 to 10.70; q<0.001) and fast food (adjusted hazard ratio, 4.34; 95% confidence interval, 2.55 to 7.40; q<0.001). Novel predictors of human disease may be identified using an unbiased approach to analyze text from the electronic health record. Copyright © 2016 by the American Society of Nephrology.

Forest Type and Above Ground Biomass Estimation Based on Sentinel-2A and WorldView-2 Data Evaluation of Predictor nd Data Suitability

NASA Astrophysics Data System (ADS)

Fritz, Andreas; Enßle, Fabian; Zhang, Xiaoli; Koch, Barbara

2016-08-01

The present study analyses the two earth observation sensors regarding their capability of modelling forest above ground biomass and forest density. Our research is carried out at two different demonstration sites. The first is located in south-western Germany (region Karlsruhe) and the second is located in southern China in Jiangle County (Province Fujian). A set of spectral and spatial predictors are computed from both, Sentinel-2A and WorldView-2 data. Window sizes in the range of 3*3 pixels to 21*21 pixels are computed in order to cover the full range of the canopy sizes of mature forest stands. Textural predictors of first and second order (grey-level-co-occurrence matrix) are calculated and are further used within a feature selection procedure. Additionally common spectral predictors from WorldView-2 and Sentinel-2A data such as all relevant spectral bands and NDVI are integrated in the analyses. To examine the most important predictors, a predictor selection algorithm is applied to the data, whereas the entire predictor set of more than 1000 predictors is used to find most important ones. Out of the original set only the most important predictors are then further analysed. Predictor selection is done with the Boruta package in R (Kursa and Rudnicki (2010)), whereas regression is computed with random forest. Prior the classification and regression a tuning of parameters is done by a repetitive model selection (100 runs), based on the .632 bootstrapping. Both are implemented in the caret R pack- age (Kuhn et al. (2016)). To account for the variability in the data set 100 independent runs are performed. Within each run 80 percent of the data is used for training and the 20 percent are used for an independent validation. With the subset of original predictors mapping of above ground biomass is performed.

Simplified filtered Smith predictor for MIMO processes with multiple time delays.

PubMed

Santos, Tito L M; Torrico, Bismark C; Normey-Rico, Julio E

2016-11-01

This paper proposes a simplified tuning strategy for the multivariable filtered Smith predictor. It is shown that offset-free control can be achieved with step references and disturbances regardless of the poles of the primary controller, i.e., integral action is not explicitly required. This strategy reduces the number of design parameters and simplifies tuning procedure because the implicit integrative poles are not considered for design purposes. The simplified approach can be used to design continuous-time or discrete-time controllers. Three case studies are used to illustrate the advantages of the proposed strategy if compared with the standard approach, which is based on the explicit integrative action. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Impact of telephone reinforcement and negotiated contracts on behavioral predictors of exercise maintenance in older adults with osteoarthritis.

PubMed

Desai, Pakaja M; Hughes, Susan L; Peters, Karen E; Mermelstein, Robin J

2014-05-01

To examine the impact of telephone reinforcement (TR) on predictors of physical activity (PA) maintenance in older adults with osteoarthritis. Mixed effects modeling was conducted of data from a randomized PA trial that used negotiated maintenance contracts, supplemented by TR, to test impact of TR on barriers, decisional balance, and stage of change at multiple points in time. Participants who were referred to a PA program and received TR improved the most in barriers and decisional balance. Participants who negotiated a tailored maintenance contract but did not receive TR improved the most in stage. TR appears to positively affect perceptions around engagement, whereas negotiation positively impacts PA behavior. Further research should examine the effectiveness of specific PA maintenance strategies.

Fuzzy neural network technique for system state forecasting.

PubMed

Li, Dezhi; Wang, Wilson; Ismail, Fathy

2013-10-01

In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

The impact of SOA for achieving healthcare interoperability. An empirical investigation based on a hypothetical adoption.

PubMed

Daskalakis, S; Mantas, J

2009-01-01

The evaluation of a service-oriented prototype implementation for healthcare interoperability. A prototype framework was developed, aiming to exploit the use of service-oriented architecture (SOA) concepts for achieving healthcare interoperability and to move towards a virtual patient record (VPR) paradigm. The prototype implementation was evaluated for its hypothetical adoption. The evaluation strategy was based on the initial proposition of the DeLone and McLean model of information systems (IS) success [1], as modeled by Iivari [2]. A set of SOA and VPR characteristics were empirically encapsulated within the dimensions of IS success model, combined with measures from previous research works. The data gathered was analyzed using partial least squares (PLS). The results highlighted that system quality is a partial predictor of system use but not of user satisfaction. On the contrary, information quality proved to be a significant predictor of user satisfaction and partially a strong significant predictor of system use. Moreover, system use did not prove to be a significant predictor of individual impact whereas the bi-directional relation between use and user satisfaction did not confirm. Additionally, user satisfaction was found to be a strong significant predictor of individual impact. Finally, individual impact proved to be a strong significant predictor of organizational impact. The empirical study attempted to obtain hypothetical, but still useful beliefs and perceptions regarding the SOA prototype implementation. The deduced observations can form the basis for further investigation regarding the adaptability of SOA implementations with VPR characteristics in the healthcare domain.

Continuity and Change in, and Child Predictors of, Caregiver Reported Anxiety Symptoms in Young People with Autism Spectrum Disorder: A Follow-Up Study

ERIC Educational Resources Information Center

Teh, Elizabeth J.; Chan, Diana Mei-En; Tan, Germaine Ke Jia; Magiati, Iliana

2017-01-01

Little is known about continuity, change and predictors of anxiety in ASD. This follow-up study investigated changes in caregiver-reported anxiety in 54 non-referred youth with ASD after 10-19 months. Earlier child predictors of later anxiety were also examined. Anxiety scores were generally stable. Time 1 ASD repetitive behavior symptoms, but not…

Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations.

PubMed

Hodgson, Luke Eliot; Sarnowski, Alexander; Roderick, Paul J; Dimitrov, Borislav D; Venn, Richard M; Forni, Lui G

2017-09-27

Critically appraise prediction models for hospital-acquired acute kidney injury (HA-AKI) in general populations. Systematic review. Medline, Embase and Web of Science until November 2016. Studies describing development of a multivariable model for predicting HA-AKI in non-specialised adult hospital populations. Published guidance followed for data extraction reporting and appraisal. 14 046 references were screened. Of 53 HA-AKI prediction models, 11 met inclusion criteria (general medicine and/or surgery populations, 474 478 patient episodes) and five externally validated. The most common predictors were age (n=9 models), diabetes (5), admission serum creatinine (SCr) (5), chronic kidney disease (CKD) (4), drugs (diuretics (4) and/or ACE inhibitors/angiotensin-receptor blockers (3)), bicarbonate and heart failure (4 models each). Heterogeneity was identified for outcome definition. Deficiencies in reporting included handling of predictors, missing data and sample size. Admission SCr was frequently taken to represent baseline renal function. Most models were considered at high risk of bias. Area under the receiver operating characteristic curves to predict HA-AKI ranged 0.71-0.80 in derivation (reported in 8/11 studies), 0.66-0.80 for internal validation studies (n=7) and 0.65-0.71 in five external validations. For calibration, the Hosmer-Lemeshow test or a calibration plot was provided in 4/11 derivations, 3/11 internal and 3/5 external validations. A minority of the models allow easy bedside calculation and potential electronic automation. No impact analysis studies were found. AKI prediction models may help address shortcomings in risk assessment; however, in general hospital populations, few have external validation. Similar predictors reflect an elderly demographic with chronic comorbidities. Reporting deficiencies mirrors prediction research more broadly, with handling of SCr (baseline function and use as a predictor) a concern. Future research should focus on validation, exploration of electronic linkage and impact analysis. The latter could combine a prediction model with AKI alerting to address prevention and early recognition of evolving AKI. © 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.

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Predictors of Weapon Carrying in Youth Attending Drop-in Centers

PubMed Central

Blumberg, Elaine J.; Liles, Sandy; Kelley, Norma J.; Hovell, Melbourne F.; Bousman, Chad A.; Shillington, Audrey M.; Ji, Ming; Clapp, John

2012-01-01

Objective To test and compare 2 predictive models of weapon carrying in youth (n=308) recruited from 4 drop-in centers in San Diego and Imperial counties. Methods Both models were based on the Behavioral Ecological Model (BEM). Results The first and second models significantly explained 39% and 53% of the variance in weapon carrying, respectively, and both full models shared the significant predictors of being black(−), being Hispanic (−), peer modeling of weapon carrying/jail time(+), and school suspensions(+). Conclusions Results suggest that the BEM offers a generalizable conceptual model that may inform prevention strategies for youth at greatest risk of weapon carrying. PMID:19320622

Mathematical skills in 3- and 5-year-olds with spina bifida and their typically developing peers: a longitudinal approach.

PubMed

Barnes, Marcia A; Stubbs, Allison; Raghubar, Kimberly P; Agostino, Alba; Taylor, Heather; Landry, Susan; Fletcher, Jack M; Smith-Chant, Brenda

2011-05-01

Preschoolers with spina bifida (SB) were compared to typically developing (TD) children on tasks tapping mathematical knowledge at 36 months (n = 102) and 60 months of age (n = 98). The group with SB had difficulty compared to TD peers on all mathematical tasks except for transformation on quantities in the subitizable range. At 36 months, vocabulary knowledge, visual-spatial, and fine motor abilities predicted achievement on a measure of informal math knowledge in both groups. At 60 months of age, phonological awareness, visual-spatial ability, and fine motor skill were uniquely and differentially related to counting knowledge, oral counting, object-based arithmetic skills, and quantitative concepts. Importantly, the patterns of association between these predictors and mathematical performance were similar across the groups. A novel finding is that fine motor skill uniquely predicted object-based arithmetic abilities in both groups, suggesting developmental continuity in the neurocognitive correlates of early object-based and later symbolic arithmetic problem solving. Models combining 36-month mathematical ability and these language-based, visual-spatial, and fine motor abilities at 60 months accounted for considerable variance on 60-month informal mathematical outcomes. Results are discussed with reference to models of mathematical development and early identification of risk in preschoolers with neurodevelopmental disorder.

Mathematical Skills in 3- and 5-Year-Olds with Spina Bifida and Their Typically Developing Peers: A Longitudinal Approach

PubMed Central

Barnes, Marcia A.; Stubbs, Allison; Raghubar, Kimberly P.; Agostino, Alba; Taylor, Heather; Landry, Susan; Fletcher, Jack M.; Smith-Chant, Brenda

2011-01-01

Preschoolers with spina bifida (SB) were compared to typically developing (TD) children on tasks tapping mathematical knowledge at 36 months (n = 102) and 60 months of age (n = 98). The group with SB had difficulty compared to TD peers on all mathematical tasks except for transformation on quantities in the subitizable range. At 36 months, vocabulary knowledge, visual–spatial, and fine motor abilities predicted achievement on a measure of informal math knowledge in both groups. At 60 months of age, phonological awareness, visual–spatial ability, and fine motor skill were uniquely and differentially related to counting knowledge, oral counting, object-based arithmetic skills, and quantitative concepts. Importantly, the patterns of association between these predictors and mathematical performance were similar across the groups. A novel finding is that fine motor skill uniquely predicted object-based arithmetic abilities in both groups, suggesting developmental continuity in the neurocognitive correlates of early object-based and later symbolic arithmetic problem solving. Models combining 36-month mathematical ability and these language-based, visual–spatial, and fine motor abilities at 60 months accounted for considerable variance on 60-month informal mathematical outcomes. Results are discussed with reference to models of mathematical development and early identification of risk in preschoolers with neurodevelopmental disorder. PMID:21418718

An investigation of meaningful understanding and effectiveness of the implementation of Piagetian and Ausubelian theories in physics instruction

NASA Astrophysics Data System (ADS)

Williams, Karen Ann

One section of college students (N = 25) enrolled in an algebra-based physics course was selected for a Piagetian-based learning cycle (LC) treatment while a second section (N = 25) studied in an Ausubelian-based meaningful verbal reception learning treatment (MVRL). This study examined the students' overall (concept + problem solving + mental model) meaningful understanding of force, density/Archimedes Principle, and heat. Also examined were students' meaningful understanding as measured by conceptual questions, problems, and mental models. In addition, students' learning orientations were examined. There were no significant posttest differences between the LC and MVRL groups for students' meaningful understanding or learning orientation. Piagetian and Ausubelian theories explain meaningful understanding for each treatment. Students from each treatment increased their meaningful understanding. However, neither group altered their learning orientation. The results of meaningful understanding as measured by conceptual questions, problem solving, and mental models were mixed. Differences were attributed to the weaknesses and strengths of each treatment. This research also examined four variables (treatment, reasoning ability, learning orientation, and prior knowledge) to find which best predicted students' overall meaningful understanding of physics concepts. None of these variables were significant predictors at the.05 level. However, when the same variables were used to predict students' specific understanding (i.e. concept, problem solving, or mental model understanding), the results were mixed. For forces and density/Archimedes Principle, prior knowledge and reasoning ability significantly predicted students' conceptual understanding. For heat, however, reasoning ability was the only significant predictor of concept understanding. Reasoning ability and treatment were significant predictors of students' problem solving for heat and forces. For density/Archimedes Principle, treatment was the only significant predictor of students' problem solving. None of the variables were significant predictors of mental model understanding. This research suggested that Piaget and Ausubel used different terminology to describe learning yet these theories are similar. Further research is needed to validate this premise and validate the blending of the two theories.

Sarnoff JND Vision Model for Flat-Panel Design

NASA Technical Reports Server (NTRS)

Brill, Michael H.; Lubin, Jeffrey

1998-01-01

This document describes adaptation of the basic Sarnoff JND Vision Model created in response to the NASA/ARPA need for a general-purpose model to predict the perceived image quality attained by flat-panel displays. The JND model predicts the perceptual ratings that humans will assign to a degraded color-image sequence relative to its nondegraded counterpart. Substantial flexibility is incorporated into this version of the model so it may be used to model displays at the sub-pixel and sub-frame level. To model a display (e.g., an LCD), the input-image data can be sampled at many times the pixel resolution and at many times the digital frame rate. The first stage of the model downsamples each sequence in time and in space to physiologically reasonable rates, but with minimum interpolative artifacts and aliasing. Luma and chroma parts of the model generate (through multi-resolution pyramid representation) a map of differences-between test and reference called the JND map, from which a summary rating predictor is derived. The latest model extensions have done well in calibration against psychophysical data and against image-rating data given a CRT-based front-end. THe software was delivered to NASA Ames and is being integrated with LCD display models at that facility,

Social Victimization Trajectories From Middle Childhood Through Late Adolescence

PubMed Central

Rosen, Lisa H.; Beron, Kurt J.; Underwood, Marion K.

2016-01-01

Social victimization refers to being targeted by behaviors intended to harm one’s social status or relationships (Underwood, 2003), including malicious gossip, friendship manipulation, and social exclusion (both verbal and non-verbal). The current study examined social victimization experiences longitudinally from middle childhood through late adolescence. Participants (N = 273, 139 females) reported on their social victimization experiences in grades 4–11 (ages 9 to 16 years). Using mixture (group-based) modeling, four social victimization trajectories were identified: low, medium decreasing, medium increasing, and elevated. High parent-child relationship quality decreased the odds of being in the elevated group compared to the low group; however, parent-child relationship quality was no longer a significant predictor when emotional dysfunction was added to the model. Higher emotional dysfunction and male gender increased the odds of being in the elevated group and medium increaser group relative to the low group even after controlling for parent-child relationship quality. Implications for intervention and future research directions are discussed. PMID:28408789

Pattern and Predictors of Outpatient Palliative Care Referral Among Thoracic Medical Oncologists.

PubMed

Hui, David; Kilgore, Kelly; Park, Minjeong; Liu, Diane; Kim, Yu Jung; Park, Ji Chan; Fossella, Frank; Bruera, Eduardo

2018-06-12

There is significant variation in access to palliative care. We examined the pattern of outpatient palliative care referral among thoracic medical oncologists and identified oncologist characteristics associated with greater referral. We retrieved data on all patients who died of advanced thoracic malignancies at our institution between January 1, 2007, and December 31, 2012. Using median as a cutoff, we defined two groups (high-referring and low-referring oncologists) based on their frequency of referral. We examined various oncologist- and patient-related characteristics associated with outpatient referral. Of 1,642 decedents, 444 (27%) had an outpatient palliative care referral. The median proportion of referral among 26 thoracic oncologists was 30% (range 9%-45%; median proportion of high-referring 37% vs. low-referring 24% when divided into two groups at median). High-referring oncologists were significantly younger (age 45 vs. 56) than low-referring oncologists; they were also significantly more likely to refer patients earlier (median interval between oncology consultation and palliative care consultation 90 days vs. 170 days) and to refer those without metastatic disease (7% vs. 2%). In multivariable mixed-effect logistic regression, younger oncologists (odds ratio [OR] = 0.97 per year increase, 95% confidence interval [CI] 0.95-0.995), younger patients (OR = 0.98 per year increase, 95% CI 0.97-0.99), and nonmetastatic disease status (OR = 0.48, 95% CI 0.29-0.78) were significantly associated with outpatient palliative care referral. The pattern of referral to outpatient palliative care varied widely among thoracic oncologists. Younger oncologists were not only referring a higher proportion of patients, but also referring patients earlier in the disease trajectory. This retrospective cohort study found that younger thoracic medical oncologists were significantly more likely to refer patients to outpatient palliative care and to do so earlier in the disease trajectory compared with older oncologists, even after adjusting for other known predictors such as patient demographics. The findings highlight the role of education to standardize palliative care access and imply that outpatient palliative care referral is likely to continue to increase with a shifting oncology workforce. © AlphaMed Press 2018.

Predicting Speech Intelligibility Decline in Amyotrophic Lateral Sclerosis Based on the Deterioration of Individual Speech Subsystems

PubMed Central

Yunusova, Yana; Wang, Jun; Zinman, Lorne; Pattee, Gary L.; Berry, James D.; Perry, Bridget; Green, Jordan R.

2016-01-01

Purpose To determine the mechanisms of speech intelligibility impairment due to neurologic impairments, intelligibility decline was modeled as a function of co-occurring changes in the articulatory, resonatory, phonatory, and respiratory subsystems. Method Sixty-six individuals diagnosed with amyotrophic lateral sclerosis (ALS) were studied longitudinally. The disease-related changes in articulatory, resonatory, phonatory, and respiratory subsystems were quantified using multiple instrumental measures, which were subjected to a principal component analysis and mixed effects models to derive a set of speech subsystem predictors. A stepwise approach was used to select the best set of subsystem predictors to model the overall decline in intelligibility. Results Intelligibility was modeled as a function of five predictors that corresponded to velocities of lip and jaw movements (articulatory), number of syllable repetitions in the alternating motion rate task (articulatory), nasal airflow (resonatory), maximum fundamental frequency (phonatory), and speech pauses (respiratory). The model accounted for 95.6% of the variance in intelligibility, among which the articulatory predictors showed the most substantial independent contribution (57.7%). Conclusion Articulatory impairments characterized by reduced velocities of lip and jaw movements and resonatory impairments characterized by increased nasal airflow served as the subsystem predictors of the longitudinal decline of speech intelligibility in ALS. Declines in maximum performance tasks such as the alternating motion rate preceded declines in intelligibility, thus serving as early predictors of bulbar dysfunction. Following the rapid decline in speech intelligibility, a precipitous decline in maximum performance tasks subsequently occurred. PMID:27148967

Predicting Speech Intelligibility Decline in Amyotrophic Lateral Sclerosis Based on the Deterioration of Individual Speech Subsystems.

PubMed

Rong, Panying; Yunusova, Yana; Wang, Jun; Zinman, Lorne; Pattee, Gary L; Berry, James D; Perry, Bridget; Green, Jordan R

2016-01-01

To determine the mechanisms of speech intelligibility impairment due to neurologic impairments, intelligibility decline was modeled as a function of co-occurring changes in the articulatory, resonatory, phonatory, and respiratory subsystems. Sixty-six individuals diagnosed with amyotrophic lateral sclerosis (ALS) were studied longitudinally. The disease-related changes in articulatory, resonatory, phonatory, and respiratory subsystems were quantified using multiple instrumental measures, which were subjected to a principal component analysis and mixed effects models to derive a set of speech subsystem predictors. A stepwise approach was used to select the best set of subsystem predictors to model the overall decline in intelligibility. Intelligibility was modeled as a function of five predictors that corresponded to velocities of lip and jaw movements (articulatory), number of syllable repetitions in the alternating motion rate task (articulatory), nasal airflow (resonatory), maximum fundamental frequency (phonatory), and speech pauses (respiratory). The model accounted for 95.6% of the variance in intelligibility, among which the articulatory predictors showed the most substantial independent contribution (57.7%). Articulatory impairments characterized by reduced velocities of lip and jaw movements and resonatory impairments characterized by increased nasal airflow served as the subsystem predictors of the longitudinal decline of speech intelligibility in ALS. Declines in maximum performance tasks such as the alternating motion rate preceded declines in intelligibility, thus serving as early predictors of bulbar dysfunction. Following the rapid decline in speech intelligibility, a precipitous decline in maximum performance tasks subsequently occurred.

Ethnic distribution of ECG predictors of atrial fibrillation and its impact on understanding the ethnic distribution of ischemic stroke in the Atherosclerosis Risk in Communities (ARIC) study.

PubMed

Soliman, Elsayed Z; Prineas, Ronald J; Case, L Douglas; Zhang, Zhu-ming; Goff, David C

2009-04-01

The paradox of the reported low prevalence of atrial fibrillation (AF) in blacks compared with whites despite higher stroke rates in the former could be related to limitations in the current methods used to diagnose AF in population-based studies. Hence, this study aimed to use the ethnic distribution of ECG predictors of AF as measures of AF propensity in different ethnic groups. The distribution of baseline measures of P-wave terminal force, P-wave duration, P-wave area, and PR duration (referred to as AF predictors) were compared by ethnicity in 15 429 participants (27% black) from the Atherosclerosis Risk in Communities (ARIC) study by unpaired t test, chi(2), and logistic-regression analysis, as appropriate. Cox proportional-hazards analysis was used to separately examine the association of AF predictors with incident AF and ischemic stroke. Whereas AF was significantly less common in blacks compared with whites (0.24% vs 0.95%, P<0.0001), similar to what has been reported in previous studies, blacks had significantly higher and more abnormal values of AF predictors (P<0.0001 for all comparisons). Black ethnicity was significantly associated with abnormal AF predictors compared with whites; odds ratios for different AF predictors ranged from 2.1 to 3.1. AF predictors were significantly and independently associated with AF and ischemic stroke with no significant interaction between ethnicity and AF predictors, findings that further justify using AF predictors as an earlier indicator of future risk of AF and stroke. There is a disconnect between the ethnic distribution of AF predictors and the ethnic distribution of AF, probably because the former, unlike the latter, do not suffer from low sensitivity. These results raise the possibility that blacks might actually have a higher prevalence of AF that might have been missed by previous studies owing to limited methodology, a difference that could partially explain the greater stroke risk in blacks.

Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model

NASA Technical Reports Server (NTRS)

Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long

2001-01-01

This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships

PubMed Central

Sachindra, D. A.; Perera, B. J. C.

2016-01-01

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950–2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950–2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950–69, 1970–89 and 1990–99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP). PMID:27997609

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships.

PubMed

Sachindra, D A; Perera, B J C

2016-01-01

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950-2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950-2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950-69, 1970-89 and 1990-99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP).

Epidemiology and predictive factors for early and late mortality in Candida bloodstream infections: a population-based surveillance in Spain.

PubMed

Puig-Asensio, M; Padilla, B; Garnacho-Montero, J; Zaragoza, O; Aguado, J M; Zaragoza, R; Montejo, M; Muñoz, P; Ruiz-Camps, I; Cuenca-Estrella, M; Almirante, B

2014-04-01

A prospective, multicentre, population-based surveillance programme for Candida bloodstream infections was implemented in five metropolitan areas of Spain to determine its incidence and the prevalence of antifungal resistance, and to identify predictors of death. Between May 2010 and April 2011, Candida isolates were centralized to a reference laboratory for species identification by DNA sequencing and for susceptibility testing by EUCAST reference procedure. Prognostic factors associated with early (0-7 days) and late (8-30 days) death were analysed using logistic regression modelling. We detected 773 episodes: annual incidence of 8.1 cases/100 000 inhabitants, 0.89/1000 admissions and 1.36/10 000 patient-days. Highest incidence was found in infants younger than 1 year (96.4/100 000 inhabitants). Candida albicans was the predominant species (45.4%), followed by Candida parapsilosis (24.9%), Candida glabrata (13.4%) and Candida tropicalis (7.7%). Overall, 79% of Candida isolates were susceptible to fluconazole. Cumulative mortality at 7 and 30 days after the first episode of candidaemia was 12.8% and 30.6%, respectively. Multivariate analysis showed that therapeutic measures within the first 48 h may improve early mortality: antifungal treatment (OR 0.51, 95% CI 0.27-0.95) and central venous catheter removal (OR 0.43, 95% CI 0.21-0.87). Predictors of late death included host factors (e.g. patients' comorbid status and signs of organ dysfunction), primary source (OR 1.63, 95% CI 1.03-2.61), and severe sepsis or septic shock (OR 1.77, 95% CI 1.05-3.00). In Spain, the proportion of Candida isolates non-susceptible to fluconazole is higher than in previous reports. Early mortality may be improved with strict adherence to guidelines. © 2013 The Authors Clinical Microbiology and Infection © 2013 European Society of Clinical Microbiology and Infectious Diseases.

The Northern Norway Mother-and-Child Contaminant Cohort (MISA) Study: PCA analyses of environmental contaminants in maternal sera and dietary intake in early pregnancy.

PubMed

Veyhe, Anna Sofía; Hofoss, Dag; Hansen, Solrunn; Thomassen, Yngvar; Sandanger, Torkjel M; Odland, Jon Øyvind; Nieboer, Evert

2015-03-01

Although predictors of contaminants in serum or whole blood are usually examined by chemical groups (e.g., POPs, toxic and/or essential elements; dietary sources), principal component analysis (PCA) permits consideration of both individual substances and combined variables. Our study had two primary objectives: (i) Characterize the sources and predictors of a suite of eight PCBs, four organochlorine (OC) pesticides, five essential and five toxic elements in serum and/or whole blood of pregnant women recruited as part of the Mother-and-Child Contaminant Cohort Study conducted in Northern Norway (The MISA study); and (ii) determine the influence of personal and social characteristics on both dietary and contaminant factors. Recruitment and sampling started in May 2007 and continued for the next 31 months until December 2009. Blood/serum samples were collected during the 2nd trimester (mean: 18.2 weeks, range 9.0-36.0). A validated questionnaire was administered to obtain personal information. The samples were analysed by established laboratories employing verified methods and reference standards. PCA involved Varimax rotation, and significant predictors (p≤0.05) in linear regression models were included in the multivariable linear regression analysis. When considering all the contaminants, three prominent PCA axes stood out with prominent loadings of: all POPs; arsenic, selenium and mercury; and cadmium and lead. Respectively, in the multivariate models the following were predictors: maternal age, parity and consumption of freshwater fish and land-based wild animals; marine fish; cigarette smoking, dietary PCA axes reflecting consumption of grains and cereals, and food items involving hunting. PCA of only the POPs separated them into two axes that, in terms of recently published findings, could be understood to reflect longitudinal trends and their relative contributions to summed POPs. The linear combinations of variables generated by PCA identified prominent dietary sources of OC groups and of prominent toxic elements and highlighted the importance of maternal characteristics. Copyright © 2014 Elsevier GmbH. All rights reserved.

Regression calibration for models with two predictor variables measured with error and their interaction, using instrumental variables and longitudinal data.

PubMed

Strand, Matthew; Sillau, Stefan; Grunwald, Gary K; Rabinovitch, Nathan

2014-02-10

Regression calibration provides a way to obtain unbiased estimators of fixed effects in regression models when one or more predictors are measured with error. Recent development of measurement error methods has focused on models that include interaction terms between measured-with-error predictors, and separately, methods for estimation in models that account for correlated data. In this work, we derive explicit and novel forms of regression calibration estimators and associated asymptotic variances for longitudinal models that include interaction terms, when data from instrumental and unbiased surrogate variables are available but not the actual predictors of interest. The longitudinal data are fit using linear mixed models that contain random intercepts and account for serial correlation and unequally spaced observations. The motivating application involves a longitudinal study of exposure to two pollutants (predictors) - outdoor fine particulate matter and cigarette smoke - and their association in interactive form with levels of a biomarker of inflammation, leukotriene E4 (LTE 4 , outcome) in asthmatic children. Because the exposure concentrations could not be directly observed, we used measurements from a fixed outdoor monitor and urinary cotinine concentrations as instrumental variables, and we used concentrations of fine ambient particulate matter and cigarette smoke measured with error by personal monitors as unbiased surrogate variables. We applied the derived regression calibration methods to estimate coefficients of the unobserved predictors and their interaction, allowing for direct comparison of toxicity of the different pollutants. We used simulations to verify accuracy of inferential methods based on asymptotic theory. Copyright © 2013 John Wiley & Sons, Ltd.

On the Logical Development of Statistical Models.

DTIC Science & Technology

1983-12-01

David Steinberg, and Jean Wallis for their helpful cosments to a preliminary draft of this work. -21- REFERENCES [1] Anscombe, F. J. and Tukey, J. W ...effect of a known vector x of predictor variables. In this way we obtain an explicative-static or type 11 model and -3- W .- r󈧬 41 U) 3 0) H + N 1 0...dependency is: Yt M it + ut p(ut ) - U It P(ut M-t) - I - t (2.1) t W P01 + Vt-1(P11 - P0 1) where Yt can only have the values zero and one, p is its

Ecological Predictors of Disciplinary Style and Child Abuse Potential in a Hispanic and Anglo-American Sample

ERIC Educational Resources Information Center

Rodriguez, Christina M.

2008-01-01

Recent attention to multicultural issues has sparked recognition that parenting is also a culturally construed phenomenon. The present study involved a diverse sample of 90 Anglo-American and Hispanic parents examining predictors based on distal/proximal levels as conceptualized in the ecological model. The study examined background…

Analysis of the interannual variability of tropical cyclones striking the California coast based on statistical downscaling

NASA Astrophysics Data System (ADS)

Mendez, F. J.; Rueda, A.; Barnard, P.; Mori, N.; Nakajo, S.; Espejo, A.; del Jesus, M.; Diez Sierra, J.; Cofino, A. S.; Camus, P.

2016-02-01

Hurricanes hitting California have a very low ocurrence probability due to typically cool ocean temperature and westward tracks. However, damages associated to these improbable events would be dramatic in Southern California and understanding the oceanographic and atmospheric drivers is of paramount importance for coastal risk management for present and future climates. A statistical analysis of the historical events is very difficult due to the limited resolution of atmospheric and oceanographic forcing data available. In this work, we propose a combination of: (a) statistical downscaling methods (Espejo et al, 2015); and (b) a synthetic stochastic tropical cyclone (TC) model (Nakajo et al, 2014). To build the statistical downscaling model, Y=f(X), we apply a combination of principal component analysis and the k-means classification algorithm to find representative patterns from a potential TC index derived from large-scale SST fields in Eastern Central Pacific (predictor X) and the associated tropical cyclone ocurrence (predictand Y). SST data comes from NOAA Extended Reconstructed SST V3b providing information from 1854 to 2013 on a 2.0 degree x 2.0 degree global grid. As data for the historical occurrence and paths of tropical cycloneas are scarce, we apply a stochastic TC model which is based on a Monte Carlo simulation of the joint distribution of track, minimum sea level pressure and translation speed of the historical events in the Eastern Central Pacific Ocean. Results will show the ability of the approach to explain seasonal-to-interannual variability of the predictor X, which is clearly related to El Niño Southern Oscillation. References Espejo, A., Méndez, F.J., Diez, J., Medina, R., Al-Yahyai, S. (2015) Seasonal probabilistic forecasting of tropical cyclone activity in the North Indian Ocean, Journal of Flood Risk Management, DOI: 10.1111/jfr3.12197 Nakajo, S., N. Mori, T. Yasuda, and H. Mase (2014) Global Stochastic Tropical Cyclone Model Based on Principal Component Analysis and Cluster Analysis, Journal of Applied Meteorology and Climatology, DOI: 10.1175/JAMC-D-13-08.1

Normative reference values for strength and flexibility of 1,000 children and adults.

PubMed

McKay, Marnee J; Baldwin, Jennifer N; Ferreira, Paulo; Simic, Milena; Vanicek, Natalie; Burns, Joshua

2017-01-03

To establish reference values for isometric strength of 12 muscle groups and flexibility of 13 joint movements in 1,000 children and adults and investigate the influence of demographic and anthropometric factors. A standardized reliable protocol of hand-held and fixed dynamometry for isometric strength of ankle, knee, hip, elbow, and shoulder musculature as well as goniometry for flexibility of the ankle, knee, hip, elbow, shoulder, and cervical spine was performed in an observational study investigating 1,000 healthy male and female participants aged 3-101 years. Correlation and multiple regression analyses were performed to identify factors independently associated with strength and flexibility of children, adolescents, adults, and older adults. Normative reference values of 25 strength and flexibility measures were generated. Strong linear correlations between age and strength were identified in the first 2 decades of life. Muscle strength significantly decreased with age in older adults. Regression modeling identified increasing height as the most significant predictor of strength in children, higher body mass in adolescents, and male sex in adults and older adults. Joint flexibility gradually decreased with age, with little sex difference. Waist circumference was a significant predictor of variability in joint flexibility in adolescents, adults, and older adults. Reference values and associated age- and sex-stratified z scores generated from this study can be used to determine the presence and extent of impairments associated with neuromuscular and other neurologic disorders, monitor disease progression over time in natural history studies, and evaluate the effect of new treatments in clinical trials. © 2016 American Academy of Neurology.

Can Psychological, Social and Demographical Factors Predict Clinical Characteristics Symptomatology of Bipolar Affective Disorder and Schizophrenia?

PubMed

Maciukiewicz, Malgorzata; Pawlak, Joanna; Kapelski, Pawel; Łabędzka, Magdalena; Skibinska, Maria; Zaremba, Dorota; Leszczynska-Rodziewicz, Anna; Dmitrzak-Weglarz, Monika; Hauser, Joanna

2016-09-01

Schizophrenia (SCH) is a complex, psychiatric disorder affecting 1 % of population. Its clinical phenotype is heterogeneous with delusions, hallucinations, depression, disorganized behaviour and negative symptoms. Bipolar affective disorder (BD) refers to periodic changes in mood and activity from depression to mania. It affects 0.5-1.5 % of population. Two types of disorder (type I and type II) are distinguished by severity of mania episodes. In our analysis, we aimed to check if clinical and demographical characteristics of the sample are predictors of symptom dimensions occurrence in BD and SCH cases. We included total sample of 443 bipolar and 439 schizophrenia patients. Diagnosis was based on DSM-IV criteria using Structured Clinical Interview for DSM-IV. We applied regression models to analyse associations between clinical and demographical traits from OPCRIT and symptom dimensions. We used previously computed dimensions of schizophrenia and bipolar affective disorder as quantitative traits for regression models. Male gender seemed protective factor for depression dimension in schizophrenia and bipolar disorder sample. Presence of definite psychosocial stressor prior disease seemed risk factor for depressive and suicidal domain in BD and SCH. OPCRIT items describing premorbid functioning seemed related with depression, positive and disorganised dimensions in schizophrenia and psychotic in BD. We proved clinical and demographical characteristics of the sample are predictors of symptom dimensions of schizophrenia and bipolar disorder. We also saw relation between clinical dimensions and course of disorder and impairment during disorder.

Efficacy of the theory of planned behavior in predicting breastfeeding: Meta-analysis and structural equation modeling.

PubMed

Guo, J L; Wang, T F; Liao, J Y; Huang, C M

2016-02-01

This study assessed the applicability and efficacy of the theory of planned behavior (TPB) in predicting breastfeeding. The TPB assumes a rational approach for engaging in various behaviors, and has been used extensively for explaining health behavior. However, most studies have tested the effectiveness of TPB constructs in predicting how people perform actions for their own benefit rather than performing behaviors that are beneficial to others, such as breastfeeding infants. A meta-analysis approach could help clarify the breastfeeding practice to promote breastfeeding. This study used meta-analytic procedures. We searched for studies to include in our analysis, examining those published between January 1, 1990 and December 31, 2013 in PubMed, Medline, CINAHL, ProQuest, and Mosby's Index. We also reviewed journals with a history of publishing breastfeeding studies and searched reference lists for potential articles to include. Ten studies comprising a total of 2694 participants were selected for analysis. These studies yielded 10 effect sizes from the TPB, which ranged from 0.20 to 0.59. Structural equation model analysis using the pooled correlation matrix enabled us to determine the relative coefficients among TPB constructs. Attitude, subjective norms, and perceived behavioral control were all significant predictors of breastfeeding intention, whereas intention was a strong predictor of breastfeeding behavior. Perceived behavioral control reached a borderline level of significance to breastfeeding behavior. Theoretical and empirical implications are discussed from the perspective of evidence-based practice. Copyright © 2015 Elsevier Inc. All rights reserved.

Contingent self-esteem, stressors and burnout in working women and men.

PubMed

Blom, Victoria

2012-01-01

A high work involvement is considered central in the burnout process. Yet, research investigating how high work involvement and psychosocial stressors relate to burnout is scarce. High involvement in terms of performance-based self-esteem (PBSE) refers to individuals' strivings to validate self-worth by achievements, a disposition linked to poor health. The aim of the present study was to examine longitudinally PBSE in relation to burnout while also taking into account work- and private life stressors. The sample consisted of 2121 working women and men. Main- and mediation effects were investigated using hierarchical regression analysis. The results showed performance-based self-esteem mediated partially between the stressors and burnout. Performance-based self-esteem was the strongest predictor of burnout over time, followed by private life stressors. Women experienced more work stress than did men. Men had stronger associations between work stressors and burnout, while women had stronger associations between performance-based self-esteem and burnout. Individual characteristics along with both private life and work stressors are important predictors of burnout. Factors associated with burnout differ somewhat between women and men.

Reference-dependent risk sensitivity as rational inference.

PubMed

Denrell, Jerker C

2015-07-01

Existing explanations of reference-dependent risk sensitivity attribute it to cognitive imperfections and heuristic choice processes. This article shows that behavior consistent with an S-shaped value function could be an implication of rational inferences about the expected values of alternatives. Theoretically, I demonstrate that even a risk-neutral Bayesian decision maker, who is uncertain about the reliability of observations, should use variability in observed outcomes as a predictor of low expected value for outcomes above a reference level, and as a predictor of high expected value for outcomes below a reference level. Empirically, I show that combining past outcomes using an S-shaped value function leads to accurate predictions about future values. The theory also offers a rationale for why risk sensitivity consistent with an inverse S-shaped value function should occur in experiments on decisions from experience with binary payoff distributions. (c) 2015 APA, all rights reserved).

Service use and costs for people with headache: a UK primary care study.

PubMed

McCrone, Paul; Seed, Paul T; Dowson, Andrew J; Clark, Lucy V; Goldstein, Laura H; Morgan, Myfanwy; Ridsdale, Leone

2011-12-01

This paper aims to estimate the service and social costs of headache presenting in primary care and to identify predictors of headache costs. Patients were recruited from GP practices in England and service use and lost employment recorded. Predictors of cost were identified using regression models. Service and social costs were available on 288 and 282 patients, respectively. Average service costs over 3 months were £117 whilst total costs (including lost production) were £582. Patients referred to neurologists had service costs that were £82 higher than those not referred (90% CI £36-£128). Costs including lost employment were higher by £150, but this was not significant (90% CI -£139-£439). The annual mean service and social costs, weighted to represent population rates of referral, were £468 and £2328, respectively. Higher costs were significantly related to pain. Age was linked to higher service costs and lower social costs. The figures extrapolated to the whole of the UK suggest £956 million due to service use and £4.8 billion including lost employment. These are likely to be underestimates because many people experiencing headaches do not consult their GP.

Control algorithms for aerobraking in the Martian atmosphere

NASA Technical Reports Server (NTRS)

Ward, Donald T.; Shipley, Buford W., Jr.

1991-01-01

The Analytic Predictor Corrector (APC) and Energy Controller (EC) atmospheric guidance concepts were adapted to control an interplanetary vehicle aerobraking in the Martian atmosphere. Changes are made to the APC to improve its robustness to density variations. These changes include adaptation of a new exit phase algorithm, an adaptive transition velocity to initiate the exit phase, refinement of the reference dynamic pressure calculation and two improved density estimation techniques. The modified controller with the hybrid density estimation technique is called the Mars Hybrid Predictor Corrector (MHPC), while the modified controller with a polynomial density estimator is called the Mars Predictor Corrector (MPC). A Lyapunov Steepest Descent Controller (LSDC) is adapted to control the vehicle. The LSDC lacked robustness, so a Lyapunov tracking exit phase algorithm is developed to guide the vehicle along a reference trajectory. This algorithm, when using the hybrid density estimation technique to define the reference path, is called the Lyapunov Hybrid Tracking Controller (LHTC). With the polynomial density estimator used to define the reference trajectory, the algorithm is called the Lyapunov Tracking Controller (LTC). These four new controllers are tested using a six degree of freedom computer simulation to evaluate their robustness. The MHPC, MPC, LHTC, and LTC show dramatic improvements in robustness over the APC and EC.

A calibration hierarchy for risk models was defined: from utopia to empirical data.

PubMed

Van Calster, Ben; Nieboer, Daan; Vergouwe, Yvonne; De Cock, Bavo; Pencina, Michael J; Steyerberg, Ewout W

2016-06-01

Calibrated risk models are vital for valid decision support. We define four levels of calibration and describe implications for model development and external validation of predictions. We present results based on simulated data sets. A common definition of calibration is "having an event rate of R% among patients with a predicted risk of R%," which we refer to as "moderate calibration." Weaker forms of calibration only require the average predicted risk (mean calibration) or the average prediction effects (weak calibration) to be correct. "Strong calibration" requires that the event rate equals the predicted risk for every covariate pattern. This implies that the model is fully correct for the validation setting. We argue that this is unrealistic: the model type may be incorrect, the linear predictor is only asymptotically unbiased, and all nonlinear and interaction effects should be correctly modeled. In addition, we prove that moderate calibration guarantees nonharmful decision making. Finally, results indicate that a flexible assessment of calibration in small validation data sets is problematic. Strong calibration is desirable for individualized decision support but unrealistic and counter productive by stimulating the development of overly complex models. Model development and external validation should focus on moderate calibration. Copyright © 2016 Elsevier Inc. All rights reserved.

Interval Predictor Models with a Formal Characterization of Uncertainty and Reliability

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Giesy, Daniel P.; Kenny, Sean P.

2014-01-01

This paper develops techniques for constructing empirical predictor models based on observations. By contrast to standard models, which yield a single predicted output at each value of the model's inputs, Interval Predictors Models (IPM) yield an interval into which the unobserved output is predicted to fall. The IPMs proposed prescribe the output as an interval valued function of the model's inputs, render a formal description of both the uncertainty in the model's parameters and of the spread in the predicted output. Uncertainty is prescribed as a hyper-rectangular set in the space of model's parameters. The propagation of this set through the empirical model yields a range of outputs of minimal spread containing all (or, depending on the formulation, most) of the observations. Optimization-based strategies for calculating IPMs and eliminating the effects of outliers are proposed. Outliers are identified by evaluating the extent by which they degrade the tightness of the prediction. This evaluation can be carried out while the IPM is calculated. When the data satisfies mild stochastic assumptions, and the optimization program used for calculating the IPM is convex (or, when its solution coincides with the solution to an auxiliary convex program), the model's reliability (that is, the probability that a future observation would be within the predicted range of outputs) can be bounded rigorously by a non-asymptotic formula.

Functional Status Outperforms Comorbidities as a Predictor of 30-Day Acute Care Readmissions in the Inpatient Rehabilitation Population.

PubMed

Shih, Shirley L; Zafonte, Ross; Bates, David W; Gerrard, Paul; Goldstein, Richard; Mix, Jacqueline; Niewczyk, Paulette; Greysen, S Ryan; Kazis, Lewis; Ryan, Colleen M; Schneider, Jeffrey C

2016-10-01

Functional status is associated with patient outcomes, but is rarely included in hospital readmission risk models. The objective of this study was to determine whether functional status is a better predictor of 30-day acute care readmission than traditionally investigated variables including demographics and comorbidities. Retrospective database analysis between 2002 and 2011. 1158 US inpatient rehabilitation facilities. 4,199,002 inpatient rehabilitation facility admissions comprising patients from 16 impairment groups within the Uniform Data System for Medical Rehabilitation database. Logistic regression models predicting 30-day readmission were developed based on age, gender, comorbidities (Elixhauser comorbidity index, Deyo-Charlson comorbidity index, and Medicare comorbidity tier system), and functional status [Functional Independence Measure (FIM)]. We hypothesized that (1) function-based models would outperform demographic- and comorbidity-based models and (2) the addition of demographic and comorbidity data would not significantly enhance function-based models. For each impairment group, Function Only Models were compared against Demographic-Comorbidity Models and Function Plus Models (Function-Demographic-Comorbidity Models). The primary outcome was 30-day readmission, and the primary measure of model performance was the c-statistic. All-cause 30-day readmission rate from inpatient rehabilitation facilities to acute care hospitals was 9.87%. C-statistics for the Function Only Models were 0.64 to 0.70. For all 16 impairment groups, the Function Only Model demonstrated better c-statistics than the Demographic-Comorbidity Models (c-statistic difference: 0.03-0.12). The best-performing Function Plus Models exhibited negligible improvements in model performance compared to Function Only Models, with c-statistic improvements of only 0.01 to 0.05. Readmissions are currently used as a marker of hospital performance, with recent financial penalties to hospitals for excessive readmissions. Function-based readmission models outperform models based only on demographics and comorbidities. Readmission risk models would benefit from the inclusion of functional status as a primary predictor. Copyright © 2016 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

Forecasting malaria in a highly endemic country using environmental and clinical predictors.

PubMed

Zinszer, Kate; Kigozi, Ruth; Charland, Katia; Dorsey, Grant; Brewer, Timothy F; Brownstein, John S; Kamya, Moses R; Buckeridge, David L

2015-06-18

Malaria thrives in poor tropical and subtropical countries where local resources are limited. Accurate disease forecasts can provide public and clinical health services with the information needed to implement targeted approaches for malaria control that make effective use of limited resources. The objective of this study was to determine the relevance of environmental and clinical predictors of malaria across different settings in Uganda. Forecasting models were based on health facility data collected by the Uganda Malaria Surveillance Project and satellite-derived rainfall, temperature, and vegetation estimates from 2006 to 2013. Facility-specific forecasting models of confirmed malaria were developed using multivariate autoregressive integrated moving average models and produced weekly forecast horizons over a 52-week forecasting period. The model with the most accurate forecasts varied by site and by forecast horizon. Clinical predictors were retained in the models with the highest predictive power for all facility sites. The average error over the 52 forecasting horizons ranged from 26 to 128% whereas the cumulative burden forecast error ranged from 2 to 22%. Clinical data, such as drug treatment, could be used to improve the accuracy of malaria predictions in endemic settings when coupled with environmental predictors. Further exploration of malaria forecasting is necessary to improve its accuracy and value in practice, including examining other environmental and intervention predictors, including insecticide-treated nets.

Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

NASA Astrophysics Data System (ADS)

Chattopadhyay, Surajit; Chattopadhyay, Goutami

2012-10-01

In the work discussed in this paper we considered total ozone time series over Kolkata (22°34'10.92″N, 88°22'10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.

Predictors of early change in bulimia nervosa after a brief psychoeducational therapy.

PubMed

Fernàndez-Aranda, Fernando; Álvarez-Moya, Eva M; Martínez-Viana, Cristina; Sànchez, Isabel; Granero, Roser; Penelo, Eva; Forcano, Laura; Peñas-Lledó, Eva

2009-06-01

We aimed to examine baseline predictors of treatment response in bulimic patients. 241 seeking-treatment females with bulimia nervosa completed an exhaustive assessment and were referred to a six-session psychoeducational group. Regression analyses of treatment response were performed. Childhood obesity, lower frequency of eating symptomatology, lower body mass index, older age, and lower family's and patient's concern about the disorder were predictors of poor abstinence. Suicidal ideation, alcohol abuse, higher maximum BMI, higher novelty seeking and lower baseline purging frequency predicted dropouts. Predictors of early symptom changes and dropouts were similar to those identified in longer CBT interventions.

A Concept–Wide Association Study of Clinical Notes to Discover New Predictors of Kidney Failure

PubMed Central

Betensky, Rebecca A.; Wright, Adam; Curhan, Gary C.; Bates, David W.; Waikar, Sushrut S.

2016-01-01

Background and objectives Identifying predictors of kidney disease progression is critical toward the development of strategies to prevent kidney failure. Clinical notes provide a unique opportunity for big data approaches to identify novel risk factors for disease. Design, setting, participants, & measurements We used natural language processing tools to extract concepts from the preceding year’s clinical notes among patients newly referred to a tertiary care center’s outpatient nephrology clinics and retrospectively evaluated these concepts as predictors for the subsequent development of ESRD using proportional subdistribution hazards (competing risk) regression. The primary outcome was time to ESRD, accounting for a competing risk of death. We identified predictors from univariate and multivariate (adjusting for Tangri linear predictor) models using a 5% threshold for false discovery rate (q value <0.05). We included all patients seen by an adult outpatient nephrologist between January 1, 2004 and June 18, 2014 and excluded patients seen only by transplant nephrology, with preexisting ESRD, with fewer than five clinical notes, with no follow-up, or with no baseline creatinine values. Results Among the 4013 patients selected in the final study cohort, we identified 960 concepts in the unadjusted analysis and 885 concepts in the adjusted analysis. Novel predictors identified included high–dose ascorbic acid (adjusted hazard ratio, 5.48; 95% confidence interval, 2.80 to 10.70; q<0.001) and fast food (adjusted hazard ratio, 4.34; 95% confidence interval, 2.55 to 7.40; q<0.001). Conclusions Novel predictors of human disease may be identified using an unbiased approach to analyze text from the electronic health record. PMID:27927892

Dynamic linear models using the Kalman filter for early detection and early warning of malaria outbreaks

NASA Astrophysics Data System (ADS)

Merkord, C. L.; Liu, Y.; DeVos, M.; Wimberly, M. C.

2015-12-01

Malaria early detection and early warning systems are important tools for public health decision makers in regions where malaria transmission is seasonal and varies from year to year with fluctuations in rainfall and temperature. Here we present a new data-driven dynamic linear model based on the Kalman filter with time-varying coefficients that are used to identify malaria outbreaks as they occur (early detection) and predict the location and timing of future outbreaks (early warning). We fit linear models of malaria incidence with trend and Fourier form seasonal components using three years of weekly malaria case data from 30 districts in the Amhara Region of Ethiopia. We identified past outbreaks by comparing the modeled prediction envelopes with observed case data. Preliminary results demonstrated the potential for improved accuracy and timeliness over commonly-used methods in which thresholds are based on simpler summary statistics of historical data. Other benefits of the dynamic linear modeling approach include robustness to missing data and the ability to fit models with relatively few years of training data. To predict future outbreaks, we started with the early detection model for each district and added a regression component based on satellite-derived environmental predictor variables including precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and land surface temperature (LST) and spectral indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). We included lagged environmental predictors in the regression component of the model, with lags chosen based on cross-correlation of the one-step-ahead forecast errors from the first model. Our results suggest that predictions of future malaria outbreaks can be improved by incorporating lagged environmental predictors.

A Data-Driven Approach to Develop Physically Sound Predictors: Application to Depth-Averaged Velocities and Drag Coefficients on Vegetated Flows

NASA Astrophysics Data System (ADS)

Tinoco, R. O.; Goldstein, E. B.; Coco, G.

2016-12-01

We use a machine learning approach to seek accurate, physically sound predictors, to estimate two relevant flow parameters for open-channel vegetated flows: mean velocities and drag coefficients. A genetic programming algorithm is used to find a robust relationship between properties of the vegetation and flow parameters. We use data published from several laboratory experiments covering a broad range of conditions to obtain: a) in the case of mean flow, an equation that matches the accuracy of other predictors from recent literature while showing a less complex structure, and b) for drag coefficients, a predictor that relies on both single element and array parameters. We investigate different criteria for dataset size and data selection to evaluate their impact on the resulting predictor, as well as simple strategies to obtain only dimensionally consistent equations, and avoid the need for dimensional coefficients. The results show that a proper methodology can deliver physically sound models representative of the processes involved, such that genetic programming and machine learning techniques can be used as powerful tools to study complicated phenomena and develop not only purely empirical, but "hybrid" models, coupling results from machine learning methodologies into physics-based models.

The Relationship Between Motor Skills, Social Problems, and ADHD Symptomatology: Does It Vary According to Parent and Teacher Report?

PubMed

Goulardins, Juliana B; Rigoli, Daniela; Loh, Pek Ru; Kane, Robert; Licari, Melissa; Hands, Beth; Oliveira, Jorge A; Piek, Jan

2018-06-01

This study investigated the relationship between motor performance; attentional, hyperactive, and impulsive symptoms; and social problems. Correlations between parents' versus teachers' ratings of social problems and ADHD symptomatology were also examined. A total of 129 children aged 9 to 12 years were included. ADHD symptoms and social problems were identified based on Conners' Rating Scales-Revised: L, and the McCarron Assessment of Neuromuscular Development was used to assess motor skills. After controlling for ADHD symptomatology, motor skills remained a significant predictor of social problems in the teacher model but not in the parent model. After controlling for motor skills, inattentive (not hyperactive-impulsive) symptoms were a significant predictor of social problems in the parent model, whereas hyperactive-impulsive (not inattentive) symptoms were a significant predictor of social problems in the teacher model. The findings suggested that intervention strategies should consider the interaction between symptoms and environmental contexts.

Remote Sensing-Based Detection and Spatial Pattern Analysis for Geo-Ecological Niche Modeling of Tillandsia SPP. In the Atacama, Chile

NASA Astrophysics Data System (ADS)

Wolf, N.; Siegmund, A.; del Río, C.; Osses, P.; García, J. L.

2016-06-01

In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called `fog oases' dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of Tillandsia spp. in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of Tillandsia spp. Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF) are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning.

PubMed

Roysden, Nathaniel; Wright, Adam

2015-01-01

Mental health problems are an independent predictor of increased healthcare utilization. We created random forest classifiers for predicting two outcomes following a patient's first behavioral health encounter: decreased utilization by any amount (AUROC 0.74) and ultra-high absolute utilization (AUROC 0.88). These models may be used for clinical decision support by referring providers, to automatically detect patients who may benefit from referral, for cost management, or for risk/protection factor analysis.

Nonlinear techniques for forecasting solar activity directly from its time series

NASA Technical Reports Server (NTRS)

Ashrafi, S.; Roszman, L.; Cooley, J.

1992-01-01

Numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series are presented. This approach makes it possible to extract dynamical invariants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), given a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

Nonlinear techniques for forecasting solar activity directly from its time series

NASA Technical Reports Server (NTRS)

Ashrafi, S.; Roszman, L.; Cooley, J.

1993-01-01

This paper presents numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series. This approach makes it possible to extract dynamical in variants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), give a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

Estimation of Subpixel Snow-Covered Area by Nonparametric Regression Splines

NASA Astrophysics Data System (ADS)

Kuter, S.; Akyürek, Z.; Weber, G.-W.

2016-10-01

Measurement of the areal extent of snow cover with high accuracy plays an important role in hydrological and climate modeling. Remotely-sensed data acquired by earth-observing satellites offer great advantages for timely monitoring of snow cover. However, the main obstacle is the tradeoff between temporal and spatial resolution of satellite imageries. Soft or subpixel classification of low or moderate resolution satellite images is a preferred technique to overcome this problem. The most frequently employed snow cover fraction methods applied on Moderate Resolution Imaging Spectroradiometer (MODIS) data have evolved from spectral unmixing and empirical Normalized Difference Snow Index (NDSI) methods to latest machine learning-based artificial neural networks (ANNs). This study demonstrates the implementation of subpixel snow-covered area estimation based on the state-of-the-art nonparametric spline regression method, namely, Multivariate Adaptive Regression Splines (MARS). MARS models were trained by using MODIS top of atmospheric reflectance values of bands 1-7 as predictor variables. Reference percentage snow cover maps were generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also employed to estimate the percentage snow-covered area on the same data set. The results indicated that the developed MARS model performed better than th

Attitudes and Descriptive Norms of Alcohol-Related Problems as Predictors of Alcohol Use among College Students

ERIC Educational Resources Information Center

Sheppard, Meg E.; Usdan, Stuart L.; Higginbotham, John C.; Cremeens-Matthews, Jennifer L.

2016-01-01

Background: The purpose of this study is to identify predictors of alcohol use based on personal values and several constructs from the Integrated Behavioral Model (i.e., attitudes, injunctive norms and descriptive norms) among undergraduate college students. Methods: A cross sectional study design was used with a convenience sample of college…

Predicting Post-Treatment-Initiation Alcohol Use among Patients with Severe Mental Illness and Alcohol Use Disorders

ERIC Educational Resources Information Center

Bradizza, Clara M.; Maisto, Stephen A.; Vincent, Paula C.; Stasiewicz, Paul R.; Connors, Gerard J.; Mercer, Nicole D.

2009-01-01

Few investigators studying alcohol abuse among individuals with a severe mental illness (SMI) have examined predictors of posttreatment alcohol outcomes. In the present study, a multivariate approach based on a theoretical model was used to study the relationship between psychosocial factors and post-treatment-initiation alcohol use. Predictors of…

In vivo serial MRI-based models and statistical methods to quantify sensitivity and specificity of mechanical predictors for carotid plaque rupture: location and beyond.

PubMed

Wu, Zheyang; Yang, Chun; Tang, Dalin

2011-06-01

It has been hypothesized that mechanical risk factors may be used to predict future atherosclerotic plaque rupture. Truly predictive methods for plaque rupture and methods to identify the best predictor(s) from all the candidates are lacking in the literature. A novel combination of computational and statistical models based on serial magnetic resonance imaging (MRI) was introduced to quantify sensitivity and specificity of mechanical predictors to identify the best candidate for plaque rupture site prediction. Serial in vivo MRI data of carotid plaque from one patient was acquired with follow-up scan showing ulceration. 3D computational fluid-structure interaction (FSI) models using both baseline and follow-up data were constructed and plaque wall stress (PWS) and strain (PWSn) and flow maximum shear stress (FSS) were extracted from all 600 matched nodal points (100 points per matched slice, baseline matching follow-up) on the lumen surface for analysis. Each of the 600 points was marked "ulcer" or "nonulcer" using follow-up scan. Predictive statistical models for each of the seven combinations of PWS, PWSn, and FSS were trained using the follow-up data and applied to the baseline data to assess their sensitivity and specificity using the 600 data points for ulcer predictions. Sensitivity of prediction is defined as the proportion of the true positive outcomes that are predicted to be positive. Specificity of prediction is defined as the proportion of the true negative outcomes that are correctly predicted to be negative. Using probability 0.3 as a threshold to infer ulcer occurrence at the prediction stage, the combination of PWS and PWSn provided the best predictive accuracy with (sensitivity, specificity) = (0.97, 0.958). Sensitivity and specificity given by PWS, PWSn, and FSS individually were (0.788, 0.968), (0.515, 0.968), and (0.758, 0.928), respectively. The proposed computational-statistical process provides a novel method and a framework to assess the sensitivity and specificity of various risk indicators and offers the potential to identify the optimized predictor for plaque rupture using serial MRI with follow-up scan showing ulceration as the gold standard for method validation. While serial MRI data with actual rupture are hard to acquire, this single-case study suggests that combination of multiple predictors may provide potential improvement to existing plaque assessment schemes. With large-scale patient studies, this predictive modeling process may provide more solid ground for rupture predictor selection strategies and methods for image-based plaque vulnerability assessment.

Risk estimates for persistent high-risk human papillomavirus infections as surrogate endpoints of progressive cervical disease critically depend on reference category: analysis of the combined prospective cohort of the New Independent States of the Former Soviet Union and Latin American Screening studies.

PubMed

Syrjänen, K; Shabalova, I; Naud, P; Kozachenko, V; Derchain, S; Zakharchenko, S; Roteli-Martins, C; Nerovjna, R; Longatto-Filho, A; Kljukina, L; Tatti, S; Branovskaja, M; Hammes, L S; Branca, M; Grunjberga, V; Eržen, M; Juschenko, A; Costa, S; Sarian, L; Podistov, J; Syrjänen, S

2011-06-01

To make feasible future clinical trials with new-generation human papillomavirus (HPV) vaccines, novel virological surrogate endpoints of progressive disease have been proposed, including high-risk HPV (HR-HPV) persistence for six months (6M+) or 12 months (12M+). The risk estimates (relative risks [RRs]) of these 'virological endpoints' are influenced by several variables, not yet validated adequately. We compared the impact of three referent groups: (i) HPV-negative, (ii) HPV-transient, (iii) HPV-mixed outcome on the risk estimates for 6M+ or 12M+ HR-HPV persistence as predictors of progressive disease. Generalized estimating equation models were used to estimate the strength of 6M+ and 12M+ HR-HPV persistence with disease progression to squamous intraepithelial lesions (SILs), cervical intraepithelial neoplasia (CIN) grade 1+, CIN2+, CIN/SIL endpoints, comparing three optional reference categories (i)-(iii) in a prospective sub-cohort of 1865 women from the combined New Independent States of the Former Soviet Union (NIS) and Latin American Screening (LAMS) studies cohort (n = 15,301). The RRs of these viral endpoints as predictors of progressive disease are affected by the length of viral persistence (6M+ or 12M+) and the surrogate endpoint (SIL, CIN1, CIN2, CIN/SIL). Most dramatic is the effect of the referent group used in risk estimates, with the HPV-negative referent group giving the highest and most consistent RRs for both 6M+ and 12M+ viral persistence, irrespective of which surrogate is used. In addition to deciding on whether to use 6M+ or 12M+ persistence criteria, and cytological, histological or combined surrogate endpoints, one should adopt the HPV-negative referent group as the gold standard in all future studies using viral persistence as the surrogate endpoint of progressive disease.

Predictors of quitting behaviour with special reference to nicotine dependence among adult tobacco-users in a slum of Burdwan district, West Bengal, India.

PubMed

Islam, Kamirul; Saha, Indranil; Saha, Rajib; Samim Khan, Sufi Abdul; Thakur, Rupali; Shivam, Swapnil

2014-04-01

Information on predictors of quitting behaviour in adult tobacco users is scarce in Indian context. Hence, this study was undertaken to assess the intention of tobacco-users towards quitting and its predictors with reference to nicotine dependence. A community-based observational, cross-sectional study was conducted on 128 adult tobacco-users (89.8% male) with mean age of 41.1 ± 15.7 yr selected by complete enumeration method. Data were collected by interview using pre-designed, pre-tested schedule. Nicotine dependence was assessed by Fagerstrφm Test for Nicotine Dependence (FTND) questionnaire. Of the 128 users, 63.3 per cent had intention to quit. Majority of the tobacco users who did not intend to quit belonged to the age group of > 40 yr (66.0%), were illiterate (55.3%), started tobacco use at 11 - 15 yr of age (57.4%), had been using tobacco for 20 yr or more (70.2%), were daily tobacco users (91.5%), and highly dependent on nicotine (80.9%). Tobacco users having high FTND score and who started tobacco use early in life were 1.83 and 3.30 times more unintended to quit, respectively. Suitable plan for quitting should be developed depending on the FTND score of an individual, the most important determinant of quitting that would be beneficial for categorization of the treatment leading to successful quitting.

Relationship between general health of older health service users and their self-esteem in Isfahan in 2014

PubMed Central

Molavi, Razieh; Alavi, Mousa; Keshvari, Mahrokh

2015-01-01

Background: Self-esteem is known to be one of the most important markers of successful aging. Older people's self-esteem is influenced by several factors that particularly may be health related. Therefore, this study aimed to explore some important general health-related predictors of the older people's self-esteem. Materials and Methods: In this study, 200 people, aged 65 years and older, who referred to health care centers were selected through stratified random sampling method. Data were collected by using Rosenberg's self-esteem scale and the 28-item Goldberg's general health questionnaire. Data were analyzed by Pearson's coefficient tests and multiple regression analysis. Results: Findings showed that the entered predictor variables accounted for 49% of the total variance (R2) of self-esteem in the model (P < 0.001, F4,195 = 46.717). Three out of the four predictor variables including somatic signs, anxiety/insomnia, and depression, significantly predicted the self-esteem. The results emphasized on the determinant role of both physical (somatic signs) and mental (anxiety/insomnia and depression) aspects of health in older patients’ self-esteem. Conclusions: The significant general health-related predictors found in the present study emphasize on some of the significant points that should be considered in planning for improving older patients’ self-esteem. PMID:26793259

Main predictors for repetition of suicidal behaviour among women referred to a single public sector tertiary care hospital in Iran.

PubMed

Mostafazadeh, Babak; Farzaneh, Esmaeil

2017-09-01

To assess the main predictors for repetition of suicidal behaviour among women. This cross-sectional study was conducted at Loghman Hakim Hospital, Tehran, Iran, in 2014, and comprised women patients. The patients were divided into two groups, i.e. women repeating suicide and women without repeating suicide. Data was collected through a checklist and then analysed with SPSS 20. Of the 300 women, 121(40.3%) repeated suicide and 179(59.7%) did not. The overall mean age was 26.9±9.1 years (range: 14-80 years). High prevalence of psychological drug usage, alcohol use, history of self-mutilation (self-harm), psychotic disturbances, sexual relationships, as well as smoking and opium addition was revealed as major factors in repeated suicidal behaviour in women when compared with other women. The result of multivariate logistic regression model showed two factors of self-mutilation (odds ratio =2.692, p=0.002) and underlying psychotic disorders (odds ratio = 2.780, p<0.001) as main predictors of suicide in women. In this regard, demographic characteristics could not predict repeating suicidal attempts (p>0.05). The presence of underlying psychotic disorders and self-mutilation were main predictors for repetition of suicidal behaviour.

Relationship between general health of older health service users and their self-esteem in Isfahan in 2014.

PubMed

Molavi, Razieh; Alavi, Mousa; Keshvari, Mahrokh

2015-01-01

Self-esteem is known to be one of the most important markers of successful aging. Older people's self-esteem is influenced by several factors that particularly may be health related. Therefore, this study aimed to explore some important general health-related predictors of the older people's self-esteem. In this study, 200 people, aged 65 years and older, who referred to health care centers were selected through stratified random sampling method. Data were collected by using Rosenberg's self-esteem scale and the 28-item Goldberg's general health questionnaire. Data were analyzed by Pearson's coefficient tests and multiple regression analysis. Findings showed that the entered predictor variables accounted for 49% of the total variance (R(2)) of self-esteem in the model (P < 0.001, F4,195 = 46.717). Three out of the four predictor variables including somatic signs, anxiety/insomnia, and depression, significantly predicted the self-esteem. The results emphasized on the determinant role of both physical (somatic signs) and mental (anxiety/insomnia and depression) aspects of health in older patients' self-esteem. The significant general health-related predictors found in the present study emphasize on some of the significant points that should be considered in planning for improving older patients' self-esteem.

Empirical Evaluation of Hunk Metrics as Bug Predictors

NASA Astrophysics Data System (ADS)

Ferzund, Javed; Ahsan, Syed Nadeem; Wotawa, Franz

Reducing the number of bugs is a crucial issue during software development and maintenance. Software process and product metrics are good indicators of software complexity. These metrics have been used to build bug predictor models to help developers maintain the quality of software. In this paper we empirically evaluate the use of hunk metrics as predictor of bugs. We present a technique for bug prediction that works at smallest units of code change called hunks. We build bug prediction models using random forests, which is an efficient machine learning classifier. Hunk metrics are used to train the classifier and each hunk metric is evaluated for its bug prediction capabilities. Our classifier can classify individual hunks as buggy or bug-free with 86 % accuracy, 83 % buggy hunk precision and 77% buggy hunk recall. We find that history based and change level hunk metrics are better predictors of bugs than code level hunk metrics.

Statistical forecast of seasonal discharge in Central Asia using observational records: development of a generic linear modelling tool for operational water resource management

NASA Astrophysics Data System (ADS)

Apel, Heiko; Abdykerimova, Zharkinay; Agalhanova, Marina; Baimaganbetov, Azamat; Gavrilenko, Nadejda; Gerlitz, Lars; Kalashnikova, Olga; Unger-Shayesteh, Katy; Vorogushyn, Sergiy; Gafurov, Abror

2018-04-01

The semi-arid regions of Central Asia crucially depend on the water resources supplied by the mountainous areas of the Tien Shan and Pamir and Altai mountains. During the summer months the snow-melt- and glacier-melt-dominated river discharge originating in the mountains provides the main water resource available for agricultural production, but also for storage in reservoirs for energy generation during the winter months. Thus a reliable seasonal forecast of the water resources is crucial for sustainable management and planning of water resources. In fact, seasonal forecasts are mandatory tasks of all national hydro-meteorological services in the region. In order to support the operational seasonal forecast procedures of hydro-meteorological services, this study aims to develop a generic tool for deriving statistical forecast models of seasonal river discharge based solely on observational records. The generic model structure is kept as simple as possible in order to be driven by meteorological and hydrological data readily available at the hydro-meteorological services, and to be applicable for all catchments in the region. As snow melt dominates summer runoff, the main meteorological predictors for the forecast models are monthly values of winter precipitation and temperature, satellite-based snow cover data, and antecedent discharge. This basic predictor set was further extended by multi-monthly means of the individual predictors, as well as composites of the predictors. Forecast models are derived based on these predictors as linear combinations of up to four predictors. A user-selectable number of the best models is extracted automatically by the developed model fitting algorithm, which includes a test for robustness by a leave-one-out cross-validation. Based on the cross-validation the predictive uncertainty was quantified for every prediction model. Forecasts of the mean seasonal discharge of the period April to September are derived every month from January until June. The application of the model for several catchments in Central Asia - ranging from small to the largest rivers (240 to 290 000 km2 catchment area) - for the period 2000-2015 provided skilful forecasts for most catchments already in January, with adjusted R2 values of the best model in the range of 0.6-0.8 for most of the catchments. The skill of the prediction increased every following month, i.e. with reduced lead time, with adjusted R2 values usually in the range 0.8-0.9 for the best and 0.7-0.8 on average for the set of models in April just before the prediction period. The later forecasts in May and June improve further due to the high predictive power of the discharge in the first 2 months of the snow melt period. The improved skill of the set of forecast models with decreasing lead time resulted in narrow predictive uncertainty bands at the beginning of the snow melt period. In summary, the proposed generic automatic forecast model development tool provides robust predictions for seasonal water availability in Central Asia, which will be tested against the official forecasts in the upcoming years, with the vision of operational implementation.

PROTAX-Sound: A probabilistic framework for automated animal sound identification

PubMed Central

Somervuo, Panu; Ovaskainen, Otso

2017-01-01

Autonomous audio recording is stimulating new field in bioacoustics, with a great promise for conducting cost-effective species surveys. One major current challenge is the lack of reliable classifiers capable of multi-species identification. We present PROTAX-Sound, a statistical framework to perform probabilistic classification of animal sounds. PROTAX-Sound is based on a multinomial regression model, and it can utilize as predictors any kind of sound features or classifications produced by other existing algorithms. PROTAX-Sound combines audio and image processing techniques to scan environmental audio files. It identifies regions of interest (a segment of the audio file that contains a vocalization to be classified), extracts acoustic features from them and compares with samples in a reference database. The output of PROTAX-Sound is the probabilistic classification of each vocalization, including the possibility that it represents species not present in the reference database. We demonstrate the performance of PROTAX-Sound by classifying audio from a species-rich case study of tropical birds. The best performing classifier achieved 68% classification accuracy for 200 bird species. PROTAX-Sound improves the classification power of current techniques by combining information from multiple classifiers in a manner that yields calibrated classification probabilities. PMID:28863178

PROTAX-Sound: A probabilistic framework for automated animal sound identification.

PubMed

de Camargo, Ulisses Moliterno; Somervuo, Panu; Ovaskainen, Otso

2017-01-01

Autonomous audio recording is stimulating new field in bioacoustics, with a great promise for conducting cost-effective species surveys. One major current challenge is the lack of reliable classifiers capable of multi-species identification. We present PROTAX-Sound, a statistical framework to perform probabilistic classification of animal sounds. PROTAX-Sound is based on a multinomial regression model, and it can utilize as predictors any kind of sound features or classifications produced by other existing algorithms. PROTAX-Sound combines audio and image processing techniques to scan environmental audio files. It identifies regions of interest (a segment of the audio file that contains a vocalization to be classified), extracts acoustic features from them and compares with samples in a reference database. The output of PROTAX-Sound is the probabilistic classification of each vocalization, including the possibility that it represents species not present in the reference database. We demonstrate the performance of PROTAX-Sound by classifying audio from a species-rich case study of tropical birds. The best performing classifier achieved 68% classification accuracy for 200 bird species. PROTAX-Sound improves the classification power of current techniques by combining information from multiple classifiers in a manner that yields calibrated classification probabilities.

Predictors of Competitive Employment among Consumers with Co-Occurring Mental and Substance Use Disorders

ERIC Educational Resources Information Center

Biegel, David E.; Stevenson, Lauren D.; Beimers, David; Ronis, Robert J.; Boyle, Patrick

2010-01-01

Objectives: This study examines consumer and agency level predictors of competitive employment for consumers with co-occurring disorders. Methods: The study sample included 191 consumers from mental health agencies receiving Integrated Dual Diagnosis Treatment services, including a subgroup which was referred for Supported Employment Services.…

Penalized nonparametric scalar-on-function regression via principal coordinates

PubMed Central

Reiss, Philip T.; Miller, David L.; Wu, Pei-Shien; Hua, Wen-Yu

2016-01-01

A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. In the proposed method, which we call principal coordinate ridge regression, one regresses the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, principal coordinate ridge regression, with dynamic time warping distance used to define the principal coordinates, is shown to outperform a functional generalized linear model. PMID:29217963

Using genetic algorithms to optimize the analogue method for precipitation prediction in the Swiss Alps

NASA Astrophysics Data System (ADS)

Horton, Pascal; Jaboyedoff, Michel; Obled, Charles

2018-01-01

Analogue methods provide a statistical precipitation prediction based on synoptic predictors supplied by general circulation models or numerical weather prediction models. The method samples a selection of days in the archives that are similar to the target day to be predicted, and consider their set of corresponding observed precipitation (the predictand) as the conditional distribution for the target day. The relationship between the predictors and predictands relies on some parameters that characterize how and where the similarity between two atmospheric situations is defined. This relationship is usually established by a semi-automatic sequential procedure that has strong limitations: (i) it cannot automatically choose the pressure levels and temporal windows (hour of the day) for a given meteorological variable, (ii) it cannot handle dependencies between parameters, and (iii) it cannot easily handle new degrees of freedom. In this work, a global optimization approach relying on genetic algorithms could optimize all parameters jointly and automatically. The global optimization was applied to some variants of the analogue method for the Rhône catchment in the Swiss Alps. The performance scores increased compared to reference methods, especially for days with high precipitation totals. The resulting parameters were found to be relevant and coherent between the different subregions of the catchment. Moreover, they were obtained automatically and objectively, which reduces the effort that needs to be invested in exploration attempts when adapting the method to a new region or for a new predictand. For example, it obviates the need to assess a large number of combinations of pressure levels and temporal windows of predictor variables that were manually selected beforehand. The optimization could also take into account parameter inter-dependencies. In addition, the approach allowed for new degrees of freedom, such as a possible weighting between pressure levels, and non-overlapping spatial windows.

On the interest of combining an analog model to a regression model for the adaptation of the downscaling link. Application to probabilistic prediction of precipitation over France.

NASA Astrophysics Data System (ADS)

Chardon, Jérémy; Hingray, Benoit; Favre, Anne-Catherine

2016-04-01

Scenarios of surface weather required for the impact studies have to be unbiased and adapted to the space and time scales of the considered hydro-systems. Hence, surface weather scenarios obtained from global climate models and/or numerical weather prediction models are not really appropriated. Outputs of these models have to be post-processed, which is often carried out thanks to Statistical Downscaling Methods (SDMs). Among those SDMs, approaches based on regression are often applied. For a given station, a regression link can be established between a set of large scale atmospheric predictors and the surface weather variable. These links are then used for the prediction of the latter. However, physical processes generating surface weather vary in time. This is well known for precipitation for instance. The most relevant predictors and the regression link are also likely to vary in time. A better prediction skill is thus classically obtained with a seasonal stratification of the data. Another strategy is to identify the most relevant predictor set and establish the regression link from dates that are similar - or analog - to the target date. In practice, these dates can be selected thanks to an analog model. In this study, we explore the possibility of improving the local performance of an analog model - where the analogy is applied to the geopotential heights 1000 and 500 hPa - using additional local scale predictors for the probabilistic prediction of the Safran precipitation over France. For each prediction day, the prediction is obtained from two GLM regression models - for both the occurrence and the quantity of precipitation - for which predictors and parameters are estimated from the analog dates. Firstly, the resulting combined model noticeably allows increasing the prediction performance by adapting the downscaling link for each prediction day. Secondly, the selected predictors for a given prediction depend on the large scale situation and on the considered region. Finally, even with such an adaptive predictor identification, the downscaling link appears to be robust: for a same prediction day, predictors selected for different locations of a given region are similar and the regression parameters are consistent within the region of interest.

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

USGS Publications Warehouse

Ackers, Steven H.; Davis, Raymond J.; Olsen, K.; Dugger, Catherine

2015-01-01

Wildlife habitat mapping has evolved at a rapid pace over the last few decades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls (Strix occidentalis caurina), whose geographic covers about 23 million ha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as predictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map.Recent increases in the use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. Instead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetation height for use in SDMs. We expect a SDM habitat map produced from directly measured predictor variables to be more accurate than one produced from modeled predictors.We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and estimates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo-interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC = 0.809 ± 0.011), but the performance of the Landsat-based map was within acceptable limits (AUC = 0.717 ± 0.021). As is common with photo-interpreted maps, there was no accuracy assessment available for comparison. The photo-interpreted map produced the highest and lowest estimates of habitat area, depending on which habitat classes were included (nesting, roosting, and foraging habitat = 9962 ha, nesting habitat only = 6036 ha). The Landsat-based map produced an estimate of habitat area that was within this range (95% CI: 6679–9592 ha), while the lidar-based map produced an area estimate similar to what was interpreted by local wildlife biologists as nesting (i.e., high quality) habitat using aerial imagery (95% CI: 5453–7216). Confidence intervals of habitat area estimates from the SDMs based on Landsat and lidar overlapped.We concluded that both Landsat- and lidar-based SDMs produced reasonable maps and area estimates for northern spotted owl habitat within the study area. The lidar-based map was more precise and spatially similar to what local wildlife biologists considered spotted owl nesting habitat. The Landsat-based map provided a less precise spatial representation of habitat within the relatively small geographic confines of the study area, but habitat area estimates were similar to both the photo-interpreted and lidar-based maps.Photo-interpreted maps are time consuming to produce, subjective in nature, and difficult to replicate. SDMs provide a framework for efficiently producing habitat maps that can be replicated as habitat conditions change over time, provided that comparable remotely sensed data are available. When the SDM uses predictor variables extracted from lidar data, it can produce a habitat map that is both accurate and useful at large and small spatial scales. In comparison, SDMs using Landsat-based data are more appropriate for large scale analyses of amounts and general spatial patterns of habitat at regional scales.

Cardiac rehabilitation following an acute coronary syndrome: Trends in referral, predictors and mortality outcome in a multicenter national registry between years 2006-2013: Report from the Working Group on Cardiac Rehabilitation, the Israeli Heart Society.

PubMed

Chernomordik, Fernando; Sabbag, Avi; Tzur, Boaz; Kopel, Eran; Goldkorn, Ronen; Matetzky, Shlomi; Goldenberg, Ilan; Shlomo, Nir; Klempfner, Robert

2017-01-01

Background Utilization of cardiac rehabilitation is suboptimal. The aim of the study was to assess referral trends over the past decade, to identify predictors for referral to a cardiac rehabilitation program, and to evaluate the association with one-year mortality in a large national registry of acute coronary syndrome patients. Design and methods Data were extracted from the Acute Coronary Syndrome Israeli Survey national surveys between 2006-2013. A total of 6551 patients discharged with a diagnosis of acute coronary syndrome were included. Results Referral to cardiac rehabilitation following an acute coronary syndrome increased from 38% in 2006 to 57% in 2013 ( p for trend < 0.001). Multivariate modeling identified the following independent predictors for non-referral: 2006 survey, older age, female sex, past stroke, heart or renal failure, prior myocardial infarction, minority group, and lack of in-hospital cardiac rehabilitation center (all p < 0.01). Kaplan-Meier survival analyses showed one-year survival rates of 97% vs 92% in patients referred for cardiac rehabilitation as compared to those not referred (log-rank p < 0.01). Multivariate analysis showed that referral for cardiac rehabilitation was associated with a 27% mortality risk reduction at one-year follow-up ( p = 0.03). Consistently, a 32% lower one-year mortality risk was evident in a propensity score matched group of 3340 patients (95% confidence interval 0.48-0.95, p = 0.02). Conclusions Over the past decade there was a significant increase in cardiac rehabilitation referral following an acute coronary syndrome. However, cardiac rehabilitation is still under-utilized in important high-risk subsets of this population. Patients referred to cardiac rehabilitation have a lower adjusted mortality risk.

A mixed model for the relationship between climate and human cranial form.

PubMed

Katz, David C; Grote, Mark N; Weaver, Timothy D

2016-08-01

We expand upon a multivariate mixed model from quantitative genetics in order to estimate the magnitude of climate effects in a global sample of recent human crania. In humans, genetic distances are correlated with distances based on cranial form, suggesting that population structure influences both genetic and quantitative trait variation. Studies controlling for this structure have demonstrated significant underlying associations of cranial distances with ecological distances derived from climate variables. However, to assess the biological importance of an ecological predictor, estimates of effect size and uncertainty in the original units of measurement are clearly preferable to significance claims based on units of distance. Unfortunately, the magnitudes of ecological effects are difficult to obtain with distance-based methods, while models that produce estimates of effect size generally do not scale to high-dimensional data like cranial shape and form. Using recent innovations that extend quantitative genetics mixed models to highly multivariate observations, we estimate morphological effects associated with a climate predictor for a subset of the Howells craniometric dataset. Several measurements, particularly those associated with cranial vault breadth, show a substantial linear association with climate, and the multivariate model incorporating a climate predictor is preferred in model comparison. Previous studies demonstrated the existence of a relationship between climate and cranial form. The mixed model quantifies this relationship concretely. Evolutionary questions that require population structure and phylogeny to be disentangled from potential drivers of selection may be particularly well addressed by mixed models. Am J Phys Anthropol 160:593-603, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Using Proactivity, Time Discounting, and the Theory of Planned Behavior to Identify Predictors of Retirement Planning

ERIC Educational Resources Information Center

Griffin, Barbara; Loe, David; Hesketh, Beryl

2012-01-01

This study developed and tested a model to identify the predictors of retirement planning based on an extension of the theory of planned behavior ([TPB], Ajzen, 1991) that included individual differences in proactivity and time discounting. The results showed that personal attitudes, sense of control, social influence, and stable traits have a…

Sheltering the self from the storm: self-construal abstractness and the stability of self-esteem.

PubMed

Updegraff, John A; Emanuel, Amber S; Suh, Eunkook M; Gallagher, Kristel M

2010-01-01

Self-construal abstractness (SCA) refers to the degree to which people construe important bases of self-esteem in a broad, flexible, and abstract rather than a concrete and specific manner. This article hypothesized that SCA would be a unique predictor of self-esteem stability, capturing the degree to which people's most important bases of self-worth are resistant to disconfirmation. Two studies using a daily diary methodology examined relationships between SCA, daily self-esteem, and daily emotions and/or events. In Study 1, individual differences in SCA emerged as the most consistent and unique predictor of self-esteem stability. Furthermore, SCA contributed to self-esteem stability by buffering the influence of daily negative emotions on self-esteem. Study 2 manipulated SCA via a daily self-construal task and found an abstract versus concrete self-focus to buffer the influence of daily negative events on self-esteem. Implications of these findings for the study of the self and well-being are discussed.

Research on Multi - Person Parallel Modeling Method Based on Integrated Model Persistent Storage

NASA Astrophysics Data System (ADS)

Qu, MingCheng; Wu, XiangHu; Tao, YongChao; Liu, Ying

2018-03-01

This paper mainly studies the multi-person parallel modeling method based on the integrated model persistence storage. The integrated model refers to a set of MDDT modeling graphics system, which can carry out multi-angle, multi-level and multi-stage description of aerospace general embedded software. Persistent storage refers to converting the data model in memory into a storage model and converting the storage model into a data model in memory, where the data model refers to the object model and the storage model is a binary stream. And multi-person parallel modeling refers to the need for multi-person collaboration, the role of separation, and even real-time remote synchronization modeling.

Reference values and equations reference of balance for children of 8 to 12 years.

PubMed

Libardoni, Thiele de Cássia; Silveira, Carolina Buzzi da; Sinhorim, Larissa Milani Brognoli; Oliveira, Anamaria Siriani de; Santos, Márcio José Dos; Santos, Gilmar Moraes

2018-02-01

There are still no normative data in balance sway for school-age children in Brazil. We aimed to establish the reference ranges for balance scores and to develop prediction equations for estimation of balance scores in children aged 8 to 12 years old. The study included 165 healthy children (83 boys and 82 girls; age, 8-12 years) recruited from a public school in the city of Florianópolis, Santa Catarina, Brazil. We used the Sensory Organization Test to assess the balance scores and both a digital scale and a stadiometer to measure the anthropometric variables. We tested a stepwise multiple-regression model with sex, height, weight, and mid-thigh circumference of the dominant leg as predictors of the balance score. For all experimental conditions, girls' age accounted for over 85% of the variability in balance scores; while, boys' age accounted only 55% of the variability in balance scores. Therefore, balance scores increase with age for boys and girls. This study described the ranges of age- and sex-specific normative values for balance scores in children during 6 different testing conditions established by the sensory organization test. We confirmed that age was the predictor that best explained the variability in balance scores in children between 8 and 12 years old. This study stimulates a new and more comprehensive study to estimate balance scores from prediction equations for overall Brazilian pediatric population. Copyright © 2017 Elsevier B.V. All rights reserved.

Binary Logistic Regression Versus Boosted Regression Trees in Assessing Landslide Susceptibility for Multiple-Occurring Regional Landslide Events: Application to the 2009 Storm Event in Messina (Sicily, southern Italy).

NASA Astrophysics Data System (ADS)

Lombardo, L.; Cama, M.; Maerker, M.; Parisi, L.; Rotigliano, E.

2014-12-01

This study aims at comparing the performances of Binary Logistic Regression (BLR) and Boosted Regression Trees (BRT) methods in assessing landslide susceptibility for multiple-occurrence regional landslide events within the Mediterranean region. A test area was selected in the north-eastern sector of Sicily (southern Italy), corresponding to the catchments of the Briga and the Giampilieri streams both stretching for few kilometres from the Peloritan ridge (eastern Sicily, Italy) to the Ionian sea. This area was struck on the 1st October 2009 by an extreme climatic event resulting in thousands of rapid shallow landslides, mainly of debris flows and debris avalanches types involving the weathered layer of a low to high grade metamorphic bedrock. Exploiting the same set of predictors and the 2009 landslide archive, BLR- and BRT-based susceptibility models were obtained for the two catchments separately, adopting a random partition (RP) technique for validation; besides, the models trained in one of the two catchments (Briga) were tested in predicting the landslide distribution in the other (Giampilieri), adopting a spatial partition (SP) based validation procedure. All the validation procedures were based on multi-folds tests so to evaluate and compare the reliability of the fitting, the prediction skill, the coherence in the predictor selection and the precision of the susceptibility estimates. All the obtained models for the two methods produced very high predictive performances, with a general congruence between BLR and BRT in the predictor importance. In particular, the research highlighted that BRT-models reached a higher prediction performance with respect to BLR-models, for RP based modelling, whilst for the SP-based models the difference in predictive skills between the two methods dropped drastically, converging to an analogous excellent performance. However, when looking at the precision of the probability estimates, BLR demonstrated to produce more robust models in terms of selected predictors and coefficients, as well as of dispersion of the estimated probabilities around the mean value for each mapped pixel. The difference in the behaviour could be interpreted as the result of overfitting effects, which heavily affect decision tree classification more than logistic regression techniques.

The behaviour of random forest permutation-based variable importance measures under predictor correlation.

PubMed

Nicodemus, Kristin K; Malley, James D; Strobl, Carolin; Ziegler, Andreas

2010-02-27

Random forests (RF) have been increasingly used in applications such as genome-wide association and microarray studies where predictor correlation is frequently observed. Recent works on permutation-based variable importance measures (VIMs) used in RF have come to apparently contradictory conclusions. We present an extended simulation study to synthesize results. In the case when both predictor correlation was present and predictors were associated with the outcome (HA), the unconditional RF VIM attributed a higher share of importance to correlated predictors, while under the null hypothesis that no predictors are associated with the outcome (H0) the unconditional RF VIM was unbiased. Conditional VIMs showed a decrease in VIM values for correlated predictors versus the unconditional VIMs under HA and was unbiased under H0. Scaled VIMs were clearly biased under HA and H0. Unconditional unscaled VIMs are a computationally tractable choice for large datasets and are unbiased under the null hypothesis. Whether the observed increased VIMs for correlated predictors may be considered a "bias" - because they do not directly reflect the coefficients in the generating model - or if it is a beneficial attribute of these VIMs is dependent on the application. For example, in genetic association studies, where correlation between markers may help to localize the functionally relevant variant, the increased importance of correlated predictors may be an advantage. On the other hand, we show examples where this increased importance may result in spurious signals.

Use and Appreciation of a Tailored Self-Management eHealth Intervention for Early Cancer Survivors: Process Evaluation of a Randomized Controlled Trial.

PubMed

Kanera, Iris Maria; Willems, Roy A; Bolman, Catherine A W; Mesters, Ilse; Zambon, Victor; Gijsen, Brigitte Cm; Lechner, Lilian

2016-08-23

A fully automated computer-tailored Web-based self-management intervention, Kanker Nazorg Wijzer (KNW [Cancer Aftercare Guide]), was developed to support early cancer survivors to adequately cope with psychosocial complaints and to promote a healthy lifestyle. The KNW self-management training modules target the following topics: return to work, fatigue, anxiety and depression, relationships, physical activity, diet, and smoking cessation. Participants were guided to relevant modules by personalized module referral advice that was based on participants’ current complaints and identified needs. The aim of this study was to evaluate the adherence to the module referral advice, examine the KNW module use and its predictors, and describe the appreciation of the KNW and its predictors. Additionally, we explored predictors of personal relevance. This process evaluation was conducted as part of a randomized controlled trial. Early cancer survivors with various types of cancer were recruited from 21 Dutch hospitals. Data from online self-report questionnaires and logging data were analyzed from participants allocated to the intervention condition. Chi-square tests were applied to assess the adherence to the module referral advice, negative binominal regression analysis was used to identify predictors of module use, multiple linear regression analysis was applied to identify predictors of the appreciation, and ordered logistic regression analysis was conducted to explore possible predictors of perceived personal relevance. From the respondents (N=231; mean age 55.6, SD 11.5; 79.2% female [183/231]), 98.3% (227/231) were referred to one or more KNW modules (mean 2.9, SD 1.5), and 85.7% (198/231) of participants visited at least one module (mean 2.1, SD 1.6). Significant positive associations were found between the referral to specific modules (range 1-7) and the use of corresponding modules. The likelihoods of visiting modules were higher when respondents were referred to those modules by the module referral advice. Predictors of visiting a higher number of modules were a higher number of referrals by the module referral advice (β=.136, P=.009), and having a partner was significantly related with a lower number of modules used (β=-.256, P=.044). Overall appreciation was high (mean 7.5, SD 1.2; scale 1-10) and was significantly predicted by a higher perceived personal relevance (β=.623, P=.000). None of the demographic and cancer-related characteristics significantly predicted the perceived personal relevance. The KNW in general and more specifically the KNW modules were well used and highly appreciated by early cancer survivors. Indications were found that the module referral advice might be a meaningful intervention component to guide the users in following a preferred selection of modules. These results indicate that the fully automated Web-based KNW provides personal relevant and valuable information and support for early cancer survivors. Therefore, this intervention can complement usual cancer aftercare and may serve as a first step in a stepped-care approach. Nederlands Trial Register: NTR3375; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=3375 (Archived by WebCite at http://www.webcitation.org/6jo4jO7kb).

The diagnostic value of specific IgE to Ara h 2 to predict peanut allergy in children is comparable to a validated and updated diagnostic prediction model.

PubMed

Klemans, Rob J B; Otte, Dianne; Knol, Mirjam; Knol, Edward F; Meijer, Yolanda; Gmelig-Meyling, Frits H J; Bruijnzeel-Koomen, Carla A F M; Knulst, André C; Pasmans, Suzanne G M A

2013-01-01

A diagnostic prediction model for peanut allergy in children was recently published, using 6 predictors: sex, age, history, skin prick test, peanut specific immunoglobulin E (sIgE), and total IgE minus peanut sIgE. To validate this model and update it by adding allergic rhinitis, atopic dermatitis, and sIgE to peanut components Ara h 1, 2, 3, and 8 as candidate predictors. To develop a new model based only on sIgE to peanut components. Validation was performed by testing discrimination (diagnostic value) with an area under the receiver operating characteristic curve and calibration (agreement between predicted and observed frequencies of peanut allergy) with the Hosmer-Lemeshow test and a calibration plot. The performance of the (updated) models was similarly analyzed. Validation of the model in 100 patients showed good discrimination (88%) but poor calibration (P < .001). In the updating process, age, history, and additional candidate predictors did not significantly increase discrimination, being 94%, and leaving only 4 predictors of the original model: sex, skin prick test, peanut sIgE, and total IgE minus sIgE. When building a model with sIgE to peanut components, Ara h 2 was the only predictor, with a discriminative ability of 90%. Cutoff values with 100% positive and negative predictive values could be calculated for both the updated model and sIgE to Ara h 2. In this way, the outcome of the food challenge could be predicted with 100% accuracy in 59% (updated model) and 50% (Ara h 2) of the patients. Discrimination of the validated model was good; however, calibration was poor. The discriminative ability of Ara h 2 was almost comparable to that of the updated model, containing 4 predictors. With both models, the need for peanut challenges could be reduced by at least 50%. Copyright © 2012 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

LinkEHR-Ed: a multi-reference model archetype editor based on formal semantics.

PubMed

Maldonado, José A; Moner, David; Boscá, Diego; Fernández-Breis, Jesualdo T; Angulo, Carlos; Robles, Montserrat

2009-08-01

To develop a powerful archetype editing framework capable of handling multiple reference models and oriented towards the semantic description and standardization of legacy data. The main prerequisite for implementing tools providing enhanced support for archetypes is the clear specification of archetype semantics. We propose a formalization of the definition section of archetypes based on types over tree-structured data. It covers the specialization of archetypes, the relationship between reference models and archetypes and conformance of data instances to archetypes. LinkEHR-Ed, a visual archetype editor based on the former formalization with advanced processing capabilities that supports multiple reference models, the editing and semantic validation of archetypes, the specification of mappings to data sources, and the automatic generation of data transformation scripts, is developed. LinkEHR-Ed is a useful tool for building, processing and validating archetypes based on any reference model.

Binary recursive partitioning: background, methods, and application to psychology.

PubMed

Merkle, Edgar C; Shaffer, Victoria A

2011-02-01

Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's 'goodness' is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.

Predicting Australian adults' sun-safe behaviour: examining the role of personal and social norms.

PubMed

White, Katherine M; Starfelt, Louise C; Young, Ross McD; Hawkes, Anna L; Leske, Stuart; Hamilton, Kyra

2015-05-01

To address the scarcity of comprehensive, theory-based research in the Australian context, this study, using a theory of planned behaviour (TPB) framework, investigated the role of personal and social norms to identify the key predictors of adult Australians' sun-safe intentions and behaviour. The study used a prospective design with two waves of data collection, 1 week apart. Participants were 816 adults (48.2% men) aged between 18 and 88 years recruited from urban, regional, and rural areas of Australia. At baseline, participants completed a questionnaire assessing the standard TPB predictors (attitude, subjective norm, and perceived behavioural control [PBC]), past behaviour, behavioural intention, and additional measures of group norm for the referent groups of friends and family, image norm, personal norm, personal choice/responsibility, and Australian identity. Seventy-one per cent of the participants (n = 577) reported on their sun-safe behaviour in the subsequent week. Via path modelling, past behaviour, attitude, group norm (friends), personal norm, and personal choice/responsibility emerged as independent predictors of intentions which, in turn, predicted sun-safe behaviour prospectively. Past behaviour, but not PBC, had direct effects on sun-safe behaviour. The model explained 61.6% and 43.9% of the variance in intention and behaviour, respectively. This study provides support for the use of a comprehensive theoretical decision-making model to explain Australian adults' sun-safe intentions and behaviours and identifies viable targets for health-promoting messages in this high-risk context. Statement of contribution What is already known on this subject? Identifying determinants of sun-safe behaviour is vital in high-risk cancer areas like Australia. For young Australians, friendship group norm is a key influence of intentions and behaviour. Little is known about drivers of sun safety, especially norms, among Australian adults in general. What does this study add? This study drew on qualitative data and reconceptualized norms for Australians' sun-safe decisions. Friendship group norm and personal norm, not family group norm, influence sun-safe intentions. Perceived responsibility and choice to be sun safe also impact on people's intentions. © 2014 The British Psychological Society.

Predicting dropout using student- and school-level factors: An ecological perspective.

PubMed

Wood, Laura; Kiperman, Sarah; Esch, Rachel C; Leroux, Audrey J; Truscott, Stephen D

2017-03-01

High school dropout has been associated with negative outcomes, including increased rates of unemployment, incarceration, and mortality. Dropout rates vary significantly depending on individual and environmental factors. The purpose of our study was to use an ecological perspective to concurrently explore student- and school-level predictors associated with dropout for the purpose of better understanding how to prevent it. We used the Education Longitudinal Study of 2002 dataset. Participants included 14,106 sophomores across 684 public and private schools. We identified variables of interest based on previous research on dropout and implemented hierarchical generalized linear modeling. In the final model, significant student-level predictors included academic achievement, retention, sex, family socioeconomic status (SES), and extracurricular involvement. Significant school-level predictors included school SES and school size. Race/ethnicity, special education status, born in the United States, English as first language, school urbanicity, and school region did not significantly predict dropout after controlling for the aforementioned predictors. Implications for prevention and intervention efforts within a multitiered intervention model are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing

USGS Publications Warehouse

Marshall, Michael T.; Thenkabail, Prasad S.

2015-01-01

Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors included: crop height (H), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), and fraction of vegetation cover (FVC). The spectral predictors included 196 hyperspectral narrowbands (HNBs) from 350 to 2500 nm. The models for rice, maize, cotton, and alfalfa included H and HNBs in the near infrared (NIR); H, FAPAR, and HNBs in the NIR; H and HNBs in the visible and NIR; and FVC and HNBs in the visible; respectively. In each case, the non-spectral predictors were the most important, while the HNBs explained additional and statistically significant predictors, but with lower variance. The final models selected for validation yielded an R2 of 0.84, 0.59, 0.91, and 0.86 for rice, maize, cotton, and alfalfa, which when compared to models using HNBs alone from a previous study using the same spectral data, explained an additional 12%, 29%, 14%, and 6% in AWB variance. These integrated models will be used in an up-coming study to extrapolate AWB over 60 × 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands.

Maximal exercise oxygen pulse as a predictor of mortality among male veterans referred for exercise testing.

PubMed

Oliveira, Ricardo B; Myers, Jonathan; Araújo, Claudio Gil S; Abella, Joshua; Mandic, Sandra; Froelicher, Victor

2009-06-01

Maximal oxygen pulse (O(2) pulse) mirrors the stroke volume response to exercise, and should therefore be a strong predictor of mortality. Limited and conflicting data are, however, available on this issue. Nine hundred forty-eight participants, classified as those with cardiopulmonary disease (CPD) and those without (non-CPD), underwent cardiopulmonary exercise testing (CPX) for clinical reasons between 1993 and 2003. The ability of maximal O(2) pulse and maximal oxygen uptake (peak VO(2)) to predict mortality was investigated using proportional hazards and Akaike information criterion analyses. All-cause mortality was the endpoint. Over a mean follow-up of 6.3+/-3.2 years, there were 126 deaths. Maximal O(2) pulse, expressed in either absolute or relative to age-predicted terms, and peak VO(2) were significant and independent predictors of mortality in those with and without CPD (P<0.04). Akaike information criterion analysis revealed that the model including both maximal O(2) pulse and peak VO(2) had the highest accuracy for predicting mortality. The optimal cut-points for O(2) pulse and peak VO(2) (<12; > or =12 ml/beat and <16; > or =16 ml/(kg.min) respectively) were established by the area under the receiver-operating-characteristic curve. The relative risks of mortality were 3.4 and 2.2 (CPD and non-CPD, respectively) among participants with both maximal O(2) pulse and peak VO(2) responses below these cut-points compared with participants with both responses above these cut-points. These results indicate that maximal O(2) pulse is a significant predictor of mortality in patients with and without CPD. The addition of absolute and relative O(2) pulse data provides complementary information for risk-stratifying heterogeneous participants referred for CPX and should be routinely included in the CPX report.

Predictors of dyadic planning: Perspectives of prostate cancer survivors and their partners.

PubMed

Keller, Jan; Wiedemann, Amelie U; Hohl, Diana Hilda; Scholz, Urte; Burkert, Silke; Schrader, Mark; Knoll, Nina

2017-02-01

Extending individual planning of health behaviour change to the level of the dyad, dyadic planning refers to a target person and a planning partner jointly planning the target person's health behaviour change. To date, predictors of dyadic planning have not been systematically investigated. Integrating cognitive predictors of individual planning with four established predictor domains of social support provision, we propose a framework of predictors of dyadic planning. Including target persons' and partners' perspectives, we examine these predictor domains in the context of prostate cancer patients' rehabilitative pelvic floor exercise (PFE) following radical prostatectomy. Longitudinal data from 175 patients and their partners were analysed in a study with four post-surgery assessments across 6 months. PFE-related dyadic planning was assessed from both partners together with indicators from four predictor domains: context, target person, partner, and relationship factors. Individual planning and social support served as covariates. Findings from two-level models nesting repeated assessments in individuals showed that context (patients' incontinence), target person (i.e., positive affect and self-efficacy), and relationship factors (i.e., relationship satisfaction) were uniquely associated with dyadic planning, whereas partner factors (i.e., positive and negative affects) were not. Factors predicting patients' and partners' accounts of dyadic planning differed. Resembling prior findings on antecedents of support provision in this context, partner factors did not prevail as unique predictors of dyadic planning, whereas indicators from all other predictor domains did. To establish predictive direction, future work should use lagged predictions with shorter intermeasurement intervals. Statement of contribution What is already known on this subject? Dyadic planning has been shown to be linked to health behaviour change. However, its role in behaviour regulation frameworks is not well investigated, especially regarding factors that might be predictive of dyadic planning. What does this study add? A framework of predictors of dyadic planning in the health behaviour change process is presented. The framework is investigated accounting for both planning partners' perspectives. Context, target person, and relationship factors were related to dyadic planning. © 2016 The British Psychological Society.

Ethnic Distribution of ECG Predictors of Atrial Fibrillation and Its Impact on Understanding the Ethnic Distribution of Ischemic Stroke in the Atherosclerosis Risk in Communities (ARIC) Study

PubMed Central

Soliman, Elsayed Z.; Prineas, Ronald J.; Case, L. Douglas; Zhang, Zhu-ming; Goff, David C.

2009-01-01

Background and Purpose The paradox of the reported low prevalence of atrial fibrillation (AF) in blacks compared with whites despite higher stroke rates in the former could be related to limitations in the current methods used to diagnose AF in population-based studies. Hence, this study aimed to use the ethnic distribution of ECG predictors of AF as measures of AF propensity in different ethnic groups. Methods The distribution of baseline measures of P-wave terminal force, P-wave duration, P-wave area, and PR duration (referred to as AF predictors) were compared by ethnicity in 15 429 participants (27% black) from the Atherosclerosis Risk in Communities (ARIC) study by unpaired t test, χ2, and logistic-regression analysis, as appropriate. Cox proportional-hazards analysis was used to separately examine the association of AF predictors with incident AF and ischemic stroke. Results Whereas AF was significantly less common in blacks compared with whites (0.24% vs 0.95%, P<0.0001), similar to what has been reported in previous studies, blacks had significantly higher and more abnormal values of AF predictors (P<0.0001 for all comparisons). Black ethnicity was significantly associated with abnormal AF predictors compared with whites; odds ratios for different AF predictors ranged from 2.1 to 3.1. AF predictors were significantly and independently associated with AF and ischemic stroke with no significant interaction between ethnicity and AF predictors, findings that further justify using AF predictors as an earlier indicator of future risk of AF and stroke. Conclusions There is a disconnect between the ethnic distribution of AF predictors and the ethnic distribution of AF, probably because the former, unlike the latter, do not suffer from low sensitivity. These results raise the possibility that blacks might actually have a higher prevalence of AF that might have been missed by previous studies owing to limited methodology, a difference that could partially explain the greater stroke risk in blacks. PMID:19213946

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Longitudinal modelling of academic buoyancy and motivation: do the '5Cs' hold up over time?

PubMed

Martin, Andrew J; Colmar, Susan H; Davey, Louise A; Marsh, Herbert W

2010-09-01

Academic buoyancy is students' ability to successfully deal with setbacks and challenges that are typical of academic life. The present study extends previous preliminary cross-sectional work that tentatively identified five motivational predictors of academic buoyancy - referred to as the '5Cs' of academic buoyancy: confidence (self-efficacy), coordination (planning), commitment (persistence), composure (low anxiety), and control (low uncertain control). The study seeks to more clearly ascertain the effects of motivation (and its mediating role) on academic buoyancy over and above prior academic buoyancy. The study comprised N=1,866 high school students from six schools. Longitudinal data were collected (1 year apart) and the hypothesized model exploring longitudinal effects was tested using structural equation modelling. After controlling for prior variance in academic buoyancy, the 5Cs were significant predictors of subsequent academic buoyancy. Furthermore, over and above the direct effects of prior academic buoyancy on subsequent academic buoyancy, the 5Cs significantly mediated this relationship. The study concludes with a discussion of the substantive, applied, and methodological implications for researchers and practitioners seeking to investigate and address the academic buoyancy of students who require the capacity to effectively function in an ever-challenging school environment.

Applying the Health Belief Model to college students' health behavior

PubMed Central

Kim, Hak-Seon; Ahn, Joo

2012-01-01

The purpose of this research was to investigate how university students' nutrition beliefs influence their health behavioral intention. This study used an online survey engine (Qulatrics.com) to collect data from college students. Out of 253 questionnaires collected, 251 questionnaires (99.2%) were used for the statistical analysis. Confirmatory Factor Analysis (CFA) revealed that six dimensions, "Nutrition Confidence," "Susceptibility," "Severity," "Barrier," "Benefit," "Behavioral Intention to Eat Healthy Food," and "Behavioral Intention to do Physical Activity," had construct validity; Cronbach's alpha coefficient and composite reliabilities were tested for item reliability. The results validate that objective nutrition knowledge was a good predictor of college students' nutrition confidence. The results also clearly showed that two direct measures were significant predictors of behavioral intentions as hypothesized. Perceived benefit of eating healthy food and perceived barrier for eat healthy food to had significant effects on Behavioral Intentions and was a valid measurement to use to determine Behavioral Intentions. These findings can enhance the extant literature on the universal applicability of the model and serve as useful references for further investigations of the validity of the model within other health care or foodservice settings and for other health behavioral categories. PMID:23346306

Reference Values of Impulse Oscillometric Lung Function Indices in Adults of Advanced Age

PubMed Central

Schulz, Holger; Flexeder, Claudia; Behr, Jürgen; Heier, Margit; Holle, Rolf; Huber, Rudolf M.; Jörres, Rudolf A.; Nowak, Dennis; Peters, Annette; Wichmann, H.-Erich; Heinrich, Joachim; Karrasch, Stefan

2013-01-01

Background Impulse oscillometry (IOS) is a non-demanding lung function test. Its diagnostic use may be particularly useful in patients of advanced age with physical or mental limitations unable to perform spirometry. Only few reference equations are available for Caucasians, none of them covering the old age. Here, we provide reference equations up to advanced age and compare them with currently available equations. Methods IOS was performed in a population-based sample of 1990 subjects, aged 45–91 years, from KORA cohorts (Augsburg, Germany). From those, 397 never-smoking, lung healthy subjects with normal spirometry were identified and sex-specific quantile regression models with age, height and body weight as predictors for respiratory system impedance, resistance, reactance, and other parameters of IOS applied. Results Women (n = 243) showed higher resistance values than men (n = 154), while reactance at low frequencies (up to 20 Hz) was lower (p<0.05). A significant age dependency was observed for the difference between resistance values at 5 Hz and 20 Hz (R5–R20), the integrated area of low-frequency reactance (AX), and resonant frequency (Fres) in both sexes whereas reactance at 5 Hz (X5) was age dependent only in females. In the healthy subjects (n = 397), mean differences between observed values and predictions for resistance (5 Hz and 20 Hz) and reactance (5 Hz) ranged between −1% and 5% when using the present model. In contrast, differences based on the currently applied equations (Vogel & Smidt 1994) ranged between −34% and 76%. Regarding our equations the indices were beyond the limits of normal in 8.1% to 18.6% of the entire KORA cohort (n = 1990), and in 0.7% to 9.4% with the currently applied equations. Conclusions Our study provides up-to-date reference equations for IOS in Caucasians aged 45 to 85 years. We suggest the use of the present equations particularly in advanced age in order to detect airway dysfunction. PMID:23691036

Discrete-time pilot model. [human dynamics and digital simulation

NASA Technical Reports Server (NTRS)

Cavalli, D.

1978-01-01

Pilot behavior is considered as a discrete-time process where the decision making has a sequential nature. This model differs from both the quasilinear model which follows from classical control theory and from the optimal control model which considers the human operator as a Kalman estimator-predictor. An additional factor considered is that the pilot's objective may not be adequately formulated as a quadratic cost functional to be minimized, but rather as a more fuzzy measure of the closeness with which the aircraft follows a reference trajectory. All model parameters, in the digital program simulating the pilot's behavior, were successfully compared in terms of standard-deviation and performance with those of professional pilots in IFR configuration. The first practical application of the model was in the study of its performance degradation when the aircraft model static margin decreases.

SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.

PubMed

Yang, Xiaoxia; Wang, Jia; Sun, Jun; Liu, Rong

2015-01-01

Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder) by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

The Adams formulas for numerical integration of differential equations from 1st to 20th order

NASA Technical Reports Server (NTRS)

Kirkpatrick, J. C.

1976-01-01

The Adams Bashforth predictor coefficients and the Adams Moulton corrector coefficients for the integration of differential equations are presented for methods of 1st to 20th order. The order of the method as presented refers to the highest order difference formula used in Newton's backward difference interpolation formula, on which the Adams method is based. The Adams method is a polynomial approximation method derived from Newton's backward difference interpolation formula. The Newton formula is derived and expanded to 20th order. The Adams predictor and corrector formulas are derived and expressed in terms of differences of the derivatives, as well as in terms of the derivatives themselves. All coefficients are given to 18 significant digits. For the difference formula only, the ratio coefficients are given to 10th order.

Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer.

PubMed

Hoogendoorn, Mark; Szolovits, Peter; Moons, Leon M G; Numans, Mattijs E

2016-05-01

Machine learning techniques can be used to extract predictive models for diseases from electronic medical records (EMRs). However, the nature of EMRs makes it difficult to apply off-the-shelf machine learning techniques while still exploiting the rich content of the EMRs. In this paper, we explore the usage of a range of natural language processing (NLP) techniques to extract valuable predictors from uncoded consultation notes and study whether they can help to improve predictive performance. We study a number of existing techniques for the extraction of predictors from the consultation notes, namely a bag of words based approach and topic modeling. In addition, we develop a dedicated technique to match the uncoded consultation notes with a medical ontology. We apply these techniques as an extension to an existing pipeline to extract predictors from EMRs. We evaluate them in the context of predictive modeling for colorectal cancer (CRC), a disease known to be difficult to diagnose before performing an endoscopy. Our results show that we are able to extract useful information from the consultation notes. The predictive performance of the ontology-based extraction method moves significantly beyond the benchmark of age and gender alone (area under the receiver operating characteristic curve (AUC) of 0.870 versus 0.831). We also observe more accurate predictive models by adding features derived from processing the consultation notes compared to solely using coded data (AUC of 0.896 versus 0.882) although the difference is not significant. The extracted features from the notes are shown be equally predictive (i.e. there is no significant difference in performance) compared to the coded data of the consultations. It is possible to extract useful predictors from uncoded consultation notes that improve predictive performance. Techniques linking text to concepts in medical ontologies to derive these predictors are shown to perform best for predicting CRC in our EMR dataset. Copyright © 2016 Elsevier B.V. All rights reserved.

Model Updating of Complex Structures Using the Combination of Component Mode Synthesis and Kriging Predictor

PubMed Central

Li, Yan; Wang, Dejun; Zhang, Shaoyi

2014-01-01

Updating the structural model of complex structures is time-consuming due to the large size of the finite element model (FEM). Using conventional methods for these cases is computationally expensive or even impossible. A two-level method, which combined the Kriging predictor and the component mode synthesis (CMS) technique, was proposed to ensure the successful implementing of FEM updating of large-scale structures. In the first level, the CMS was applied to build a reasonable condensed FEM of complex structures. In the second level, the Kriging predictor that was deemed as a surrogate FEM in structural dynamics was generated based on the condensed FEM. Some key issues of the application of the metamodel (surrogate FEM) to FEM updating were also discussed. Finally, the effectiveness of the proposed method was demonstrated by updating the FEM of a real arch bridge with the measured modal parameters. PMID:24634612

Dynamic modeling of Listeria monocytogenes growth in pasteurized vanilla cream after postprocessing contamination.

PubMed

Panagou, Efstathios Z; Nychas, George-John E

2008-09-01

A product-specific model was developed and validated under dynamic temperature conditions for predicting the growth of Listeria monocytogenes in pasteurized vanilla cream, a traditional milk-based product. Model performance was also compared with Growth Predictor and Sym'Previus predictive microbiology software packages. Commercially prepared vanilla cream samples were artificially inoculated with a five-strain cocktail of L. monocytogenes, with an initial concentration of 102 CFU g(-1), and stored at 3, 5, 10, and 15 degrees C for 36 days. The growth kinetic parameters at each temperature were determined by the primary model of Baranyi and Roberts. The maximum specific growth rate (mu(max)) was further modeled as a function of temperature by means of a square root-type model. The performance of the model in predicting the growth of the pathogen under dynamic temperature conditions was based on two different temperature scenarios with periodic changes from 4 to 15 degrees C. Growth prediction for dynamic temperature profiles was based on the square root model and the differential equations of the Baranyi and Roberts model, which were numerically integrated with respect to time. Model performance was based on the bias factor (B(f)), the accuracy factor (A(f)), the goodness-of-fit index (GoF), and the percent relative errors between observed and predicted growth. The product-specific model developed in the present study accurately predicted the growth of L. monocytogenes under dynamic temperature conditions. The average values for the performance indices were 1.038, 1.068, and 0.397 for B(f), A(f), and GoF, respectively for both temperature scenarios assayed. Predictions from Growth Predictor and Sym'Previus overestimated pathogen growth. The average values of B(f), A(f), and GoF were 1.173, 1.174, 1.162, and 0.956, 1.115, 0.713 for [corrected] Growth Predictor and Sym'Previus, respectively.

Evaluating the influence of spatial resolution of Landsat predictors on the accuracy of biomass models for large-area estimation across the eastern USA

NASA Astrophysics Data System (ADS)

Deo, Ram K.; Domke, Grant M.; Russell, Matthew B.; Woodall, Christopher W.; Andersen, Hans-Erik

2018-05-01

Aboveground biomass (AGB) estimates for regional-scale forest planning have become cost-effective with the free access to satellite data from sensors such as Landsat and MODIS. However, the accuracy of AGB predictions based on passive optical data depends on spatial resolution and spatial extent of target area as fine resolution (small pixels) data are associated with smaller coverage and longer repeat cycles compared to coarse resolution data. This study evaluated various spatial resolutions of Landsat-derived predictors on the accuracy of regional AGB models at three different sites in the eastern USA: Maine, Pennsylvania-New Jersey, and South Carolina. We combined national forest inventory data with Landsat-derived predictors at spatial resolutions ranging from 30–1000 m to understand the optimal spatial resolution of optical data for large-area (regional) AGB estimation. Ten generic models were developed using the data collected in 2014, 2015 and 2016, and the predictions were evaluated (i) at the county-level against the estimates of the USFS Forest Inventory and Analysis Program which relied on EVALIDator tool and national forest inventory data from the 2009–2013 cycle and (ii) within a large number of strips (~1 km wide) predicted via LiDAR metrics at 30 m spatial resolution. The county-level estimates by the EVALIDator and Landsat models were highly related (R 2 > 0.66), although the R 2 varied significantly across sites and resolution of predictors. The mean and standard deviation of county-level estimates followed increasing and decreasing trends, respectively, with models of coarser resolution. The Landsat-based total AGB estimates were larger than the LiDAR-based total estimates within the strips, however the mean of AGB predictions by LiDAR were mostly within one-standard deviations of the mean predictions obtained from the Landsat-based model at any of the resolutions. We conclude that satellite data at resolutions up to 1000 m provide acceptable accuracy for continental scale analysis of AGB.

A Canonical Ensemble Correlation Prediction Model for Seasonal Precipitation Anomaly

NASA Technical Reports Server (NTRS)

Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Guilong

2001-01-01

This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

PubMed Central

Sharafoddini, Anis; Dubin, Joel A

2017-01-01

Background Physicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions.. Objective The aim is to summarize and review published studies describing computer-based approaches for predicting patients’ future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research. Methods The method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results. Results After duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods. Conclusions Interest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health data, wavelet transform and term frequency-inverse document frequency methods were employed to extract predictors. Selecting predictors with potential to highlight special cases and defining new patient similarity metrics were among the gaps identified in the existing literature that provide starting points for future work. Patient status prediction models based on patient similarity and health data offer exciting potential for personalizing and ultimately improving health care, leading to better patient outcomes. PMID:28258046

Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by fibrillatory wave amplitude computation in multiple electrocardiogram leads.

PubMed

Zarzoso, Vicente; Latcu, Decebal G; Hidalgo-Muñoz, Antonio R; Meo, Marianna; Meste, Olivier; Popescu, Irina; Saoudi, Nadir

2016-12-01

Catheter ablation (CA) of persistent atrial fibrillation (AF) is challenging, and reported results are capable of improvement. A better patient selection for the procedure could enhance its success rate while avoiding the risks associated with ablation, especially for patients with low odds of favorable outcome. CA outcome can be predicted non-invasively by atrial fibrillatory wave (f-wave) amplitude, but previous works focused mostly on manual measures in single electrocardiogram (ECG) leads only. To assess the long-term prediction ability of f-wave amplitude when computed in multiple ECG leads. Sixty-two patients with persistent AF (52 men; mean age 61.5±10.4years) referred for CA were enrolled. A standard 1-minute 12-lead ECG was acquired before the ablation procedure for each patient. F-wave amplitudes in different ECG leads were computed by a non-invasive signal processing algorithm, and combined into a mutivariate prediction model based on logistic regression. During an average follow-up of 13.9±8.3months, 47 patients had no AF recurrence after ablation. A lead selection approach relying on the Wald index pointed to I, V1, V2 and V5 as the most relevant ECG leads to predict jointly CA outcome using f-wave amplitudes, reaching an area under the curve of 0.854, and improving on single-lead amplitude-based predictors. Analysing the f-wave amplitude in several ECG leads simultaneously can significantly improve CA long-term outcome prediction in persistent AF compared with predictors based on single-lead measures. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Predictors of Urgent Findings on Abdominopelvic CT in Patients with Crohn's Disease Presenting to the Emergency Department.

PubMed

Jung, Yoon Suk; Park, Dong Il; Hong, Sung Noh; Kim, Eun Ran; Kim, Young Ho; Cheon, Jae Hee; Eun, Chang Soo; Han, Dong Soo; Lee, Chang Kyun; Kim, Jae Hak; Huh, Kyu Chan; Yoon, Soon Man; Song, Hyun Joo; Shin, Jeong Eun; Jeon, Seong Ran

2015-04-01

Patients with Crohn's disease (CD) are frequently exposed to diagnostic radiation, mainly as a result of abdominopelvic computed tomography (APCT) examinations. However, there are limited data on the impact of APCT on clinical management in this population. To investigate clinical predictors of urgent findings on APCT in patients with CD who presented to the emergency department (ED). A retrospective study was performed among patients with CD presenting to 11 EDs with a gastrointestinal complaint. The primary outcome, OPAN (obstruction, perforation, abscess, or non-CD-related urgent findings), included new or worsening CD-related urgent findings or non-CD-related urgent findings that required urgent or emergency treatment. Variables with P < 0.1 in univariate analyses were included in a multivariable logistic regression model. Of the 266 APCTs performed, 103 (38.7 %) had OPAN and 113 (42.5 %) required changes in treatment plan. Stricturing or penetrating disease (odds ratio [OR] 2.72, 95 % confidence interval [CI] 1.21-6.13), heart rate >100 beats/min (OR 2.33, 95 % CI 1.10-4.93), leukocyte count >10,000/mm(3) (OR 4.38, 95 % CI 2.10-9.13), and CRP >2.5 mg/dL (OR 3.11, 95 % CI 1.23-7.86) were identified as the independent predictors of OPAN, whereas biologic agent use (OR 0.37; 95 % CI 0.15-0.90) was identified as the negative predictor in patients with CD. Only 39 % of the APCTs performed in the ED among patients with CD showed urgent findings. Stricturing or penetrating disease, tachycardia, leukocytosis, and high CRP level were predictors of urgent CT findings, while biologic agent use was a negative predictor. To reduce unnecessary radiation exposure, the selection process for CD patients referred for APCT must be improved.

Externalizing behavior from early childhood to adolescence: Prediction from inhibition, language, parenting, and attachment.

PubMed

Roskam, Isabelle

2018-03-22

The aim of the current research was to disentangle four theoretically sound models of externalizing behavior etiology (i.e., attachment, language, inhibition, and parenting) by testing their relation with behavioral trajectories from early childhood to adolescence. The aim was achieved through a 10-year prospective longitudinal study conducted over five waves with 111 referred children aged 3 to 5 years at the onset of the study. Clinical referral was primarily based on externalizing behavior. A multimethod (questionnaires, testing, and observations) approach was used to estimate the four predictors in early childhood. In line with previous studies, the results show a significant decrease of externalizing behavior from early childhood to adolescence. The decline was negatively related to mothers' coercive parenting and positively related to attachment security in early childhood, but not related to inhibition and language. The study has implications for research into externalizing behavior etiology recommending to gather hypotheses from various theoretically sound models to put them into competition with one another. The study also has implications for clinical practice by providing clear indications for prevention and early intervention.

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11

PubMed Central

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2015-01-01

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. PMID:26369671

Predictors of Entering a Hearing Aid Evaluation Period: A Prospective Study in Older Hearing-Help Seekers

PubMed Central

Deeg, Dorly J.H.; Versfeld, Niek J.; Heymans, Martijn W.; Naylor, Graham; Kramer, Sophia E.

2017-01-01

This study aimed to determine the predictors of entering a hearing aid evaluation period (HAEP) using a prospective design drawing on the health belief model and the transtheoretical model. In total, 377 older persons who presented with hearing problems to an Ear, Nose, and Throat specialist (n = 110) or a hearing aid dispenser (n = 267) filled in a baseline questionnaire. After 4 months, it was determined via a telephone interview whether or not participants had decided to enter a HAEP. Multivariable logistic regression analyses were applied to determine which baseline variables predicted HAEP status. A priori, candidate predictors were divided into ‘likely’ and ‘novel’ predictors based on the literature. The following variables turned out to be significant predictors: more expected hearing aid benefits, greater social pressure, and greater self-reported hearing disability. In addition, greater hearing loss severity and stigma were predictors in women but not in men. Of note, the predictive effect of self-reported hearing disability was modified by readiness such that with higher readiness, the positive predictive effect became stronger. None of the ‘novel’ predictors added significant predictive value. The results support the notion that predictors of hearing aid uptake are also predictive of entering a HAEP. This study shows that some of these predictors appear to be gender specific or are dependent on a person’s readiness for change. After assuring the external validity of the predictors, an important next step would be to develop prediction rules for use in clinical practice, so that older persons’ hearing help-seeking journey can be facilitated. PMID:29237333

Unified dead-time compensation structure for SISO processes with multiple dead times.

PubMed

Normey-Rico, Julio E; Flesch, Rodolfo C C; Santos, Tito L M

2014-11-01

This paper proposes a dead-time compensation structure for processes with multiple dead times. The controller is based on the filtered Smith predictor (FSP) dead-time compensator structure and it is able to control stable, integrating, and unstable processes with multiple input/output dead times. An equivalent model of the process is first computed in order to define the predictor structure. Using this equivalent model, the primary controller and the predictor filter are tuned to obtain an internally stable closed-loop system which also attempts some closed-loop specifications in terms of set-point tracking, disturbance rejection, and robustness. Some simulation case studies are used to illustrate the good properties of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Sociodemographic Factors Associated With Changes in Successful Aging in Spain: A Follow-Up Study.

PubMed

Domènech-Abella, Joan; Perales, Jaime; Lara, Elvira; Moneta, Maria Victoria; Izquierdo, Ana; Rico-Uribe, Laura Alejandra; Mundó, Jordi; Haro, Josep Maria

2017-06-01

Successful aging (SA) refers to maintaining well-being in old age. Several definitions or models of SA exist (biomedical, psychosocial, and mixed). We examined the longitudinal association between various SA models and sociodemographic factors, and analyzed the patterns of change within these models. This was a nationally representative follow-up in Spain including 3,625 individuals aged ≥50 years. Some 1,970 individuals were interviewed after 3 years. Linear regression models were used to analyze the survey data. Age, sex, and occupation predicted SA in the biomedical model, while marital status, educational level, and urbanicity predicted SA in the psychosocial model. The remaining models included different sets of these predictors as significant. In the psychosocial model, individuals tended to improve over time but this was not the case in the biomedical model. The biomedical and psychosocial components of SA need to be addressed specifically to achieve the best aging trajectories.

On neural networks in identification and control of dynamic systems

NASA Technical Reports Server (NTRS)

Phan, Minh; Juang, Jer-Nan; Hyland, David C.

1993-01-01

This paper presents a discussion of the applicability of neural networks in the identification and control of dynamic systems. Emphasis is placed on the understanding of how the neural networks handle linear systems and how the new approach is related to conventional system identification and control methods. Extensions of the approach to nonlinear systems are then made. The paper explains the fundamental concepts of neural networks in their simplest terms. Among the topics discussed are feed forward and recurrent networks in relation to the standard state-space and observer models, linear and nonlinear auto-regressive models, linear, predictors, one-step ahead control, and model reference adaptive control for linear and nonlinear systems. Numerical examples are presented to illustrate the application of these important concepts.

Predictive value of the DASH tool for predicting return to work of injured workers with musculoskeletal disorders of the upper extremity.

PubMed

Armijo-Olivo, Susan; Woodhouse, Linda J; Steenstra, Ivan A; Gross, Douglas P

2016-12-01

To determine whether the Disabilities of the Arm, Shoulder, and Hand (DASH) tool added to the predictive ability of established prognostic factors, including patient demographic and clinical outcomes, to predict return to work (RTW) in injured workers with musculoskeletal (MSK) disorders of the upper extremity. A retrospective cohort study using a population-based database from the Workers' Compensation Board of Alberta (WCB-Alberta) that focused on claimants with upper extremity injuries was used. Besides the DASH, potential predictors included demographic, occupational, clinical and health usage variables. Outcome was receipt of compensation benefits after 3 months. To identify RTW predictors, a purposeful logistic modelling strategy was used. A series of receiver operating curve analyses were performed to determine which model provided the best discriminative ability. The sample included 3036 claimants with upper extremity injuries. The final model for predicting RTW included the total DASH score in addition to other established predictors. The area under the curve for this model was 0.77, which is interpreted as fair discrimination. This model was statistically significantly different than the model of established predictors alone (p<0.001). When comparing the DASH total score versus DASH item 23, a non-significant difference was obtained between the models (p=0.34). The DASH tool together with other established predictors significantly helped predict RTW after 3 months in participants with upper extremity MSK disorders. An appealing result for clinicians and busy researchers is that DASH item 23 has equal predictive ability to the total DASH score. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

WAVELET-DOMAIN REGRESSION AND PREDICTIVE INFERENCE IN PSYCHIATRIC NEUROIMAGING

PubMed Central

Reiss, Philip T.; Huo, Lan; Zhao, Yihong; Kelly, Clare; Ogden, R. Todd

2016-01-01

An increasingly important goal of psychiatry is the use of brain imaging data to develop predictive models. Here we present two contributions to statistical methodology for this purpose. First, we propose and compare a set of wavelet-domain procedures for fitting generalized linear models with scalar responses and image predictors: sparse variants of principal component regression and of partial least squares, and the elastic net. Second, we consider assessing the contribution of image predictors over and above available scalar predictors, in particular via permutation tests and an extension of the idea of confounding to the case of functional or image predictors. Using the proposed methods, we assess whether maps of a spontaneous brain activity measure, derived from functional magnetic resonance imaging, can meaningfully predict presence or absence of attention deficit/hyperactivity disorder (ADHD). Our results shed light on the role of confounding in the surprising outcome of the recent ADHD-200 Global Competition, which challenged researchers to develop algorithms for automated image-based diagnosis of the disorder. PMID:27330652

Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data

USGS Publications Warehouse

Junttila, Virpi; Kauranne, Tuomo; Finley, Andrew O.; Bradford, John B.

2015-01-01

Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%–15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model’s lack of fit.

Gene Environment Interactions and Predictors of Colorectal Cancer in Family-Based, Multi-Ethnic Groups.

PubMed

Shiao, S Pamela K; Grayson, James; Yu, Chong Ho; Wasek, Brandi; Bottiglieri, Teodoro

2018-02-16

For the personalization of polygenic/omics-based health care, the purpose of this study was to examine the gene-environment interactions and predictors of colorectal cancer (CRC) by including five key genes in the one-carbon metabolism pathways. In this proof-of-concept study, we included a total of 54 families and 108 participants, 54 CRC cases and 54 matched family friends representing four major racial ethnic groups in southern California (White, Asian, Hispanics, and Black). We used three phases of data analytics, including exploratory, family-based analyses adjusting for the dependence within the family for sharing genetic heritage, the ensemble method, and generalized regression models for predictive modeling with a machine learning validation procedure to validate the results for enhanced prediction and reproducibility. The results revealed that despite the family members sharing genetic heritage, the CRC group had greater combined gene polymorphism rates than the family controls ( p < 0.05), on MTHFR C677T , MTR A2756G , MTRR A66G, and DHFR 19 bp except MTHFR A1298C. Four racial groups presented different polymorphism rates for four genes (all p < 0.05) except MTHFR A1298C. Following the ensemble method, the most influential factors were identified, and the best predictive models were generated by using the generalized regression models, with Akaike's information criterion and leave-one-out cross validation methods. Body mass index (BMI) and gender were consistent predictors of CRC for both models when individual genes versus total polymorphism counts were used, and alcohol use was interactive with BMI status. Body mass index status was also interactive with both gender and MTHFR C677T gene polymorphism, and the exposure to environmental pollutants was an additional predictor. These results point to the important roles of environmental and modifiable factors in relation to gene-environment interactions in the prevention of CRC.

Accuracy of visual inspection performed by community health workers in cervical cancer screening.

PubMed

Driscoll, Susan D; Tappen, Ruth M; Newman, David; Voege-Harvey, Kathi

2018-05-22

Cervical cancer remains the leading cause of cancer and mortality in low-resource areas with healthcare personnel shortages. Visual inspection is a low-resource alternative method of cervical cancer screening in areas with limited access to healthcare. To assess accuracy of visual inspection performed by community health workers (CHWs) and licensed providers, and the effect of provider training on visual inspection accuracy. Five databases and four websites were queried for studies published in English up to December 31, 2015. Derivations of "cervical cancer screening" and "visual inspection" were search terms. Visual inspection screening studies with provider definitions, colposcopy reference standards, and accuracy data were included. A priori variables were extracted by two independent reviewers. Bivariate linear mixed-effects models were used to compare visual inspection accuracy. Provider type was a significant predictor of visual inspection sensitivity (P=0.048); sensitivity was 15 percentage points higher among CHWs than physicians (P=0.014). Components of provider training were significant predictors of sensitivity and specificity. Community-based visual inspection programs using adequately trained CHWs could reduce barriers and expand access to screening, thereby decreasing cervical cancer incidence and mortality for women at highest risk and those living in remote areas with limited access to healthcare personnel. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported.

PubMed

Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S; Riley, Richard D

2018-05-18

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risk. Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorised as high risk of error, however this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions. Copyright © 2018. Published by Elsevier Inc.

Tracing the source of numerical climate model uncertainties in precipitation simulations using a feature-oriented statistical model

NASA Astrophysics Data System (ADS)

Xu, Y.; Jones, A. D.; Rhoades, A.

2017-12-01

Precipitation is a key component in hydrologic cycles, and changing precipitation regimes contribute to more intense and frequent drought and flood events around the world. Numerical climate modeling is a powerful tool to study climatology and to predict future changes. Despite the continuous improvement in numerical models, long-term precipitation prediction remains a challenge especially at regional scales. To improve numerical simulations of precipitation, it is important to find out where the uncertainty in precipitation simulations comes from. There are two types of uncertainty in numerical model predictions. One is related to uncertainty in the input data, such as model's boundary and initial conditions. These uncertainties would propagate to the final model outcomes even if the numerical model has exactly replicated the true world. But a numerical model cannot exactly replicate the true world. Therefore, the other type of model uncertainty is related the errors in the model physics, such as the parameterization of sub-grid scale processes, i.e., given precise input conditions, how much error could be generated by the in-precise model. Here, we build two statistical models based on a neural network algorithm to predict long-term variation of precipitation over California: one uses "true world" information derived from observations, and the other uses "modeled world" information using model inputs and outputs from the North America Coordinated Regional Downscaling Project (NA CORDEX). We derive multiple climate feature metrics as the predictors for the statistical model to represent the impact of global climate on local hydrology, and include topography as a predictor to represent the local control. We first compare the predictors between the true world and the modeled world to determine the errors contained in the input data. By perturbing the predictors in the statistical model, we estimate how much uncertainty in the model's final outcomes is accounted for by each predictor. By comparing the statistical model derived from true world information and modeled world information, we assess the errors lying in the physics of the numerical models. This work provides a unique insight to assess the performance of numerical climate models, and can be used to guide improvement of precipitation prediction.

A biological tool to assess flow connectivity in reference temporary streams from the Mediterranean Basin.

PubMed

Cid, N; Verkaik, I; García-Roger, E M; Rieradevall, M; Bonada, N; Sánchez-Montoya, M M; Gómez, R; Suárez, M L; Vidal-Abarca, M R; Demartini, D; Buffagni, A; Erba, S; Karaouzas, I; Skoulikidis, N; Prat, N

2016-01-01

Many streams in the Mediterranean Basin have temporary flow regimes. While timing for seasonal drought is predictable, they undergo strong inter-annual variability in flow intensity. This high hydrological variability and associated ecological responses challenge the ecological status assessment of temporary streams, particularly when setting reference conditions. This study examined the effects of flow connectivity in aquatic macroinvertebrates from seven reference temporary streams across the Mediterranean Basin where hydrological variability and flow conditions are well studied. We tested for the effect of flow cessation on two streamflow indices and on community composition, and, by performing random forest and classification tree analyses we identified important biological predictors for classifying the aquatic state either as flowing or disconnected pools. Flow cessation was critical for one of the streamflow indices studied and for community composition. Macroinvertebrate families found to be important for classifying the aquatic state were Hydrophilidae, Simuliidae, Hydropsychidae, Planorbiidae, Heptageniidae and Gerridae. For biological traits, trait categories associated to feeding habits, food, locomotion and substrate relation were the most important and provided more accurate predictions compared to taxonomy. A combination of selected metrics and associated thresholds based on the most important biological predictors (i.e. Bio-AS Tool) were proposed in order to assess the aquatic state in reference temporary streams, especially in the absence of hydrological data. Although further development is needed, the tool can be of particular interest for monitoring, restoration, and conservation purposes, representing an important step towards an adequate management of temporary rivers not only in the Mediterranean Basin but also in other regions vulnerable to the effects of climate change. Copyright © 2015 Elsevier B.V. All rights reserved.

Prediction models of health-related quality of life in different neck pain conditions: a cross-sectional study.

PubMed

Beltran-Alacreu, Hector; López-de-Uralde-Villanueva, Ibai; Calvo-Lobo, César; La Touche, Roy; Cano-de-la-Cuerda, Roberto; Gil-Martínez, Alfonso; Fernández-Ayuso, David; Fernández-Carnero, Josué

2018-01-01

The main aim of the study was to predict the health-related quality of life (HRQoL) based on physical, functional, and psychological measures in patients with different types of neck pain (NP). This cross-sectional study included 202 patients from a primary health center and the physiotherapy outpatient department of a hospital. Patients were divided into four groups according to their NP characteristics: chronic (CNP), acute whiplash (WHIP), chronic NP associated with temporomandibular dysfunction (NP-TMD), or chronic NP associated with chronic primary headache (NP-PH). The following measures were performed: Short Form-12 Health Survey (SF-12), Neck Disability Index (NDI), visual analog scale (VAS), State-Trait Anxiety Inventory (STAI), Beck Depression Inventory (BECK), and cervical range of movement (CROM). The regression models based on the SF-12 total HRQoL for CNP and NP-TMD groups showed that only NDI was a significant predictor of the worst HRQoL (48.9% and 48.4% of the variance, respectively). In the WHIP group, the regression model showed that BECK was the only significant predictor variable for the worst HRQoL (31.7% of the variance). Finally, in the NP-PH group, the regression showed that the BECK, STAI, and VAS model predicted the worst HRQoL (75.1% of the variance). Chronic nonspecific NP and chronic NP associated with temporomandibular dysfunction were the main predictors of neck disability. In addition, depression, anxiety, and pain were the main predictors of WHIP or primary headache associated with CNP.

Species distribution model transferability and model grain size - finer may not always be better.

PubMed

Manzoor, Syed Amir; Griffiths, Geoffrey; Lukac, Martin

2018-05-08

Species distribution models have been used to predict the distribution of invasive species for conservation planning. Understanding spatial transferability of niche predictions is critical to promote species-habitat conservation and forecasting areas vulnerable to invasion. Grain size of predictor variables is an important factor affecting the accuracy and transferability of species distribution models. Choice of grain size is often dependent on the type of predictor variables used and the selection of predictors sometimes rely on data availability. This study employed the MAXENT species distribution model to investigate the effect of the grain size on model transferability for an invasive plant species. We modelled the distribution of Rhododendron ponticum in Wales, U.K. and tested model performance and transferability by varying grain size (50 m, 300 m, and 1 km). MAXENT-based models are sensitive to grain size and selection of variables. We found that over-reliance on the commonly used bioclimatic variables may lead to less accurate models as it often compromises the finer grain size of biophysical variables which may be more important determinants of species distribution at small spatial scales. Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.

ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration

PubMed Central

Bottolo, Leonardo; Langley, Sarah R.; Petretto, Enrico; Tiret, Laurence; Tregouet, David; Richardson, Sylvia

2011-01-01

Summary: ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the ‘large p, small n’ case. In the current version, ESS++ can handle several hundred observations, thousands of predictors and a few responses simultaneously. The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo. Our implementation is open source, allowing community-based alterations and improvements. Availability: C++ source code and documentation including compilation instructions are available under GNU licence at http://bgx.org.uk/software/ESS.html. Contact: l.bottolo@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21233165

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

PubMed

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Assessment of insulin sensitivity (S I) and glucose effectiveness (S G) from a standardized hyperglucidic breakfast test in type 2 diabetics exhibiting various levels of insulin resistance.

PubMed

Brun, Jean-Frédéric; Ghanassia, Edouard; Fédou, Christine; Bordenave, Sylvain; Raynaud de Mauverger, Eric; Mercier, Jacques

2013-04-01

We investigated the measurement of insulin sensitivity (S I) with a standardized hyperglucidic breakfast (SHB) compared to minimal model analysis of an intravenous glucose tolerance test (S I-IVGTT) in 17 patients clinically referred as type 2 diabetics, not yet treated by insulin, and representing a wide range of body mass index and S I. To classify the patients, ten meal-tolerance test-based calculations of S I (MTT-S I) were compared to S I-IVGTT, and their reference values and distribution were measured on a separate sample of 200 control SHBs and 209 control IVGTTs. Eight MTT-SI indices exhibit significant correlations with S I-IVGTT: Mari's OGIS index, BIGTT-SI|0-30-120, BIGTT-SI|0-60-120, 1/G b I m, Caumo's oral minimal model (OMM), Sluiter's index "A" = 10(4)/(I p·G p), Matsuda's composite index given by the formula ISIcomp = 10(4)/(I b G b I m G m)(0.5), S I = 1/I b G b I m G m with r (2) ranging between 0,53 and 0,28. S I-IVGTT and S I-MTT exhibited in the lower range a very different (non-normal) pattern of distribution and thus the cutoff value for defining insulin resistance varied among indices. With such cutoffs, S I-MTT < 6.3 min(-1)/(μU/ml) 10(-4) with Caumo's OMM was the best predictor of insulin resistance defined as S I-IVGTT < 2 min(-1)/(μU/ml) 10(-4). Other indices, including OGIS and BIGTT, resulted in more misclassifications of patients. HOMA-IR and QUICKI were poor predictors. The formula [Formula: see text] satisfactorily predicts IVGTT-derived glucose effectiveness in type 2 diabetics. Thus, SHB appears suitable for the measurement of S I and S G in type 2 diabetics, and the OMM seems to provide the most accurate SHB-derived index in this population.

Gene-Environment Interactions in Cardiovascular Disease

PubMed Central

Flowers, Elena; Froelicher, Erika Sivarajan; Aouizerat, Bradley E.

2011-01-01

Background Historically, models to describe disease were exclusively nature-based or nurture-based. Current theoretical models for complex conditions such as cardiovascular disease acknowledge the importance of both biologic and non-biologic contributors to disease. A critical feature is the occurrence of interactions between numerous risk factors for disease. The interaction between genetic (i.e. biologic, nature) and environmental (i.e. non-biologic, nurture) causes of disease is an important mechanism for understanding both the etiology and public health impact of cardiovascular disease. Objectives The purpose of this paper is to describe theoretical underpinnings of gene-environment interactions, models of interaction, methods for studying gene-environment interactions, and the related concept of interactions between epigenetic mechanisms and the environment. Discussion Advances in methods for measurement of genetic predictors of disease have enabled an increasingly comprehensive understanding of the causes of disease. In order to fully describe the effects of genetic predictors of disease, it is necessary to place genetic predictors within the context of known environmental risk factors. The additive or multiplicative effect of the interaction between genetic and environmental risk factors is often greater than the contribution of either risk factor alone. PMID:21684212

Life course epidemiology: Modeling educational attainment with administrative data.

PubMed

Roos, Leslie L; Wall-Wieler, Elizabeth

2017-01-01

Understanding the processes across childhood and adolescence that affect later life inequalities depends on many variables for a large number of individuals measured over substantial time periods. Linkable administrative data were used to generate birth cohorts and to study pathways of inequity in childhood and early adolescence leading to differences in educational attainment. Advantages and disadvantages of using large administrative data bases for such research were highlighted. Children born in Manitoba, Canada between 1982 and 1995 were followed until age 19 (N = 89,763), with many time-invariant measures serving as controls. Five time-varying predictors of high school graduation-three social and two health-were modelled using logistic regression and a framework for examining predictors across the life course. For each time-varying predictor, six temporal patterns were tested: full, accumulation of risk, sensitive period, and three critical period models. Predictors measured in early adolescence generated the highest odds ratios, suggesting the importance of adolescence. Full models provided the best fit for the three time-varying social measures. Residence in a low-income neighborhood was a particularly influential predictor of not graduating from high school. The transmission of risk across developmental periods was also highlighted; exposure in one period had significant implications for subsequent life stages. This study advances life course epidemiology, using administrative data to clarify the relationships among several measures of social behavior, cognitive development, and health. Analyses of temporal patterns can be useful in studying such other outcomes as educational achievement, teen pregnancy, and workforce participation.

Machine learning-based dual-energy CT parametric mapping

NASA Astrophysics Data System (ADS)

Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W.; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Helo, Rose Al; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C.; Rassouli, Negin; Gilkeson, Robert C.; Traughber, Bryan J.; Cheng, Chee-Wai; Muzic, Raymond F., Jr.

2018-06-01

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρ e), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

Machine learning-based dual-energy CT parametric mapping.

PubMed

Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Al Helo, Rose; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C; Rassouli, Negin; Gilkeson, Robert C; Traughber, Bryan J; Cheng, Chee-Wai; Muzic, Raymond F

2018-06-08

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z eff ), relative electron density (ρ e ), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

Distribution, genetic and cardiovascular determinants of FVIII:c - Data from the population-based Gutenberg Health Study.

PubMed

Hermanns, M Iris; Grossmann, Vera; Spronk, Henri M H; Schulz, Andreas; Jünger, Claus; Laubert-Reh, Dagmar; Mazur, Johanna; Gori, Tommaso; Zeller, Tanja; Pfeiffer, Norbert; Beutel, Manfred; Blankenberg, Stefan; Münzel, Thomas; Lackner, Karl J; Ten Cate-Hoek, Arina J; Ten Cate, Hugo; Wild, Philipp S

2015-01-01

Elevated levels of c are associated with risk for both venous and arterial thromboembolism. However, no population-based study on the sex-specific distribution and reference ranges of plasma c and its cardiovascular determinants is available. c was analyzed in a randomly selected sample of 2533 males and 2440 females from the Gutenberg Health Study in Germany. Multivariable regression analyses for c were performed under adjustment for genetic determinants, cardiovascular risk factors and cardiovascular disease. Females (126.6% (95% CI: 125.2/128)) showed higher c levels than males (121.2% (119.8/122.7)). c levels increased with age in both sexes (ß per decade: 5.67% (4.22/7.13) male, 6.15% (4.72/7.57) female; p<0.001). Sex-specific reference limits and categories indicating the grade of deviation from the reference were calculated, and nomograms for c were created. c was approximately 25% higher in individuals with non-O blood type. Adjusted for sex and age, ABO-blood group accounted for 18.3% of c variation. In multivariable analysis, c was notably positively associated with diabetes mellitus, obesity, hypertension and dyslipidemia and negatively with current smoking. In a fully adjusted multivariable model, the strongest associations observed were of elevated c with diabetes and peripheral artery disease in both sexes and with obesity in males. Effects of SNPs in the vWF, STAB2 and SCARA5 gene were stronger in females than in males. The use of nomograms for valuation of c might be useful to identify high-risk cohorts for thromboembolism. Additionally, the prospective evaluation of c as a risk predictor becomes feasible. Copyright © 2015. Published by Elsevier Ireland Ltd.

Exploratory Long-Range Models to Estimate Summer Climate Variability over Southern Africa.

NASA Astrophysics Data System (ADS)

Jury, Mark R.; Mulenga, Henry M.; Mason, Simon J.

1999-07-01

Teleconnection predictors are explored using multivariate regression models in an effort to estimate southern African summer rainfall and climate impacts one season in advance. The preliminary statistical formulations include many variables influenced by the El Niño-Southern Oscillation (ENSO) such as tropical sea surface temperatures (SST) in the Indian and Atlantic Oceans. Atmospheric circulation responses to ENSO include the alternation of tropical zonal winds over Africa and changes in convective activity within oceanic monsoon troughs. Numerous hemispheric-scale datasets are employed to extract predictors and include global indexes (Southern Oscillation index and quasi-biennial oscillation), SST principal component scores for the global oceans, indexes of tropical convection (outgoing longwave radiation), air pressure, and surface and upper winds over the Indian and Atlantic Oceans. Climatic targets include subseasonal, area-averaged rainfall over South Africa and the Zambezi river basin, and South Africa's annual maize yield. Predictors and targets overlap in the years 1971-93, the defined training period. Each target time series is fitted by an optimum group of predictors from the preceding spring, in a linear multivariate formulation. To limit artificial skill, predictors are restricted to three, providing 17 degrees of freedom. Models with colinear predictors are screened out, and persistence of the target time series is considered. The late summer rainfall models achieve a mean r2 fit of 72%, contributed largely through ENSO modulation. Early summer rainfall cross validation correlations are lower (61%). A conceptual understanding of the climate dynamics and ocean-atmosphere coupling processes inherent in the exploratory models is outlined.Seasonal outlooks based on the exploratory models could help mitigate the impacts of southern Africa's fluctuating climate. It is believed that an advance warning of drought risk and seasonal rainfall prospects will improve the economic growth potential of southern Africa and provide additional security for food and water supplies.

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

NASA Astrophysics Data System (ADS)

Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

2017-04-01

Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger-)causes vegetation dynamics in most regions globally. More specifically, water availability is the most dominant vegetation driver, being the dominant vegetation driver in 54% of the vegetated surface. Furthermore, our results show that precipitation and soil moisture have prolonged impacts on vegetation in semiarid regions, with up to 10% of additional explained variance on the vegetation dynamics occurring three months later. Finally, hydro-climatic extremes seem to have a remarkable impact on vegetation, since they also explain up to 10% of additional variance of vegetation in certain regions despite their infrequent occurrence. References [1] Papagiannopoulou, C., Miralles, D. G., Verhoest, N. E. C., Dorigo, W. A., and Waegeman, W.: A non-linear Granger causality framework to investigate climate-vegetation dynamics, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-266, in review, 2016.

Predictors of Self-care among the Elderly with Diabetes Type 2: Using Social Cognitive Theory.

PubMed

Borhaninejad, Vahidreza; Iranpour, Abedin; Shati, Mohsen; Tahami, Ahmad Naghibzadeh; Yousefzadeh, Gholamrezan; Fadayevatan, Reza

Diabetes is one of the most common chronic diseases among the elderly and is also a very serious health problem. Adopting theory-based self-care behaviors is an effective means in managing such diseases. This study aimed to determine the predictors of diabetes self-care in the elderly in Kerman based on a social cognitive theory. In this cross-sectional study, 384 elderly diabetic patients who had referred to health screening centers in Kerman were chosen via cluster sampling. To collect information about self-care and its predictors, Toobert Glasgow's diabetes self-efficacy scale as well as a questionnaire was used which was based on social cognitive theory constructs. The validity and reliability of the questionnaire was confirmed. The data were analyzed using Pearson correlation and linear regression analysis in SPSS software 17. Among the subjects, 67.37% (252) had poor self-care ability; 29.14% (109) had average ability, and 3.40% (13) enjoyed a proper level of self- care ability. There was a significant relationship between the constructs of the social cognitive theory (knowledge, self- efficacy, social support, outcome expectations, outcome expectancy and self-regulation) and the self-care score. Furthermore, the mentioned constructs could predict 0.47% of the variance of the self-care behaviors. self-care behaviors in this study were poor. Therefore, it is necessary to develop an educational intervention based on cognitive theory constructs with the goal of properly managing diabetes in the elderly patients. Copyright © 2016 Diabetes India. Published by Elsevier Ltd. All rights reserved.

Phytoplankton absorption predicts patterns in primary productivity in Australian coastal shelf waters

NASA Astrophysics Data System (ADS)

Robinson, C. M.; Cherukuru, N.; Hardman-Mountford, N. J.; Everett, J. D.; McLaughlin, M. J.; Davies, K. P.; Van Dongen-Vogels, V.; Ralph, P. J.; Doblin, M. A.

2017-06-01

The phytoplankton absorption coefficient (aPHY) has been suggested as a suitable alternate first order predictor of net primary productivity (NPP). We compiled a dataset of surface bio-optical properties and phytoplankton NPP measurements in coastal waters around Australia to examine the utility of an in-situ absorption model to estimate NPP. The magnitude of surface NPP (0.20-19.3 mmol C m-3 d-1) across sites was largely driven by phytoplankton biomass, with higher rates being attributed to the microplankton (>20 μm) size class. The phytoplankton absorption coefficient aPHY for PAR (photosynthetically active radiation; āPHY)) ranged from 0.003 to 0.073 m-1, influenced by changes in phytoplankton community composition, physiology and environmental conditions. The aPHY coefficient also reflected changes in NPP and the absorption model-derived NPP could explain 73% of the variability in measured surface NPP (n = 41; RMSE = 2.49). The absorption model was applied to two contrasting coastal locations to examine NPP dynamics: a high chlorophyll-high variation (HCHV; Port Hacking National Reference Station) and moderate chlorophyll-low variation (MCLV; Yongala National Reference Station) location in eastern Australia using the GIOP-DC satellite aPHY product. Mean daily NPP rates between 2003 and 2015 were higher at the HCHV site (1.71 ± 0.03 mmol C m-3 d-1) with the annual maximum NPP occurring during the austral winter. In contrast, the MCLV site annual NPP peak occurred during the austral wet season and had lower mean daily NPP (1.43 ± 0.03 mmol C m-3 d-1) across the time-series. An absorption-based model to estimate NPP is a promising approach for exploring the spatio-temporal dynamics in phytoplankton NPP around the Australian continental shelf.

Ecological and personal predictors of science achievement in an urban center

NASA Astrophysics Data System (ADS)

Guidubaldi, John Michael

This study sought to examine selected personal and environmental factors that predict urban students' achievement test scores on the science subject area of the Ohio standardized test. Variables examined were in the general categories of teacher/classroom, student, and parent/home. It assumed that these clusters might add independent variance to a best predictor model, and that discovering relative strength of different predictors might lead to better selection of intervention strategies to improve student performance. This study was conducted in an urban school district and was comprised of teachers and students enrolled in ninth grade science in three of this district's high schools. Consenting teachers (9), students (196), and parents (196) received written surveys with questions designed to examine the predictive power of each variable cluster. Regression analyses were used to determine which factors best correlate with student scores and classroom science grades. Selected factors were then compiled into a best predictive model, predicting success on standardized science tests. Students t tests of gender and racial subgroups confirmed that there were racial differences in OPT scores, and both gender and racial differences in science grades. Additional examinations were therefore conducted for all 12 variables to determine whether gender and race had an impact on the strength of individual variable predictions and on the final best predictor model. Of the 15 original OPT and cluster variable hypotheses, eight showed significant positive relationships that occurred in the expected direction. However, when more broadly based end-of-the-year science class grade was used as a criterion, 13 of the 15 hypotheses showed significant relationships in the expected direction. With both criteria, significant gender and racial differences were observed in the strength of individual predictors and in the composition of best predictor models.

Association of Discharge Home with Home Health Care and 30-day Readmission after Pancreatectomy

PubMed Central

Sanford, Dominic E; Olsen, Margaret A; Bommarito, Kerry M; Shah, Manish; Fields, Ryan C; Hawkins, William G; Jaques, David P; Linehan, David C

2014-01-01

Background We sought to determine if discharge home with home health care (HHC) is an independent predictor of increased readmission following pancreatectomy. Study Design We examined 30-day readmissions in patients undergoing pancreatectomy using the Healthcare Cost and Utilization Project State Inpatient Database for California from 2009 to 2011. Readmissions were categorized as severe or non-severe using the Modified Accordion Severity Grading System. Multivariable logistic regression models were used to examine the association of discharge home with HHC and 30-day readmission using discharge home without HHC as the reference group. Propensity score matching was used as an additional analysis to compare the rate of 30-day readmission between patients discharged home with HHC to patients discharged home without HHC. Results 3,573 patients underwent pancreatectomy and 752 (21.0%) were readmitted within 30 days of discharge. In a multivariable logistic regression model, discharge home with HHC was an independent predictor of increased 30-day readmission (OR=1.37; 95%CI=1.11-1.69, p=0.004). Using propensity score matching, patients who received HHC had a significantly increased rate of 30-day readmission compared to patients discharged home without HHC (24.3% vs 19.8%, p<0.001). Patients discharged home with HHC had a significantly increased rate of non-severe readmission compared to those discharged home without HHC by univariate comparison (19.2% vs 13.9%, p<0.001), but not severe readmission (6.4% vs 4.7%, p= 0.08). In multivariable logistic regression models, excluding patients discharged to facilities, discharge home with HHC was an independent predictor of increased non-severe readmissions (OR=1.41; 95%CI=1.11-1.79, p=0.005), but not severe readmissions (OR=1.31; 95%CI=0.88-1.93, p=0.18). Conclusions Discharge home with HHC following pancreatectomy is an independent predictor of increased 30-day readmission; specifically, these services are associated with increased non-severe readmissions, but not severe readmissions. PMID:25440026

A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables

NASA Astrophysics Data System (ADS)

Madonna, Erica; Ginsbourger, David; Martius, Olivia

2018-05-01

In Switzerland, hail regularly causes substantial damage to agriculture, cars and infrastructure, however, little is known about its long-term variability. To study the variability, the monthly number of days with hail in northern Switzerland is modeled in a regression framework using large-scale predictors derived from ERA-Interim reanalysis. The model is developed and verified using radar-based hail observations for the extended summer season (April-September) in the period 2002-2014. The seasonality of hail is explicitly modeled with a categorical predictor (month) and monthly anomalies of several large-scale predictors are used to capture the year-to-year variability. Several regression models are applied and their performance tested with respect to standard scores and cross-validation. The chosen model includes four predictors: the monthly anomaly of the two meter temperature, the monthly anomaly of the logarithm of the convective available potential energy (CAPE), the monthly anomaly of the wind shear and the month. This model well captures the intra-annual variability and slightly underestimates its inter-annual variability. The regression model is applied to the reanalysis data back in time to 1980. The resulting hail day time series shows an increase of the number of hail days per month, which is (in the model) related to an increase in temperature and CAPE. The trend corresponds to approximately 0.5 days per month per decade. The results of the regression model have been compared to two independent data sets. All data sets agree on the sign of the trend, but the trend is weaker in the other data sets.

Prior hospitalization and age as predictors of mental health resource utilization in Israel.

PubMed

Ginsberg, G; Lerner, Y; Mark, M; Popper, M

1997-03-01

A two-part demand model based on data from a psychiatric case registry was estimated in order to search for predictors of hospital-based psychiatric care utilization. Using only age as an independent variable, explanation of future resource utilization is considerably weaker than when number of cumulative days of psychiatric hospital-based service use during the previous five years is also included. Only a small marginal gain is achieved by also adding diagnoses. Prospective remuneration by capitating sick funds according to age and past hospital-based service utilization records is recommended to avoid the twin pitfalls of cream-skimming and a distorted allocation of resources for psychiatric services.

Parent predictors of child weight change in family based behavioral obesity treatment.

PubMed

Boutelle, Kerri N; Cafri, Guy; Crow, Scott J

2012-07-01

Family based behavioral treatment for overweight and obese children includes parenting skills targeting the modification of child eating and activity change. The purpose of this study was to examine parenting skills and parent weight change as predictors of child weight change in a sample of 80 parent/child dyads who were enrolled in a family based behavioral weight loss program for childhood obesity. Eighty overweight and obese children and their parents who enrolled in treatment in two sites were included in the study. Variables included those related to parent modeling (parent BMI), home food environment, parenting (parent and child report), and demographics. Results suggested that parent BMI change was a significant predictor of child weight, in that a reduction of 1 BMI unit in the parent was associated with a 0.255 reduction in child BMI. None of the other variables were significant in the final model. This study is consistent with other research showing that parent weight change is a key contributor to child weight change in behavioral treatment for childhood obesity. Researchers and clinicians should focus on encouraging parents to lose weight to assist their overweight and obese child in weight management.

Predictors of outcome after reconstructive hepatico-jejunostomy for post cholecystectomy bile duct injuries.

PubMed

Gomes, Rachel M; Doctor, Nilesh H

2015-01-01

Reconstructive hepatico-jejunostomy is recommended for major bile duct injuries (BDIs) during cholecystectomy. Complications of biliary leak, cholangitis, bleeding, anastomotic strictures and biliary cirrhosis remain a major concern affecting a patient's outcome after surgery. The aim of this study was to analyse the results of surgical repair of major BDIs at our institution and identify predictors for the development of major complications. A retrospective study of 57 patients with major BDI after cholecystectomy referred to a tertiary hepato-biliary centre from July 1999 to July 2011 and subsequently managed with reconstructive bilio-enteric anastomosis was performed. Of 57 patents 35 (61.4%) were primary referred. 22 (38.6 %) were secondary referred, of which 17 were for correct reconstructive surgery performed elsewhere and 5 were following attempted endoscopic management. 17 (29.8%) had local and systemic perioperative complications. 13 (22.8%) had major complications (bile leak, bleed, stricture and/or biliary cirrhosis). No association was found between age, type of cholecystectomy, type of injury, vascular injury and occurrence of major complications. Secondarily referred patients after therapeutic interventions (p = 0.010) and reconstructive surgery after repair performed by non-specialists suffered an increased incidence of major complications (p = 0.032). Secondary referral was also an independent predictor of major complications (p = 0.024). Early referral of patients with no previous intervention to a tertiary hepato-biliary center and specialist surgical repair is recommended for improved outcome after reconstructive hepatico-jejunostomy for major BDIs during cholecystectomy.

Predictors of relationship power among drug-involved women.

PubMed

Campbell, Aimee N C; Tross, Susan; Hu, Mei-chen; Pavlicova, Martina; Nunes, Edward V

2012-08-01

Gender-based relationship power is frequently linked to women's capacity to reduce sexual risk behaviors. This study offers an exploration of predictors of relationship power, as measured by the multidimensional and theoretically grounded sexual relationship power scale, among women in outpatient substance abuse treatment. Linear models were used to test nine predictors (age, race/ethnicity, education, time in treatment, economic dependence, substance use, sexual concurrency, partner abuse, and sex role orientation) of relationship power among 513 women participating in a multi-site HIV risk reduction intervention study. Significant predictors of relationship control included having a non-abusive male partner, only one male partner, and endorsing traditional masculine (or both masculine and feminine) sex role attributes. Predictors of decision-making dominance were interrelated, with substance use × partner abuse and age × sex role orientation interactions. Results contribute to the understanding of factors which may influence relationship power and to their potential role in HIV sexual risk reduction interventions.

Adherence predictors in an Internet-based Intervention program for depression.

PubMed

Castro, Adoración; López-Del-Hoyo, Yolanda; Peake, Christian; Mayoral, Fermín; Botella, Cristina; García-Campayo, Javier; Baños, Rosa María; Nogueira-Arjona, Raquel; Roca, Miquel; Gili, Margalida

2018-05-01

Internet-delivered psychotherapy has been demonstrated to be effective in the treatment of depression. Nevertheless, the study of the adherence in this type of the treatment reported divergent results. The main objective of this study is to analyze predictors of adherence in a primary care Internet-based intervention for depression in Spain. A multi-center, three arm, parallel, randomized controlled trial was conducted with 194 depressive patients, who were allocated in self-guided or supported-guided intervention. Sociodemographic and clinical characteristics were gathered using a case report form. The Mini international neuropsychiatric interview diagnoses major depression. Beck Depression Inventory was used to assess depression severity. The visual analogic scale assesses the respondent's self-rated health and Short Form Health Survey was used to measure the health-related quality of life. Age results a predictor variable for both intervention groups (with and without therapist support). Perceived health is a negative predictor of adherence for the self-guided intervention when change in depression severity was included in the model. Change in depression severity results a predictor of adherence in the support-guided intervention. Our findings demonstrate that in our sample, there are differences in sociodemographic and clinical variables between active and dropout participants and we provide adherence predictors in each intervention condition of this Internet-based program for depression (self-guided and support-guided). It is important to point that further research in this area is essential to improve tailored interventions and to know specific patients groups can benefit from these interventions.

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

NASA Astrophysics Data System (ADS)

Chardon, Jérémy; Hingray, Benoit; Favre, Anne-Catherine

2018-01-01

Statistical downscaling models (SDMs) are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

Can Classification Tree Analyses Help Improve Decision Making About Treatments for Depression and Anxiety Disorders? A Preliminary Investigation

PubMed Central

Rhodes, Louisa; Naumann, Ulrike M.

2011-01-01

Objective: To identify how decisions about treatment are being made in secondary services for anxiety disorders and depression and, specifically, whether it was possible to predict the decisions to refer for evidence-based treatments. Method: Post hoc classification tree analysis was performed using a sample from an audit on implementation of the National Institute for Health and Clinical Excellence Guidelines for Depression and Anxiety Disorders. The audit was of 5 teams offering secondary care services; they included psychiatrists, psychologists, community psychiatric nurses, social workers, dual-diagnosis workers, and vocational workers. The patient sample included all of those with a primary problem of depression (n = 56) or an anxiety disorder (n = 16) who were offered treatment from February 16 to April 3, 2009. The outcome variable was whether or not evidence-based treatments were offered, and the predictor variables were presenting problem, risk, comorbid problem, social problems, and previous psychiatric history. Results: Treatment decisions could be more accurately predicted for anxiety disorders (93% correct) than for depression (55%). For anxiety disorders, the presence or absence of social problems was a good predictor for whether evidence-based or non–evidence-based treatments were offered; 44% (4/9) of those with social problems vs 100% (6/6) of those without social problems were offered evidence-based treatments. For depression, patients’ risk rating had the largest impact on treatment decisions, although no one variable could be identified as individually predictive of all treatment decisions. Conclusions: Treatment decisions were generally consistent for anxiety disorders but more idiosyncratic for depression, making the development of a decision-making model very difficult for depression. The lack of clarity of some terms in the clinical guidelines and the more complex nature of depression could be factors contributing to this difficulty. Further research is needed to understand the complex nature of decision making with depressed patients. PMID:22295255

Mining Rare Events Data for Assessing Customer Attrition Risk

NASA Astrophysics Data System (ADS)

Au, Tom; Chin, Meei-Ling Ivy; Ma, Guangqin

Customer attrition refers to the phenomenon whereby a customer leaves a service provider. As competition intensifies, preventing customers from leaving is a major challenge to many businesses such as telecom service providers. Research has shown that retaining existing customers is more profitable than acquiring new customers due primarily to savings on acquisition costs, the higher volume of service consumption, and customer referrals. For a large enterprise, its customer base consists of tens of millions service subscribers, more often the events, such as switching to competitors or canceling services are large in absolute number, but rare in percentage, far less than 5%. Based on a simple random sample, popular statistical procedures, such as logistic regression, tree-based method and neural network, can sharply underestimate the probability of rare events, and often result a null model (no significant predictors). To improve efficiency and accuracy for event probability estimation, a case-based data collection technique is then considered. A case-based sample is formed by taking all available events and a small, but representative fraction of nonevents from a dataset of interest. In this article we showed a consistent prior correction method for events probability estimation and demonstrated the performance of the above data collection techniques in predicting customer attrition with actual telecommunications data.

Tuning algorithms for fractional order internal model controllers for time delay processes

NASA Astrophysics Data System (ADS)

Muresan, Cristina I.; Dutta, Abhishek; Dulf, Eva H.; Pinar, Zehra; Maxim, Anca; Ionescu, Clara M.

2016-03-01

This paper presents two tuning algorithms for fractional-order internal model control (IMC) controllers for time delay processes. The two tuning algorithms are based on two specific closed-loop control configurations: the IMC control structure and the Smith predictor structure. In the latter, the equivalency between IMC and Smith predictor control structures is used to tune a fractional-order IMC controller as the primary controller of the Smith predictor structure. Fractional-order IMC controllers are designed in both cases in order to enhance the closed-loop performance and robustness of classical integer order IMC controllers. The tuning procedures are exemplified for both single-input-single-output as well as multivariable processes, described by first-order and second-order transfer functions with time delays. Different numerical examples are provided, including a general multivariable time delay process. Integer order IMC controllers are designed in each case, as well as fractional-order IMC controllers. The simulation results show that the proposed fractional-order IMC controller ensures an increased robustness to modelling uncertainties. Experimental results are also provided, for the design of a multivariable fractional-order IMC controller in a Smith predictor structure for a quadruple-tank system.

Candidate Predictors of Health-Related Quality of Life of Colorectal Cancer Survivors: A Systematic Review

PubMed Central

van der Linden, Bernadette W.A.; Winkels, Renate M.; van Duijnhoven, Fränzel J.; Mols, Floortje; van Roekel, Eline H.; Kampman, Ellen; Beijer, Sandra; Weijenberg, Matty P.

2016-01-01

The population of colorectal cancer (CRC) survivors is growing and many survivors experience deteriorated health-related quality of life (HRQoL) in both early and late post-treatment phases. Identification of CRC survivors at risk for HRQoL deterioration can be improved by using prediction models. However, such models are currently not available for oncology practice. As a starting point for developing prediction models of HRQoL for CRC survivors, a comprehensive overview of potential candidate HRQoL predictors is necessary. Therefore, a systematic literature review was conducted to identify candidate predictors of HRQoL of CRC survivors. Original research articles on associations of biopsychosocial factors with HRQoL of CRC survivors were searched in PubMed, Embase, and Google Scholar. Two independent reviewers assessed eligibility and selected articles for inclusion (N = 53). Strength of evidence for candidate HRQoL predictors was graded according to predefined methodological criteria. The World Health Organization’s International Classification of Functioning, Disability and Health (ICF) was used to develop a biopsychosocial framework in which identified candidate HRQoL predictors were mapped across the main domains of the ICF: health condition, body structures and functions, activities, participation, and personal and environmental factors. The developed biopsychosocial ICF framework serves as a basis for selecting candidate HRQoL predictors, thereby providing conceptual guidance for developing comprehensive, evidence-based prediction models of HRQoL for CRC survivors. Such models are useful in clinical oncology practice to aid in identifying individual CRC survivors at risk for HRQoL deterioration and could also provide potential targets for a biopsychosocial intervention aimed at safeguarding the HRQoL of at-risk individuals. Implications for Practice: More and more people now survive a diagnosis of colorectal cancer. The quality of life of these cancer survivors is threatened by health problems persisting for years after diagnosis and treatment. Early identification of survivors at risk of experiencing low quality of life in the future is thus important for taking preventive measures. Clinical prediction models are tools that can help oncologists identify at-risk individuals. However, such models are currently not available for clinical oncology practice. This systematic review outlines candidate predictors of low quality of life of colorectal cancer survivors, providing a firm conceptual basis for developing prediction models. PMID:26911406

Vegetation cover, tidal amplitude and land area predict short-term marsh vulnerability in Coastal Louisiana

USGS Publications Warehouse

Schoolmaster, Donald; Stagg, Camille L.; Sharp, Leigh Anne; McGinnis, Tommy S.; Wood, Bernard; Piazza, Sarai

2018-01-01

The loss of coastal marshes is a topic of great concern, because these habitats provide tangible ecosystem services and are at risk from sea-level rise and human activities. In recent years, significant effort has gone into understanding and modeling the relationships between the biological and physical factors that contribute to marsh stability. Simulation-based process models suggest that marsh stability is the product of a complex feedback between sediment supply, flooding regime and vegetation response, resulting in elevation gains sufficient to match the combination of relative sea-level rise and losses from erosion. However, there have been few direct, empirical tests of these models, because long-term datasets that have captured sufficient numbers of marsh loss events in the context of a rigorous monitoring program are rare. We use a multi-year data set collected by the Coastwide Reference Monitoring System (CRMS) that includes transitions of monitored vegetation plots to open water to build and test a predictive model of near-term marsh vulnerability. We found that despite the conclusions of previous process models, elevation change had no ability to predict the transition of vegetated marsh to open water. However, we found that the processes that drive elevation change were significant predictors of transitions. Specifically, vegetation cover in prior year, land area in the surrounding 1 km2 (an estimate of marsh fragmentation), and the interaction of tidal amplitude and position in tidal frame were all significant factors predicting marsh loss. This suggests that 1) elevation change is likely better a predictor of marsh loss at time scales longer than we consider in this study and 2) the significant predictive factors affect marsh vulnerability through pathways other than elevation change, such as resistance to erosion. In addition, we found that, while sensitivity of marsh vulnerability to the predictive factors varied spatially across coastal Louisiana, vegetation cover in prior year was the best single predictor of subsequent loss in most sites followed by changes in percent land and tidal amplitude. The model’s predicted land loss rates correlated well with land loss rates derived from satellite data, although agreement was spatially variable. These results indicate 1) monitoring the loss of small scale vegetation plots can inform patterns of land loss at larger scales 2) the drivers of land loss vary spatially across coastal Louisiana, and 3) relatively simple models have potential as highly informative tools for bioassessment, directing future research, and management planning.

Predictors of Better Self-Care in Patients with Heart Failure after Six Months of Follow-Up Home Visits

PubMed Central

Trojahn, Melina Maria; Ruschel, Karen Brasil; Nogueira de Souza, Emiliane; Mussi, Cláudia Motta; Naomi Hirakata, Vânia; Nogueira Mello Lopes, Alexandra; Rabelo-Silva, Eneida Rejane

2013-01-01

This study aimed to examine the predictors of better self-care behavior in patients with heart failure (HF) in a home visiting program. This is a longitudinal study nested in a randomized controlled trial (ISRCTN01213862) in which the home-based educational intervention consisted of a six-month followup that included four home visits by a nurse, interspersed with four telephone calls. The self-care score was measured at baseline and at six months using the Brazilian version of the European Heart Failure Self-Care Behaviour Scale. The associations included eight variables: age, sex, schooling, having received the intervention, social support, income, comorbidities, and symptom severity. A simple linear regression model was developed using significant variables (P ≤ 0.20), followed by a multivariate model to determine the predictors of better self-care. One hundred eighty-eight patients completed the study. A better self-care behavior was associated with patients who received intervention (P < 0.001), had more years of schooling (P = 0.016), and had more comorbidities (P = 0.008). Having received the intervention (P < 0.001) and having a greater number of comorbidities (P = 0.038) were predictors of better self-care. In the multivariate regression model, being in the intervention group and having more comorbidities were a predictor of better self-care. PMID:24083023

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

PubMed Central

Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction. PMID:27128736

Improving the Efficiency of Abdominal Aortic Aneurysm Wall Stress Computations

PubMed Central

Zelaya, Jaime E.; Goenezen, Sevan; Dargon, Phong T.; Azarbal, Amir-Farzin; Rugonyi, Sandra

2014-01-01

An abdominal aortic aneurysm is a pathological dilation of the abdominal aorta, which carries a high mortality rate if ruptured. The most commonly used surrogate marker of rupture risk is the maximal transverse diameter of the aneurysm. More recent studies suggest that wall stress from models of patient-specific aneurysm geometries extracted, for instance, from computed tomography images may be a more accurate predictor of rupture risk and an important factor in AAA size progression. However, quantification of wall stress is typically computationally intensive and time-consuming, mainly due to the nonlinear mechanical behavior of the abdominal aortic aneurysm walls. These difficulties have limited the potential of computational models in clinical practice. To facilitate computation of wall stresses, we propose to use a linear approach that ensures equilibrium of wall stresses in the aneurysms. This proposed linear model approach is easy to implement and eliminates the burden of nonlinear computations. To assess the accuracy of our proposed approach to compute wall stresses, results from idealized and patient-specific model simulations were compared to those obtained using conventional approaches and to those of a hypothetical, reference abdominal aortic aneurysm model. For the reference model, wall mechanical properties and the initial unloaded and unstressed configuration were assumed to be known, and the resulting wall stresses were used as reference for comparison. Our proposed linear approach accurately approximates wall stresses for varying model geometries and wall material properties. Our findings suggest that the proposed linear approach could be used as an effective, efficient, easy-to-use clinical tool to estimate patient-specific wall stresses. PMID:25007052

Crop weather models of barley and spring wheat yield for agrophysical units in North Dakota

NASA Technical Reports Server (NTRS)

Leduc, S. (Principal Investigator)

1982-01-01

Models based on multiple regression were developed to estimate barley yield and spring wheat yield from weather data for Agrophysical units(APU) in North Dakota. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for Crop Reporting Districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU models show sight improvements in some of the statistics of the models, e.g., explained variation. These models are to be independently evaluated and compared to the previously evaluated CRD models. The comparison will indicate the preferred model area for this application, i.e., APU or CRD.

Prognostic model of survival for typical bronchial carcinoid tumours: analysis of 1109 patients on behalf of the European Association of Thoracic Surgeons (ESTS) Neuroendocrine Tumours Working Group.

PubMed

Filosso, Pier Luigi; Guerrera, Francesco; Evangelista, Andrea; Welter, Stefan; Thomas, Pascal; Casado, Paula Moreno; Rendina, Erino Angelo; Venuta, Federico; Ampollini, Luca; Brunelli, Alessandro; Stella, Franco; Nosotti, Mario; Raveglia, Federico; Larocca, Valentina; Rena, Ottavio; Margaritora, Stefano; Ardissone, Francesco; Travis, William D; Sarkaria, Inderpal; Sagan, Dariusz

2015-09-01

Typical carcinoids (TCs) are uncommon, slow-growing neoplasms, usually with high 5-year survival rates. As these are rare tumours, their management is still based on small clinical observations and no international guidelines exist. Based on the European Society of Thoracic Surgeon Neuroendocrine Tumours Working Group (NET-WG) Database, we evaluated factors that may influence TCs mortality. Using the NET-WG database, an analysis on TC survival was performed. Overall survival (OS) was calculated starting from the date of intervention. Predictors of OS were investigated using the Cox model with shared frailty (accounting for the within-centre correlation). Candidate predictors were: gender, age, smoking habit, tumour location, previous malignancy, Eastern Cooperative Oncology Group (ECOG) performance status (PS), pT, pN, TNM stage and tumour vascular invasion. The final model included predictors with P ≤ 0.15 after a backward selection. Missing data in the evaluated predictors were multiple-imputed and combined estimates were obtained from five imputed data sets. For 58 of 1167 TC patients vital status was unavailable and analyses were therefore performed on 1109 patients from 17 institutions worldwide. During a median follow-up of 50 months, 87 patients died, with a 5-year OS rate of 93.7% (95% confidence interval: 91.7-95.3). Backward selection resulted in a prediction model for mortality containing age, gender, previous malignancies, peripheral tumour, TNM stage and ECOG PS. The final model showed a good discrimination ability with a C-statistic equal to 0.836 (bootstrap optimism-corrected 0.806). We presented and validated a promising prognostic model for TC survival, showing good calibration and discrimination ability. Further analyses are needed and could be focused on an external validation of this model. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Cognitive predictors of adaptive functioning in children with symptomatic epilepsy.

PubMed

Kerr, Elizabeth N; Fayed, Nora

2017-10-01

The current study sought to understand the contribution of the attention and working memory challenges experienced by children with active epilepsy without an intellectual disability to adaptive functioning (AF) while taking into account intellectual ability, co-occurring brain-based psychosocial diagnoses, and epilepsy-related variables. The relationship of attention and working memory with AF was examined in 76 children with active epilepsy with intellectual ability above the 2nd percentile recruited from a tertiary care center. AF was measured using the Scales of Independent Behavior-Revised (SIB-R) and compared with norm-referenced data. Standardized clinical assessments of attention span, sustained attention, as well as basic and more complex working memory were administered to children. Commonality analysis was used to investigate the importance of the variables with respect to the prediction of AF and to construct parsimonious models to elucidate the factors most important in explaining AF. Seventy-one percent of parents reported that their child experienced mild to severe difficulties with overall AF. Similar proportions of children displayed limitations in domain-specific areas of AF (Motor, Social/Communication, Person Living, and Community Living). The reduced models for Broad and domain-specific AF produced a maximum of seven predictor variables, with little loss in overall explained variance compared to the full models. Intellectual ability was a powerful predictor of Broad and domain-specific AF. Complex working memory was the only other cognitive predictor retained in each of the parsimonious models of AF. Sustained attention and complex working memory explained a large amount of the total variance in Motor AF. Children with a previously diagnosed comorbidity displayed lower Social/Communication, Personal Living, and Broad AF than those without a diagnosis. At least one epilepsy-related variable appeared in each of the reduced models, with age of seizure onset and seizure type (generalized or partial) being the main predictors. Intellectual ability was the most powerful predictor of AF in children with epilepsy whose intellectual functioning was above the 2nd percentile. Co-occurring brain-based cognitive and psychosocial issues experienced by children with living epilepsy, particularly complex working memory and diagnosed comorbidities, contribute to AF and may be amenable to intervention. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Unique effects and moderators of effects of sources on self-efficacy: A model-based meta-analysis.

PubMed

Byars-Winston, Angela; Diestelmann, Jacob; Savoy, Julia N; Hoyt, William T

2017-11-01

Self-efficacy beliefs are strong predictors of academic pursuits, performance, and persistence, and in theory are developed and maintained by 4 classes of experiences Bandura (1986) referred to as sources: performance accomplishments (PA), vicarious learning (VL), social persuasion (SP), and affective arousal (AA). The effects of sources on self-efficacy vary by performance domain and individual difference factors. In this meta-analysis (k = 61 studies of academic self-efficacy; N = 8,965), we employed B. J. Becker's (2009) model-based approach to examine cumulative effects of the sources as a set and unique effects of each source, controlling for the others. Following Becker's recommendations, we used available data to create a correlation matrix for the 4 sources and self-efficacy, then used these meta-analytically derived correlations to test our path model. We further examined moderation of these associations by subject area (STEM vs. non-STEM), grade, sex, and ethnicity. PA showed by far the strongest unique association with self-efficacy beliefs. Subject area was a significant moderator, with sources collectively predicting self-efficacy more strongly in non-STEM (k = 14) compared with STEM (k = 47) subjects (R2 = .37 and .22, respectively). Within studies of STEM subjects, grade level was a significant moderator of the coefficients in our path model, as were 2 continuous study characteristics (percent non-White and percent female). Practical implications of the findings and future research directions are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2016-09-01

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. Proteins 2016; 84(Suppl 1):247-259. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Is questionnaire-based sitting time inaccurate and can it be improved? A cross-sectional investigation using accelerometer-based sitting time

PubMed Central

Gupta, Nidhi; Christiansen, Caroline Stordal; Hanisch, Christiana; Bay, Hans; Burr, Hermann; Holtermann, Andreas

2017-01-01

Objectives To investigate the differences between a questionnaire-based and accelerometer-based sitting time, and develop a model for improving the accuracy of questionnaire-based sitting time for predicting accelerometer-based sitting time. Methods 183 workers in a cross-sectional study reported sitting time per day using a single question during the measurement period, and wore 2 Actigraph GT3X+ accelerometers on the thigh and trunk for 1–4 working days to determine their actual sitting time per day using the validated Acti4 software. Least squares regression models were fitted with questionnaire-based siting time and other self-reported predictors to predict accelerometer-based sitting time. Results Questionnaire-based and accelerometer-based average sitting times were ≈272 and ≈476 min/day, respectively. A low Pearson correlation (r=0.32), high mean bias (204.1 min) and wide limits of agreement (549.8 to −139.7 min) between questionnaire-based and accelerometer-based sitting time were found. The prediction model based on questionnaire-based sitting explained 10% of the variance in accelerometer-based sitting time. Inclusion of 9 self-reported predictors in the model increased the explained variance to 41%, with 10% optimism using a resampling bootstrap validation. Based on a split validation analysis, the developed prediction model on ≈75% of the workers (n=132) reduced the mean and the SD of the difference between questionnaire-based and accelerometer-based sitting time by 64% and 42%, respectively, in the remaining 25% of the workers. Conclusions This study indicates that questionnaire-based sitting time has low validity and that a prediction model can be one solution to materially improve the precision of questionnaire-based sitting time. PMID:28093433

Predictors of outcome in myxoedema coma

PubMed Central

Beynon, Jennifer; Akhtar, Simeen; Kearney, Tara

2008-01-01

Myxoedema coma is a rare and life-threatening illness the outcome of which has not been robustly studied in large numbers, partly due to its low incidence. Dutta and colleagues have explored outcome predictors in a developing country where access to thyroid function tests is more limited than in the Western world. Cardiovascular instability, reduced consciousness, persistent hypothermia, and sepsis all contributed to a poorer outcome, as has been demonstrated before, but a generic outcome predictor model was shown to be useful in this group of patients. Unfortunately, this observational study was unable to show differences in outcome based on replacement treatment methods and the mortality remains at 40%. PMID:18254932

Predictors of outcome in myxoedema coma.

PubMed

Beynon, Jennifer; Akhtar, Simeen; Kearney, Tara

2008-01-01

Myxoedema coma is a rare and life-threatening illness the outcome of which has not been robustly studied in large numbers, partly due to its low incidence. Dutta and colleagues have explored outcome predictors in a developing country where access to thyroid function tests is more limited than in the Western world. Cardiovascular instability, reduced consciousness, persistent hypothermia, and sepsis all contributed to a poorer outcome, as has been demonstrated before, but a generic outcome predictor model was shown to be useful in this group of patients. Unfortunately, this observational study was unable to show differences in outcome based on replacement treatment methods and the mortality remains at 40%.

A Stress-Coping Model of Mental Illness Stigma: I. Predictors of Cognitive Stress Appraisal

PubMed Central

Rüsch, Nicolas; Corrigan, Patrick W.; Wassel, Abigail; Michaels, Patrick; Olschewski, Manfred; Wilkniss, Sandra; Batia, Karen

2009-01-01

Stigma can be a major stressor for individuals with schizophrenia and other mental illnesses. It is unclear, however, why some stigmatized individuals appraise stigma as more stressful, while others feel they can cope with the potential harm posed by public prejudice. We tested the hypothesis that the level of perceived public stigma and personal factors such as rejection sensitivity, perceived legitimacy of discrimination and ingroup perceptions (group value; group identification; entitativity, or the perception of the ingroup of people with mental illness as a coherent unit) predict the cognitive appraisal of stigma as a stressor. Stigma stress appraisal refers to perceived stigma-related harm exceeding perceived coping resources. Stress appraisal, stress predictors and social cue recognition were assessed in 85 people with schizophrenia, schizoaffective or affective disorders. Stress appraisal did not differ between diagnostic subgroups, but was positively correlated with rejection sensitivity. Higher levels of perceived societal stigma and holding the group of people with mental illness in low regard (low group value) independently predicted high stigma stress appraisal. These predictors remained significant after controlling for social cognitive deficits, depressive symptoms and diagnosis. Our findings support the model that public and personal factors predict stigma stress appraisal among people with mental illness, independent of diagnosis and clinical symptoms. Interventions that aim to reduce the impact of stigma on people with mental illness could focus on variables such as rejection sensitivity, a personal vulnerability factor, low group value and the cognitive appraisal of stigma as a stressor. PMID:19269140

A simulation study on Bayesian Ridge regression models for several collinearity levels

NASA Astrophysics Data System (ADS)

Efendi, Achmad; Effrihan

2017-12-01

When analyzing data with multiple regression model if there are collinearities, then one or several predictor variables are usually omitted from the model. However, there sometimes some reasons, for instance medical or economic reasons, the predictors are all important and should be included in the model. Ridge regression model is not uncommon in some researches to use to cope with collinearity. Through this modeling, weights for predictor variables are used for estimating parameters. The next estimation process could follow the concept of likelihood. Furthermore, for the estimation nowadays the Bayesian version could be an alternative. This estimation method does not match likelihood one in terms of popularity due to some difficulties; computation and so forth. Nevertheless, with the growing improvement of computational methodology recently, this caveat should not at the moment become a problem. This paper discusses about simulation process for evaluating the characteristic of Bayesian Ridge regression parameter estimates. There are several simulation settings based on variety of collinearity levels and sample sizes. The results show that Bayesian method gives better performance for relatively small sample sizes, and for other settings the method does perform relatively similar to the likelihood method.

Sense of coherence and hardiness as predictors of the mental health of college students.

PubMed

Knowlden, Adam P; Sharma, Manoj; Kanekar, Amar; Atri, Ashutosh

Psychological distress has a deleterious impact on the mental health of college students. The purpose of this study was to specify a theoretical, sense of coherence, and hardiness-based regression model to predict the mental health of college students. The instruments employed to build the model included the Kessler Psychological Distress Scale K-6, the Sense of Coherence-29, and the College Student Hardiness Measure. Data were collected from a sample of college students (n = 220) attending a Midwestern university. Each of the theoretical predictors regressed on mental health was deemed significant. Collectively, the significant predictors produced an R2 adjusted value of 0.434 (p < 0.001), suggesting the final specified model explained 43.4% of the variance in mental health in the sample of participants. Qualitative cut-points were developed for each scale to aid in measurement of health promotion and education interventions designed to improve the mental health of college students.

Modeling of geogenic radon in Switzerland based on ordered logistic regression.

PubMed

Kropat, Georg; Bochud, François; Murith, Christophe; Palacios Gruson, Martha; Baechler, Sébastien

2017-01-01

The estimation of the radon hazard of a future construction site should ideally be based on the geogenic radon potential (GRP), since this estimate is free of anthropogenic influences and building characteristics. The goal of this study was to evaluate terrestrial gamma dose rate (TGD), geology, fault lines and topsoil permeability as predictors for the creation of a GRP map based on logistic regression. Soil gas radon measurements (SRC) are more suited for the estimation of GRP than indoor radon measurements (IRC) since the former do not depend on ventilation and heating habits or building characteristics. However, SRC have only been measured at a few locations in Switzerland. In former studies a good correlation between spatial aggregates of IRC and SRC has been observed. That's why we used IRC measurements aggregated on a 10 km × 10 km grid to calibrate an ordered logistic regression model for geogenic radon potential (GRP). As predictors we took into account terrestrial gamma doserate, regrouped geological units, fault line density and the permeability of the soil. The classification success rate of the model results to 56% in case of the inclusion of all 4 predictor variables. Our results suggest that terrestrial gamma doserate and regrouped geological units are more suited to model GRP than fault line density and soil permeability. Ordered logistic regression is a promising tool for the modeling of GRP maps due to its simplicity and fast computation time. Future studies should account for additional variables to improve the modeling of high radon hazard in the Jura Mountains of Switzerland. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Guide to solar reference spectra and irradiance models

NASA Astrophysics Data System (ADS)

Tobiska, W. Kent

The international standard for determining solar irradiances was published by the International Standards Organization (ISO) in May 2007. The document, ISO 21348 Space Environment (natural and artificial) - Process for determining solar irradiances, describes the process for representing solar irradiances. We report on the next progression of standards work, i.e., the development of a guide that identifies solar reference spectra and irradiance models for use in engineering design or scientific research. This document will be produced as an AIAA Guideline and ISO Technical Report. It will describe the content of the reference spectra and models, uncertainties and limitations, technical basis, data bases from which the reference spectra and models are formed, publication references, and sources of computer code for reference spectra and solar irradiance models, including those which provide spectrally-resolved lines as well as solar indices and proxies and which are generally recognized in the solar sciences. The document is intended to assist aircraft and space vehicle designers and developers, heliophysicists, geophysicists, aeronomers, meteorologists, and climatologists in understanding available models, comparing sources of data, and interpreting engineering and scientific results based on different solar reference spectra and irradiance models.

Decision curve analysis assessing the clinical benefit of NMP22 in the detection of bladder cancer: secondary analysis of a prospective trial.

PubMed

Barbieri, Christopher E; Cha, Eugene K; Chromecki, Thomas F; Dunning, Allison; Lotan, Yair; Svatek, Robert S; Scherr, Douglas S; Karakiewicz, Pierre I; Sun, Maxine; Mazumdar, Madhu; Shariat, Shahrokh F

2012-03-01

• To employ decision curve analysis to determine the impact of nuclear matrix protein 22 (NMP22) on clinical decision making in the detection of bladder cancer using data from a prospective trial. • The study included 1303 patients at risk for bladder cancer who underwent cystoscopy, urine cytology and measurement of urinary NMP22 levels. • We constructed several prediction models to estimate risk of bladder cancer. The base model was generated using patient characteristics (age, gender, race, smoking and haematuria); cytology and NMP22 were added to the base model to determine effects on predictive accuracy. • Clinical net benefit was calculated by summing the benefits and subtracting the harms and weighting these by the threshold probability at which a patient or clinician would opt for cystoscopy. • In all, 72 patients were found to have bladder cancer (5.5%). In univariate analyses, NMP22 was the strongest predictor of bladder cancer presence (predictive accuracy 71.3%), followed by age (67.5%) and cytology (64.3%). • In multivariable prediction models, NMP22 improved the predictive accuracy of the base model by 8.2% (area under the curve 70.2-78.4%) and of the base model plus cytology by 4.2% (area under the curve 75.9-80.1%). • Decision curve analysis revealed that adding NMP22 to other models increased clinical benefit, particularly at higher threshold probabilities. • NMP22 is a strong, independent predictor of bladder cancer. • Addition of NMP22 improves the accuracy of standard predictors by a statistically and clinically significant margin. • Decision curve analysis suggests that integration of NMP22 into clinical decision making helps avoid unnecessary cystoscopies, with minimal increased risk of missing a cancer. © 2011 THE AUTHORS. BJU INTERNATIONAL © 2011 BJU INTERNATIONAL.

Spectral Classes for FAA's Integrated Noise Model Version 6.0.

DOT National Transportation Integrated Search

1999-12-07

The starting point in any empirical model such as the Federal Aviation Administrations (FAA) : Integrated Noise Model (INM) is a reference data base. In Version 5.2 and in previous versions : the reference data base consisted solely of a set of no...

Improving transmembrane protein consensus topology prediction using inter-helical interaction.

PubMed

Wang, Han; Zhang, Chao; Shi, Xiaohu; Zhang, Li; Zhou, You

2012-11-01

Alpha helix transmembrane proteins (αTMPs) represent roughly 30% of all open reading frames (ORFs) in a typical genome and are involved in many critical biological processes. Due to the special physicochemical properties, it is hard to crystallize and obtain high resolution structures experimentally, thus, sequence-based topology prediction is highly desirable for the study of transmembrane proteins (TMPs), both in structure prediction and function prediction. Various model-based topology prediction methods have been developed, but the accuracy of those individual predictors remain poor due to the limitation of the methods or the features they used. Thus, the consensus topology prediction method becomes practical for high accuracy applications by combining the advances of the individual predictors. Here, based on the observation that inter-helical interactions are commonly found within the transmembrane helixes (TMHs) and strongly indicate the existence of them, we present a novel consensus topology prediction method for αTMPs, CNTOP, which incorporates four top leading individual topology predictors, and further improves the prediction accuracy by using the predicted inter-helical interactions. The method achieved 87% prediction accuracy based on a benchmark dataset and 78% accuracy based on a non-redundant dataset which is composed of polytopic αTMPs. Our method derives the highest topology accuracy than any other individual predictors and consensus predictors, at the same time, the TMHs are more accurately predicted in their length and locations, where both the false positives (FPs) and the false negatives (FNs) decreased dramatically. The CNTOP is available at: http://ccst.jlu.edu.cn/JCSB/cntop/CNTOP.html. Copyright © 2012 Elsevier B.V. All rights reserved.

Command generator tracker based direct model reference adaptive tracking guidance for Mars atmospheric entry

NASA Astrophysics Data System (ADS)

Li, Shuang; Peng, Yuming

2012-01-01

In order to accurately deliver an entry vehicle through the Martian atmosphere to the prescribed parachute deployment point, active Mars entry guidance is essential. This paper addresses the issue of Mars atmospheric entry guidance using the command generator tracker (CGT) based direct model reference adaptive control to reduce the adverse effect of the bounded uncertainties on atmospheric density and aerodynamic coefficients. Firstly, the nominal drag acceleration profile meeting a variety of constraints is planned off-line in the longitudinal plane as the reference model to track. Then, the CGT based direct model reference adaptive controller and the feed-forward compensator are designed to robustly track the aforementioned reference drag acceleration profile and to effectively reduce the downrange error. Afterwards, the heading alignment logic is adopted in the lateral plane to reduce the crossrange error. Finally, the validity of the guidance algorithm proposed in this paper is confirmed by Monte Carlo simulation analysis.

Predictors of Willingness to Read in English: Testing a Model Based on Possible Selves and Self-Confidence

ERIC Educational Resources Information Center

Khajavy, Gholam Hassan; Ghonsooly, Behzad

2017-01-01

The aim of the present study is twofold. First, it tests a model of willingness to read (WTR) based on L2 motivation and communication confidence (communication anxiety and perceived communicative competence). Second, it applies the recent theory of L2 motivation proposed by Dörnyei [2005. "The Psychology of Language Learner: Individual…

Risk prediction models for major adverse cardiac event (MACE) following percutaneous coronary intervention (PCI): A review

NASA Astrophysics Data System (ADS)

Manan, Norhafizah A.; Abidin, Basir

2015-02-01

Five percent of patients who went through Percutaneous Coronary Intervention (PCI) experienced Major Adverse Cardiac Events (MACE) after PCI procedure. Risk prediction of MACE following a PCI procedure therefore is helpful. This work describes a review of such prediction models currently in use. Literature search was done on PubMed and SCOPUS database. Thirty literatures were found but only 4 studies were chosen based on the data used, design, and outcome of the study. Particular emphasis was given and commented on the study design, population, sample size, modeling method, predictors, outcomes, discrimination and calibration of the model. All the models had acceptable discrimination ability (C-statistics >0.7) and good calibration (Hosmer-Lameshow P-value >0.05). Most common model used was multivariate logistic regression and most popular predictor was age.

Clinical Prediction Models for Patients With Nontraumatic Knee Pain in Primary Care: A Systematic Review and Internal Validation Study.

PubMed

Panken, Guus; Verhagen, Arianne P; Terwee, Caroline B; Heymans, Martijn W

2017-08-01

Study Design Systematic review and validation study. Background Many prognostic models of knee pain outcomes have been developed for use in primary care. Variability among published studies with regard to patient population, outcome measures, and relevant prognostic factors hampers the generalizability and implementation of these models. Objectives To summarize existing prognostic models in patients with knee pain in a primary care setting and to develop and internally validate new summary prognostic models. Methods After a sensitive search strategy, 2 reviewers independently selected prognostic models for patients with nontraumatic knee pain and assessed the methodological quality of the included studies. All predictors of the included studies were evaluated, summarized, and classified. The predictors assessed in multiple studies of sufficient quality are presented in this review. Using data from the Musculoskeletal System Study (BAS) cohort of patients with a new episode of knee pain, recruited consecutively by Dutch general medical practitioners (n = 372), we used predictors with a strong level of evidence to develop new prognostic models for each outcome measure and internally validated these models. Results Sixteen studies were eligible for inclusion. We considered 11 studies to be of sufficient quality. None of these studies validated their models. Five predictors with strong evidence were related to function and 6 to recovery, and were used to compose 2 prognostic models for patients with knee pain at 1 year. Running these new models in another data set showed explained variances (R 2 ) of 0.36 (function) and 0.33 (recovery). The area under the curve of the recovery model was 0.79. After internal validation, the adjusted R 2 values of the models were 0.30 (function) and 0.20 (recovery), and the area under the curve was 0.73. Conclusion We developed 2 valid prognostic models for function and recovery for patients with nontraumatic knee pain, based on predictors with strong evidence. A longer duration of complaints predicted poorer function but did not adequately predict chance of recovery. Level of Evidence Prognosis, levels 1a and 1b. J Orthop Sports Phys Ther 2017;47(8):518-529. Epub 16 Jun 2017. doi:10.2519/jospt.2017.7142.

Internet-Communication Disorder: It's a Matter of Social Aspects, Coping, and Internet-Use Expectancies

PubMed Central

Wegmann, Elisa; Brand, Matthias

2016-01-01

Online communication applications such as Facebook, WhatsApp, and Twitter are some of the most frequently used Internet applications. There is a growing amount of individuals suffering diminished control over their use of online communication applications which leads to diverse negative consequences in offline life. This could be referred to as Internet-communication disorder (ICD). The current study investigates the role of individual characteristics (e.g., psychopathological symptoms, feelings of loneliness) and specific cognitions. In a sample of 485 participants a structural equation model was tested to investigate predictors and mediators which may predict an excessive use. The results emphasize that a higher level of social loneliness and less perceived social support enhance the risk of a pathological use. The effects of psychopathological symptoms (depression and social anxiety) as well as individual characteristics (self-esteem, self-efficacy, and stress vulnerability) on ICD symptoms are mediated by Internet-use expectancies and dysfunctional coping mechanisms. The results illustrate mediation effects which are in line with the theoretical model by Brand et al. (2016). As suggested in the model social aspects seem to be key predictors of ICD symptoms. Further research should investigate convergent and divergent factors of other types of specific Internet-use disorders. PMID:27891107

Internet-Communication Disorder: It's a Matter of Social Aspects, Coping, and Internet-Use Expectancies.

PubMed

Wegmann, Elisa; Brand, Matthias

2016-01-01

Online communication applications such as Facebook, WhatsApp, and Twitter are some of the most frequently used Internet applications. There is a growing amount of individuals suffering diminished control over their use of online communication applications which leads to diverse negative consequences in offline life. This could be referred to as Internet-communication disorder (ICD). The current study investigates the role of individual characteristics (e.g., psychopathological symptoms, feelings of loneliness) and specific cognitions. In a sample of 485 participants a structural equation model was tested to investigate predictors and mediators which may predict an excessive use. The results emphasize that a higher level of social loneliness and less perceived social support enhance the risk of a pathological use. The effects of psychopathological symptoms (depression and social anxiety) as well as individual characteristics (self-esteem, self-efficacy, and stress vulnerability) on ICD symptoms are mediated by Internet-use expectancies and dysfunctional coping mechanisms. The results illustrate mediation effects which are in line with the theoretical model by Brand et al. (2016). As suggested in the model social aspects seem to be key predictors of ICD symptoms. Further research should investigate convergent and divergent factors of other types of specific Internet-use disorders.

Effects of sample survey design on the accuracy of classification tree models in species distribution models

USGS Publications Warehouse

Edwards, T.C.; Cutler, D.R.; Zimmermann, N.E.; Geiser, L.; Moisen, Gretchen G.

2006-01-01

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater emphasis should be placed on calculating and reporting cross-validation accuracy rates rather than simple resubstitution accuracy rates. Evaluation of the DESIGN and PURPOSIVE tree models on the EVALUATION data set shows significantly lower prediction accuracy for the PURPOSIVE tree models relative to the DESIGN models, indicating that non-probabilistic sample surveys may generate models with limited predictive capability. These differences were consistent across all four lichen species, with 11 of the 12 possible species and sample survey type comparisons having significantly lower accuracy rates. Some differences in accuracy were as large as 50%. The classification tree structures also differed considerably both among and within the modelled species, depending on the sample survey form. Overlap in the predictor variables selected by the DESIGN and PURPOSIVE tree models ranged from only 20% to 38%, indicating the classification trees fit the two evaluated survey forms on different sets of predictor variables. The magnitude of these differences in predictor variables throws doubt on ecological interpretation derived from prediction models based on non-probabilistic sample surveys. ?? 2006 Elsevier B.V. All rights reserved.

Predictors of Weapon Carrying in Youth Attending Drop-in Centers

ERIC Educational Resources Information Center

Blumberg, Elaine J.; Liles, Sandy; Kelley, Norma J.; Hovell, Melbourne F.; Bousman, Chad A.; Shillington, Audrey M.; Ji, Ming; Clapp, John

2009-01-01

Objective: To test and compare 2 predictive models of weapon carrying in youth (n=308) recruited from 4 drop-in centers in San Diego and Imperial counties. Methods: Both models were based on the Behavioral Ecological Model (BEM). Results: The first and second models significantly explained 39% and 53% of the variance in weapon carrying,…

Smith predictor-based robot control for ultrasound-guided teleoperated beating-heart surgery.

PubMed

Bowthorpe, Meaghan; Tavakoli, Mahdi; Becher, Harald; Howe, Robert

2014-01-01

Performing surgery on fast-moving heart structures while the heart is freely beating is next to impossible. Nevertheless, the ability to do this would greatly benefit patients. By controlling a teleoperated robot to continuously follow the heart's motion, the heart can be made to appear stationary. The surgeon will then be able to operate on a seemingly stationary heart when in reality it is freely beating. The heart's motion is measured from ultrasound images and thus involves a non-negligible delay due to image acquisition and processing, estimated to be 150 ms that, if not compensated for, can cause the teleoperated robot's end-effector (i.e., the surgical tool) to collide with and puncture the heart. This research proposes the use of a Smith predictor to compensate for this time delay in calculating the reference position for the teleoperated robot. The results suggest that heart motion tracking is improved as the introduction of the Smith predictor significantly decreases the mean absolute error, which is the error in making the distance between the robot's end-effector and the heart follow the surgeon's motion, and the mean integrated square error.

Quantitative Model to Predict Melts on the Ol-Opx Saturation Boundary during Mantle Melting: The Role of H2O

NASA Astrophysics Data System (ADS)

Andrews, A. L.; Grove, T. L.

2014-12-01

Two quantitative, empirical models are presented that predict mantle melt compositions in equilibrium with olivine (ol) + orthopyroxene (opx) ± spinel (sp) as a function of variable pressure and H2O content. The models consist of multiple linear regressions calibrated using new data from H2O-undersaturated primitive and depleted mantle lherzolite melting experiments as well as experimental literature data. The models investigate the roles of H2O, Pressure, 1-Mg# (1-[XMg/(XMg+XFe)]), NaK# ((Na2O+K2O)/(Na2O+K2O+CaO)), TiO2, and Cr2O3 on mantle melt compositions. Melts are represented by the pseudoternary endmembers Clinopyroxene (Cpx), Olivine (Ol), Plagioclase (Plag), and Quartz (Qz) of Tormey et al. (1987). Model A returns predictive equations for the four endmembers with identical predictor variables, whereas Model B chooses predictor variables for the four compositional endmember equations and temperature independently. We employ the use of Akaike Information Criteria (Akaike, 1974) to determine the best predictor variables from initial variables chosen through thermodynamic reasoning and by previous models. In both Models A and B, the coefficients for H2O show that increasing H2O drives the melt to more Qz normative space, as the Qz component increases by +0.012(3) per 1 wt.% H2O. The other endmember components decrease and are all three times less affected by H2O (Ol: -0.004(2); Cpx: -0.004(2); Plag: -0.004(3)). Consistent with previous models and experimental data, increasing pressure moves melt compositions to more Ol normative space at the expense of the Qz component. The models presented quantitatively determine the influence of H2O, Pressure, 1-Mg#, NaK#, TiO2, and Cr2O3 on mantle melts in equilibrium with ol+opx±sp; the equations presented can be used to predict melts of known mantle source compositions saturated in ol+opx±sp. References Tormey, Grove, & Bryan (1987), doi: 10.1007/BF00375227. Akaike (1974), doi: 10.1109/TAC.1974.1100705.

Main predictors of periphyton species richness depend on adherence strategy and cell size

PubMed Central

Siqueira, Tadeu; Landeiro, Victor Lemes; Rodrigues, Liliana; Bonecker, Claudia Costa; Rodrigues, Luzia Cleide; Santana, Natália Fernanda; Thomaz, Sidinei Magela; Bini, Luis Mauricio

2017-01-01

Periphytic algae are important components of aquatic ecosystems. However, the factors driving periphyton species richness variation remain largely unexplored. Here, we used data from a subtropical floodplain (Upper Paraná River floodplain, Brazil) to quantify the influence of environmental variables (total suspended matter, temperature, conductivity, nutrient concentrations, hydrology, phytoplankton biomass, phytoplankton species richness, aquatic macrophyte species richness and zooplankton density) on overall periphytic algal species richness and on the richness of different algal groups defined by morphological traits (cell size and adherence strategy). We expected that the coefficients of determination of the models estimated for different trait-based groups would be higher than the model coefficient of determination of the entire algal community. We also expected that the relative importance of explanatory variables in predicting species richness would differ among algal groups. The coefficient of determination for the model used to predict overall periphytic algal species richness was higher than the ones obtained for models used to predict the species richness of the different groups. Thus, our first prediction was not supported. Species richness of aquatic macrophytes was the main predictor of periphyton species richness of the entire community and a significant predictor of the species richness of small mobile, large mobile and small-loosely attached algae. Abiotic variables, phytoplankton species richness, chlorophyll-a concentration, and hydrology were also significant predictors, depending on the group. These results suggest that habitat heterogeneity (as proxied by aquatic macrophytes richness) is important for maintaining periphyton species richness in floodplain environments. However, other factors played a role, suggesting that the analysis of species richness of different trait-based groups unveils relationships that were not detectable when the entire community was analysed together. PMID:28742122

Climate Drivers of Blue Intensity from Two Eastern North American Conifers

NASA Astrophysics Data System (ADS)

Rayback, S. A.; Kilbride, J.; Pontius, J.; Tait, E.; Little, J.

2016-12-01

Gaining a comprehensive understanding of the climatic factors that drive tree radial growth over time is important in the context of global climate change. Herein, we explore minimum blue intensity (BI), a measure of lignin context in the latewood of tree rings, with the objective of developing BI chronologies for two eastern North American conifers to identify and explore climatic drivers and to compare BI-climate relationships to those of tree-ring widths (TRW). Using dendrochronological techniques, Tsuga canadensis and Picea rubens TRW and BI chronologies were developed at Abbey Pond (ABP) and The Cape National Research Area (CAPE), Vermont, USA, respectively. Climate drivers (1901-2010) were investigated using correlation and response function analyses and generalized linear mixed models. The ABP T. canadensis BI model explained the highest amount of variance (R2 = 0.350, adjR2=0.324) with September Tmin and June total percent cloudiness as predictors. The ABP T. canadensis TRW model explained 34% of the variance (R2 = 0.340, adjR2=0.328) with summer total precipitation and June PDSI as predictors. The CAPE P. rubens TRW and BI models explained 31% of the variance (R2 = 0.33, adjR2=0.310), based on p July Tmax, p August Tmean and fall Tmin as predictors, and 7% (R2 = 0.068, adjR2=0.060) based on Spring Tmin as the predictor, respectively. Moving window analyses confirm the moisture sensitivity of T. canadensis TRW and now BI and suggest an extension of the growing season. Similarly, P. rubens TRW responded consistently negative to high growing season temperatures, but TRW and BI benefited from a longer growing season. This study introduces two new BI chronologies, the first from northeastern North America, and highlights shifts underway in tree response to changing climate.

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

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

Activity of invasive slug Limax maximus in relation to climate conditions based on citizen's observations and novel regularization based statistical approaches.

PubMed

Morii, Yuta; Ohkubo, Yusaku; Watanabe, Sanae

2018-05-13

Citizen science is a powerful tool that can be used to resolve the problems of introduced species. An amateur naturalist and author of this paper, S. Watanabe, recorded the total number of Limax maximus (Limacidae, Pulmonata) individuals along a fixed census route almost every day for two years on Hokkaido Island, Japan. L. maximus is an invasive slug considered a pest species of horticultural and agricultural crops. We investigated how weather conditions were correlated to the intensity of slug activity using for the first time in ecology the recently developed statistical analyses, Bayesian regularization regression with comparisons among Laplace, Horseshoe and Horseshoe+ priors for the first time in ecology. The slug counts were compared with meteorological data from 5:00 in the morning on the day of observation (OT- and OD-models) and the day before observation (DBOD-models). The OT- and OD-models were more supported than the DBOD-models based on the WAIC scores, and the meteorological predictors selected in the OT-, OD- and DBOD-models were different. The probability of slug appearance was increased on mornings with higher than 20-year-average humidity (%) and lower than average wind velocity (m/s) and precipitation (mm) values in the OT-models. OD-models showed a pattern similar to OT-models in the probability of slug appearance, but also suggested other meteorological predictors for slug activities; positive effect of solar radiation (MJ) for example. Five meteorological predictors, mean and highest temperature (°C), wind velocity (m/s), precipitation amount (mm) and atmospheric pressure (hPa), were selected as the effective factors for the counts in the DBOD-models. Therefore, the DBOD-models will be valuable for the prediction of slug activity in the future, much like a weather forecast. Copyright © 2018 Elsevier B.V. All rights reserved.

Floristic composition, beta diversity, and nestedness of reference sites for restoration of xeroriparian areas

USGS Publications Warehouse

Beauchamp, Vanessa B.; Shafroth, P.B.

2011-01-01

In restoration ecology, reference sites serve as models for areas to be restored and can provide a standard of comparison for restoration project outcomes. When reference sites are located a relatively long distance from associated restoration projects, differences in climate, disturbance history, and biogeography can increase beta diversity and may decrease the relevance of reference sites. Variation in factors at the scale of individual reference sites such as patch size, microclimate, barriers to dispersal, or soil chemistry can result in reference site species composition that is a nested subset of the regional species pool. In the western United States, restoration of riparian areas, particularly those occupied by Tamarix spp., has become a priority; however, little is known about suitable native replacement vegetation communities for relatively dry and saline riparian terraces that comprise many of the sites where Tamarix is removed prior to restoration activities. We studied plant communities on riparian terraces along five rivers in New Mexico, USA, to (1) determine whether the floristic composition of reference sites can be predicted by easily measured soil variables such as pH, salinity (electric conductivity), and texture; (2) examine the extent of distance decay in the compositional similarity of xeroriparian plant communities in the southwestern United States; and (3) determine the degree of nestedness in xeroriparian plant communities in relationship to soil variables. We found that sites clustered into groups based largely on variation in soil salinity and texture. Vegetation across all sites was highly nested with dominant, salt-tolerant species found on most soil groups and salt-intolerant subordinate species restricted to lowsalinity soils. The identity of subordinate species was largely site dependent, causing all sites to have the same low degree of similarity regardless of the distance between them. We conclude that, when planning restoration projects on dry and saline riparian sites, soil salinity and texture are good predictors of which species will be most suited to the area being restored, but a candidate species pool should be developed from the nearest possible reference sites, particularly for subordinate species. ?? 2011 by the Ecological Society of America.

Partitioning the Uncertainty in Estimates of Mean Basal Area Obtained from 10-year Diameter Growth Model Predictions

Treesearch

Ronald E. McRoberts

2005-01-01

Uncertainty in model-based predictions of individual tree diameter growth is attributed to three sources: measurement error for predictor variables, residual variability around model predictions, and uncertainty in model parameter estimates. Monte Carlo simulations are used to propagate the uncertainty from the three sources through a set of diameter growth models to...

Evaluating Level of Specificity of Normative Referents in Relation to Personal Drinking Behavior*

PubMed Central

Larimer, Mary E.; Kaysen, Debra L.; Lee, Christine M.; Kilmer, Jason R.; Lewis, Melissa A.; Dillworth, Tiara; Montoya, Heidi D.; Neighbors, Clayton

2009-01-01

Objective: Research has found perceived descriptive norms to be one of the strongest predictors of college student drinking, and several intervention approaches have incorporated normative feedback to correct misperceptions of peer drinking behavior. Little research has focused on the role of the reference group in normative perceptions. The current study sought to examine whether normative perceptions vary based on specificity of the reference group and whether perceived norms for more specific reference-group norms are related to individual drinking behavior. Method: Participants were first-year undergraduates (n = 1,276, 58% female) randomly selected from a university list of incoming students. Participants reported personal drinking behavior and perceived descriptive norms for eight reference groups, including typical student; same gender, ethnicity, or residence; and combinations of those reference groups (e.g., same gender and residence). Results: Findings indicated that participants distinguished among different reference groups in estimating descriptive drinking norms. Moreover, results indicated misperceptions in drinking norms were evident at all levels of specificity of the reference group. Additionally, findings showed perceived norms for more specific groups were uniquely related to participants' own drinking. Conclusions: These results suggest that providing normative feedback targeting at least one level of specificity to the participant (i.e., beyond what the “typical” student does) may be an important tool in normative feedback interventions. PMID:19538919

Normative values and correlates of carotid artery intima-media thickness and carotid atherosclerosis in Andean-Hispanics: The PREVENCION Study

PubMed Central

Pastorius, Catherine A.; Medina-Lezama, Josefina; Corrales-Medina, Fernando; Bernabé-Ortiz, Antonio; Paz-Manrique, Roberto; Salinas-Najarro, Belissa; Khan, Zubair A.; Takahashi, Junichiro; Toshima, Gen; Zea-Diaz, Humberto; Postigo-MacDowall, Mauricio; Chirinos-Pacheco, Julio; Ibañez, Francisco; Chirinos, Diana A.; Saif, Hassam; Chirinos, Julio A.

2010-01-01

Objectives Carotid intima-media thickness (cIMT) is an independent predictor of cardiovascular risk. Furthermore, ethnicity and gender-specific normative data are required to assess cIMT, which are not available for Andean-Hispanics. In addition, data regarding correlates of subclinical atherosclerosis in ethnic population are needed. Methods We studied 1448 adults enrolled in a population-based study in Peru. cIMT and carotid plaque were measured with high-resolution ultrasonography. A healthy reference sample (n=472) with no cardiovascular disease, normal weight and normal metabolic parameters was selected to establish normative cIMT values. Correlates of abnormal cIMT and carotid plaque were assessed in the entire population. Results In the reference sample, 95th-percentile cIMT values were both age and gender-dependent. In stepwise regression, selected predictors of increasing cIMT were: older age, impaired fasting glucose, diabetes mellitus, higher systolic blood pressure, higher LDL-cholesterol, smoking and male gender. Predictors of carotid plaque included older age, male gender, higher systolic blood pressure, lower diastolic blood pressure and higher LDL-cholesterol. HDL-cholesterol and C-reactive protein were not associated with cIMT or carotid plaque. The lack of association with HDL-cholesterol was confirmed using high performance liquid chromatography. Conclusions We present ethnic-specific cutoffs for abnormal cIMT applicable to Andean-Hispanics and correlates of subclinical atherosclerosis in this population. Pending longitudinal studies, our data supports several risk associations seen in other populations and can be used to identify Andean-Hispanics at increased risk for atherosclerotic cardiovascular disease. The lack of association between HDL-C and cIMT or carotid plaque in this population requires further investigation. PMID:20510418

A systematic review finds methodological improvements necessary for prognostic models in determining traumatic brain injury outcomes.

PubMed

Mushkudiani, Nino A; Hukkelhoven, Chantal W P M; Hernández, Adrián V; Murray, Gordon D; Choi, Sung C; Maas, Andrew I R; Steyerberg, Ewout W

2008-04-01

To describe the modeling techniques used for early prediction of outcome in traumatic brain injury (TBI) and to identify aspects for potential improvements. We reviewed key methodological aspects of studies published between 1970 and 2005 that proposed a prognostic model for the Glasgow Outcome Scale of TBI based on admission data. We included 31 papers. Twenty-four were single-center studies, and 22 reported on fewer than 500 patients. The median of the number of initially considered predictors was eight, and on average five of these were selected for the prognostic model, generally including age, Glasgow Coma Score (or only motor score), and pupillary reactivity. The most common statistical technique was logistic regression with stepwise selection of predictors. Model performance was often quantified by accuracy rate rather than by more appropriate measures such as the area under the receiver-operating characteristic curve. Model validity was addressed in 15 studies, but mostly used a simple split-sample approach, and external validation was performed in only four studies. Although most models agree on the three most important predictors, many were developed on small sample sizes within single centers and hence lack generalizability. Modeling strategies have to be improved, and include external validation.

Modern modeling techniques had limited external validity in predicting mortality from traumatic brain injury.

PubMed

van der Ploeg, Tjeerd; Nieboer, Daan; Steyerberg, Ewout W

2016-10-01

Prediction of medical outcomes may potentially benefit from using modern statistical modeling techniques. We aimed to externally validate modeling strategies for prediction of 6-month mortality of patients suffering from traumatic brain injury (TBI) with predictor sets of increasing complexity. We analyzed individual patient data from 15 different studies including 11,026 TBI patients. We consecutively considered a core set of predictors (age, motor score, and pupillary reactivity), an extended set with computed tomography scan characteristics, and a further extension with two laboratory measurements (glucose and hemoglobin). With each of these sets, we predicted 6-month mortality using default settings with five statistical modeling techniques: logistic regression (LR), classification and regression trees, random forests (RFs), support vector machines (SVM) and neural nets. For external validation, a model developed on one of the 15 data sets was applied to each of the 14 remaining sets. This process was repeated 15 times for a total of 630 validations. The area under the receiver operating characteristic curve (AUC) was used to assess the discriminative ability of the models. For the most complex predictor set, the LR models performed best (median validated AUC value, 0.757), followed by RF and support vector machine models (median validated AUC value, 0.735 and 0.732, respectively). With each predictor set, the classification and regression trees models showed poor performance (median validated AUC value, <0.7). The variability in performance across the studies was smallest for the RF- and LR-based models (inter quartile range for validated AUC values from 0.07 to 0.10). In the area of predicting mortality from TBI, nonlinear and nonadditive effects are not pronounced enough to make modern prediction methods beneficial. Copyright © 2016 Elsevier Inc. All rights reserved.

Prediction of vesico-ureteric reflux in childhood urinary tract infection: a multivariate approach.

PubMed

Oostenbrink, R; van der Heijden, A J; Moons, K G; Moll, H A

2000-07-01

In this study, independent predictors obtained from patient history, physical examination and laboratory results for vesico-ureteric reflux (VUR) in children of 0-5 y with a first urinary tract infection (UTI) were assessed and the added value of renal ultrasound (US) investigated. Information was collected from children visiting the paediatric outpatient department with a first proven UTI, defined as a urine monoculture with > or = 10(5) organism/ml, with clinical symptoms and possible white cell count > or = 20 per high-power field of spun fresh urine. Children with neurologic bladder dysfunction were excluded. VUR was determined by voiding cystourethrography (VCUG) and graded from I to V. The diagnostic value of predictors was judged using multivariate logistic modelling with the area under the receiver operating characteristic (ROC area). A risk score was derived based on the regression coefficients of the independent predictors in the logistic model. In 140 children (51 boys and 89 girls) VUR was diagnosed in 37. Independent predictors for VUR were male gender, age, family history for uropathology, serum C-reactive protein level (CRP) and dilatation of the urinary tract on US. The ROC area of this model was 0.78 (95% CI: 0.69-0.87). This prediction model identified 12% (95% CI: 7-18) of the patients without VUR without missing one case of VUR. If we used VUR > or = grade 3 as a threshold, the model assessed VUR to be absent in 34% (95% CI: 26-42). A prediction rule based on age, gender, family history, CRP and US results is useful in assessing the probability of VUR in the individual child with a first UTI and may help the physician to make decisions about performing additional imaging techniques. Prospective validation of the model in future patients, however, will be necessary before applying the rule in practice.

Relative codon adaptation: a generic codon bias index for prediction of gene expression.

PubMed

Fox, Jesse M; Erill, Ivan

2010-06-01

The development of codon bias indices (CBIs) remains an active field of research due to their myriad applications in computational biology. Recently, the relative codon usage bias (RCBS) was introduced as a novel CBI able to estimate codon bias without using a reference set. The results of this new index when applied to Escherichia coli and Saccharomyces cerevisiae led the authors of the original publications to conclude that natural selection favours higher expression and enhanced codon usage optimization in short genes. Here, we show that this conclusion was flawed and based on the systematic oversight of an intrinsic bias for short sequences in the RCBS index and of biases in the small data sets used for validation in E. coli. Furthermore, we reveal that how the RCBS can be corrected to produce useful results and how its underlying principle, which we here term relative codon adaptation (RCA), can be made into a powerful reference-set-based index that directly takes into account the genomic base composition. Finally, we show that RCA outperforms the codon adaptation index (CAI) as a predictor of gene expression when operating on the CAI reference set and that this improvement is significantly larger when analysing genomes with high mutational bias.

Predictability of the Lagrangian Motion in the Upper Ocean

NASA Astrophysics Data System (ADS)

Piterbarg, L. I.; Griffa, A.; Griffa, A.; Mariano, A. J.; Ozgokmen, T. M.; Ryan, E. H.

2001-12-01

The complex non-linear dynamics of the upper ocean leads to chaotic behavior of drifter trajectories in the ocean. Our study is focused on estimating the predictability limit for the position of an individual Lagrangian particle or a particle cluster based on the knowledge of mean currents and observations of nearby particles (predictors). The Lagrangian prediction problem, besides being a fundamental scientific problem, is also of great importance for practical applications such as search and rescue operations and for modeling the spread of fish larvae. A stochastic multi-particle model for the Lagrangian motion has been rigorously formulated and is a generalization of the well known "random flight" model for a single particle. Our model is mathematically consistent and includes a few easily interpreted parameters, such as the Lagrangian velocity decorrelation time scale, the turbulent velocity variance, and the velocity decorrelation radius, that can be estimated from data. The top Lyapunov exponent for an isotropic version of the model is explicitly expressed as a function of these parameters enabling us to approximate the predictability limit to first order. Lagrangian prediction errors for two new prediction algorithms are evaluated against simple algorithms and each other and are used to test the predictability limits of the stochastic model for isotropic turbulence. The first algorithm is based on a Kalman filter and uses the developed stochastic model. Its implementation for drifter clusters in both the Tropical Pacific and Adriatic Sea, showed good prediction skill over a period of 1-2 weeks. The prediction error is primarily a function of the data density, defined as the number of predictors within a velocity decorrelation spatial scale from the particle to be predicted. The second algorithm is model independent and is based on spatial regression considerations. Preliminary results, based on simulated, as well as, real data, indicate that it performs better than the Kalman-based algorithm in strong shear flows. An important component of our research is the optimal predictor location problem; Where should floats be launched in order to minimize the Lagrangian prediction error? Preliminary Lagrangian sampling results for different flow scenarios will be presented.

Predictive factors of gestational diabetes in pregnancies following assisted reproductive technology: a nested case-control study.

PubMed

Kouhkan, Azam; Khamseh, Mohammad E; Moini, Ashraf; Pirjani, Reihaneh; Valojerdi, Ameneh Ebrahim; Arabipoor, Arezoo; Hosseini, Roya; Baradaran, Hamid Reza

2018-05-05

To evaluate predictive factors for gestational diabetes mellitus (GDM) in singleton pregnancy following assisted reproductive technology (ART). This nested case-control study was performed during October 2016-June 2017. Pregnant women who conceived following ART procedures referred to infertility clinic were selected and categorized into GDM and non-GDM based on ADA/IAPDSG criteria. The study variables including age, educational status, first-degree family history of chronic diseases, systolic and diastolic blood pressure, previous obstetric and perinatal outcomes, infertility history, and ART cycle characteristics were collected from medical records. Prediction model to develop GDM was employed by binary logistic regression analysis after adjustment for age and body mass index, family history of diabetes, and gravidity. In total, 270 women with singleton pregnancies (consisted of 135 GDM and 135 non-GDM women) conceived were studied. According to the final model, significant predictors of GDM were history of polycystic ovarian syndrome (PCOS), previous ovarian hyper-stimulation syndrome (OHSS) risk and progesterone injections. Administration of injectable progesterone during the first 10-12 weeks of pregnancy was associated with an approximately twofold increased risk of developing GDM [odds ratio (OR) 2.28, 95% confidence interval (CI) 1.27-4.09)] compared to vaginal progesterone. In addition, the regression analysis revealed that previous OHSS risk (OR 2.40, 95% CI 1.34-4.31) and history of PCOS (OR 2.76, 95% CI 1.26-6.06) were other most important predictors of GDM. The route of progesterone administration, previous OHSS risk and history of PCOS seem to be putative risk factors for GDM in women conceived by ART.

A cutoff value based on analysis of a reference population decreases overestimation of the prevalence of nocturnal polyuria.

PubMed

van Haarst, Ernst P; Bosch, J L H Ruud

2012-09-01

We sought criteria for nocturnal polyuria in asymptomatic, nonurological adults of all ages by reporting reference values of the ratio of daytime and nighttime urine volumes, and finding nocturia predictors. Data from a database of frequency-volume charts from a reference population of 894 nonurological, asymptomatic volunteers of all age groups were analyzed. The nocturnal polyuria index and the nocturia index were calculated and factors influencing these values were determined by multivariate analysis. The nocturnal polyuria index had wide variation but a normal distribution with a mean ± SD of 30% ± 12%. The 95th percentile of the values was 53%. Above this cutoff a patient had nocturnal polyuria. This value contrasts with the International Continence Society definition of 33% but agrees with several other reports. On multivariate regression analysis with the nocturnal polyuria index as the dependent variable sleeping time, maximum voided volume and age were the covariates. However, the increase in the nocturnal polyuria index by age was small. Excluding polyuria and nocturia from analysis did not alter the results in a relevant way. The nocturnal voiding frequency depended on sleeping time and maximum voided volume but most of all on the nocturia index. The prevalence of nocturnal polyuria is overestimated. We suggest a new cutoff value for the nocturnal polyuria index, that is nocturnal polyuria exists when the nocturnal polyuria index exceeds 53%. The nocturia index is the best predictor of nocturia. Copyright © 2012 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Occupational Stress: A Comprehensive Review of the Top 50 Annual and Lifetime Cited Articles.

PubMed

Nowrouzi, Behdin; Nguyen, Christine; Casole, Jennifer; Nowrouzi-Kia, Behnam

2017-05-01

This study determined the impact and influence of published articles on the field of occupational stress. A transdisciplinary approach was used to identify the 50 work-related stress articles with the most lifetime citations and the 50 work-related stress articles with the highest annual citation rates. Studies were categorized based on their primary focus: (a) etiology, (b) predictor of outcome for which occupational stress is the outcome or predictor of outcome for which occupational stress is an independent variable, (c) management/intervention, (d) theory/model/framework, or (e) methodologies. The majority of studies with the highest number of lifetime citations as well as the highest annual citation rates used stress as a predictor or outcome of another factor. The proportion of studies that were categorized by etiology, intervention/management, theory/model/framework, or methodologies was relatively low for both lifetime and annual citations.

Towards an understanding of dimensions, predictors, and gender gap in written composition

PubMed Central

Kim, Young-Suk; Al Otaiba, Stephanie; Wanzek, Jeanne; Gatlin, Brandy

2014-01-01

We had three aims in the present study: (1) to examine the dimensionality of various evaluative approaches to scoring writing samples (e.g., quality, productivity, and curriculum based writing [CBM]) , (2) to investigate unique language and cognitive predictors of the identified dimensions, and (3) to examine gender gap in the identified dimensions of writing. These questions were addressed using data from second and third grade students (N = 494). Data were analyzed using confirmatory factor analysis and multilevel modeling. Results showed that writing quality, productivity, and CBM scoring were dissociable constructs, but that writing quality and CBM scoring were highly related (r = .82). Language and cognitive predictors differed among the writing outcomes. Boys had lower writing scores than girls even after accounting for language, reading, attention, spelling, handwriting automaticity, and rapid automatized naming. Results are discussed in light of writing evaluation and a developmental model of writing. PMID:25937667

Predictors of medication adherence in high risk youth of color living with HIV.

PubMed

Macdonell, Karen E; Naar-King, Sylvie; Murphy, Debra A; Parsons, Jeffrey T; Harper, Gary W

2010-07-01

To test predictors of medication adherence in high-risk racial or ethnic minority youth living with HIV (YLH) using a conceptual model of social cognitive predictors including a continuous measure of motivational readiness. Youth were participants in a multi-site clinical trial examining the efficacy of a motivational intervention. Racial-minority YLH (primarily African American) who were prescribed antiretroviral medication were included (N = 104). Data were collected using computer-assisted personal interviewing method via an Internet-based application and questionnaires. Using path analysis with bootstrapping, most youth reported suboptimal adherence, which predicted higher viral load. Higher motivational readiness predicted optimal adherence, and higher social support predicted readiness. Decisional balance was indirectly related to adherence. The model provided a plausible framework for understanding adherence in this population. Culturally competent interventions focused on readiness and social support may be helpful for improving adherence in YLH.

Characterization of the spatial variability of soil available zinc at various sampling densities using grouped soil type information.

PubMed

Song, Xiao-Dong; Zhang, Gan-Lin; Liu, Feng; Li, De-Cheng; Zhao, Yu-Guo

2016-11-01

The influence of anthropogenic activities and natural processes involved high uncertainties to the spatial variation modeling of soil available zinc (AZn) in plain river network regions. Four datasets with different sampling densities were split over the Qiaocheng district of Bozhou City, China. The difference of AZn concentrations regarding soil types was analyzed by the principal component analysis (PCA). Since the stationarity was not indicated and effective ranges of four datasets were larger than the sampling extent (about 400 m), two investigation tools, namely F3 test and stationarity index (SI), were employed to test the local non-stationarity. Geographically weighted regression (GWR) technique was performed to describe the spatial heterogeneity of AZn concentrations under the non-stationarity assumption. GWR based on grouped soil type information (GWRG for short) was proposed so as to benefit the local modeling of soil AZn within each soil-landscape unit. For reference, the multiple linear regression (MLR) model, a global regression technique, was also employed and incorporated the same predictors as in the GWR models. Validation results based on 100 times realization demonstrated that GWRG outperformed MLR and can produce similar or better accuracy than the GWR approach. Nevertheless, GWRG can generate better soil maps than GWR for limit soil data. Two-sample t test of produced soil maps also confirmed significantly different means. Variogram analysis of the model residuals exhibited weak spatial correlation, rejecting the use of hybrid kriging techniques. As a heuristically statistical method, the GWRG was beneficial in this study and potentially for other soil properties.

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.

PubMed

Alghamdi, Manal; Al-Mallah, Mouaz; Keteyian, Steven; Brawner, Clinton; Ehrman, Jonathan; Sakr, Sherif

2017-01-01

Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up. At the completion of the fifth year, 5,099 of those patients have developed diabetes. The dataset contained 62 attributes classified into four categories: demographic characteristics, disease history, medication use history, and stress test vital signs. We developed an Ensembling-based predictive model using 13 attributes that were selected based on their clinical importance, Multiple Linear Regression, and Information Gain Ranking methods. The negative effect of the imbalance class of the constructed model was handled by Synthetic Minority Oversampling Technique (SMOTE). The overall performance of the predictive model classifier was improved by the Ensemble machine learning approach using the Vote method with three Decision Trees (Naïve Bayes Tree, Random Forest, and Logistic Model Tree) and achieved high accuracy of prediction (AUC = 0.92). The study shows the potential of ensembling and SMOTE approaches for predicting incident diabetes using cardiorespiratory fitness data.

A prediction model-based algorithm for computer-assisted database screening of adverse drug reactions in the Netherlands.

PubMed

Scholl, Joep H G; van Hunsel, Florence P A M; Hak, Eelko; van Puijenbroek, Eugène P

2018-02-01

The statistical screening of pharmacovigilance databases containing spontaneously reported adverse drug reactions (ADRs) is mainly based on disproportionality analysis. The aim of this study was to improve the efficiency of full database screening using a prediction model-based approach. A logistic regression-based prediction model containing 5 candidate predictors was developed and internally validated using the Summary of Product Characteristics as the gold standard for the outcome. All drug-ADR associations, with the exception of those related to vaccines, with a minimum of 3 reports formed the training data for the model. Performance was based on the area under the receiver operating characteristic curve (AUC). Results were compared with the current method of database screening based on the number of previously analyzed associations. A total of 25 026 unique drug-ADR associations formed the training data for the model. The final model contained all 5 candidate predictors (number of reports, disproportionality, reports from healthcare professionals, reports from marketing authorization holders, Naranjo score). The AUC for the full model was 0.740 (95% CI; 0.734-0.747). The internal validity was good based on the calibration curve and bootstrapping analysis (AUC after bootstrapping = 0.739). Compared with the old method, the AUC increased from 0.649 to 0.740, and the proportion of potential signals increased by approximately 50% (from 12.3% to 19.4%). A prediction model-based approach can be a useful tool to create priority-based listings for signal detection in databases consisting of spontaneous ADRs. © 2017 The Authors. Pharmacoepidemiology & Drug Safety Published by John Wiley & Sons Ltd.

Multicollinearity in hierarchical linear models.

PubMed

Yu, Han; Jiang, Shanhe; Land, Kenneth C

2015-09-01

This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model. Copyright © 2015 Elsevier Inc. All rights reserved.

Predictors of emotional and physical dating violence in a sample of serious juvenile offenders.

PubMed

Sweeten, Gary; Larson, Matthew; Piquero, Alex R

2016-10-01

We estimate group-based dating violence trajectories and identify the adolescent risk factors that explain membership in each trajectory group. Using longitudinal data from the Pathways to Desistance Study, which follows a sample of 1354 serious juvenile offenders from Philadelphia, Pennsylvania and Phoenix, Arizona between mid-adolescence and early adulthood, we estimate group-based trajectory models of both emotional dating violence and physical dating violence over a span of five years in young adulthood. We then estimate multinomial logistic regression models to identify theoretically motivated risk factors that predict membership in these groups. We identified three developmental patterns of emotional dating violence: none (33%), low-level (59%) and high-level decreasing (8%). The best-fitting model for physical dating violence also had three groups: none (73%), low-level (24%) and high-level (3%). Race/ethnicity, family and psychosocial variables were among the strongest predictors of both emotional and physical dating violence. In addition, delinquency history variables predicted emotional dating violence and relationship variables predicted physical dating violence. Dating violence is quite prevalent in young adulthood among serious juvenile offenders. Numerous predictors distinguish between chronic dating violence perpetrators and other groups. These may suggest points of intervention for reducing future violence. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Utility of an Abbreviated Dizziness Questionnaire to Differentiate between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study

PubMed Central

Roland, Lauren T.; Kallogjeri, Dorina; Sinks, Belinda C.; Rauch, Steven D.; Shepard, Neil T.; White, Judith A.; Goebel, Joel A.

2015-01-01

Objective Test performance of a focused dizziness questionnaire’s ability to discriminate between peripheral and non-peripheral causes of vertigo. Study Design Prospective multi-center Setting Four academic centers with experienced balance specialists Patients New dizzy patients Interventions A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Main outcomes Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and non-peripheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. Results 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and non-peripheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central and other causes were considered good as measured by c-indices of 0.75, 0.7 and 0.78, respectively. Conclusions This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from non-peripheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed. PMID:26485598

Fluoroscopically guided transforaminal epidural steroid injections at a quaternary-care teaching institution: effect of trainee involvement and patient body mass index on fluoroscopy time and patient dose.

PubMed

Tiegs-Heiden, C A; Murthy, N S; Geske, J R; Diehn, F E; Schueler, B A; Wald, J T; Kaufmann, T J; Lehman, V T; Carr, C M; Amrami, K K; Morris, J M; Thielen, K R; Maus, T P

2016-01-01

To investigate whether there are differences in fluoroscopy time and patient dose for fluoroscopically guided lumbar transforaminal epidural steroid injections (TFESIs) performed by staff radiologists versus with trainees and to evaluate the effect of patient body mass index (BMI) on fluoroscopy time and patient dose, including their interactions with other variables. Single-level lumbar TFESIs (n=1844) between 1 January 2011 and 31 December 2013 were reviewed. Fluoroscopy time, reference point air kerma (Ka,r), and kerma area product (KAP) were recorded. BMI and trainee involvement were examined as predictors of fluoroscopy time, Ka,r, and KAP in models adjusted for age and gender in multivariable linear models. Stratified models of BMI groups by trainee presence were performed. Increased age was the only significant predictor of increased fluoroscopy time (p<0.0001). Ka,r and KAP were significantly higher in patients with a higher BMI (p<0.0001 and p=0.0009). When stratified by BMI, longer fluoroscopy time predicted increased Ka,r and KAP in all groups (p<0.0001). Trainee involvement was not a statistically significant predictor of fluoroscopy time or Ka,r in any BMI category. KAP was lower with trainees in the overweight group (p=0.0009) and higher in male patients for all BMI categories (p<0.02). Trainee involvement did not result in increased fluoroscopy time or patient dose. BMI did not affect fluoroscopy time; however, overweight and obese patients received significantly higher Ka,r and KAP. Male patients received a higher KAP in all BMI categories. Limiting fluoroscopy time and good collimation practices should be reinforced in these patients. Copyright © 2015 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Utility of an Abbreviated Dizziness Questionnaire to Differentiate Between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study.

PubMed

Roland, Lauren T; Kallogjeri, Dorina; Sinks, Belinda C; Rauch, Steven D; Shepard, Neil T; White, Judith A; Goebel, Joel A

2015-12-01

Test performance of a focused dizziness questionnaire's ability to discriminate between peripheral and nonperipheral causes of vertigo. Prospective multicenter. Four academic centers with experienced balance specialists. New dizzy patients. A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and nonperipheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. In total, 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and nonperipheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central, and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central, and other causes was considered good as measured by c-indices of 0.75, 0.7, and 0.78, respectively. This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from nonperipheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed.

Stressful Life Events and Predictors of Post-traumatic Growth among High-Risk Early Emerging Adults.

PubMed

Arpawong, Thalida E; Rohrbach, Louise A; Milam, Joel E; Unger, Jennifer B; Land, Helen; Sun, Ping; Spruijt-Metz, Donna; Sussman, Steve

2016-01-01

Stressful life events (SLEs) may elicit positive psychosocial change among youth, referred to as Post-traumatic Growth (PTG). We assessed types of SLEs experienced, degree to which participants reported PTG, and variables predicting PTG across 24 months among a sample of high risk, ethnically diverse early emerging adults. Participants were recruited from alternative high schools ( n = 564; mean age=16.8; 65% Hispanic). Multi-level regression models were constructed to examine the impact of environmental (SLE quantity, severity) and personal factors (hedonic ability, perceived stress, developmental stage, future time orientation) on a composite score of PTG. The majority of participants reported positive changes resulted from their most life-altering SLE of the past two years. Predictors of PTG included fewer SLEs, less general stress, having a future time perspective, and greater identification with the developmental stage of Emerging Adulthood. Findings suggest intervention targets to foster positive adaptation among early emerging adults who experience frequent SLEs.

International Nursing: Research on the Correlation Between Empathy and China's Big Five Personality Theory: Implications for Nursing Leaders.

PubMed

Wang, Cong; Wu, Qin; Feng, Mei; Wan, Qunfang; Wu, Xiaoling

To investigate the characteristics of nurses' empathy and explore the correlation between nurses' empathy and personality, a cross-sectional study with 250 nurses from a general hospital in China was conducted using the Chinese Big Five Personality Inventory (CBF-PI) and the Jefferson Scale of Empathy-Health Professionals (JSE-HP). The total score of the JSE-HP was 110.60 (SD = 11.71). Employment forms and child-rearing situations were the significant predictors of the JSE-HP score. Multiple hierarchical regression analysis indicated that the JSE-HP score was positively correlated with conscientiousness and agreeableness and the contribution of CBF-PI to JSE-HP scale variances was 15.1%. The results demonstrated that nurses' empathy is on the high level. The Big Five Personality model is a significant predictor of nurses' empathy. The findings of the study provide reference for nurses' humanistic care training and education. In addition, training programs emphasizing emotions, psychology, humanistic quality, and healthy personality should be strengthened to promote nurses' empathy.

The effects of consultation on over-the-counter medication purchasing decisions.

PubMed

Nichol, M B; McCombs, J S; Johnson, K A; Spacapan, S; Sclar, D A

1992-11-01

This article examines factors that predict changes in consumer purchasing decisions of nonprescription medications. Variables corresponding to factors in Andersen's behavioral model are measured, in addition to data regarding characteristics of the 17 pharmacy consultants who provided counseling services. One thousand seven hundred and thirteen consumers in five stores in southern California were provided consultation during a 6-month period, resulting in 25.4% of the patients purchasing a different drug than intended when entering the pharmacy, 1.3% being referred to a physician, and 13.4% not purchasing any over-the-counter medication at all. Logistic regression techniques demonstrated that one enabling variable (availability of generic medications), and four need factors (the discussion of clinical issues, short encounters, cough and cold products, and vitamin products) were significant predictors of the consumer's decision to purchase a different product than intended. Consultant characteristics (introversion, external locus of control) were also important predictors, but opposite the expected direction. Consumers who received information from female consultants were more likely to change their purchasing decisions.

Effects of occupational role conflict and emotional demands on subsequent psychological distress: a 3-year follow-up study of the general working population in Norway.

PubMed

Johannessen, Håkon A; Tynes, Tore; Sterud, Tom

2013-06-01

To examine the impact of occupational role conflict and emotional demands on subsequent psychological distress. A randomly drawn cohort from the general Norwegian working-age population was followed up for 3 years (n = 12,550; response rate = 67%). Eligible respondents were in paid work during the reference week in 2006 and 2009 or temporarily absent from such work (n = 6,745; response rate = 68%). In the fully adjusted model, both high role conflict (odds ratios = 1.53; 95% CI = 1.15 to 2.03) and high emotional demands (odds ratios = 1.38; 95% CI = 1.13 to 1.69) were significant predictors of psychological distress. Additional significant predictors were low job control, bullying/harassment, and job insecurity (P < 0.05). Considering all of the evaluated work-related factors, role conflict and emotional demands contributed the most to the population risk of developing psychological distress.

A SIGNIFICANCE TEST FOR THE LASSO1

PubMed Central

Lockhart, Richard; Taylor, Jonathan; Tibshirani, Ryan J.; Tibshirani, Robert

2014-01-01

In the sparse linear regression setting, we consider testing the significance of the predictor variable that enters the current lasso model, in the sequence of models visited along the lasso solution path. We propose a simple test statistic based on lasso fitted values, called the covariance test statistic, and show that when the true model is linear, this statistic has an Exp(1) asymptotic distribution under the null hypothesis (the null being that all truly active variables are contained in the current lasso model). Our proof of this result for the special case of the first predictor to enter the model (i.e., testing for a single significant predictor variable against the global null) requires only weak assumptions on the predictor matrix X. On the other hand, our proof for a general step in the lasso path places further technical assumptions on X and the generative model, but still allows for the important high-dimensional case p > n, and does not necessarily require that the current lasso model achieves perfect recovery of the truly active variables. Of course, for testing the significance of an additional variable between two nested linear models, one typically uses the chi-squared test, comparing the drop in residual sum of squares (RSS) to a χ12 distribution. But when this additional variable is not fixed, and has been chosen adaptively or greedily, this test is no longer appropriate: adaptivity makes the drop in RSS stochastically much larger than χ12 under the null hypothesis. Our analysis explicitly accounts for adaptivity, as it must, since the lasso builds an adaptive sequence of linear models as the tuning parameter λ decreases. In this analysis, shrinkage plays a key role: though additional variables are chosen adaptively, the coefficients of lasso active variables are shrunken due to the l1 penalty. Therefore, the test statistic (which is based on lasso fitted values) is in a sense balanced by these two opposing properties—adaptivity and shrinkage—and its null distribution is tractable and asymptotically Exp(1). PMID:25574062

Topography and vegetation as predictors of snow water equivalent across the alpine treeline ecotone at Lee Ridge, Glacier National Park, Montana, U.S.A.

USGS Publications Warehouse

Geddes, C.A.; Brown, D.G.; Fagre, D.B.

2005-01-01

We derived and implemented two spatial models of May snow water equivalent (SWE) at Lee Ridge in Glacier National Park, Montana. We used the models to test the hypothesis that vegetation structure is a control on snow redistribution at the alpine treeline ecotone (ATE). The statistical models were derived using stepwise and "best" subsets regression techniques. The first model was derived from field measurements of SWE, topography, and vegetation taken at 27 sample points. The second model was derived using GIS-based measures of topography and vegetation. Both the field- (R² = 0.93) and GIS-based models (R² = 0.69) of May SWE included the following variables: site type (based on vegetation), elevation, maximum slope, and general slope aspect. Site type was identified as the most important predictor of SWE in both models, accounting for 74.0% and 29.5% of the variation, respectively. The GIS-based model was applied to create a predictive map of SWE across Lee Ridge, predicting little snow accumulation on the top of the ridge where vegetation is scarce. The GIS model failed in large depressions, including ephemeral stream channels. The models supported the hypothesis that upright vegetation has a positive effect on accumulation of SWE above and beyond the effects of topography. Vegetation, therefore, creates a positive feedback in which it modifies its, environment and could affect the ability of additional vegetation to become established.

Persistence of Sleep Problems in Children with Anxiety and Attention Deficit Hyperactivity Disorders

ERIC Educational Resources Information Center

Hansen, Berit Hjelde; Skirbekk, Benedicte; Oerbeck, Beate; Wentzel-Larsen, Tore; Kristensen, Hanne

2013-01-01

This study examines the persistence of sleep problems over 18 months in 76 referred children with anxiety disorders and/or attention deficit hyperactivity disorders (ADHD) and 31 nonreferred controls, and explores predictors of sleep problems at follow-up (T2) in the referred children. Diagnoses were assessed at initial assessment (T1) using the…

Predicting exercise adherence in cancer patients and survivors: a systematic review and meta-analysis of motivational and behavioural factors.

PubMed

Husebø, Anne M Lunde; Dyrstad, Sindre M; Søreide, Jon A; Bru, Edvin

2013-01-01

To examine research findings regarding predictors of adherence to exercise programmes in cancer populations. Cancer patients are advised to participate in daily exercise. Whether they comply with the recommendations for physical activity or not remains unclear. A systematic review and meta-analysis. Empirical articles published in English between 1995 and 2011 were searched in electronic databases and in reference lists, using the search terms 'adherence', 'predictors', 'exercise', and 'cancer' in varying combinations. Twelve of 541 screened abstracts met the inclusion criteria. The included studies' eligibility considering predictors of exercise adherence were reviewed. A quality assessment process evaluating the studies methodological quality was performed. Eight of the reviewed studies were considered eligible for a meta-analysis involving Pearson's r correlations. Exercise stage of change, derived from the transtheoretical model of behaviour change (TTM) was found to be statistically significant and a strong predictor of exercise adherence. In addition, the theory of planned behaviour (TPB) construct; intention to engage in a health-changing behaviour and perceived behavioural control, demonstrated significant correlations with exercise adherence. The review identified that both the TPB and the TTM frameworks include aspects that predicts exercise adherence in cancer patients, and thus contributes to the understanding of motivational factors of change in exercise behaviour in cancer populations. However, the strengths of predictions were relatively weak. More research is needed to identify predictors of greater importance. Surveying the patients' readiness and intention to initiate and maintain exercise levels, as well as tailoring exercise programmes to individual needs may be important for nurses in order to help patients meet exercise guidelines and stay active. © 2012 Blackwell Publishing Ltd.

Systematic analysis of ECG predictors of sinus rhythm maintenance after electrical cardioversion for persistent atrial fibrillation.

PubMed

Lankveld, Theo; de Vos, Cees B; Limantoro, Ione; Zeemering, Stef; Dudink, Elton; Crijns, Harry J; Schotten, Ulrich

2016-05-01

Electrical cardioversion (ECV) is one of the rhythm control strategies in patients with persistent atrial fibrillation (AF). Unfortunately, recurrences of AF are common after ECV, which significantly limits the practical benefit of this treatment in patients with AF. The objectives of this study were to identify noninvasive complexity or frequency parameters obtained from the surface electrocardiogram (ECG) to predict sinus rhythm (SR) maintenance after ECV and to compare these ECG parameters with clinical predictors. We studied a wide variety of ECG-derived time- and frequency-domain AF complexity parameters in a prospective cohort of 502 patients with persistent AF referred for ECV. During 1-year follow-up, 161 patients (32%) maintained SR. The best clinical predictor of SR maintenance was antiarrhythmic drug (AAD) treatment. A model including clinical parameters predicted SR maintenance with a mean cross-validated area under the receiver operating characteristic curve (AUC) of 0.62 ± 0.05. The best single ECG parameter was the dominant frequency (DF) on lead V6. Combining several ECG parameters predicted SR maintenance with a mean AUC of 0.64 ± 0.06. Combining clinical and ECG parameters improved prediction to a mean AUC of 0.67 ± 0.05. Although the DF was affected by AAD treatment, excluding patients taking AADs did not significantly lower the predictive performance captured by the ECG. ECG-derived parameters predict SR maintenance during 1-year follow-up after ECV at least as good as known clinical predictors of rhythm outcome. The DF proved to be the most powerful ECG-derived predictor. Copyright © 2016 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation.

PubMed

Nikzad-Langerodi, Ramin; Lughofer, Edwin; Cernuda, Carlos; Reischer, Thomas; Kantner, Wolfgang; Pawliczek, Marcin; Brandstetter, Markus

2018-07-12

The physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. On-line supervision of the turbidity point by means of vibrational spectroscopy has recently emerged as a promising technique to monitor the DP of MF resins. However, spectroscopic determination of the DP relies on chemometric models, which are usually sensitive to drifts caused by instrumental and/or sample-associated changes occurring over time. In order to detect the time point when drifts start causing prediction bias, we here explore a universal drift detector based on a faded version of the Page-Hinkley (PH) statistic, which we test in three data streams from an industrial MF resin production process. We employ committee disagreement (CD), computed as the variance of model predictions from an ensemble of partial least squares (PLS) models, as a measure for sample-wise prediction uncertainty and use the PH statistic to detect changes in this quantity. We further explore supervised and unsupervised strategies for (semi-)automatic model adaptation upon detection of a drift. For the former, manual reference measurements are requested whenever statistical thresholds on Hotelling's T 2 and/or Q-Residuals are violated. Models are subsequently re-calibrated using weighted partial least squares in order to increase the influence of newer samples, which increases the flexibility when adapting to new (drifted) states. Unsupervised model adaptation is carried out exploiting the dual antecedent-consequent structure of a recently developed fuzzy systems variant of PLS termed FLEXFIS-PLS. In particular, antecedent parts are updated while maintaining the internal structure of the local linear predictors (i.e. the consequents). We found improved drift detection capability of the CD compared to Hotelling's T 2 and Q-Residuals when used in combination with the proposed PH test. Furthermore, we found that active selection of samples by active learning (AL) used for subsequent model adaptation is advantageous compared to passive (random) selection in case that a drift leads to persistent prediction bias allowing more rapid adaptation at lower reference measurement rates. Fully unsupervised adaptation using FLEXFIS-PLS could improve predictive accuracy significantly for light drifts but was not able to fully compensate for prediction bias in case of significant lack of fit w.r.t. the latent variable space. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimization of pyDock for the new CAPRI challenges: Docking of homology-based models, domain-domain assembly and protein-RNA binding.

PubMed

Pons, Carles; Solernou, Albert; Perez-Cano, Laura; Grosdidier, Solène; Fernandez-Recio, Juan

2010-11-15

We describe here our results in the last CAPRI edition. We have participated in all targets, both as predictors and as scorers, using our pyDock docking methodology. The new challenges (homology-based modeling of the interacting subunits, domain-domain assembling, and protein-RNA interactions) have pushed our computer tools to the limits and have encouraged us to devise new docking approaches. Overall, the results have been quite successful, in line with previous editions, especially considering the high difficulty of some of the targets. Our docking approaches succeeded in five targets as predictors or as scorers (T29, T34, T35, T41, and T42). Moreover, with the inclusion of available information on the residues expected to be involved in the interaction, our protocol would have also succeeded in two additional cases (T32 and T40). In the remaining targets (except T37), results were equally poor for most of the groups. We submitted the best model (in ligand RMSD) among scorers for the unbound-bound target T29, the second best model among scorers for the protein-RNA target T34, and the only correct model among predictors for the domain assembly target T35. In summary, our excellent results for the new proposed challenges in this CAPRI edition showed the limitations and applicability of our approaches and encouraged us to continue developing methodologies for automated biomolecular docking. © 2010 Wiley-Liss, Inc.

Modeling of Thermospheric Neutral Density Variations in Response to Geomagnetic Forcing using GRACE Accelerometer Data

NASA Astrophysics Data System (ADS)

Calabia, A.; Matsuo, T.; Jin, S.

2017-12-01

The upper atmospheric expansion refers to an increase in the temperature and density of Earth's thermosphere due to increased geomagnetic and space weather activities, producing anomalous atmospheric drag on LEO spacecraft. Increased drag decelerates satellites, moving their orbit closer to Earth, decreasing the lifespan of satellites, and making satellite orbit determination difficult. In this study, thermospheric neutral density variations due to geomagnetic forcing are investigated from 10 years (2003-2013) of GRACE's accelerometer-based estimates. In order to isolate the variations produced by geomagnetic forcing, 99.8% of the total variability has been modeled and removed through the parameterization of annual, LST, and solar-flux variations included in the primary Empirical Orthogonal Functions. The residual disturbances of neutral density variations have been investigated further in order to unravel their relationship to several geomagnetic indices and space weather activity indicators. Stronger fluctuations have been found in the southern polar cap, following the dipole-tilt angle variations. While the parameterization of the residual disturbances in terms of Dst index results in the best fit to training data, the use of merging electric field as a predictor leads to the best forecasting performance. An important finding is that modeling of neutral density variations in response geomagnetic forcing can be improved by accounting for the latitude-dependent delay. Our data-driven modeling results are further compared to modeling with TIEGCM.

Requirements for data integration platforms in biomedical research networks: a reference model.

PubMed

Ganzinger, Matthias; Knaup, Petra

2015-01-01

Biomedical research networks need to integrate research data among their members and with external partners. To support such data sharing activities, an adequate information technology infrastructure is necessary. To facilitate the establishment of such an infrastructure, we developed a reference model for the requirements. The reference model consists of five reference goals and 15 reference requirements. Using the Unified Modeling Language, the goals and requirements are set into relation to each other. In addition, all goals and requirements are described textually in tables. This reference model can be used by research networks as a basis for a resource efficient acquisition of their project specific requirements. Furthermore, a concrete instance of the reference model is described for a research network on liver cancer. The reference model is transferred into a requirements model of the specific network. Based on this concrete requirements model, a service-oriented information technology architecture is derived and also described in this paper.

Predictors of placement for children who initially remained in their homes after an investigation for abuse or neglect

PubMed Central

Horwitz, Sarah McCue; Hurlburt, Michael S.; Cohen, Steven D.; Zhang, Jinjin; Landsverk, John

2011-01-01

Objective To examine the frequency and predictors of out-of-home placement in a 30 month follow-up for a nationally representative sample of children investigated for a report of maltreatment who remained in their homes following the index child welfare report. Methods Data came from the National Survey of Child and Adolescent Well-being (NSCAW), a 3-year longitudinal study of 5,501 youth 0-14 years old referred to child welfare agencies for potential maltreatment between 10/1999 and 12/2000. These analyses focused on the children who had not been placed out-of-home at the baseline interview and examined child, family and case characteristics as predictors of subsequent out-of-home placement. Weighted logistic regression models were used to determine which baseline characteristics were related to out-of-home placement in the follow-up. Results For the total study sample, predictors of placement in the 30 month follow-up period included elevated Conflict Tactics Scale scores, prior history of child welfare involvement, high family risk scores and caseworkers’ assessment of likelihood of re-report without receipt of services. Higher family income was protective. For children without any prior child welfare history (incident cases), younger children, low family income and a high family risk score were strongly related to subsequent placement but receipt of services and case workers’ assessments were not. Conclusions/Practice implications Family risk variables are strongly related to out-of-home placement in a 30 month follow-up, but receipt of child welfare services is not related to further placements. Considering family risk factors and income, 25% of the children who lived in poor families, with high family risk scores, were subsequently placed out-of-home, even among children in families who received child welfare services. Given that relevant evidence-based interventions are available for these families, more widespread tests of their use should be explored to understand whether their use could make a substantial difference in the lives of vulnerable children. PMID:21489626

The nature of violence: a multilevel analysis of gun use and victim injury in violent interpersonal encounters.

PubMed

Burgason, Kyle A; Thomas, Shaun A; Berthelot, Emily R

2014-02-01

A large number of studies have examined predictors of crime quantities yet considerably less attention has been directed toward exploring patterns in the nature or quality of violence within and across communities. The current study adds to the literature on qualitative variations in violence by assessing the incident and contextual-level predictors of offender gun use and physical injuries sustained by victims of robbery and aggravated assault. Specifically, we examine incident-level data from the National Incident Based Reporting System in conjunction with contextual-level data on the cities in which the incidents occurred. We use hierarchical linear and nonlinear modeling techniques to explore variations in predictors of offender gun use and extent of victim injury. Supporting cultural effects explicated by Anderson, results reveal certain individual-level predictors are conditioned by community characteristics.

Predicting Ideological Prejudice

PubMed Central

Brandt, Mark J.

2017-01-01

A major shortcoming of current models of ideological prejudice is that although they can anticipate the direction of the association between participants’ ideology and their prejudice against a range of target groups, they cannot predict the size of this association. I developed and tested models that can make specific size predictions for this association. A quantitative model that used the perceived ideology of the target group as the primary predictor of the ideology-prejudice relationship was developed with a representative sample of Americans (N = 4,940) and tested against models using the perceived status of and choice to belong to the target group as predictors. In four studies (total N = 2,093), ideology-prejudice associations were estimated, and these observed estimates were compared with the models’ predictions. The model that was based only on perceived ideology was the most parsimonious with the smallest errors. PMID:28394693

Recognition of predictors for mid-long term runoff prediction based on lasso

NASA Astrophysics Data System (ADS)

Xie, S.; Huang, Y.

2017-12-01

Reliable and accuracy mid-long term runoff prediction is of great importance in integrated management of reservoir. And many methods are proposed to model runoff time series. Almost all forecast lead times (LT) of these models are 1 month, and the predictors are previous runoff with different time lags. However, runoff prediction with increased LT, which is more beneficial, is not popular in current researches. It is because the connection between previous runoff and current runoff will be weakened with the increase of LT. So 74 atmospheric circulation factors (ACFs) together with pre-runoff are used as alternative predictors for mid-long term runoff prediction of Longyangxia reservoir in this study. Because pre-runoff and 74 ACFs with different time lags are so many and most of these factors are useless, lasso, which means `least absolutely shrinkage and selection operator', is used to recognize predictors. And the result demonstrates that 74 ACFs are beneficial for runoff prediction in both validation and test sets when LT is greater than 6. And there are 6 factors other than pre-runoff, most of which are with big time lag, are selected as predictors frequently. In order to verify the effect of 74 ACFs, 74 stochastic time series generated from normalized 74 ACFs are used as input of model. The result shows that these 74 stochastic time series are useless, which confirm the effect of 74 ACFs on mid-long term runoff prediction.

Predictors of Playing Augmented Reality Mobile Games While Walking Based on the Theory of Planned Behavior: Web-Based Survey.

PubMed

Koh, Hyeseung Elizabeth; Oh, Jeeyun; Mackert, Michael

2017-12-11

There has been a sharp increase in the number of pedestrians injured while using a mobile phone, but little research has been conducted to explain how and why people use mobile devices while walking. Therefore, we conducted a survey study to explicate the motivations of mobile phone use while walking. The purpose of this study was to identify the critical predictors of behavioral intention to play a popular mobile game, Pokemon Go, while walking, based on the theory of planned behavior (TPB). In addition to the three components of TPB, automaticity, immersion, and enjoyment were added to the model. This study is a theory-based investigation that explores the underlying mechanisms of mobile phone use while walking focusing on a mobile game behavior. Participants were recruited from a university (study 1; N=262) and Amazon Mechanical Turk (MTurk) (study 2; N=197) in the United States. Participants completed a Web-based questionnaire, which included measures of attitude, subjective norms, perceived behavioral control (PBC), automaticity, immersion, and enjoyment. Participants also answered questions regarding demographic items. Hierarchical regression analyses were conducted to examine hypotheses. The model we tested explained about 41% (study 1) and 63% (study 2) of people's intention to play Pokemon Go while walking. The following 3 TPB variables were significant predictors of intention to play Pokemon Go while walking in study 1 and study 2: attitude (P<.001), subjective norms (P<.001), and PBC (P=.007 in study 1; P<.001 in study 2). Automaticity tendency (P<.001), immersion (P=.02), and enjoyment (P=.04) were significant predictors in study 1, whereas enjoyment was the only significant predictor in study 2 (P=.01). Findings from this study demonstrated the utility of TPB in predicting a new behavioral domain-mobile use while walking. To sum up, younger users who are habitual, impulsive, and less immersed players are more likely to intend to play a mobile game while walking. ©Hyeseung Elizabeth Koh, Jeeyun Oh, Michael Mackert. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 11.12.2017.

Predictors of Playing Augmented Reality Mobile Games While Walking Based on the Theory of Planned Behavior: Web-Based Survey

PubMed Central

Oh, Jeeyun; Mackert, Michael

2017-01-01

Background There has been a sharp increase in the number of pedestrians injured while using a mobile phone, but little research has been conducted to explain how and why people use mobile devices while walking. Therefore, we conducted a survey study to explicate the motivations of mobile phone use while walking Objective The purpose of this study was to identify the critical predictors of behavioral intention to play a popular mobile game, Pokemon Go, while walking, based on the theory of planned behavior (TPB). In addition to the three components of TPB, automaticity, immersion, and enjoyment were added to the model. This study is a theory-based investigation that explores the underlying mechanisms of mobile phone use while walking focusing on a mobile game behavior. Methods Participants were recruited from a university (study 1; N=262) and Amazon Mechanical Turk (MTurk) (study 2; N=197) in the United States. Participants completed a Web-based questionnaire, which included measures of attitude, subjective norms, perceived behavioral control (PBC), automaticity, immersion, and enjoyment. Participants also answered questions regarding demographic items. Results Hierarchical regression analyses were conducted to examine hypotheses. The model we tested explained about 41% (study 1) and 63% (study 2) of people’s intention to play Pokemon Go while walking. The following 3 TPB variables were significant predictors of intention to play Pokemon Go while walking in study 1 and study 2: attitude (P<.001), subjective norms (P<.001), and PBC (P=.007 in study 1; P<.001 in study 2). Automaticity tendency (P<.001), immersion (P=.02), and enjoyment (P=.04) were significant predictors in study 1, whereas enjoyment was the only significant predictor in study 2 (P=.01). Conclusions Findings from this study demonstrated the utility of TPB in predicting a new behavioral domain—mobile use while walking. To sum up, younger users who are habitual, impulsive, and less immersed players are more likely to intend to play a mobile game while walking. PMID:29229586

A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales

NASA Astrophysics Data System (ADS)

Ghosh, Aniruddha; Fassnacht, Fabian Ewald; Joshi, P. K.; Koch, Barbara

2014-02-01

Knowledge of tree species distribution is important worldwide for sustainable forest management and resource evaluation. The accuracy and information content of species maps produced using remote sensing images vary with scale, sensor (optical, microwave, LiDAR), classification algorithm, verification design and natural conditions like tree age, forest structure and density. Imaging spectroscopy reduces the inaccuracies making use of the detailed spectral response. However, the scale effect still has a strong influence and cannot be neglected. This study aims to bridge the knowledge gap in understanding the scale effect in imaging spectroscopy when moving from 4 to 30 m pixel size for tree species mapping, keeping in mind that most current and future hyperspectral satellite based sensors work with spatial resolution around 30 m or more. Two airborne (HyMAP) and one spaceborne (Hyperion) imaging spectroscopy dataset with pixel sizes of 4, 8 and 30 m, respectively were available to examine the effect of scale over a central European forest. The forest under examination is a typical managed forest with relatively homogenous stands featuring mostly two canopy layers. Normalized digital surface model (nDSM) derived from LiDAR data was used additionally to examine the effect of height information in tree species mapping. Six different sets of predictor variables (reflectance value of all bands, selected components of a Minimum Noise Fraction (MNF), Vegetation Indices (VI) and each of these sets combined with LiDAR derived height) were explored at each scale. Supervised kernel based (Support Vector Machines) and ensemble based (Random Forest) machine learning algorithms were applied on the dataset to investigate the effect of the classifier. Iterative bootstrap-validation with 100 iterations was performed for classification model building and testing for all the trials. For scale, analysis of overall classification accuracy and kappa values indicated that 8 m spatial resolution (reaching kappa values of over 0.83) slightly outperformed the results obtained from 4 m for the study area and five tree species under examination. The 30 m resolution Hyperion image produced sound results (kappa values of over 0.70), which in some areas of the test site were comparable with the higher spatial resolution imagery when qualitatively assessing the map outputs. Considering input predictor sets, MNF bands performed best at 4 and 8 m resolution. Optical bands were found to be best for 30 m spatial resolution. Classification with MNF as input predictors produced better visual appearance of tree species patches when compared with reference maps. Based on the analysis, it was concluded that there is no significant effect of height information on tree species classification accuracies for the present framework and study area. Furthermore, in the examined cases there was no single best choice among the two classifiers across scales and predictors. It can be concluded that tree species mapping from imaging spectroscopy for forest sites comparable to the one under investigation is possible with reliable accuracies not only from airborne but also from spaceborne imaging spectroscopy datasets.

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations

USGS Publications Warehouse

Gerber, Brian D.; Kendall, William L.; Hooten, Mevin B.; Dubovsky, James A.; Drewien, Roderick C.

2015-01-01

Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment.Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression.Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring–summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect.Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond.Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.

Beyond the usual suspects: target group- and behavior-specific factors add to a theory-based sun protection intervention for teenagers.

PubMed

Schüz, Natalie; Eid, Michael

2013-10-01

Sun protection standards among teenagers are low while sun exposure peaks in this age group. Study 1 explores predictors of adolescent protection intentions and exposure behavior. Study 2 tests the effectiveness of an intervention based on these predictors. Study 1(cross-sectional, N = 207, ages 15-18) and Study 2 (RCT, N = 253, ages 13-19) were conducted in schools. Path models were used to analyze data. Self-efficacy (β = .26, p < .001) and time perspective (β = .17, p = .014) were the strongest predictors of intentions; appearance motivation (β = .54, p < .001) and intention (β = -.18, p = .015) predicted behavior. The intervention effected changes in all predictors except self-efficacy. Changes in outcome expectancies (β = .19, p < .001) and time perspective (β = .09, p = .039) predicted changes in intention, while changes in intention (β = -.17, p = .002) and appearance motivation (β = .29, p < .001) predicted behavior changes. Target group- and behavior-specific intervention components are as important for changes in intentions and behavior as components derived from common health behavior theories.

Predicting chroma from luma with frequency domain intra prediction

NASA Astrophysics Data System (ADS)

Egge, Nathan E.; Valin, Jean-Marc

2015-03-01

This paper describes a technique for performing intra prediction of the chroma planes based on the reconstructed luma plane in the frequency domain. This prediction exploits the fact that while RGB to YUV color conversion has the property that it decorrelates the color planes globally across an image, there is still some correlation locally at the block level.1 Previous proposals compute a linear model of the spatial relationship between the luma plane (Y) and the two chroma planes (U and V).2 In codecs that use lapped transforms this is not possible since transform support extends across the block boundaries3 and thus neighboring blocks are unavailable during intra- prediction. We design a frequency domain intra predictor for chroma that exploits the same local correlation with lower complexity than the spatial predictor and which works with lapped transforms. We then describe a low- complexity algorithm that directly uses luma coefficients as a chroma predictor based on gain-shape quantization and band partitioning. An experiment is performed that compares these two techniques inside the experimental Daala video codec and shows the lower complexity algorithm to be a better chroma predictor.

Using the Random Nearest Neighbor Data Mining Method to Extract Maximum Information Content from Weather Forecasts from Multiple Predictors of Weather and One Predictand (Low-Level Turbulence)

DTIC Science & Technology

2014-10-30

Force Weather Agency (AFWA) WRF 15-km atmospheric model forecast data and low-level turbulence. Archives of historical model data forecast predictors...Relationships between WRF model predictors and PIREPS were developed using the new data mining methodology. The new methodology was inspired...convection. Predictors of turbulence were collected from the AFWA WRF 15km model, and corresponding PIREPS (the predictand) were collected between 2013

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression

NASA Astrophysics Data System (ADS)

de Oliveira, Isadora R. N.; Roque, Jussara V.; Maia, Mariza P.; Stringheta, Paulo C.; Teófilo, Reinaldo F.

2018-04-01

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.

2012-01-01

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

Predictive Modeling of Response to Pregabalin for the Treatment of Neuropathic Pain Using 6-Week Observational Data: A Spectrum of Modern Analytics Applications.

PubMed

Emir, Birol; Johnson, Kjell; Kuhn, Max; Parsons, Bruce

2017-01-01

This post hoc analysis used 11 predictive models of data from a large observational study in Germany to evaluate potential predictors of achieving at least 50% pain reduction by week 6 after treatment initiation (50% pain response) with pregabalin (150-600 mg/d) in patients with neuropathic pain (NeP). The potential predictors evaluated included baseline demographic and clinical characteristics, such as patient-reported pain severity (0 [no pain] to 10 [worst possible pain]) and pain-related sleep disturbance scores (0 [sleep not impaired] to 10 [severely impaired sleep]) that were collected during clinic visits (baseline and weeks 1, 3, and 6). Baseline characteristics were also evaluated combined with pain change at week 1 or weeks 1 and 3 as potential predictors of end-of-treatment 50% pain response. The 11 predictive models were linear, nonlinear, and tree based, and all predictors in the training dataset were ranked according to their variable importance and normalized to 100%. The training dataset comprised 9187 patients, and the testing dataset had 6114 patients. To adjust for the high imbalance in the responder distribution (75% of patients were 50% responders), which can skew the parameter tuning process, the training set was balanced into sets of 1000 responders and 1000 nonresponders. The predictive modeling approaches that were used produced consistent results. Baseline characteristics alone had fair predictive value (accuracy range, 0.61-0.72; κ range, 0.17-0.30). Baseline predictors combined with pain change at week 1 had moderate predictive value (accuracy, 0.73-0.81; κ range, 0.37-0.49). Baseline predictors with pain change at weeks 1 and 3 had substantial predictive value (accuracy, 0.83-0.89; κ range, 0.54-0.71). When variable importance across the models was estimated, the best predictor of 50% responder status was pain change at week 3 (average importance 100.0%), followed by pain change at week 1 (48.1%), baseline pain score (14.1%), baseline depression (13.9%), and using pregabalin as a monotherapy (11.7%). The finding that pain changes by week 1 or weeks 1 and 3 are the best predictors of pregabalin response at 6 weeks suggests that adhering to a pregabalin medication regimen is important for an optimal end-of-treatment outcome. Regarding baseline predictors alone, considerable published evidence supports the importance of high baseline pain score and presence of depression as factors that can affect treatment response. Future research would be required to elucidate why using pregabalin as a monotherapy also had more than a 10% variable importance as a potential predictor. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Model reference adaptive control (MRAC)-based parameter identification applied to surface-mounted permanent magnet synchronous motor

NASA Astrophysics Data System (ADS)

Zhong, Chongquan; Lin, Yaoyao

2017-11-01

In this work, a model reference adaptive control-based estimated algorithm is proposed for online multi-parameter identification of surface-mounted permanent magnet synchronous machines. By taking the dq-axis equations of a practical motor as the reference model and the dq-axis estimation equations as the adjustable model, a standard model-reference-adaptive-system-based estimator was established. Additionally, the Popov hyperstability principle was used in the design of the adaptive law to guarantee accurate convergence. In order to reduce the oscillation of identification result, this work introduces a first-order low-pass digital filter to improve precision regarding the parameter estimation. The proposed scheme was then applied to an SPM synchronous motor control system without any additional circuits and implemented using a DSP TMS320LF2812. For analysis, the experimental results reveal the effectiveness of the proposed method.

Determining Predictor Importance in Hierarchical Linear Models Using Dominance Analysis

ERIC Educational Resources Information Center

Luo, Wen; Azen, Razia

2013-01-01

Dominance analysis (DA) is a method used to evaluate the relative importance of predictors that was originally proposed for linear regression models. This article proposes an extension of DA that allows researchers to determine the relative importance of predictors in hierarchical linear models (HLM). Commonly used measures of model adequacy in…

Clinical Risk Stratification for Primary Prevention Implantable Cardioverter Defibrillators

PubMed Central

Hardy, Judy; Yee, Raymond; Healey, Jeffrey S.; Birnie, David; Simpson, Christopher S.; Crystal, Eugene; Mangat, Iqwal; Nanthakumar, Kumaraswamy; Wang, Xuesong; Krahn, Andrew D.; Dorian, Paul; Austin, Peter C.; Tu, Jack V.

2015-01-01

Background— A conceptualized model may be useful for understanding risk stratification of primary prevention implantable cardioverter defibrillators considering the competing risks of appropriate implantable cardioverter defibrillator shock versus mortality. Methods and Results— In a prospective, multicenter, population-based cohort with left ventricular ejection fraction ≤35% referred for primary prevention implantable cardioverter defibrillator, we developed dual risk stratification models to determine the competing risks of appropriate defibrillator shock versus mortality using a Fine-Gray subdistribution hazard model. Among 7020 patients referred, 3445 underwent defibrillator implant (79.7% men, median, 66 years [25th, 75th: 58–73]). During 5918 person-years of follow-up, appropriate shock occurred in 204 patients (3.6 shocks/100 person-years) and 292 died (4.9 deaths/100 person-years). Competing risk predictors of appropriate shock included nonsustained ventricular tachycardia, atrial fibrillation, serum creatinine concentration, digoxin or amiodarone use, and QRS duration near 130-ms peak. One-year cumulative incidence of appropriate shock was 0.9% in the lowest risk category, and 1.7%, 2.5%, 4.9%, and 9.3% in low, intermediate, high, and highest risk groups, respectively. Hazard ratios for appropriate shock ranged from 4.04 to 7.79 in the highest 3 deciles (all P≤0.001 versus lowest risk). Cumulative incidence of 1-year death was 0.6%, 1.9%, 3.3%, 6.2%, and 17.7% in lowest, low, intermediate, high, and highest risk groups, respectively. Mortality hazard ratios ranged from 11.48 to 36.22 in the highest 3 deciles (all P<0.001 versus lowest risk). Conclusions— Simultaneous estimation of risks of appropriate shock and mortality can be performed using clinical variables, providing a potential framework for identification of patients who are unlikely to benefit from prophylactic implantable cardioverter defibrillator. PMID:26224792

The BREAST-V: a unifying predictive formula for volume assessment in small, medium, and large breasts.

PubMed

Longo, Benedetto; Farcomeni, Alessio; Ferri, Germano; Campanale, Antonella; Sorotos, Micheal; Santanelli, Fabio

2013-07-01

Breast volume assessment enhances preoperative planning of both aesthetic and reconstructive procedures, helping the surgeon in the decision-making process of shaping the breast. Numerous methods of breast size determination are currently reported but are limited by methodologic flaws and variable estimations. The authors aimed to develop a unifying predictive formula for volume assessment in small to large breasts based on anthropomorphic values. Ten anthropomorphic breast measurements and direct volumes of 108 mastectomy specimens from 88 women were collected prospectively. The authors performed a multivariate regression to build the optimal model for development of the predictive formula. The final model was then internally validated. A previously published formula was used as a reference. Mean (±SD) breast weight was 527.9 ± 227.6 g (range, 150 to 1250 g). After model selection, sternal notch-to-nipple, inframammary fold-to-nipple, and inframammary fold-to-fold projection distances emerged as the most important predictors. The resulting formula (the BREAST-V) showed an adjusted R of 0.73. The estimated expected absolute error on new breasts is 89.7 g (95 percent CI, 62.4 to 119.1 g) and the expected relative error is 18.4 percent (95 percent CI, 12.9 to 24.3 percent). Application of reference formula on the sample yielded worse predictions than those derived by the formula, showing an R of 0.55. The BREAST-V is a reliable tool for predicting small to large breast volumes accurately for use as a complementary device in surgeon evaluation. An app entitled BREAST-V for both iOS and Android devices is currently available for free download in the Apple App Store and Google Play Store. Diagnostic, II.

Relationships between Speech Intelligibility and Word Articulation Scores in Children with Hearing Loss

PubMed Central

Ertmer, David J.

2012-01-01

Purpose This investigation sought to determine whether scores from a commonly used word-based articulation test are closely associated with speech intelligibility in children with hearing loss. If the scores are closely related, articulation testing results might be used to estimate intelligibility. If not, the importance of direct assessment of intelligibility would be reinforced. Methods Forty-four children with hearing losses produced words from the Goldman-Fristoe Test of Articulation-2 and sets of 10 short sentences. Correlation analyses were conducted between scores for seven word-based predictor variables and percent-intelligible scores derived from listener judgments of stimulus sentences. Results Six of seven predictor variables were significantly correlated with percent-intelligible scores. However, regression analysis revealed that no single predictor variable or multi- variable model accounted for more than 25% of the variability in intelligibility scores. Implications The findings confirm the importance of assessing connected speech intelligibility directly. PMID:20220022

Towards an understanding of Internet-based problem shopping behaviour: The concept of online shopping addiction and its proposed predictors

PubMed Central

ROSE, SUSAN; DHANDAYUDHAM, ARUN

2014-01-01

Background: Compulsive and addictive forms of consumption and buying behaviour have been researched in both business and medical literature. Shopping enabled via the Internet now introduces new features to the shopping experience that translate to positive benefits for the shopper. Evidence now suggests that this new shopping experience may lead to problematic online shopping behaviour. This paper provides a theoretical review of the literature relevant to online shopping addiction (OSA). Based on this selective review, a conceptual model of OSA is presented. Method: The selective review of the literature draws on searches within databases relevant to both clinical and consumer behaviour literature including EBSCO, ABI Pro-Quest, Web of Science – Social Citations Index, Medline, PsycINFO and Pubmed. The article reviews current thinking on problematic, and specifically addictive, behaviour in relation to online shopping. Results: The review of the literature enables the extension of existing knowledge into the Internet-context. A conceptual model of OSA is developed with theoretical support provided for the inclusion of 7 predictor variables: low self-esteem, low self-regulation; negative emotional state; enjoyment; female gender; social anonymity and cognitive overload. The construct of OSA is defined and six component criteria of OSA are proposed based on established technological addiction criteria. Conclusions: Current Internet-based shopping experiences may trigger problematic behaviours which can be classified on a spectrum which at the extreme end incorporates OSA. The development of a conceptual model provides a basis for the future measurement and testing of proposed predictor variables and the outcome variable OSA. PMID:25215218

Towards an understanding of Internet-based problem shopping behaviour: The concept of online shopping addiction and its proposed predictors.

PubMed

Rose, Susan; Dhandayudham, Arun

2014-06-01

Compulsive and addictive forms of consumption and buying behaviour have been researched in both business and medical literature. Shopping enabled via the Internet now introduces new features to the shopping experience that translate to positive benefits for the shopper. Evidence now suggests that this new shopping experience may lead to problematic online shopping behaviour. This paper provides a theoretical review of the literature relevant to online shopping addiction (OSA). Based on this selective review, a conceptual model of OSA is presented. The selective review of the literature draws on searches within databases relevant to both clinical and consumer behaviour literature including EBSCO, ABI Pro-Quest, Web of Science - Social Citations Index, Medline, PsycINFO and Pubmed. The article reviews current thinking on problematic, and specifically addictive, behaviour in relation to online shopping. The review of the literature enables the extension of existing knowledge into the Internet-context. A conceptual model of OSA is developed with theoretical support provided for the inclusion of 7 predictor variables: low self-esteem, low self-regulation; negative emotional state; enjoyment; female gender; social anonymity and cognitive overload. The construct of OSA is defined and six component criteria of OSA are proposed based on established technological addiction criteria. Current Internet-based shopping experiences may trigger problematic behaviours which can be classified on a spectrum which at the extreme end incorporates OSA. The development of a conceptual model provides a basis for the future measurement and testing of proposed predictor variables and the outcome variable OSA.

Predictors of Relationship Power among Drug-involved Women

PubMed Central

Campbell, Aimee N. C.; Tross, Susan; Hu, Mei-chen; Pavlicova, Martina; Nunes, Edward V.

2012-01-01

Gender-based relationship power is frequently linked to women’s capacity to reduce sexual risk behaviors. This study offers an exploration of predictors of relationship power, as measured by the multidimensional and theoretically grounded Sexual Relationship Power Scale (SRPS), among women in outpatient substance abuse treatment. Linear models were used to test nine predictors (age, race/ethnicity, education, time in treatment, economic dependence, substance use, sexual concurrency, partner abuse, sex role orientation) of relationship power among 513 women participating in a multi-site HIV risk reduction intervention study. Significant predictors of relationship control included having a non-abusive male partner, only one male partner, and endorsing traditional masculine (or both masculine and feminine) sex role attributes. Predictors of decision-making dominance were interrelated, with substance use x partner abuse and age x sex role orientation interactions. Results contribute to the understanding of factors which may influence relationship power and to their potential role in HIV sexual risk reduction interventions. PMID:22614746

Psychosocial predictors of the onset of anxiety disorders in women: Results from a prospective 3-year longitudinal study

PubMed Central

Calkins, Amanda W.; Otto, Michael W.; Cohen, Lee S.; Soares, Claudio N.; Vitonis, Alison F.; Hearon, Bridget A.; Harlow, Bernard L.

2009-01-01

In a prospective, longitudinal, population-based study of 643 women participating in the Harvard Study of Moods and Cycles we examined whether psychosocial variables predicted a new or recurrent onset of an anxiety disorder. Presence of anxiety disorders was assessed every six months over three years via structured clinical interviews. Among individuals who had a new episode of anxiety, we confirmed previous findings that history of anxiety, increased anxiety sensitivity (the fear of anxiety related sensations), and increased neuroticism were significant predictors. We also found trend level support for assertiveness as a predictor of anxiety onset. However, of these variables, only history of anxiety and anxiety sensitivity provided unique prediction. We did not find evidence for negative life events as a predictor of onset of anxiety either alone or in interaction with other variables in a diathesis-stress model. These findings from a prospective longitudinal study are discussed in relation to the potential role of such predictors in primary or relapse prevention efforts. PMID:19699609

[Predictive model based multimetric index of macroinvertebrates for river health assessment].

PubMed

Chen, Kai; Yu, Hai Yan; Zhang, Ji Wei; Wang, Bei Xin; Chen, Qiu Wen

2017-06-18

Improving the stability of integrity of biotic index (IBI; i.e., multi-metric indices, MMI) across temporal and spatial scales is one of the most important issues in water ecosystem integrity bioassessment and water environment management. Using datasets of field-based macroinvertebrate and physicochemical variables and GIS-based natural predictors (e.g., geomorphology and climate) and land use variables collected at 227 river sites from 2004 to 2011 across the Zhejiang Province, China, we used random forests (RF) to adjust the effects of natural variations at temporal and spatial scales on macroinvertebrate metrics. We then developed natural variations adjusted (predictive) and unadjusted (null) MMIs and compared performance between them. The core me-trics selected for predictive and null MMIs were different from each other, and natural variations within core metrics in predictive MMI explained by RF models ranged between 11.4% and 61.2%. The predictive MMI was more precise and accurate, but less responsive and sensitive than null MMI. The multivariate nearest-neighbor test determined that 9 test sites and 1 most degraded site were flagged outside of the environmental space of the reference site network. We found that combination of predictive MMI developed by using predictive model and the nearest-neighbor test performed best and decreased risks of inferring type I (designating a water body as being in poor biological condition, when it was actually in good condition) and type II (designating a water body as being in good biological condition, when it was actually in poor condition) errors. Our results provided an effective method to improve the stability and performance of integrity of biotic index.

Variability in symptom expression among sexually abused girls: developing multivariate models.

PubMed

Spaccarelli, S; Fuchs, C

1997-03-01

Examined which of several apparent risk variables were predictors of internalizing and externalizing problems in 48 girls who were referred for therapy after disclosing sexual abuse. Specifically, the effects of abuse characteristics, support from nonoffending parents, victims' coping strategies, and victims' cognitive appraisals on symptomatology were assessed. As hypothesized, results indicated that internalizing and externalizing problems were associated with different sets of predictor variables. Victims' self-reports of depression and anxiety were related to lower perceived support from nonoffending parents, more use of cognitive avoidance coping, and more negative appraisals of the abuse. These results were partially replicated when using parent-report measures of depression, but were not replicated for parent reports of victim anxiety. Incest was the only variable that was significantly related to parent-reported anxiety. Parent-reported aggressive behaviors were predicted by level of abuse-related stress; and aggression, social problems, and sexual problems were all related to the tendency to cope by controlling others. Social problems were also related to coping by self-distraction. Regression analyses were done for each dependent variable to examine which predictors accounted for unique variance when controlling for other significant zero-order correlates. Implications of these results for understanding variability in symptom expression among sexual abuse victims are discussed.

Social and moral norm differences among Portuguese 1st and 6th year medical students towards their intention to comply with hand hygiene.

PubMed

Roberto, Magda S; Mearns, Kathryn; Silva, Silvia A

2012-01-01

This study examines social and moral norms towards the intention to comply with hand hygiene among Portuguese medical students from 1st and 6th years (N = 175; 121 from the 1st year, 54 from the 6th year). The study extended the theory of planned behaviour theoretical principles and hypothesised that both subjective and moral norms will be the best predictors of 1st and 6th year medical students' intention to comply with hand hygiene; however, these predictors ability to explain intention variance will change according to medical students' school year. Results indicated that the subjective norm, whose referent focuses on professors, is a relevant predictor of 1st year medical students' intention, while the subjective norm that emphasises the relevance of colleagues predicts the intentions of medical students from the 6th year. In terms of the moral norm, 6th year students' intention is better predicted by a norm that interferes with compliance; whereas intentions from 1st year students are better predicted by a norm that favours compliance. Implications of the findings highlight the importance of role models and mentors as key factors in teaching hand hygiene in medical undergraduate curricula.

Predictors of adherence among community users of a cognitive behavior therapy website

PubMed Central

Batterham, Philip J; Neil, Alison L; Bennett, Kylie; Griffiths, Kathleen M; Christensen, Helen

2008-01-01

Objective To investigate the predictors of early and late dropout among community users of the MoodGYM website, a five module online intervention for reducing the symptoms of depression. Method Approximately 82,000 users accessed the site in 2006, of which 27% completed one module and 10% completed two or more modules. Adherence was modeled as a trichotomous variable representing non-starters (0 modules), early dropouts (1 module) and late dropouts (2–5 modules). Predictor variables included age, gender, education, location, referral source, depression severity, anxiety severity, dysfunctional thinking, and change in symptom count. Results Better adherence was predicted by higher depression severity, higher anxiety severity, a greater level of dysfunctional thinking, younger age, higher education, being female, and being referred to the site by a mental health professional. In addition, users whose depression severity had improved or remained stable after the first intervention module had higher odds of completing subsequent modules. Conclusions While the effect of age and the null effect of location were in accordance with prior adherence research, the significant effects of gender, education and depression severity were not, and may reflect user characteristics, the content of the intervention and unique aspects of online interventions. Further research directions are suggested to investigate the elements of open access online interventions that facilitate adherence. PMID:19920949

Breastfeeding performance in Iranian women.

PubMed

Faridvand, Fatemeh; Mirghafourvand, Mojgan; Mohammad-Alizadeh-Charandabi, Sakineh; Malakouti, Jamileh

2018-04-20

Studies have shown that breastfeeding has both short-term and long-term useful effects on mother's and newborn's health. This study was conducted with the aim of determining predictors of breastfeeding performance in women who were referred to health centres in Tabriz City, Iran, in 2014 to 2015. This cross-sectional study cluster-sampled 220 breastfeeding women with infants aged 4 to 6 months. The Breastfeeding Self-Efficacy Scale, the Iowa Infant Feeding Attitude Scale, the personal resource questionnaire-85, and a researcher-developed knowledge questionnaire were used to collect data. Multivariate linear regression model was used to determine predictors of breastfeeding performance. The results showed that participants' breastfeeding performance mean (SD) value was 3.6 (1.2) of 6. There were significant relationships between breastfeeding performance and breastfeeding self-efficacy (P = .033) but not between social support, knowledge, attitudes, and breastfeeding performance (P > .05). Breastfeeding self-efficacy, occupation, family income sufficiency, and living with the family were identified as predictors of breastfeeding performance. Given the relationship between breastfeeding self-efficacy and breastfeeding performance, strengthening mothers' self-efficacy should be considered, especially when compiling programs to promote breastfeeding. Increasing breastfeeding self-efficacy in women improves their breastfeeding performance: In developing programs to promote breastfeeding culture, women's self-efficacy should be considered. © 2018 John Wiley & Sons Australia, Ltd.

A three-gene expression signature model for risk stratification of patients with neuroblastoma.

PubMed

Garcia, Idoia; Mayol, Gemma; Ríos, José; Domenech, Gema; Cheung, Nai-Kong V; Oberthuer, André; Fischer, Matthias; Maris, John M; Brodeur, Garrett M; Hero, Barbara; Rodríguez, Eva; Suñol, Mariona; Galvan, Patricia; de Torres, Carmen; Mora, Jaume; Lavarino, Cinzia

2012-04-01

Neuroblastoma is an embryonal tumor with contrasting clinical courses. Despite elaborate stratification strategies, precise clinical risk assessment still remains a challenge. The purpose of this study was to develop a PCR-based predictor model to improve clinical risk assessment of patients with neuroblastoma. The model was developed using real-time PCR gene expression data from 96 samples and tested on separate expression data sets obtained from real-time PCR and microarray studies comprising 362 patients. On the basis of our prior study of differentially expressed genes in favorable and unfavorable neuroblastoma subgroups, we identified three genes, CHD5, PAFAH1B1, and NME1, strongly associated with patient outcome. The expression pattern of these genes was used to develop a PCR-based single-score predictor model. The model discriminated patients into two groups with significantly different clinical outcome [set 1: 5-year overall survival (OS): 0.93 ± 0.03 vs. 0.53 ± 0.06, 5-year event-free survival (EFS): 0.85 ± 0.04 vs. 0.042 ± 0.06, both P < 0.001; set 2 OS: 0.97 ± 0.02 vs. 0.61 ± 0.1, P = 0.005, EFS: 0.91 ± 0.8 vs. 0.56 ± 0.1, P = 0.005; and set 3 OS: 0.99 ± 0.01 vs. 0.56 ± 0.06, EFS: 0.96 ± 0.02 vs. 0.43 ± 0.05, both P < 0.001]. Multivariate analysis showed that the model was an independent marker for survival (P < 0.001, for all). In comparison with accepted risk stratification systems, the model robustly classified patients in the total cohort and in different clinically relevant risk subgroups. We propose for the first time in neuroblastoma, a technically simple PCR-based predictor model that could help refine current risk stratification systems. ©2012 AACR.

A Three-Gene Expression Signature Model for Risk Stratification of Patients with Neuroblastoma

PubMed Central

Garcia, Idoia; Mayol, Gemma; Ríos, José; Domenech, Gema; Cheung, Nai-Kong V.; Oberthuer, André; Fischer, Matthias; Maris, John M.; Brodeur, Garrett M.; Hero, Barbara; Rodríguez, Eva; Suñol, Mariona; Galvan, Patricia; de Torres, Carmen; Mora, Jaume; Lavarino, Cinzia

2014-01-01

Purpose Neuroblastoma is an embryonal tumor with contrasting clinical courses. Despite elaborate stratification strategies, precise clinical risk assessment still remains a challenge. The purpose of this study was to develop a PCR-based predictor model to improve clinical risk assessment of patients with neuroblastoma. Experimental Design The model was developed using real-time PCR gene expression data from 96 samples and tested on separate expression data sets obtained from real-time PCR and microarray studies comprising 362 patients. Results On the basis of our prior study of differentially expressed genes in favorable and unfavorable neuroblastoma subgroups, we identified three genes, CHD5, PAFAH1B1, and NME1, strongly associated with patient outcome. The expression pattern of these genes was used to develop a PCR-based single-score predictor model. The model discriminated patients into two groups with significantly different clinical outcome [set 1: 5-year overall survival (OS): 0.93 ± 0.03 vs. 0.53 ± 0.06, 5-year event-free survival (EFS): 0.85 ± 0.04 vs. 0.042 ± 0.06, both P < 0.001; set 2 OS: 0.97 ± 0.02 vs. 0.61 ± 0.1, P = 0.005, EFS: 0.91 ± 0.8 vs. 0.56 ± 0.1, P = 0.005; and set 3 OS: 0.99 ± 0.01 vs. 0.56 ± 0.06, EFS: 0.96 ± 0.02 vs. 0.43 ± 0.05, both P < 0.001]. Multivariate analysis showed that the model was an independent marker for survival (P < 0.001, for all). In comparison with accepted risk stratification systems, the model robustly classified patients in the total cohort and in different clinically relevant risk subgroups. Conclusion We propose for the first time in neuroblastoma, a technically simple PCR-based predictor model that could help refine current risk stratification systems. PMID:22328561

A Method of Relating General Circulation Model Simulated Climate to the Observed Local Climate. Part I: Seasonal Statistics.

NASA Astrophysics Data System (ADS)

Karl, Thomas R.; Wang, Wei-Chyung; Schlesinger, Michael E.; Knight, Richard W.; Portman, David

1990-10-01

Important surface observations such as the daily maximum and minimum temperature, daily precipitation, and cloud ceilings often have localized characteristics that are difficult to reproduce with the current resolution and the physical parameterizations in state-of-the-art General Circulation climate Models (GCMs). Many of the difficulties can be partially attributed to mismatches in scale, local topography. regional geography and boundary conditions between models and surface-based observations. Here, we present a method, called climatological projection by model statistics (CPMS), to relate GCM grid-point flee-atmosphere statistics, the predictors, to these important local surface observations. The method can be viewed as a generalization of the model output statistics (MOS) and perfect prog (PP) procedures used in numerical weather prediction (NWP) models. It consists of the application of three statistical methods: 1) principle component analysis (FICA), 2) canonical correlation, and 3) inflated regression analysis. The PCA reduces the redundancy of the predictors The canonical correlation is used to develop simultaneous relationships between linear combinations of the predictors, the canonical variables, and the surface-based observations. Finally, inflated regression is used to relate the important canonical variables to each of the surface-based observed variables.We demonstrate that even an early version of the Oregon State University two-level atmospheric GCM (with prescribed sea surface temperature) produces free-atmosphere statistics than can, when standardized using the model's internal means and variances (the MOS-like version of CPMS), closely approximate the observed local climate. When the model data are standardized by the observed free-atmosphere means and variances (the PP version of CPMS), however, the model does not reproduce the observed surface climate as well. Our results indicate that in the MOS-like version of CPMS the differences between the output of a ten-year GCM control run and the surface-based observations are often smaller than the differences between the observations of two ten-year periods. Such positive results suggest that GCMs may already contain important climatological information that can be used to infer the local climate.

Predictors of outcome in patients with advanced nonseminomatous germ cell testicular tumors.

PubMed

Yetisyigit, Tarkan; Babacan, Nalan; Urun, Yuksel; Seber, Erdogan Selcuk; Cihan, Sener; Arpaci, Erkan; Yildirim, Nuriye; Aksoy, Sercan; Budakoglu, Burcin; Zengin, Nurullah; Oksuzoglu, Berna; Yalcin, Banu Cicek; Alkis, Necati

2014-01-01

Predictor factors determining complete response to treatment are still not clearly defined. We aimed to evaluate clinicopathological features, risk factors, treatment responses, and survival analysis of patient with advanced nonseminomatous GCTs (NSGCTs). Between November 1999 and September 2011, 140 patients with stage II and III NSGCTs were referred to our institutions and 125 patients with complete clinical data were included in this retrospective study. Four cycles of BEP regimen were applied as a first-line treatment. Salvage chemotherapy and/or high-dose chemotherapy (HDCT) with autologous stem cell transplantation were given in patients who progressed after BEP chemotherapy. Post-chemotherapy surgery was performed in selected patients with incomplete radiographic response and normal tumor markers. The median age was 28 years. For the good, intermediate and poor risk groups, compete response rates (CRR) were, 84.6%, 67.9% and 59.4%, respectively. Extragonadal tumors, stage 3 disease, intermediate and poor risk factors, rete testis invasion were associated with worse outcomes. There were 32 patients (25.6%) with non-CR who were treated with salvage treatment. Thirty-one patients died from GCTs and 94% of them had stage III disease. Even though response rates are high, some patients with GCTs still need salvage treatment and cure cannot be achieved. Non-complete response to platinium-based first-line treatment is a negative prognostic factor. Our study confirmed the need for a prognostic and predictive model and more effective salvage approaches.

Using Propensity Score Matching to Model Retention of Developmental Math Students in Community Colleges in North Carolina

ERIC Educational Resources Information Center

Frye, Bobbie Jean

2014-01-01

Traditionally, modeling student retention has been done by deriving student success predictors and measuring the likelihood of success based on several background factors such as age, race, gender, and other pre-college variables, also known as the input-output model. Increasingly, however, researchers have used mediating factors of the student…

Assessment of the Relationship between Spiritual and Social Health and the Self-Care Ability of Elderly People Referred to Community Health Centers.

PubMed

Mohammadi, Mahboobeh; Alavi, Mousa; Bahrami, Masoud; Zandieh, Zahra

2017-01-01

Promotion of self-care ability among older people is an essential means to help maintain and improve their health. However, the role of spiritual and social health has not yet been considered in detail in the context of self-care ability among elderly. The aim of this study was to assess the relationship between spiritual and social health and self-care ability of older people referred to community health centers in Isfahan. In this cross-sectional correlation study, 200 people, aged 60 years and older, referred to healthcare centers in 2016 were recruited through convenience sampling method. Data were collected by four-part tool comprising of: (a) demographics, (b) Ellison and Palotzin's spiritual well-being scale, (c) Kees's "social health" scale, and (d) self-care ability scale for the elderly by Soderhamn's; data were analyzed by descriptive and inferential (independent t -test, analysis of variance - ANOVA, Pearson's coefficient tests, and multiple regression analysis) statistics by SPSS16 software. Findings showed that the entered predictor variables were accounted for 41% of total variance ( R 2 ) of the two self-care ability in the model ( p < 0.001, F 3, 199 = 46.02). Two out of the three predictor variables including religious well-being and social health, significantly predicted the self-care ability of older people. The results of this study emphasized on the relationship between spiritual and social health of the elderly people and their ability to self-care. Therefore, it would be recommended to keep the focus of the service resources towards improving social and spiritual health to improve self-care ability in elderly people.

Reference values for the 6-minute walk test in healthy children and adolescents in Switzerland

PubMed Central

2013-01-01

Background The six-minute walk test (6MWT) is a simple, low tech, safe and well established, self-paced assessment tool to quantify functional exercise capacity in adults. The definition of normal 6MWT in children is especially demanding since not only parameters like height, weight and ethnical background influence the measurement, but may be as crucial as age and the developmental stage. The aim of this study is establishing reference values for the 6MWT in healthy children and adolescents in Switzerland and to investigate the influence of age, anthropometrics, heart rate, blood pressure and physical activity on the distance walked. Methods Children and adolescents between 5–17 years performed a 6MWT. Short questionnaire assessments about their health state and physical activities. anthropometrics and vitals were measured before and after a 6-minute walk test and were previously defined as secondary outcomes. Results Age, height, weight and the heart rate after the 6MWT all predicted the distance walked according to different regression models: age was the best single predictor and mostly influenced walk distance in younger age, anthropometrics were more important in adolescents and females. Heart rate after the 6MWT was an important distance predictor in addition to age and outreached anthropometrics in the majority of subgroups assessed. Conclusions The 6MWT in children and adolescents is feasible and practical. The 6MWT distance depends mainly on age; however, heart rate after the 6MWT, height and weight significantly add information and should be taken into account mainly in adolescents. Reference equations allow predicting 6-minute walk test distance and may help to better assess and compare outcomes in young patients with cardiovascular and respiratory diseases and are highly warranted for different populations. PMID:23915140

Examining individuals' adoption of healthcare wearable devices: An empirical study from privacy calculus perspective.

PubMed

Li, He; Wu, Jing; Gao, Yiwen; Shi, Yao

2016-04-01

Wearable technology has shown the potential of improving healthcare efficiency and reducing healthcare cost. Different from pioneering studies on healthcare wearable devices from technical perspective, this paper explores the predictors of individuals' adoption of healthcare wearable devices. Considering the importance of individuals' privacy perceptions in healthcare wearable devices adoption, this study proposes a model based on the privacy calculus theory to investigate how individuals adopt healthcare wearable devices. The proposed conceptual model was empirically tested by using data collected from a survey. The sample covers 333 actual users of healthcare wearable devices. Structural equation modeling (SEM) method was employed to estimate the significance of the path coefficients. This study reveals several main findings: (1) individuals' decisions to adopt healthcare wearable devices are determined by their risk-benefit analyses (refer to privacy calculus). In short, if an individual's perceived benefit is higher than perceived privacy risk, s/he is more likely to adopt the device. Otherwise, the device would not be adopted; (2) individuals' perceived privacy risk is formed by health information sensitivity, personal innovativeness, legislative protection, and perceived prestige; and (3) individuals' perceived benefit is determined by perceived informativeness and functional congruence. The theoretical and practical implications, limitations, and future research directions are then discussed. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Prediction of new onset of end stage renal disease in Chinese patients with type 2 diabetes mellitus - a population-based retrospective cohort study.

PubMed

Wan, Eric Yuk Fai; Fong, Daniel Yee Tak; Fung, Colman Siu Cheung; Yu, Esther Yee Tak; Chin, Weng Yee; Chan, Anca Ka Chun; Lam, Cindy Lo Kuen

2017-08-01

Since diabetes mellitus (DM) is the leading cause of end stage renal disease (ESRD), this study aimed to develop a 5-year ESRD risk prediction model among Chinese patients with Type 2 DM (T2DM) in primary care. A retrospective cohort study was conducted on 149,333 Chinese adult T2DM primary care patients without ESRD in 2010. Using the derivation cohort over a median of 5 years follow-up, the gender-specific models including the interaction effect between predictors and age were derived using Cox regression with a forward stepwise approach. Harrell's C-statistic and calibration plot were applied to the validation cohort to assess discrimination and calibration of the models. Prediction models showed better discrimination with Harrell's C-statistics of 0.866 (males) and 0.862 (females) and calibration power from the plots than other established models. The predictors included age, usages of anti-hypertensive drugs, anti-glucose drugs, and Hemogloblin A1c, blood pressure, urine albumin/creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR). Specific predictors for male were smoking and presence of sight threatening diabetic retinopathy while additional predictors for female included longer duration of diabetes and quadratic effect of body mass index. Interaction factors with age showed a greater weighting of insulin and urine ACR in younger males, and eGFR in younger females. Our newly developed gender-specific models provide a more accurate 5-year ESRD risk predictions for Chinese diabetic primary care patients than other existing models. The models included several modifiable risk factors that clinicians can use to counsel patients, and to target at in the delivery of care to patients.

Predictors of Visualization: A Structural Equation Model.

ERIC Educational Resources Information Center

Robichaux, Rebecca R.; Guarino, A. J.

This study tested a causal model of the development of spatial visualization based on a synthesis of past and present research. During the summer and fall of 1999, 117 third- and fourth-year undergraduates majoring in architecture, mathematics, mathematics education, and mechanical engineering completed a spatial visualization test and a…

Ingroup Rejection among Women: The Role of Personal Inadequacy

ERIC Educational Resources Information Center

Cowan, Gloria; Ullman, Jodie B.

2006-01-01

We examined predictors and outcomes of women's hostility toward other women. Based on a projection model, we hypothesized and tested the theory via structural equation modeling that women's sense of personal inadequacy, the tendency to stereotype, and general anger would predict hostility toward women, and hostility toward women would predict…

Development and validation of a novel predictive scoring model for microvascular invasion in patients with hepatocellular carcinoma.

PubMed

Zhao, Hui; Hua, Ye; Dai, Tu; He, Jian; Tang, Min; Fu, Xu; Mao, Liang; Jin, Huihan; Qiu, Yudong

2017-03-01

Microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) cannot be accurately predicted preoperatively. This study aimed to establish a predictive scoring model of MVI in solitary HCC patients without macroscopic vascular invasion. A total of 309 consecutive HCC patients who underwent curative hepatectomy were divided into the derivation (n=206) and validation cohort (n=103). A predictive scoring model of MVI was established according to the valuable predictors in the derivation cohort based on multivariate logistic regression analysis. The performance of the predictive model was evaluated in the derivation and validation cohorts. Preoperative imaging features on CECT, such as intratumoral arteries, non-nodular type of HCC and absence of radiological tumor capsule were independent predictors for MVI. The predictive scoring model was established according to the β coefficients of the 3 predictors. Area under receiver operating characteristic (AUROC) of the predictive scoring model was 0.872 (95% CI, 0.817-0.928) and 0.856 (95% CI, 0.771-0.940) in the derivation and validation cohorts. The positive and negative predictive values were 76.5% and 88.0% in the derivation cohort and 74.4% and 88.3% in the validation cohort. The performance of the model was similar between the patients with tumor size ≤5cm and >5cm in AUROC (P=0.910). The predictive scoring model based on intratumoral arteries, non-nodular type of HCC, and absence of the radiological tumor capsule on preoperative CECT is of great value in the prediction of MVI regardless of tumor size. Copyright © 2017 Elsevier B.V. All rights reserved.

Using existing case-mix methods to fund trauma cases.

PubMed

Monakova, Julia; Blais, Irene; Botz, Charles; Chechulin, Yuriy; Picciano, Gino; Basinski, Antoni

2010-01-01

Policymakers frequently face the need to increase funding in isolated and frequently heterogeneous (clinically and in terms of resource consumption) patient subpopulations. This article presents a methodologic solution for testing the appropriateness of using existing grouping and weighting methodologies for funding subsets of patients in the scenario where a case-mix approach is preferable to a flat-rate based payment system. Using as an example the subpopulation of trauma cases of Ontario lead trauma hospitals, the statistical techniques of linear and nonlinear regression models, regression trees, and spline models were applied to examine the fit of the existing case-mix groups and reference weights for the trauma cases. The analyses demonstrated that for funding Ontario trauma cases, the existing case-mix systems can form the basis for rational and equitable hospital funding, decreasing the need to develop a different grouper for this subset of patients. This study confirmed that Injury Severity Score is a poor predictor of costs for trauma patients. Although our analysis used the Canadian case-mix classification system and cost weights, the demonstrated concept of using existing case-mix systems to develop funding rates for specific subsets of patient populations may be applicable internationally.

Predictors of fibromyalgia: a population-based twin cohort study.

PubMed

Markkula, Ritva A; Kalso, Eija A; Kaprio, Jaakko A

2016-01-15

Fibromyalgia (FM) is a pain syndrome, the mechanisms and predictors of which are still unclear. We have earlier validated a set of FM-symptom questions for detecting possible FM in an epidemiological survey and thereby identified a cluster with "possible FM". This study explores prospectively predictors for membership of that FM-symptom cluster. A population-based sample of 8343 subjects of the older Finnish Twin Cohort replied to health questionnaires in 1975, 1981, and 1990. Their answers to the set of FM-symptom questions in 1990 classified them in three latent classes (LC): LC1 with no or few symptoms, LC2 with some symptoms, and LC3 with many FM symptoms. We analysed putative predictors for these symptom classes using baseline (1975 and 1981) data on regional pain, headache, migraine, sleeping, body mass index (BMI), physical activity, smoking, and zygosity, adjusted for age, gender, and education. Those with a high likelihood of having fibromyalgia at baseline were excluded from the analysis. In the final multivariate regression model, regional pain, sleeping problems, and overweight were all predictors for membership in the class with many FM symptoms. The strongest non-genetic predictor was frequent headache (OR 8.6, CI 95% 3.8-19.2), followed by persistent back pain (OR 4.7, CI 95% 3.3-6.7) and persistent neck pain (OR 3.3, CI 95% 1.8-6.0). Regional pain, frequent headache, and persistent back or neck pain, sleeping problems, and overweight are predictors for having a cluster of symptoms consistent with fibromyalgia.

The use of geographic information system and 1860s cadastral data to model agricultural suitability before heavy mechanization. A case study from Malta.

PubMed

Alberti, Gianmarco; Grima, Reuben; Vella, Nicholas C

2018-01-01

The present study seeks to understand the determinants of land agricultural suitability in Malta before heavy mechanization. A GIS-based Logistic Regression model is built on the basis of the data from mid-1800s cadastral maps (cabreo). This is the first time that such data are being used for the purpose of building a predictive model. The maps record the agricultural quality of parcels (ranging from good to lowest), which is represented by different colours. The study treats the agricultural quality as a depended variable with two levels: optimal (corresponding to the good class) vs. non-optimal quality (mediocre, bad, low, and lowest classes). Seventeen predictors are isolated on the basis of literature review and data availability. Logistic Regression is used to isolate the predictors that can be considered determinants of the agricultural quality. Our model has an optimal discriminatory power (AUC: 0.92). The positive effect on land agricultural quality of the following predictors is considered and discussed: sine of the aspect (odds ratio 1.42), coast distance (2.46), Brown Rendzinas (2.31), Carbonate Raw (2.62) and Xerorendzinas (9.23) soils, distance to minor roads (4.88). Predictors resulting having a negative effect are: terrain elevation (0.96), slope (0.97), distance to the nearest geological fault lines (0.09), Terra Rossa soil (0.46), distance to secondary roads (0.19) and footpaths (0.41). The model isolates a host of topographic and cultural variables, the latter related to human mobility and landscape accessibility, which differentially contributed to the agricultural suitability, providing the bases for the creation of the fragmented and extremely variegated agricultural landscape that is the hallmark of the Maltese Islands. Our findings are also useful to suggest new questions that may be posed to the more meagre evidence from earlier periods.

Empirical downscaling of atmospheric key variables above a tropical glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, M.; Kaser, G.; Mölg, T.; Juen, I.; Wagnon, P.

2009-04-01

Glaciers in the outer tropical Cordillera Blanca (Peru, South America) are of major socio-economic importance, since glacier runoff represents the primary water source during the dry season, when little or no rainfall occurs. Due to their location at high elevations, the glaciers moreover provide important information about climate change in the tropical troposphere, where measurements are sparse. This study targets the local reconstruction of air temperature, specific humidity and wind speed above the surface of an outer tropical glacier from NCEP/NCAR reanalysis data as large scale predictors. Since a farther scope is to provide input data for process based glacier mass balance modelling, the reconstruction pursues a high temporal resolution. Hence an empirical downscaling scheme is developed, based on a few years' time series of hourly observations from automatic weather stations, located at the glacier Artesonraju and nearby moraines (Northern Cordillera Blanca). Principal component and multiple regression analyses are applied to define the appropriate spatial downscaling domain, suitable predictor variables, and the statistical transfer functions. The model performance is verified using an independent data set. The best predictors are lower tropospheric air temperature and specific humidity, at reanalysis model grid points that represent the Bolivian Altiplano, located in the South of the Cordillera Blanca. The developed downscaling model explaines a considerable portion (more than 60%) of the diurnal variance of air temperature and specific humidity at the moraine stations, and air temperature above the glacier surface. Specific humidity above the glacier surface, however, can be reconstructed well in the seasonal, but not in the required diurnal time resolution. Wind speed can only be poorly determined by the large scale predictors (r² lower than 0.3) at both sites. We assume a complex local interaction between valley and glacier wind system to be the main cause for the differences between model and observations.

The use of geographic information system and 1860s cadastral data to model agricultural suitability before heavy mechanization. A case study from Malta

PubMed Central

Grima, Reuben; Vella, Nicholas C.

2018-01-01

The present study seeks to understand the determinants of land agricultural suitability in Malta before heavy mechanization. A GIS-based Logistic Regression model is built on the basis of the data from mid-1800s cadastral maps (cabreo). This is the first time that such data are being used for the purpose of building a predictive model. The maps record the agricultural quality of parcels (ranging from good to lowest), which is represented by different colours. The study treats the agricultural quality as a depended variable with two levels: optimal (corresponding to the good class) vs. non-optimal quality (mediocre, bad, low, and lowest classes). Seventeen predictors are isolated on the basis of literature review and data availability. Logistic Regression is used to isolate the predictors that can be considered determinants of the agricultural quality. Our model has an optimal discriminatory power (AUC: 0.92). The positive effect on land agricultural quality of the following predictors is considered and discussed: sine of the aspect (odds ratio 1.42), coast distance (2.46), Brown Rendzinas (2.31), Carbonate Raw (2.62) and Xerorendzinas (9.23) soils, distance to minor roads (4.88). Predictors resulting having a negative effect are: terrain elevation (0.96), slope (0.97), distance to the nearest geological fault lines (0.09), Terra Rossa soil (0.46), distance to secondary roads (0.19) and footpaths (0.41). The model isolates a host of topographic and cultural variables, the latter related to human mobility and landscape accessibility, which differentially contributed to the agricultural suitability, providing the bases for the creation of the fragmented and extremely variegated agricultural landscape that is the hallmark of the Maltese Islands. Our findings are also useful to suggest new questions that may be posed to the more meagre evidence from earlier periods. PMID:29415059

Model-based control strategies for systems with constraints of the program type

NASA Astrophysics Data System (ADS)

Jarzębowska, Elżbieta

2006-08-01

The paper presents a model-based tracking control strategy for constrained mechanical systems. Constraints we consider can be material and non-material ones referred to as program constraints. The program constraint equations represent tasks put upon system motions and they can be differential equations of orders higher than one or two, and be non-integrable. The tracking control strategy relies upon two dynamic models: a reference model, which is a dynamic model of a system with arbitrary order differential constraints and a dynamic control model. The reference model serves as a motion planner, which generates inputs to the dynamic control model. It is based upon a generalized program motion equations (GPME) method. The method enables to combine material and program constraints and merge them both into the motion equations. Lagrange's equations with multipliers are the peculiar case of the GPME, since they can be applied to systems with constraints of first orders. Our tracking strategy referred to as a model reference program motion tracking control strategy enables tracking of any program motion predefined by the program constraints. It extends the "trajectory tracking" to the "program motion tracking". We also demonstrate that our tracking strategy can be extended to a hybrid program motion/force tracking.

High-sensitivity cardiac troponin I and risk of heart failure in patients with suspected acute coronary syndrome: a cohort study.

PubMed

Stelzle, Dominik; Shah, Anoop S V; Anand, Atul; Strachan, Fiona E; Chapman, Andrew R; Denvir, Martin A; Mills, Nicholas L; McAllister, David A

2018-01-01

Heart failure may occur following acute myocardial infarction, but with the use of high-sensitivity cardiac troponin assays we increasingly diagnose patients with minor myocardial injury. Whether troponin concentrations remain a useful predictor of heart failure in patients with acute coronary syndrome is uncertain. We identified all consecutive patients (n = 4748) with suspected acute coronary syndrome (61 ± 16 years, 57% male) presenting to three secondary and tertiary care hospitals. Cox-regression models were used to evaluate the association between high-sensitivity cardiac troponin I concentration and subsequent heart failure hospitalization. C-statistics were estimated to evaluate the predictive value of troponin for heart failure hospitalization. Over 2071 years of follow-up there were 83 heart failure hospitalizations. Patients with troponin concentrations above the upper reference limit (URL) were more likely to be hospitalized with heart failure than patients below the URL (118/1000 vs. 17/1000 person years, adjusted hazard ratio: 7.0). Among patients with troponin concentrations

Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting.

PubMed

Suchting, Robert; Gowin, Joshua L; Green, Charles E; Walss-Bass, Consuelo; Lane, Scott D

2018-01-01

Rationale : Given datasets with a large or diverse set of predictors of aggression, machine learning (ML) provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior. Objectives : The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5) polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults. Methods : The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a) select variables from an initial set of 20 to build a model of trait aggression; and then (b) reduce that model to maximize parsimony and generalizability. Results : From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ) total score, with R 2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect), childhood trauma (physical abuse and neglect), and the FKBP5_13 gene (rs1360780). The six-factor model approximated the initial eight-factor model at 99.4% of R 2 . Conclusions : Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for replication. This approach provides utility for the prediction of aggression behavior, particularly in the context of large multivariate datasets.

An Agent-Based Modeling Template for a Cohort of Veterans with Diabetic Retinopathy.

PubMed

Day, Theodore Eugene; Ravi, Nathan; Xian, Hong; Brugh, Ann

2013-01-01

Agent-based models are valuable for examining systems where large numbers of discrete individuals interact with each other, or with some environment. Diabetic Veterans seeking eye care at a Veterans Administration hospital represent one such cohort. The objective of this study was to develop an agent-based template to be used as a model for a patient with diabetic retinopathy (DR). This template may be replicated arbitrarily many times in order to generate a large cohort which is representative of a real-world population, upon which in-silico experimentation may be conducted. Agent-based template development was performed in java-based computer simulation suite AnyLogic Professional 6.6. The model was informed by medical data abstracted from 535 patient records representing a retrospective cohort of current patients of the VA St. Louis Healthcare System Eye clinic. Logistic regression was performed to determine the predictors associated with advancing stages of DR. Predicted probabilities obtained from logistic regression were used to generate the stage of DR in the simulated cohort. The simulated cohort of DR patients exhibited no significant deviation from the test population of real-world patients in proportion of stage of DR, duration of diabetes mellitus (DM), or the other abstracted predictors. Simulated patients after 10 years were significantly more likely to exhibit proliferative DR (P<0.001). Agent-based modeling is an emerging platform, capable of simulating large cohorts of individuals based on manageable data abstraction efforts. The modeling method described may be useful in simulating many different conditions where course of disease is described in categorical stages.

Women's Endorsement of Models of Sexual Response: Correlates and Predictors.

PubMed

Nowosielski, Krzysztof; Wróbel, Beata; Kowalczyk, Robert

2016-02-01

Few studies have investigated endorsement of female sexual response models, and no single model has been accepted as a normative description of women's sexual response. The aim of the study was to establish how women from a population-based sample endorse current theoretical models of the female sexual response--the linear models and circular model (partial and composite Basson models)--as well as predictors of endorsement. Accordingly, 174 heterosexual women aged 18-55 years were included in a cross-sectional study: 74 women diagnosed with female sexual dysfunction (FSD) based on DSM-5 criteria and 100 non-dysfunctional women. The description of sexual response models was used to divide subjects into four subgroups: linear (Masters-Johnson and Kaplan models), circular (partial Basson model), mixed (linear and circular models in similar proportions, reflective of the composite Basson model), and a different model. Women were asked to choose which of the models best described their pattern of sexual response and how frequently they engaged in each model. Results showed that 28.7% of women endorsed the linear models, 19.5% the partial Basson model, 40.8% the composite Basson model, and 10.9% a different model. Women with FSD endorsed the partial Basson model and a different model more frequently than did non-dysfunctional controls. Individuals who were dissatisfied with a partner as a lover were more likely to endorse a different model. Based on the results, we concluded that the majority of women endorsed a mixed model combining the circular response with the possibility of an innate desire triggering a linear response. Further, relationship difficulties, not FSD, predicted model endorsement.

A generalized conditional heteroscedastic model for temperature downscaling

NASA Astrophysics Data System (ADS)

Modarres, R.; Ouarda, T. B. M. J.

2014-11-01

This study describes a method for deriving the time varying second order moment, or heteroscedasticity, of local daily temperature and its association to large Coupled Canadian General Circulation Models predictors. This is carried out by applying a multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) approach to construct the conditional variance-covariance structure between General Circulation Models (GCMs) predictors and maximum and minimum temperature time series during 1980-2000. Two MGARCH specifications namely diagonal VECH and dynamic conditional correlation (DCC) are applied and 25 GCM predictors were selected for a bivariate temperature heteroscedastic modeling. It is observed that the conditional covariance between predictors and temperature is not very strong and mostly depends on the interaction between the random process governing temporal variation of predictors and predictants. The DCC model reveals a time varying conditional correlation between GCM predictors and temperature time series. No remarkable increasing or decreasing change is observed for correlation coefficients between GCM predictors and observed temperature during 1980-2000 while weak winter-summer seasonality is clear for both conditional covariance and correlation. Furthermore, the stationarity and nonlinearity Kwiatkowski-Phillips-Schmidt-Shin (KPSS) and Brock-Dechert-Scheinkman (BDS) tests showed that GCM predictors, temperature and their conditional correlation time series are nonlinear but stationary during 1980-2000 according to BDS and KPSS test results. However, the degree of nonlinearity of temperature time series is higher than most of the GCM predictors.

Developmental trajectories of paediatric headache - sex-specific analyses and predictors.

PubMed

Isensee, Corinna; Fernandez Castelao, Carolin; Kröner-Herwig, Birgit

2016-01-01

Headache is the most common pain disorder in children and adolescents and is associated with diverse dysfunctions and psychological symptoms. Several studies evidenced sex-specific differences in headache frequency. Until now no study exists that examined sex-specific patterns of change in paediatric headache across time and included pain-related somatic and (socio-)psychological predictors. Latent Class Growth Analysis (LCGA) was used in order to identify different trajectory classes of headache across four annual time points in a population-based sample (n = 3 227; mean age 11.34 years; 51.2 % girls). In multinomial logistic regression analyses the influence of several predictors on the class membership was examined. For girls, a four-class model was identified as the best fitting model. While the majority of girls reported no (30.5 %) or moderate headache frequencies (32.5 %) across time, one class with a high level of headache days (20.8 %) and a class with an increasing headache frequency across time (16.2 %) were identified. For boys a two class model with a 'no headache class' (48.6 %) and 'moderate headache class' (51.4 %) showed the best model fit. Regarding logistic regression analyses, migraine and parental headache proved to be stable predictors across sexes. Depression/anxiety was a significant predictor for all pain classes in girls. Life events, dysfunctional stress coping and school burden were also able to differentiate at least between some classes in both sexes. The identified trajectories reflect sex-specific differences in paediatric headache, as seen in the number and type of classes extracted. The documented risk factors can deliver ideas for preventive actions and considerations for treatment programmes.

The association of clinical indication for exercise stress testing with all-cause mortality: the FIT Project

PubMed Central

Kim, Joonseok; Al-Mallah, Mouaz; Juraschek, Stephen P.; Brawner, Clinton; Keteyian, Steve J.; Nasir, Khurram; Dardari, Zeina A.; Blumenthal, Roger S.

2016-01-01

Introduction We hypothesized that the indication for stress testing provided by the referring physician would be an independent predictor of all-cause mortality. Material and methods We studied 48,914 patients from The Henry Ford Exercise Testing Project (The FIT Project) without known congestive heart failure who were referred for a clinical treadmill stress test and followed for 11 ±4.7 years. The reason for stress test referral was abstracted from the clinical test order, and should be considered the primary concerning symptom or indication as stated by the ordering clinician. Hierarchical multivariable Cox proportional hazards regression was performed, after controlling for potential confounders including demographics, risk factors, and medication use as well as additional adjustment for exercise capacity in the final model. Results A total of 67% of the patients were referred for chest pain, 12% for shortness of breath (SOB), 4% for palpitations, 3% for pre-operative evaluation, 6% for abnormal prior testing, and 7% for risk factors only. There were 6,211 total deaths during follow-up. Compared to chest pain, those referred for palpitations (HR = 0.72, 95% CI: 0.60–0.86) and risk factors only (HR = 0.72, 95% CI: 0.63–0.82) had a lower risk of all-cause mortality, whereas those referred for SOB (HR = 1.15, 95% CI: 1.07–1.23) and pre-operative evaluation (HR = 2.11, 95% CI: 1.94–2.30) had an increased risk. In subgroup analysis, referral for palpitations was protective only in those without coronary artery disease (CAD) (HR = 0.75, 95% CI: 0.62–0.90), while SOB increased mortality risk only in those with established CAD (HR = 1.25, 95% CI: 1.10–1.44). Conclusions The indication for stress testing is an independent predictor of mortality, showing an interaction with CAD status. Importantly, SOB may be associated with higher mortality risk than chest pain, particularly in patients with CAD. PMID:27186173

Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.

PubMed

Lourenco, D A L; Tsuruta, S; Fragomeni, B O; Masuda, Y; Aguilar, I; Legarra, A; Bertrand, J K; Amen, T S; Wang, L; Moser, D W; Misztal, I

2015-06-01

Predictive ability of genomic EBV when using single-step genomic BLUP (ssGBLUP) in Angus cattle was investigated. Over 6 million records were available on birth weight (BiW) and weaning weight (WW), almost 3.4 million on postweaning gain (PWG), and over 1.3 million on calving ease (CE). Genomic information was available on, at most, 51,883 animals, which included high and low EBV accuracy animals. Traditional EBV was computed by BLUP and genomic EBV by ssGBLUP and indirect prediction based on SNP effects was derived from ssGBLUP; SNP effects were calculated based on the following reference populations: ref_2k (contains top bulls and top cows that had an EBV accuracy for BiW ≥0.85), ref_8k (contains all parents that were genotyped), and ref_33k (contains all genotyped animals born up to 2012). Indirect prediction was obtained as direct genomic value (DGV) or as an index of DGV and parent average (PA). Additionally, runs with ssGBLUP used the inverse of the genomic relationship matrix calculated by an algorithm for proven and young animals (APY) that uses recursions on a small subset of reference animals. An extra reference subset included 3,872 genotyped parents of genotyped animals (ref_4k). Cross-validation was used to assess predictive ability on a validation population of 18,721 animals born in 2013. Computations for growth traits used multiple-trait linear model and, for CE, a bivariate CE-BiW threshold-linear model. With BLUP, predictivities were 0.29, 0.34, 0.23, and 0.12 for BiW, WW, PWG, and CE, respectively. With ssGBLUP and ref_2k, predictivities were 0.34, 0.35, 0.27, and 0.13 for BiW, WW, PWG, and CE, respectively, and with ssGBLUP and ref_33k, predictivities were 0.39, 0.38, 0.29, and 0.13 for BiW, WW, PWG, and CE, respectively. Low predictivity for CE was due to low incidence rate of difficult calving. Indirect predictions with ref_33k were as accurate as with full ssGBLUP. Using the APY and recursions on ref_4k gave 88% gains of full ssGBLUP and using the APY and recursions on ref_8k gave 97% gains of full ssGBLUP. Genomic evaluation in beef cattle with ssGBLUP is feasible while keeping the models (maternal, multiple trait, and threshold) already used in regular BLUP. Gains in predictivity are dependent on the composition of the reference population. Indirect predictions via SNP effects derived from ssGBLUP allow for accurate genomic predictions on young animals, with no advantage of including PA in the index if the reference population is large. With the APY conditioning on about 10,000 reference animals, ssGBLUP is potentially applicable to a large number of genotyped animals without compromising predictive ability.

Referral Consultant: An Expert System for Guiding Teachers in Referring Students for Special Education Placement. Final Report.

ERIC Educational Resources Information Center

Baer, Richard; And Others

In light of evidence indicating that referral itself often predicts student placement, an expert system was designed to assist educators to reduce bias in the process of referring students with suspected disabilities. A preliminary review of the literature looks at teacher perceptions as a predictor of handicapping conditions, referral bias, and…

What happens if we compare chopsticks with forks? The impact of making inappropriate comparisons in cross-cultural research.

PubMed

Chen, Fang Fang

2008-11-01

It is a common practice to export instruments developed in one culture to another. Little is known about the consequences of making inappropriate comparisons in cross-cultural research. Several studies were conducted to fill in this gap. Study 1 examined the impact of lacking factor loading invariance on regression slope comparisons. When factor loadings of a predictor are higher in the reference group (e.g., United States), for which the scale was developed, than in the focal group (e.g., China), into which the scale was imported, the predictive relationship (e.g., self-esteem predicting life satisfaction) is artificially stronger in the reference group but weaker in the focal group, creating a bogus interaction effect of predictor by group (e.g., self-esteem by culture); the opposite pattern is found when the reference group has higher loadings in an outcome variable. Studies 2 and 3 examined the impact of lacking loading and intercept (i.e., point of origin) invariance on factor means, respectively. When the reference group has higher loadings or intercepts, the mean is overestimated in that group but underestimated in the focal group, resulting in a pseudo group difference. (c) 2008 APA, all rights reserved.

Improving Transferability of Introduced Species’ Distribution Models: New Tools to Forecast the Spread of a Highly Invasive Seaweed

PubMed Central

Verbruggen, Heroen; Tyberghein, Lennert; Belton, Gareth S.; Mineur, Frederic; Jueterbock, Alexander; Hoarau, Galice; Gurgel, C. Frederico D.; De Clerck, Olivier

2013-01-01

The utility of species distribution models for applications in invasion and global change biology is critically dependent on their transferability between regions or points in time, respectively. We introduce two methods that aim to improve the transferability of presence-only models: density-based occurrence thinning and performance-based predictor selection. We evaluate the effect of these methods along with the impact of the choice of model complexity and geographic background on the transferability of a species distribution model between geographic regions. Our multifactorial experiment focuses on the notorious invasive seaweed Caulerpacylindracea (previously Caulerpa racemosa var. cylindracea ) and uses Maxent, a commonly used presence-only modeling technique. We show that model transferability is markedly improved by appropriate predictor selection, with occurrence thinning, model complexity and background choice having relatively minor effects. The data shows that, if available, occurrence records from the native and invaded regions should be combined as this leads to models with high predictive power while reducing the sensitivity to choices made in the modeling process. The inferred distribution model of Caulerpacylindracea shows the potential for this species to further spread along the coasts of Western Europe, western Africa and the south coast of Australia. PMID:23950789

Is it beneficial to approximate pre-failure topography to predict landslide susceptibility with empirical models?

NASA Astrophysics Data System (ADS)

Steger, Stefan; Schmaltz, Elmar; Glade, Thomas

2017-04-01

Empirical landslide susceptibility maps spatially depict the areas where future slope failures are likely due to specific environmental conditions. The underlying statistical models are based on the assumption that future landsliding is likely to occur under similar circumstances (e.g. topographic conditions, lithology, land cover) as past slope failures. This principle is operationalized by applying a supervised classification approach (e.g. a regression model with a binary response: landslide presence/absence) that enables discrimination between conditions that favored past landslide occurrences and the circumstances typical for landslide absences. The derived empirical relation is then transferred to each spatial unit of an area. Literature reveals that the specific topographic conditions representative for landslide presences are frequently extracted from derivatives of digital terrain models at locations were past landslides were mapped. The underlying morphology-based landslide identification becomes possible due to the fact that the topography at a specific locality usually changes after landslide occurrence (e.g. hummocky surface, concave and steep scarp). In a strict sense, this implies that topographic predictors used within conventional statistical landslide susceptibility models relate to post-failure topographic conditions - and not to the required pre-failure situation. This study examines the assumption that models calibrated on the basis of post-failure topographies may not be appropriate to predict future landslide locations, because (i) post-failure and pre-failure topographic conditions may differ and (ii) areas were future landslides will occur do not yet exhibit such a distinct post-failure morphology. The study was conducted for an area located in the Walgau region (Vorarlberg, western Austria), where a detailed inventory consisting of shallow landslides was available. The methodology comprised multiple systematic comparisons of models generated on the basis of post-failure conditions (i.e. the standard approach) with models based on an approximated pre-failure topography. Pre-failure topography was approximated by (i) erasing the area of mapped landslide polygons within a digital terrain model and (ii) filling these "empty" areas by interpolating elevation points located outside the mapped landslides. Landslide presence information was extracted from the respective landslide scarp locations while an equal number of randomly sampled points represented landslide absences. After an initial exploratory data analysis, mixed-effects logistic regression was applied to model landslide susceptibility on the basis of two predictor sets (post-failure versus pre-failure predictors). Furthermore, all analyses were separately conducted for five different modelling resolutions to elaborate the suspicion that the degree of generalization of topographic parameters may as well play a role on how the respective models may differ. Model evaluation was conducted by means of multiple procedures (i.e. odds ratios, k-fold cross validation, permutation-based variable importance, difference maps of predictions). The results revealed that models based on highest resolutions (e.g. 1 m, 2.5 m) and post-failure topography performed best from a purely quantitative perspective. A confrontation of models (post-failure versus pre-failure based models) based on an identical modelling resolution exposed that validation results, modelled relationships as well as the prediction pattern tended to converge with a decreasing raster resolution. Based on the results, we concluded that an approximation of pre-failure topography does not significantly contribute to improved landslide susceptibility models in the case (i) the underlying inventory consists of small landslide features and (ii) the models are based on coarse raster resolutions (e.g. 25 m). However, in the case modelling with high raster resolutions is envisaged (e.g. 1 m, 2.5 m) or the inventory mainly consists of larger events, a reconstruction of pre-failure conditions might be highly expedient, even though conventional validation results might indicate an opposite tendency. Finally, we recommend to consider that topographic predictors highly useful to detect past slope movements (e.g. roughness) are not necessarily valuable to predict future slope instabilities.

A Systematic Review of Predictors of, and Reasons for, Adherence to Online Psychological Interventions.

PubMed

Beatty, Lisa; Binnion, Claire

2016-12-01

A key issue regarding the provision of psychological therapy in a self-guided online format is low rates of adherence. The aim of this systematic review was to assess both quantitative and qualitative data on the predictors of adherence, as well as participant reported reasons for adhering or not adhering to online psychological interventions. Database searches of PsycINFO, Medline, and CINAHL identified 1721 potentially relevant articles published between 1 January 2000 and 25 November 2015. A further 34 potentially relevant articles were retrieved from reference lists. Articles that reported predictors of, or reasons for, adherence to an online psychological intervention were included. A total of 36 studies met the inclusion criteria. Predictors assessed included demographic, psychological, characteristics of presenting problem, and intervention/computer-related predictors. Evidence suggested that female gender, higher treatment expectancy, sufficient time, and personalized intervention content each predicted higher adherence. Age, baseline symptom severity, and control group allocation had mixed findings. The majority of assessed variables however, did not predict adherence. Few clear predictors of adherence emerged overall, and most results were either mixed or too preliminary to draw conclusions. More research of predictors associated with adherence to online interventions is warranted.

Comparative evaluation of urinary PCA3 and TMPRSS2: ERG scores and serum PHI in predicting prostate cancer aggressiveness.

PubMed

Tallon, Lucile; Luangphakdy, Devillier; Ruffion, Alain; Colombel, Marc; Devonec, Marian; Champetier, Denis; Paparel, Philippe; Decaussin-Petrucci, Myriam; Perrin, Paul; Vlaeminck-Guillem, Virginie

2014-07-30

It has been suggested that urinary PCA3 and TMPRSS2:ERG fusion tests and serum PHI correlate to cancer aggressiveness-related pathological criteria at prostatectomy. To evaluate and compare their ability in predicting prostate cancer aggressiveness, PHI and urinary PCA3 and TMPRSS2:ERG (T2) scores were assessed in 154 patients who underwent radical prostatectomy for biopsy-proven prostate cancer. Univariate and multivariate analyses using logistic regression and decision curve analyses were performed. All three markers were predictors of a tumor volume≥0.5 mL. Only PHI predicted Gleason score≥7. T2 score and PHI were both independent predictors of extracapsular extension(≥pT3), while multifocality was only predicted by PCA3 score. Moreover, when compared to a base model (age, digital rectal examination, serum PSA, and Gleason sum at biopsy), the addition of both PCA3 score and PHI to the base model induced a significant increase (+12%) when predicting tumor volume>0.5 mL. PHI and urinary PCA3 and T2 scores can be considered as complementary predictors of cancer aggressiveness at prostatectomy.

Sociodemographic and career history predictors of suicide mortality in the United States Army 2004-2009.

PubMed

Gilman, S E; Bromet, E J; Cox, K L; Colpe, L J; Fullerton, C S; Gruber, M J; Heeringa, S G; Lewandowski-Romps, L; Millikan-Bell, A M; Naifeh, J A; Nock, M K; Petukhova, M V; Sampson, N A; Schoenbaum, M; Stein, M B; Ursano, R J; Wessely, S; Zaslavsky, A M; Kessler, R C

2014-09-01

The US Army suicide rate has increased sharply in recent years. Identifying significant predictors of Army suicides in Army and Department of Defense (DoD) administrative records might help focus prevention efforts and guide intervention content. Previous studies of administrative data, although documenting significant predictors, were based on limited samples and models. A career history perspective is used here to develop more textured models. The analysis was carried out as part of the Historical Administrative Data Study (HADS) of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). De-identified data were combined across numerous Army and DoD administrative data systems for all Regular Army soldiers on active duty in 2004-2009. Multivariate associations of sociodemographics and Army career variables with suicide were examined in subgroups defined by time in service, rank and deployment history. Several novel results were found that could have intervention implications. The most notable of these were significantly elevated suicide rates (69.6-80.0 suicides per 100 000 person-years compared with 18.5 suicides per 100 000 person-years in the total Army) among enlisted soldiers deployed either during their first year of service or with less than expected (based on time in service) junior enlisted rank; a substantially greater rise in suicide among women than men during deployment; and a protective effect of marriage against suicide only during deployment. A career history approach produces several actionable insights missed in less textured analyses of administrative data predictors. Expansion of analyses to a richer set of predictors might help refine understanding of intervention implications.

Sociodemographic and career history predictors of suicide mortality in the United States Army 2004–2009

PubMed Central

Gilman, S. E.; Bromet, E. J.; Cox, K. L.; Colpe, L. J.; Fullerton, C. S.; Gruber, M. J.; Heeringa, S.G.; Lewandowski-Romps, L.; Millikan-Bell, A.M.; Naifeh, J. A.; Nock, M. K.; Petukhova, M. V.; Sampson, N. A.; Schoenbaum, M.; Stein, M. B.; Ursano, R. J.; Wessely, S.; Zaslavsky, A.M.; Kessler, R. C.

2014-01-01

Background The US Army suicide rate has increased sharply in recent years. Identifying significant predictors of Army suicides in Army and Department of Defense (DoD) administrative records might help focus prevention efforts and guide intervention content. Previous studies of administrative data, although documenting significant predictors, were based on limited samples and models. A career history perspective is used here to develop more textured models. Method The analysis was carried out as part of the Historical Administrative Data Study (HADS) of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). De-identified data were combined across numerous Army and DoD administrative data systems for all Regular Army soldiers on active duty in 2004–2009. Multivariate associations of sociodemographics and Army career variables with suicide were examined in subgroups defined by time in service, rank and deployment history. Results Several novel results were found that could have intervention implications. The most notable of these were significantly elevated suicide rates (69.6–80.0 suicides per 100000 person-years compared with 18.5 suicides per 100000 person-years in the total Army) among enlisted soldiers deployed either during their first year of service or with less than expected (based on time in service) junior enlisted rank; a substantially greater rise in suicide among women than men during deployment; and a protective effect of marriage against suicide only during deployment. Conclusions A career history approach produces several actionable insights missed in less textured analyses of administrative data predictors. Expansion of analyses to a richer set of predictors might help refine understanding of intervention implications. PMID:25055175

Predictors of Initiation and Engagement of Cognitive Processing Therapy Among Veterans With PTSD Enrolled in Collaborative Care.

PubMed

Grubbs, Kathleen M; Fortney, John C; Pyne, Jeffrey M; Hudson, Teresa; Moore, William Mark; Custer, Paul; Schneider, Ronald; Schnurr, Paula P

2015-12-01

Collaborative care (CC) increases access to evidence-based pharmacotherapy and psychotherapy. The study aim was to identify the characteristics of rural veterans receiving a telemedicine-based CC intervention for posttraumatic stress disorder (PTSD) who initiated and engaged in cognitive processing therapy (CPT) delivered via interactive video. Veterans diagnosed with PTSD were recruited from 11 community-based outpatient clinics (N = 133). Chart abstraction identified all mental health encounters received during the 12-month study. General linear mixed models were used to identify characteristics that predicted CPT initiation and engagement (attendance at 8 or more sessions). For initiation, higher PTSD severity according to the Clinician Administered PTSD Scale (d = -0.39, p = .038) and opt-out recruitment (vs. self-referral; d = -0.49, p = .010) were negative predictors. For engagement, major depression (d = -1.32, p = .006) was a negative predictor whereas a pending claim for military service connected disability (d = 2.02, p = .008) was a positive predictor. In general, veterans enrolled in CC initiated and engaged in CPT at higher rates than usual care. Those with more severe symptoms and comorbidity, however, were at risk of not starting or completing CPT. © 2015 International Society for Traumatic Stress Studies.

Appendix 2: Risk-based framework and risk case studies. Risk Assessment for two bird species in northern Wisconsin.

Treesearch

Megan M. Friggens; Stephen N. Matthews

2012-01-01

Species distribution models for 147 bird species have been derived using climate, elevation, and distribution of current tree species as potential predictors (Matthews et al. 2011). In this case study, a risk matrix was developed for two bird species (fig. A2-5), with projected change in bird habitat (the x axis) based on models of changing suitable habitat resulting...

Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia

PubMed Central

Brown, Fred; Adelson, David; White, Deborah; Hughes, Timothy; Chaudhri, Naeem

2017-01-01

Background Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction ‘rules’ for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia. Principle Findings The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models. PMID:28045960

An aggregate method to calibrate the reference point of cumulative prospect theory-based route choice model for urban transit network

NASA Astrophysics Data System (ADS)

Zhang, Yufeng; Long, Man; Luo, Sida; Bao, Yu; Shen, Hanxia

2015-12-01

Transit route choice model is the key technology of public transit systems planning and management. Traditional route choice models are mostly based on expected utility theory which has an evident shortcoming that it cannot accurately portray travelers' subjective route choice behavior for their risk preferences are not taken into consideration. Cumulative prospect theory (CPT), a brand new theory, can be used to describe travelers' decision-making process under the condition of uncertainty of transit supply and risk preferences of multi-type travelers. The method to calibrate the reference point, a key parameter to CPT-based transit route choice model, determines the precision of the model to a great extent. In this paper, a new method is put forward to obtain the value of reference point which combines theoretical calculation and field investigation results. Comparing the proposed method with traditional method, it shows that the new method can promote the quality of CPT-based model by improving the accuracy in simulating travelers' route choice behaviors based on transit trip investigation from Nanjing City, China. The proposed method is of great significance to logical transit planning and management, and to some extent makes up the defect that obtaining the reference point is solely based on qualitative analysis.

Integrated Reconfigurable Intelligent Systems (IRIS) for Complex Naval Systems

DTIC Science & Technology

2010-02-21

RKF45] and Adams Variable Step- Size Predictor - Corrector methods). While such algorithms naturally are usually used to numerically solve differential...verified by yet another function call. Due to their nature, such methods are referred to as predictor - corrector methods. While computationally expensive...CONTRACT NUMBER N00014-09- C -0394 5b. GRANT NUMBER N/A 5c. PROGRAM ELEMENT NUMBER N/A 6. Author(s) Dr. Dimitri N. Mavris Dr. Yongchang Li 5d

Clinical predictors of advanced sellar masses.

PubMed

Rambaldini, Gloria M; Butalia, Sonia; Ezzat, Shereen; Kucharczyk, Walter; Sawka, Anna M

2007-10-01

To identify clinical variables associated with the presence of a structurally advanced sellar mass (ASM). We performed a retrospective study of patients referred for evaluation of suspected new pituitary disease or sellar mass to the Endocrine Oncology Unit of Mount Sinai Hospital in Toronto, Ontario, Canada. By multivariate analysis, we examined predictors of a structurally ASM (a sellar lesion with any of the following characteristics: diameter of >or=1 cm on magnetic resonance imaging [MRI], optic chiasmal compression on MRI, or clinical or biochemical evidence of hypopituitarism). Data from 152 patients were analyzed. Of the 152 sellar masses, 142 (93%) were pituitary adenomas. An ASM was noted in 85 of the 152 patients (56%). In the final multivariate model, male sex (odds ratio [OR], 6.23; 95% confidence interval [CI], 2.84 to 13.56; P<0.001) and self-reported visual field defect (OR, 3.62; 95% CI, 1.07 to 12.25; P = 0.039) were significantly independently associated with the presence of an ASM. The presence of new or changed headaches also tended to be associated with an ASM (OR, 2.11; 95% CI, 0.96 to 4.64; P = 0.063). Age and self-reported galactorrhea were not independently associated with the presence of an ASM and were conditionally removed from the final model. In patients with suspected sellar or pituitary disease, male sex and self-reported visual field defects independently predict the presence of an ASM. New or changed headaches also tend to be related to the presence of an ASM. The presence of predictors of an ASM should prompt expedited sellar MRI and biochemical evaluation.

Predictor-corrector framework for the sequential assembly of optical systems based on wavefront sensing.

PubMed

Schindlbeck, Christopher; Pape, Christian; Reithmeier, Eduard

2018-04-16

Alignment of optical components is crucial for the assembly of optical systems to ensure their full functionality. In this paper we present a novel predictor-corrector framework for the sequential assembly of serial optical systems. Therein, we use a hybrid optical simulation model that comprises virtual and identified component positions. The hybrid model is constantly adapted throughout the assembly process with the help of nonlinear identification techniques and wavefront measurements. This enables prediction of the future wavefront at the detector plane and therefore allows for taking corrective measures accordingly during the assembly process if a user-defined tolerance on the wavefront error is violated. We present a novel notation for the so-called hybrid model and outline the work flow of the presented predictor-corrector framework. A beam expander is assembled as demonstrator for experimental verification of the framework. The optical setup consists of a laser, two bi-convex spherical lenses each mounted to a five degree-of-freedom stage to misalign and correct components, and a Shack-Hartmann sensor for wavefront measurements.

Hysteretic Models Considering Axial-Shear-Flexure Interaction

NASA Astrophysics Data System (ADS)

Ceresa, Paola; Negrisoli, Giorgio

2017-10-01

Most of the existing numerical models implemented in finite element (FE) software, at the current state of the art, are not capable to describe, with enough reliability, the interaction between axial, shear and flexural actions under cyclic loading (e.g. seismic actions), neglecting crucial effects for predicting the nature of the collapse of reinforced concrete (RC) structural elements. Just a few existing 3D volume models or fibre beam models can lead to a quite accurate response, but they are still computationally inefficient for typical applications in earthquake engineering and also characterized by very complex formulation. Thus, discrete models with lumped plasticity hinges may be the preferred choice for modelling the hysteretic behaviour due to cyclic loading conditions, in particular with reference to its implementation in a commercial software package. These considerations lead to this research work focused on the development of a model for RC beam-column elements able to consider degradation effects and interaction between the actions under cyclic loading conditions. In order to develop a model for a general 3D discrete hinge element able to take into account the axial-shear-flexural interaction, it is necessary to provide an implementation which involves a corrector-predictor iterative scheme. Furthermore, a reliable constitutive model based on damage plasticity theory is formulated and implemented for its numerical validation. Aim of this research work is to provide the formulation of a numerical model, which will allow implementation within a FE software package for nonlinear cyclic analysis of RC structural members. The developed model accounts for stiffness degradation effect and stiffness recovery for loading reversal.

Comparison of different statistical modelling approaches for deriving spatial air temperature patterns in an urban environment

NASA Astrophysics Data System (ADS)

Straub, Annette; Beck, Christoph; Breitner, Susanne; Cyrys, Josef; Geruschkat, Uta; Jacobeit, Jucundus; Kühlbach, Benjamin; Kusch, Thomas; Richter, Katja; Schneider, Alexandra; Umminger, Robin; Wolf, Kathrin

2017-04-01

Frequently spatial variations of air temperature of considerable magnitude occur within urban areas. They correspond to varying land use/land cover characteristics and vary with season, time of day and synoptic conditions. These temperature differences have an impact on human health and comfort directly by inducing thermal stress as well as indirectly by means of affecting air quality. Therefore, knowledge of the spatial patterns of air temperature in cities and the factors causing them is of great importance, e.g. for urban planners. A multitude of studies have shown statistical modelling to be a suitable tool for generating spatial air temperature patterns. This contribution presents a comparison of different statistical modelling approaches for deriving spatial air temperature patterns in the urban environment of Augsburg, Southern Germany. In Augsburg there exists a measurement network for air temperature and humidity currently comprising 48 stations in the city and its rural surroundings (corporately operated by the Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health and the Institute of Geography, University of Augsburg). Using different datasets for land surface characteristics (Open Street Map, Urban Atlas) area percentages of different types of land cover were calculated for quadratic buffer zones of different size (25, 50, 100, 250, 500 m) around the stations as well for source regions of advective air flow and used as predictors together with additional variables such as sky view factor, ground level and distance from the city centre. Multiple Linear Regression and Random Forest models for different situations taking into account season, time of day and weather condition were applied utilizing selected subsets of these predictors in order to model spatial distributions of mean hourly and daily air temperature deviations from a rural reference station. Furthermore, the different model setups were evaluated and the relative importance of individual predictors was examined via averaging over orderings (for MLR) and permutation importance (for RF) respectively. The results indicate that MLR is superior to RF with mean squared skill scores reaching up to 0.85 and R2 in leave-one-out cross validation up to 65% for individual situations and setups. The best performing models are obtained for situations with low to medium wind velocities before sunrise and after sunset. Important predictor variables for these situations are percentage of built-up area, sky view factor, and distance from the city centre.

Predictors of persistent maternal depression trajectories in early childhood: results from the EDEN mother-child cohort study in France.

PubMed

van der Waerden, J; Galéra, C; Saurel-Cubizolles, M-J; Sutter-Dallay, A-L; Melchior, M

2015-07-01

Maternal depression in the pre- and postpartum period may set women on a course of chronic depressive symptoms. Little is known about predictors of persistently elevated depressive symptoms in mothers from pregnancy onwards. The aims of this study are to determine maternal depression trajectories from pregnancy to the child's fifth birthday and identify associated risk factors. Mothers (N = 1807) from the EDEN mother-child birth cohort study based in France (2003-2011) were followed from 24-28 weeks of pregnancy to their child's fifth birthday. Maternal depression trajectories were determined with a semi-parametric group-based modelling strategy. Sociodemographic, psychosocial and psychiatric predictors were explored for their association with trajectory class membership. Five trajectories of maternal symptoms of depression from pregnancy onwards were identified: no symptoms (60.2%); persistent intermediate-level depressive symptoms (25.2%); persistent high depressive symptoms (5.0%); high symptoms in pregnancy only (4.7%); high symptoms in the child's preschool period only (4.9%). Socio-demographic predictors associated with persistent depression were non-French origin; psychosocial predictors were childhood adversities, life events during pregnancy and work overinvestment; psychiatric predictors were previous mental health problems, psychological help, and high anxiety during pregnancy. Persistent depression in mothers of young children is associated to several risk factors present prior to or during pregnancy, notably anxiety. These characteristics precede depression trajectories and offer a possible entry point to enhance mother's mental health and reduce its burden on children.

Predicting PDZ domain mediated protein interactions from structure

PubMed Central

2013-01-01

Background PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. Results We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling. Conclusions We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training–testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW. PMID:23336252

Requirements for data integration platforms in biomedical research networks: a reference model

PubMed Central

Knaup, Petra

2015-01-01

Biomedical research networks need to integrate research data among their members and with external partners. To support such data sharing activities, an adequate information technology infrastructure is necessary. To facilitate the establishment of such an infrastructure, we developed a reference model for the requirements. The reference model consists of five reference goals and 15 reference requirements. Using the Unified Modeling Language, the goals and requirements are set into relation to each other. In addition, all goals and requirements are described textually in tables. This reference model can be used by research networks as a basis for a resource efficient acquisition of their project specific requirements. Furthermore, a concrete instance of the reference model is described for a research network on liver cancer. The reference model is transferred into a requirements model of the specific network. Based on this concrete requirements model, a service-oriented information technology architecture is derived and also described in this paper. PMID:25699205

Translational systems pharmacology‐based predictive assessment of drug‐induced cardiomyopathy

PubMed Central

Messinis, Dimitris E.; Melas, Ioannis N.; Hur, Junguk; Varshney, Navya; Alexopoulos, Leonidas G.

2018-01-01

Drug‐induced cardiomyopathy contributes to drug attrition. We compared two pipelines of predictive modeling: (1) applying elastic net (EN) to differentially expressed genes (DEGs) of drugs; (2) applying integer linear programming (ILP) to construct each drug's signaling pathway starting from its targets to downstream proteins, to transcription factors, and to its DEGs in human cardiomyocytes, and then subjecting the genes/proteins in the drugs' signaling networks to EN regression. We classified 31 drugs with availability of DEGs into 13 toxic and 18 nontoxic drugs based on a clinical cardiomyopathy incidence cutoff of 0.1%. The ILP‐augmented modeling increased prediction accuracy from 79% to 88% (sensitivity: 88%; specificity: 89%) under leave‐one‐out cross validation. The ILP‐constructed signaling networks of drugs were better predictors than DEGs. Per literature, the microRNAs that reportedly regulate expression of our six top predictors are of diagnostic value for natural heart failure or doxorubicin‐induced cardiomyopathy. This translational predictive modeling might uncover potential biomarkers. PMID:29341478

Accounting for care: Healthcare Resource Groups for paediatric critical care.

PubMed

Murphy, Janet; Morris, Kevin

2008-02-01

Healthcare Resource Groups are a way of grouping patients in relation to the amount of healthcare resources they consume. They are the basis for implementation of Payment by Results by the Department of Health in England. An expert working group was set up to define a dataset for paediatric critical care that would in turn support the derivation of Healthcare Resource Groups. Three relevant classification systems were identified and tested with data from ten PICUs, including data about diagnoses, number of organ systems supported, interventions and nursing activity. Each PICU provided detailed costing for the financial year 2005/2006. Eighty-three per cent of PICU costs were found to be related to staff costs, with the largest cost being nursing costs. The Nursing Activity Score system was found to be a poor predictor of staff resource use, as was the adult HRG model based on the number of organ systems supported. It was decided to develop the HRGs based on a 'levels of care' approach; 32 data items were defined to support HRG allocation. From October 2007, data have been collected daily to identify the HRGs for each PICU patient and are being used by the Department of Health to estimate reference costs for PICU services. The data can also be used to support improved audit of PICU activity nationally as well as comparison of workload across different units and modelling of staff requirements within a unit.

Landscape capability predicts upland game bird abundance and occurrence

USGS Publications Warehouse

Loman, Zachary G.; Blomberg, Erik J.; DeLuca, William; Harrison, Daniel J.; Loftin, Cyndy; Wood, Petra B.

2017-01-01

Landscape capability (LC) models are a spatial tool with potential applications in conservation planning. We used survey data to validate LC models as predictors of occurrence and abundance at broad and fine scales for American woodcock (Scolopax minor) and ruffed grouse (Bonasa umbellus). Landscape capability models were reliable predictors of occurrence but were less indicative of relative abundance at route (11.5–14.6 km) and point scales (0.5–1 km). As predictors of occurrence, LC models had high sensitivity (0.71–0.93) and were accurate (0.71–0.88) and precise (0.88 and 0.92 for woodcock and grouse, respectively). Models did not predict point-scale abundance independent of the ability to predict occurrence of either species. The LC models are useful predictors of patterns of occurrences in the northeastern United States, but they have limited utility as predictors of fine-scale or route-specific abundances. 

Prevention of Incontinence Associated Skin Damage in Nursing Homes: Disparities and Predictors

PubMed Central

Bliss, Donna Z.; Gurvich, Olga V.; Mathiason, Michelle A.; Eberly, Lynn E.; Savik, Kay; Harms, Susan; Mueller, Christine; Wyman, Jean F.; Virnig, Beth

2016-01-01

Racial/ethnic disparities in preventing health problems have been reported in nursing homes. Incontinence is common among nursing home residents and can result in inflammatory-type skin damage, referred to as incontinence associated skin damage (IASD). Little is known about the prevention of IASD and whether there are racial/ethnic disparities in its prevention. This study assessed the proportion of older nursing home residents receiving IASD prevention after developing incontinence after admission (n=10,713) and whether there were racial/ethnic disparities in IASD prevention. Predictors of preventing IASD were also examined. Four national datasets provided potential predictors at multiple levels. Disparities were analyzed using the Peters-Belson method; predictors of preventing IASD were assessed using hierarchical logistic regression. Prevention of IASD was received by 0.12 of residents and no racial/ethnic disparities were found. Predictors of preventing IASD were primarily resident level factors including limitations in activities of daily living, poor nutrition, and more oxygenation problems. PMID:27586441

Use of generalised additive models to categorise continuous variables in clinical prediction

PubMed Central

2013-01-01

Background In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind. Methods We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology. Results The three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically significant differences being found between the two AUCs (p =0.079). The four-category proposal for PCO2 was ≤ 43;(43,52];(52,65];> 65, for which the following values were obtained: AIC=258.1 and AUC=0.81. No statistically significant differences were found between the AUC of the four-category option and that of the continuous predictor, which yielded an AIC of 250.3 and an AUC of 0.825 (p =0.115). Conclusions Our proposed method provides clinicians with the number and location of cut points for categorising variables, and performs as successfully as the original continuous predictor when it comes to developing clinical prediction rules. PMID:23802742

Using association rules to measure Subjective Organization after Acquired Brain Injury.

PubMed

Parente, Frederick; Finley, John-Christopher

2018-01-01

Subjective Organization (SO) refers to the human tendency to impose organization on our environment. Persons with Acquired Brain Injury (ABI) often lose the ability to organize however, there are no performance based measures of organization that can be used to document this disability. The authors propose a method of association rule analysis (AR) that can be used as a clinical tool for assessing a patient's ability to organize. Twenty three patients with ABI recalled a list of twelve unrelated nouns over twelve study and test trials. Several measures of AR computed on these data were correlated with various measures of short-term, long-term, and delayed recall of the words. All of the AR measures correlated significantly with the short-term and long-term memory measures. The confidence measure was the best predictor of memory and the number of association rules generated was the best predictor of learning. The confidence measure can be used as a clinical tool to assess SO with individual ABI survivors.

Predicting Incursion of Plant Invaders into Kruger National Park, South Africa: The Interplay of General Drivers and Species-Specific Factors

PubMed Central

Jarošík, Vojtěch; Pyšek, Petr; Foxcroft, Llewellyn C.; Richardson, David M.; Rouget, Mathieu; MacFadyen, Sandra

2011-01-01

Background Overcoming boundaries is crucial for incursion of alien plant species and their successful naturalization and invasion within protected areas. Previous work showed that in Kruger National Park, South Africa, this process can be quantified and that factors determining the incursion of invasive species can be identified and predicted confidently. Here we explore the similarity between determinants of incursions identified by the general model based on a multispecies assemblage, and those identified by species-specific models. We analyzed the presence and absence of six invasive plant species in 1.0×1.5 km segments along the border of the park as a function of environmental characteristics from outside and inside the KNP boundary, using two data-mining techniques: classification trees and random forests. Principal Findings The occurrence of Ageratum houstonianum, Chromolaena odorata, Xanthium strumarium, Argemone ochroleuca, Opuntia stricta and Lantana camara can be reliably predicted based on landscape characteristics identified by the general multispecies model, namely water runoff from surrounding watersheds and road density in a 10 km radius. The presence of main rivers and species-specific combinations of vegetation types are reliable predictors from inside the park. Conclusions The predictors from the outside and inside of the park are complementary, and are approximately equally reliable for explaining the presence/absence of current invaders; those from the inside are, however, more reliable for predicting future invasions. Landscape characteristics determined as crucial predictors from outside the KNP serve as guidelines for management to enact proactive interventions to manipulate landscape features near the KNP to prevent further incursions. Predictors from the inside the KNP can be used reliably to identify high-risk areas to improve the cost-effectiveness of management, to locate invasive plants and target them for eradication. PMID:22194893

Predicting incursion of plant invaders into Kruger National Park, South Africa: the interplay of general drivers and species-specific factors.

PubMed

Jarošík, Vojtěch; Pyšek, Petr; Foxcroft, Llewellyn C; Richardson, David M; Rouget, Mathieu; MacFadyen, Sandra

2011-01-01

Overcoming boundaries is crucial for incursion of alien plant species and their successful naturalization and invasion within protected areas. Previous work showed that in Kruger National Park, South Africa, this process can be quantified and that factors determining the incursion of invasive species can be identified and predicted confidently. Here we explore the similarity between determinants of incursions identified by the general model based on a multispecies assemblage, and those identified by species-specific models. We analyzed the presence and absence of six invasive plant species in 1.0×1.5 km segments along the border of the park as a function of environmental characteristics from outside and inside the KNP boundary, using two data-mining techniques: classification trees and random forests. The occurrence of Ageratum houstonianum, Chromolaena odorata, Xanthium strumarium, Argemone ochroleuca, Opuntia stricta and Lantana camara can be reliably predicted based on landscape characteristics identified by the general multispecies model, namely water runoff from surrounding watersheds and road density in a 10 km radius. The presence of main rivers and species-specific combinations of vegetation types are reliable predictors from inside the park. The predictors from the outside and inside of the park are complementary, and are approximately equally reliable for explaining the presence/absence of current invaders; those from the inside are, however, more reliable for predicting future invasions. Landscape characteristics determined as crucial predictors from outside the KNP serve as guidelines for management to enact proactive interventions to manipulate landscape features near the KNP to prevent further incursions. Predictors from the inside the KNP can be used reliably to identify high-risk areas to improve the cost-effectiveness of management, to locate invasive plants and target them for eradication.

A New Class of Highly Accurate Differentiation Schemes Based on the Prolate Spheroidal Wave Functions

DTIC Science & Technology

2011-04-07

predictor - corrector scheme. Such an approach for the solution of time-dependent PDEs, which is some- times referred to as the “method of lines,” is studied...particular, λj = i j |λj |. We define the self -adjoint operator Qc : L 2([−1, 1]) → L2([−1, 1]) by the formula Qc(φ) = 1 π ∫ 1 −1 sin( c (x− t)) x− t φ...Gaussian quadratures for bandlimited functions is to use the Newton-type nonlinear optimization algorithm of [14]. Specifically, for bandlimit c and

Predictability and prediction of the total number of winter extremely cold days over China

NASA Astrophysics Data System (ADS)

Luo, Xiao; Wang, Bin

2018-03-01

The current dynamical climate models have limited skills in predicting winter temperature in China. The present study uses physics-based empirical models (PEMs) to explore the sources and limits of the seasonal predictability in the total number of extremely cold days (NECD) over China. A combined cluster-rotated EOF analysis reveals two sub-regions of homogeneous variability among hundreds of stations, namely the Northeast China (NE) and Main China (MC). This reduces the large-number of predictands to only two indices, the NCED-NE and NCED-MC, which facilitates detection of the common sources of predictability for all stations. The circulation anomalies associated with the NECD-NE exhibit a zonally symmetric Arctic Oscillation-like pattern, whereas those associated with the NECD-MC feature a North-South dipolar pattern over Asia. The predictability of the NECD originates from SST and snow cover anomalies in the preceding September and October. However, the two regions have different SST predictors: The NE predictor is in the western Eurasian Arctic while the MC predictor is over the tropical-North Pacific. The October snow cover predictors also differ: The NE predictor primarily resides in the central Eurasia while the MC predictor is over the western and eastern Eurasia. The PEM prediction results suggest that about 60% (55%) of the total variance of winter NECD over the NE (Main) China are likely predictable 1 month in advance. The NECD at each station can also be predicted by using the four predictors that were detected for the two indices. The cross-validated temporal correlation skills exceed 0.70 at most stations. The physical mechanisms by which the autumn Arctic sea ice, snow cover, and tropical-North Pacific SST anomalies affect winter NECD over the NE and Main China are discussed.

Performance of time-varying predictors in multilevel models under an assumption of fixed or random effects.

PubMed

Baird, Rachel; Maxwell, Scott E

2016-06-01

Time-varying predictors in multilevel models are a useful tool for longitudinal research, whether they are the research variable of interest or they are controlling for variance to allow greater power for other variables. However, standard recommendations to fix the effect of time-varying predictors may make an assumption that is unlikely to hold in reality and may influence results. A simulation study illustrates that treating the time-varying predictor as fixed may allow analyses to converge, but the analyses have poor coverage of the true fixed effect when the time-varying predictor has a random effect in reality. A second simulation study shows that treating the time-varying predictor as random may have poor convergence, except when allowing negative variance estimates. Although negative variance estimates are uninterpretable, results of the simulation show that estimates of the fixed effect of the time-varying predictor are as accurate for these cases as for cases with positive variance estimates, and that treating the time-varying predictor as random and allowing negative variance estimates performs well whether the time-varying predictor is fixed or random in reality. Because of the difficulty of interpreting negative variance estimates, 2 procedures are suggested for selection between fixed-effect and random-effect models: comparing between fixed-effect and constrained random-effect models with a likelihood ratio test or fitting a fixed-effect model when an unconstrained random-effect model produces negative variance estimates. The performance of these 2 procedures is compared. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Is questionnaire-based sitting time inaccurate and can it be improved? A cross-sectional investigation using accelerometer-based sitting time.

PubMed

Gupta, Nidhi; Christiansen, Caroline Stordal; Hanisch, Christiana; Bay, Hans; Burr, Hermann; Holtermann, Andreas

2017-01-16

To investigate the differences between a questionnaire-based and accelerometer-based sitting time, and develop a model for improving the accuracy of questionnaire-based sitting time for predicting accelerometer-based sitting time. 183 workers in a cross-sectional study reported sitting time per day using a single question during the measurement period, and wore 2 Actigraph GT3X+ accelerometers on the thigh and trunk for 1-4 working days to determine their actual sitting time per day using the validated Acti4 software. Least squares regression models were fitted with questionnaire-based siting time and other self-reported predictors to predict accelerometer-based sitting time. Questionnaire-based and accelerometer-based average sitting times were ≈272 and ≈476 min/day, respectively. A low Pearson correlation (r=0.32), high mean bias (204.1 min) and wide limits of agreement (549.8 to -139.7 min) between questionnaire-based and accelerometer-based sitting time were found. The prediction model based on questionnaire-based sitting explained 10% of the variance in accelerometer-based sitting time. Inclusion of 9 self-reported predictors in the model increased the explained variance to 41%, with 10% optimism using a resampling bootstrap validation. Based on a split validation analysis, the developed prediction model on ≈75% of the workers (n=132) reduced the mean and the SD of the difference between questionnaire-based and accelerometer-based sitting time by 64% and 42%, respectively, in the remaining 25% of the workers. This study indicates that questionnaire-based sitting time has low validity and that a prediction model can be one solution to materially improve the precision of questionnaire-based sitting time. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Brazil soybean yield covariance model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate soybean yields for the seven soybean-growing states of Brazil. The meteorological data of these seven states were pooled and the years 1975 to 1980 were used to model since there was no technological trend in the yields during these years. Predictor variables were derived from monthly total precipitation and monthly average temperature.

Predicting adolescents' intake of fruits and vegetables.

PubMed

Lytle, Leslie A; Varnell, Sherri; Murray, David M; Story, Mary; Perry, Cheryl; Birnbaum, Amanda S; Kubik, Martha Y

2003-01-01

To explore potential predictors of adolescents' fruit and vegetable intake by expanding on current theory and drawing from other adolescent research. This research reports on baseline and interim data from a school-based intervention study. Data were collected through surveys administered to students at the beginning and end of their 7th grade year. The students attended 16 public schools in Minnesota. Data were collected on 3878 students; approximately half were female and 67% were white. All students in the 7th grade cohort were invited to participate in the surveys and over 94% completed both surveys. Our dependent variable, fruit and vegetable intake, was assessed by a validated fruit and vegetable food frequency scale. Predictive factors assessed included parenting style, spirituality/religiosity, depressive symptoms, and other commonly assessed predictors. Generalized linear mixed model regression. Omnibus test of association using P <.05 is reported. Subjective norms, barriers, knowledge, usual food choice, parenting style, spirituality/religiosity, and depressive symptoms were statistically significant predictors of intake. The model explained about 31% of the variance in fruit and vegetable consumption. To better understand adolescents' fruit and vegetable intake, we must explore novel predictors. Our results need to be replicated, and more exploratory research in this field is needed.