Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.

PubMed

Enns, Eva A; Cipriano, Lauren E; Simons, Cyrena T; Kong, Chung Yin

2015-02-01

To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting. For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results. Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. © The Author(s) 2014.

The Comparative Performance of Conditional Independence Indices

ERIC Educational Resources Information Center

Kim, Doyoung; De Ayala, R. J.; Ferdous, Abdullah A.; Nering, Michael L.

2011-01-01

To realize the benefits of item response theory (IRT), one must have model-data fit. One facet of a model-data fit investigation involves assessing the tenability of the conditional item independence (CII) assumption. In this Monte Carlo study, the comparative performance of 10 indices for identifying conditional item dependence is assessed. The…

Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation.

PubMed

Clark, D Angus; Bowles, Ryan P

2018-04-23

In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker-Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA.

Fitting Item Response Theory Models to Two Personality Inventories: Issues and Insights.

PubMed

Chernyshenko, O S; Stark, S; Chan, K Y; Drasgow, F; Williams, B

2001-10-01

The present study compared the fit of several IRT models to two personality assessment instruments. Data from 13,059 individuals responding to the US-English version of the Fifth Edition of the Sixteen Personality Factor Questionnaire (16PF) and 1,770 individuals responding to Goldberg's 50 item Big Five Personality measure were analyzed. Various issues pertaining to the fit of the IRT models to personality data were considered. We examined two of the most popular parametric models designed for dichotomously scored items (i.e., the two- and three-parameter logistic models) and a parametric model for polytomous items (Samejima's graded response model). Also examined were Levine's nonparametric maximum likelihood formula scoring models for dichotomous and polytomous data, which were previously found to provide good fits to several cognitive ability tests (Drasgow, Levine, Tsien, Williams, & Mead, 1995). The two- and three-parameter logistic models fit some scales reasonably well but not others; the graded response model generally did not fit well. The nonparametric formula scoring models provided the best fit of the models considered. Several implications of these findings for personality measurement and personnel selection were described.

Phenomenology and Signal Processing for UXO/Clutter Discrimination

DTIC Science & Technology

2009-08-01

29 Figure 25. (a) Pasion -Oldenburg model fit to the EMI response of a 4.2 inch mortar aligned transverse to the primary field...b) Comparison between the two-component and Pasion - Oldenburg model fits to the 4.2 inch mortar response...30 Figure 26. Pasion -Oldenburg exponent γ compared to the magnetic crossover time τM for model fits to EMI data collected

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach

PubMed Central

Enns, Eva A.; Cipriano, Lauren E.; Simons, Cyrena T.; Kong, Chung Yin

2014-01-01

Background To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single “goodness-of-fit” (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. Methods We demonstrate the Pareto frontier approach in the calibration of two models: a simple, illustrative Markov model and a previously-published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to two possible weighted-sum GOF scoring systems, and compare the health economic conclusions arising from these different definitions of best-fitting. Results For the simple model, outcomes evaluated over the best-fitting input sets according to the two weighted-sum GOF schemes were virtually non-overlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95%CI: 72,500 – 87,600] vs. $139,700 [95%CI: 79,900 - 182,800] per QALY gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95%CI: 64,900 – 156,200] per QALY gained). The TAVR model yielded similar results. Conclusions Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. PMID:24799456

A new approach to correct the QT interval for changes in heart rate using a nonparametric regression model in beagle dogs.

PubMed

Watanabe, Hiroyuki; Miyazaki, Hiroyasu

2006-01-01

Over- and/or under-correction of QT intervals for changes in heart rate may lead to misleading conclusions and/or masking the potential of a drug to prolong the QT interval. This study examines a nonparametric regression model (Loess Smoother) to adjust the QT interval for differences in heart rate, with an improved fitness over a wide range of heart rates. 240 sets of (QT, RR) observations collected from each of 8 conscious and non-treated beagle dogs were used as the materials for investigation. The fitness of the nonparametric regression model to the QT-RR relationship was compared with four models (individual linear regression, common linear regression, and Bazett's and Fridericia's correlation models) with reference to Akaike's Information Criterion (AIC). Residuals were visually assessed. The bias-corrected AIC of the nonparametric regression model was the best of the models examined in this study. Although the parametric models did not fit, the nonparametric regression model improved the fitting at both fast and slow heart rates. The nonparametric regression model is the more flexible method compared with the parametric method. The mathematical fit for linear regression models was unsatisfactory at both fast and slow heart rates, while the nonparametric regression model showed significant improvement at all heart rates in beagle dogs.

Development of a modified independent parallel reactions kinetic model and comparison with the distributed activation energy model for the pyrolysis of a wide variety of biomass fuels.

PubMed

Sfakiotakis, Stelios; Vamvuka, Despina

2015-12-01

The pyrolysis of six waste biomass samples was studied and the fuels were kinetically evaluated. A modified independent parallel reactions scheme (IPR) and a distributed activation energy model (DAEM) were developed and their validity was assessed and compared by checking their accuracy of fitting the experimental results, as well as their prediction capability in different experimental conditions. The pyrolysis experiments were carried out in a thermogravimetric analyzer and a fitting procedure, based on least squares minimization, was performed simultaneously at different experimental conditions. A modification of the IPR model, considering dependence of the pre-exponential factor on heating rate, was proved to give better fit results for the same number of tuned kinetic parameters, comparing to the known IPR model and very good prediction results for stepwise experiments. Fit of calculated data to the experimental ones using the developed DAEM model was also proved to be very good. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dust in the Small Magellanic Cloud

NASA Technical Reports Server (NTRS)

Rodrigues, C. V.; Coyne, G. V.; Magalhaes, A. M.

1995-01-01

We discuss simultaneous dust model fits to our extinction and polarization data for the Small Magellanic Cloud (SMC) using existing dust models. Dust model fits to the wavelength dependent polarization are possible for stars with small lambda(sub max). They generally imply size distributions which are narrower and have smaller average sizes compared to those in the Galaxy. The best fits for the extinction curves are obtained with a power law size distribution. The typical, monotonic SMC extinction curve can be well fit with graphite and silicate grains if a small fraction of the SMC carbon is locked up in the grains. Amorphous carbon and silicate grains also fit the data well.

Comparative testing of dark matter models with 15 HSB and 15 LSB galaxies

NASA Astrophysics Data System (ADS)

Kun, E.; Keresztes, Z.; Simkó, A.; Szűcs, G.; Gergely, L. Á.

2017-12-01

Context. We assemble a database of 15 high surface brightness (HSB) and 15 low surface brightness (LSB) galaxies, for which surface brightness density and spectroscopic rotation curve data are both available and representative for various morphologies. We use this dataset to test the Navarro-Frenk-White, the Einasto, and the pseudo-isothermal sphere dark matter models. Aims: We investigate the compatibility of the pure baryonic model and baryonic plus one of the three dark matter models with observations on the assembled galaxy database. When a dark matter component improves the fit with the spectroscopic rotational curve, we rank the models according to the goodness of fit to the datasets. Methods: We constructed the spatial luminosity density of the baryonic component based on the surface brightness profile of the galaxies. We estimated the mass-to-light (M/L) ratio of the stellar component through a previously proposed color-mass-to-light ratio relation (CMLR), which yields stellar masses independent of the photometric band. We assumed an axissymetric baryonic mass model with variable axis ratios together with one of the three dark matter models to provide the theoretical rotational velocity curves, and we compared them with the dataset. In a second attempt, we addressed the question whether the dark component could be replaced by a pure baryonic model with fitted M/L ratios, varied over ranges consistent with CMLR relations derived from the available stellar population models. We employed the Akaike information criterion to establish the performance of the best-fit models. Results: For 7 galaxies (2 HSB and 5 LSB), neither model fits the dataset within the 1σ confidence level. For the other 23 cases, one of the models with dark matter explains the rotation curve data best. According to the Akaike information criterion, the pseudo-isothermal sphere emerges as most favored in 14 cases, followed by the Navarro-Frenk-White (6 cases) and the Einasto (3 cases) dark matter models. We find that the pure baryonic model with fitted M/L ratios falls within the 1σ confidence level for 10 HSB and 2 LSB galaxies, at the price of growing the M/Ls on average by a factor of two, but the fits are inferior compared to the best-fitting dark matter model.

Three-dimensional whole-brain perfusion quantification using pseudo-continuous arterial spin labeling MRI at multiple post-labeling delays: accounting for both arterial transit time and impulse response function.

PubMed

Qin, Qin; Huang, Alan J; Hua, Jun; Desmond, John E; Stevens, Robert D; van Zijl, Peter C M

2014-02-01

Measurement of the cerebral blood flow (CBF) with whole-brain coverage is challenging in terms of both acquisition and quantitative analysis. In order to fit arterial spin labeling-based perfusion kinetic curves, an empirical three-parameter model which characterizes the effective impulse response function (IRF) is introduced, which allows the determination of CBF, the arterial transit time (ATT) and T(1,eff). The accuracy and precision of the proposed model were compared with those of more complicated models with four or five parameters through Monte Carlo simulations. Pseudo-continuous arterial spin labeling images were acquired on a clinical 3-T scanner in 10 normal volunteers using a three-dimensional multi-shot gradient and spin echo scheme at multiple post-labeling delays to sample the kinetic curves. Voxel-wise fitting was performed using the three-parameter model and other models that contain two, four or five unknown parameters. For the two-parameter model, T(1,eff) values close to tissue and blood were assumed separately. Standard statistical analysis was conducted to compare these fitting models in various brain regions. The fitted results indicated that: (i) the estimated CBF values using the two-parameter model show appreciable dependence on the assumed T(1,eff) values; (ii) the proposed three-parameter model achieves the optimal balance between the goodness of fit and model complexity when compared among the models with explicit IRF fitting; (iii) both the two-parameter model using fixed blood T1 values for T(1,eff) and the three-parameter model provide reasonable fitting results. Using the proposed three-parameter model, the estimated CBF (46 ± 14 mL/100 g/min) and ATT (1.4 ± 0.3 s) values averaged from different brain regions are close to the literature reports; the estimated T(1,eff) values (1.9 ± 0.4 s) are higher than the tissue T1 values, possibly reflecting a contribution from the microvascular arterial blood compartment. Copyright © 2013 John Wiley & Sons, Ltd.

The role of social capital and community belongingness for exercise adherence: An exploratory study of the CrossFit gym model.

PubMed

Whiteman-Sandland, Jessica; Hawkins, Jemma; Clayton, Debbie

2016-08-01

This is the first study to measure the 'sense of community' reportedly offered by the CrossFit gym model. A cross-sectional study adapted Social Capital and General Belongingness scales to compare perceptions of a CrossFit gym and a traditional gym. CrossFit gym members reported significantly higher levels of social capital (both bridging and bonding) and community belongingness compared with traditional gym members. However, regression analysis showed neither social capital, community belongingness, nor gym type was an independent predictor of gym attendance. Exercise and health professionals may benefit from evaluating further the 'sense of community' offered by gym-based exercise programmes.

Application of separable parameter space techniques to multi-tracer PET compartment modeling.

PubMed

Zhang, Jeff L; Michael Morey, A; Kadrmas, Dan J

2016-02-07

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg-Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.

Application of separable parameter space techniques to multi-tracer PET compartment modeling

NASA Astrophysics Data System (ADS)

Zhang, Jeff L.; Morey, A. Michael; Kadrmas, Dan J.

2016-02-01

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg-Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.

Comparison among cognitive diagnostic models for the TIMSS 2007 fourth grade mathematics assessment.

PubMed

Yamaguchi, Kazuhiro; Okada, Kensuke

2018-01-01

A variety of cognitive diagnostic models (CDMs) have been developed in recent years to help with the diagnostic assessment and evaluation of students. Each model makes different assumptions about the relationship between students' achievement and skills, which makes it important to empirically investigate which CDMs better fit the actual data. In this study, we examined this question by comparatively fitting representative CDMs to the Trends in International Mathematics and Science Study (TIMSS) 2007 assessment data across seven countries. The following two major findings emerged. First, in accordance with former studies, CDMs had a better fit than did the item response theory models. Second, main effects models generally had a better fit than other parsimonious or the saturated models. Related to the second finding, the fit of the traditional parsimonious models such as the DINA and DINO models were not optimal. The empirical educational implications of these findings are discussed.

Comparison among cognitive diagnostic models for the TIMSS 2007 fourth grade mathematics assessment

PubMed Central

Okada, Kensuke

2018-01-01

A variety of cognitive diagnostic models (CDMs) have been developed in recent years to help with the diagnostic assessment and evaluation of students. Each model makes different assumptions about the relationship between students’ achievement and skills, which makes it important to empirically investigate which CDMs better fit the actual data. In this study, we examined this question by comparatively fitting representative CDMs to the Trends in International Mathematics and Science Study (TIMSS) 2007 assessment data across seven countries. The following two major findings emerged. First, in accordance with former studies, CDMs had a better fit than did the item response theory models. Second, main effects models generally had a better fit than other parsimonious or the saturated models. Related to the second finding, the fit of the traditional parsimonious models such as the DINA and DINO models were not optimal. The empirical educational implications of these findings are discussed. PMID:29394257

Genetic algorithm dynamics on a rugged landscape

NASA Astrophysics Data System (ADS)

Bornholdt, Stefan

1998-04-01

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.

Network growth models: A behavioural basis for attachment proportional to fitness

NASA Astrophysics Data System (ADS)

Bell, Michael; Perera, Supun; Piraveenan, Mahendrarajah; Bliemer, Michiel; Latty, Tanya; Reid, Chris

2017-02-01

Several growth models have been proposed in the literature for scale-free complex networks, with a range of fitness-based attachment models gaining prominence recently. However, the processes by which such fitness-based attachment behaviour can arise are less well understood, making it difficult to compare the relative merits of such models. This paper analyses an evolutionary mechanism that would give rise to a fitness-based attachment process. In particular, it is proven by analytical and numerical methods that in homogeneous networks, the minimisation of maximum exposure to node unfitness leads to attachment probabilities that are proportional to node fitness. This result is then extended to heterogeneous networks, with supply chain networks being used as an example.

Bayesian Evaluation of Dynamical Soil Carbon Models Using Soil Carbon Flux Data

NASA Astrophysics Data System (ADS)

Xie, H. W.; Romero-Olivares, A.; Guindani, M.; Allison, S. D.

2017-12-01

2016 was Earth's hottest year in the modern temperature record and the third consecutive record-breaking year. As the planet continues to warm, temperature-induced changes in respiration rates of soil microbes could reduce the amount of carbon sequestered in the soil organic carbon (SOC) pool, one of the largest terrestrial stores of carbon. This would accelerate temperature increases. In order to predict the future size of the SOC pool, mathematical soil carbon models (SCMs) describing interactions between the biosphere and atmosphere are needed. SCMs must be validated before they can be chosen for predictive use. In this study, we check two SCMs called CON and AWB for consistency with observed data using Bayesian goodness of fit testing that can be used in the future to compare other models. We compare the fit of the models to longitudinal soil respiration data from a meta-analysis of soil heating experiments using a family of Bayesian goodness of fit metrics called information criteria (IC), including the Widely Applicable Information Criterion (WAIC), the Leave-One-Out Information Criterion (LOOIC), and the Log Pseudo Marginal Likelihood (LPML). These IC's take the entire posterior distribution into account, rather than just one outputted model fit line. A lower WAIC and LOOIC and larger LPML indicate a better fit. We compare AWB and CON with fixed steady state model pool sizes. At equivalent SOC, dissolved organic carbon, and microbial pool sizes, CON always outperforms AWB quantitatively by all three IC's used. AWB monotonically improves in fit as we reduce the SOC steady state pool size while fixing all other pool sizes, and the same is almost true for CON. The AWB model with the lowest SOC is the best performing AWB model, while the CON model with the second lowest SOC is the best performing model. We observe that AWB displays more changes in slope sign and qualitatively displays more adaptive dynamics, which prevents AWB from being fully ruled out for predictive use, but based on IC's, CON is clearly the superior model for fitting the data. Hence, we demonstrate that Bayesian goodness of fit testing with information criteria helps us rigorously determine the consistency of models with data. Models that demonstrate their consistency to multiple data sets with our approach can then be selected for further refinement.

The 6300 A O/1-D/ airglow and dissociative recombination

NASA Technical Reports Server (NTRS)

Wickwar, V. B.; Cogger, L. L.; Carlson, H. C.

1974-01-01

Measurements of night-time 6300 A airglow intensities at the Arecibo Observatory have been compared with dissociative recombination calculations based on electron densities derived from simultaneous incoherent backscatter measurements. The agreement indicates that the nightglow can be fully accounted for by dissociative recombination. The comparisons are examined to determine the importance of quenching, heavy ions, ionization above the F-layer peak, and the temperature parameter of the model atmosphere. Comparable fits between the observed and calculated intensities are found for several available model atmospheres. The least-squares fitting process, used to make the comparisons, produces comparable fits over a wide range of combinations of neutral densities and of reaction constants. Yet, the fitting places constraints upon the possible combinations; these constraints indicate that the latest laboratory chemical constants and densities extrapolated to a base altitude are mutually consistent.

Comparison of statistical models to estimate parasite growth rate in the induced blood stage malaria model.

PubMed

Wockner, Leesa F; Hoffmann, Isabell; O'Rourke, Peter; McCarthy, James S; Marquart, Louise

2017-08-25

The efficacy of vaccines aimed at inhibiting the growth of malaria parasites in the blood can be assessed by comparing the growth rate of parasitaemia in the blood of subjects treated with a test vaccine compared to controls. In studies using induced blood stage malaria (IBSM), a type of controlled human malaria infection, parasite growth rate has been measured using models with the intercept on the y-axis fixed to the inoculum size. A set of statistical models was evaluated to determine an optimal methodology to estimate parasite growth rate in IBSM studies. Parasite growth rates were estimated using data from 40 subjects published in three IBSM studies. Data was fitted using 12 statistical models: log-linear, sine-wave with the period either fixed to 48 h or not fixed; these models were fitted with the intercept either fixed to the inoculum size or not fixed. All models were fitted by individual, and overall by study using a mixed effects model with a random effect for the individual. Log-linear models and sine-wave models, with the period fixed or not fixed, resulted in similar parasite growth rate estimates (within 0.05 log 10 parasites per mL/day). Average parasite growth rate estimates for models fitted by individual with the intercept fixed to the inoculum size were substantially lower by an average of 0.17 log 10 parasites per mL/day (range 0.06-0.24) compared with non-fixed intercept models. Variability of parasite growth rate estimates across the three studies analysed was substantially higher (3.5 times) for fixed-intercept models compared with non-fixed intercept models. The same tendency was observed in models fitted overall by study. Modelling data by individual or overall by study had minimal effect on parasite growth estimates. The analyses presented in this report confirm that fixing the intercept to the inoculum size influences parasite growth estimates. The most appropriate statistical model to estimate the growth rate of blood-stage parasites in IBSM studies appears to be a log-linear model fitted by individual and with the intercept estimated in the log-linear regression. Future studies should use this model to estimate parasite growth rates.

Statistical modelling for recurrent events: an application to sports injuries

PubMed Central

Ullah, Shahid; Gabbett, Tim J; Finch, Caroline F

2014-01-01

Background Injuries are often recurrent, with subsequent injuries influenced by previous occurrences and hence correlation between events needs to be taken into account when analysing such data. Objective This paper compares five different survival models (Cox proportional hazards (CoxPH) model and the following generalisations to recurrent event data: Andersen-Gill (A-G), frailty, Wei-Lin-Weissfeld total time (WLW-TT) marginal, Prentice-Williams-Peterson gap time (PWP-GT) conditional models) for the analysis of recurrent injury data. Methods Empirical evaluation and comparison of different models were performed using model selection criteria and goodness-of-fit statistics. Simulation studies assessed the size and power of each model fit. Results The modelling approach is demonstrated through direct application to Australian National Rugby League recurrent injury data collected over the 2008 playing season. Of the 35 players analysed, 14 (40%) players had more than 1 injury and 47 contact injuries were sustained over 29 matches. The CoxPH model provided the poorest fit to the recurrent sports injury data. The fit was improved with the A-G and frailty models, compared to WLW-TT and PWP-GT models. Conclusions Despite little difference in model fit between the A-G and frailty models, in the interest of fewer statistical assumptions it is recommended that, where relevant, future studies involving modelling of recurrent sports injury data use the frailty model in preference to the CoxPH model or its other generalisations. The paper provides a rationale for future statistical modelling approaches for recurrent sports injury. PMID:22872683

Word frequencies: A comparison of Pareto type distributions

NASA Astrophysics Data System (ADS)

Wiegand, Martin; Nadarajah, Saralees; Si, Yuancheng

2018-03-01

Mehri and Jamaati (2017) [18] used Zipf's law to model word frequencies in Holy Bible translations for one hundred live languages. We compare the fit of Zipf's law to a number of Pareto type distributions. The latter distributions are shown to provide the best fit, as judged by a number of comparative plots and error measures. The fit of Zipf's law appears generally poor.

Computing Robust, Bootstrap-Adjusted Fit Indices for Use with Nonnormal Data

ERIC Educational Resources Information Center

Walker, David A.; Smith, Thomas J.

2017-01-01

Nonnormality of data presents unique challenges for researchers who wish to carry out structural equation modeling. The subsequent SPSS syntax program computes bootstrap-adjusted fit indices (comparative fit index, Tucker-Lewis index, incremental fit index, and root mean square error of approximation) that adjust for nonnormality, along with the…

Structure of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Criteria for Obsessive–Compulsive Personality Disorder in Patients With Binge Eating Disorder

PubMed Central

Ansell, Emily B; Pinto, Anthony; Edelen, Maria Orlando; Grilo, Carlos M

2013-01-01

Objective To examine 1-, 2-, and 3-factor model structures through confirmatory analytic procedures for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) obsessive–compulsive personality disorder (OCPD) criteria in patients with binge eating disorder (BED). Method Participants were consecutive outpatients (n = 263) with binge eating disorder and were assessed with semi-structured interviews. The 8 OCPD criteria were submitted to confirmatory factor analyses in Mplus Version 4.2 (Los Angeles, CA) in which previously identified factor models of OCPD were compared for fit, theoretical relevance, and parsimony. Nested models were compared for significant improvements in model fit. Results Evaluation of indices of fit in combination with theoretical considerations suggest a multifactorial model is a significant improvement in fit over the current DSM-IV single-factor model of OCPD. Though the data support both 2- and 3-factor models, the 3-factor model is hindered by an underspecified third factor. Conclusion A multifactorial model of OCPD incorporating the factors perfectionism and rigidity represents the best compromise of fit and theory in modelling the structure of OCPD in patients with BED. A third factor representing miserliness may be relevant in BED populations but needs further development. The perfectionism and rigidity factors may represent distinct intrapersonal and interpersonal attempts at control and may have implications for the assessment of OCPD. PMID:19087485

Structure of diagnostic and statistical manual of mental disorders, fourth edition criteria for obsessive-compulsive personality disorder in patients with binge eating disorder.

PubMed

Ansell, Emily B; Pinto, Anthony; Edelen, Maria Orlando; Grilo, Carlos M

2008-12-01

To examine 1-, 2-, and 3-factor model structures through confirmatory analytic procedures for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) obsessive-compulsive personality disorder (OCPD) criteria in patients with binge eating disorder (BED). Participants were consecutive outpatients (n = 263) with binge eating disorder and were assessed with semi-structured interviews. The 8 OCPD criteria were submitted to confirmatory factor analyses in Mplus Version 4.2 (Los Angeles, CA) in which previously identified factor models of OCPD were compared for fit, theoretical relevance, and parsimony. Nested models were compared for significant improvements in model fit. Evaluation of indices of fit in combination with theoretical considerations suggest a multifactorial model is a significant improvement in fit over the current DSM-IV single- factor model of OCPD. Though the data support both 2- and 3-factor models, the 3-factor model is hindered by an underspecified third factor. A multifactorial model of OCPD incorporating the factors perfectionism and rigidity represents the best compromise of fit and theory in modelling the structure of OCPD in patients with BED. A third factor representing miserliness may be relevant in BED populations but needs further development. The perfectionism and rigidity factors may represent distinct intrapersonal and interpersonal attempts at control and may have implications for the assessment of OCPD.

Decomposition of mineral absorption bands using nonlinear least squares curve fitting: Application to Martian meteorites and CRISM data

NASA Astrophysics Data System (ADS)

Parente, Mario; Makarewicz, Heather D.; Bishop, Janice L.

2011-04-01

This study advances curve-fitting modeling of absorption bands of reflectance spectra and applies this new model to spectra of Martian meteorites ALH 84001 and EETA 79001 and data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM). This study also details a recently introduced automated parameter initialization technique. We assess the performance of this automated procedure by comparing it to the currently available initialization method and perform a sensitivity analysis of the fit results to variation in initial guesses. We explore the issues related to the removal of the continuum, offer guidelines for continuum removal when modeling the absorptions and explore different continuum-removal techniques. We further evaluate the suitability of curve fitting techniques using Gaussians/Modified Gaussians to decompose spectra into individual end-member bands. We show that nonlinear least squares techniques such as the Levenberg-Marquardt algorithm achieve comparable results to the MGM model ( Sunshine and Pieters, 1993; Sunshine et al., 1990) for meteorite spectra. Finally we use Gaussian modeling to fit CRISM spectra of pyroxene and olivine-rich terrains on Mars. Analysis of CRISM spectra of two regions show that the pyroxene-dominated rock spectra measured at Juventae Chasma were modeled well with low Ca pyroxene, while the pyroxene-rich spectra acquired at Libya Montes required both low-Ca and high-Ca pyroxene for a good fit.

The Work Role Functioning Questionnaire v2.0 Showed Consistent Factor Structure Across Six Working Samples.

PubMed

Abma, Femke I; Bültmann, Ute; Amick Iii, Benjamin C; Arends, Iris; Dorland, Heleen F; Flach, Peter A; van der Klink, Jac J L; van de Ven, Hardy A; Bjørner, Jakob Bue

2017-09-09

Objective The Work Role Functioning Questionnaire v2.0 (WRFQ) is an outcome measure linking a persons' health to the ability to meet work demands in the twenty-first century. We aimed to examine the construct validity of the WRFQ in a heterogeneous set of working samples in the Netherlands with mixed clinical conditions and job types to evaluate the comparability of the scale structure. Methods Confirmatory factor and multi-group analyses were conducted in six cross-sectional working samples (total N = 2433) to evaluate and compare a five-factor model structure of the WRFQ (work scheduling demands, output demands, physical demands, mental and social demands, and flexibility demands). Model fit indices were calculated based on RMSEA ≤ 0.08 and CFI ≥ 0.95. After fitting the five-factor model, the multidimensional structure of the instrument was evaluated across samples using a second order factor model. Results The factor structure was robust across samples and a multi-group model had adequate fit (RMSEA = 0.63, CFI = 0.972). In sample specific analyses, minor modifications were necessary in three samples (final RMSEA 0.055-0.080, final CFI between 0.955 and 0.989). Applying the previous first order specifications, a second order factor model had adequate fit in all samples. Conclusion A five-factor model of the WRFQ showed consistent structural validity across samples. A second order factor model showed adequate fit, but the second order factor loadings varied across samples. Therefore subscale scores are recommended to compare across different clinical and working samples.

Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS

USGS Publications Warehouse

Bolker, Benjamin M.; Gardner, Beth; Maunder, Mark; Berg, Casper W.; Brooks, Mollie; Comita, Liza; Crone, Elizabeth; Cubaynes, Sarah; Davies, Trevor; de Valpine, Perry; Ford, Jessica; Gimenez, Olivier; Kéry, Marc; Kim, Eun Jung; Lennert-Cody, Cleridy; Magunsson, Arni; Martell, Steve; Nash, John; Nielson, Anders; Regentz, Jim; Skaug, Hans; Zipkin, Elise

2013-01-01

1. Ecologists often use nonlinear fitting techniques to estimate the parameters of complex ecological models, with attendant frustration. This paper compares three open-source model fitting tools and discusses general strategies for defining and fitting models. 2. R is convenient and (relatively) easy to learn, AD Model Builder is fast and robust but comes with a steep learning curve, while BUGS provides the greatest flexibility at the price of speed. 3. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield) to specific suggestions about how to change the mathematical description of models to make them more amenable to parameter estimation. 4. A companion web site (https://groups.nceas.ucsb.edu/nonlinear-modeling/projects) presents detailed examples of application of the three tools to a variety of typical ecological estimation problems; each example links both to a detailed project report and to full source code and data.

Application of separable parameter space techniques to multi-tracer PET compartment modeling

PubMed Central

Zhang, Jeff L; Morey, A Michael; Kadrmas, Dan J

2016-01-01

Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg–Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models. PMID:26788888

Statistical Methodology for the Analysis of Repeated Duration Data in Behavioral Studies.

PubMed

Letué, Frédérique; Martinez, Marie-José; Samson, Adeline; Vilain, Anne; Vilain, Coriandre

2018-03-15

Repeated duration data are frequently used in behavioral studies. Classical linear or log-linear mixed models are often inadequate to analyze such data, because they usually consist of nonnegative and skew-distributed variables. Therefore, we recommend use of a statistical methodology specific to duration data. We propose a methodology based on Cox mixed models and written under the R language. This semiparametric model is indeed flexible enough to fit duration data. To compare log-linear and Cox mixed models in terms of goodness-of-fit on real data sets, we also provide a procedure based on simulations and quantile-quantile plots. We present two examples from a data set of speech and gesture interactions, which illustrate the limitations of linear and log-linear mixed models, as compared to Cox models. The linear models are not validated on our data, whereas Cox models are. Moreover, in the second example, the Cox model exhibits a significant effect that the linear model does not. We provide methods to select the best-fitting models for repeated duration data and to compare statistical methodologies. In this study, we show that Cox models are best suited to the analysis of our data set.

Exploratory structural equation modeling of personality data.

PubMed

Booth, Tom; Hughes, David J

2014-06-01

The current article compares the use of exploratory structural equation modeling (ESEM) as an alternative to confirmatory factor analytic (CFA) models in personality research. We compare model fit, factor distinctiveness, and criterion associations of factors derived from ESEM and CFA models. In Sample 1 (n = 336) participants completed the NEO-FFI, the Trait Emotional Intelligence Questionnaire-Short Form, and the Creative Domains Questionnaire. In Sample 2 (n = 425) participants completed the Big Five Inventory and the depression and anxiety scales of the General Health Questionnaire. ESEM models provided better fit than CFA models, but ESEM solutions did not uniformly meet cutoff criteria for model fit. Factor scores derived from ESEM and CFA models correlated highly (.91 to .99), suggesting the additional factor loadings within the ESEM model add little in defining latent factor content. Lastly, criterion associations of each personality factor in CFA and ESEM models were near identical in both inventories. We provide an example of how ESEM and CFA might be used together in improving personality assessment. © The Author(s) 2014.

Lee-Carter state space modeling: Application to the Malaysia mortality data

NASA Astrophysics Data System (ADS)

Zakiyatussariroh, W. H. Wan; Said, Z. Mohammad; Norazan, M. R.

2014-06-01

This article presents an approach that formalizes the Lee-Carter (LC) model as a state space model. Maximum likelihood through Expectation-Maximum (EM) algorithm was used to estimate the model. The methodology is applied to Malaysia's total population mortality data. Malaysia's mortality data was modeled based on age specific death rates (ASDR) data from 1971-2009. The fitted ASDR are compared to the actual observed values. However, results from the comparison of the fitted and actual values between LC-SS model and the original LC model shows that the fitted values from the LC-SS model and original LC model are quite close. In addition, there is not much difference between the value of root mean squared error (RMSE) and Akaike information criteria (AIC) from both models. The LC-SS model estimated for this study can be extended for forecasting ASDR in Malaysia. Then, accuracy of the LC-SS compared to the original LC can be further examined by verifying the forecasting power using out-of-sample comparison.

Statistical evaluation of the metallurgical test data in the ORR-PSF-PVS irradiation experiment. [PWR; BWR

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

Stallmann, F.W.

1984-08-01

A statistical analysis of Charpy test results of the two-year Pressure Vessel Simulation metallurgical irradiation experiment was performed. Determination of transition temperature and upper shelf energy derived from computer fits compare well with eyeball fits. Uncertainties for all results can be obtained with computer fits. The results were compared with predictions in Regulatory Guide 1.99 and other irradiation damage models.

Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance.

ERIC Educational Resources Information Center

Cheung, Gordon W.; Rensvold, Roger B.

2002-01-01

Examined 20 goodness-of-fit indexes based on the minimum fit function using a simulation under the 2-group situation. Results support the use of the delta comparative fit index, delta Gamma hat, and delta McDonald's Noncentrality Index to evaluation measurement invariance. These three approaches are independent of model complexity and sample size.…

Effect of conductor geometry on source localization: Implications for epilepsy studies

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

Schlitt, H.; Heller, L.; Best, E.

1994-07-01

We shall discuss the effects of conductor geometry on source localization for applications in epilepsy studies. The most popular conductor model for clinical MEG studies is a homogeneous sphere. However, several studies have indicated that a sphere is a poor model for the head when the sources are deep, as is the case for epileptic foci in the mesial temporal lobe. We believe that replacing the spherical model with a more realistic one in the inverse fitting procedure will improve the accuracy of localizing epileptic sources. In order to include a realistic head model in the inverse problem, we mustmore » first solve the forward problem for the realistic conductor geometry. We create a conductor geometry model from MR images, and then solve the forward problem via a boundary integral equation for the electric potential due to a specified primary source. One the electric potential is known, the magnetic field can be calculated directly. The most time-intensive part of the problem is generating the conductor model; fortunately, this needs to be done only once for each patient. It takes little time to change the primary current and calculate a new magnetic field for use in the inverse fitting procedure. We present the results of a series of computer simulations in which we investigate the localization accuracy due to replacing the spherical model with the realistic head model in the inverse fitting procedure. The data to be fit consist of a computer generated magnetic field due to a known current dipole in a realistic head model, with added noise. We compare the localization errors when this field is fit using a spherical model to the fit using a realistic head model. Using a spherical model is comparable to what is usually done when localizing epileptic sources in humans, where the conductor model used in the inverse fitting procedure does not correspond to the actual head.« less

An Improved Statistical Solution for Global Seismicity by the HIST-ETAS Approach

NASA Astrophysics Data System (ADS)

Chu, A.; Ogata, Y.; Katsura, K.

2010-12-01

For long-term global seismic model fitting, recent work by Chu et al. (2010) applied the spatial-temporal ETAS model (Ogata 1998) and analyzed global data partitioned into tectonic zones based on geophysical characteristics (Bird 2003), and it has shown tremendous improvements of model fitting compared with one overall global model. While the ordinary ETAS model assumes constant parameter values across the complete region analyzed, the hierarchical space-time ETAS model (HIST-ETAS, Ogata 2004) is a newly introduced approach by proposing regional distinctions of the parameters for more accurate seismic prediction. As the HIST-ETAS model has been fit to regional data of Japan (Ogata 2010), our work applies the model to describe global seismicity. Employing the Akaike's Bayesian Information Criterion (ABIC) as an assessment method, we compare the MLE results with zone divisions considered to results obtained by an overall global model. Location dependent parameters of the model and Gutenberg-Richter b-values are optimized, and seismological interpretations are discussed.

Induced subgraph searching for geometric model fitting

NASA Astrophysics Data System (ADS)

Xiao, Fan; Xiao, Guobao; Yan, Yan; Wang, Xing; Wang, Hanzi

2017-11-01

In this paper, we propose a novel model fitting method based on graphs to fit and segment multiple-structure data. In the graph constructed on data, each model instance is represented as an induced subgraph. Following the idea of pursuing the maximum consensus, the multiple geometric model fitting problem is formulated as searching for a set of induced subgraphs including the maximum union set of vertices. After the generation and refinement of the induced subgraphs that represent the model hypotheses, the searching process is conducted on the "qualified" subgraphs. Multiple model instances can be simultaneously estimated by solving a converted problem. Then, we introduce the energy evaluation function to determine the number of model instances in data. The proposed method is able to effectively estimate the number and the parameters of model instances in data severely corrupted by outliers and noises. Experimental results on synthetic data and real images validate the favorable performance of the proposed method compared with several state-of-the-art fitting methods.

New formulation feed method in tariff model of solar PV in Indonesia

NASA Astrophysics Data System (ADS)

Djamal, Muchlishah Hadi; Setiawan, Eko Adhi; Setiawan, Aiman

2017-03-01

Geographically, Indonesia has 18 latitudes that correlated strongly with the potential of solar radiation for the implementation of solar photovoltaic (PV) technologies. This is becoming the basis assumption to develop a proportional model of Feed In Tariff (FIT), consequently the FIT will be vary, according to the various of latitudes in Indonesia. This paper proposed a new formulation of solar PV FIT based on the potential of solar radiation and some independent variables such as latitude, longitude, Levelized Cost of Electricity (LCOE), and also socio-economic. The Principal Component Regression (PCR) method is used to analyzed the correlation of six independent variables C1-C6 then three models of FIT are presented. Model FIT-2 is chosen because it has a small residual value and has higher financial benefit compared to the other models. This study reveals the value of variable FIT associated with solar energy potential in each region, can reduce the total FIT to be paid by the state around 80 billion rupiahs in 10 years of 1 MW photovoltaic operation at each 34 provinces in Indonesia.

Isochrone Fitting of Hubble Photometry in UV-Vis Bands

NASA Astrophysics Data System (ADS)

Barker, Hallie; Paust, Nathaniel

2017-01-01

We present the results of isochrone fitting of color-magnitude diagrams from Hubble Space Telescope Wide Field Camera 3 (WFC3) and Advanced Camera for Surveys (ACS) photometry of the globular clusters M13 and M80 in five bands from the ultraviolet to near infrared. Fits from both the Dartmouth Stellar Evolution Program (DSEP) and the PAdova and TRieste Stellar Evolution Code (PARSEC) are examined. Ages, extinctions, and distances are found from the isochrone fitting, and metallicities are confirmed. We conduct careful qualitative analysis on the inconsistencies of the fits across all of the color combinations possible with the five observed bands, and find that the (F606W-F814W) color generally produces very good fits, but that there are large discrepancies when the data is fit using colors including UV bands for both models. Finally, we directly compare the two models by performing isochrone-isochrone fitting, and find that the age in PARSEC is on average 1.5 Gyr younger than DSEP for similar-appearing models at the same metallicity, and that the two models become less discrepant at lower metallicities.

Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes

ERIC Educational Resources Information Center

Leite, Walter L.; Stapleton, Laura M.

2011-01-01

In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…

Genomic Prediction Accounting for Residual Heteroskedasticity

PubMed Central

Ou, Zhining; Tempelman, Robert J.; Steibel, Juan P.; Ernst, Catherine W.; Bates, Ronald O.; Bello, Nora M.

2015-01-01

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. PMID:26564950

Premium analysis for copula model: A case study for Malaysian motor insurance claims

NASA Astrophysics Data System (ADS)

Resti, Yulia; Ismail, Noriszura; Jaaman, Saiful Hafizah

2014-06-01

This study performs premium analysis for copula models with regression marginals. For illustration purpose, the copula models are fitted to the Malaysian motor insurance claims data. In this study, we consider copula models from Archimedean and Elliptical families, and marginal distributions of Gamma and Inverse Gaussian regression models. The simulated results from independent model, which is obtained from fitting regression models separately to each claim category, and dependent model, which is obtained from fitting copula models to all claim categories, are compared. The results show that the dependent model using Frank copula is the best model since the risk premiums estimated under this model are closely approximate to the actual claims experience relative to the other copula models.

Health benefits and cost-effectiveness of a hybrid screening strategy for colorectal cancer.

PubMed

Dinh, Tuan; Ladabaum, Uri; Alperin, Peter; Caldwell, Cindy; Smith, Robert; Levin, Theodore R

2013-09-01

Colorectal cancer (CRC) screening guidelines recommend screening schedules for each single type of test except for concurrent sigmoidoscopy and fecal occult blood test (FOBT). We investigated the cost-effectiveness of a hybrid screening strategy that was based on a fecal immunological test (FIT) and colonoscopy. We conducted a cost-effectiveness analysis by using the Archimedes Model to evaluate the effects of different CRC screening strategies on health outcomes and costs related to CRC in a population that represents members of Kaiser Permanente Northern California. The Archimedes Model is a large-scale simulation of human physiology, diseases, interventions, and health care systems. The CRC submodel in the Archimedes Model was derived from public databases, published epidemiologic studies, and clinical trials. A hybrid screening strategy led to substantial reductions in CRC incidence and mortality, gains in quality-adjusted life years (QALYs), and reductions in costs, comparable with those of the best single-test strategies. Screening by annual FIT of patients 50-65 years old and then a single colonoscopy when they were 66 years old (FIT/COLOx1) reduced CRC incidence by 72% and gained 110 QALYs for every 1000 people during a period of 30 years, compared with no screening. Compared with annual FIT, FIT/COLOx1 gained 1400 QALYs/100,000 persons at an incremental cost of $9700/QALY gained and required 55% fewer FITs. Compared with FIT/COLOx1, colonoscopy at 10-year intervals gained 500 QALYs/100,000 at an incremental cost of $35,100/QALY gained but required 37% more colonoscopies. Over the ranges of parameters examined, the cost-effectiveness of hybrid screening strategies was slightly more sensitive to the adherence rate with colonoscopy than the adherence rate with yearly FIT. Uncertainties associated with estimates of FIT performance within a program setting and sensitivities for flat and right-sided lesions are expected to have significant impacts on the cost-effectiveness results. In our simulation model, a strategy of annual or biennial FIT, beginning when patients are 50 years old, with a single colonoscopy when they are 66 years old, delivers clinical and economic outcomes similar to those of CRC screening by single-modality strategies, with a favorable impact on resources demand. Copyright © 2013 AGA Institute. Published by Elsevier Inc. All rights reserved.

Automatic Fitting of Spiking Neuron Models to Electrophysiological Recordings

PubMed Central

Rossant, Cyrille; Goodman, Dan F. M.; Platkiewicz, Jonathan; Brette, Romain

2010-01-01

Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains) that can run in parallel on graphics processing units (GPUs). The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models. PMID:20224819

A Comparison of the Fit of Empirical Data to Two Latent Trait Models. Report No. 92.

ERIC Educational Resources Information Center

Hutten, Leah R.

Goodness of fit of raw test score data were compared, using two latent trait models: the Rasch model and the Birnbaum three-parameter logistic model. Data were taken from various achievement tests and the Scholastic Aptitude Test (Verbal). A minimum sample size of 1,000 was required, and the minimum test length was 40 items. Results indicated that…

Using three-dimensional computational modeling to compare the geometrical fitness of two kinds of proximal femoral intramedullary nail for Chinese femur.

PubMed

Zhang, Sheng; Zhang, Kairui; Wang, Yimin; Feng, Wei; Wang, Bowei; Yu, Bin

2013-01-01

The aim of this study was to use three-dimensional (3D) computational modeling to compare the geometric fitness of these two kinds of proximal femoral intramedullary nails in the Chinese femurs. Computed tomography (CT) scans of a total of 120 normal adult Chinese cadaveric femurs were collected for analysis. With the three-dimensional (3D) computational technology, the anatomical fitness between the nail and bone was quantified according to the impingement incidence, maximum thicknesses and lengths by which the nail was protruding into the cortex in the virtual bone model, respectively, at the proximal, middle, and distal portions of the implant in the femur. The results showed that PFNA-II may fit better for the Chinese proximal femurs than InterTan, and the distal portion of InterTan may perform better than that of PFNA-II; the anatomic fitness of both nails for Chinese patients may not be very satisfactory. As a result, both implants need further modifications to meet the needs of the Chinese population.

Adaptation in Tunably Rugged Fitness Landscapes: The Rough Mount Fuji Model

PubMed Central

Neidhart, Johannes; Szendro, Ivan G.; Krug, Joachim

2014-01-01

Much of the current theory of adaptation is based on Gillespie’s mutational landscape model (MLM), which assumes that the fitness values of genotypes linked by single mutational steps are independent random variables. On the other hand, a growing body of empirical evidence shows that real fitness landscapes, while possessing a considerable amount of ruggedness, are smoother than predicted by the MLM. In the present article we propose and analyze a simple fitness landscape model with tunable ruggedness based on the rough Mount Fuji (RMF) model originally introduced by Aita et al. in the context of protein evolution. We provide a comprehensive collection of results pertaining to the topographical structure of RMF landscapes, including explicit formulas for the expected number of local fitness maxima, the location of the global peak, and the fitness correlation function. The statistics of single and multiple adaptive steps on the RMF landscape are explored mainly through simulations, and the results are compared to the known behavior in the MLM model. Finally, we show that the RMF model can explain the large number of second-step mutations observed on a highly fit first-step background in a recent evolution experiment with a microvirid bacteriophage. PMID:25123507

Why Might Relative Fit Indices Differ between Estimators?

ERIC Educational Resources Information Center

Weng, Li-Jen; Cheng, Chung-Ping

1997-01-01

Relative fit indices using the null model as the reference point in computation may differ across estimation methods, as this article illustrates by comparing maximum likelihood, ordinary least squares, and generalized least squares estimation in structural equation modeling. The illustration uses a covariance matrix for six observed variables…

Micro-CT evaluation of the marginal fit of CAD/CAM all ceramic crowns

NASA Astrophysics Data System (ADS)

Brenes, Christian

Objectives: Evaluate the marginal fit of CAD/CAM all ceramic crowns made from lithium disilicate and zirconia using two different fabrication protocols (model and model-less). METHODS: Forty anterior all ceramic restorations (20 lithium disilicate, 20 zirconia) were fabricated using a CEREC Bluecam scanner. Two different fabrication methods were used: a full digital approach and a printed model. Completed crowns were cemented and marginal gap was evaluated using Micro-CT. Each specimen was analyzed in sagittal and trans-axial orientations, allowing a 360° evaluation of the vertical and horizontal fit. RESULTS: Vertical measurements in the lingual, distal and mesial views had and estimated marginal gap from 101.9 to 133.9 microns for E-max crowns and 126.4 to 165.4 microns for zirconia. No significant differences were found between model and model-less techniques. CONCLUSION: Lithium disilicate restorations exhibited a more accurate and consistent marginal adaptation when compared to zirconia crowns. No statistically significant differences were observed when comparing model or model-less approaches.

Robust and fast nonlinear optimization of diffusion MRI microstructure models.

PubMed

Harms, R L; Fritz, F J; Tobisch, A; Goebel, R; Roebroeck, A

2017-07-15

Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of run time, fit, accuracy and precision. Parameter initialization approaches were found to be relevant especially for more complex models, such as those involving several fiber orientations per voxel. For these, a fitting cascade initializing or fixing parameter values in a later optimization step from simpler models in an earlier optimization step further improved run time, fit, accuracy and precision compared to a single step fit. This establishes and makes available standards by which robust fit and accuracy can be achieved in shorter run times. This is especially relevant for the use of diffusion microstructure modeling in large group or population studies and in combining microstructure parameter maps with tractography results. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Tumor Control Probability Modeling for Stereotactic Body Radiation Therapy of Early-Stage Lung Cancer Using Multiple Bio-physical Models

PubMed Central

Liu, Feng; Tai, An; Lee, Percy; Biswas, Tithi; Ding, George X.; El Naqa, Isaam; Grimm, Jimm; Jackson, Andrew; Kong, Feng-Ming (Spring); LaCouture, Tamara; Loo, Billy; Miften, Moyed; Solberg, Timothy; Li, X Allen

2017-01-01

Purpose To analyze pooled clinical data using different radiobiological models and to understand the relationship between biologically effective dose (BED) and tumor control probability (TCP) for stereotactic body radiotherapy (SBRT) of early-stage non-small cell lung cancer (NSCLC). Method and Materials The clinical data of 1-, 2-, 3-, and 5-year actuarial or Kaplan-Meier TCP from 46 selected studies were collected for SBRT of NSCLC in the literature. The TCP data were separated for Stage T1 and T2 tumors if possible, otherwise collected for combined stages. BED was calculated at isocenters using six radiobiological models. For each model, the independent model parameters were determined from a fit to the TCP data using the least chi-square (χ2) method with either one set of parameters regardless of tumor stages or two sets for T1 and T2 tumors separately. Results The fits to the clinic data yield consistent results of large α/β ratios of about 20 Gy for all models investigated. The regrowth model that accounts for the tumor repopulation and heterogeneity leads to a better fit to the data, compared to other 5 models where the fits were indistinguishable between the models. The models based on the fitting parameters predict that the T2 tumors require about additional 1 Gy physical dose at isocenters per fraction (≤5 fractions) to achieve the optimal TCP when compared to the T1 tumors. Conclusion This systematic analysis of a large set of published clinical data using different radiobiological models shows that local TCP for SBRT of early-stage NSCLC has strong dependence on BED with large α/β ratios of about 20 Gy. The six models predict that a BED (calculated with α/β of 20) of 90 Gy is sufficient to achieve TCP ≥ 95%. Among the models considered, the regrowth model leads to a better fit to the clinical data. PMID:27871671

Evaluation of Statistical Methods for Modeling Historical Resource Production and Forecasting

NASA Astrophysics Data System (ADS)

Nanzad, Bolorchimeg

This master's thesis project consists of two parts. Part I of the project compares modeling of historical resource production and forecasting of future production trends using the logit/probit transform advocated by Rutledge (2011) with conventional Hubbert curve fitting, using global coal production as a case study. The conventional Hubbert/Gaussian method fits a curve to historical production data whereas a logit/probit transform uses a linear fit to a subset of transformed production data. Within the errors and limitations inherent in this type of statistical modeling, these methods provide comparable results. That is, despite that apparent goodness-of-fit achievable using the Logit/Probit methodology, neither approach provides a significant advantage over the other in either explaining the observed data or in making future projections. For mature production regions, those that have already substantially passed peak production, results obtained by either method are closely comparable and reasonable, and estimates of ultimately recoverable resources obtained by either method are consistent with geologically estimated reserves. In contrast, for immature regions, estimates of ultimately recoverable resources generated by either of these alternative methods are unstable and thus, need to be used with caution. Although the logit/probit transform generates high quality-of-fit correspondence with historical production data, this approach provides no new information compared to conventional Gaussian or Hubbert-type models and may have the effect of masking the noise and/or instability in the data and the derived fits. In particular, production forecasts for immature or marginally mature production systems based on either method need to be regarded with considerable caution. Part II of the project investigates the utility of a novel alternative method for multicyclic Hubbert modeling tentatively termed "cycle-jumping" wherein overlap of multiple cycles is limited. The model is designed in a way that each cycle is described by the same three parameters as conventional multicyclic Hubbert model and every two cycles are connected with a transition width. Transition width indicates the shift from one cycle to the next and is described as weighted coaddition of neighboring two cycles. It is determined by three parameters: transition year, transition width, and gamma parameter for weighting. The cycle-jumping method provides superior model compared to the conventional multicyclic Hubbert model and reflects historical production behavior more reasonably and practically, by better modeling of the effects of technological transitions and socioeconomic factors that affect historical resource production behavior by explicitly considering the form of the transitions between production cycles.

The Benefits of Including Clinical Factors in Rectal Normal Tissue Complication Probability Modeling After Radiotherapy for Prostate Cancer

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

Defraene, Gilles, E-mail: gilles.defraene@uzleuven.be; Van den Bergh, Laura; Al-Mamgani, Abrahim

2012-03-01

Purpose: To study the impact of clinical predisposing factors on rectal normal tissue complication probability modeling using the updated results of the Dutch prostate dose-escalation trial. Methods and Materials: Toxicity data of 512 patients (conformally treated to 68 Gy [n = 284] and 78 Gy [n = 228]) with complete follow-up at 3 years after radiotherapy were studied. Scored end points were rectal bleeding, high stool frequency, and fecal incontinence. Two traditional dose-based models (Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) and a logistic model were fitted using a maximum likelihood approach. Furthermore, these model fits were improved by including themore » most significant clinical factors. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminating ability of all fits. Results: Including clinical factors significantly increased the predictive power of the models for all end points. In the optimal LKB, RS, and logistic models for rectal bleeding and fecal incontinence, the first significant (p = 0.011-0.013) clinical factor was 'previous abdominal surgery.' As second significant (p = 0.012-0.016) factor, 'cardiac history' was included in all three rectal bleeding fits, whereas including 'diabetes' was significant (p = 0.039-0.048) in fecal incontinence modeling but only in the LKB and logistic models. High stool frequency fits only benefitted significantly (p = 0.003-0.006) from the inclusion of the baseline toxicity score. For all models rectal bleeding fits had the highest AUC (0.77) where it was 0.63 and 0.68 for high stool frequency and fecal incontinence, respectively. LKB and logistic model fits resulted in similar values for the volume parameter. The steepness parameter was somewhat higher in the logistic model, also resulting in a slightly lower D{sub 50}. Anal wall DVHs were used for fecal incontinence, whereas anorectal wall dose best described the other two endpoints. Conclusions: Comparable prediction models were obtained with LKB, RS, and logistic NTCP models. Including clinical factors improved the predictive power of all models significantly.« less

FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code

NASA Astrophysics Data System (ADS)

De Geyter, G.; Baes, M.; Fritz, J.; Camps, P.

2013-02-01

We present FitSKIRT, a method to efficiently fit radiative transfer models to UV/optical images of dusty galaxies. These images have the advantage that they have better spatial resolution compared to FIR/submm data. FitSKIRT uses the GAlib genetic algorithm library to optimize the output of the SKIRT Monte Carlo radiative transfer code. Genetic algorithms prove to be a valuable tool in handling the multi- dimensional search space as well as the noise induced by the random nature of the Monte Carlo radiative transfer code. FitSKIRT is tested on artificial images of a simulated edge-on spiral galaxy, where we gradually increase the number of fitted parameters. We find that we can recover all model parameters, even if all 11 model parameters are left unconstrained. Finally, we apply the FitSKIRT code to a V-band image of the edge-on spiral galaxy NGC 4013. This galaxy has been modeled previously by other authors using different combinations of radiative transfer codes and optimization methods. Given the different models and techniques and the complexity and degeneracies in the parameter space, we find reasonable agreement between the different models. We conclude that the FitSKIRT method allows comparison between different models and geometries in a quantitative manner and minimizes the need of human intervention and biasing. The high level of automation makes it an ideal tool to use on larger sets of observed data.

Statistical Power of Alternative Structural Models for Comparative Effectiveness Research: Advantages of Modeling Unreliability.

PubMed

Coman, Emil N; Iordache, Eugen; Dierker, Lisa; Fifield, Judith; Schensul, Jean J; Suggs, Suzanne; Barbour, Russell

2014-05-01

The advantages of modeling the unreliability of outcomes when evaluating the comparative effectiveness of health interventions is illustrated. Adding an action-research intervention component to a regular summer job program for youth was expected to help in preventing risk behaviors. A series of simple two-group alternative structural equation models are compared to test the effect of the intervention on one key attitudinal outcome in terms of model fit and statistical power with Monte Carlo simulations. Some models presuming parameters equal across the intervention and comparison groups were underpowered to detect the intervention effect, yet modeling the unreliability of the outcome measure increased their statistical power and helped in the detection of the hypothesized effect. Comparative Effectiveness Research (CER) could benefit from flexible multi-group alternative structural models organized in decision trees, and modeling unreliability of measures can be of tremendous help for both the fit of statistical models to the data and their statistical power.

A robust and fast active contour model for image segmentation with intensity inhomogeneity

NASA Astrophysics Data System (ADS)

Ding, Keyan; Weng, Guirong

2018-04-01

In this paper, a robust and fast active contour model is proposed for image segmentation in the presence of intensity inhomogeneity. By introducing the local image intensities fitting functions before the evolution of curve, the proposed model can effectively segment images with intensity inhomogeneity. And the computation cost is low because the fitting functions do not need to be updated in each iteration. Experiments have shown that the proposed model has a higher segmentation efficiency compared to some well-known active contour models based on local region fitting energy. In addition, the proposed model is robust to initialization, which allows the initial level set function to be a small constant function.

Foveal Curvature and Asymmetry Assessed Using Optical Coherence Tomography.

PubMed

VanNasdale, Dean A; Eilerman, Amanda; Zimmerman, Aaron; Lai, Nicky; Ramsey, Keith; Sinnott, Loraine T

2017-06-01

The aims of this study were to use cross-sectional optical coherence tomography imaging and custom curve fitting software to evaluate and model the foveal curvature as a spherical surface and to compare the radius of curvature in the horizontal and vertical meridians and test the sensitivity of this technique to anticipated meridional differences. Six 30-degree foveal-centered radial optical coherence tomography cross-section scans were acquired in the right eye of 20 clinically normal subjects. Cross sections were manually segmented, and custom curve fitting software was used to determine foveal pit radius of curvature using the central 500, 1000, and 1500 μm of the foveal contour. Radius of curvature was compared across different fitting distances. Root mean square error was used to determine goodness of fit. The radius of curvature was compared between the horizontal and vertical meridians for each fitting distance. There radius of curvature was significantly different when comparing each of the three fitting distances (P < .01 for each comparison). The average radii of curvature were 970 μm (95% confidence interval [CI], 913 to 1028 μm), 1386 μm (95% CI, 1339 to 1439 μm), and 2121 μm (95% CI, 2066 to 2183) for the 500-, 1000-, and 1500-μm fitting distances, respectively. Root mean square error was also significantly different when comparing each fitting distance (P < .01 for each comparison). The average root mean square errors were 2.48 μm (95% CI, 2.41 to 2.53 μm), 6.22 μm (95% CI, 5.77 to 6.60 μm), and 13.82 μm (95% CI, 12.93 to 14.58 μm) for the 500-, 1000-, and 1500-μm fitting distances, respectively. The radius of curvature between the horizontal and vertical meridian radii was statistically different only in the 1000- and 1500-μm fitting distances (P < .01 for each), with the horizontal meridian being flatter than the vertical. The foveal contour can be modeled as a sphere with low curve fitting error over a limited distance and capable of detecting subtle foveal contour differences between meridians.

Comparison of random regression models with Legendre polynomials and linear splines for production traits and somatic cell score of Canadian Holstein cows.

PubMed

Bohmanova, J; Miglior, F; Jamrozik, J; Misztal, I; Sullivan, P G

2008-09-01

A random regression model with both random and fixed regressions fitted by Legendre polynomials of order 4 was compared with 3 alternative models fitting linear splines with 4, 5, or 6 knots. The effects common for all models were a herd-test-date effect, fixed regressions on days in milk (DIM) nested within region-age-season of calving class, and random regressions for additive genetic and permanent environmental effects. Data were test-day milk, fat and protein yields, and SCS recorded from 5 to 365 DIM during the first 3 lactations of Canadian Holstein cows. A random sample of 50 herds consisting of 96,756 test-day records was generated to estimate variance components within a Bayesian framework via Gibbs sampling. Two sets of genetic evaluations were subsequently carried out to investigate performance of the 4 models. Models were compared by graphical inspection of variance functions, goodness of fit, error of prediction of breeding values, and stability of estimated breeding values. Models with splines gave lower estimates of variances at extremes of lactations than the model with Legendre polynomials. Differences among models in goodness of fit measured by percentages of squared bias, correlations between predicted and observed records, and residual variances were small. The deviance information criterion favored the spline model with 6 knots. Smaller error of prediction and higher stability of estimated breeding values were achieved by using spline models with 5 and 6 knots compared with the model with Legendre polynomials. In general, the spline model with 6 knots had the best overall performance based upon the considered model comparison criteria.

Genomic Prediction Accounting for Residual Heteroskedasticity.

PubMed

Ou, Zhining; Tempelman, Robert J; Steibel, Juan P; Ernst, Catherine W; Bates, Ronald O; Bello, Nora M

2015-11-12

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit. Copyright © 2016 Ou et al.

Model error in covariance structure models: Some implications for power and Type I error

PubMed Central

Coffman, Donna L.

2010-01-01

The present study investigated the degree to which violation of the parameter drift assumption affects the Type I error rate for the test of close fit and power analysis procedures proposed by MacCallum, Browne, and Sugawara (1996) for both the test of close fit and the test of exact fit. The parameter drift assumption states that as sample size increases both sampling error and model error (i.e. the degree to which the model is an approximation in the population) decrease. Model error was introduced using a procedure proposed by Cudeck and Browne (1992). The empirical power for both the test of close fit, in which the null hypothesis specifies that the Root Mean Square Error of Approximation (RMSEA) ≤ .05, and the test of exact fit, in which the null hypothesis specifies that RMSEA = 0, is compared with the theoretical power computed using the MacCallum et al. (1996) procedure. The empirical power and theoretical power for both the test of close fit and the test of exact fit are nearly identical under violations of the assumption. The results also indicated that the test of close fit maintains the nominal Type I error rate under violations of the assumption. PMID:21331302

Comparison of all atom, continuum, and linear fitting empirical models for charge screening effect of aqueous medium surrounding a protein molecule

NASA Astrophysics Data System (ADS)

Takahashi, Takuya; Sugiura, Junnnosuke; Nagayama, Kuniaki

2002-05-01

To investigate the role hydration plays in the electrostatic interactions of proteins, the time-averaged electrostatic potential of the B1 domain of protein G in an aqueous solution was calculated with full atomic molecular dynamics simulations that explicitly considers every atom (i.e., an all atom model). This all atom calculated potential was compared with the potential obtained from an electrostatic continuum model calculation. In both cases, the charge-screening effect was fairly well formulated with an effective relative dielectric constant which increased linearly with increasing charge-charge distance. This simulated linear dependence agrees with the experimentally determined linear relation proposed by Pickersgill. Cut-off approximations for Coulomb interactions failed to reproduce this linear relation. Correlation between the all atom model and the continuum models was found to be better than the respective correlation calculated for linear fitting to the two models. This confirms that the continuum model is better at treating the complicated shapes of protein conformations than the simple linear fitting empirical model. We have tried a sigmoid fitting empirical model in addition to the linear one. When weights of all data were treated equally, the sigmoid model, which requires two fitting parameters, fits results of both the all atom and the continuum models less accurately than the linear model which requires only one fitting parameter. When potential values are chosen as weighting factors, the fitting error of the sigmoid model became smaller, and the slope of both linear fitting curves became smaller. This suggests the screening effect of an aqueous medium within a short range, where potential values are relatively large, is smaller than that expected from the linear fitting curve whose slope is almost 4. To investigate the linear increase of the effective relative dielectric constant, the Poisson equation of a low-dielectric sphere in a high-dielectric medium was solved and charges distributed near the molecular surface were indicated as leading to the apparent linearity.

Assessing item fit for unidimensional item response theory models using residuals from estimated item response functions.

PubMed

Haberman, Shelby J; Sinharay, Sandip; Chon, Kyong Hee

2013-07-01

Residual analysis (e.g. Hambleton & Swaminathan, Item response theory: principles and applications, Kluwer Academic, Boston, 1985; Hambleton, Swaminathan, & Rogers, Fundamentals of item response theory, Sage, Newbury Park, 1991) is a popular method to assess fit of item response theory (IRT) models. We suggest a form of residual analysis that may be applied to assess item fit for unidimensional IRT models. The residual analysis consists of a comparison of the maximum-likelihood estimate of the item characteristic curve with an alternative ratio estimate of the item characteristic curve. The large sample distribution of the residual is proved to be standardized normal when the IRT model fits the data. We compare the performance of our suggested residual to the standardized residual of Hambleton et al. (Fundamentals of item response theory, Sage, Newbury Park, 1991) in a detailed simulation study. We then calculate our suggested residuals using data from an operational test. The residuals appear to be useful in assessing the item fit for unidimensional IRT models.

Calibrating White Dwarf Asteroseismic Fitting Techniques

NASA Astrophysics Data System (ADS)

Castanheira, B. G.; Romero, A. D.; Bischoff-Kim, A.

2017-03-01

The main goal of looking for intrinsic variability in stars is the unique opportunity to study their internal structure. Once we have extracted independent modes from the data, it appears to be a simple matter of comparing the period spectrum with those from theoretical model grids to learn the inner structure of that star. However, asteroseismology is much more complicated than this simple description. We must account not only for observational uncertainties in period determination, but most importantly for the limitations of the model grids, coming from the uncertainties in the constitutive physics, and of the fitting techniques. In this work, we will discuss results of numerical experiments where we used different independently calculated model grids (white dwarf cooling models WDEC and fully evolutionary LPCODE-PUL) and fitting techniques to fit synthetic stars. The advantage of using synthetic stars is that we know the details of their interior structure so we can assess how well our models and fitting techniques are able to the recover the interior structure, as well as the stellar parameters.

FIT-MART: Quantum Magnetism with a Gentle Learning Curve

NASA Astrophysics Data System (ADS)

Engelhardt, Larry; Garland, Scott C.; Rainey, Cameron; Freeman, Ray A.

We present a new open-source software package, FIT-MART, that allows non-experts to quickly get started sim- ulating quantum magnetism. FIT-MART can be downloaded as a platform-idependent executable Java (JAR) file. It allows the user to define (Heisenberg) Hamiltonians by electronically drawing pictures that represent quantum spins and operators. Sliders are automatically generated to control the values of the parameters in the model, and when the values change, several plots are updated in real time to display both the resulting energy spectra and the equilibruim magnetic properties. Several experimental data sets for real magnetic molecules are included in FIT-MART to allow easy comparison between simulated and experimental data, and FIT-MART users can also import their own data for analysis and compare the goodness of fit for different models.

Effect of heating rate and kinetic model selection on activation energy of nonisothermal crystallization of amorphous felodipine.

PubMed

Chattoraj, Sayantan; Bhugra, Chandan; Li, Zheng Jane; Sun, Changquan Calvin

2014-12-01

The nonisothermal crystallization kinetics of amorphous materials is routinely analyzed by statistically fitting the crystallization data to kinetic models. In this work, we systematically evaluate how the model-dependent crystallization kinetics is impacted by variations in the heating rate and the selection of the kinetic model, two key factors that can lead to significant differences in the crystallization activation energy (Ea ) of an amorphous material. Using amorphous felodipine, we show that the Ea decreases with increase in the heating rate, irrespective of the kinetic model evaluated in this work. The model that best describes the crystallization phenomenon cannot be identified readily through the statistical fitting approach because several kinetic models yield comparable R(2) . Here, we propose an alternate paired model-fitting model-free (PMFMF) approach for identifying the most suitable kinetic model, where Ea obtained from model-dependent kinetics is compared with those obtained from model-free kinetics. The most suitable kinetic model is identified as the one that yields Ea values comparable with the model-free kinetics. Through this PMFMF approach, nucleation and growth is identified as the main mechanism that controls the crystallization kinetics of felodipine. Using this PMFMF approach, we further demonstrate that crystallization mechanism from amorphous phase varies with heating rate. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Random genetic drift, natural selection, and noise in human cranial evolution.

PubMed

Roseman, Charles C

2016-08-01

This study assesses the extent to which relationships among groups complicate comparative studies of adaptation in recent human cranial variation and the extent to which departures from neutral additive models of evolution hinder the reconstruction of population relationships among groups using cranial morphology. Using a maximum likelihood evolutionary model fitting approach and a mixed population genomic and cranial data set, I evaluate the relative fits of several widely used models of human cranial evolution. Moreover, I compare the goodness of fit of models of cranial evolution constrained by genomic variation to test hypotheses about population specific departures from neutrality. Models from population genomics are much better fits to cranial variation than are traditional models from comparative human biology. There is not enough evolutionary information in the cranium to reconstruct much of recent human evolution but the influence of population history on cranial variation is strong enough to cause comparative studies of adaptation serious difficulties. Deviations from a model of random genetic drift along a tree-like population history show the importance of environmental effects, gene flow, and/or natural selection on human cranial variation. Moreover, there is a strong signal of the effect of natural selection or an environmental factor on a group of humans from Siberia. The evolution of the human cranium is complex and no one evolutionary process has prevailed at the expense of all others. A holistic unification of phenome, genome, and environmental context, gives us a strong point of purchase on these problems, which is unavailable to any one traditional approach alone. Am J Phys Anthropol 160:582-592, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Transformation Model Choice in Nonlinear Regression Analysis of Fluorescence-based Serial Dilution Assays

PubMed Central

Fong, Youyi; Yu, Xuesong

2016-01-01

Many modern serial dilution assays are based on fluorescence intensity (FI) readouts. We study optimal transformation model choice for fitting five parameter logistic curves (5PL) to FI-based serial dilution assay data. We first develop a generalized least squares-pseudolikelihood type algorithm for fitting heteroscedastic logistic models. Next we show that the 5PL and log 5PL functions can approximate each other well. We then compare four 5PL models with different choices of log transformation and variance modeling through a Monte Carlo study and real data. Our findings are that the optimal choice depends on the intended use of the fitted curves. PMID:27642502

A model of the endogenous glucose balance incorporating the characteristics of glucose transporters.

PubMed

Arleth, T; Andreassen, S; Federici, M O; Benedetti, M M

2000-07-01

This paper describes the development and preliminary test of a model of the endogenous glucose balance that incorporates the characteristics of the glucose transporters GLUT1, GLUT3 and GLUT4. In the modeling process the model is parameterized with nine parameters that are subsequently estimated from data in the literature on the hepatic- and endogenous- balances at various combinations of blood glucose and insulin levels. The ability of the resulting endogenous balance to fit blood glucose measured from patients was tested on 20 patients. The fit obtained with this model compared favorably with the fit obtained with the endogenous balance currently incorporated in the DIAS system.

Health Promotion Behavior of Chinese International Students in Korea Including Acculturation Factors: A Structural Equation Model.

PubMed

Kim, Sun Jung; Yoo, Il Young

2016-03-01

The purpose of this study was to explain the health promotion behavior of Chinese international students in Korea using a structural equation model including acculturation factors. A survey using self-administered questionnaires was employed. Data were collected from 272 Chinese students who have resided in Korea for longer than 6 months. The data were analyzed using structural equation modeling. The p value of final model is .31. The fitness parameters of the final model such as goodness of fit index, adjusted goodness of fit index, normed fit index, non-normed fit index, and comparative fit index were more than .95. Root mean square of residual and root mean square error of approximation also met the criteria. Self-esteem, perceived health status, acculturative stress and acculturation level had direct effects on health promotion behavior of the participants and the model explained 30.0% of variance. The Chinese students in Korea with higher self-esteem, perceived health status, acculturation level, and lower acculturative stress reported higher health promotion behavior. The findings can be applied to develop health promotion strategies for this population. Copyright © 2016. Published by Elsevier B.V.

Cost-effectiveness of the faecal immunochemical test at a range of positivity thresholds compared with the guaiac faecal occult blood test in the NHS Bowel Cancer Screening Programme in England

PubMed Central

Halloran, Stephen

2017-01-01

Objectives Through the National Health Service (NHS) Bowel Cancer Screening Programme (BCSP), men and women in England aged between 60 and 74 years are invited for colorectal cancer (CRC) screening every 2 years using the guaiac faecal occult blood test (gFOBT). The aim of this analysis was to estimate the cost–utility of the faecal immunochemical test for haemoglobin (FIT) compared with gFOBT for a cohort beginning screening aged 60 years at a range of FIT positivity thresholds. Design We constructed a cohort-based Markov state transition model of CRC disease progression and screening. Screening uptake, detection, adverse event, mortality and cost data were taken from BCSP data and national sources, including a recent large pilot study of FIT screening in the BCSP. Results Our results suggest that FIT is cost-effective compared with gFOBT at all thresholds, resulting in cost savings and quality-adjusted life years (QALYs) gained over a lifetime time horizon. FIT was cost-saving (p<0.001) and resulted in QALY gains of 0.014 (95% CI 0.012 to 0.017) at the base case threshold of 180 µg Hb/g faeces. Greater health gains and cost savings were achieved as the FIT threshold was decreased due to savings in cancer management costs. However, at lower thresholds, FIT was also associated with more colonoscopies (increasing from 32 additional colonoscopies per 1000 people invited for screening for FIT 180 µg Hb/g faeces to 421 additional colonoscopies per 1000 people invited for screening for FIT 20 µg Hb/g faeces over a 40-year time horizon). Parameter uncertainty had limited impact on the conclusions. Conclusions This is the first published economic analysis of FIT screening in England using data directly comparing FIT with gFOBT in the NHS BSCP. These results for a cohort starting screening aged 60 years suggest that FIT is highly cost-effective at all thresholds considered. Further modelling is needed to estimate economic outcomes for screening across all age cohorts simultaneously. PMID:29079605

Factor structure and psychometric properties of the english version of the trier inventory for chronic stress (TICS-E).

PubMed

Petrowski, Katja; Kliem, Sören; Sadler, Michael; Meuret, Alicia E; Ritz, Thomas; Brähler, Elmar

2018-02-06

Demands placed on individuals in occupational and social settings, as well as imbalances in personal traits and resources, can lead to chronic stress. The Trier Inventory for Chronic Stress (TICS) measures chronic stress while incorporating domain-specific aspects, and has been found to be a highly reliable and valid research tool. The aims of the present study were to confirm the German version TICS factorial structure in an English translation of the instrument (TICS-E) and to report its psychometric properties. A random route sample of healthy participants (N = 483) aged 18-30 years completed the TICS-E. The robust maximum likelihood estimation with a mean-adjusted chi-square test statistic was applied due to the sample's significant deviation from the multivariate normal distribution. Goodness of fit, absolute model fit, and relative model fit were assessed by means of the root mean square error of approximation (RMSEA), the Comparative Fit Index (CFI) and the Tucker Lewis Index (TLI). Reliability estimates (Cronbach's α and adjusted split-half reliability) ranged from .84 to .92. Item-scale correlations ranged from .50 to .85. Measures of fit showed values of .052 for RMSEA (Cl = 0.50-.054) and .067 for SRMR for absolute model fit, and values of .846 (TLI) and .855 (CFI) for relative model-fit. Factor loadings ranged from .55 to .91. The psychometric properties and factor structure of the TICS-E are comparable to the German version of the TICS. The instrument therefore meets quality standards for an adequate measurement of chronic stress.

A single factor underlies the metabolic syndrome: a confirmatory factor analysis.

PubMed

Pladevall, Manel; Singal, Bonita; Williams, L Keoki; Brotons, Carlos; Guyer, Heidi; Sadurni, Josep; Falces, Carles; Serrano-Rios, Manuel; Gabriel, Rafael; Shaw, Jonathan E; Zimmet, Paul Z; Haffner, Steven

2006-01-01

Confirmatory factor analysis (CFA) was used to test the hypothesis that the components of the metabolic syndrome are manifestations of a single common factor. Three different datasets were used to test and validate the model. The Spanish and Mauritian studies included 207 men and 203 women and 1,411 men and 1,650 women, respectively. A third analytical dataset including 847 men was obtained from a previously published CFA of a U.S. population. The one-factor model included the metabolic syndrome core components (central obesity, insulin resistance, blood pressure, and lipid measurements). We also tested an expanded one-factor model that included uric acid and leptin levels. Finally, we used CFA to compare the goodness of fit of one-factor models with the fit of two previously published four-factor models. The simplest one-factor model showed the best goodness-of-fit indexes (comparative fit index 1, root mean-square error of approximation 0.00). Comparisons of one-factor with four-factor models in the three datasets favored the one-factor model structure. The selection of variables to represent the different metabolic syndrome components and model specification explained why previous exploratory and confirmatory factor analysis, respectively, failed to identify a single factor for the metabolic syndrome. These analyses support the current clinical definition of the metabolic syndrome, as well as the existence of a single factor that links all of the core components.

An MLE method for finding LKB NTCP model parameters using Monte Carlo uncertainty estimates

NASA Astrophysics Data System (ADS)

Carolan, Martin; Oborn, Brad; Foo, Kerwyn; Haworth, Annette; Gulliford, Sarah; Ebert, Martin

2014-03-01

The aims of this work were to establish a program to fit NTCP models to clinical data with multiple toxicity endpoints, to test the method using a realistic test dataset, to compare three methods for estimating confidence intervals for the fitted parameters and to characterise the speed and performance of the program.

Fitting species-accumulation functions and assessing regional land use impacts on avian diversity

Treesearch

Curtis H. Flather

1996-01-01

As one samples species from a particular assemblage, the initial rapid rate with which new species are encountered declines with increasing effort. Nine candidate models to characterize species-accumulation functions were compared in a search for a model that consistently fit geographically extensive avian survey data from a wide range of environmental conditions....

Derivation of surface properties from Magellan altimetry data

NASA Astrophysics Data System (ADS)

Lovell, Amy J.; Schloerb, F. Peter; McGill, George E.

1992-12-01

The fit of the Hagfors model to the Magellan altimetry data provides a means to characterize the surface properties of Venus. However, the derived surface properties are only meaningful if the model provides a good representation of the data. The Hagfors model provides a good representation of the data. The Hagfors model is generally a realistic fit to surface scattering properties of a nadir-directed antenna such as the Magellan altimeter; however, some regions of the surface of Venus are poorly described by the existing model, according to the goodness of fit parameter provided on the ARCDR CD-ROMs. Poorly characterized regions need to be identified and fit to new models in order to derive more accurate surface properties for use in inferring the geological processes that affect the surface in those regions. We have compared the goodness of fit of the Hagfors model to the distribution of features across the planet, and preliminary results show a correlation between steep topographic slopes and poor fits to the standard model, as has been noticed by others. In this paper, we investigate possible relations between many classes of features and the ability of the Hagfors model to fit the observed echo profiles. In the regions that are not well characterized by existing models, we calculate new models that compensate for topographic relief in order to derive improved estimates of surface properties. Areas investigated to date span from longitude 315 through 45, at all latitudes covered by Magellan. A survey of those areas yields preliminary results that suggest that topographically high regions are well suited to the current implementation of the Hagfors model. Striking examples of such large-scale good fits are Alpha Regio, the northern edges of Lada Terra, and the southern edge of Ishtar Terra. Other features that are typically well fit are the rims of coronae such as Heng-O and the peaks of volcanos such as Gula Mons. Surprisingly, topographically low regions, such as the ubiquitous plains areas, are modeled poorly in comparison. However, this generalization has has exceptions: Lakshmi Planum is an elevated region that is not well fit compared to the rest of neighboring Ishtar, while the southern parts of topographically low Guinevere Planitia are characterized quite well by the Hagfors model. Features that are candidates for improved models are impact craters, coronae, ridges of significant scale, complex ridged terrains, moderate-sized mountains, and sharp terrain boundaries. These features are chosen because the goodness of fit is likely to be most affected either by departures from normal incidence angles or by sharp changes in terrain type within a single footprint. Most large features that are elevated with respect to their surroundings will suffer from steep slope effects, and smaller coronae and impact craters will probably suffer due to rapid changes in their appearance within a single footprint (10-20 km).

Derivation of surface properties from Magellan altimetry data

NASA Technical Reports Server (NTRS)

Lovell, Amy J.; Schloerb, F. Peter; Mcgill, George E.

1992-01-01

The fit of the Hagfors model to the Magellan altimetry data provides a means to characterize the surface properties of Venus. However, the derived surface properties are only meaningful if the model provides a good representation of the data. The Hagfors model provides a good representation of the data. The Hagfors model is generally a realistic fit to surface scattering properties of a nadir-directed antenna such as the Magellan altimeter; however, some regions of the surface of Venus are poorly described by the existing model, according to the goodness of fit parameter provided on the ARCDR CD-ROMs. Poorly characterized regions need to be identified and fit to new models in order to derive more accurate surface properties for use in inferring the geological processes that affect the surface in those regions. We have compared the goodness of fit of the Hagfors model to the distribution of features across the planet, and preliminary results show a correlation between steep topographic slopes and poor fits to the standard model, as has been noticed by others. In this paper, we investigate possible relations between many classes of features and the ability of the Hagfors model to fit the observed echo profiles. In the regions that are not well characterized by existing models, we calculate new models that compensate for topographic relief in order to derive improved estimates of surface properties. Areas investigated to date span from longitude 315 through 45, at all latitudes covered by Magellan. A survey of those areas yields preliminary results that suggest that topographically high regions are well suited to the current implementation of the Hagfors model. Striking examples of such large-scale good fits are Alpha Regio, the northern edges of Lada Terra, and the southern edge of Ishtar Terra. Other features that are typically well fit are the rims of coronae such as Heng-O and the peaks of volcanos such as Gula Mons. Surprisingly, topographically low regions, such as the ubiquitous plains areas, are modeled poorly in comparison. However, this generalization has has exceptions: Lakshmi Planum is an elevated region that is not well fit compared to the rest of neighboring Ishtar, while the southern parts of topographically low Guinevere Planitia are characterized quite well by the Hagfors model. Features that are candidates for improved models are impact craters, coronae, ridges of significant scale, complex ridged terrains, moderate-sized mountains, and sharp terrain boundaries. These features are chosen because the goodness of fit is likely to be most affected either by departures from normal incidence angles or by sharp changes in terrain type within a single footprint. Most large features that are elevated with respect to their surroundings will suffer from steep slope effects, and smaller coronae and impact craters will probably suffer due to rapid changes in their appearance within a single footprint (10-20 km).

A comparison of modelling techniques used to characterise oxygen uptake kinetics during the on-transient of exercise.

PubMed

Bell, C; Paterson, D H; Kowalchuk, J M; Padilla, J; Cunningham, D A

2001-09-01

We compared estimates for the phase 2 time constant (tau) of oxygen uptake (VO2) during moderate- and heavy-intensity exercise, and the slow component of VO2 during heavy-intensity exercise using previously published exponential models. Estimates for tau and the slow component were different (P < 0.05) among models. For moderate-intensity exercise, a two-component exponential model, or a mono-exponential model fitted from 20 s to 3 min were best. For heavy-intensity exercise, a three-component model fitted throughout the entire 6 min bout of exercise, or a two-component model fitted from 20 s were best. When the time delays for the two- and three-component models were equal the best statistical fit was obtained; however, this model produced an inappropriately low DeltaVO2/DeltaWR (WR, work rate) for the projected phase 2 steady state, and the estimate of phase 2 tau was shortened compared with other models. The slow component was quantified as the difference between VO2 at end-exercise (6 min) and at 3 min (DeltaVO2 (6-3 min)); 259 ml x min(-1)), and also using the phase 3 amplitude terms (truncated to end-exercise) from exponential fits (409-833 ml x min(-1)). Onset of the slow component was identified by the phase 3 time delay parameter as being of delayed onset approximately 2 min (vs. arbitrary 3 min). Using this delay DeltaVO2 (6-2 min) was approximately 400 ml x min(-1). Use of valid consistent methods to estimate tau and the slow component in exercise are needed to advance physiological understanding.

Fluidized bed drying characteristics and modeling of ginger ( zingiber officinale) slices

NASA Astrophysics Data System (ADS)

Parlak, Nezaket

2015-08-01

In this study fluidized bed drying characteristics of ginger have been investigated. The effects of the fluidizing air temperature, velocity, humidity and bed height on the drying performance of ginger slices have been found. The experimental moisture loss data of ginger slices has been fitted to the eight thin layer drying models. Two-term model drying model has shown a better fit to the experimental data with R2 of 0.998 as compared to others.

Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks

PubMed Central

Richter, Philipp; Toledano-Ayala, Manuel

2015-01-01

Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area network (WLAN) location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas; we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate. PMID:26370996

A New Z Score Curve of the Coronary Arterial Internal Diameter Using the Lambda-Mu-Sigma Method in a Pediatric Population.

PubMed

Kobayashi, Tohru; Fuse, Shigeto; Sakamoto, Naoko; Mikami, Masashi; Ogawa, Shunichi; Hamaoka, Kenji; Arakaki, Yoshio; Nakamura, Tsuneyuki; Nagasawa, Hiroyuki; Kato, Taichi; Jibiki, Toshiaki; Iwashima, Satoru; Yamakawa, Masaru; Ohkubo, Takashi; Shimoyama, Shinya; Aso, Kentaro; Sato, Seiichi; Saji, Tsutomu

2016-08-01

Several coronary artery Z score models have been developed. However, a Z score model derived by the lambda-mu-sigma (LMS) method has not been established. Echocardiographic measurements of the proximal right coronary artery, left main coronary artery, proximal left anterior descending coronary artery, and proximal left circumflex artery were prospectively collected in 3,851 healthy children ≤18 years of age and divided into developmental and validation data sets. In the developmental data set, smooth curves were fitted for each coronary artery using linear, logarithmic, square-root, and LMS methods for both sexes. The relative goodness of fit of these models was compared using the Bayesian information criterion. The best-fitting model was tested for reproducibility using the validation data set. The goodness of fit of the selected model was visually compared with that of the previously reported regression models using a Q-Q plot. Because the internal diameter of each coronary artery was not similar between sexes, sex-specific Z score models were developed. The LMS model with body surface area as the independent variable showed the best goodness of fit; therefore, the internal diameter of each coronary artery was transformed into a sex-specific Z score on the basis of body surface area using the LMS method. In the validation data set, a Q-Q plot of each model indicated that the distribution of Z scores in the LMS models was closer to the normal distribution compared with previously reported regression models. Finally, the final models for each coronary artery in both sexes were developed using the developmental and validation data sets. A Microsoft Excel-based Z score calculator was also created, which is freely available online (http://raise.umin.jp/zsp/calculator/). Novel LMS models with which to estimate the sex-specific Z score of each internal coronary artery diameter were generated and validated using a large pediatric population. Copyright © 2016 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.

MGS-TES thermal inertia study of the Arsia Mons Caldera

USGS Publications Warehouse

Cushing, G.E.; Titus, T.N.

2008-01-01

Temperatures of the Arsia Mons caldera floor and two nearby control areas were obtained by the Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES). These observations revealed that the Arsia Mons caldera floor exhibits thermal behavior different from the surrounding Tharsis region when compared with thermal models. Our technique compares modeled and observed data to determine best fit values of thermal inertia, layer depth, and albedo. Best fit modeled values are accurate in the two control regions, but those in the Arsia Mons' caldera are consistently either up to 15 K warmer than afternoon observations, or have albedo values that are more than two standard deviations higher than the observed mean. Models of both homogeneous and layered (such as dust over bedrock) cases were compared, with layered-cases indicating a surface layer at least thick enough to insulate itself from diurnal effects of an underlying substrate material. Because best fit models of the caldera floor poorly match observations, it is likely that the caldera floor experiences some physical process not incorporated into our thermal model. Even on Mars, Arsia Mons is an extreme environment where CO2 condenses upon the caldera floor every night, diurnal temperatures range each day by a factor of nearly 2, and annual average atmospheric pressure is only around one millibar. Here, we explore several possibilities that may explain the poor modeled fits to caldera floor and conclude that temperature dependent thermal conductivity may cause thermal inertia to vary diurnally, and this effect may be exaggerated by presence of water-ice clouds, which occur frequently above Arsia Mons. Copyright 2008 by the American Geophysical Union.

Stability of INFIT and OUTFIT Compared to Simulated Estimates in Applied Setting.

PubMed

Hodge, Kari J; Morgan, Grant B

Residual-based fit statistics are commonly used as an indication of the extent to which the item response data fit the Rash model. Fit statistic estimates are influenced by sample size and rules-of thumb estimates may result in incorrect conclusions about the extent to which the model fits the data. Estimates obtained in this analysis were compared to 250 simulated data sets to examine the stability of the estimates. All INFIT estimates were within the rule-of-thumb range of 0.7 to 1.3. However, only 82% of the INFIT estimates fell within the 2.5th and 97.5th percentile of the simulated item's INFIT distributions using this 95% confidence-like interval. This is a 18 percentage point difference in items that were classified as acceptable. Fourty-eight percent of OUTFIT estimates fell within the 0.7 to 1.3 rule- of-thumb range. Whereas 34% of OUTFIT estimates fell within the 2.5th and 97.5th percentile of the simulated item's OUTFIT distributions. This is a 13 percentage point difference in items that were classified as acceptable. When using the rule-of- thumb ranges for fit estimates the magnitude of misfit was smaller than with the 95% confidence interval of the simulated distribution. The findings indicate that the use of confidence intervals as critical values for fit statistics leads to different model data fit conclusions than traditional rule of thumb critical values.

[Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model].

PubMed

Ke-Wei, Wang; Yu, Wu; Jin-Ping, Li; Yu-Yu, Jiang

2016-07-12

To explore the effect of the autoregressive integrated moving average model-nonlinear auto-regressive neural network (ARIMA-NARNN) model on predicting schistosomiasis infection rates of population. The ARIMA model, NARNN model and ARIMA-NARNN model were established based on monthly schistosomiasis infection rates from January 2005 to February 2015 in Jiangsu Province, China. The fitting and prediction performances of the three models were compared. Compared to the ARIMA model and NARNN model, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model were the least with the values of 0.011 1, 0.090 0 and 0.282 4, respectively. The ARIMA-NARNN model could effectively fit and predict schistosomiasis infection rates of population, which might have a great application value for the prevention and control of schistosomiasis.

Hybrid active contour model for inhomogeneous image segmentation with background estimation

NASA Astrophysics Data System (ADS)

Sun, Kaiqiong; Li, Yaqin; Zeng, Shan; Wang, Jun

2018-03-01

This paper proposes a hybrid active contour model for inhomogeneous image segmentation. The data term of the energy function in the active contour consists of a global region fitting term in a difference image and a local region fitting term in the original image. The difference image is obtained by subtracting the background from the original image. The background image is dynamically estimated from a linear filtered result of the original image on the basis of the varying curve locations during the active contour evolution process. As in existing local models, fitting the image to local region information makes the proposed model robust against an inhomogeneous background and maintains the accuracy of the segmentation result. Furthermore, fitting the difference image to the global region information makes the proposed model robust against the initial contour location, unlike existing local models. Experimental results show that the proposed model can obtain improved segmentation results compared with related methods in terms of both segmentation accuracy and initial contour sensitivity.

Framework based on stochastic L-Systems for modeling IP traffic with multifractal behavior

NASA Astrophysics Data System (ADS)

Salvador, Paulo S.; Nogueira, Antonio; Valadas, Rui

2003-08-01

In a previous work we have introduced a multifractal traffic model based on so-called stochastic L-Systems, which were introduced by biologist A. Lindenmayer as a method to model plant growth. L-Systems are string rewriting techniques, characterized by an alphabet, an axiom (initial string) and a set of production rules. In this paper, we propose a novel traffic model, and an associated parameter fitting procedure, which describes jointly the packet arrival and the packet size processes. The packet arrival process is modeled through a L-System, where the alphabet elements are packet arrival rates. The packet size process is modeled through a set of discrete distributions (of packet sizes), one for each arrival rate. In this way the model is able to capture correlations between arrivals and sizes. We applied the model to measured traffic data: the well-known pOct Bellcore, a trace of aggregate WAN traffic and two traces of specific applications (Kazaa and Operation Flashing Point). We assess the multifractality of these traces using Linear Multiscale Diagrams. The suitability of the traffic model is evaluated by comparing the empirical and fitted probability mass and autocovariance functions; we also compare the packet loss ratio and average packet delay obtained with the measured traces and with traces generated from the fitted model. Our results show that our L-System based traffic model can achieve very good fitting performance in terms of first and second order statistics and queuing behavior.

Investigating the correlation of the U.S. Air Force Physical Fitness Test to combat-based fitness: a women-only study.

PubMed

Mitchell, Tarah; White, Edward D; Ritschel, Daniel

2014-06-01

The primary objective in this research involves determining the Air Force Physical Fitness Test's (AFPFT) predictability of combat fitness and whether measures within the AFPFT require modification to increase this predictability further. We recruited 60 female volunteers and compared their performance on the AFPFT to the Marine Combat Fitness Test, the proxy for combat fitness. We discovered little association between the two (R(2) of 0.35), however, this association significantly increased (adjusted R(2) of 0.56) when utilizing the raw scores of the AFPFT instead of using the gender/age scoring tables. Improving on these associations, we develop and propose a simple ordinary least squares regression model that minimally impacts the AFPFT testing routine. This two-event model for predicting combat fitness incorporates the 1.5-mile run along with the number of repetitions of a 30-lb dumbbell from chest height to overhead with arms extended during a 2-minute time span. These two events predicted combat fitness as assessed by the Marine Combat Fitness Test with an adjusted R(2) of 0.82. By adopting this model, we greatly improve the Air Force's ability to assess combat fitness for women. Reprint & Copyright © 2014 Association of Military Surgeons of the U.S.

Hybrid Forecasting of Daily River Discharges Considering Autoregressive Heteroscedasticity

NASA Astrophysics Data System (ADS)

Szolgayová, Elena Peksová; Danačová, Michaela; Komorniková, Magda; Szolgay, Ján

2017-06-01

It is widely acknowledged that in the hydrological and meteorological communities, there is a continuing need to improve the quality of quantitative rainfall and river flow forecasts. A hybrid (combined deterministic-stochastic) modelling approach is proposed here that combines the advantages offered by modelling the system dynamics with a deterministic model and a deterministic forecasting error series with a data-driven model in parallel. Since the processes to be modelled are generally nonlinear and the model error series may exhibit nonstationarity and heteroscedasticity, GARCH-type nonlinear time series models are considered here. The fitting, forecasting and simulation performance of such models have to be explored on a case-by-case basis. The goal of this paper is to test and develop an appropriate methodology for model fitting and forecasting applicable for daily river discharge forecast error data from the GARCH family of time series models. We concentrated on verifying whether the use of a GARCH-type model is suitable for modelling and forecasting a hydrological model error time series on the Hron and Morava Rivers in Slovakia. For this purpose we verified the presence of heteroscedasticity in the simulation error series of the KLN multilinear flow routing model; then we fitted the GARCH-type models to the data and compared their fit with that of an ARMA - type model. We produced one-stepahead forecasts from the fitted models and again provided comparisons of the model's performance.

Longitudinal factorial invariance of the PedsQL 4.0 Generic Core Scales child self-report Version: one year prospective evidence from the California State Children's Health Insurance Program (SCHIP).

PubMed

Varni, James W; Limbers, Christine A; Newman, Daniel A; Seid, Michael

2008-11-01

The measurement of health-related quality of life (HRQOL) in pediatric medicine and health services research has grown significantly over the past decade. The paradigm shift toward patient-reported outcomes (PROs) has provided the opportunity to emphasize the value and critical need for pediatric patient self-report. In order for changes in HRQOL/PRO outcomes to be meaningful over time, it is essential to demonstrate longitudinal factorial invariance. This study examined the longitudinal factor structure of the PedsQL 4.0 Generic Core Scales over a one-year period for child self-report ages 5-17 in 2,887 children from a statewide evaluation of the California State Children's Health Insurance Program (SCHIP) utilizing a structural equation modeling framework. Specifying four- and five-factor measurement models, longitudinal structural equation modeling was used to compare factor structures over a one-year interval on the PedsQL 4.0 Generic Core Scales. While the four-factor conceptually-derived measurement model for the PedsQL 4.0 Generic Core Scales produced an acceptable fit, the five-factor empirically-derived measurement model from the initial field test of the PedsQL 4.0 Generic Core Scales produced a marginally superior fit in comparison to the four-factor model. For the five-factor measurement model, the best fitting model, strict factorial invariance of the PedsQL 4.0 Generic Core Scales across the two measurement occasions was supported by the stability of the comparative fit index between the unconstrained and constrained models, and several additional indices of practical fit including the root mean squared error of approximation, the non-normed fit index, and the parsimony normed fit index. The findings support an equivalent factor structure on the PedsQL 4.0 Generic Core Scales over time. Based on these data, it can be concluded that over a one-year period children in our study interpreted items on the PedsQL 4.0 Generic Core Scales in a similar manner.

A Comparison of Graded Response and Rasch Partial Credit Models with Subjective Well-Being.

ERIC Educational Resources Information Center

Baker, John G.; Rounds, James B.; Zevon, Michael A.

2000-01-01

Compared two multiple category item response theory models using a data set of 52 mood terms with 713 undergraduate psychology students. Comparative model fit for the Samejima (F. Samejima, 1966) logistic model for graded responses and the Masters (G. Masters, 1982) partial credit model favored the former model for this data set. (SLD)

Comparative evaluation of human heat stress indices on selected hospital admissions in Sydney, Australia.

PubMed

Goldie, James; Alexander, Lisa; Lewis, Sophie C; Sherwood, Steven

2017-08-01

To find appropriate regression model specifications for counts of the daily hospital admissions of a Sydney cohort and determine which human heat stress indices best improve the models' fit. We built parent models of eight daily counts of admission records using weather station observations, census population estimates and public holiday data. We added heat stress indices; models with lower Akaike Information Criterion scores were judged a better fit. Five of the eight parent models demonstrated adequate fit. Daily maximum Simplified Wet Bulb Globe Temperature (sWBGT) consistently improved fit more than most other indices; temperature and heatwave indices also modelled some health outcomes well. Humidity and heat-humidity indices better fit counts of patients who died following admission. Maximum sWBGT is an ideal measure of heat stress for these types of Sydney hospital admissions. Simple temperature indices are a good fallback where a narrower range of conditions is investigated. Implications for public health: This study confirms the importance of selecting appropriate heat stress indices for modelling. Epidemiologists projecting Sydney hospital admissions should use maximum sWBGT as a common measure of heat stress. Health organisations interested in short-range forecasting may prefer simple temperature indices. © 2017 The Authors.

One-point fitting of the flux density produced by a heliostat

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

Collado, Francisco J.

Accurate and simple models for the flux density reflected by an isolated heliostat should be one of the basic tools for the design and optimization of solar power tower systems. In this work, the ability and the accuracy of the Universidad de Zaragoza (UNIZAR) and the DLR (HFCAL) flux density models to fit actual energetic spots are checked against heliostat energetic images measured at Plataforma Solar de Almeria (PSA). Both the fully analytic models are able to acceptably fit the spot with only one-point fitting, i.e., the measured maximum flux. As a practical validation of this one-point fitting, the interceptmore » percentage of the measured images, i.e., the percentage of the energetic spot sent by the heliostat that gets the receiver surface, is compared with the intercept calculated through the UNIZAR and HFCAL models. As main conclusions, the UNIZAR and the HFCAL models could be quite appropriate tools for the design and optimization, provided the energetic images from the heliostats to be used in the collector field were previously analyzed. Also note that the HFCAL model is much simpler and slightly more accurate than the UNIZAR model. (author)« less

Feasibility of quasi-random band model in evaluating atmospheric radiance

NASA Technical Reports Server (NTRS)

Tiwari, S. N.; Mirakhur, N.

1980-01-01

The use of the quasi-random band model in evaluating upwelling atmospheric radiation is investigated. The spectral transmittance and total band adsorptance are evaluated for selected molecular bands by using the line by line model, quasi-random band model, exponential sum fit method, and empirical correlations, and these are compared with the available experimental results. The atmospheric transmittance and upwelling radiance were calculated by using the line by line and quasi random band models and were compared with the results of an existing program called LOWTRAN. The results obtained by the exponential sum fit and empirical relations were not in good agreement with experimental results and their use cannot be justified for atmospheric studies. The line by line model was found to be the best model for atmospheric applications, but it is not practical because of high computational costs. The results of the quasi random band model compare well with the line by line and experimental results. The use of the quasi random band model is recommended for evaluation of the atmospheric radiation.

Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python.

PubMed

Irvine, Michael A; Hollingsworth, T Déirdre

2018-05-26

Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adaptive tolerance scheme. We implement a novel kernel density estimation scheme to capture both dispersed and multi-dimensional data, and directly compare this technique to standard Bayesian approaches. We then apply the procedure to a complex individual-based simulation of lymphatic filariasis, a human parasitic disease. The procedure and examples are released alongside this article as an open access library, with examples to aid researchers to rapidly fit models to data. This demonstrates that an adaptive ABC scheme with a general summary and distance metric is capable of performing model fitting for a variety of epidemiological data. It also does not require significant theoretical background to use and can be made accessible to the diverse epidemiological research community. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Polynomials to model the growth of young bulls in performance tests.

PubMed

Scalez, D C B; Fragomeni, B O; Passafaro, T L; Pereira, I G; Toral, F L B

2014-03-01

The use of polynomial functions to describe the average growth trajectory and covariance functions of Nellore and MA (21/32 Charolais+11/32 Nellore) young bulls in performance tests was studied. The average growth trajectories and additive genetic and permanent environmental covariance functions were fit with Legendre (linear through quintic) and quadratic B-spline (with two to four intervals) polynomials. In general, the Legendre and quadratic B-spline models that included more covariance parameters provided a better fit with the data. When comparing models with the same number of parameters, the quadratic B-spline provided a better fit than the Legendre polynomials. The quadratic B-spline with four intervals provided the best fit for the Nellore and MA groups. The fitting of random regression models with different types of polynomials (Legendre polynomials or B-spline) affected neither the genetic parameters estimates nor the ranking of the Nellore young bulls. However, fitting different type of polynomials affected the genetic parameters estimates and the ranking of the MA young bulls. Parsimonious Legendre or quadratic B-spline models could be used for genetic evaluation of body weight of Nellore young bulls in performance tests, whereas these parsimonious models were less efficient for animals of the MA genetic group owing to limited data at the extreme ages.

Can a one-layer optical skin model including melanin and inhomogeneously distributed blood explain spatially resolved diffuse reflectance spectra?

NASA Astrophysics Data System (ADS)

Karlsson, Hanna; Pettersson, Anders; Larsson, Marcus; Strömberg, Tomas

2011-02-01

Model based analysis of calibrated diffuse reflectance spectroscopy can be used for determining oxygenation and concentration of skin chromophores. This study aimed at assessing the effect of including melanin in addition to hemoglobin (Hb) as chromophores and compensating for inhomogeneously distributed blood (vessel packaging), in a single-layer skin model. Spectra from four humans were collected during different provocations using a twochannel fiber optic probe with source-detector separations 0.4 and 1.2 mm. Absolute calibrated spectra using data from either a single distance or both distances were analyzed using inverse Monte Carlo for light transport and Levenberg-Marquardt for non-linear fitting. The model fitting was excellent using a single distance. However, the estimated model failed to explain spectra from the other distance. The two-distance model did not fit the data well at either distance. Model fitting was significantly improved including melanin and vessel packaging. The most prominent effect when fitting data from the larger separation compared to the smaller separation was a different light scattering decay with wavelength, while the tissue fraction of Hb and saturation were similar. For modeling spectra at both distances, we propose using either a multi-layer skin model or a more advanced model for the scattering phase function.

An evaluation of the structural validity of the shoulder pain and disability index (SPADI) using the Rasch model.

PubMed

Jerosch-Herold, Christina; Chester, Rachel; Shepstone, Lee; Vincent, Joshua I; MacDermid, Joy C

2018-02-01

The shoulder pain and disability index (SPADI) has been extensively evaluated for its psychometric properties using classical test theory (CTT). The purpose of this study was to evaluate its structural validity using Rasch model analysis. Responses to the SPADI from 1030 patients referred for physiotherapy with shoulder pain and enrolled in a prospective cohort study were available for Rasch model analysis. Overall fit, individual person and item fit, response format, dependence, unidimensionality, targeting, reliability and differential item functioning (DIF) were examined. The SPADI pain subscale initially demonstrated a misfit due to DIF by age and gender. After iterative analysis it showed good fit to the Rasch model with acceptable targeting and unidimensionality (overall fit Chi-square statistic 57.2, p = 0.1; mean item fit residual 0.19 (1.5) and mean person fit residual 0.44 (1.1); person separation index (PSI) of 0.83. The disability subscale however shows significant misfit due to uniform DIF even after iterative analyses were used to explore different solutions to the sources of misfit (overall fit (Chi-square statistic 57.2, p = 0.1); mean item fit residual 0.54 (1.26) and mean person fit residual 0.38 (1.0); PSI 0.84). Rasch Model analysis of the SPADI has identified some strengths and limitations not previously observed using CTT methods. The SPADI should be treated as two separate subscales. The SPADI is a widely used outcome measure in clinical practice and research; however, the scores derived from it must be interpreted with caution. The pain subscale fits the Rasch model expectations well. The disability subscale does not fit the Rasch model and its current format does not meet the criteria for true interval-level measurement required for use as a primary endpoint in clinical trials. Clinicians should therefore exercise caution when interpreting score changes on the disability subscale and attempt to compare their scores to age- and sex-stratified data.

Evolution of complex dynamics

NASA Astrophysics Data System (ADS)

Wilds, Roy; Kauffman, Stuart A.; Glass, Leon

2008-09-01

We study the evolution of complex dynamics in a model of a genetic regulatory network. The fitness is associated with the topological entropy in a class of piecewise linear equations, and the mutations are associated with changes in the logical structure of the network. We compare hill climbing evolution, in which only mutations that increase the fitness are allowed, with neutral evolution, in which mutations that leave the fitness unchanged are allowed. The simple structure of the fitness landscape enables us to estimate analytically the rates of hill climbing and neutral evolution. In this model, allowing neutral mutations accelerates the rate of evolutionary advancement for low mutation frequencies. These results are applicable to evolution in natural and technological systems.

A nonlinear model of gold production in Malaysia

NASA Astrophysics Data System (ADS)

Ramli, Norashikin; Muda, Nora; Umor, Mohd Rozi

2014-06-01

Malaysia is a country which is rich in natural resources and one of it is a gold. Gold has already become an important national commodity. This study is conducted to determine a model that can be well fitted with the gold production in Malaysia from the year 1995-2010. Five nonlinear models are presented in this study which are Logistic model, Gompertz, Richard, Weibull and Chapman-Richard model. These model are used to fit the cumulative gold production in Malaysia. The best model is then selected based on the model performance. The performance of the fitted model is measured by sum squares error, root mean squares error, coefficient of determination, mean relative error, mean absolute error and mean absolute percentage error. This study has found that a Weibull model is shown to have significantly outperform compare to the other models. To confirm that Weibull is the best model, the latest data are fitted to the model. Once again, Weibull model gives the lowest readings at all types of measurement error. We can concluded that the future gold production in Malaysia can be predicted according to the Weibull model and this could be important findings for Malaysia to plan their economic activities.

Biomechanical modeling of acetabular component polyethylene stresses, fracture risk, and wear rate following press-fit implantation.

PubMed

Ong, Kevin L; Rundell, Steve; Liepins, Imants; Laurent, Ryan; Markel, David; Kurtz, Steven M

2009-11-01

Press-fit implantation may result in acetabular component deformation between the ischial-ilial columns ("pinching"). The biomechanical and clinical consequences of liner pinching due to press-fit implantation have not been well studied. We compared the effects of pinching on the polyethylene fracture risk, potential wear rate, and stresses for two different thickness liners using computational methods. Line-to-line ("no pinch") reaming and 2 mm underreaming press fit ("pinch") conditions were examined for Trident cups with X3 polyethylene liner wall thicknesses of 5.9 mm (36E) and 3.8 mm (40E). Press-fit cup deformations were measured from a foam block configuration. A hybrid material model, calibrated to experimentally determined stress-strain behavior of sequentially annealed polyethylene, was applied to the computational model. Molecular chain stretch did not exceed the fracture threshold in any cases. Nominal shell pinch of 0.28 mm was estimated to increase the volumetric wear rate by 70% for both cups and peak contact stresses by 140 and 170% for the 5.9 and 3.8 mm-thick liners, respectively. Although pinching increases liner stresses, polyethylene fracture is highly unlikely, and the volumetric wear rates are likely to be low compared to conventional polyethylene. (c) 2009 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Sensitivity analysis of respiratory parameter uncertainties: impact of criterion function form and constraints.

PubMed

Lutchen, K R

1990-08-01

A sensitivity analysis based on weighted least-squares regression is presented to evaluate alternative methods for fitting lumped-parameter models to respiratory impedance data. The goal is to maintain parameter accuracy simultaneously with practical experiment design. The analysis focuses on predicting parameter uncertainties using a linearized approximation for joint confidence regions. Applications are with four-element parallel and viscoelastic models for 0.125- to 4-Hz data and a six-element model with separate tissue and airway properties for input and transfer impedance data from 2-64 Hz. The criterion function form was evaluated by comparing parameter uncertainties when data are fit as magnitude and phase, dynamic resistance and compliance, or real and imaginary parts of input impedance. The proper choice of weighting can make all three criterion variables comparable. For the six-element model, parameter uncertainties were predicted when both input impedance and transfer impedance are acquired and fit simultaneously. A fit to both data sets from 4 to 64 Hz could reduce parameter estimate uncertainties considerably from those achievable by fitting either alone. For the four-element models, use of an independent, but noisy, measure of static compliance was assessed as a constraint on model parameters. This may allow acceptable parameter uncertainties for a minimum frequency of 0.275-0.375 Hz rather than 0.125 Hz. This reduces data acquisition requirements from a 16- to a 5.33- to 8-s breath holding period. These results are approximations, and the impact of using the linearized approximation for the confidence regions is discussed.

Performance of a quantitative fecal immunochemical test for detecting advanced colorectal neoplasia: a prospective cohort study.

PubMed

Liles, Elizabeth G; Perrin, Nancy; Rosales, Ana G; Smith, David H; Feldstein, Adrianne C; Mosen, David M; Levin, Theodore R

2018-05-02

The fecal immunochemical test (FIT) is easier to use and more sensitive than the guaiac fecal occult blood test, but it is unclear how to optimize FIT performance. We compared the sensitivity and specificity for detecting advanced colorectal neoplasia between single-sample (1-FIT) and two-sample (2-FIT) FIT protocols at a range of hemoglobin concentration cutoffs for a positive test. We recruited 2,761 average-risk men and women ages 49-75 referred for colonoscopy within a large nonprofit, group-model health maintenance organization (HMO), and asked them to complete two separate single-sample FITs. We generated receiver-operating characteristic (ROC) curves to compare sensitivity and specificity estimates for 1-FIT and 2-FIT protocols among those who completed both FIT kits and colonoscopy. We similarly compared sensitivity and specificity between hemoglobin concentration cutoffs for a single-sample FIT. Differences in sensitivity and specificity between the 1-FIT and 2-FIT protocols were not statistically significant at any of the pre-specified hemoglobin concentration cutoffs (10, 15, 20, 25, and 30 μg/g). There was a significant difference in test performance of the one-sample FIT between 50 ng/ml (10 μg/g) and each of the higher pre-specified cutoffs. Disease prevalence was low. A two-sample FIT is not superior to a one-sample FIT in detection of advanced adenomas; the one-sample FIT at a hemoglobin concentration cutoff of 50 ng/ml (10 μg/g) is significantly more sensitive for advanced adenomas than at higher cutoffs. These findings apply to a population of younger, average-risk patients in a U.S. integrated care system with high rates of prior screening.

Cost-effectiveness of one versus two sample faecal immunochemical testing for colorectal cancer screening.

PubMed

Goede, S Lucas; van Roon, Aafke H C; Reijerink, Jacqueline C I Y; van Vuuren, Anneke J; Lansdorp-Vogelaar, Iris; Habbema, J Dik F; Kuipers, Ernst J; van Leerdam, Monique E; van Ballegooijen, Marjolein

2013-05-01

The sensitivity and specificity of a single faecal immunochemical test (FIT) are limited. The performance of FIT screening can be improved by increasing the screening frequency or by providing more than one sample in each screening round. This study aimed to evaluate if two-sample FIT screening is cost-effective compared with one-sample FIT. The MISCAN-colon microsimulation model was used to estimate costs and benefits of strategies with either one or two-sample FIT screening. The FIT cut-off level varied between 50 and 200 ng haemoglobin/ml, and the screening schedule was varied with respect to age range and interval. In addition, different definitions for positivity of the two-sample FIT were considered: at least one positive sample, two positive samples, or the mean of both samples being positive. Within an exemplary screening strategy, biennial FIT from the age of 55-75 years, one-sample FIT provided 76.0-97.0 life-years gained (LYG) per 1000 individuals, at a cost of € 259,000-264,000 (range reflects different FIT cut-off levels). Two-sample FIT screening with at least one sample being positive provided 7.3-12.4 additional LYG compared with one-sample FIT at an extra cost of € 50,000-59,000. However, when all screening intervals and age ranges were considered, intensifying screening with one-sample FIT provided equal or more LYG at lower costs compared with two-sample FIT. If attendance to screening does not differ between strategies it is recommended to increase the number of screening rounds with one-sample FIT screening, before considering increasing the number of FIT samples provided per screening round.

NGMIX: Gaussian mixture models for 2D images

NASA Astrophysics Data System (ADS)

Sheldon, Erin

2015-08-01

NGMIX implements Gaussian mixture models for 2D images. Both the PSF profile and the galaxy are modeled using mixtures of Gaussians. Convolutions are thus performed analytically, resulting in fast model generation as compared to methods that perform the convolution in Fourier space. For the galaxy model, NGMIX supports exponential disks and de Vaucouleurs and Sérsic profiles; these are implemented approximately as a sum of Gaussians using the fits from Hogg & Lang (2013). Additionally, any number of Gaussians can be fit, either completely free or constrained to be cocentric and co-elliptical.

Mars - Hellas Planitia gravity analysis

NASA Technical Reports Server (NTRS)

Sjogren, W. L.; Wimberley, R. N.

1981-01-01

Doppler radio tracking data from Viking Orbiter 1 has provided new detailed observations of gravity variations over Hellas Planitia. Line-of-sight Bouguer gravity definitely indicates that isostatic adjustment has occurred. Two theoretical models were tested to obtain fits to the gravity data. Results for a surface deficit model, and a model with a surface deficit and a mass excess at depth are displayed. The mass-at-depth model produced very marked improvement in the data fit as compared to the surface deficit model. The optimum depth for the mass excess is 130 km.

Quantifying Confidence in Model Predictions for Hypersonic Aircraft Structures

DTIC Science & Technology

2015-03-01

of isolating calibrations of models in the network, segmented and simultaneous calibration are compared using the Kullback - Leibler ...value of θ. While not all test -statistics are as simple as measuring goodness or badness of fit , their directional interpretations tend to remain...data quite well, qualitatively. Quantitative goodness - of - fit tests are problematic because they assume a true empirical CDF is being tested or

On the accuracy of models for predicting sound propagation in fitted rooms.

PubMed

Hodgson, M

1990-08-01

The objective of this article is to make a contribution to the evaluation of the accuracy and applicability of models for predicting the sound propagation in fitted rooms such as factories, classrooms, and offices. The models studied are 1:50 scale models; the method-of-image models of Jovicic, Lindqvist, Hodgson, Kurze, and of Lemire and Nicolas; the emprical formula of Friberg; and Ondet and Barbry's ray-tracing model. Sound propagation predictions by the analytic models are compared with the results of sound propagation measurements in a 1:50 scale model and in a warehouse, both containing various densities of approximately isotropically distributed, rectangular-parallelepipedic fittings. The results indicate that the models of Friberg and of Lemire and Nicolas are fundamentally incorrect. While more generally applicable versions exist, the versions of the models of Jovicic and Kurze studied here are found to be of limited applicability since they ignore vertical-wall reflections. The Hodgson and Lindqvist models appear to be accurate in certain limited cases. This preliminary study found the ray-tracing model of Ondet and Barbry to be the most accurate of all the cases studied. Furthermore, it has the necessary flexibility with respect to room geometry, surface-absorption distribution, and fitting distribution. It appears to be the model with the greatest applicability to fitted-room sound propagation prediction.

Comparative effectiveness and cost-effectiveness of screening colonoscopy vs. sigmoidoscopy and alternative strategies.

PubMed

Sharaf, Ravi N; Ladabaum, Uri

2013-01-01

Fecal occult blood testing (FOBT) and sigmoidoscopy are proven to decrease colorectal cancer (CRC) incidence and mortality. Sigmoidoscopy's benefit is limited to the distal colon. Observational data are conflicting regarding the degree to which colonoscopy affords protection against proximal CRC. Our aim was to explore the comparative effectiveness and cost-effectiveness of colonoscopy vs. sigmoidoscopy and alternative CRC screening strategies in light of the latest published data. We performed a contemporary cost-utility analysis using a Markov model validated against data from randomized controlled trials of FOBT and sigmoidoscopy. Persons at average CRC risk within the general US population were modeled. Screening strategies included those recommended by the United States (US) Preventive Services Task Force, including colonoscopy every 10 years (COLO), flexible sigmoidoscopy every 5 years (FS), annual fecal occult blood testing, annual fecal immunochemical testing (FIT), and the combination FS/FIT. The main outcome measures were quality-adjusted life-years (QALYs) and costs. In the base case, FIT dominated other strategies. The advantage of FIT over FS and COLO was contingent on rates of uptake and adherence that are well above current US rates. Compared with FIT, FS and COLO both cost <$50,000/QALY gained when FIT per-cycle adherence was <50%. COLO cost $56,800/QALY gained vs. FS in the base case. COLO cost <$100,000/QALY gained vs. FS when COLO yielded a relative risk of proximal CRC of <0.5 vs. no screening. In probabilistic analyses, COLO was cost-effective vs. FS at a willingness-to-pay threshold of $100,000/QALY gained in 84% of iterations. Screening colonoscopy may be cost-effective compared with FIT and sigmoidoscopy, depending on the relative rates of screening uptake and adherence and the protective benefit of colonoscopy in the proximal colon. Colonoscopy's cost-effectiveness compared with sigmoidoscopy is contingent on the ability to deliver ~50% protection against CRC in the proximal colon.

Experimentally validated modification to Cook-Torrance BRDF model for improved accuracy

NASA Astrophysics Data System (ADS)

Butler, Samuel D.; Ethridge, James A.; Nauyoks, Stephen E.; Marciniak, Michael A.

2017-09-01

The BRDF describes optical scatter off realistic surfaces. The microfacet BRDF model assumes geometric optics but is computationally simple compared to wave optics models. In this work, MERL BRDF data is fitted to the original Cook-Torrance microfacet model, and a modified Cook-Torrance model using the polarization factor in place of the mathematically problematic cross section conversion and geometric attenuation terms. The results provide experimental evidence that this modified Cook-Torrance model leads to improved fits, particularly for large incident and scattered angles. These results are expected to lead to more accurate BRDF modeling for remote sensing.

Syndromes of collateral-reported psychopathology for ages 18-59 in 18 Societies

PubMed Central

Ivanova, Masha Y.; Achenbach, Thomas M.; Rescorla, Leslie A.; Turner, Lori V.; Árnadóttir, Hervör Alma; Au, Alma; Caldas, J. Carlos; Chaalal, Nebia; Chen, Yi Chuen; da Rocha, Marina M.; Decoster, Jeroen; Fontaine, Johnny R.J.; Funabiki, Yasuko; Guðmundsson, Halldór S.; Kim, Young Ah; Leung, Patrick; Liu, Jianghong; Malykh, Sergey; Marković, Jasminka; Oh, Kyung Ja; Petot, Jean-Michel; Samaniego, Virginia C.; Silvares, Edwiges Ferreira de Mattos; Šimulionienė, Roma; Šobot, Valentina; Sokoli, Elvisa; Sun, Guiju; Talcott, Joel B.; Vázquez, Natalia; Zasępa, Ewa

2017-01-01

The purpose was to advance research and clinical methodology for assessing psychopathology by testing the international generalizability of an 8-syndrome model derived from collateral ratings of adult behavioral, emotional, social, and thought problems. Collateral informants rated 8,582 18–59-year-old residents of 18 societies on the Adult Behavior Checklist (ABCL). Confirmatory factor analyses tested the fit of the 8-syndrome model to ratings from each society. The primary model fit index (Root Mean Square Error of Approximation) showed good model fit for all societies, while secondary indices (Tucker Lewis Index, Comparative Fit Index) showed acceptable to good fit for 17 societies. Factor loadings were robust across societies and items. Of the 5,007 estimated parameters, 4 (0.08%) were outside the admissible parameter space, but 95% confidence intervals included the admissible space, indicating that the 4 deviant parameters could be due to sampling fluctuations. The findings are consistent with previous evidence for the generalizability of the 8-syndrome model in self-ratings from 29 societies, and support the 8-syndrome model for operationalizing phenotypes of adult psychopathology from multi-informant ratings in diverse societies. PMID:29399019

Analysis and fit of stellar spectra using a mega-database of CMFGEN models

NASA Astrophysics Data System (ADS)

Fierro-Santillán, Celia; Zsargó, Janos; Klapp, Jaime; Díaz-Azuara, Santiago Alfredo; Arrieta, Anabel; Arias, Lorena

2017-11-01

We present a tool for analysis and fit of stellar spectra using a mega database of 15,000 atmosphere models for OB stars. We have developed software tools, which allow us to find the model that best fits to an observed spectrum, comparing equivalent widths and line ratios in the observed spectrum with all models of the database. We use the Hα, Hβ, Hγ, and Hδ lines as criterion of stellar gravity and ratios of He II λ4541/He I λ4471, He II λ4200/(He I+He II λ4026), He II λ4541/He I λ4387, and He II λ4200/He I λ4144 as criterion of T eff.

Fit of interim crowns fabricated using photopolymer-jetting 3D printing.

PubMed

Mai, Hang-Nga; Lee, Kyu-Bok; Lee, Du-Hyeong

2017-08-01

The fit of interim crowns fabricated using 3-dimensional (3D) printing is unknown. The purpose of this in vitro study was to evaluate the fit of interim crowns fabricated using photopolymer-jetting 3D printing and to compare it with that of milling and compression molding methods. Twelve study models were fabricated by making an impression of a metal master model of the mandibular first molar. On each study model, interim crowns (N=36) were fabricated using compression molding (molding group, n=12), milling (milling group, n=12), and 3D polymer-jetting methods. The crowns were prepared as follows: molding group, overimpression technique; milling group, a 5-axis dental milling machine; and polymer-jetting group using a 3D printer. The fit of interim crowns was evaluated in the proximal, marginal, internal axial, and internal occlusal regions by using the image-superimposition and silicone-replica techniques. The Mann-Whitney U test and Kruskal-Wallis tests were used to compare the results among groups (α=.05). Compared with the molding group, the milling and polymer-jetting groups showed more accurate results in the proximal and marginal regions (P<.001). In the axial regions, even though the mean discrepancy was smallest in the molding group, the data showed large deviations. In the occlusal region, the polymer-jetting group was the most accurate, and compared with the other groups, the milling group showed larger internal discrepancies (P<.001). Polymer-jet 3D printing significantly enhanced the fit of interim crowns, particularly in the occlusal region. Copyright © 2016 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

A Smoluchowski model of crystallization dynamics of small colloidal clusters

NASA Astrophysics Data System (ADS)

Beltran-Villegas, Daniel J.; Sehgal, Ray M.; Maroudas, Dimitrios; Ford, David M.; Bevan, Michael A.

2011-10-01

We investigate the dynamics of colloidal crystallization in a 32-particle system at a fixed value of interparticle depletion attraction that produces coexisting fluid and solid phases. Free energy landscapes (FELs) and diffusivity landscapes (DLs) are obtained as coefficients of 1D Smoluchowski equations using as order parameters either the radius of gyration or the average crystallinity. FELs and DLs are estimated by fitting the Smoluchowski equations to Brownian dynamics (BD) simulations using either linear fits to locally initiated trajectories or global fits to unbiased trajectories using Bayesian inference. The resulting FELs are compared to Monte Carlo Umbrella Sampling results. The accuracy of the FELs and DLs for modeling colloidal crystallization dynamics is evaluated by comparing mean first-passage times from BD simulations with analytical predictions using the FEL and DL models. While the 1D models accurately capture dynamics near the free energy minimum fluid and crystal configurations, predictions near the transition region are not quantitatively accurate. A preliminary investigation of ensemble averaged 2D order parameter trajectories suggests that 2D models are required to capture crystallization dynamics in the transition region.

Tanning Shade Gradations of Models in Mainstream Fitness and Muscle Enthusiast Magazines: Implications for Skin Cancer Prevention in Men.

PubMed

Basch, Corey H; Hillyer, Grace Clarke; Ethan, Danna; Berdnik, Alyssa; Basch, Charles E

2015-07-01

Tanned skin has been associated with perceptions of fitness and social desirability. Portrayal of models in magazines may reflect and perpetuate these perceptions. Limited research has investigated tanning shade gradations of models in men's versus women's fitness and muscle enthusiast magazines. Such findings are relevant in light of increased incidence and prevalence of melanoma in the United States. This study evaluated and compared tanning shade gradations of adult Caucasian male and female model images in mainstream fitness and muscle enthusiast magazines. Sixty-nine U.S. magazine issues (spring and summer, 2013) were utilized. Two independent reviewers rated tanning shade gradations of adult Caucasian male and female model images on magazines' covers, advertisements, and feature articles. Shade gradations were assessed using stock photographs of Caucasian models with varying levels of tanned skin on an 8-shade scale. A total of 4,683 images were evaluated. Darkest tanning shades were found among males in muscle enthusiast magazines and lightest among females in women's mainstream fitness magazines. By gender, male model images were 54% more likely to portray a darker tanning shade. In this study, images in men's (vs. women's) fitness and muscle enthusiast magazines portrayed Caucasian models with darker skin shades. Despite these magazines' fitness-related messages, pro-tanning images may promote attitudes and behaviors associated with higher skin cancer risk. To date, this is the first study to explore tanning shades in men's magazines of these genres. Further research is necessary to identify effects of exposure to these images among male readers. © The Author(s) 2014.

Right-sizing statistical models for longitudinal data.

PubMed

Wood, Phillip K; Steinley, Douglas; Jackson, Kristina M

2015-12-01

Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeated-measures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study. (c) 2015 APA, all rights reserved).

Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis.

PubMed

Xie, Yuanchang; Lord, Dominique; Zhang, Yunlong

2007-09-01

Statistical models have frequently been used in highway safety studies. They can be utilized for various purposes, including establishing relationships between variables, screening covariates and predicting values. Generalized linear models (GLM) and hierarchical Bayes models (HBM) have been the most common types of model favored by transportation safety analysts. Over the last few years, researchers have proposed the back-propagation neural network (BPNN) model for modeling the phenomenon under study. Compared to GLMs and HBMs, BPNNs have received much less attention in highway safety modeling. The reasons are attributed to the complexity for estimating this kind of model as well as the problem related to "over-fitting" the data. To circumvent the latter problem, some statisticians have proposed the use of Bayesian neural network (BNN) models. These models have been shown to perform better than BPNN models while at the same time reducing the difficulty associated with over-fitting the data. The objective of this study is to evaluate the application of BNN models for predicting motor vehicle crashes. To accomplish this objective, a series of models was estimated using data collected on rural frontage roads in Texas. Three types of models were compared: BPNN, BNN and the negative binomial (NB) regression models. The results of this study show that in general both types of neural network models perform better than the NB regression model in terms of data prediction. Although the BPNN model can occasionally provide better or approximately equivalent prediction performance compared to the BNN model, in most cases its prediction performance is worse than the BNN model. In addition, the data fitting performance of the BPNN model is consistently worse than the BNN model, which suggests that the BNN model has better generalization abilities than the BPNN model and can effectively alleviate the over-fitting problem without significantly compromising the nonlinear approximation ability. The results also show that BNNs could be used for other useful analyses in highway safety, including the development of accident modification factors and for improving the prediction capabilities for evaluating different highway design alternatives.

Species area relationships in mediterranean-climate plant communities

USGS Publications Warehouse

Keeley, Jon E.; Fotheringham, C.J.

2003-01-01

Aim To determine the best-fit model of species–area relationships for Mediterranean-type plant communities and evaluate how community structure affects these species–area models.Location Data were collected from California shrublands and woodlands and compared with literature reports for other Mediterranean-climate regions.Methods The number of species was recorded from 1, 100 and 1000 m2 nested plots. Best fit to the power model or exponential model was determined by comparing adjusted r2 values from the least squares regression, pattern of residuals, homoscedasticity across scales, and semi-log slopes at 1–100 m2 and 100–1000 m2. Dominance–diversity curves were tested for fit to the lognormal model, MacArthur's broken stick model, and the geometric and harmonic series.Results Early successional Western Australia and California shrublands represented the extremes and provide an interesting contrast as the exponential model was the best fit for the former, and the power model for the latter, despite similar total species richness. We hypothesize that structural differences in these communities account for the different species–area curves and are tied to patterns of dominance, equitability and life form distribution. Dominance–diversity relationships for Western Australian heathlands exhibited a close fit to MacArthur's broken stick model, indicating more equitable distribution of species. In contrast, Californian shrublands, both postfire and mature stands, were best fit by the geometric model indicating strong dominance and many minor subordinate species. These regions differ in life form distribution, with annuals being a major component of diversity in early successional Californian shrublands although they are largely lacking in mature stands. Both young and old Australian heathlands are dominated by perennials, and annuals are largely absent. Inherent in all of these ecosystems is cyclical disequilibrium caused by periodic fires. The potential for community reassembly is greater in Californian shrublands where only a quarter of the flora resprout, whereas three quarters resprout in Australian heathlands.Other Californian vegetation types sampled include coniferous forests, oak savannas and desert scrub, and demonstrate that different community structures may lead to a similar species–area relationship. Dominance–diversity relationships for coniferous forests closely follow a geometric model whereas associated oak savannas show a close fit to the lognormal model. However, for both communities, species–area curves fit a power model. The primary driver appears to be the presence of annuals. Desert scrub communities illustrate dramatic changes in both species diversity and dominance–diversity relationships in high and low rainfall years, because of the disappearance of annuals in drought years.Main conclusions Species–area curves for immature shrublands in California and the majority of Mediterranean plant communities fit a power function model. Exceptions that fit the exponential model are not because of sampling error or scaling effects, rather structural differences in these communities provide plausible explanations. The exponential species–area model may arise in more than one way. In the highly diverse Australian heathlands it results from a rapid increase in species richness at small scales. In mature California shrublands it results from very depauperate richness at the community scale. In both instances the exponential model is tied to a preponderance of perennials and paucity of annuals. For communities fit by a power model, coefficients z and log c exhibit a number of significant correlations with other diversity parameters, suggesting that they have some predictive value in ecological communities.

Mathematical modelling of temperature effect on growth kinetics of Pseudomonas spp. on sliced mushroom (Agaricus bisporus).

PubMed

Tarlak, Fatih; Ozdemir, Murat; Melikoglu, Mehmet

2018-02-02

The growth data of Pseudomonas spp. on sliced mushrooms (Agaricus bisporus) stored between 4 and 28°C were obtained and fitted to three different primary models, known as the modified Gompertz, logistic and Baranyi models. The goodness of fit of these models was compared by considering the mean squared error (MSE) and the coefficient of determination for nonlinear regression (pseudo-R 2 ). The Baranyi model yielded the lowest MSE and highest pseudo-R 2 values. Therefore, the Baranyi model was selected as the best primary model. Maximum specific growth rate (r max ) and lag phase duration (λ) obtained from the Baranyi model were fitted to secondary models namely, the Ratkowsky and Arrhenius models. High pseudo-R 2 and low MSE values indicated that the Arrhenius model has a high goodness of fit to determine the effect of temperature on r max . Observed number of Pseudomonas spp. on sliced mushrooms from independent experiments was compared with the predicted number of Pseudomonas spp. with the models used by considering the B f and A f values. The B f and A f values were found to be 0.974 and 1.036, respectively. The correlation between the observed and predicted number of Pseudomonas spp. was high. Mushroom spoilage was simulated as a function of temperature with the models used. The models used for Pseudomonas spp. growth can provide a fast and cost-effective alternative to traditional microbiological techniques to determine the effect of storage temperature on product shelf-life. The models can be used to evaluate the growth behaviour of Pseudomonas spp. on sliced mushroom, set limits for the quantitative detection of the microbial spoilage and assess product shelf-life. Copyright © 2017 Elsevier B.V. All rights reserved.

Validation of the Brazilian version of the 'Spanish Burnout Inventory' in teachers.

PubMed

Gil-Monte, Pedro R; Carlotto, Mary Sandra; Câmara, Sheila Gonçalves

2010-02-01

To assess factorial validity and internal consistency of the Brazilian version of the 'Spanish Burnout Inventory' (SBI). The translation process of the SBI into Brazilian Portuguese included translation, back translation, and semantic equivalence. A confirmatory factor analysis was carried out using a four-factor model, which was similar to the original SBI. The sample consisted of 714 teachers working in schools in the metropolitan area of the city of Porto Alegre, Southern Brazil, in 2008. The instrument comprises 20 items and four subscales: Enthusiasm towards job (5 items), Psychological exhaustion (4 items), Indolence (6 items), and Guilt (5 items). The model was analyzed using LISREL 8. Goodness-of-Fit statistics showed that the hypothesized model had adequate fit: chi2(164) = 605.86 (p<0.000); Goodness-of-Fit Index = 0.92; Adjusted Goodness-of-Fit Index = 0.90; Root Mean Square Error of Approximation = 0.062; Nonnormed Fit Index = 0.91; Comparative Fit Index = 0.92; and Parsimony Normed Fit Index = 0.77. Cronbach's alpha measures for all subscales were higher than 0.70. The study showed that the SBI has adequate factorial validity and internal consistency to assess burnout in Brazilian teachers.

Are all models created equal? A content analysis of women in advertisements of fitness versus fashion magazines.

PubMed

Wasylkiw, L; Emms, A A; Meuse, R; Poirier, K F

2009-03-01

The current study is a content analysis of women appearing in advertisements in two types of magazines: fitness/health versus fashion/beauty chosen because of their large and predominantly female readerships. Women appearing in advertisements of the June 2007 issue of five fitness/health magazines were compared to women appearing in advertisements of the June 2007 issue of five beauty/fashion magazines. Female models appearing in advertisements of both types of magazines were primarily young, thin Caucasians; however, images of models were more likely to emphasize appearance over performance when they appeared in fashion magazines. This difference in emphasis has implications for future research.

Quantitative Rheological Model Selection

NASA Astrophysics Data System (ADS)

Freund, Jonathan; Ewoldt, Randy

2014-11-01

The more parameters in a rheological the better it will reproduce available data, though this does not mean that it is necessarily a better justified model. Good fits are only part of model selection. We employ a Bayesian inference approach that quantifies model suitability by balancing closeness to data against both the number of model parameters and their a priori uncertainty. The penalty depends upon prior-to-calibration expectation of the viable range of values that model parameters might take, which we discuss as an essential aspect of the selection criterion. Models that are physically grounded are usually accompanied by tighter physical constraints on their respective parameters. The analysis reflects a basic principle: models grounded in physics can be expected to enjoy greater generality and perform better away from where they are calibrated. In contrast, purely empirical models can provide comparable fits, but the model selection framework penalizes their a priori uncertainty. We demonstrate the approach by selecting the best-justified number of modes in a Multi-mode Maxwell description of PVA-Borax. We also quantify relative merits of the Maxwell model relative to powerlaw fits and purely empirical fits for PVA-Borax, a viscoelastic liquid, and gluten.

Internal fit of single crowns produced by CAD-CAM and lost-wax metal casting technique assessed by the triple-scan protocol.

PubMed

Dahl, Bjørn Einar; Rønold, Hans Jacob; Dahl, Jon E

2017-03-01

Whether single crowns produced by computer-aided design and computer-aided manufacturing (CAD-CAM) have an internal fit comparable to crowns made by lost-wax metal casting technique is unknown. The purpose of this in vitro study was to compare the internal fit of single crowns produced with the lost-wax and metal casting technique with that of single crowns produced with the CAD-CAM technique. The internal fit of 5 groups of single crowns produced with the CAD-CAM technique was compared with that of single crowns produced in cobalt-chromium with the conventional lost-wax and metal casting technique. Comparison was performed using the triple-scan protocol; scans of the master model, the crown on the master model, and the intaglio of the crown were superimposed and analyzed with computer software. The 5 groups were milled presintered zirconia, milled hot isostatic pressed zirconia, milled lithium disilicate, milled cobalt-chromium, and laser-sintered cobalt-chromium. The cement space in both the mesiodistal and buccopalatal directions was statistically smaller (P<.05) for crowns made by the conventional lost-wax and metal casting technique compared with that of crowns produced by the CAD-CAM technique. Single crowns made using the conventional lost-wax and metal casting technique have better internal fit than crowns produced using the CAD-CAM technique. Copyright © 2016 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Accuracy of methods for calculating volumetric wear from coordinate measuring machine data of retrieved metal-on-metal hip joint implants.

PubMed

Lu, Zhen; McKellop, Harry A

2014-03-01

This study compared the accuracy and sensitivity of several numerical methods employing spherical or plane triangles for calculating the volumetric wear of retrieved metal-on-metal hip joint implants from coordinate measuring machine measurements. Five methods, one using spherical triangles and four using plane triangles to represent the bearing and the best-fit surfaces, were assessed and compared on a perfect hemisphere model and a hemi-ellipsoid model (i.e. unworn models), computer-generated wear models and wear-tested femoral balls, with point spacings of 0.5, 1, 2 and 3 mm. The results showed that the algorithm (Method 1) employing spherical triangles to represent the bearing surface and to scale the mesh to the best-fit surfaces produced adequate accuracy for the wear volume with point spacings of 0.5, 1, 2 and 3 mm. The algorithms (Methods 2-4) using plane triangles to represent the bearing surface and to scale the mesh to the best-fit surface also produced accuracies that were comparable to that with spherical triangles. In contrast, if the bearing surface was represented with a mesh of plane triangles and the best-fit surface was taken as a smooth surface without discretization (Method 5), the algorithm produced much lower accuracy with a point spacing of 0.5 mm than Methods 1-4 with a point spacing of 3 mm.

FracFit: A Robust Parameter Estimation Tool for Anomalous Transport Problems

NASA Astrophysics Data System (ADS)

Kelly, J. F.; Bolster, D.; Meerschaert, M. M.; Drummond, J. D.; Packman, A. I.

2016-12-01

Anomalous transport cannot be adequately described with classical Fickian advection-dispersion equations (ADE). Rather, fractional calculus models may be used, which capture non-Fickian behavior (e.g. skewness and power-law tails). FracFit is a robust parameter estimation tool based on space- and time-fractional models used to model anomalous transport. Currently, four fractional models are supported: 1) space fractional advection-dispersion equation (sFADE), 2) time-fractional dispersion equation with drift (TFDE), 3) fractional mobile-immobile equation (FMIE), and 4) tempered fractional mobile-immobile equation (TFMIE); additional models may be added in the future. Model solutions using pulse initial conditions and continuous injections are evaluated using stable distribution PDFs and CDFs or subordination integrals. Parameter estimates are extracted from measured breakthrough curves (BTCs) using a weighted nonlinear least squares (WNLS) algorithm. Optimal weights for BTCs for pulse initial conditions and continuous injections are presented, facilitating the estimation of power-law tails. Two sample applications are analyzed: 1) continuous injection laboratory experiments using natural organic matter and 2) pulse injection BTCs in the Selke river. Model parameters are compared across models and goodness-of-fit metrics are presented, assisting model evaluation. The sFADE and time-fractional models are compared using space-time duality (Baeumer et. al., 2009), which links the two paradigms.

Estimation of parameters of dose volume models and their confidence limits

NASA Astrophysics Data System (ADS)

van Luijk, P.; Delvigne, T. C.; Schilstra, C.; Schippers, J. M.

2003-07-01

Predictions of the normal-tissue complication probability (NTCP) for the ranking of treatment plans are based on fits of dose-volume models to clinical and/or experimental data. In the literature several different fit methods are used. In this work frequently used methods and techniques to fit NTCP models to dose response data for establishing dose-volume effects, are discussed. The techniques are tested for their usability with dose-volume data and NTCP models. Different methods to estimate the confidence intervals of the model parameters are part of this study. From a critical-volume (CV) model with biologically realistic parameters a primary dataset was generated, serving as the reference for this study and describable by the NTCP model. The CV model was fitted to this dataset. From the resulting parameters and the CV model, 1000 secondary datasets were generated by Monte Carlo simulation. All secondary datasets were fitted to obtain 1000 parameter sets of the CV model. Thus the 'real' spread in fit results due to statistical spreading in the data is obtained and has been compared with estimates of the confidence intervals obtained by different methods applied to the primary dataset. The confidence limits of the parameters of one dataset were estimated using the methods, employing the covariance matrix, the jackknife method and directly from the likelihood landscape. These results were compared with the spread of the parameters, obtained from the secondary parameter sets. For the estimation of confidence intervals on NTCP predictions, three methods were tested. Firstly, propagation of errors using the covariance matrix was used. Secondly, the meaning of the width of a bundle of curves that resulted from parameters that were within the one standard deviation region in the likelihood space was investigated. Thirdly, many parameter sets and their likelihood were used to create a likelihood-weighted probability distribution of the NTCP. It is concluded that for the type of dose response data used here, only a full likelihood analysis will produce reliable results. The often-used approximations, such as the usage of the covariance matrix, produce inconsistent confidence limits on both the parameter sets and the resulting NTCP values.

Temperature-viscosity models reassessed.

PubMed

Peleg, Micha

2017-05-04

The temperature effect on viscosity of liquid and semi-liquid foods has been traditionally described by the Arrhenius equation, a few other mathematical models, and more recently by the WLF and VTF (or VFT) equations. The essence of the Arrhenius equation is that the viscosity is proportional to the absolute temperature's reciprocal and governed by a single parameter, namely, the energy of activation. However, if the absolute temperature in K in the Arrhenius equation is replaced by T + b where both T and the adjustable b are in °C, the result is a two-parameter model, which has superior fit to experimental viscosity-temperature data. This modified version of the Arrhenius equation is also mathematically equal to the WLF and VTF equations, which are known to be equal to each other. Thus, despite their dissimilar appearances all three equations are essentially the same model, and when used to fit experimental temperature-viscosity data render exactly the same very high regression coefficient. It is shown that three new hybrid two-parameter mathematical models, whose formulation bears little resemblance to any of the conventional models, can also have excellent fit with r 2 ∼ 1. This is demonstrated by comparing the various models' regression coefficients to published viscosity-temperature relationships of 40% sucrose solution, soybean oil, and 70°Bx pear juice concentrate at different temperature ranges. Also compared are reconstructed temperature-viscosity curves using parameters calculated directly from 2 or 3 data points and fitted curves obtained by nonlinear regression using a larger number of experimental viscosity measurements.

Assessing Goodness of Fit in Item Response Theory with Nonparametric Models: A Comparison of Posterior Probabilities and Kernel-Smoothing Approaches

ERIC Educational Resources Information Center

Sueiro, Manuel J.; Abad, Francisco J.

2011-01-01

The distance between nonparametric and parametric item characteristic curves has been proposed as an index of goodness of fit in item response theory in the form of a root integrated squared error index. This article proposes to use the posterior distribution of the latent trait as the nonparametric model and compares the performance of an index…

Harms, benefits and costs of fecal immunochemical testing versus guaiac fecal occult blood testing for colorectal cancer screening.

PubMed

Goede, S Lucas; Rabeneck, Linda; van Ballegooijen, Marjolein; Zauber, Ann G; Paszat, Lawrence F; Hoch, Jeffrey S; Yong, Jean H E; Kroep, Sonja; Tinmouth, Jill; Lansdorp-Vogelaar, Iris

2017-01-01

The ColonCancerCheck screening program for colorectal cancer (CRC) in Ontario, Canada, is considering switching from biennial guaiac fecal occult blood test (gFOBT) screening between age 50-74 years to the more sensitive, but also less specific fecal immunochemical test (FIT). The aim of this study is to estimate whether the additional benefits of FIT screening compared to gFOBT outweigh the additional costs and harms. We used microsimulation modeling to estimate quality adjusted life years (QALYs) gained and costs of gFOBT and FIT, compared to no screening, in a cohort of screening participants. We compared strategies with various age ranges, screening intervals, and cut-off levels for FIT. Cost-efficient strategies were determined for various levels of available colonoscopy capacity. Compared to no screening, biennial gFOBT screening between age 50-74 years provided 20 QALYs at a cost of CAN$200,900 per 1,000 participants, and required 17 colonoscopies per 1,000 participants per year. FIT screening was more effective and less costly. For the same level of colonoscopy requirement, biennial FIT (with a high cut-off level of 200 ng Hb/ml) between age 50-74 years provided 11 extra QALYs gained while saving CAN$333,300 per 1000 participants, compared to gFOBT. Without restrictions in colonoscopy capacity, FIT (with a low cut-off level of 50 ng Hb/ml) every year between age 45-80 years was the most cost-effective strategy providing 27 extra QALYs gained per 1000 participants, while saving CAN$448,300. Compared to gFOBT screening, switching to FIT at a high cut-off level could increase the health benefits of a CRC screening program without considerably increasing colonoscopy demand.

Nonbleeding adenomas: Evidence of systematic false-negative fecal immunochemical test results and their implications for screening effectiveness-A modeling study.

PubMed

van der Meulen, Miriam P; Lansdorp-Vogelaar, Iris; van Heijningen, Else-Mariëtte B; Kuipers, Ernst J; van Ballegooijen, Marjolein

2016-06-01

If some adenomas do not bleed over several years, they will cause systematic false-negative fecal immunochemical test (FIT) results. The long-term effectiveness of FIT screening has been estimated without accounting for such systematic false-negativity. There are now data with which to evaluate this issue. The authors developed one microsimulation model (MISCAN [MIcrosimulation SCreening ANalysis]-Colon) without systematic false-negative FIT results and one model that allowed a percentage of adenomas to be systematically missed in successive FIT screening rounds. Both variants were adjusted to reproduce the first-round findings of the Dutch CORERO FIT screening trial. The authors then compared simulated detection rates in the second screening round with those observed, and adjusted the simulated percentage of systematically missed adenomas to those data. Finally, the authors calculated the impact of systematic false-negative FIT results on the effectiveness of repeated FIT screening. The model without systematic false-negativity simulated higher detection rates in the second screening round than observed. These observed rates could be reproduced when assuming that FIT systematically missed 26% of advanced and 73% of nonadvanced adenomas. To reduce the false-positive rate in the second round to the observed level, the authors also had to assume that 30% of false-positive findings were systematically false-positive. Systematic false-negative FIT testing limits the long-term reduction of biennial FIT screening in the incidence of colorectal cancer (35.6% vs 40.9%) and its mortality (55.2% vs 59.0%) in participants. The results of the current study provide convincing evidence based on the combination of real-life and modeling data that a percentage of adenomas are systematically missed by repeat FIT screening. This impairs the efficacy of FIT screening. Cancer 2016;122:1680-8. © 2016 American Cancer Society. © 2016 American Cancer Society.

Engelmann Spruce Site Index Models: A Comparison of Model Functions and Parameterizations

PubMed Central

Nigh, Gordon

2015-01-01

Engelmann spruce (Picea engelmannii Parry ex Engelm.) is a high-elevation species found in western Canada and western USA. As this species becomes increasingly targeted for harvesting, better height growth information is required for good management of this species. This project was initiated to fill this need. The objective of the project was threefold: develop a site index model for Engelmann spruce; compare the fits and modelling and application issues between three model formulations and four parameterizations; and more closely examine the grounded-Generalized Algebraic Difference Approach (g-GADA) model parameterization. The model fitting data consisted of 84 stem analyzed Engelmann spruce site trees sampled across the Engelmann Spruce – Subalpine Fir biogeoclimatic zone. The fitted models were based on the Chapman-Richards function, a modified Hossfeld IV function, and the Schumacher function. The model parameterizations that were tested are indicator variables, mixed-effects, GADA, and g-GADA. Model evaluation was based on the finite-sample corrected version of Akaike’s Information Criteria and the estimated variance. Model parameterization had more of an influence on the fit than did model formulation, with the indicator variable method providing the best fit, followed by the mixed-effects modelling (9% increase in the variance for the Chapman-Richards and Schumacher formulations over the indicator variable parameterization), g-GADA (optimal approach) (335% increase in the variance), and the GADA/g-GADA (with the GADA parameterization) (346% increase in the variance). Factors related to the application of the model must be considered when selecting the model for use as the best fitting methods have the most barriers in their application in terms of data and software requirements. PMID:25853472

A Three-Component Model for Magnetization Transfer. Solution by Projection-Operator Technique, and Application to Cartilage

NASA Astrophysics Data System (ADS)

Adler, Ronald S.; Swanson, Scott D.; Yeung, Hong N.

1996-01-01

A projection-operator technique is applied to a general three-component model for magnetization transfer, extending our previous two-component model [R. S. Adler and H. N. Yeung,J. Magn. Reson. A104,321 (1993), and H. N. Yeung, R. S. Adler, and S. D. Swanson,J. Magn. Reson. A106,37 (1994)]. The PO technique provides an elegant means of deriving a simple, effective rate equation in which there is natural separation of relaxation and source terms and allows incorporation of Redfield-Provotorov theory without any additional assumptions or restrictive conditions. The PO technique is extended to incorporate more general, multicomponent models. The three-component model is used to fit experimental data from samples of human hyaline cartilage and fibrocartilage. The fits of the three-component model are compared to the fits of the two-component model.

Methods of comparing associative models and an application to retrospective revaluation.

PubMed

Witnauer, James E; Hutchings, Ryan; Miller, Ralph R

2017-11-01

Contemporary theories of associative learning are increasingly complex, which necessitates the use of computational methods to reveal predictions of these models. We argue that comparisons across multiple models in terms of goodness of fit to empirical data from experiments often reveal more about the actual mechanisms of learning and behavior than do simulations of only a single model. Such comparisons are best made when the values of free parameters are discovered through some optimization procedure based on the specific data being fit (e.g., hill climbing), so that the comparisons hinge on the psychological mechanisms assumed by each model rather than being biased by using parameters that differ in quality across models with respect to the data being fit. Statistics like the Bayesian information criterion facilitate comparisons among models that have different numbers of free parameters. These issues are examined using retrospective revaluation data. Copyright © 2017 Elsevier B.V. All rights reserved.

Detection of shallow buried objects using an autoregressive model on the ground penetrating radar signal

NASA Astrophysics Data System (ADS)

Nabelek, Daniel P.; Ho, K. C.

2013-06-01

The detection of shallow buried low-metal content objects using ground penetrating radar (GPR) is a challenging task. This is because these targets are right underneath the ground and the ground bounce reflection interferes with their detections. They do not create distinctive hyperbolic signatures as required by most existing GPR detection algorithms due to their special geometric shapes and low metal content. This paper proposes the use of the Autoregressive (AR) modeling method for the detection of these targets. We fit an A-scan of the GPR data to an AR model. It is found that the fitting error will be small when such a target is present and large when it is absent. The ratio of the energy in an Ascan before and after AR model fitting is used as the confidence value for detection. We also apply AR model fitting over scans and utilize the fitting residual energies over several scans to form a feature vector for improving the detections. Using the data collected from a government test site, the proposed method can improve the detection of this kind of targets by 30% compared to the pre-screener, at a false alarm rate of 0.002/m2.

A dual-process approach to exploring the role of delay discounting in obesity.

PubMed

Price, Menna; Higgs, Suzanne; Maw, James; Lee, Michelle

2016-08-01

Delay discounting of financial rewards has been related to overeating and obesity. Neuropsychological evidence supports a dual-system account of both discounting and overeating behaviour where the degree of impulsive decision making is determined by the relative strength of reward desire and executive control. A dual-parameter model of discounting behaviour is consistent with this theory. In this study, the fit of the commonly used one-parameter model was compared to a new dual-parameter model for the first time in a sample of adults with wide ranging BMI. Delay discounting data from 79 males and females (males=26) across a wide age (M=28.44years (SD=8.81)) and BMI range (M=25.42 (SD=5.16)) was analysed. A dual-parameter model (saturating-hyperbolic; Doya, [Doya (2008) ]) was applied to the data and compared on model fit indices to the single-parameter model. Discounting was significantly greater in the overweight/obese participants using both models, however, the two parameter model showed a superior fit to data (p<0.0001). The two parameters were shown to be related yet distinct measures consistent with a dual-system account of inter-temporal choice behaviour. The dual-parameter model showed superior fit to data and the two parameters were shown to be related yet distinct indices sensitive to differences between weight groups. Findings are discussed in terms of the impulsive reward and executive control systems that contribute to unhealthy food choice and within the context of obesity related research. Copyright © 2016 Elsevier Inc. All rights reserved.

An efficient sequential strategy for realizing cross-gradient joint inversion: method and its application to 2-D cross borehole seismic traveltime and DC resistivity tomography

NASA Astrophysics Data System (ADS)

Gao, Ji; Zhang, Haijiang

2018-05-01

Cross-gradient joint inversion that enforces structural similarity between different models has been widely utilized in jointly inverting different geophysical data types. However, it is a challenge to combine different geophysical inversion systems with the cross-gradient structural constraint into one joint inversion system because they may differ greatly in the model representation, forward modelling and inversion algorithm. Here we propose a new joint inversion strategy that can avoid this issue. Different models are separately inverted using the existing inversion packages and model structure similarity is only enforced through cross-gradient minimization between two models after each iteration. Although the data fitting and structural similarity enforcing processes are decoupled, our proposed strategy is still able to choose appropriate models to balance the trade-off between geophysical data fitting and structural similarity. This is realized by using model perturbations from separate data inversions to constrain the cross-gradient minimization process. We have tested this new strategy on 2-D cross borehole synthetic seismic traveltime and DC resistivity data sets. Compared to separate geophysical inversions, our proposed joint inversion strategy fits the separate data sets at comparable levels while at the same time resulting in a higher structural similarity between the velocity and resistivity models.

Fitting milk production curves through nonlinear mixed models.

PubMed

Piccardi, Monica; Macchiavelli, Raúl; Funes, Ariel Capitaine; Bó, Gabriel A; Balzarini, Mónica

2017-05-01

The aim of this work was to fit and compare three non-linear models (Wood, Milkbot and diphasic) to model lactation curves from two approaches: with and without cow random effect. Knowing the behaviour of lactation curves is critical for decision-making in a dairy farm. Knowledge of the model of milk production progress along each lactation is necessary not only at the mean population level (dairy farm), but also at individual level (cow-lactation). The fits were made in a group of high production and reproduction dairy farms; in first and third lactations in cool seasons. A total of 2167 complete lactations were involved, of which 984 were first-lactations and the remaining ones, third lactations (19 382 milk yield tests). PROC NLMIXED in SAS was used to make the fits and estimate the model parameters. The diphasic model resulted to be computationally complex and barely practical. Regarding the classical Wood and MilkBot models, although the information criteria suggest the selection of MilkBot, the differences in the estimation of production indicators did not show a significant improvement. The Wood model was found to be a good option for fitting the expected value of lactation curves. Furthermore, the three models fitted better when the subject (cow) random effect was considered, which is related to magnitude of production. The random effect improved the predictive potential of the models, but it did not have a significant effect on the production indicators derived from the lactation curves, such as milk yield and days in milk to peak.

Kinematic modelling of disc galaxies using graphics processing units

NASA Astrophysics Data System (ADS)

Bekiaris, G.; Glazebrook, K.; Fluke, C. J.; Abraham, R.

2016-01-01

With large-scale integral field spectroscopy (IFS) surveys of thousands of galaxies currently under-way or planned, the astronomical community is in need of methods, techniques and tools that will allow the analysis of huge amounts of data. We focus on the kinematic modelling of disc galaxies and investigate the potential use of massively parallel architectures, such as the graphics processing unit (GPU), as an accelerator for the computationally expensive model-fitting procedure. We review the algorithms involved in model-fitting and evaluate their suitability for GPU implementation. We employ different optimization techniques, including the Levenberg-Marquardt and nested sampling algorithms, but also a naive brute-force approach based on nested grids. We find that the GPU can accelerate the model-fitting procedure up to a factor of ˜100 when compared to a single-threaded CPU, and up to a factor of ˜10 when compared to a multithreaded dual CPU configuration. Our method's accuracy, precision and robustness are assessed by successfully recovering the kinematic properties of simulated data, and also by verifying the kinematic modelling results of galaxies from the GHASP and DYNAMO surveys as found in the literature. The resulting GBKFIT code is available for download from: http://supercomputing.swin.edu.au/gbkfit.

Changes in Collegiate Ice Hockey Player Anthropometrics and Aerobic Fitness Over Three Decades.

PubMed

Triplett, Ashley N; Ebbing, Amy C; Green, Matthew R; Connolly, Christopher P; Carrier, David P; Pivarnik, James M

2018-04-09

Over the past several decades, an increased emphasis on fitness training has emerged among collegiate ice hockey teams, with the objective to improve on-ice performance. However, it is unknown if this increase in training has translated over time to changes in anthropometric and fitness profiles of collegiate ice hockey players. The purposes of this study were to describe anthropometric (height, weight, BMI, %fat) and aerobic fitness (VO_2peak) characteristics of collegiate ice hockey players over 36 years, and to evaluate whether these characteristics differ between player positions. Anthropometric and physiologic data were obtained through preseason fitness testing of players (N=279) from a NCAA Division I men's ice hockey team from the years of 1980 through 2015. Changes over time in the anthropometric and physiologic variables were evaluated via regression analysis using linear and polynomial models and differences between player position were compared via ANOVA (p<0.05). Regression analysis revealed a cubic model best predicted changes in mean height (R²=0.65), weight (R²=0.77), and BMI (R²=0.57), while a quadratic model best fit change in %fat by year (R²=0.30). Little change was observed over time in the anthropometric characteristics. Defensemen were significantly taller than forwards (184.7±12.1 vs. 181.3±5.9cm)(p=0.007) and forwards had a higher relative VO_2peak compared to defensemen (58.7±4.7 vs. 57.2±4.4ml/kg/min)(p=0.032). No significant differences were observed in %fat or weight by position. While average player heights and weights fluctuated over time, increased emphasis on fitness training did not affect athletes' relative aerobic fitness. Differences in height and aerobic fitness levels were observed between player position.

SU-F-BRD-16: Relative Biological Effectiveness of Double-Strand Break Induction for Modeling Cell Survival in Pristine Proton Beams of Different Dose-Averaged Linear Energy Transfers

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

Peeler, C; Bronk, L; UT Graduate School of Biomedical Sciences at Houston, Houston, TX

2015-06-15

Purpose: High throughput in vitro experiments assessing cell survival following proton radiation indicate that both the alpha and the beta parameters of the linear quadratic model increase with increasing proton linear energy transfer (LET). We investigated the relative biological effectiveness (RBE) of double-strand break (DSB) induction as a means of explaining the experimental results. Methods: Experiments were performed with two lung cancer cell lines and a range of proton LET values (0.94 – 19.4 keV/µm) using an experimental apparatus designed to irradiate cells in a 96 well plate such that each column encounters protons of different dose-averaged LET (LETd). Traditionalmore » linear quadratic survival curve fitting was performed, and alpha, beta, and RBE values obtained. Survival curves were also fit with a model incorporating RBE of DSB induction as the sole fit parameter. Fitted values of the RBE of DSB induction were then compared to values obtained using Monte Carlo Damage Simulation (MCDS) software and energy spectra calculated with Geant4. Other parameters including alpha, beta, and number of DSBs were compared to those obtained from traditional fitting. Results: Survival curve fitting with RBE of DSB induction yielded alpha and beta parameters that increase with proton LETd, which follows from the standard method of fitting; however, relying on a single fit parameter provided more consistent trends. The fitted values of RBE of DSB induction increased beyond what is predicted from MCDS data above proton LETd of approximately 10 keV/µm. Conclusion: In order to accurately model in vitro proton irradiation experiments performed with high throughput methods, the RBE of DSB induction must increase more rapidly than predicted by MCDS above LETd of 10 keV/µm. This can be explained by considering the increased complexity of DSBs or the nature of intra-track pairwise DSB interactions in this range of LETd values. NIH Grant 2U19CA021239-35.« less

Growth characterisation of intra-thoracic organs of children on CT scans.

PubMed

Coulongeat, François; Jarrar, Mohamed-Salah; Thollon, Lionel; Serre, Thierry

2013-01-01

This paper analyses the geometry of intra-thoracic organs from computed tomography (CT) scans performed on 20 children aged from 4 months to 16 years. The aim is to find the most reliable measurements to characterise the growth of heart and lungs from CT data. Standard measurements available on chest radiographies are compared with original measurements only available on CT scans. These measurements should characterise the growth of organs as well as the changes in their position relative to the thorax. Measurements were considered as functions of age. Quadratic regression models were fitted to the data. Goodness of fit of the models was then evaluated. Positions of organs relative to the thorax have a high variability compared with their changes with age. The length and volume of the heart and lungs as well as the diameter of the thorax fit well to the models of growth. It could be interesting to study these measurements with a larger sample size in order to define growth standards.

The effects of the WISE/ GALEX photometry for the SED-fitting with M31 star clusters and candidates

NASA Astrophysics Data System (ADS)

Fan, Zhou; Wang, Song

2017-10-01

Spectral energy distribution (SED) fitting of stellar population synthesis models is an important and popular way to constrain the physical parameters—e.g., the ages, metallicities, masses for stellar population analysis. The previous works suggest that both blue-bands and red-bands photometry works for the SED-fitting. Either blue-domained or red-domained SED-fitting usually lead to the unreliable or biased results. Meanwhile, it seems that extending the wavelength coverage could be helpful. Since the Galaxy Evolution Explorer ( GALEX) and Wide-field Infrared Survey Explorer (WISE) provide the FUV/NUV and mid-infrared W1/W2 band data, we extend the SED-fitting to a wider wavelength coverage. In our work, we analyzed the effect of adding the FUV/NUV and W1/W2 band to the optical and near-infrared UBVRIJHK bands for the fitting with the (Bruzual and Charlot in Mon. Not. R. Astron. Soc. 344, 1000, 2003) (BC03) models and galev models. It is found that the FUV/NUV bands data affect the fitting results of both ages and metallicities much more significantly than that of the WISE W1/W2 band with the BC03 models. While for the galev models, the effect of the WISE W1/W2 band for the metallicity fitting seems comparable to that of GALEX FUV/NUV bands, but for age the effect of the W1/W2 band seems less crucial than that of the FUV/NUV bands. Thus we conclude that the GALEX FUV/NUV bands are more crucial for the SED-fitting of ages and metallicities, than the other bands, and the high-quality UV data (with high photometry precision) are required.

Alterations to the relativistic Love-Franey model and their application to inelastic scattering

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

Zeile, J.R.

The fictitious axial-vector and tensor mesons for the real part of the relativistic Love-Franey interaction are removed. In an attempt to make up for this loss, derivative couplings are used for the {pi} and {rho} mesons. Such derivative couplings require the introduction of axial-vector and tensor contact term corrections. Meson parameters are then fit to free nucleon-nucleon scattering data. The resulting fits are comparable to those of the relativistic Love-Franey model provided that the contact term corrections are included and the fits are weighted over the physically significant quantity of twice the tensor minus the axial-vector Lorentz invariants. Failure tomore » include contact term corrections leads to poor fits at higher energies. The off-shell behavior of this model is then examined by looking at several applications from inelastic proton-nucleus scattering.« less

Model-independent partial wave analysis using a massively-parallel fitting framework

NASA Astrophysics Data System (ADS)

Sun, L.; Aoude, R.; dos Reis, A. C.; Sokoloff, M.

2017-10-01

The functionality of GooFit, a GPU-friendly framework for doing maximum-likelihood fits, has been extended to extract model-independent {\\mathscr{S}}-wave amplitudes in three-body decays such as D + → h + h + h -. A full amplitude analysis is done where the magnitudes and phases of the {\\mathscr{S}}-wave amplitudes are anchored at a finite number of m 2(h + h -) control points, and a cubic spline is used to interpolate between these points. The amplitudes for {\\mathscr{P}}-wave and {\\mathscr{D}}-wave intermediate states are modeled as spin-dependent Breit-Wigner resonances. GooFit uses the Thrust library, with a CUDA backend for NVIDIA GPUs and an OpenMP backend for threads with conventional CPUs. Performance on a variety of platforms is compared. Executing on systems with GPUs is typically a few hundred times faster than executing the same algorithm on a single CPU.

Atmospheric Properties Of T Dwarfs Inferred From Model Fits At Low Spectral Resolution

NASA Astrophysics Data System (ADS)

Giorla Godfrey, Paige A.; Rice, Emily L.; Filippazzo, Joseph C.; Douglas, Stephanie E.

2016-09-01

Brown dwarf spectral types (M, L, T, Y) correlate with spectral morphology, and generally appear to correspond with decreasing mass and effective temperature (Teff). Model fits to observed spectra suggest, however, that spectral subclasses do not share this monotonic temperature correlation, indicating that secondary parameters (gravity, metallicity, dust) significantly influence spectral morphology. We seekto disentangle the fundamental parameters that underlie the spectral type sequence of the coolest fully populated spectral class of brown dwarfs using atmosphere models. We investigate the relationship between spectral type and best fit model parameters for a sample of over 150 T dwarfs with low resolution (R 75-100) near-infrared ( 0.8-2.5 micron) SpeX Prism spectra. We use synthetic spectra from four model grids (Saumon & Marley 2008, Morley+ 2012, Saumon+ 2012, BT Settl 2013) and a Markov-Chain Monte Carlo (MCMC) analysis to determine robust best fit parameters and their uncertainties. We compare the consistency of each model grid by performing our analysis on the full spectrum and also on individual wavelength bands (Y,J,H,K). We find more consistent results between the J band and full spectrum fits and that our best fit spectral type-Teff results agree with the polynomial relationships of Stephens+2009 and Filippazzo+ 2015 using bolometric luminosities. Our analysis consists of the most extensive low resolution T dwarf model comparison to date, and lays the foundation for interpretation of cool brown dwarf and exoplanet spectra.

MODELING THE NEAR-UV BAND OF GK STARS. II. NON-LTE MODELS

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

Ian Short, C.; Campbell, Eamonn A.; Pickup, Heather

We present a grid of atmospheric models and synthetic spectral energy distributions (SEDs) for late-type dwarfs and giants of solar and 1/3 solar metallicity with many opacity sources computed in self-consistent non-local thermodynamic equilibrium (NLTE), and compare them to the LTE grid of Short and Hauschildt (Paper I). We describe, for the first time, how the NLTE treatment affects the thermal equilibrium of the atmospheric structure (T({tau}) relation) and the SED as a finely sampled function of T{sub eff}, log g, and [A/H] among solar metallicity and mildly metal-poor red giants. We compare the computed SEDs to the library ofmore » observed spectrophotometry described in Paper I across the entire visible band, and in the blue and red regions of the spectrum separately. We find that for the giants of both metallicities, the NLTE models yield best-fit T{sub eff} values that are 30-90 K lower than those provided by LTE models, while providing greater consistency between log g values, and, for Arcturus, T{sub eff} values, fitted separately to the blue and red spectral regions. There is marginal evidence that NLTE models give more consistent best-fit T{sub eff} values between the red and blue bands for earlier spectral classes among the solar metallicity GK giants than they do for the later classes, but no model fits the blue-band spectrum well for any class. For the two dwarf spectral classes that we are able to study, the effect of NLTE on derived parameters is less significant. We compare our derived T{sub eff} values to several other spectroscopic and photometric T{sub eff} calibrations for red giants, including one that is less model dependent based on the infrared flux method (IRFM). We find that the NLTE models provide slightly better agreement to the IRFM calibration among the warmer stars in our sample, while giving approximately the same level of agreement for the cooler stars.« less

Comparing Models of Intelligence in Project TALENT: The VPR Model Fits Better than the CHC and Extended Gf-Gc Models

ERIC Educational Resources Information Center

Major, Jason T.; Johnson, Wendy; Deary, Ian J.

2012-01-01

Three prominent theories of intelligence, the Cattell-Horn-Carroll (CHC), extended fluid-crystallized (Gf-Gc) and verbal-perceptual-image rotation (VPR) theories, provide differing descriptions of the structure of intelligence (McGrew, 2009; Horn & Blankson, 2005; Johnson & Bouchard, 2005b). To compare these theories, models representing them were…

ASSESSMENT OF MODELS OF GALACTIC THERMAL DUST EMISSION USING COBE /FIRAS AND COBE /DIRBE OBSERVATIONS

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

Odegard, N.; Kogut, A.; Miller, N. J.

2016-09-01

Accurate modeling of the spectrum of thermal dust emission at millimeter wavelengths is important for improving the accuracy of foreground subtraction for cosmic microwave background (CMB) measurements, for improving the accuracy with which the contributions of different foreground emission components can be determined, and for improving our understanding of dust composition and dust physics. We fit four models of dust emission to high Galactic latitude COBE /FIRAS and COBE /DIRBE observations from 3 mm to 100 μ m and compare the quality of the fits. We consider the two-level systems (TLS) model because it provides a physically motivated explanation formore » the observed long wavelength flattening of the dust spectrum and the anti-correlation between emissivity index and dust temperature. We consider the model of Finkbeiner et al. because it has been widely used for CMB studies, and the generalized version of this model that was recently applied to Planck data by Meisner and Finkbeiner. For comparison we have also fit a phenomenological model consisting of the sum of two graybody components. We find that the two-graybody model gives the best fit and the FDS model gives a significantly poorer fit than the other models. The Meisner and Finkbeiner model and the TLS model remain viable for use in Galactic foreground subtraction, but the FIRAS data do not have a sufficient signal-to-noise ratio to provide a strong test of the predicted spectrum at millimeter wavelengths.« less

Modelling lactation curve for milk fat to protein ratio in Iranian buffaloes (Bubalus bubalis) using non-linear mixed models.

PubMed

Hossein-Zadeh, Navid Ghavi

2016-08-01

The aim of this study was to compare seven non-linear mathematical models (Brody, Wood, Dhanoa, Sikka, Nelder, Rook and Dijkstra) to examine their efficiency in describing the lactation curves for milk fat to protein ratio (FPR) in Iranian buffaloes. Data were 43 818 test-day records for FPR from the first three lactations of Iranian buffaloes which were collected on 523 dairy herds in the period from 1996 to 2012 by the Animal Breeding Center of Iran. Each model was fitted to monthly FPR records of buffaloes using the non-linear mixed model procedure (PROC NLMIXED) in SAS and the parameters were estimated. The models were tested for goodness of fit using Akaike's information criterion (AIC), Bayesian information criterion (BIC) and log maximum likelihood (-2 Log L). The Nelder and Sikka mixed models provided the best fit of lactation curve for FPR in the first and second lactations of Iranian buffaloes, respectively. However, Wood, Dhanoa and Sikka mixed models provided the best fit of lactation curve for FPR in the third parity buffaloes. Evaluation of first, second and third lactation features showed that all models, except for Dijkstra model in the third lactation, under-predicted test time at which daily FPR was minimum. On the other hand, minimum FPR was over-predicted by all equations. Evaluation of the different models used in this study indicated that non-linear mixed models were sufficient for fitting test-day FPR records of Iranian buffaloes.

Hepatic function imaging using dynamic Gd-EOB-DTPA enhanced MRI and pharmacokinetic modeling.

PubMed

Ning, Jia; Yang, Zhiying; Xie, Sheng; Sun, Yongliang; Yuan, Chun; Chen, Huijun

2017-10-01

To determine whether pharmacokinetic modeling parameters with different output assumptions of dynamic contrast-enhanced MRI (DCE-MRI) using Gd-EOB-DTPA correlate with serum-based liver function tests, and compare the goodness of fit of the different output assumptions. A 6-min DCE-MRI protocol was performed in 38 patients. Four dual-input two-compartment models with different output assumptions and a published one-compartment model were used to calculate hepatic function parameters. The Akaike information criterion fitting error was used to evaluate the goodness of fit. Imaging-based hepatic function parameters were compared with blood chemistry using correlation with multiple comparison correction. The dual-input two-compartment model assuming venous flow equals arterial flow plus portal venous flow and no bile duct output better described the liver tissue enhancement with low fitting error and high correlation with blood chemistry. The relative uptake rate Kir derived from this model was found to be significantly correlated with direct bilirubin (r = -0.52, P = 0.015), prealbumin concentration (r = 0.58, P = 0.015), and prothrombin time (r = -0.51, P = 0.026). It is feasible to evaluate hepatic function by proper output assumptions. The relative uptake rate has the potential to serve as a biomarker of function. Magn Reson Med 78:1488-1495, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Density dependence and risk of extinction in a small population of sea otters

USGS Publications Warehouse

Gerber, L.R.; Buenau, K.E.; VanBlaricom, G.

2004-01-01

Sea otters (Enhydra lutris (L.)) were hunted to extinction off the coast of Washington State early in the 20th century. A new population was established by translocations from Alaska in 1969 and 1970. The population, currently numbering at least 550 animals, A major threat to the population is the ongoing risk of majour oil spills in sea otter habitat. We apply population models to census and demographic data in order to evaluate the status of the population. We fit several density dependent models to test for density dependence and determine plausible values for the carrying capacity (K) by comparing model goodness of fit to an exponential model. Model fits were compared using Akaike Information Criterion (AIC). A significant negative relationship was found between the population growth rate and population size (r2=0.27, F=5.57, df=16, p<0.05), suggesting density dependence in Washington state sea otters. Information criterion statistics suggest that the model is the most parsimonious, followed closely by the logistic Beverton-Holt model. Values of K ranged from 612 to 759 with best-fit parameter estimates for the Beverton-Holt model including 0.26 for r and 612 for K. The latest (2001) population index count (555) puts the population at 87-92% of the estimated carrying capacity, above the suggested range for optimum sustainable population (OSP). Elasticity analysis was conducted to examine the effects of proportional changes in vital rates on the population growth rate (??). The elasticity values indicate the population is most sensitive to changes in survival rates (particularly adult survival).

Relationship between long working hours and depression: a 3-year longitudinal study of clerical workers.

PubMed

Amagasa, Takashi; Nakayama, Takeo

2013-08-01

To clarify how long working hours affect the likelihood of current and future depression. Using data from four repeated measurements collected from 218 clerical workers, four models associating work-related factors to the depressive mood scale were established. The final model was constructed after comparing and testing the goodness-of-fit index using structural equation modeling. Multiple logistic regression analysis was also performed. The final model showed the best fit (normed fit index = 0.908; goodness-of-fit index = 0.936; root-mean-square error of approximation = 0.018). Its standardized total effect indicated that long working hours affected depression at the time of evaluation and 1 to 3 years later. The odds ratio for depression risk was 14.7 in employees who were not long-hours overworked according to the initial survey but who were long-hours overworked according to the second survey. Long working hours increase current and future risks of depression.

COMPARING THE IMPAIRMENT PROFILES OF OLDER DRIVERS AND NON-DRIVERS: TOWARD THE DEVELOPMENT OF A FITNESS-TO-DRIVE MODEL

PubMed Central

Antin, Jonathan F.; Stanley, Laura M.; Guo, Feng

2011-01-01

The purpose of this research effort was to compare older driver and non-driver functional impairment profiles across some 60 assessment metrics in an initial effort to contribute to the development of fitness-to-drive assessment models. Of the metrics evaluated, 21 showed statistically significant differences, almost all favoring the drivers. Also, it was shown that a logistic regression model comprised of five of the assessment scores could completely and accurately separate the two groups. The results of this study imply that older drivers are far less functionally impaired than non-drivers of similar ages, and that a parsimonious model can accurately assign individuals to either group. With such models, any driver classified or diagnosed as a non-driver would be a strong candidate for further investigation and intervention. PMID:22058607

Comparing different Ultraviolet Imaging Spectrograph (UVIS) occultation observations using modeling of water vapor jets

NASA Astrophysics Data System (ADS)

Portyankina, Ganna; Esposito, Larry W.; Hansen, Candice; Aye, Klaus-Michael

2016-10-01

Motivation: On March 11, 2016 the Cassini UVIS observed its 6th star occultation by Enceladus' plume. This observation was aimed to determine variability in the total gas flux from the Enceladus' southern polar region. The analysis of the received data suggests that the total gas flux is moderately increased comparing to the average gas flux observed by UVIS from 2005 to 2011 [1]. However, UVIS detected variability in individual jets. In particular, Baghdad 1 is more collimated in 2016 than in 2005, meaning its gas escapes at higher velocity.Model and fits: We use 3D DSMC model for water vapor jets to compare different UVIS occultation observations from 2005 to 2016. The model traces test articles from jets' sources [2] into space and results in coordinates and velocities for a set of test particles. We convert particle positions into the particle number density and integrate along UVIS line of sight (LoS) for each time step of the UVIS observation using precise observational geometry derived from SPICE [3]. We integrate all jets that are crossed by the LoS and perform constrained least-squares fit of resulting modeled opacities to the observed data to solved for relative strengths of jets. The geometry of each occultation is specific, for example, during solar occultation in 2010 UVIS LoS was almost parallel to tiger stripes, which made it possible to distinguish jets venting from different tiger stripes. In 2011 Eps Orionis occultation LoS was perpendicular to tiger stripes and thus many of the jets were geometrically overlapping. Solar occultation provided us with the largest inventory of active jets - our model fit detects at least 43 non-zero jet contributions. Stellar occultations generally have lower temporal resolution and observe only a sub-set of these jets: 2011 Eps Orionis needs minimum 25 non-zero jets to fit UVIS data. We will discuss different occultations and models fits, including the most recent Epsilon Orionis occultation of 2016.[1] Hansen et al., DPS 48, 2016 [2] Porco et al. 2014 The Astronomical Journal 148, 4 [3] Acton, C.H., 1996 PSS 44, 65-70

Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications.

PubMed

Sabry, A H; W Hasan, W Z; Ab Kadir, M Z A; Radzi, M A M; Shafie, S

2018-01-01

The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.

Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications

PubMed Central

W. Hasan, W. Z.

2018-01-01

The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system’s modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model. PMID:29351554

Carbon dioxide stripping in aquaculture -- part III: model verification

USGS Publications Warehouse

Colt, John; Watten, Barnaby; Pfeiffer, Tim

2012-01-01

Based on conventional mass transfer models developed for oxygen, the use of the non-linear ASCE method, 2-point method, and one parameter linear-regression method were evaluated for carbon dioxide stripping data. For values of KLaCO2 < approximately 1.5/h, the 2-point or ASCE method are a good fit to experimental data, but the fit breaks down at higher values of KLaCO2. How to correct KLaCO2 for gas phase enrichment remains to be determined. The one-parameter linear regression model was used to vary the C*CO2 over the test, but it did not result in a better fit to the experimental data when compared to the ASCE or fixed C*CO2 assumptions.

Assessment of Models of Galactic Thermal Dust Emission Using COBE/FIRAS and COBE/DIRBE Observations

NASA Technical Reports Server (NTRS)

Odegard, N.; Kogut, A.; Chuss, D. T.; Miller, N. J.

2016-01-01

Accurate modeling of the spectrum of thermal dust emission at millimeter wavelengths is important for improving the accuracy of foreground subtraction for cosmic microwave background (CMB) measurements, for improving the accuracy with which the contributions of different foreground emission components can be determined, and for improving our understanding of dust composition and dust physics. We fit four models of dust emission to high Galactic latitude COBE/FIRAS and COBE/DIRBE observations from 3 mm to 100m and compare the quality of the fits. We consider the two-level systems (TLS) model because it provides a physically motivated explanation for the observed long wavelength flattening of the dust spectrum and the anti-correlation between emissivity index and dust temperature. We consider the model of Finkbeiner et al. because it has been widely used for CMB studies, and the generalized version of this model that was recently applied to Planck data by Meisner and Finkbeiner. For comparison we have also fit a phenomenological model consisting of the sum of two-graybody components. We find that the two-graybody model gives the best fit and the FDS model gives a significantly poorer fit than the othermodels. The Meisner and Finkbeiner model and the TLS model remain viable for use in Galactic foreground subtraction, but the FIRAS data do not have a sufficient signal-to-noise ratio to provide a strong test of the predicted spectrum at millimeter wavelengths.

Fast and exact Newton and Bidirectional fitting of Active Appearance Models.

PubMed

Kossaifi, Jean; Tzimiropoulos, Yorgos; Pantic, Maja

2016-12-21

Active Appearance Models (AAMs) are generative models of shape and appearance that have proven very attractive for their ability to handle wide changes in illumination, pose and occlusion when trained in the wild, while not requiring large training dataset like regression-based or deep learning methods. The problem of fitting an AAM is usually formulated as a non-linear least squares one and the main way of solving it is a standard Gauss-Newton algorithm. In this paper we extend Active Appearance Models in two ways: we first extend the Gauss-Newton framework by formulating a bidirectional fitting method that deforms both the image and the template to fit a new instance. We then formulate a second order method by deriving an efficient Newton method for AAMs fitting. We derive both methods in a unified framework for two types of Active Appearance Models, holistic and part-based, and additionally show how to exploit the structure in the problem to derive fast yet exact solutions. We perform a thorough evaluation of all algorithms on three challenging and recently annotated inthe- wild datasets, and investigate fitting accuracy, convergence properties and the influence of noise in the initialisation. We compare our proposed methods to other algorithms and show that they yield state-of-the-art results, out-performing other methods while having superior convergence properties.

An Improved Cryosat-2 Sea Ice Freeboard Retrieval Algorithm Through the Use of Waveform Fitting

NASA Technical Reports Server (NTRS)

Kurtz, Nathan T.; Galin, N.; Studinger, M.

2014-01-01

We develop an empirical model capable of simulating the mean echo power cross product of CryoSat-2 SAR and SAR In mode waveforms over sea ice covered regions. The model simulations are used to show the importance of variations in the radar backscatter coefficient with incidence angle and surface roughness for the retrieval of surfaceelevation of both sea ice floes and leads. The numerical model is used to fit CryoSat-2 waveforms to enable retrieval of surface elevation through the use of look-up tables and a bounded trust region Newton least squares fitting approach. The use of a model to fit returns from sea ice regions offers advantages over currently used threshold retrackingmethods which are here shown to be sensitive to the combined effect of bandwidth limited range resolution and surface roughness variations. Laxon et al. (2013) have compared ice thickness results from CryoSat-2 and IceBridge, and found good agreement, however consistent assumptions about the snow depth and density of sea ice werenot used in the comparisons. To address this issue, we directly compare ice freeboard and thickness retrievals from the waveform fitting and threshold tracker methods of CryoSat-2 to Operation IceBridge data using a consistent set of parameterizations. For three IceBridge campaign periods from March 20112013, mean differences (CryoSat-2 IceBridge) of 0.144m and 1.351m are respectively found between the freeboard and thickness retrievals using a 50 sea ice floe threshold retracker, while mean differences of 0.019m and 0.182m are found when using the waveform fitting method. This suggests the waveform fitting technique is capable of better reconciling the seaice thickness data record from laser and radar altimetry data sets through the usage of consistent physical assumptions.

Model-Free Estimation of Tuning Curves and Their Attentional Modulation, Based on Sparse and Noisy Data.

PubMed

Helmer, Markus; Kozyrev, Vladislav; Stephan, Valeska; Treue, Stefan; Geisel, Theo; Battaglia, Demian

2016-01-01

Tuning curves are the functions that relate the responses of sensory neurons to various values within one continuous stimulus dimension (such as the orientation of a bar in the visual domain or the frequency of a tone in the auditory domain). They are commonly determined by fitting a model e.g. a Gaussian or other bell-shaped curves to the measured responses to a small subset of discrete stimuli in the relevant dimension. However, as neuronal responses are irregular and experimental measurements noisy, it is often difficult to determine reliably the appropriate model from the data. We illustrate this general problem by fitting diverse models to representative recordings from area MT in rhesus monkey visual cortex during multiple attentional tasks involving complex composite stimuli. We find that all models can be well-fitted, that the best model generally varies between neurons and that statistical comparisons between neuronal responses across different experimental conditions are affected quantitatively and qualitatively by specific model choices. As a robust alternative to an often arbitrary model selection, we introduce a model-free approach, in which features of interest are extracted directly from the measured response data without the need of fitting any model. In our attentional datasets, we demonstrate that data-driven methods provide descriptions of tuning curve features such as preferred stimulus direction or attentional gain modulations which are in agreement with fit-based approaches when a good fit exists. Furthermore, these methods naturally extend to the frequent cases of uncertain model selection. We show that model-free approaches can identify attentional modulation patterns, such as general alterations of the irregular shape of tuning curves, which cannot be captured by fitting stereotyped conventional models. Finally, by comparing datasets across different conditions, we demonstrate effects of attention that are cell- and even stimulus-specific. Based on these proofs-of-concept, we conclude that our data-driven methods can reliably extract relevant tuning information from neuronal recordings, including cells whose seemingly haphazard response curves defy conventional fitting approaches.

Model-Free Estimation of Tuning Curves and Their Attentional Modulation, Based on Sparse and Noisy Data

PubMed Central

Helmer, Markus; Kozyrev, Vladislav; Stephan, Valeska; Treue, Stefan; Geisel, Theo; Battaglia, Demian

2016-01-01

Tuning curves are the functions that relate the responses of sensory neurons to various values within one continuous stimulus dimension (such as the orientation of a bar in the visual domain or the frequency of a tone in the auditory domain). They are commonly determined by fitting a model e.g. a Gaussian or other bell-shaped curves to the measured responses to a small subset of discrete stimuli in the relevant dimension. However, as neuronal responses are irregular and experimental measurements noisy, it is often difficult to determine reliably the appropriate model from the data. We illustrate this general problem by fitting diverse models to representative recordings from area MT in rhesus monkey visual cortex during multiple attentional tasks involving complex composite stimuli. We find that all models can be well-fitted, that the best model generally varies between neurons and that statistical comparisons between neuronal responses across different experimental conditions are affected quantitatively and qualitatively by specific model choices. As a robust alternative to an often arbitrary model selection, we introduce a model-free approach, in which features of interest are extracted directly from the measured response data without the need of fitting any model. In our attentional datasets, we demonstrate that data-driven methods provide descriptions of tuning curve features such as preferred stimulus direction or attentional gain modulations which are in agreement with fit-based approaches when a good fit exists. Furthermore, these methods naturally extend to the frequent cases of uncertain model selection. We show that model-free approaches can identify attentional modulation patterns, such as general alterations of the irregular shape of tuning curves, which cannot be captured by fitting stereotyped conventional models. Finally, by comparing datasets across different conditions, we demonstrate effects of attention that are cell- and even stimulus-specific. Based on these proofs-of-concept, we conclude that our data-driven methods can reliably extract relevant tuning information from neuronal recordings, including cells whose seemingly haphazard response curves defy conventional fitting approaches. PMID:26785378

Data from fitting Gaussian process models to various data sets using eight Gaussian process software packages.

PubMed

Erickson, Collin B; Ankenman, Bruce E; Sanchez, Susan M

2018-06-01

This data article provides the summary data from tests comparing various Gaussian process software packages. Each spreadsheet represents a single function or type of function using a particular input sample size. In each spreadsheet, a row gives the results for a particular replication using a single package. Within each spreadsheet there are the results from eight Gaussian process model-fitting packages on five replicates of the surface. There is also one spreadsheet comparing the results from two packages performing stochastic kriging. These data enable comparisons between the packages to determine which package will give users the best results.

Additive manufactured push-fit implant fixation with screw-strength pull out.

PubMed

van Arkel, Richard J; Ghouse, Shaaz; Milner, Piers E; Jeffers, Jonathan R T

2017-10-11

Additive manufacturing offers exciting new possibilities for improving long-term metallic implant fixation in bone through enabling open porous structures for bony ingrowth. The aim of this research was to investigate how the technology could also improve initial fixation, a precursor to successful long-term fixation. A new barbed fixation mechanism, relying on flexible struts was proposed and manufactured as a push-fit peg. The technology was optimized using a synthetic bone model and compared with conventional press-fit peg controls tested over a range of interference fits. Optimum designs, achieving maximum pull-out force, were subsequently tested in a cadaveric femoral condyle model. The barbed fixation surface provided more than double the pull-out force for less than a third of the insertion force compared to the best performing conventional press-fit peg (p < 0.001). Indeed, it provided screw-strength pull out from a push-fit device (1,124 ± 146 N). This step change in implant fixation potential offers new capabilities for low profile, minimally invasive implant design, while providing new options to simplify surgery, allowing for one-piece push-fit components with high levels of initial stability. © 2017 The Authors. Journal of Orthopaedic Research Published by WileyPeriodicals, Inc. on behalf of the Orthopaedic Research Society. J Orthop Res 9999:1-11, 2017. © 2017 The Authors. Journal of Orthopaedic Research Published by WileyPeriodicals, Inc. on behalf of the Orthopaedic Research Society.

A data-centric approach to understanding the pricing of financial options

NASA Astrophysics Data System (ADS)

Healy, J.; Dixon, M.; Read, B.; Cai, F. F.

2002-05-01

We investigate what can be learned from a purely phenomenological study of options prices without modelling assumptions. We fitted neural net (NN) models to LIFFE ``ESX'' European style FTSE 100 index options using daily data from 1992 to 1997. These non-parametric models reproduce the Black-Scholes (BS) analytic model in terms of fit and performance measures using just the usual five inputs (S, X, t, r, IV). We found that adding transaction costs (bid-ask spread) to these standard five parameters gives a comparable fit and performance. Tests show that the bid-ask spread can be a statistically significant explanatory variable for option prices. The difference in option prices between the models with transaction costs and those without ranges from about -3.0 to +1.5 index points, varying with maturity date. However, the difference depends on the moneyness (S/X), being greatest in-the-money. This suggests that use of a five-factor model can result in a pricing difference of up to #10 to #30 per call option contract compared with modelling under transaction costs. We found that the influence of transaction costs varied between different yearly subsets of the data. Open interest is also a significant explanatory variable, but volume is not.

[Primary branch size of Pinus koraiensis plantation: a prediction based on linear mixed effect model].

PubMed

Dong, Ling-Bo; Liu, Zhao-Gang; Li, Feng-Ri; Jiang, Li-Chun

2013-09-01

By using the branch analysis data of 955 standard branches from 60 sampled trees in 12 sampling plots of Pinus koraiensis plantation in Mengjiagang Forest Farm in Heilongjiang Province of Northeast China, and based on the linear mixed-effect model theory and methods, the models for predicting branch variables, including primary branch diameter, length, and angle, were developed. Considering tree effect, the MIXED module of SAS software was used to fit the prediction models. The results indicated that the fitting precision of the models could be improved by choosing appropriate random-effect parameters and variance-covariance structure. Then, the correlation structures including complex symmetry structure (CS), first-order autoregressive structure [AR(1)], and first-order autoregressive and moving average structure [ARMA(1,1)] were added to the optimal branch size mixed-effect model. The AR(1) improved the fitting precision of branch diameter and length mixed-effect model significantly, but all the three structures didn't improve the precision of branch angle mixed-effect model. In order to describe the heteroscedasticity during building mixed-effect model, the CF1 and CF2 functions were added to the branch mixed-effect model. CF1 function improved the fitting effect of branch angle mixed model significantly, whereas CF2 function improved the fitting effect of branch diameter and length mixed model significantly. Model validation confirmed that the mixed-effect model could improve the precision of prediction, as compare to the traditional regression model for the branch size prediction of Pinus koraiensis plantation.

Bifactor Models Show a Superior Model Fit: Examination of the Factorial Validity of Parent-Reported and Self-Reported Symptoms of Attention-Deficit/Hyperactivity Disorders in Children and Adolescents.

PubMed

Rodenacker, Klaas; Hautmann, Christopher; Görtz-Dorten, Anja; Döpfner, Manfred

2016-01-01

Various studies have demonstrated that bifactor models yield better solutions than models with correlated factors. However, the kind of bifactor model that is most appropriate is yet to be examined. The current study is the first to test bifactor models across the full age range (11-18 years) of adolescents using self-reports, and the first to test bifactor models with German subjects and German questionnaires. The study sample included children and adolescents aged between 6 and 18 years recruited from a German clinical sample (n = 1,081) and a German community sample (n = 642). To examine the factorial validity, we compared unidimensional, correlated factors and higher-order and bifactor models and further tested a modified incomplete bifactor model for measurement invariance. Bifactor models displayed superior model fit statistics compared to correlated factor models or second-order models. However, a more parsimonious incomplete bifactor model with only 2 specific factors (inattention and impulsivity) showed a good model fit and a better factor structure than the other bifactor models. Scalar measurement invariance was given in most group comparisons. An incomplete bifactor model would suggest that the specific inattention and impulsivity factors represent entities separable from the general attention-deficit/hyperactivity disorder construct and might, therefore, give way to a new approach to subtyping of children beyond and above attention-deficit/hyperactivity disorder. © 2016 S. Karger AG, Basel.

Depression, stress, and intimate partner violence among Latino migrant and seasonal farmworkers in rural Southeastern North Carolina.

PubMed

Kim-Godwin, Yeoun Soo; Maume, Michael O; Fox, Jane A

2014-12-01

The purpose of the study is to identify the predictors of depression and intimate partner violence (IPV) among Latinos in rural Southeastern North Carolina. A sample of 291 migrant and seasonal farmworkers was interviewed to complete the demographic questionnaire, HITS (intimate violence tendency), Migrant Farmworker Stress Inventory, Center for Epidemiologic Studies Depression Scale (depression), and CAGE/4M (alcohol abuse). OLS regression and structural equation modeling were used to test the hypothesized relations between predictors of IPV and depression. The findings indicated that respondents reporting higher levels of stress also reported higher levels of IPV and depression. The goodness-of-fit statistics for the overall model again indicated a moderate fit of the model to the data (χ2 = 5,612, p < .001; root mean square error for approximation = 0.09; adjusted goodness-of-fit index = 0.44; comparative fit index = 0.52). Although the findings were not robust to estimation in the structural equation models, the OLS regression models indicated direct associations between IPV and depression.

Isochrone Fitting of Hubble Photometry in UV–VIS–IR Bands

NASA Astrophysics Data System (ADS)

Barker, Hallie; Paust, Nathaniel E. Q.

2018-03-01

We present new isochrone fits to color–magnitude diagrams from Hubble Space Telescope Wide Field Camera 3 and Advanced Camera for Surveys photometry of the globular clusters M13 and M80 in five bands from the ultraviolet to near-infrared. Isochrone fits to the photometry using the Dartmouth Stellar Evolution Program (DSEP), the PAdova and TRieste Stellar Evolution Code (PARSEC), and MESA Isochrones and Stellar Tracks (MIST) are examined to study the isochrone morphology. Additionally, cluster ages, extinctions, and distances are found from the visible-infrared color–magnitude diagrams. We conduct careful qualitative analysis on the inconsistencies of the fits across twelve color combinations of the five observed bands, and find that the (F606W‑F814W) color generally produces very good fits, but that there are large discrepancies when the data is fit using colors including UV bands for all three models. We also find that the best fits in the UV are achieved using MIST isochrones, but that they require metallicities that are lower than the other two models, as well published spectroscopic values. Finally, we directly compare DSEP and PARSEC by performing isochrone-isochrone fitting, and find that, for globular cluster aged populations, similar appearing PARSEC isochrones are on average 1.5 Gyr younger than DSEP isochrones. We find that the two models become less discrepant at lower metallicities.

Measuring Systematic Error with Curve Fits

ERIC Educational Resources Information Center

Rupright, Mark E.

2011-01-01

Systematic errors are often unavoidable in the introductory physics laboratory. As has been demonstrated in many papers in this journal, such errors can present a fundamental problem for data analysis, particularly when comparing the data to a given model. In this paper I give three examples in which my students use popular curve-fitting software…

Performance of DIMTEST-and NOHARM-Based Statistics for Testing Unidimensionality

ERIC Educational Resources Information Center

Finch, Holmes; Habing, Brian

2007-01-01

This Monte Carlo study compares the ability of the parametric bootstrap version of DIMTEST with three goodness-of-fit tests calculated from a fitted NOHARM model to detect violations of the assumption of unidimensionality in testing data. The effectiveness of the procedures was evaluated for different numbers of items, numbers of examinees,…

Goodness of Fit and Misspecification in Quantile Regressions

ERIC Educational Resources Information Center

Furno, Marilena

2011-01-01

The article considers a test of specification for quantile regressions. The test relies on the increase of the objective function and the worsening of the fit when unnecessary constraints are imposed. It compares the objective functions of restricted and unrestricted models and, in its different formulations, it verifies (a) forecast ability, (b)…

An Approximation to the Adaptive Exponential Integrate-and-Fire Neuron Model Allows Fast and Predictive Fitting to Physiological Data.

PubMed

Hertäg, Loreen; Hass, Joachim; Golovko, Tatiana; Durstewitz, Daniel

2012-01-01

For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which neurons may operate in vivo. Here we present an approach to parameter estimation for a simple spiking neuron model mainly based on standard f-I curves obtained from in vitro recordings. Such recordings are routinely obtained in standard protocols and assess a neuron's response under a wide range of mean-input currents. Our fitting procedure makes use of closed-form expressions for the firing rate derived from an approximation to the adaptive exponential integrate-and-fire (AdEx) model. The resulting fitting process is simple and about two orders of magnitude faster compared to methods based on numerical integration of the differential equations. We probe this method on different cell types recorded from rodent prefrontal cortex. After fitting to the f-I current-clamp data, the model cells are tested on completely different sets of recordings obtained by fluctuating ("in vivo-like") input currents. For a wide range of different input regimes, cell types, and cortical layers, the model could predict spike times on these test traces quite accurately within the bounds of physiological reliability, although no information from these distinct test sets was used for model fitting. Further analyses delineated some of the empirical factors constraining model fitting and the model's generalization performance. An even simpler adaptive LIF neuron was also examined in this context. Hence, we have developed a "high-throughput" model fitting procedure which is simple and fast, with good prediction performance, and which relies only on firing rate information and standard physiological data widely and easily available.

Assessing spatio-temporal eruption forecasts in a monogenetic volcanic field

NASA Astrophysics Data System (ADS)

Bebbington, Mark S.

2013-02-01

Many spatio-temporal models have been proposed for forecasting the location and timing of the next eruption in a monogenetic volcanic field. These have almost invariably been fitted retrospectively. That is, the model has been tuned to all of the data, and hence an assessment of the goodness of fit has not been carried out on independent data. The low rate of eruptions in monogenetic fields means that there is not the opportunity to carry out a purely prospective test, as thousands of years would be required to accumulate the necessary data. This leaves open the possibility of a retrospective sequential test, where the parameters are calculated only on the basis of prior events and the resulting forecast compared statistically with the location and time of the next eruption. In general, events in volcanic fields are not dated with sufficient accuracy and precision to pursue this line of investigation; An exception is the Auckland Volcanic Field (New Zealand), consisting of c. 50 centers formed during the last c. 250 kyr, for which an age-order model exists in the form of a Monte Carlo sampling algorithm, facilitating repeated sequential testing. I examine a suite of spatial, temporal and spatio-temporal hazard models, comparing the degree of fit, and attempt to draw lessons from how and where each model is particularly successful or unsuccessful. A relatively simple (independent) combination of a renewal model (temporal term) and a spatially uniform ellipse (spatial term) performs as well as any other model. Both avoid over fitting the data, and hence large errors, when the spatio-temporal occurrence pattern changes.

Modeling the near-ultraviolet band of GK stars. III. Dependence on abundance pattern

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

Short, C. Ian; Campbell, Eamonn A., E-mail: ishort@ap.smu.ca

2013-06-01

We extend the grid of non-LTE (NLTE) models presented in Paper II to explore variations in abundance pattern in two ways: (1) the adoption of the Asplund et al. (GASS10) abundances, (2) for stars of metallicity, [M/H], of –0.5, the adoption of a non-solar enhancement of α-elements by +0.3 dex. Moreover, our grid of synthetic spectral energy distributions (SEDs) is interpolated to a finer numerical resolution in both T {sub eff} (ΔT {sub eff} = 25 K) and log g (Δlog g = 0.25). We compare the values of T {sub eff} and log g inferred from fitting LTE andmore » NLTE SEDs to observed SEDs throughout the entire visible band, and in an ad hoc 'blue' band. We compare our spectrophotometrically derived T {sub eff} values to a variety of T {sub eff} calibrations, including more empirical ones, drawn from the literature. For stars of solar metallicity, we find that the adoption of the GASS10 abundances lowers the inferred T {sub eff} value by 25-50 K for late-type giants, and NLTE models computed with the GASS10 abundances give T {sub eff} results that are marginally in better agreement with other T {sub eff} calibrations. For stars of [M/H] = –0.5 there is marginal evidence that adoption of α-enhancement further lowers the derived T {sub eff} value by 50 K. Stellar parameters inferred from fitting NLTE models to SEDs are more dependent than LTE models on the wavelength region being fitted, and we find that the effect depends on how heavily line blanketed the fitting region is, whether the fitting region is to the blue of the Wien peak of the star's SED, or both.« less

[Equilibrium sorption isotherm for Cu2+ onto Hydrilla verticillata Royle and Myriophyllum spicatum].

PubMed

Yan, Chang-zhou; Zeng, A-yan; Jin, Xiang-can; Wang, Sheng-rui; Xu, Qiu-jin; Zhao, Jing-zhu

2006-06-01

Equilibrium sorption isotherms for Cu2+ onto Hydrilla verticillata Royle and Myriophyllum spicatum were studied. Both methods of linear and non-linear fitting were applied to describe the sorption isotherms, and their applicability were analyzed and compared. The results were: (1) The applicability of simulated equation can't be compared only by R2 and chi2 when equilibrium sorption model was used to quantify and contrast the performance of different biosorbents. Both methods of linear and non-linear fitting can be applied in different fitting equations to describe the equilibrium sorption isotherms respectively in order to obtain the actual and credible fitting results, and the fitting equation best accorded with experimental data can be selected; (2) In this experiment, the Langmuir model is more suitable to describe the sorption isotherm of Cu2+ biosorption by H. verticillata and M. spicatum, and there is greater difference between the experimental data and the calculated value of Freundlich model, especially for the linear form of Freundlich model; (3) The content of crude cellulose in dry matter is one of the main factor affecting the biosorption capacity of a submerged aquatic plant, and -OH and -CONH2 groups of polysaccharides on cell wall maybe are active center of biosorption; (4) According to the coefficients qm of the linear form of Langmuir model, the maximum sorption capacity of Cu2+ was found to be 21.55 mg/g and 10.80mg/g for H. verticillata and M. spicatum, respectively. The maximum specific surface area for H. verticillata for binding Cu2+ was 3.23m2/g, and it was 1.62m2/g for M. spicatum.

Validation of a Best-Fit Pharmacokinetic Model for Scopolamine Disposition after Intranasal Administration

NASA Technical Reports Server (NTRS)

Wu, L.; Chow, D. S-L.; Tam, V.; Putcha, L.

2015-01-01

An intranasal gel formulation of scopolamine (INSCOP) was developed for the treatment of Motion Sickness. Bioavailability and pharmacokinetics (PK) were determined per Investigative New Drug (IND) evaluation guidance by the Food and Drug Administration. Earlier, we reported the development of a PK model that can predict the relationship between plasma, saliva and urinary scopolamine (SCOP) concentrations using data collected from an IND clinical trial with INSCOP. This data analysis project is designed to validate the reported best fit PK model for SCOP by comparing observed and model predicted SCOP concentration-time profiles after administration of INSCOP.

Using multiple group modeling to test moderators in meta-analysis.

PubMed

Schoemann, Alexander M

2016-12-01

Meta-analysis is a popular and flexible analysis that can be fit in many modeling frameworks. Two methods of fitting meta-analyses that are growing in popularity are structural equation modeling (SEM) and multilevel modeling (MLM). By using SEM or MLM to fit a meta-analysis researchers have access to powerful techniques associated with SEM and MLM. This paper details how to use one such technique, multiple group analysis, to test categorical moderators in meta-analysis. In a multiple group meta-analysis a model is fit to each level of the moderator simultaneously. By constraining parameters across groups any model parameter can be tested for equality. Using multiple groups to test for moderators is especially relevant in random-effects meta-analysis where both the mean and the between studies variance of the effect size may be compared across groups. A simulation study and the analysis of a real data set are used to illustrate multiple group modeling with both SEM and MLM. Issues related to multiple group meta-analysis and future directions for research are discussed. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Modeling dust emission in the Magellanic Clouds with Spitzer and Herschel

NASA Astrophysics Data System (ADS)

Chastenet, Jérémy; Bot, Caroline; Gordon, Karl D.; Bocchio, Marco; Roman-Duval, Julia; Jones, Anthony P.; Ysard, Nathalie

2017-05-01

Context. Dust modeling is crucial to infer dust properties and budget for galaxy studies. However, there are systematic disparities between dust grain models that result in corresponding systematic differences in the inferred dust properties of galaxies. Quantifying these systematics requires a consistent fitting analysis. Aims: We compare the output dust parameters and assess the differences between two dust grain models, the DustEM model and THEMIS. In this study, we use a single fitting method applied to all the models to extract a coherent and unique statistical analysis. Methods: We fit the models to the dust emission seen by Spitzer and Herschel in the Small and Large Magellanic Clouds (SMC and LMC). The observations cover the infrared (IR) spectrum from a few microns to the sub-millimeter range. For each fitted pixel, we calculate the full n-D likelihood based on a previously described method. The free parameters are both environmental (U, the interstellar radiation field strength; αISRF, power-law coefficient for a multi-U environment; Ω∗, the starlight strength) and intrinsic to the model (YI: abundances of the grain species I; αsCM20, coefficient in the small carbon grain size distribution). Results: Fractional residuals of five different sets of parameters show that fitting THEMIS brings a more accurate reproduction of the observations than the DustEM model. However, independent variations of the dust species show strong model-dependencies. We find that the abundance of silicates can only be constrained to an upper-limit and that the silicate/carbon ratio is different than that seen in our Galaxy. In the LMC, our fits result in dust masses slightly lower than those found in the literature, by a factor lower than 2. In the SMC, we find dust masses in agreement with previous studies.

Efficient occupancy model-fitting for extensive citizen-science data.

PubMed

Dennis, Emily B; Morgan, Byron J T; Freeman, Stephen N; Ridout, Martin S; Brereton, Tom M; Fox, Richard; Powney, Gary D; Roy, David B

2017-01-01

Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species' range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists.

Efficient occupancy model-fitting for extensive citizen-science data

PubMed Central

Morgan, Byron J. T.; Freeman, Stephen N.; Ridout, Martin S.; Brereton, Tom M.; Fox, Richard; Powney, Gary D.; Roy, David B.

2017-01-01

Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species’ range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists. PMID:28328937

Cardiorespiratory fitness and muscle strength in pancreatic cancer patients.

PubMed

Clauss, Dorothea; Tjaden, Christine; Hackert, Thilo; Schneider, Lutz; Ulrich, Cornelia M; Wiskemann, Joachim; Steindorf, Karen

2017-09-01

Cancer patients frequently experience reduced physical fitness due to the disease itself as well as treatment-related side effects. However, studies on physical fitness in pancreatic cancer patients are missing. Therefore, we assessed cardiorespiratory fitness and muscle strength of pancreatic cancer patients. We included 65 pancreatic cancer patients, mostly after surgical resection. Cardiorespiratory fitness was assessed using cardiopulmonary exercise testing (CPET) and 6-min walk test (6MWT). Hand-held dynamometry was used to evaluate isometric muscle strength. Physical fitness values were compared to reference values of a healthy population. Associations between sociodemographic and clinical variables with patients' physical fitness were analyzed using multiple regression models. Cardiorespiratory fitness (VO 2 peak, 20.5 ± 6.9 ml/min/kg) was significantly lower (-24%) compared to healthy reference values. In the 6MWT pancreatic cancer patients nearly reached predicted values (555 vs. 562 m). Maximal voluntary isometric contraction (MVIC) of the upper (-4.3%) and lower extremities (-13.8%) were significantly lower compared to reference values. Overall differences were larger in men than those in women. Participating in regular exercise in the year before diagnosis was associated with greater VO 2 peak (p < .05) and MVIC of the knee extensors (p < .05). Pancreatic cancer patients had significantly impaired physical fitness with regard to both cardiorespiratory function and isometric muscle strength, already in the early treatment phase (median 95 days after surgical resection). Our findings underline the need to investigate exercise training in pancreatic cancer patients to counteract the loss of physical fitness.

Estimation of the linear mixed integrated Ornstein–Uhlenbeck model

PubMed Central

Hughes, Rachael A.; Kenward, Michael G.; Sterne, Jonathan A. C.; Tilling, Kate

2017-01-01

ABSTRACT The linear mixed model with an added integrated Ornstein–Uhlenbeck (IOU) process (linear mixed IOU model) allows for serial correlation and estimation of the degree of derivative tracking. It is rarely used, partly due to the lack of available software. We implemented the linear mixed IOU model in Stata and using simulations we assessed the feasibility of fitting the model by restricted maximum likelihood when applied to balanced and unbalanced data. We compared different (1) optimization algorithms, (2) parameterizations of the IOU process, (3) data structures and (4) random-effects structures. Fitting the model was practical and feasible when applied to large and moderately sized balanced datasets (20,000 and 500 observations), and large unbalanced datasets with (non-informative) dropout and intermittent missingness. Analysis of a real dataset showed that the linear mixed IOU model was a better fit to the data than the standard linear mixed model (i.e. independent within-subject errors with constant variance). PMID:28515536

Properties of young massive clusters obtained with different massive-star evolutionary models

NASA Astrophysics Data System (ADS)

Wofford, Aida; Charlot, Stéphane

We undertake a comprehensive comparative test of seven widely-used spectral synthesis models using multi-band HST photometry of a sample of eight YMCs in two galaxies. We provide a first quantitative estimate of the accuracies and uncertainties of new models, show the good progress of models in fitting high-quality observations, and highlight the need of further comprehensive comparative tests.

Detection and correction of laser induced breakdown spectroscopy spectral background based on spline interpolation method

NASA Astrophysics Data System (ADS)

Tan, Bing; Huang, Min; Zhu, Qibing; Guo, Ya; Qin, Jianwei

2017-12-01

Laser-induced breakdown spectroscopy (LIBS) is an analytical technique that has gained increasing attention because of many applications. The production of continuous background in LIBS is inevitable because of factors associated with laser energy, gate width, time delay, and experimental environment. The continuous background significantly influences the analysis of the spectrum. Researchers have proposed several background correction methods, such as polynomial fitting, Lorenz fitting and model-free methods. However, less of them apply these methods in the field of LIBS Technology, particularly in qualitative and quantitative analyses. This study proposes a method based on spline interpolation for detecting and estimating the continuous background spectrum according to its smooth property characteristic. Experiment on the background correction simulation indicated that, the spline interpolation method acquired the largest signal-to-background ratio (SBR) over polynomial fitting, Lorenz fitting and model-free method after background correction. These background correction methods all acquire larger SBR values than that acquired before background correction (The SBR value before background correction is 10.0992, whereas the SBR values after background correction by spline interpolation, polynomial fitting, Lorentz fitting, and model-free methods are 26.9576, 24.6828, 18.9770, and 25.6273 respectively). After adding random noise with different kinds of signal-to-noise ratio to the spectrum, spline interpolation method acquires large SBR value, whereas polynomial fitting and model-free method obtain low SBR values. All of the background correction methods exhibit improved quantitative results of Cu than those acquired before background correction (The linear correlation coefficient value before background correction is 0.9776. Moreover, the linear correlation coefficient values after background correction using spline interpolation, polynomial fitting, Lorentz fitting, and model-free methods are 0.9998, 0.9915, 0.9895, and 0.9940 respectively). The proposed spline interpolation method exhibits better linear correlation and smaller error in the results of the quantitative analysis of Cu compared with polynomial fitting, Lorentz fitting and model-free methods, The simulation and quantitative experimental results show that the spline interpolation method can effectively detect and correct the continuous background.

3D Modeling of Iran and Surrounding Areas From Simultaneous Inversion of Multiple Geophysical Datasets

DTIC Science & Technology

2010-09-01

which are primarily sensitive to upper crustal structures, are difficult to measure and especially true in tectonically and geologically complex areas...slice through the model (compare Figure 6 and Figure 9). The fit to the receiver function is not perfect and the spread of the slower deep crustal ...Although the final fit is certainly not perfect, note the improvement in timing of the main crustal conversion and reverberation (vertical lines) from the

The consistency of standard cosmology and the BATSE number versus brightness relation

NASA Technical Reports Server (NTRS)

Wickramasinghe, W. A. D. T.; Nemiroff, R. J.; Norris, J. P.; Kouveliotou, C.; Fishman, G. J.; Meegan, C. A.; Wilson, R. B.; Paciesas, W. S.

1993-01-01

The integrated number-peak-flux relation measured by the Burst and Transient Source Experiment (BATSE) on board the Compton Gamma Ray Observatory is compared with several standard cosmological distributions for gamma-ray bursts (GRBs). Friedmann-Robertson-Walker models were used along with the assumption that the bursts are standard candles and have no number or luminosity evolution. For a given Omega spectral shape, we used a free parameter, essentially the comoving number density of bursts, to generate a best fit between the cosmology and the measured relation. Our results are shown for a subsample of the first 260 GRBs recorded by BATSE. We find acceptable fits between simple cosmological models and the brightness distribution data, as determined by the Kolmogorov-Smirnov one-distribution statistical test. One cannot distinguish a single best cosmological model from the goodness of the fits. The best fit implies that BATSE GRBs are complete out to a redshift of about unity. However, significantly higher and lower redshifts, by as much as a factor of 2, are possible for other marginally acceptable fits.

The Blazar 3C 66A in 2003-2004: hadronic versus leptonic model fits

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

Reimer, A.; Joshi, M.; Boettcher, M.

2008-12-24

The low-frequency peaked BL Lac object 3C 66A was the subject of an extensive multi-wavelength campaign from July 2003 till April 2004, which included quasi-simultaneous observations at optical, X-rays and very high energy gamma-rays. Here we apply the hadronic Synchrotron-Proton Blazar (SPB) model to the observed spectral energy distribution time-averaged over a flaring state, and compare the resulting model fits to those obtained from the application of the leptonic Synchrotron-Self-Compton (SSC) model. The results are used to identify diagnostic key predictions of the two blazar models for future multi-wavelength observations.

Migration confers winter survival benefits in a partially migratory songbird

PubMed Central

Zúñiga, Daniel; Gager, Yann; Kokko, Hanna; Fudickar, Adam Michael; Schmidt, Andreas; Naef-Daenzer, Beat; Wikelski, Martin

2017-01-01

To evolve and to be maintained, seasonal migration, despite its risks, has to yield fitness benefits compared with year-round residency. Empirical data supporting this prediction have remained elusive in the bird literature. To test fitness related benefits of migration, we studied a partial migratory population of European blackbirds (Turdus merula) over 7 years. Using a combination of capture-mark-recapture and radio telemetry, we compared survival probabilities between migrants and residents estimated by multi-event survival models, showing that migrant blackbirds had 16% higher probability to survive the winter compared to residents. A subsequent modelling exercise revealed that residents should have 61.25% higher breeding success than migrants, to outweigh the survival costs of residency. Our results support theoretical models that migration should confer survival benefits to evolve, and thus provide empirical evidence to understand the evolution and maintenance of migration. PMID:29157357

Evaluation of psychometric properties of the German Hospital Survey on Patient Safety Culture and its potential for cross-cultural comparisons: a cross-sectional study.

PubMed

Gambashidze, Nikoloz; Hammer, Antje; Brösterhaus, Mareen; Manser, Tanja

2017-11-09

To study the psychometric characteristics of German version of the Hospital Survey on Patient Safety Culture and to compare its dimensionality to other language versions in order to understand the instrument's potential for cross-national studies. Cross-sectional multicentre study to establish psychometric properties of German version of the survey instrument. 73 units from 37 departments of two German university hospitals. Clinical personnel (n=995 responses, response rate 39.6%). Psychometric properties (eg, model fit, internal consistency, construct validity) of the instrument and comparison of dimensionality across different language translations. The instrument demonstrated acceptable to good internal consistency (Cronbach's alpha 0.64-0.88). Confirmatory factor analysis of the original 12-factor model resulted in marginally satisfactory model fit (root mean square error of approximation (RMSEA)=0.05; standardised root mean residual (SRMR)=0.05; comparative fit index (CFI)=0.90; goodness of fit index (GFI)=0.88; Tucker-Lewis Index (TLI)=0.88). Exploratory factor analysis resulted in an alternative eight-factor model with good model fit (RMSEA=0.05; SRMR=0.05; CFI=0.95; GFI=0.91; TLI=0.94) and good internal consistency (Cronbach's alpha 0.73-0.87) and construct validity. Analysis of the dimensionality compared with models from 10 other language versions revealed eight dimensions with relatively stable composition and appearance across different versions and four dimensions requiring further improvement. The German version of Hospital Survey on Patient Safety Culture demonstrated satisfactory psychometric properties for use in German hospitals. However, our comparison of instrument dimensionality across different language versions indicates limitations concerning cross-national studies. Results of this study can be considered in interpreting findings across national contexts, in further refinement of the instrument for cross-national studies and in better understanding the various facets and dimensions of patient safety culture. © 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.

Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation

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

Daly, Don S.; Anderson, Kevin K.; White, Amanda M.

Background: A microarray of enzyme-linked immunosorbent assays, or ELISA microarray, predicts simultaneously the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Making sound biological inferences as well as improving the ELISA microarray process require require both concentration predictions and creditable estimates of their errors. Methods: We present a statistical method based on monotonic spline statistical models, penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict concentrations and estimate prediction errors in ELISA microarray. PCLS restrains the flexible spline to a fit of assay intensitymore » that is a monotone function of protein concentration. With MC, both modeling and measurement errors are combined to estimate prediction error. The spline/PCLS/MC method is compared to a common method using simulated and real ELISA microarray data sets. Results: In contrast to the rigid logistic model, the flexible spline model gave credible fits in almost all test cases including troublesome cases with left and/or right censoring, or other asymmetries. For the real data sets, 61% of the spline predictions were more accurate than their comparable logistic predictions; especially the spline predictions at the extremes of the prediction curve. The relative errors of 50% of comparable spline and logistic predictions differed by less than 20%. Monte Carlo simulation rendered acceptable asymmetric prediction intervals for both spline and logistic models while propagation of error produced symmetric intervals that diverged unrealistically as the standard curves approached horizontal asymptotes. Conclusions: The spline/PCLS/MC method is a flexible, robust alternative to a logistic/NLS/propagation-of-error method to reliably predict protein concentrations and estimate their errors. The spline method simplifies model selection and fitting, and reliably estimates believable prediction errors. For the 50% of the real data sets fit well by both methods, spline and logistic predictions are practically indistinguishable, varying in accuracy by less than 15%. The spline method may be useful when automated prediction across simultaneous assays of numerous proteins must be applied routinely with minimal user intervention.« less

The effects of changes in physical fitness on academic performance among New York City youth.

PubMed

Bezold, Carla P; Konty, Kevin J; Day, Sophia E; Berger, Magdalena; Harr, Lindsey; Larkin, Michael; Napier, Melanie D; Nonas, Cathy; Saha, Subir; Harris, Tiffany G; Stark, James H

2014-12-01

To evaluate whether a change in fitness is associated with academic outcomes in New York City (NYC) middle-school students using longitudinal data and to evaluate whether this relationship is modified by student household poverty. This was a longitudinal study of 83,111 New York City middle-school students enrolled between 2006-2007 and 2011-2012. Fitness was measured as a composite percentile based on three fitness tests and categorized based on change from the previous year. The effect of the fitness change level on academic outcomes, measured as a composite percentile based on state standardized mathematics and English Language Arts test scores, was estimated using a multilevel growth model. Models were stratified by sex, and additional models were tested stratified by student household poverty. For both girls and boys, a substantial increase in fitness from the previous year resulted in a greater improvement in academic ranking than was seen in the reference group (girls: .36 greater percentile point improvement, 95% confidence interval: .09-.63; boys: .38 greater percentile point improvement, 95% confidence interval: .09-.66). A substantial decrease in fitness was associated with a decrease in academics in both boys and girls. Effects of fitness on academics were stronger in high-poverty boys and girls than in low-poverty boys and girls. Academic rankings improved for boys and girls who increased their fitness level by >20 percentile points compared to other students. Opportunities for increased physical fitness may be important to support academic performance. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Soft X-ray spectral fits of Geminga with model neutron star atmospheres

NASA Technical Reports Server (NTRS)

Meyer, R. D.; Pavlov, G. G.; Meszaros, P.

1994-01-01

The spectrum of the soft X-ray pulsar Geminga consists of two components, a softer one which can be interpreted as thermal-like radiation from the surface of the neutron star, and a harder one interpreted as radiation from a polar cap heated by relativistic particles. We have fitted the soft spectrum using a detailed magnetized hydrogen atmosphere model. The fitting parameters are the hydrogen column density, the effective temperature T(sub eff), the gravitational redshift z, and the distance to radius ratio, for different values of the magnetic field B. The best fits for this model are obtained when B less than or approximately 1 x 10(exp 12) G and z lies on the upper boundary of the explored range (z = 0.45). The values of T(sub eff) approximately = (2-3) x 10(exp 5) K are a factor of 2-3 times lower than the value of T(sub eff) obtained for blackbody fits with the same z. The lower T(sub eff) increases the compatibility with some proposed schemes for fast neutrino cooling of neutron stars (NSs) by the direct Urca process or by exotic matter, but conventional cooling cannot be excluded. The hydrogen atmosphere fits also imply a smaller distance to Geminga than that inferred from a blackbody fit. An accurate evaluation of the distance would require a better knowledge of the ROSAT Position Sensitive Proportional Counter (PSPC) response to the low-energy region of the incident spectrum. Our modeling of the soft component with a cooler magnetized atmosphere also implies that the hard-component fit requires a characteristic temperature which is higher (by a factor of approximately 2-3) and a surface area which is smaller (by a factor of 10(exp 3), compared to previous blackbody fits.

Mathematical models of human mobility of relevance to malaria transmission in Africa.

PubMed

Marshall, John M; Wu, Sean L; Sanchez C, Hector M; Kiware, Samson S; Ndhlovu, Micky; Ouédraogo, André Lin; Touré, Mahamoudou B; Sturrock, Hugh J; Ghani, Azra C; Ferguson, Neil M

2018-05-16

As Africa-wide malaria prevalence declines, an understanding of human movement patterns is essential to inform how best to target interventions. We fitted movement models to trip data from surveys conducted at 3-5 sites throughout each of Mali, Burkina Faso, Zambia and Tanzania. Two models were compared in terms of their ability to predict the observed movement patterns - a gravity model, in which movement rates between pairs of locations increase with population size and decrease with distance, and a radiation model, in which travelers are cumulatively "absorbed" as they move outwards from their origin of travel. The gravity model provided a better fit to the data overall and for travel to large populations, while the radiation model provided a better fit for nearby populations. One strength of the data set was that trips could be categorized according to traveler group - namely, women traveling with children in all survey countries and youth workers in Mali. For gravity models fitted to data specific to these groups, youth workers were found to have a higher travel frequency to large population centers, and women traveling with children a lower frequency. These models may help predict the spatial transmission of malaria parasites and inform strategies to control their spread.

Evaluating models of remember-know judgments: complexity, mimicry, and discriminability.

PubMed

Cohen, Andrew L; Rotello, Caren M; Macmillan, Neil A

2008-10-01

Remember-know judgments provide additional information in recognition memory tests, but the nature of this information and the attendant decision process are in dispute. Competing models have proposed that remember judgments reflect a sum of familiarity and recollective information (the one-dimensional model), are based on a difference between these strengths (STREAK), or are purely recollective (the dual-process model). A choice among these accounts is sometimes made by comparing the precision of their fits to data, but this strategy may be muddied by differences in model complexity: Some models that appear to provide good fits may simply be better able to mimic the data produced by other models. To evaluate this possibility, we simulated data with each of the models in each of three popular remember-know paradigms, then fit those data to each of the models. We found that the one-dimensional model is generally less complex than the others, but despite this handicap, it dominates the others as the best-fitting model. For both reasons, the one-dimensional model should be preferred. In addition, we found that some empirical paradigms are ill-suited for distinguishing among models. For example, data collected by soliciting remember/know/new judgments--that is, the trinary task--provide a particularly weak ground for distinguishing models. Additional tables and figures may be downloaded from the Psychonomic Society's Archive of Norms, Stimuli, and Data, at www.psychonomic.org/archive.

Assessing performance of Bayesian state-space models fit to Argos satellite telemetry locations processed with Kalman filtering.

PubMed

Silva, Mónica A; Jonsen, Ian; Russell, Deborah J F; Prieto, Rui; Thompson, Dave; Baumgartner, Mark F

2014-01-01

Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to "true" GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6 ± 5.6 km) was nearly half that of LS estimates (11.6 ± 8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales' behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates.

Assessing Performance of Bayesian State-Space Models Fit to Argos Satellite Telemetry Locations Processed with Kalman Filtering

PubMed Central

Silva, Mónica A.; Jonsen, Ian; Russell, Deborah J. F.; Prieto, Rui; Thompson, Dave; Baumgartner, Mark F.

2014-01-01

Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to “true” GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6±5.6 km) was nearly half that of LS estimates (11.6±8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales’ behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates. PMID:24651252

Foreground Bias from Parametric Models of Far-IR Dust Emission

NASA Technical Reports Server (NTRS)

Kogut, A.; Fixsen, D. J.

2016-01-01

We use simple toy models of far-IR dust emission to estimate the accuracy to which the polarization of the cosmic microwave background can be recovered using multi-frequency fits, if the parametric form chosen for the fitted dust model differs from the actual dust emission. Commonly used approximations to the far-IR dust spectrum yield CMB residuals comparable to or larger than the sensitivities expected for the next generation of CMB missions, despite fitting the combined CMB plus foreground emission to precision 0.1 percent or better. The Rayleigh-Jeans approximation to the dust spectrum biases the fitted dust spectral index by (Delta)(Beta)(sub d) = 0.2 and the inflationary B-mode amplitude by (Delta)(r) = 0.03. Fitting the dust to a modified blackbody at a single temperature biases the best-fit CMB by (Delta)(r) greater than 0.003 if the true dust spectrum contains multiple temperature components. A 13-parameter model fitting two temperature components reduces this bias by an order of magnitude if the true dust spectrum is in fact a simple superposition of emission at different temperatures, but fails at the level (Delta)(r) = 0.006 for dust whose spectral index varies with frequency. Restricting the observing frequencies to a narrow region near the foreground minimum reduces these biases for some dust spectra but can increase the bias for others. Data at THz frequencies surrounding the peak of the dust emission can mitigate these biases while providing a direct determination of the dust temperature profile.

Assessment of family functioning in Caucasian and Hispanic Americans: reliability, validity, and factor structure of the Family Assessment Device.

PubMed

Aarons, Gregory A; McDonald, Elizabeth J; Connelly, Cynthia D; Newton, Rae R

2007-12-01

The purpose of this study was to examine the factor structure, reliability, and validity of the Family Assessment Device (FAD) among a national sample of Caucasian and Hispanic American families receiving public sector mental health services. A confirmatory factor analysis conducted to test model fit yielded equivocal findings. With few exceptions, indices of model fit, reliability, and validity were poorer for Hispanic Americans compared with Caucasian Americans. Contrary to our expectation, an exploratory factor analysis did not result in a better fitting model of family functioning. Without stronger evidence supporting a reformulation of the FAD, we recommend against such a course of action. Findings highlight the need for additional research on the role of culture in measurement of family functioning.

Core overshoot and convection in δ Scuti and γ Doradus stars

NASA Astrophysics Data System (ADS)

Lovekin, Catherine; Guzik, Joyce A.

2017-09-01

The effects of rotation on pulsation in δ Scuti and γ Doradus stars are poorly understood. Stars in this mass range span the transition from convective envelopes to convective cores, and realistic models of convection are thus a key part of understanding these stars. In this work, we use 2D asteroseismic modelling of 5 stars observed with the Kepler spacecraft to provide constraints on the age, mass, rotation rate, and convective core overshoot. We use Period04 to calculate the frequencies based on short cadence Kepler observations of five γ Doradus and δ Scuti stars. We fit these stars with rotating models calculated using MESA and adiabatic pulsation frequencies calculated with GYRE. Comparison of these models with the pulsation frequencies of three stars observed with Kepler allowed us to place constraints on the age, mass, and rotation rate of these stars. All frequencies not identified as possible combinations were compared to theoretical frequencies calculated using models including the effects of rotation and overshoot. The best fitting models for all five stars are slowly rotating at the best fitting age and have moderate convective core overshoot. In this work, we will discuss the results of the frequency extraction and fitting process.

Simultaneous quantification of soil phosphorus labile pool and desorption kinetics using DGTs and 3D-DIFS

NASA Astrophysics Data System (ADS)

Menezes-Blackburn, Daniel; Sun, Jiahui; Lehto, Niklas; Zhang, Hao; Stutter, Marc; Giles, Courtney D.; Darch, Tegan; George, Timothy S.; Shand, Charles; Lumsdon, David; Blackwell, Martin; Wearing, Catherine; Cooper, Patricia; Wendler, Renate; Brown, Lawrie; Haygarth, Philip M.

2017-04-01

The phosphorus (P) labile pool and desorption kinetics were simultaneously evaluated in ten representative UK soils using the technique of Diffusive gradients in thin films (DGT). The DGT-induced fluxes in soil and sediments model (DIFS) was fitted to the time series of DGT deployment (1h to 240h). The desorbable P concentration (labile P) was obtained by multiplying the fitted Kd by the soil solution P concentration obtained using Diffusive Equilibration in Thin Films (DET) devices. The labile P was then compared to several soil P extracts including Olsen P, Resin P, FeO-P and water extractable P, in order to assess if these analytical procedures can be used to represent the labile P across different soils. The Olsen P, commonly used as a representation of the soil labile P pool, overestimated the desorbable P concentration by a seven fold factor. The use of this approach for the quantification of soil P desorption kinetics parameters was somewhat unprecise, showing a wide range of equally valid solutions for the response of the system P equilibration time (Tc). Additionally, the performance of different DIFS model versions (1D, 2D and 3D) was compared. Although these models had a good fit to experimental DGT time series data, the fitted parameters showed a poor agreement between different model versions. The limitations of the DIFS model family are associated with the assumptions taken in the modelling approach and the 3D version is here considered to be the most precise among them.

Curve fitting and modeling with splines using statistical variable selection techniques

NASA Technical Reports Server (NTRS)

Smith, P. L.

1982-01-01

The successful application of statistical variable selection techniques to fit splines is demonstrated. Major emphasis is given to knot selection, but order determination is also discussed. Two FORTRAN backward elimination programs, using the B-spline basis, were developed. The program for knot elimination is compared in detail with two other spline-fitting methods and several statistical software packages. An example is also given for the two-variable case using a tensor product basis, with a theoretical discussion of the difficulties of their use.

The relationship between offspring size and fitness: integrating theory and empiricism.

PubMed

Rollinson, Njal; Hutchings, Jeffrey A

2013-02-01

How parents divide the energy available for reproduction between size and number of offspring has a profound effect on parental reproductive success. Theory indicates that the relationship between offspring size and offspring fitness is of fundamental importance to the evolution of parental reproductive strategies: this relationship predicts the optimal division of resources between size and number of offspring, it describes the fitness consequences for parents that deviate from optimality, and its shape can predict the most viable type of investment strategy in a given environment (e.g., conservative vs. diversified bet-hedging). Many previous attempts to estimate this relationship and the corresponding value of optimal offspring size have been frustrated by a lack of integration between theory and empiricism. In the present study, we draw from C. Smith and S. Fretwell's classic model to explain how a sound estimate of the offspring size--fitness relationship can be derived with empirical data. We evaluate what measures of fitness can be used to model the offspring size--fitness curve and optimal size, as well as which statistical models should and should not be used to estimate offspring size--fitness relationships. To construct the fitness curve, we recommend that offspring fitness be measured as survival up to the age at which the instantaneous rate of offspring mortality becomes random with respect to initial investment. Parental fitness is then expressed in ecologically meaningful, theoretically defensible, and broadly comparable units: the number of offspring surviving to independence. Although logistic and asymptotic regression have been widely used to estimate offspring size-fitness relationships, the former provides relatively unreliable estimates of optimal size when offspring survival and sample sizes are low, and the latter is unreliable under all conditions. We recommend that the Weibull-1 model be used to estimate this curve because it provides modest improvements in prediction accuracy under experimentally relevant conditions.

Comparing Simulated and Theoretical Sampling Distributions of the U3 Person-Fit Statistic.

ERIC Educational Resources Information Center

Emons, Wilco H. M.; Meijer, Rob R.; Sijtsma, Klaas

2002-01-01

Studied whether the theoretical sampling distribution of the U3 person-fit statistic is in agreement with the simulated sampling distribution under different item response theory models and varying item and test characteristics. Simulation results suggest that the use of standard normal deviates for the standardized version of the U3 statistic may…

Measuring the Processes of Change From the Transtheoretical Model for Physical Activity and Exercise in Overweight and Obese Adults.

PubMed

Romain, Ahmed Jerôme; Bernard, Paquito; Hokayem, Marie; Gernigon, Christophe; Avignon, Antoine

2016-03-01

This study aimed to test three factorial structures conceptualizing the processes of change (POC) from the transtheoretical model and to examine the relationships between the POC and stages of change (SOC) among overweight and obese adults. Cross-sectional study. This study was conducted at the University Hospital of Montpellier, France. A sample of 289 overweight or obese participants (199 women) was enrolled in the study. Participants completed the POC and SOC questionnaires during a 5-day hospitalization for weight management. Structural equation modeling was used to compare the different factorial structures. The unweighted least-squares method was used to identify the best-fit indices for the five fully correlated model (goodness-of-fit statistic = .96; adjusted goodness-of-fit statistic = .95; standardized root mean residual = .062; normed-fit index = .95; parsimonious normed-fit index = .83; parsimonious goodness-of-fit statistic = .78). The multivariate analysis of variance was significant (p < .001). A post hoc test showed that individuals in advanced SOC used more of both experiential and behavioral POC than those in preaction stages, with effect sizes ranging from .06 to .29. This study supports the validity of the factorial structure of POC concerning physical activity and confirms the assumption that, in this context, people with excess weight use both experiential and behavioral processes. These preliminary results should be confirmed in a longitudinal study. © The Author(s) 2016.

Workplace support, discrimination, and person-organization fit: tests of the theory of work adjustment with LGB individuals.

PubMed

Velez, Brandon L; Moradi, Bonnie

2012-07-01

The present study explored the links of 2 workplace contextual variables--perceptions of workplace heterosexist discrimination and lesbian, gay, and bisexual (LGB)-supportive climates--with job satisfaction and turnover intentions in a sample of LGB employees. An extension of the theory of work adjustment (TWA) was used as the conceptual framework for the study; as such, perceived person-organization (P-O) fit was tested as a mediator of the relations between the workplace contextual variables and job outcomes. Data were analyzed from 326 LGB employees. Zero-order correlations indicated that perceptions of workplace heterosexist discrimination and LGB-supportive climates were correlated in expected directions with P-O fit, job satisfaction, and turnover intentions. Structural equation modeling (SEM) was used to compare multiple alternative measurement models evaluating the discriminant validity of the 2 workplace contextual variables relative to one another, and the 3 TWA job variables relative to one another; SEM was also used to test the hypothesized mediation model. Comparisons of multiple alternative measurement models supported the construct distinctiveness of the variables of interest. The test of the hypothesized structural model revealed that only LGB-supportive climates (and not workplace heterosexist discrimination) had a unique direct positive link with P-O fit and, through the mediating role of P-O fit, had significant indirect positive and negative relations with job satisfaction and turnover intentions, respectively. Moreover, P-O fit had a significant indirect negative link with turnover intentions through job satisfaction.

Balancing Adherence and Expense: The Cost-Effectiveness of Two-Sample vs One-Sample Fecal Immunochemical Test.

PubMed

Smith, David H; O'Keeffe Rosetti, Maureen; Mosen, David M; Rosales, A Gabriela; Keast, Erin; Perrin, Nancy; Feldstein, Adrianne C; Levin, Theodore R; Liles, Elizabeth G

2018-06-21

Colorectal cancer (CRC) causes more than 50,000 deaths each year in the United States but early detection through screening yields survival gains; those diagnosed with early stage disease have a 5-year survival greater than 90%, compared to 12% for those diagnosed with late stage disease. Using data from a large integrated health system, this study evaluates the cost-effectiveness of fecal immunochemical testing (FIT), a common CRC screening tool. A probabilistic decision-analytic model was used to examine the costs and outcomes of positive test results from a 1-FIT regimen compared with a 2-FIT regimen. The authors compared 5 diagnostic cutoffs of hemoglobin concentration for each test (for a total of 10 screening options). The principal outcome from the analysis was the cost per additional advanced neoplasia (AN) detected. The authors also estimated the number of cancers detected and life-years gained from detecting AN. The following costs were included: program management of the screening program, patient identification, FIT kits and their processing, and diagnostic colonoscopy following a positive FIT. Per-person costs ranged from $33 (1-FIT at 150ng/ml) to $92 (2-FIT at 50ng/ml) across screening options. Depending on willingness to pay, the 1-FIT 50 ng/ml and the 2-FIT 50 ng/ml are the dominant strategies with cost-effectiveness of $11,198 and $28,389, respectively, for an additional AN detected. The estimates of cancers avoided per 1000 screens ranged from 1.46 to 4.86, depending on the strategy and the assumptions of AN to cancer progression.

Testing spectral models for stellar populations with star clusters - II. Results

NASA Astrophysics Data System (ADS)

González Delgado, Rosa M.; Cid Fernandes, Roberto

2010-04-01

High spectral resolution evolutionary synthesis models have become a routinely used ingredient in extragalactic work, and as such deserve thorough testing. Star clusters are ideal laboratories for such tests. This paper applies the spectral fitting methodology outlined in Paper I to a sample of clusters, mainly from the Magellanic Clouds and spanning a wide range in age and metallicity, fitting their integrated light spectra with a suite of modern evolutionary synthesis models for single stellar populations. The combinations of model plus spectral library employed in this investigation are Galaxev/STELIB, Vazdekis/MILES, SED@/GRANADA and Galaxev/MILES+GRANADA, which provide a representative sample of models currently available for spectral fitting work. A series of empirical tests are performed with these models, comparing the quality of the spectral fits and the values of age, metallicity and extinction obtained with each of them. A comparison is also made between the properties derived from these spectral fits and literature data on these nearby, well studied clusters. These comparisons are done with the general goal of providing useful feedback for model makers, as well as guidance to the users of such models. We find the following. (i) All models are able to derive ages that are in good agreement both with each other and with literature data, although ages derived from spectral fits are on average slightly older than those based on the S-colour-magnitude diagram (S-CMD) method as calibrated by Girardi et al. (ii) There is less agreement between the models for the metallicity and extinction. In particular, Galaxev/STELIB models underestimate the metallicity by ~0.6 dex, and the extinction is overestimated by 0.1 mag. (iii) New generations of models using the GRANADA and MILES libraries are superior to STELIB-based models both in terms of spectral fit quality and regarding the accuracy with which age and metallicity are retrieved. Accuracies of about 0.1 dex in age and 0.3 dex in metallicity can be achieved as long as the models are not extrapolated beyond their expected range of validity.

Constructing the Japanese version of the Maslach Burnout Inventory-Student Survey: Confirmatory factor analysis.

PubMed

Tsubakita, Takashi; Shimazaki, Kazuyo

2016-01-01

To examine the factorial validity of the Maslach Burnout Inventory-Student Survey, using a sample of 2061 Japanese university students majoring in the medical and natural sciences (67.9% male, 31.8% female; Mage  = 19.6 years, standard deviation = 1.5). The back-translated scale used unreversed items to assess inefficacy. The inventory's descriptive properties and Cronbach's alphas were calculated using SPSS software. The present authors compared fit indices of the null, one factor, and default three factor models via confirmatory factor analysis with maximum-likelihood estimation using AMOS software, version 21.0. Intercorrelations between exhaustion, cynicism, and inefficacy were relatively higher than in prior studies. Cronbach's alphas were 0.76, 0.85, and 0.78, respectively. Although fit indices of the hypothesized three factor model did not meet the respective criteria, the model demonstrated better fit than did the null and one factor models. The present authors added four paths between error variables within items, but the modified model did not show satisfactory fit. Subsequent analysis revealed that a bi-factor model fit the data better than did the hypothesized or modified three factor models. The Japanese version of the Maslach Burnout Inventory-Student Survey needs minor changes to improve the fit of its three factor model, but the scale as a whole can be used to adequately assess overall academic burnout in Japanese university students. Although the scale was back-translated, two items measuring exhaustion whose expressions overlapped should be modified, and all items measuring inefficacy should be reversed in order to statistically clarify the factorial difference between the scale's three factors. © 2015 The Authors. Japan Journal of Nursing Science © 2015 Japan Academy of Nursing Science.

Frequency dependence 3.0: an attempt at codifying the evolutionary ecology perspective.

PubMed

Metz, Johan A J; Geritz, Stefan A H

2016-03-01

The fitness concept and perforce the definition of frequency independent fitnesses from population genetics is closely tied to discrete time population models with non-overlapping generations. Evolutionary ecologists generally focus on trait evolution through repeated mutant substitutions in populations with complicated life histories. This goes with using the per capita invasion speed of mutants as their fitness. In this paper we develop a concept of frequency independence that attempts to capture the practical use of the term by ecologists, which although inspired by population genetics rarely fits its strict definition. We propose to call the invasion fitnesses of an eco-evolutionary model frequency independent when the phenotypes can be ranked by competitive strength, measured by who can invade whom. This is equivalent to the absence of weak priority effects, protected dimorphisms and rock-scissor-paper configurations. Our concept differs from that of Heino et al. (TREE 13:367-370, 1998) in that it is based only on the signs of the invasion fitnesses, whereas Heino et al. based their definitions on the structure of the feedback environment, summarising the effect of all direct and indirect interactions between individuals on fitness. As it turns out, according to our new definition an eco-evolutionary model has frequency independent fitnesses if and only if the effect of the feedback environment on the fitness signs can be summarised by a single scalar with monotonic effect. This may be compared with Heino et al.'s concept of trivial frequency dependence defined by the environmental feedback influencing fitness, and not just its sign, in a scalar manner, without any monotonicity restriction. As it turns out, absence of the latter restriction leaves room for rock-scissor-paper configurations. Since in 'realistic' (as opposed to toy) models frequency independence is exceedingly rare, we also define a concept of weak frequency dependence, which can be interpreted intuitively as almost frequency independence, and analyse in which sense and to what extent the restrictions on the potential model outcomes of the frequency independent case stay intact for models with weak frequency dependence.

Do Men and Women Need to Be Screened Differently with Fecal Immunochemical Testing? A Cost-Effectiveness Analysis.

PubMed

Meulen, Miriam P van der; Kapidzic, Atija; Leerdam, Monique E van; van der Steen, Alex; Kuipers, Ernst J; Spaander, Manon C W; de Koning, Harry J; Hol, Lieke; Lansdorp-Vogelaar, Iris

2017-08-01

Background: Several studies suggest that test characteristics for the fecal immunochemical test (FIT) differ by gender, triggering a debate on whether men and women should be screened differently. We used the microsimulation model MISCAN-Colon to evaluate whether screening stratified by gender is cost-effective. Methods: We estimated gender-specific FIT characteristics based on first-round positivity and detection rates observed in a FIT screening pilot (CORERO-1). Subsequently, we used the model to estimate harms, benefits, and costs of 480 gender-specific FIT screening strategies and compared them with uniform screening. Results: Biennial FIT screening from ages 50 to 75 was less effective in women than men [35.7 vs. 49.0 quality-adjusted life years (QALY) gained, respectively] at higher costs (€42,161 vs. -€5,471, respectively). However, the incremental QALYs gained and costs of annual screening compared with biennial screening were more similar for both genders (8.7 QALYs gained and €26,394 for women vs. 6.7 QALYs gained and €20,863 for men). Considering all evaluated screening strategies, optimal gender-based screening yielded at most 7% more QALYs gained than optimal uniform screening and even resulted in equal costs and QALYs gained from a willingness-to-pay threshold of €1,300. Conclusions: FIT screening is less effective in women, but the incremental cost-effectiveness is similar in men and women. Consequently, screening stratified by gender is not more cost-effective than uniform FIT screening. Impact: Our conclusions support the current policy of uniform FIT screening. Cancer Epidemiol Biomarkers Prev; 26(8); 1328-36. ©2017 AACR . ©2017 American Association for Cancer Research.

[Predicting Incidence of Hepatitis E in Chinausing Fuzzy Time Series Based on Fuzzy C-Means Clustering Analysis].

PubMed

Luo, Yi; Zhang, Tao; Li, Xiao-song

2016-05-01

To explore the application of fuzzy time series model based on fuzzy c-means clustering in forecasting monthly incidence of Hepatitis E in mainland China. Apredictive model (fuzzy time series method based on fuzzy c-means clustering) was developed using Hepatitis E incidence data in mainland China between January 2004 and July 2014. The incidence datafrom August 2014 to November 2014 were used to test the fitness of the predictive model. The forecasting results were compared with those resulted from traditional fuzzy time series models. The fuzzy time series model based on fuzzy c-means clustering had 0.001 1 mean squared error (MSE) of fitting and 6.977 5 x 10⁻⁴ MSE of forecasting, compared with 0.0017 and 0.0014 from the traditional forecasting model. The results indicate that the fuzzy time series model based on fuzzy c-means clustering has a better performance in forecasting incidence of Hepatitis E.

Stem geometry changes initial femoral fixation stability of a revised press-fit hip prosthesis: A finite element study.

PubMed

Russell, Robert D; Huo, Michael H; Rodrigues, Danieli C; Kosmopoulos, Victor

2016-11-14

Stable femoral fixation during uncemented total hip arthroplasty is critical to allow for subsequent osseointegration of the prosthesis. Varying stem designs provide surgeons with multiple options to gain femoral fixation. The purpose of this study was to compare the initial fixation stability of cylindrical and tapered stem implants using two different underreaming techniques (press-fit conditions) for revision total hip arthroplasty (THA). A finite element femur model was created from three-dimensional computed tomography images simulating a trabecular bone defect commonly observed in revision THA. Two 18-mm generic femoral hip implants were modeled using the same geometry, differing only in that one had a cylindrical stem and the other had a 2 degree tapered stem. Surgery was simulated using a 0.05-mm and 0.01-mm press-fit and tested with a physiologically relevant loading protocol. Mean contact pressure was influenced more by the surgical technique than by the stem geometry. The 0.05-mm press-fit condition resulted in the highest contact pressures for both the cylindrical (27.35 MPa) and tapered (20.99 MPa) stems. Changing the press-fit to 0.01-mm greatly decreased the contact pressure by 79.8% and 78.5% for the cylindrical (5.53 MPa) and tapered (4.52 MPa) models, respectively. The cylindrical stem geometry consistently showed less relative micromotion at all the cross-sections sampled as compared to the tapered stem regardless of press-fit condition. This finite element analysis study demonstrates that tapered stem results in lower average contact pressure and greater micromotion at the implant-bone interface than a cylindrical stem geometry. More studies are needed to establish how these different stem geometries perform in such non-ideal conditions encountered in revision THA cases where less bone stock is available.

Effect of fit testing on the protection offered by n95 filtering facepiece respirators against fine particles in a laboratory setting.

PubMed

Reponen, Tiina; Lee, Shu-An; Grinshpun, Sergey A; Johnson, Erik; McKay, Roy

2011-04-01

This study investigated particle-size-selective protection factors (PFs) of four models of N95 filtering facepiece respirators (FFRs) that passed and failed fit testing. Particle size ranges were representative of individual viruses and bacteria (aerodynamic diameter d(a) = 0.04-1.3 μm). Standard respirator fit testing was followed by particle-size-selective measurement of PFs while subjects wore N95 FFRs in a test chamber. PF values obtained for all subjects were then compared to those obtained for the subjects who passed the fit testing. Overall fit test passing rate for all four models of FFRs was 67%. Of these, 29% had PFs <10 (the Occupational Safety and Health Administration Assigned Protection Factor designated for this type of respirator). When only subjects that passed fit testing were included, PFs improved with 9% having values <10. On average, the PFs were 1.4 times (29.5/21.5) higher when only data for those who passed fit testing were included. The minimum PFs were consistently observed in the particle size range of 0.08-0.2 μm. Overall PFs increased when subjects passed fit testing. The results support the value of fit testing but also show for the first time that PFs are dependent on particle size regardless of fit testing status.

How Good Are Statistical Models at Approximating Complex Fitness Landscapes?

PubMed Central

du Plessis, Louis; Leventhal, Gabriel E.; Bonhoeffer, Sebastian

2016-01-01

Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations. PMID:27189564

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

PubMed

Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

2017-05-22

PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.

Model-based estimation of individual fitness

USGS Publications Warehouse

Link, W.A.; Cooch, E.G.; Cam, E.

2002-01-01

Fitness is the currency of natural selection, a measure of the propagation rate of genotypes into future generations. Its various definitions have the common feature that they are functions of survival and fertility rates. At the individual level, the operative level for natural selection, these rates must be understood as latent features, genetically determined propensities existing at birth. This conception of rates requires that individual fitness be defined and estimated by consideration of the individual in a modelled relation to a group of similar individuals; the only alternative is to consider a sample of size one, unless a clone of identical individuals is available. We present hierarchical models describing individual heterogeneity in survival and fertility rates and allowing for associations between these rates at the individual level. We apply these models to an analysis of life histories of Kittiwakes (Rissa tridactyla) observed at several colonies on the Brittany coast of France. We compare Bayesian estimation of the population distribution of individual fitness with estimation based on treating individual life histories in isolation, as samples of size one (e.g. McGraw and Caswell, 1996).

Model-based estimation of individual fitness

USGS Publications Warehouse

Link, W.A.; Cooch, E.G.; Cam, E.

2002-01-01

Fitness is the currency of natural selection, a measure of the propagation rate of genotypes into future generations. Its various definitions have the common feature that they are functions of survival and fertility rates. At the individual level, the operative level for natural selection, these rates must be understood as latent features, genetically determined propensities existing at birth. This conception of rates requires that individual fitness be defined and estimated by consideration of the individual in a modelled relation to a group of similar individuals; the only alternative is to consider a sample of size one, unless a clone of identical individuals is available. We present hierarchical models describing individual heterogeneity in survival and fertility rates and allowing for associations between these rates at the individual level. We apply these models to an analysis of life histories of Kittiwakes (Rissa tridactyla ) observed at several colonies on the Brittany coast of France. We compare Bayesian estimation of the population distribution of individual fitness with estimation based on treating individual life histories in isolation, as samples of size one (e.g. McGraw & Caswell, 1996).

Estimating Dynamical Systems: Derivative Estimation Hints From Sir Ronald A. Fisher.

PubMed

Deboeck, Pascal R

2010-08-06

The fitting of dynamical systems to psychological data offers the promise of addressing new and innovative questions about how people change over time. One method of fitting dynamical systems is to estimate the derivatives of a time series and then examine the relationships between derivatives using a differential equation model. One common approach for estimating derivatives, Local Linear Approximation (LLA), produces estimates with correlated errors. Depending on the specific differential equation model used, such correlated errors can lead to severely biased estimates of differential equation model parameters. This article shows that the fitting of dynamical systems can be improved by estimating derivatives in a manner similar to that used to fit orthogonal polynomials. Two applications using simulated data compare the proposed method and a generalized form of LLA when used to estimate derivatives and when used to estimate differential equation model parameters. A third application estimates the frequency of oscillation in observations of the monthly deaths from bronchitis, emphysema, and asthma in the United Kingdom. These data are publicly available in the statistical program R, and functions in R for the method presented are provided.

Seasonal and Non-Seasonal Generalized Pareto Distribution to Estimate Extreme Significant Wave Height in The Banda Sea

NASA Astrophysics Data System (ADS)

Nursamsiah; Nugroho Sugianto, Denny; Suprijanto, Jusup; Munasik; Yulianto, Bambang

2018-02-01

The information of extreme wave height return level was required for maritime planning and management. The recommendation methods in analyzing extreme wave were better distributed by Generalized Pareto Distribution (GPD). Seasonal variation was often considered in the extreme wave model. This research aims to identify the best model of GPD by considering a seasonal variation of the extreme wave. By using percentile 95 % as the threshold of extreme significant wave height, the seasonal GPD and non-seasonal GPD fitted. The Kolmogorov-Smirnov test was applied to identify the goodness of fit of the GPD model. The return value from seasonal and non-seasonal GPD was compared with the definition of return value as criteria. The Kolmogorov-Smirnov test result shows that GPD fits data very well both seasonal and non-seasonal model. The seasonal return value gives better information about the wave height characteristics.

Analysis of Mining-Induced Subsidence Prediction by Exponent Knothe Model Combined with Insar and Leveling

NASA Astrophysics Data System (ADS)

Chen, Lei; Zhang, Liguo; Tang, Yixian; Zhang, Hong

2018-04-01

The principle of exponent Knothe model was introduced in detail and the variation process of mining subsidence with time was analysed based on the formulas of subsidence, subsidence velocity and subsidence acceleration in the paper. Five scenes of radar images and six levelling measurements were collected to extract ground deformation characteristics in one coal mining area in this study. Then the unknown parameters of exponent Knothe model were estimated by combined levelling data with deformation information along the line of sight obtained by InSAR technique. By compared the fitting and prediction results obtained by InSAR and levelling with that obtained only by levelling, it was shown that the accuracy of fitting and prediction combined with InSAR and levelling was obviously better than the other that. Therefore, the InSAR measurements can significantly improve the fitting and prediction accuracy of exponent Knothe model.

MODELING THE NONLINEAR CLUSTERING IN MODIFIED GRAVITY MODELS. I. A FITTING FORMULA FOR THE MATTER POWER SPECTRUM OF f(R) GRAVITY

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

Zhao, Gong-Bo, E-mail: gongbo@icosmology.info; Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth PO1 3FX

2014-04-01

Based on a suite of N-body simulations of the Hu-Sawicki model of f(R) gravity with different sets of model and cosmological parameters, we develop a new fitting formula with a numeric code, MGHalofit, to calculate the nonlinear matter power spectrum P(k) for the Hu-Sawicki model. We compare the MGHalofit predictions at various redshifts (z ≤ 1) to the f(R) simulations and find that the relative error of the MGHalofit fitting formula of P(k) is no larger than 6% at k ≤ 1 h Mpc{sup –1} and 12% at k in (1, 10] h Mpc{sup –1}, respectively. Based on a sensitivitymore » study of an ongoing and a future spectroscopic survey, we estimate the detectability of a signal of modified gravity described by the Hu-Sawicki model using the power spectrum up to quasi-nonlinear scales.« less

Genome-wide heterogeneity of nucleotide substitution model fit.

PubMed

Arbiza, Leonardo; Patricio, Mateus; Dopazo, Hernán; Posada, David

2011-01-01

At a genomic scale, the patterns that have shaped molecular evolution are believed to be largely heterogeneous. Consequently, comparative analyses should use appropriate probabilistic substitution models that capture the main features under which different genomic regions have evolved. While efforts have concentrated in the development and understanding of model selection techniques, no descriptions of overall relative substitution model fit at the genome level have been reported. Here, we provide a characterization of best-fit substitution models across three genomic data sets including coding regions from mammals, vertebrates, and Drosophila (24,000 alignments). According to the Akaike Information Criterion (AIC), 82 of 88 models considered were selected as best-fit models at least in one occasion, although with very different frequencies. Most parameter estimates also varied broadly among genes. Patterns found for vertebrates and Drosophila were quite similar and often more complex than those found in mammals. Phylogenetic trees derived from models in the 95% confidence interval set showed much less variance and were significantly closer to the tree estimated under the best-fit model than trees derived from models outside this interval. Although alternative criteria selected simpler models than the AIC, they suggested similar patterns. All together our results show that at a genomic scale, different gene alignments for the same set of taxa are best explained by a large variety of different substitution models and that model choice has implications on different parameter estimates including the inferred phylogenetic trees. After taking into account the differences related to sample size, our results suggest a noticeable diversity in the underlying evolutionary process. All together, we conclude that the use of model selection techniques is important to obtain consistent phylogenetic estimates from real data at a genomic scale.

Substance use avoidance among Iranian male adolescents: a comparison of three versions of the theory of reasoned action.

PubMed

Tavousi, Mahmoud; Montazeri, Ali; Hidarnia, Alireza; Hajizadeh, Ebrahim; Taremian, Farhad; Haerimehrizi, Aliasghar

2015-08-01

The theory of reasoned action (TRA) is one of the most common models in predicting health-related behaviors and is used more often in health education studies. This study aimed to add two control constructs (perceived behavioral control - PBC and self-efficacy - SE) to the TRA and compare them using the structural equation modeling (SEM) for substance use avoidance among Iranian male adolescents in order to find out which model was a better fit in predicting the intention. This was a cross-sectional study carried out in Tehran, Iran. Data were collected from a random sample of high school male students (15-19 years of age) using a questionnaire containing items related to the TRA plus items reflecting two additional constructs (SE and PBC). In all, 433 students completed the questionnaires. The results obtained from SEM indicated a better fit to the data for the TRA with SE compared to the TPB (TRA with PBC) and TRA (χ2/df=2.55, RMSEA=0.072, CFI=0.96, NFI=0.94, NNFI=0.95, SRMR=0.058). Comparing SE and PBC, the results showed that self-efficacy was a better control construct in improving the TRA and predicting substance use avoidance intention (41%). The TRA with SE had a better model fit than TPB and the original version of the TRA.

Active appearance pyramids for object parametrisation and fitting.

PubMed

Zhang, Qiang; Bhalerao, Abhir; Dickenson, Edward; Hutchinson, Charles

2016-08-01

Object class representation is one of the key problems in various medical image analysis tasks. We propose a part-based parametric appearance model we refer to as an Active Appearance Pyramid (AAP). The parts are delineated by multi-scale Local Feature Pyramids (LFPs) for superior spatial specificity and distinctiveness. An AAP models the variability within a population with local translations of multi-scale parts and linear appearance variations of the assembly of the parts. It can fit and represent new instances by adjusting the shape and appearance parameters. The fitting process uses a two-step iterative strategy: local landmark searching followed by shape regularisation. We present a simultaneous local feature searching and appearance fitting algorithm based on the weighted Lucas and Kanade method. A shape regulariser is derived to calculate the maximum likelihood shape with respect to the prior and multiple landmark candidates from multi-scale LFPs, with a compact closed-form solution. We apply the 2D AAP on the modelling of variability in patients with lumbar spinal stenosis (LSS) and validate its performance on 200 studies consisting of routine axial and sagittal MRI scans. Intervertebral sagittal and parasagittal cross-sections are typically used for the diagnosis of LSS, we therefore build three AAPs on L3/4, L4/5 and L5/S1 axial cross-sections and three on parasagittal slices. Experiments show significant improvement in convergence range, robustness to local minima and segmentation precision compared with Constrained Local Models (CLMs), Active Shape Models (ASMs) and Active Appearance Models (AAMs), as well as superior performance in appearance reconstruction compared with AAMs. We also validate the performance on 3D CT volumes of hip joints from 38 studies. Compared to AAMs, AAPs achieve a higher segmentation and reconstruction precision. Moreover, AAPs have a significant improvement in efficiency, consuming about half the memory and less than 10% of the training time and 15% of the testing time. Copyright © 2016 Elsevier B.V. All rights reserved.

Tests of a habitat suitability model for black-capped chickadees

USGS Publications Warehouse

Schroeder, Richard L.

1990-01-01

The black-capped chickadee (Parus atricapillus) Habitat Suitability Index (HSI) model provides a quantitative rating of the capability of a habitat to support breeding, based on measures related to food and nest site availability. The model assumption that tree canopy volume can be predicted from measures of tree height and canopy closure was tested using data from foliage volume studies conducted in the riparian cottonwood habitat along the South Platte River in Colorado. Least absolute deviations (LAD) regression showed that canopy cover and over story tree height yielded volume predictions significantly lower than volume estimated by more direct methods. Revisions to these model relations resulted in improved predictions of foliage volume. The relation between the HSI and estimates of black-capped chickadee population densities was examined using LAD regression for both the original model and the model with the foliage volume revisions. Residuals from these models were compared to residuals from both a zero slope model and an ideal model. The fit model for the original HSI differed significantly from the ideal model, whereas the fit model for the original HSI did not differ significantly from the ideal model. However, both the fit model for the original HSI and the fit model for the revised HSI did not differ significantly from a model with a zero slope. Although further testing of the revised model is needed, its use is recommended for more realistic estimates of tree canopy volume and habitat suitability.

Assessing posttraumatic stress disorder's latent structure in elderly bereaved European trauma survivors: evidence for a five-factor dysphoric and anxious arousal model.

PubMed

Armour, Cherie; O'Connor, Maja; Elklit, Ask; Elhai, Jon D

2013-10-01

The three-factor structure of posttraumatic stress disorder (PTSD) specified by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, is not supported in the empirical literature. Two alternative four-factor models have received a wealth of empirical support. However, a consensus regarding which is superior has not been reached. A recent five-factor model has been shown to provide superior fit over the existing four-factor models. The present study investigated the fit of the five-factor model against the existing four-factor models and assessed the resultant factors' association with depression in a bereaved European trauma sample (N = 325). The participants were assessed for PTSD via the Harvard Trauma Questionnaire and depression via the Beck Depression Inventory. The five-factor model provided superior fit to the data compared with the existing four-factor models. In the dysphoric arousal model, depression was equally related to both dysphoric arousal and emotional numbing, whereas depression was more related to dysphoric arousal than to anxious arousal.

Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model.

PubMed

Zhang, Xujun; Pang, Yuanyuan; Cui, Mengjing; Stallones, Lorann; Xiang, Huiyun

2015-02-01

Road traffic injuries have become a major public health problem in China. This study aimed to develop statistical models for predicting road traffic deaths and to analyze seasonality of deaths in China. A seasonal autoregressive integrated moving average (SARIMA) model was used to fit the data from 2000 to 2011. Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were used to evaluate the constructed models. Autocorrelation function and partial autocorrelation function of residuals and Ljung-Box test were used to compare the goodness-of-fit between the different models. The SARIMA model was used to forecast monthly road traffic deaths in 2012. The seasonal pattern of road traffic mortality data was statistically significant in China. SARIMA (1, 1, 1) (0, 1, 1)12 model was the best fitting model among various candidate models; the Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were -483.679, -475.053, and 4.937, respectively. Goodness-of-fit testing showed nonautocorrelations in the residuals of the model (Ljung-Box test, Q = 4.86, P = .993). The fitted deaths using the SARIMA (1, 1, 1) (0, 1, 1)12 model for years 2000 to 2011 closely followed the observed number of road traffic deaths for the same years. The predicted and observed deaths were also very close for 2012. This study suggests that accurate forecasting of road traffic death incidence is possible using SARIMA model. The SARIMA model applied to historical road traffic deaths data could provide important evidence of burden of road traffic injuries in China. Copyright © 2015 Elsevier Inc. All rights reserved.

WIMPs at the galactic center

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

Agrawal, Prateek; Batell, Brian; Fox, Patrick J.

2015-05-07

Simple models of weakly interacting massive particles (WIMPs) predict dark matter annihilations into pairs of electroweak gauge bosons, Higgses or tops, which through their subsequent cascade decays produce a spectrum of gamma rays. Intriguingly, an excess in gamma rays coming from near the Galactic center has been consistently observed in Fermi data. A recent analysis by the Fermi collaboration confirms these earlier results. Taking into account the systematic uncertainties in the modelling of the gamma ray backgrounds, we show for the first time that this excess can be well fit by these final states. In particular, for annihilations to (WW,more » ZZ, hh, tt{sup -bar}), dark matter with mass between threshold and approximately (165, 190, 280, 310) GeV gives an acceptable fit. The fit range for bb{sup -bar} is also enlarged to 35 GeV≲m{sub χ}≲165 GeV. These are to be compared to previous fits that concluded only much lighter dark matter annihilating into b, τ, and light quark final states could describe the excess. We demonstrate that simple, well-motivated models of WIMP dark matter including a thermal-relic neutralino of the MSSM, Higgs portal models, as well as other simplified models can explain the excess.« less

WIMPs at the galactic center

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

Agrawal, Prateek; Batell, Brian; Fox, Patrick J.

Simple models of weakly interacting massive particles (WIMPs) predict dark matter annihilations into pairs of electroweak gauge bosons, Higgses or tops, which through their subsequent cascade decays produce a spectrum of gamma rays. Intriguingly, an excess in gamma rays coming from near the Galactic center has been consistently observed in Fermi data. A recent analysis by the Fermi collaboration confirms these earlier results. Taking into account the systematic uncertainties in the modelling of the gamma ray backgrounds, we show for the first time that this excess can be well fit by these final states. In particular, for annihilations to (WW,more » ZZ, hh, tt¯), dark matter with mass between threshold and approximately (165, 190, 280, 310) GeV gives an acceptable fit. The fit range for bb¯ is also enlarged to 35 GeV ≲ m χ ≲ 165 GeV. These are to be compared to previous fits that concluded only much lighter dark matter annihilating into b, τ, and light quark final states could describe the excess. We demonstrate that simple, well-motivated models of WIMP dark matter including a thermal-relic neutralino of the MSSM, Higgs portal models, as well as other simplified models can explain the excess.« less

WIMPs at the galactic center

DOE PAGES

Agrawal, Prateek; Batell, Brian; Fox, Patrick J.; ...

2015-05-07

Simple models of weakly interacting massive particles (WIMPs) predict dark matter annihilations into pairs of electroweak gauge bosons, Higgses or tops, which through their subsequent cascade decays produce a spectrum of gamma rays. Intriguingly, an excess in gamma rays coming from near the Galactic center has been consistently observed in Fermi data. A recent analysis by the Fermi collaboration confirms these earlier results. Taking into account the systematic uncertainties in the modelling of the gamma ray backgrounds, we show for the first time that this excess can be well fit by these final states. In particular, for annihilations to (WW,more » ZZ, hh, tt¯), dark matter with mass between threshold and approximately (165, 190, 280, 310) GeV gives an acceptable fit. The fit range for bb¯ is also enlarged to 35 GeV ≲ m χ ≲ 165 GeV. These are to be compared to previous fits that concluded only much lighter dark matter annihilating into b, τ, and light quark final states could describe the excess. We demonstrate that simple, well-motivated models of WIMP dark matter including a thermal-relic neutralino of the MSSM, Higgs portal models, as well as other simplified models can explain the excess.« less

Confirmatory factor analysis of the female sexual function index.

PubMed

Opperman, Emily A; Benson, Lindsay E; Milhausen, Robin R

2013-01-01

The Female Sexual Functioning Index (Rosen et al., 2000 ) was designed to assess the key dimensions of female sexual functioning using six domains: desire, arousal, lubrication, orgasm, satisfaction, and pain. A full-scale score was proposed to represent women's overall sexual function. The fifth revision to the Diagnostic and Statistical Manual (DSM) is currently underway and includes a proposal to combine desire and arousal problems. The objective of this article was to evaluate and compare four models of the Female Sexual Functioning Index: (a) single-factor model, (b) six-factor model, (c) second-order factor model, and (4) five-factor model combining the desire and arousal subscales. Cross-sectional and observational data from 85 women were used to conduct a confirmatory factor analysis on the Female Sexual Functioning Index. Local and global goodness-of-fit measures, the chi-square test of differences, squared multiple correlations, and regression weights were used. The single-factor model fit was not acceptable. The original six-factor model was confirmed, and good model fit was found for the second-order and five-factor models. Delta chi-square tests of differences supported best fit for the six-factor model validating usage of the six domains. However, when revisions are made to the DSM-5, the Female Sexual Functioning Index can adapt to reflect these changes and remain a valid assessment tool for women's sexual functioning, as the five-factor structure was also supported.

Extraction of Generalized Parton Distributions from combined Deeply Virtual Compton Scattering and Timelike Compton scattering fits

NASA Astrophysics Data System (ADS)

Boer, Marie

2017-09-01

Generalized Parton Distributions (GPDs) contain the correlation between the parton's longitudinal momentum and their transverse distribution. They are accessed through hard exclusive processes, such as Deeply Virtual Compton Scattering (DVCS). DVCS has already been measured in several experiments and several models allow for extracting GPDs from these measurements. Timelike Compton Scattering (TCS) is, at leading order, the time-reversal equivalent process to DVCS and accesses GPDs at the same kinematics. Comparing GPDs extracted from DVCS and TCS is a unique way for proving GPD universality. Combining fits from the two processes will also allow for better constraining the GPDs. We will present our method for extracting GPDs from DVCS and TCS pseudo-data. We will compare fit results from the two processes in similar conditions and present what can be expected in term of contraints on GPDs from combined fits.

The Impact of Intraclass Correlation on the Effectiveness of Level-Specific Fit Indices in Multilevel Structural Equation Modeling

PubMed Central

Hsu, Hsien-Yuan; Lin, Jr-Hung; Kwok, Oi-Man; Acosta, Sandra; Willson, Victor

2016-01-01

Several researchers have recommended that level-specific fit indices should be applied to detect the lack of model fit at any level in multilevel structural equation models. Although we concur with their view, we note that these studies did not sufficiently consider the impact of intraclass correlation (ICC) on the performance of level-specific fit indices. Our study proposed to fill this gap in the methodological literature. A Monte Carlo study was conducted to investigate the performance of (a) level-specific fit indices derived by a partially saturated model method (e.g., CFIPS_B and CFIPS_W) and (b) SRMRW and SRMRB in terms of their performance in multilevel structural equation models across varying ICCs. The design factors included intraclass correlation (ICC: ICC1 = 0.091 to ICC6 = 0.500), numbers of groups in between-level models (NG: 50, 100, 200, and 1,000), group size (GS: 30, 50, and 100), and type of misspecification (no misspecification, between-level misspecification, and within-level misspecification). Our simulation findings raise a concern regarding the performance of between-level-specific partial saturated fit indices in low ICC conditions: the performances of both TLIPS_B and RMSEAPS_B were more influenced by ICC compared with CFIPS_B and SRMRB. However, when traditional cutoff values (RMSEA≤ 0.06; CFI, TLI≥ 0.95; SRMR≤ 0.08) were applied, CFIPS_B and TLIPS_B were still able to detect misspecified between-level models even when ICC was as low as 0.091 (ICC1). On the other hand, both RMSEAPS_B and SRMRB were not recommended under low ICC conditions. PMID:29795901

Neutron star mass and radius measurements from atmospheric model fits to X-ray burst cooling tail spectra

NASA Astrophysics Data System (ADS)

Nättilä, J.; Miller, M. C.; Steiner, A. W.; Kajava, J. J. E.; Suleimanov, V. F.; Poutanen, J.

2017-12-01

Observations of thermonuclear X-ray bursts from accreting neutron stars (NSs) in low-mass X-ray binary systems can be used to constrain NS masses and radii. Most previous work of this type has set these constraints using Planck function fits as a proxy: the models and the data are both fit with diluted blackbody functions to yield normalizations and temperatures that are then compared with each other. For the first time, we here fit atmosphere models of X-ray bursting NSs directly to the observed spectra. We present a hierarchical Bayesian fitting framework that uses current X-ray bursting NS atmosphere models with realistic opacities and relativistic exact Compton scattering kernels as a model for the surface emission. We test our approach against synthetic data and find that for data that are well described by our model, we can obtain robust radius, mass, distance, and composition measurements. We then apply our technique to Rossi X-ray Timing Explorer observations of five hard-state X-ray bursts from 4U 1702-429. Our joint fit to all five bursts shows that the theoretical atmosphere models describe the data well, but there are still some unmodeled features in the spectrum corresponding to a relative error of 1-5% of the energy flux. After marginalizing over this intrinsic scatter, we find that at 68% credibility, the circumferential radius of the NS in 4U 1702-429 is R = 12.4±0.4 km, the gravitational mass is M = 1.9±0.3 M⊙, the distance is 5.1 < D/ kpc < 6.2, and the hydrogen mass fraction is X < 0.09.

Dust in the small Magellanic Cloud. 2: Dust models from interstellar polarization and extinction data

NASA Technical Reports Server (NTRS)

Rodrigues, C. V.; Magalhaes, A. M.; Coyne, G. V.

1995-01-01

We study the dust in the Small Magellanic Cloud using our polarization and extinction data (Paper 1) and existing dust models. The data suggest that the monotonic SMC extinction curve is related to values of lambda(sub max), the wavelength of maximum polarization, which are on the average smaller than the mean for the Galaxy. On the other hand, AZV 456, a star with an extinction similar to that for the Galaxy, shows a value of lambda(sub max) similar to the mean for the Galaxy. We discuss simultaneous dust model fits to extinction and polarization. Fits to the wavelength dependent polarization data are possible for stars with small lambda(sub max). In general, they imply dust size distributions which are narrower and have smaller mean sizes compared to typical size distributions for the Galaxy. However, stars with lambda(sub max) close to the Galactic norm, which also have a narrower polarization curve, cannot be fit adequately. This holds true for all of the dust models considered. The best fits to the extinction curves are obtained with a power law size distribution by assuming that the cylindrical and spherical silicate grains have a volume distribution which is continuous from the smaller spheres to the larger cylinders. The size distribution for the cylinders is taken from the fit to the polarization. The 'typical', monotonic SMC extinction curve can be fit well with graphite and silicate grains if a small fraction of the SMC carbon is locked up in the grain. However, amorphous carbon and silicate grains also fit the data well. AZV456, which has an extinction curve similar to that for the Galaxy, has a UV bump which is too blue to be fit by spherical graphite grains.

In Search of Optimal Cognitive Diagnostic Model(s) for ESL Grammar Test Data

ERIC Educational Resources Information Center

Yi, Yeon-Sook

2017-01-01

This study compares five cognitive diagnostic models in search of optimal one(s) for English as a Second Language grammar test data. Using a unified modeling framework that can represent specific models with proper constraints, the article first fit the full model (the log-linear cognitive diagnostic model, LCDM) and investigated which model…

Topology in two dimensions. IV - CDM models with non-Gaussian initial conditions

NASA Astrophysics Data System (ADS)

Coles, Peter; Moscardini, Lauro; Plionis, Manolis; Lucchin, Francesco; Matarrese, Sabino; Messina, Antonio

1993-02-01

The results of N-body simulations with both Gaussian and non-Gaussian initial conditions are used here to generate projected galaxy catalogs with the same selection criteria as the Shane-Wirtanen counts of galaxies. The Euler-Poincare characteristic is used to compare the statistical nature of the projected galaxy clustering in these simulated data sets with that of the observed galaxy catalog. All the models produce a topology dominated by a meatball shift when normalized to the known small-scale clustering properties of galaxies. Models characterized by a positive skewness of the distribution of primordial density perturbations are inconsistent with the Lick data, suggesting problems in reconciling models based on cosmic textures with observations. Gaussian CDM models fit the distribution of cell counts only if they have a rather high normalization but possess too low a coherence length compared with the Lick counts. This suggests that a CDM model with extra large scale power would probably fit the available data.

Comparing and validating models of driver steering behaviour in collision avoidance and vehicle stabilisation

NASA Astrophysics Data System (ADS)

Markkula, G.; Benderius, O.; Wahde, M.

2014-12-01

A number of driver models were fitted to a large data set of human truck driving, from a simulated near-crash, low-friction scenario, yielding two main insights: steering to avoid a collision was best described as an open-loop manoeuvre of predetermined duration, but with situation-adapted amplitude, and subsequent vehicle stabilisation could to a large extent be accounted for by a simple yaw rate nulling control law. These two phenomena, which could be hypothesised to generalise to passenger car driving, were found to determine the ability of four driver models adopted from the literature to fit the human data. Based on the obtained results, it is argued that the concept of internal vehicle models may be less valuable when modelling driver behaviour in non-routine situations such as near-crashes, where behaviour may be better described as direct responses to salient perceptual cues. Some methodological issues in comparing and validating driver models are also discussed.

One-dimensional GIS-based model compared with a two-dimensional model in urban floods simulation.

PubMed

Lhomme, J; Bouvier, C; Mignot, E; Paquier, A

2006-01-01

A GIS-based one-dimensional flood simulation model is presented and applied to the centre of the city of Nîmes (Gard, France), for mapping flow depths or velocities in the streets network. The geometry of the one-dimensional elements is derived from the Digital Elevation Model (DEM). The flow is routed from one element to the next using the kinematic wave approximation. At the crossroads, the flows in the downstream branches are computed using a conceptual scheme. This scheme was previously designed to fit Y-shaped pipes junctions, and has been modified here to fit X-shaped crossroads. The results were compared with the results of a two-dimensional hydrodynamic model based on the full shallow water equations. The comparison shows that good agreements can be found in the steepest streets of the study zone, but differences may be important in the other streets. Some reasons that can explain the differences between the two models are given and some research possibilities are proposed.

Effect of a checklist on advanced trauma life support workflow deviations during trauma resuscitations without pre-arrival notification.

PubMed

Kelleher, Deirdre C; Jagadeesh Chandra Bose, R P; Waterhouse, Lauren J; Carter, Elizabeth A; Burd, Randall S

2014-03-01

Trauma resuscitations without pre-arrival notification are often initially chaotic, which can potentially compromise patient care. We hypothesized that trauma resuscitations without pre-arrival notification are performed with more variable adherence to ATLS protocol and that implementation of a checklist would improve performance. We analyzed event logs of trauma resuscitations from two 4-month periods before (n = 222) and after (n = 215) checklist implementation. Using process mining techniques, individual resuscitations were compared with an ideal workflow model of 6 ATLS primary survey tasks performed by the bedside evaluator and given model fitness scores (range 0 to 1). Mean fitness scores and frequency of conformance (fitness = 1) were compared (using Student's t-test or chi-square test, as appropriate) for activations with and without notification both before and after checklist implementation. Multivariable linear regression, controlling for patient and resuscitation characteristics, was also performed to assess the association between pre-arrival notification and model fitness before and after checklist implementation. Fifty-five (12.6%) resuscitations lacked pre-arrival notification (23 pre-implementation and 32 post-implementation; p = 0.15). Before checklist implementation, resuscitations without notification had lower fitness (0.80 vs 0.90; p < 0.001) and conformance (26.1% vs 50.8%; p = 0.03) than those with notification. After checklist implementation, the fitness (0.80 vs 0.91; p = 0.007) and conformance (26.1% vs 59.4%; p = 0.01) improved for resuscitations without notification, but still remained lower than activations with notification. In multivariable analysis, activations without notification had lower fitness both before (b = -0.11, p < 0.001) and after checklist implementation (b = -0.04, p = 0.02). Trauma resuscitations without pre-arrival notification are associated with a decreased adherence to key components of the ATLS primary survey protocol. The addition of a checklist improves protocol adherence and reduces the effect of notification on task performance. Copyright © 2014 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Modeling of boldine alkaloid adsorption onto pure and propyl-sulfonic acid-modified mesoporous silicas. A comparative study.

PubMed

Geszke-Moritz, Małgorzata; Moritz, Michał

2016-12-01

The present study deals with the adsorption of boldine onto pure and propyl-sulfonic acid-functionalized SBA-15, SBA-16 and mesocellular foam (MCF) materials. Siliceous adsorbents were characterized by nitrogen sorption analysis, transmission electron microscopy (TEM), scanning electron microscopy (SEM), Fourier-transform infrared (FT-IR) spectroscopy and thermogravimetric analysis. The equilibrium adsorption data were analyzed using the Langmuir, Freundlich, Redlich-Peterson, and Temkin isotherms. Moreover, the Dubinin-Radushkevich and Dubinin-Astakhov isotherm models based on the Polanyi adsorption potential were employed. The latter was calculated using two alternative formulas including solubility-normalized (S-model) and empirical C-model. In order to find the best-fit isotherm, both linear regression and nonlinear fitting analysis were carried out. The Dubinin-Astakhov (S-model) isotherm revealed the best fit to the experimental points for adsorption of boldine onto pure mesoporous materials using both linear and nonlinear fitting analysis. Meanwhile, the process of boldine sorption onto modified silicas was described the best by the Langmuir and Temkin isotherms using linear regression and nonlinear fitting analysis, respectively. The values of adsorption energy (below 8kJ/mol) indicate the physical nature of boldine adsorption onto unmodified silicas whereas the ionic interactions seem to be the main force of alkaloid adsorption onto functionalized sorbents (energy of adsorption above 8kJ/mol). Copyright © 2016 Elsevier B.V. All rights reserved.

A new simple local muscle recovery model and its theoretical and experimental validation.

PubMed

Ma, Liang; Zhang, Wei; Wu, Su; Zhang, Zhanwu

2015-01-01

This study was conducted to provide theoretical and experimental validation of a local muscle recovery model. Muscle recovery has been modeled in different empirical and theoretical approaches to determine work-rest allowance for musculoskeletal disorder (MSD) prevention. However, time-related parameters and individual attributes have not been sufficiently considered in conventional approaches. A new muscle recovery model was proposed by integrating time-related task parameters and individual attributes. Theoretically, this muscle recovery model was compared to other theoretical models mathematically. Experimentally, a total of 20 subjects participated in the experimental validation. Hand grip force recovery and shoulder joint strength recovery were measured after a fatiguing operation. The recovery profile was fitted by using the recovery model, and individual recovery rates were calculated as well after fitting. Good fitting values (r(2) > .8) were found for all the subjects. Significant differences in recovery rates were found among different muscle groups (p < .05). The theoretical muscle recovery model was primarily validated by characterization of the recovery process after fatiguing operation. The determined recovery rate may be useful to represent individual recovery attribute.

Identifying the Source of Misfit in Item Response Theory Models.

PubMed

Liu, Yang; Maydeu-Olivares, Alberto

2014-01-01

When an item response theory model fails to fit adequately, the items for which the model provides a good fit and those for which it does not must be determined. To this end, we compare the performance of several fit statistics for item pairs with known asymptotic distributions under maximum likelihood estimation of the item parameters: (a) a mean and variance adjustment to bivariate Pearson's X(2), (b) a bivariate subtable analog to Reiser's (1996) overall goodness-of-fit test, (c) a z statistic for the bivariate residual cross product, and (d) Maydeu-Olivares and Joe's (2006) M2 statistic applied to bivariate subtables. The unadjusted Pearson's X(2) with heuristically determined degrees of freedom is also included in the comparison. For binary and ordinal data, our simulation results suggest that the z statistic has the best Type I error and power behavior among all the statistics under investigation when the observed information matrix is used in its computation. However, if one has to use the cross-product information, the mean and variance adjusted X(2) is recommended. We illustrate the use of pairwise fit statistics in 2 real-data examples and discuss possible extensions of the current research in various directions.

Dynamical modeling and multi-experiment fitting with PottersWheel

PubMed Central

Maiwald, Thomas; Timmer, Jens

2008-01-01

Motivation: Modelers in Systems Biology need a flexible framework that allows them to easily create new dynamic models, investigate their properties and fit several experimental datasets simultaneously. Multi-experiment-fitting is a powerful approach to estimate parameter values, to check the validity of a given model, and to discriminate competing model hypotheses. It requires high-performance integration of ordinary differential equations and robust optimization. Results: We here present the comprehensive modeling framework Potters-Wheel (PW) including novel functionalities to satisfy these requirements with strong emphasis on the inverse problem, i.e. data-based modeling of partially observed and noisy systems like signal transduction pathways and metabolic networks. PW is designed as a MATLAB toolbox and includes numerous user interfaces. Deterministic and stochastic optimization routines are combined by fitting in logarithmic parameter space allowing for robust parameter calibration. Model investigation includes statistical tests for model-data-compliance, model discrimination, identifiability analysis and calculation of Hessian- and Monte-Carlo-based parameter confidence limits. A rich application programming interface is available for customization within own MATLAB code. Within an extensive performance analysis, we identified and significantly improved an integrator–optimizer pair which decreases the fitting duration for a realistic benchmark model by a factor over 3000 compared to MATLAB with optimization toolbox. Availability: PottersWheel is freely available for academic usage at http://www.PottersWheel.de/. The website contains a detailed documentation and introductory videos. The program has been intensively used since 2005 on Windows, Linux and Macintosh computers and does not require special MATLAB toolboxes. Contact: maiwald@fdm.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:18614583

ElEvoHI: A Novel CME Prediction Tool for Heliospheric Imaging Combining an Elliptical Front with Drag-based Model Fitting

NASA Astrophysics Data System (ADS)

Rollett, T.; Möstl, C.; Isavnin, A.; Davies, J. A.; Kubicka, M.; Amerstorfer, U. V.; Harrison, R. A.

2016-06-01

In this study, we present a new method for forecasting arrival times and speeds of coronal mass ejections (CMEs) at any location in the inner heliosphere. This new approach enables the adoption of a highly flexible geometrical shape for the CME front with an adjustable CME angular width and an adjustable radius of curvature of its leading edge, I.e., the assumed geometry is elliptical. Using, as input, Solar TErrestrial RElations Observatory (STEREO) heliospheric imager (HI) observations, a new elliptic conversion (ElCon) method is introduced and combined with the use of drag-based model (DBM) fitting to quantify the deceleration or acceleration experienced by CMEs during propagation. The result is then used as input for the Ellipse Evolution Model (ElEvo). Together, ElCon, DBM fitting, and ElEvo form the novel ElEvoHI forecasting utility. To demonstrate the applicability of ElEvoHI, we forecast the arrival times and speeds of 21 CMEs remotely observed from STEREO/HI and compare them to in situ arrival times and speeds at 1 AU. Compared to the commonly used STEREO/HI fitting techniques (Fixed-ϕ, Harmonic Mean, and Self-similar Expansion fitting), ElEvoHI improves the arrival time forecast by about 2 to ±6.5 hr and the arrival speed forecast by ≈ 250 to ±53 km s-1, depending on the ellipse aspect ratio assumed. In particular, the remarkable improvement of the arrival speed prediction is potentially beneficial for predicting geomagnetic storm strength at Earth.

Factorial invariance of child self-report across healthy and chronic health condition groups: a confirmatory factor analysis utilizing the PedsQLTM 4.0 Generic Core Scales.

PubMed

Limbers, Christine A; Newman, Daniel A; Varni, James W

2008-07-01

The objective of the present study was to examine the factorial invariance of the PedsQL 4.0 Generic Core Scales for child self-report across 11,433 children ages 5-18 with chronic health conditions and healthy children. Multigroup Confirmatory Factor Analysis was performed specifying a five-factor model. Two multigroup structural equation models, one with constrained parameters and the other with unconstrained parameters, were proposed in order to compare the factor loadings across children with chronic health conditions and healthy children. Metric invariance (i.e., equal factor loadings) was demonstrated based on stability of the Comparative Fit Index (CFI) between the two models, and several additional indices of practical fit including the root mean squared error of approximation, the Non-normed Fit Index, and the Parsimony Normed Fit Index. The findings support an equivalent five-factor structure on the PedsQL 4.0 Generic Core Scales across healthy and chronic health condition groups. These findings suggest that when differences are found across chronic health condition and healthy groups when utilizing the PedsQL, these differences are more likely real differences in self-perceived health-related quality of life, rather than differences in interpretation of the PedsQL items as a function of health status.

Marginal and internal fit of cobalt-chromium copings fabricated using the conventional and the direct metal laser sintering techniques: A comparative in vitro study.

PubMed

Ullattuthodi, Sujana; Cherian, Kandathil Phillip; Anandkumar, R; Nambiar, M Sreedevi

2017-01-01

This in vitro study seeks to evaluate and compare the marginal and internal fit of cobalt-chromium copings fabricated using the conventional and direct metal laser sintering (DMLS) techniques. A master model of a prepared molar tooth was made using cobalt-chromium alloy. Silicone impression of the master model was made and thirty standardized working models were then produced; twenty working models for conventional lost-wax technique and ten working models for DMLS technique. A total of twenty metal copings were fabricated using two different production techniques: conventional lost-wax method and DMLS; ten samples in each group. The conventional and DMLS copings were cemented to the working models using glass ionomer cement. Marginal gap of the copings were measured at predetermined four points. The die with the cemented copings are standardized-sectioned with a heavy duty lathe. Then, each sectioned samples were analyzed for the internal gap between the die and the metal coping using a metallurgical microscope. Digital photographs were taken at ×50 magnification and analyzed using measurement software. Statistical analysis was done by unpaired t -test and analysis of variance (ANOVA). The results of this study reveal that no significant difference was present in the marginal gap of conventional and DMLS copings ( P > 0.05) by means of ANOVA. The mean values of internal gap of DMLS copings were significantly greater than that of conventional copings ( P < 0.05). Within the limitations of this in vitro study, it was concluded that the internal fit of conventional copings was superior to that of the DMLS copings. Marginal fit of the copings fabricated by two different techniques had no significant difference.

Ecologically-focused Calibration of Hydrological Models for Environmental Flow Applications

NASA Astrophysics Data System (ADS)

Adams, S. K.; Bledsoe, B. P.

2015-12-01

Hydrologic alteration resulting from watershed urbanization is a common cause of aquatic ecosystem degradation. Developing environmental flow criteria for urbanizing watersheds requires quantitative flow-ecology relationships that describe biological responses to streamflow alteration. Ideally, gaged flow data are used to develop flow-ecology relationships; however, biological monitoring sites are frequently ungaged. For these ungaged locations, hydrologic models must be used to predict streamflow characteristics through calibration and testing at gaged sites, followed by extrapolation to ungaged sites. Physically-based modeling of rainfall-runoff response has frequently utilized "best overall fit" calibration criteria, such as the Nash-Sutcliffe Efficiency (NSE), that do not necessarily focus on specific aspects of the flow regime relevant to biota of interest. This study investigates the utility of employing flow characteristics known a priori to influence regional biological endpoints as "ecologically-focused" calibration criteria compared to traditional, "best overall fit" criteria. For this study, 19 continuous HEC-HMS 4.0 models were created in coastal southern California and calibrated to hourly USGS streamflow gages with nearby biological monitoring sites using one "best overall fit" and three "ecologically-focused" criteria: NSE, Richards-Baker Flashiness Index (RBI), percent of time when the flow is < 1 cfs (%<1), and a Combined Calibration (RBI and %<1). Calibrated models were compared using calibration accuracy, environmental flow metric reproducibility, and the strength of flow-ecology relationships. Results indicate that "ecologically-focused" criteria can be calibrated with high accuracy and may provide stronger flow-ecology relationships than "best overall fit" criteria, especially when multiple "ecologically-focused" criteria are used in concert, despite inabilities to accurately reproduce additional types of ecological flow metrics to which the models are not explicitly calibrated.

Accelerated pharmacokinetic map determination for dynamic contrast enhanced MRI using frequency-domain based Tofts model.

PubMed

Vajuvalli, Nithin N; Nayak, Krupa N; Geethanath, Sairam

2014-01-01

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is widely used in the diagnosis of cancer and is also a promising tool for monitoring tumor response to treatment. The Tofts model has become a standard for the analysis of DCE-MRI. The process of curve fitting employed in the Tofts equation to obtain the pharmacokinetic (PK) parameters is time-consuming for high resolution scans. Current work demonstrates a frequency-domain approach applied to the standard Tofts equation to speed-up the process of curve-fitting in order to obtain the pharmacokinetic parameters. The results obtained show that using the frequency domain approach, the process of curve fitting is computationally more efficient compared to the time-domain approach.

The influence of acetabular bone cracks in the press-fit hip replacement: Numerical and experimental analysis.

PubMed

Ramos, A; Duarte, R J; Relvas, C; Completo, A; Simões, J A

2013-07-01

The press-fit hip acetabular prosthesis implantation can cause crack formation in the thin regions surrounding the acetabular. As a consequence the presence of cracks in this region can lead to poor fixation and fibrous tissue formation. Numerical and experimental models of commercial press-fit hip replacements were developed to compare the behavior between the intact and implanted joints. Numerical models with an artificial crack and without crack were considered. The iliac and the femur were created through 3D geometry acquisition based on composite human replicas and 3D-Finite Element models were generated. The mechanical behavior was assessed numerically and experimentally considering the principal strains. The comparison between Finite Element model predictions and experimental measurements revealed a maximum difference of 9%. Similar distribution of the principal strains around the acetabular cavity was obtained for the intact and implanted models. When comparing the Von Mises stresses, it is possible to observe that the intact model is the one that presents the highest stress values in the entire acetabular cavity surface. The crack in the posterior side changes significantly the principal strain distribution, suggesting bone loss after hip replacement. Relatively to micromotions, these were higher on the superior side of the acetabular cavity and can change the implant stability and bone ingrowth. Copyright © 2013 Elsevier Ltd. All rights reserved.

The association between anthropometric measures and lung function in a population-based study of Canadian adults.

PubMed

Rowe, A; Hernandez, P; Kuhle, S; Kirkland, S

2017-10-01

Decreased lung function has health impacts beyond diagnosable lung disease. It is therefore important to understand the factors that may influence even small changes in lung function including obesity, physical fitness and physical activity. The aim of this study was to determine the anthropometric measure most useful in examining the association with lung function and to determine how physical activity and physical fitness influence this association. The current study used cross-sectional data on 4662 adults aged 40-79 years from the Canadian Health Measures Survey Cycles 1 and 2. Linear regression models were used to examine the association between the anthropometric and lung function measures (forced expiratory volume in 1 s [FEV 1 ] and forced vital capacity [FVC]); R 2 values were compared among models. Physical fitness and physical activity terms were added to the models and potential confounding was assessed. Models using sum of 5 skinfolds and waist circumference consistently had the highest R 2 values for FEV 1 and FVC, while models using body mass index consistently had among the lowest R 2 values for FEV 1 and FVC and for men and women. Physical activity and physical fitness were confounders of the relationships between waist circumference and the lung function measures. Waist circumference remained a significant predictor of FVC but not FEV 1 after adjustment for physical activity or physical fitness. Waist circumference is an important predictor of lung function. Physical activity and physical fitness should be considered as potential confounders of the relationship between anthropometric measures and lung function. Copyright © 2017. Published by Elsevier Ltd.

Hot Dust in Panchromatic SED Fitting: Identification of Active Galactic Nuclei and Improved Galaxy Properties

NASA Astrophysics Data System (ADS)

Leja, Joel; Johnson, Benjamin D.; Conroy, Charlie; van Dokkum, Pieter

2018-02-01

Forward modeling of the full galaxy SED is a powerful technique, providing self-consistent constraints on stellar ages, dust properties, and metallicities. However, the accuracy of these results is contingent on the accuracy of the model. One significant source of uncertainty is the contribution of obscured AGN, as they are relatively common and can produce substantial mid-IR (MIR) emission. Here we include emission from dusty AGN torii in the Prospector SED-fitting framework, and fit the UV–IR broadband photometry of 129 nearby galaxies. We find that 10% of the fitted galaxies host an AGN contributing >10% of the observed galaxy MIR luminosity. We demonstrate the necessity of this AGN component in the following ways. First, we compare observed spectral features to spectral features predicted from our model fit to the photometry. We find that the AGN component greatly improves predictions for observed Hα and Hβ luminosities, as well as mid-infrared Akari and Spitzer/IRS spectra. Second, we show that inclusion of the AGN component changes stellar ages and SFRs by up to a factor of 10, and dust attenuations by up to a factor of 2.5. Finally, we show that the strength of our model AGN component correlates with independent AGN indicators, suggesting that these galaxies truly host AGN. Notably, only 46% of the SED-detected AGN would be detected with a simple MIR color selection. Based on these results, we conclude that SED models which fit MIR data without AGN components are vulnerable to substantial bias in their derived parameters.

How do physicians become medical experts? A test of three competing theories: distinct domains, independent influence and encapsulation models.

PubMed

Violato, Claudio; Gao, Hong; O'Brien, Mary Claire; Grier, David; Shen, E

2018-05-01

The distinction between basic sciences and clinical knowledge which has led to a theoretical debate on how medical expertise is developed has implications for medical school and lifelong medical education. This longitudinal, population based observational study was conducted to test the fit of three theories-knowledge encapsulation, independent influence, distinct domains-of the development of medical expertise employing structural equation modelling. Data were collected from 548 physicians (292 men-53.3%; 256 women-46.7%; mean age = 24.2 years on admission) who had graduated from medical school 2009-2014. They included (1) Admissions data of undergraduate grade point average and Medical College Admission Test sub-test scores, (2) Course performance data from years 1, 2, and 3 of medical school, and (3) Performance on the NBME exams (i.e., Step 1, Step 2 CK, and Step 3). Statistical fit indices (Goodness of Fit Index-GFI; standardized root mean squared residual-SRMR; root mean squared error of approximation-RSMEA) and comparative fit [Formula: see text] of three theories of cognitive development of medical expertise were used to assess model fit. There is support for the knowledge encapsulation three factor model of clinical competency (GFI = 0.973, SRMR = 0.043, RSMEA = 0.063) which had superior fit indices to both the independent influence and distinct domains theories ([Formula: see text] vs [Formula: see text] [[Formula: see text

Numerical modelling of the Black Sea eigen-oscillations on a curvilinear boundary fitted coordinate system

NASA Astrophysics Data System (ADS)

Koychev Demirov, Encho

1994-12-01

The paper presents a numerical solution of barotropic and two-layer eigen-oscillation problems for the Black Sea on a boundary fitted coordinate system. This solution is compared with model and empirical data obtained by other workers. Frequencies of the eigen-oscillations found by the numerical solution of spectral problem are compared with the data obtained by spectral analysis of the sea-level oscillations measured near the town of Achtopol and Cape Irakli in stormy sea on 17-21 February 1979. Extreme oscillations of the sea-level result from resonant amplifications of three eigenmodes of the Black Sea of 68.3 -1, 36.6 -1 and 27.3 -1 cycles h -1 frequency.

A back-fitting algorithm to improve real-time flood forecasting

NASA Astrophysics Data System (ADS)

Zhang, Xiaojing; Liu, Pan; Cheng, Lei; Liu, Zhangjun; Zhao, Yan

2018-07-01

Real-time flood forecasting is important for decision-making with regards to flood control and disaster reduction. The conventional approach involves a postprocessor calibration strategy that first calibrates the hydrological model and then estimates errors. This procedure can simulate streamflow consistent with observations, but obtained parameters are not optimal. Joint calibration strategies address this issue by refining hydrological model parameters jointly with the autoregressive (AR) model. In this study, five alternative schemes are used to forecast floods. Scheme I uses only the hydrological model, while scheme II includes an AR model for error correction. In scheme III, differencing is used to remove non-stationarity in the error series. A joint inference strategy employed in scheme IV calibrates the hydrological and AR models simultaneously. The back-fitting algorithm, a basic approach for training an additive model, is adopted in scheme V to alternately recalibrate hydrological and AR model parameters. The performance of the five schemes is compared with a case study of 15 recorded flood events from China's Baiyunshan reservoir basin. Our results show that (1) schemes IV and V outperform scheme III during the calibration and validation periods and (2) scheme V is inferior to scheme IV in the calibration period, but provides better results in the validation period. Joint calibration strategies can therefore improve the accuracy of flood forecasting. Additionally, the back-fitting recalibration strategy produces weaker overcorrection and a more robust performance compared with the joint inference strategy.

The Structure of Psychopathology: Toward an Expanded Quantitative Empirical Model

PubMed Central

Wright, Aidan G.C.; Krueger, Robert F.; Hobbs, Megan J.; Markon, Kristian E.; Eaton, Nicholas R.; Slade, Tim

2013-01-01

There has been substantial recent interest in the development of a quantitative, empirically based model of psychopathology. However, the majority of pertinent research has focused on analyses of diagnoses, as described in current official nosologies. This is a significant limitation because existing diagnostic categories are often heterogeneous. In the current research, we aimed to redress this limitation of the existing literature, and to directly compare the fit of categorical, continuous, and hybrid (i.e., combined categorical and continuous) models of syndromes derived from indicators more fine-grained than diagnoses. We analyzed data from a large representative epidemiologic sample (the 2007 Australian National Survey of Mental Health and Wellbeing; N = 8,841). Continuous models provided the best fit for each syndrome we observed (Distress, Obsessive Compulsivity, Fear, Alcohol Problems, Drug Problems, and Psychotic Experiences). In addition, the best fitting higher-order model of these syndromes grouped them into three broad spectra: Internalizing, Externalizing, and Psychotic Experiences. We discuss these results in terms of future efforts to refine emerging empirically based, dimensional-spectrum model of psychopathology, and to use the model to frame psychopathology research more broadly. PMID:23067258

Local Intrinsic Dimension Estimation by Generalized Linear Modeling.

PubMed

Hino, Hideitsu; Fujiki, Jun; Akaho, Shotaro; Murata, Noboru

2017-07-01

We propose a method for intrinsic dimension estimation. By fitting the power of distance from an inspection point and the number of samples included inside a ball with a radius equal to the distance, to a regression model, we estimate the goodness of fit. Then, by using the maximum likelihood method, we estimate the local intrinsic dimension around the inspection point. The proposed method is shown to be comparable to conventional methods in global intrinsic dimension estimation experiments. Furthermore, we experimentally show that the proposed method outperforms a conventional local dimension estimation method.

a Middle-Ear Reverse Transfer Function Computed from Vibration Measurements of Otoacoustic Emissions on the Ear Drum of the Guinea PIG

NASA Astrophysics Data System (ADS)

Dalhoff, Ernst; Turcanu, Diana; Gummer, Anthony W.

2009-02-01

Using distortion products measured as vibration of the umbo and as sound pressure in the ear canal of guinea pigs, we calculated the corresponding reverse transfer function. We compare the measurements with a middle-ear model taken from the literature and adapted to the guinea pig. A reasonable fit could be achieved. We conclude that the reverse transfer function will be useful to aid fitting a middle-ear model to measured transfer functions of human subjects.

Competition-based phenotyping reveals a fitness cost for maintaining phycobilisomes under fluctuating light in the cyanobacterium Fremyella diplosiphon

DOE PAGES

Agostoni, Marco; Lucker, Ben F.; Smith, Matthew A. Y.; ...

2016-02-21

Phycobilisomes (PBSs) are pigment-rich super-complexes required for efficient harvest and transfer of light energy to photosynthetic reaction centers of cyanobacteria. The model cyanobacterium Fremyella diplosiphon is able to adjust PBS pigmentation and size in response to the prevailing light spectrum through a process called complementary chromatic acclimation to optimize spectral light absorption, concomitantly optimizing photosynthesis and growth. We explored the fitness costs versus advantages of modulating antennae size and composition under sinusoidal continuous and fluctuating light conditions in F. diplosiphon by comparing growth of wild-type (WT) cells with a mutant strain deficient in PBSs in both monoculture and polyculture conditions.more » Comparative analyses of WT and the PBS-deficient FdCh1 strain under continuous vs. fluctuating sinusoidal light suggest a potential fitness advantage for maintaining PBSs in WT cells during continuous light and a fitness cost during transitions to and acclimation under fluctuating light. Here, we explored the physiological changes correlated with the observed differential growth to understand the dynamics and biochemical bases of comparative fitness of distinct strains under defined growth conditions. Wild-type F. diplosiphon appears to accumulate longer PBS rods and exhibits higher oxidative stress under fluctuating light conditions than continuous sinusoidal light, which may impact responses and the fitness of cells that do not adapt to rapid changes in external light.« less

Non-Targeted Effects and the Dose Response for Heavy Ion Tumorigenesis

NASA Technical Reports Server (NTRS)

Chappelli, Lori J.; Cucinotta, Francis A.

2010-01-01

BACKGROUND: There is no human epidemiology data available to estimate the heavy ion cancer risks experienced by astronauts in space. Studies of tumor induction in mice are a necessary step to estimate risks to astronauts. Previous experimental data can be better utilized to model dose response for heavy ion tumorigenesis and plan future low dose studies. DOSE RESPONSE MODELS: The Harderian Gland data of Alpen et al.[1-3] was re-analyzed [4] using non-linear least square regression. The data set measured the induction of Harderian gland tumors in mice by high-energy protons, helium, neon, iron, niobium and lanthanum with LET s ranging from 0.4 to 950 keV/micron. We were able to strengthen the individual ion models by combining data for all ions into a model that relates both radiation dose and LET for the ion to tumor prevalence. We compared models based on Targeted Effects (TE) to one motivated by Non-targeted Effects (NTE) that included a bystander term that increased tumor induction at low doses non-linearly. When comparing fitted models to the experimental data, we considered the adjusted R2, the Akaike Information Criteria (AIC), and the Bayesian Information Criteria (BIC) to test for Goodness of fit.In the adjusted R2test, the model with the highest R2values provides a better fit to the available data. In the AIC and BIC tests, the model with the smaller values of the summary value provides the better fit. The non-linear NTE models fit the combined data better than the TE models that are linear at low doses. We evaluated the differences in the relative biological effectiveness (RBE) and found the NTE model provides a higher RBE at low dose compared to the TE model. POWER ANALYSIS: The final NTE model estimates were used to simulate example data to consider the design of new experiments to detect NTE at low dose for validation. Power and sample sizes were calculated for a variety of radiation qualities including some not considered in the Harderian Gland data set and with different background tumor incidences. We considered different experimental designs with varying number of doses and varying low doses dependant on the LET of the radiation. The optimal design to detect a NTE for an individual ion had 4 doses equally spaced below a maximal dose where bending due to cell sterilization was < 2%. For example at 100 keV/micron we would irradiate at 0.03 Gy, 0.065 Gy, 0.13 Gy, and 0.26 Gy and require 850 mice including a control dose for a sensitivity to detect NTE with 80% power. Sample sizes could be improved by combining ions similar to the methods used with the Harderian Gland data.

Implications of a Need-Press-Competence Model for Institutionalized Elderly.

ERIC Educational Resources Information Center

Wirzbicki, Philip J.; Smith, Barry D.

The predictive utility of a proposed need-press competence (NPC) model of satisfaction was compared with that of the traditional need-press fit model. Structured interviews with 30 residents from two nursing homes provided measures of needs, press, competence, and satisfaction. The NPC model was a better predictor of expressed satisfaction than…

Testing a Mediational Model of Communication Among Medical Staff and Families of Cancer Patients

ERIC Educational Resources Information Center

Gionta, Dana A.; Harlow, Lisa L.; Loitman, Jane E.; Leeman, Joanne M.

2005-01-01

Three structural equation models of communication between family members and medical staff were examined to understand relations among staff accessibility, inhibitory family attitudes, getting communication needs met, perceived stress, and satisfaction with communication. Compared to full and direct models, a mediational model fit best in which…

Predicting People's Environmental Behaviour: Theory of Planned Behaviour and Model of Responsible Environmental Behaviour

ERIC Educational Resources Information Center

Chao, Yu-Long

2012-01-01

Using different measures of self-reported and other-reported environmental behaviour (EB), two important theoretical models explaining EB--Hines, Hungerford and Tomera's model of responsible environmental behaviour (REB) and Ajzen's theory of planned behaviour (TPB)--were compared regarding the fit between model and data, predictive ability,…

Some Empirical Evidence for Latent Trait Model Selection.

ERIC Educational Resources Information Center

Hutten, Leah R.

The results of this study suggest that for purposes of estimating ability by latent trait methods, the Rasch model compares favorably with the three-parameter logistic model. Using estimated parameters to make predictions about 25 actual number-correct score distributions with samples of 1,000 cases each, those predicted by the Rasch model fit the…

IRT Model Selection Methods for Dichotomous Items

ERIC Educational Resources Information Center

Kang, Taehoon; Cohen, Allan S.

2007-01-01

Fit of the model to the data is important if the benefits of item response theory (IRT) are to be obtained. In this study, the authors compared model selection results using the likelihood ratio test, two information-based criteria, and two Bayesian methods. An example illustrated the potential for inconsistency in model selection depending on…

Improving Measures via Examining the Behavior of Distractors in Multiple-Choice Tests

PubMed Central

Sideridis, Georgios; Tsaousis, Ioannis; Al Harbi, Khaleel

2017-01-01

The purpose of the present article was to illustrate, using an example from a national assessment, the value from analyzing the behavior of distractors in measures that engage the multiple-choice format. A secondary purpose of the present article was to illustrate four remedial actions that can potentially improve the measurement of the construct(s) under study. Participants were 2,248 individuals who took a national examination of chemistry. The behavior of the distractors was analyzed by modeling their behavior within the Rasch model. Potentially informative distractors were (a) further modeled using the partial credit model, (b) split onto separate items and retested for model fit and parsimony, (c) combined to form a “super” item or testlet, and (d) reexamined after deleting low-ability individuals who likely guessed on those informative, albeit erroneous, distractors. Results indicated that all but the item split strategies were associated with better model fit compared with the original model. The best fitted model, however, involved modeling and crediting informative distractors via the partial credit model or eliminating the responses of low-ability individuals who likely guessed on informative distractors. The implications, advantages, and disadvantages of modeling informative distractors for measurement purposes are discussed. PMID:29795904

Intravoxel incoherent motion modeling in the kidneys: Comparison of mono-, bi-, and triexponential fit.

PubMed

van Baalen, Sophie; Leemans, Alexander; Dik, Pieter; Lilien, Marc R; Ten Haken, Bennie; Froeling, Martijn

2017-07-01

To evaluate if a three-component model correctly describes the diffusion signal in the kidney and whether it can provide complementary anatomical or physiological information about the underlying tissue. Ten healthy volunteers were examined at 3T, with T 2 -weighted imaging, diffusion tensor imaging (DTI), and intravoxel incoherent motion (IVIM). Diffusion tensor parameters (mean diffusivity [MD] and fractional anisotropy [FA]) were obtained by iterative weighted linear least squares fitting of the DTI data and mono-, bi-, and triexponential fit parameters (D 1 , D 2 , D 3 , f fast2 , f fast3 , and f interm ) using a nonlinear fit of the IVIM data. Average parameters were calculated for three regions of interest (ROIs) (cortex, medulla, and rest) and from fiber tractography. Goodness of fit was assessed with adjusted R 2 ( Radj2) and the Shapiro-Wilk test was used to test residuals for normality. Maps of diffusion parameters were also visually compared. Fitting the diffusion signal was feasible for all models. The three-component model was best able to describe fast signal decay at low b values (b < 50), which was most apparent in Radj2 of the ROI containing high diffusion signals (ROI rest ), which was 0.42 ± 0.14, 0.61 ± 0.11, 0.77 ± 0.09, and 0.81 ± 0.08 for DTI, one-, two-, and three-component models, respectively, and in visual comparison of the fitted and measured S 0 . None of the models showed significant differences (P > 0.05) between the diffusion constant of the medulla and cortex, whereas the f fast component of the two and three-component models were significantly different (P < 0.001). Triexponential fitting is feasible for the diffusion signal in the kidney, and provides additional information. 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:228-239. © 2016 The Authors Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

Random regression models using Legendre polynomials or linear splines for test-day milk yield of dairy Gyr (Bos indicus) cattle.

PubMed

Pereira, R J; Bignardi, A B; El Faro, L; Verneque, R S; Vercesi Filho, A E; Albuquerque, L G

2013-01-01

Studies investigating the use of random regression models for genetic evaluation of milk production in Zebu cattle are scarce. In this study, 59,744 test-day milk yield records from 7,810 first lactations of purebred dairy Gyr (Bos indicus) and crossbred (dairy Gyr × Holstein) cows were used to compare random regression models in which additive genetic and permanent environmental effects were modeled using orthogonal Legendre polynomials or linear spline functions. Residual variances were modeled considering 1, 5, or 10 classes of days in milk. Five classes fitted the changes in residual variances over the lactation adequately and were used for model comparison. The model that fitted linear spline functions with 6 knots provided the lowest sum of residual variances across lactation. On the other hand, according to the deviance information criterion (DIC) and bayesian information criterion (BIC), a model using third-order and fourth-order Legendre polynomials for additive genetic and permanent environmental effects, respectively, provided the best fit. However, the high rank correlation (0.998) between this model and that applying third-order Legendre polynomials for additive genetic and permanent environmental effects, indicates that, in practice, the same bulls would be selected by both models. The last model, which is less parameterized, is a parsimonious option for fitting dairy Gyr breed test-day milk yield records. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Patterns of ecosystem services supply across farm properties: Implications for ecosystem services-based policy incentives.

PubMed

Nahuelhual, Laura; Benra, Felipe; Laterra, Pedro; Marin, Sandra; Arriagada, Rodrigo; Jullian, Cristobal

2018-09-01

In developing countries, the protection of biodiversity and ecosystem services (ES) rests on the hands of millions of small landowners that coexist with large properties, in a reality of highly unequal land distribution. Guiding the effective allocation of ES-based incentives in such contexts requires researchers and practitioners to tackle a largely overlooked question: for a given targeted area, will single large farms or several small ones provide the most ES supply? The answer to this question has important implications for conservation planning and rural development alike, which transcend efficiency to involve equity issues. We address this question by proposing and testing ES supply-area relations (ESSARs) around three basic hypothesized models, characterized by constant (model 1), increasing (model 2), and decreasing increments (model 3) of ES supply per unit of area or ES "productivity". Data to explore ESSARs came from 3384 private landholdings located in southern Chile ranging from 0.5ha to over 30,000ha and indicators of four ES (forage, timber, recreation opportunities, and water supply). Forage provision best fit model 3, which suggests that targeting several small farms to provide this ES should be a preferred choice, as compared to a single large farm. Timber provision best fit model 2, suggesting that in this case targeting a single large farm would be a more effective choice. Recreation opportunities best fit model 1, which indicates that several small or a single large farm of a comparable size would be equally effective in delivering this ES. Water provision fit model 1 or model 2 depending on the study site. The results corroborate that ES provision is not independent from property area and therefore understanding ESSARs is a necessary condition for setting conservation incentives that are both efficient (deliver the highest conservation outcome at the least cost) and fair for landowners. Copyright © 2018 Elsevier B.V. All rights reserved.

Inference of gene regulatory networks from genome-wide knockout fitness data

PubMed Central

Wang, Liming; Wang, Xiaodong; Arkin, Adam P.; Samoilov, Michael S.

2013-01-01

Motivation: Genome-wide fitness is an emerging type of high-throughput biological data generated for individual organisms by creating libraries of knockouts, subjecting them to broad ranges of environmental conditions, and measuring the resulting clone-specific fitnesses. Since fitness is an organism-scale measure of gene regulatory network behaviour, it may offer certain advantages when insights into such phenotypical and functional features are of primary interest over individual gene expression. Previous works have shown that genome-wide fitness data can be used to uncover novel gene regulatory interactions, when compared with results of more conventional gene expression analysis. Yet, to date, few algorithms have been proposed for systematically using genome-wide mutant fitness data for gene regulatory network inference. Results: In this article, we describe a model and propose an inference algorithm for using fitness data from knockout libraries to identify underlying gene regulatory networks. Unlike most prior methods, the presented approach captures not only structural, but also dynamical and non-linear nature of biomolecular systems involved. A state–space model with non-linear basis is used for dynamically describing gene regulatory networks. Network structure is then elucidated by estimating unknown model parameters. Unscented Kalman filter is used to cope with the non-linearities introduced in the model, which also enables the algorithm to run in on-line mode for practical use. Here, we demonstrate that the algorithm provides satisfying results for both synthetic data as well as empirical measurements of GAL network in yeast Saccharomyces cerevisiae and TyrR–LiuR network in bacteria Shewanella oneidensis. Availability: MATLAB code and datasets are available to download at http://www.duke.edu/∼lw174/Fitness.zip and http://genomics.lbl.gov/supplemental/fitness-bioinf/ Contact: wangx@ee.columbia.edu or mssamoilov@lbl.gov Supplementary information: Supplementary data are available at Bioinformatics online PMID:23271269

PTSD's latent structure in Malaysian tsunami victims: assessing the newly proposed Dysphoric Arousal model.

PubMed

Armour, Cherie; Raudzah Ghazali, Siti; Elklit, Ask

2013-03-30

The underlying latent structure of Posttraumatic Stress Disorder (PTSD) is widely researched. However, despite a plethora of factor analytic studies, no single model has consistently been shown as superior to alternative models. The two most often supported models are the Emotional Numbing and the Dysphoria models. However, a recently proposed five-factor Dysphoric Arousal model has been gathering support over and above existing models. Data for the current study were gathered from Malaysian Tsunami survivors (N=250). Three competing models (Emotional Numbing/Dysphoria/Dysphoric Arousal) were specified and estimated using Confirmatory Factor Analysis (CFA). The Dysphoria model provided superior fit to the data compared to the Emotional Numbing model. However, using chi-square difference tests, the Dysphoric Arousal model showed a superior fit compared to both the Emotional Numbing and Dysphoria models. In conclusion, the current results suggest that the Dysphoric Arousal model better represents PTSD's latent structure and that items measuring sleeping difficulties, irritability/anger and concentration difficulties form a separate, unique PTSD factor. These results are discussed in relation to the role of Hyperarousal in PTSD's on-going symptom maintenance and in relation to the DSM-5. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Full Bayes Poisson gamma, Poisson lognormal, and zero inflated random effects models: Comparing the precision of crash frequency estimates.

PubMed

Aguero-Valverde, Jonathan

2013-01-01

In recent years, complex statistical modeling approaches have being proposed to handle the unobserved heterogeneity and the excess of zeros frequently found in crash data, including random effects and zero inflated models. This research compares random effects, zero inflated, and zero inflated random effects models using a full Bayes hierarchical approach. The models are compared not just in terms of goodness-of-fit measures but also in terms of precision of posterior crash frequency estimates since the precision of these estimates is vital for ranking of sites for engineering improvement. Fixed-over-time random effects models are also compared to independent-over-time random effects models. For the crash dataset being analyzed, it was found that once the random effects are included in the zero inflated models, the probability of being in the zero state is drastically reduced, and the zero inflated models degenerate to their non zero inflated counterparts. Also by fixing the random effects over time the fit of the models and the precision of the crash frequency estimates are significantly increased. It was found that the rankings of the fixed-over-time random effects models are very consistent among them. In addition, the results show that by fixing the random effects over time, the standard errors of the crash frequency estimates are significantly reduced for the majority of the segments on the top of the ranking. Copyright © 2012 Elsevier Ltd. All rights reserved.

Application of zero-inflated poisson mixed models in prognostic factors of hepatitis C.

PubMed

Akbarzadeh Baghban, Alireza; Pourhoseingholi, Asma; Zayeri, Farid; Jafari, Ali Akbar; Alavian, Seyed Moayed

2013-01-01

In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C. The data was collected from a longitudinal study during 2005-2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared. The results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors. The results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.

Comparing two-zone models of dust exposure.

PubMed

Jones, Rachael M; Simmons, Catherine E; Boelter, Fred W

2011-09-01

The selection and application of mathematical models to work tasks is challenging. Previously, we developed and evaluated a semi-empirical two-zone model that predicts time-weighted average (TWA) concentrations (Ctwa) of dust emitted during the sanding of drywall joint compound. Here, we fit the emission rate and random air speed variables of a mechanistic two-zone model to testing event data and apply and evaluate the model using data from two field studies. We found that the fitted random air speed values and emission rate were sensitive to (i) the size of the near-field and (ii) the objective function used for fitting, but this did not substantially impact predicted dust Ctwa. The mechanistic model predictions were lower than the semi-empirical model predictions and measured respirable dust Ctwa at Site A but were within an acceptable range. At Site B, a 10.5 m3 room, the mechanistic model did not capture the observed difference between PBZ and area Ctwa. The model predicted uniform mixing and predicted dust Ctwa up to an order of magnitude greater than was measured. We suggest that applications of the mechanistic model be limited to contexts where the near-field volume is very small relative to the far-field volume.

Extending the cost-benefit model of thermoregulation: high-temperature environments.

PubMed

Vickers, Mathew; Manicom, Carryn; Schwarzkopf, Lin

2011-04-01

The classic cost-benefit model of ectothermic thermoregulation compares energetic costs and benefits, providing a critical framework for understanding this process (Huey and Slatkin 1976 ). It considers the case where environmental temperature (T(e)) is less than the selected temperature of the organism (T(sel)), and it predicts that, to minimize increasing energetic costs of thermoregulation as habitat thermal quality declines, thermoregulatory effort should decrease until the lizard thermoconforms. We extended this model to include the case where T(e) exceeds T(sel), and we redefine costs and benefits in terms of fitness to include effects of body temperature (T(b)) on performance and survival. Our extended model predicts that lizards will increase thermoregulatory effort as habitat thermal quality declines, gaining the fitness benefits of optimal T(b) and maximizing the net benefit of activity. Further, to offset the disproportionately high fitness costs of high T(e) compared with low T(e), we predicted that lizards would thermoregulate more effectively at high values of T(e) than at low ones. We tested our predictions on three sympatric skink species (Carlia rostralis, Carlia rubrigularis, and Carlia storri) in hot savanna woodlands and found that thermoregulatory effort increased as thermal quality declined and that lizards thermoregulated most effectively at high values of T(e).

Work Engagement among Rescue Workers: Psychometric Properties of the Portuguese UWES

PubMed Central

Sinval, Jorge; Marques-Pinto, Alexandra; Queirós, Cristina; Marôco, João

2018-01-01

Rescue workers have a stressful and risky occupation where being engaged is crucial to face physical and emotional risks in order to help other persons. This study aims to estimate work engagement levels of rescue workers (namely comparing nurses, firefighters, and police officers) and to assess the validity evidence related to the internal structure of the Portuguese versions of the UWES-17 and UWES-9, namely, dimensionality, measurement invariance between occupational groups, and reliability of the scores. To evaluate the dimensionality, we compared the fit of the three-factor model with the fit of a second-order model. A Portuguese version of the instrument was applied to a convenience sample of 3,887 rescue workers (50% nurses, 39% firefighters, and 11% police officers). Work engagement levels were moderate to high, with firefighters being the highest and nurses being the lowest engaged. Psychometric properties were evaluated in the three-factor original structure revealing acceptable fit to the data in the UWES-17, although the UWES-9 had better psychometric properties. Given the observed statistically significant correlations between the three original factors, we proposed a 2nd hierarchal structure that we named work engagement. The UWES-9 first-order model obtained full uniqueness measurement invariance, and the second-order model obtained partial (metric) second-order invariance. PMID:29403403

Factor structure of a conceptual model of oral health tested among 65-year olds in Norway and Sweden.

PubMed

Astrøm, Anne Nordrehaug; Ekbäck, Gunnar; Ordell, Sven

2010-04-01

No studies have tested oral health-related quality of life models in dentate older adults across different populations. To test the factor structure of oral health outcomes within Gilbert's conceptual model among 65-year olds in Sweden and Norway. It was hypothesized that responses to 14 observed indicators could be explained by three correlated factors, symptom status, functional limitations and oral disadvantages, that each observed oral health indicator would associate more strongly with the factor it is supposed to measure than with competing factors and that the proposed 3-factor structure would possess satisfactory cross-national stability with 65-year olds in Norway and Sweden. In 2007, 6078 Swedish- and 4062 Norwegian adults borne in 1942 completed mailed questionnaires including oral symptoms, functional limitations and the eight item Oral Impacts on Daily Performances inventory. Model generation analysis was restricted to the Norwegian study group and the model achieved was tested without modifications in Swedish 65-year olds. A modified 3-factor solution with cross-loadings, improved the fit to the data compared with a 2-factor- and the initially proposed 3-factor model among the Norwegian [comparative fit index (CFI) = 0.97] and Swedish (CFI = 0.98) participants. All factor loadings for the modified 3-factor model were in the expected direction and were statistically significant at CR > 1. Multiple group confirmatory factor analyses, with Norwegian and Swedish data simultaneously revealed acceptable fit for the unconstrained model (CFI = 0.97), whereas unconstrained and constrained models were statistically significant different in nested model comparison. Within construct validity of Gilbert's model was supported with Norwegian and Swedish 65-year olds, indicating that the 14-item questionnaire reflected three constructs; symptom status, functional limitation and oral disadvantage. Measurement invariance was confirmed at the level of factor structure, suggesting that the 3-factor model is comparable to some extent across 65-year olds in Norway and Sweden.

Linear and Poisson models for genetic evaluation of tick resistance in cross-bred Hereford x Nellore cattle.

PubMed

Ayres, D R; Pereira, R J; Boligon, A A; Silva, F F; Schenkel, F S; Roso, V M; Albuquerque, L G

2013-12-01

Cattle resistance to ticks is measured by the number of ticks infesting the animal. The model used for the genetic analysis of cattle resistance to ticks frequently requires logarithmic transformation of the observations. The objective of this study was to evaluate the predictive ability and goodness of fit of different models for the analysis of this trait in cross-bred Hereford x Nellore cattle. Three models were tested: a linear model using logarithmic transformation of the observations (MLOG); a linear model without transformation of the observations (MLIN); and a generalized linear Poisson model with residual term (MPOI). All models included the classificatory effects of contemporary group and genetic group and the covariates age of animal at the time of recording and individual heterozygosis, as well as additive genetic effects as random effects. Heritability estimates were 0.08 ± 0.02, 0.10 ± 0.02 and 0.14 ± 0.04 for MLIN, MLOG and MPOI models, respectively. The model fit quality, verified by deviance information criterion (DIC) and residual mean square, indicated fit superiority of MPOI model. The predictive ability of the models was compared by validation test in independent sample. The MPOI model was slightly superior in terms of goodness of fit and predictive ability, whereas the correlations between observed and predicted tick counts were practically the same for all models. A higher rank correlation between breeding values was observed between models MLOG and MPOI. Poisson model can be used for the selection of tick-resistant animals. © 2013 Blackwell Verlag GmbH.

The manifestation of depression in the context of urban poverty: a factor analysis of the Children's Depression Inventory in low-income urban youth.

PubMed

Taylor, Jeremy J; Grant, Kathryn E; Amrhein, Kelly; Carter, Jocelyn Smith; Farahmand, Farahnaz; Harrison, Aubrey; Thomas, Kina J; Carleton, Russell A; Lugo-Hernandez, Eduardo; Katz, Brian N

2014-12-01

The current study used confirmatory factor analysis (CFA) to compare the fit of 2 factor structures for the Children's Depression Inventory (CDI) in an urban community sample of low-income youth. Results suggest that the 6-factor model developed by Craighead and colleagues (1998) was a strong fit to the pattern of symptoms reported by low-income urban youth and was a superior fit with these data than the original 5-factor model of the CDI (Kovacs, 1992). Additionally, results indicated that all 6 factors from the Craighead model contributed to the measurement of depression, including School Problems and Externalizing Problems especially for older adolescents. This pattern of findings may reflect distinct contextual influences of urban poverty on the manifestation and measurement of depression in youth. (c) 2014 APA, all rights reserved.

Efficient parallel implementation of active appearance model fitting algorithm on GPU.

PubMed

Wang, Jinwei; Ma, Xirong; Zhu, Yuanping; Sun, Jizhou

2014-01-01

The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia's GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.

Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU

PubMed Central

Wang, Jinwei; Ma, Xirong; Zhu, Yuanping; Sun, Jizhou

2014-01-01

The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia's GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures. PMID:24723812

Structure of four executive functioning tests in healthy older adults.

PubMed

de Frias, Cindy M; Dixon, Roger A; Strauss, Esther

2006-03-01

The authors examined the factor structure of 4 indicators of executive functioning derived from 2 new (i.e., Hayling and Brixton) and 2 traditional (i.e., Stroop and Color Trails) tests. Data were from a cross-sectional sample of 55- to 85-year-old healthy adults (N=427) from the Victoria Longitudinal Study. Confirmatory factor analysis (LISREL 8.52) tested both a 2-factor model of Inhibition (Hayling, Stroop) and Shifting (Brixton, Color Trails) and a single-factor model. The 2-factor model did not fit the data because the covariance matrix of the factors was not positive definite. The single-factor model fit the data well, chi(2)(2, N=427)=0.32, p=.85, root-mean-square error of approximation (RMSEA)=.00, comparative fit index (CFI)=1.00, goodness-of-fit index (GFI)=1.00. Moreover, the single-factor structure of executive functioning was invariant (configural and metric) across gender, and invariant (configural with limited metric) across age. Structural relations showed that poorer executive functioning performance was related to older age and lower fluid intelligence, chi(2)(11, N=418)=23.04, p=.02, RMSEA=.05, CFI=.97, GFI=.98.

Outcomes of a Peer Mentor Implemented Fitness Program in Older Adults: A Quasi-Randomized Controlled Trial

PubMed Central

Dorgo, Sandor; King, George A.; Bader, Julia O.; Limon, John S.

2013-01-01

Objectives To investigate the effectiveness of different applications of mentoring in an older adult exercise program, this study compared the physical fitness scores, the retention and participation rates of older adults trained by student mentors, peer mentors, peer mentors working independently of the researchers, and a non-exercising control group. Methods 106 older adults were recruited and assigned to one of the groups using quasi-randomization. All three experimental groups completed a 14-week intervention. Pre- and post-training assessments of fitness were completed, and retention and participation rates were compared. Results High retention and participation rates, as well as significant improvements in fitness scores from baseline to post-test were observed in all three mentored groups. While the control group showed improvement only in one fitness test, subjects in the mentored groups improved similarly in all measures, regardless of the type of mentoring received. Discussion These findings indicated effectiveness of the peer mentor model and suggested that with adequate preparation peer mentors may be capable of guiding older adult participants effectively without assistance from professional staff. PMID:23279966

Quantitative photoplethysmography: Lambert-Beer law or inverse function incorporating light scatter.

PubMed

Cejnar, M; Kobler, H; Hunyor, S N

1993-03-01

Finger blood volume is commonly determined from measurement of infra-red (IR) light transmittance using the Lambert-Beer law of light absorption derived for use in non-scattering media, even when such transmission involves light scatter around the phalangeal bone. Simultaneous IR transmittance and finger volume were measured over the full dynamic range of vascular volumes in seven subjects and outcomes compared with data fitted according to the Lambert-Beer exponential function and an inverse function derived for light attenuation by scattering materials. Curves were fitted by the least-squares method and goodness of fit was compared using standard errors of estimate (SEE). The inverse function gave a better data fit in six of the subjects: mean SEE 1.9 (SD 0.7, range 0.7-2.8) and 4.6 (2.2, 2.0-8.0) respectively (p < 0.02, paired t-test). Thus, when relating IR transmittance to blood volume, as occurs in the finger during measurements of arterial compliance, an inverse function derived from a model of light attenuation by scattering media gives more accurate results than the traditional exponential fit.

External validation of Vascular Study Group of New England risk predictive model of mortality after elective abdominal aorta aneurysm repair in the Vascular Quality Initiative and comparison against established models.

PubMed

Eslami, Mohammad H; Rybin, Denis V; Doros, Gheorghe; Siracuse, Jeffrey J; Farber, Alik

2018-01-01

The purpose of this study is to externally validate a recently reported Vascular Study Group of New England (VSGNE) risk predictive model of postoperative mortality after elective abdominal aortic aneurysm (AAA) repair and to compare its predictive ability across different patients' risk categories and against the established risk predictive models using the Vascular Quality Initiative (VQI) AAA sample. The VQI AAA database (2010-2015) was queried for patients who underwent elective AAA repair. The VSGNE cases were excluded from the VQI sample. The external validation of a recently published VSGNE AAA risk predictive model, which includes only preoperative variables (age, gender, history of coronary artery disease, chronic obstructive pulmonary disease, cerebrovascular disease, creatinine levels, and aneurysm size) and planned type of repair, was performed using the VQI elective AAA repair sample. The predictive value of the model was assessed via the C-statistic. Hosmer-Lemeshow method was used to assess calibration and goodness of fit. This model was then compared with the Medicare, Vascular Governance Northwest model, and Glasgow Aneurysm Score for predicting mortality in VQI sample. The Vuong test was performed to compare the model fit between the models. Model discrimination was assessed in different risk group VQI quintiles. Data from 4431 cases from the VSGNE sample with the overall mortality rate of 1.4% was used to develop the model. The internally validated VSGNE model showed a very high discriminating ability in predicting mortality (C = 0.822) and good model fit (Hosmer-Lemeshow P = .309) among the VSGNE elective AAA repair sample. External validation on 16,989 VQI cases with an overall 0.9% mortality rate showed very robust predictive ability of mortality (C = 0.802). Vuong tests yielded a significant fit difference favoring the VSGNE over then Medicare model (C = 0.780), Vascular Governance Northwest (0.774), and Glasgow Aneurysm Score (0.639). Across the 5 risk quintiles, the VSGNE model predicted observed mortality significantly with great accuracy. This simple VSGNE AAA risk predictive model showed very high discriminative ability in predicting mortality after elective AAA repair among a large external independent sample of AAA cases performed by a diverse array of physicians nationwide. The risk score based on this simple VSGNE model can reliably stratify patients according to their risk of mortality after elective AAA repair better than other established models. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

The self-transcendence scale: an investigation of the factor structure among nursing home patients.

PubMed

Haugan, Gørill; Rannestad, Toril; Garåsen, Helge; Hammervold, Randi; Espnes, Geir Arild

2012-09-01

Self-transcendence, the ability to expand personal boundaries in multiple ways, has been found to provide well-being. The purpose of this study was to examine the dimensionality of the Norwegian version of the Self-Transcendence Scale, which comprises 15 items. Reed's empirical nursing theory of self-transcendence provided the theoretical framework; self-transcendence includes an interpersonal, intrapersonal, transpersonal, and temporal dimension. Cross-sectional data were obtained from a sample of 202 cognitively intact elderly patients in 44 Norwegian nursing homes. Exploratory factor analysis revealed two and four internally consistent dimensions of self-transcendence, explaining 35.3% (two factors) and 50.7% (four factors) of the variance, respectively. Confirmatory factor analysis indicated that the hypothesized two- and four-factor models fitted better than the one-factor model (cx (2), root mean square error of approximation, standardized root mean square residual, normed fit index, nonnormed fit index, comparative fit index, goodness-of-fit index, and adjusted goodness-of-fit index). The findings indicate self-transcendence as a multifactorial construct; at present, we conclude that the two-factor model might be the most accurate and reasonable measure of self-transcendence. This research generates insights in the application of the widely used Self-Transcendence Scale by investigating its psychometric properties by applying a confirmatory factor analysis. It also generates new research-questions on the associations between self-transcendence and well-being.

Determining polarizable force fields with electrostatic potentials from quantum mechanical linear response theory

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

Wang, Hao; Yang, Weitao, E-mail: weitao.yang@duke.edu; Department of Physics, Duke University, Durham, North Carolina 27708

We developed a new method to calculate the atomic polarizabilities by fitting to the electrostatic potentials (ESPs) obtained from quantum mechanical (QM) calculations within the linear response theory. This parallels the conventional approach of fitting atomic charges based on electrostatic potentials from the electron density. Our ESP fitting is combined with the induced dipole model under the perturbation of uniform external electric fields of all orientations. QM calculations for the linear response to the external electric fields are used as input, fully consistent with the induced dipole model, which itself is a linear response model. The orientation of the uniformmore » external electric fields is integrated in all directions. The integration of orientation and QM linear response calculations together makes the fitting results independent of the orientations and magnitudes of the uniform external electric fields applied. Another advantage of our method is that QM calculation is only needed once, in contrast to the conventional approach, where many QM calculations are needed for many different applied electric fields. The molecular polarizabilities obtained from our method show comparable accuracy with those from fitting directly to the experimental or theoretical molecular polarizabilities. Since ESP is directly fitted, atomic polarizabilities obtained from our method are expected to reproduce the electrostatic interactions better. Our method was used to calculate both transferable atomic polarizabilities for polarizable molecular mechanics’ force fields and nontransferable molecule-specific atomic polarizabilities.« less

Vascular input function correction of inflow enhancement for improved pharmacokinetic modeling of liver DCE-MRI.

PubMed

Ning, Jia; Schubert, Tilman; Johnson, Kevin M; Roldán-Alzate, Alejandro; Chen, Huijun; Yuan, Chun; Reeder, Scott B

2018-06-01

To propose a simple method to correct vascular input function (VIF) due to inflow effects and to test whether the proposed method can provide more accurate VIFs for improved pharmacokinetic modeling. A spoiled gradient echo sequence-based inflow quantification and contrast agent concentration correction method was proposed. Simulations were conducted to illustrate improvement in the accuracy of VIF estimation and pharmacokinetic fitting. Animal studies with dynamic contrast-enhanced MR scans were conducted before, 1 week after, and 2 weeks after portal vein embolization (PVE) was performed in the left portal circulation of pigs. The proposed method was applied to correct the VIFs for model fitting. Pharmacokinetic parameters fitted using corrected and uncorrected VIFs were compared between different lobes and visits. Simulation results demonstrated that the proposed method can improve accuracy of VIF estimation and pharmacokinetic fitting. In animal study results, pharmacokinetic fitting using corrected VIFs demonstrated changes in perfusion consistent with changes expected after PVE, whereas the perfusion estimates derived by uncorrected VIFs showed no significant changes. The proposed correction method improves accuracy of VIFs and therefore provides more precise pharmacokinetic fitting. This method may be promising in improving the reliability of perfusion quantification. Magn Reson Med 79:3093-3102, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

On the lorentzian versus Gaussian character of time-domain spin-echo signals from the brain as sampled by means of gradient-echoes: Implications for quantitative transverse relaxation studies.

PubMed

Mulkern, Robert V; Balasubramanian, Mukund; Mitsouras, Dimitrios

2014-07-30

To determine whether Lorentzian or Gaussian intra-voxel frequency distributions are better suited for modeling data acquired with gradient-echo sampling of single spin-echoes for the simultaneous characterization of irreversible and reversible relaxation rates. Clinical studies (e.g., of brain iron deposition) using such acquisition schemes have typically assumed Lorentzian distributions. Theoretical expressions of the time-domain spin-echo signal for intra-voxel Lorentzian and Gaussian distributions were used to fit data from a human brain scanned at both 1.5 Tesla (T) and 3T, resulting in maps of irreversible and reversible relaxation rates for each model. The relative merits of the Lorentzian versus Gaussian model were compared by means of quality of fit considerations. Lorentzian fits were equivalent to Gaussian fits primarily in regions of the brain where irreversible relaxation dominated. In the multiple brain regions where reversible relaxation effects become prominent, however, Gaussian fits were clearly superior. The widespread assumption that a Lorentzian distribution is suitable for quantitative transverse relaxation studies of the brain should be reconsidered, particularly at 3T and higher field strengths as reversible relaxation effects become more prominent. Gaussian distributions offer alternate fits of experimental data that should prove quite useful in general. Magn Reson Med, 2014. © 2014 Wiley Periodicals, Inc. © 2014 Wiley Periodicals, Inc.

Photometric survey, modelling, and scaling of long-period and low-amplitude asteroids

NASA Astrophysics Data System (ADS)

Marciniak, A.; Bartczak, P.; Müller, T.; Sanabria, J. J.; Alí-Lagoa, V.; Antonini, P.; Behrend, R.; Bernasconi, L.; Bronikowska, M.; Butkiewicz-Bąk, M.; Cikota, A.; Crippa, R.; Ditteon, R.; Dudziński, G.; Duffard, R.; Dziadura, K.; Fauvaud, S.; Geier, S.; Hirsch, R.; Horbowicz, J.; Hren, M.; Jerosimic, L.; Kamiński, K.; Kankiewicz, P.; Konstanciak, I.; Korlevic, P.; Kosturkiewicz, E.; Kudak, V.; Manzini, F.; Morales, N.; Murawiecka, M.; Ogłoza, W.; Oszkiewicz, D.; Pilcher, F.; Polakis, T.; Poncy, R.; Santana-Ros, T.; Siwak, M.; Skiff, B.; Sobkowiak, K.; Stoss, R.; Żejmo, M.; Żukowski, K.

2018-02-01

Context. The available set of spin and shape modelled asteroids is strongly biased against slowly rotating targets and those with low lightcurve amplitudes. This is due to the observing selection effects. As a consequence, the current picture of asteroid spin axis distribution, rotation rates, radiometric properties, or aspects related to the object's internal structure might be affected too. Aims: To counteract these selection effects, we are running a photometric campaign of a large sample of main belt asteroids omitted in most previous studies. Using least chi-squared fitting we determined synodic rotation periods and verified previous determinations. When a dataset for a given target was sufficiently large and varied, we performed spin and shape modelling with two different methods to compare their performance. Methods: We used the convex inversion method and the non-convex SAGE algorithm, applied on the same datasets of dense lightcurves. Both methods search for the lowest deviations between observed and modelled lightcurves, though using different approaches. Unlike convex inversion, the SAGE method allows for the existence of valleys and indentations on the shapes based only on lightcurves. Results: We obtain detailed spin and shape models for the first five targets of our sample: (159) Aemilia, (227) Philosophia, (329) Svea, (478) Tergeste, and (487) Venetia. When compared to stellar occultation chords, our models obtained an absolute size scale and major topographic features of the shape models were also confirmed. When applied to thermophysical modelling (TPM), they provided a very good fit to the infrared data and allowed their size, albedo, and thermal inertia to be determined. Conclusions: Convex and non-convex shape models provide comparable fits to lightcurves. However, some non-convex models fit notably better to stellar occultation chords and to infrared data in sophisticated thermophysical modelling (TPM). In some cases TPM showed strong preference for one of the spin and shape solutions. Also, we confirmed that slowly rotating asteroids tend to have higher-than-average values of thermal inertia, which might be caused by properties of the surface layers underlying the skin depth. The photometric data is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/610/A7

Adaptive evolutionary walks require neutral intermediates in RNA fitness landscapes.

PubMed

Rendel, Mark D

2011-01-01

In RNA fitness landscapes with interconnected networks of neutral mutations, neutral precursor mutations can play an important role in facilitating the accessibility of epistatic adaptive mutant combinations. I use an exhaustively surveyed fitness landscape model based on short sequence RNA genotypes (and their secondary structure phenotypes) to calculate the minimum rate at which mutants initially appearing as neutral are incorporated into an adaptive evolutionary walk. I show first, that incorporating neutral mutations significantly increases the number of point mutations in a given evolutionary walk when compared to estimates from previous adaptive walk models. Second, that incorporating neutral mutants into such a walk significantly increases the final fitness encountered on that walk - indeed evolutionary walks including neutral steps often reach the global optimum in this model. Third, and perhaps most importantly, evolutionary paths of this kind are often extremely winding in their nature and have the potential to undergo multiple mutations at a given sequence position within a single walk; the potential of these winding paths to mislead phylogenetic reconstruction is briefly considered. Copyright © 2010 Elsevier Inc. All rights reserved.

Impact of a Higgs boson at a mass of 126 GeV on the standard model with three and four fermion generations.

PubMed

Eberhardt, Otto; Herbert, Geoffrey; Lacker, Heiko; Lenz, Alexander; Menzel, Andreas; Nierste, Ulrich; Wiebusch, Martin

2012-12-14

We perform a comprehensive statistical analysis of the standard model (SM) with three and four generations using the latest Higgs search results from LHC and Tevatron, the electroweak precision observables measured at LEP and SLD, and the latest determinations of M(W), m(t), and α(s). For the three-generation case we analyze the tensions in the electroweak fit by removing individual observables from the fit and comparing their predicted values with the measured ones. In particular, we discuss the impact of the Higgs search results on the deviations of the electroweak precision observables from their best-fit values. Our indirect prediction of the top mass is m(t) =175.7(-2.2)(+3.0) GeV at 68.3% C.L., which is in good agreement with the direct measurement. We also plot the preferred area in the M(W)-m(t) plane. The best-fit Higgs boson mass is 126.0 GeV. For the case of the SM with a perturbative sequential fourth fermion generation (SM4) we discuss the deviations of the Higgs signal strengths from their best-fit values. The H → γγ signal strength now disagrees with its best-fit SM4 value at more than 4σ. We perform a likelihood-ratio test to compare the SM and SM4 and show that the SM4 is excluded at 5.3σ. Without the Tevatron data on H → bb the significance drops to 4.8σ.

The dynamics of life stressors and depressive symptoms in early adolescence: a test of six theoretical models.

PubMed

Clements, Margaret; Aber, J Lawrence; Seidman, Edward

2008-01-01

Structural equation modeling was used to compare 6 competing theoretically based psychosocial models of the longitudinal association between life stressors and depressive symptoms in a sample of early adolescents (N= 907; 40% Hispanic, 32% Black, and 19% White; mean age at Time 1 = 11.4 years). Only two models fit the data, both of which included paths modeling the effect of depressive symptoms on stressors recall: The mood-congruent cognitive bias model included only depressive symptoms to life stressors paths (DS-->S), whereas the fully transactional model included paths representing both the DS-->S and stressors to depressive symptoms (S-->DS) effects. Social causation models and the stress generation model did not fit the data. Findings demonstrate the importance of accounting for mood-congruent cognitive bias in stressors-depressive symptoms investigations.

Closed-form solution of the Ogden-Hill's compressible hyperelastic model for ramp loading

NASA Astrophysics Data System (ADS)

Berezvai, Szabolcs; Kossa, Attila

2017-05-01

This article deals with the visco-hyperelastic modelling approach for compressible polymer foam materials. Polymer foams can exhibit large elastic strains and displacements in case of volumetric compression. In addition, they often show significant rate-dependent properties. This material behaviour can be accurately modelled using the visco-hyperelastic approach, in which the large strain viscoelastic description is combined with the rate-independent hyperelastic material model. In case of polymer foams, the most widely used compressible hyperelastic material model, the so-called Ogden-Hill's model, was applied, which is implemented in the commercial finite element (FE) software Abaqus. The visco-hyperelastic model is defined in hereditary integral form, therefore, obtaining a closed-form solution for the stress is not a trivial task. However, the parameter-fitting procedure could be much faster and accurate if closed-form solution exists. In this contribution, exact stress solutions are derived in case of uniaxial, biaxial and volumetric compression loading cases using ramp-loading history. The analytical stress solutions are compared with the stress results in Abaqus using FE analysis. In order to highlight the benefits of the analytical closed-form solution during the parameter-fitting process experimental work has been carried out on a particular open-cell memory foam material. The results of the material identification process shows significant accuracy improvement in the fitting procedure by applying the derived analytical solutions compared to the so-called separated approach applied in the engineering practice.

Identifying model error in metabolic flux analysis - a generalized least squares approach.

PubMed

Sokolenko, Stanislav; Quattrociocchi, Marco; Aucoin, Marc G

2016-09-13

The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued evolution of the methodology since its introduction, there has been little focus on validation and identification of poor model fit outside of identifying "gross measurement error". The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit. In this work, MFA calculation is framed as a generalized least squares (GLS) problem, highlighting the applicability of the common t-test for model validation. To differentiate between measurement and model error, we simulate ideal flux profiles directly from the model, perturb them with estimated measurement error, and compare their validation to real data. Application of this strategy to an established Chinese Hamster Ovary (CHO) cell model shows how fluxes validated by traditional means may be largely non-significant due to a lack of model fit. With further simulation, we explore how t-test significance relates to calculation error and show that fluxes found to be non-significant have 2-4 fold larger error (if measurement uncertainty is in the 5-10 % range). The proposed validation method goes beyond traditional detection of "gross measurement error" to identify lack of fit between model and data. Although the focus of this work is on t-test validation and traditional MFA, the presented framework is readily applicable to other regression analysis methods and MFA formulations.

Inverse Gaussian gamma distribution model for turbulence-induced fading in free-space optical communication.

PubMed

Cheng, Mingjian; Guo, Ya; Li, Jiangting; Zheng, Xiaotong; Guo, Lixin

2018-04-20

We introduce an alternative distribution to the gamma-gamma (GG) distribution, called inverse Gaussian gamma (IGG) distribution, which can efficiently describe moderate-to-strong irradiance fluctuations. The proposed stochastic model is based on a modulation process between small- and large-scale irradiance fluctuations, which are modeled by gamma and inverse Gaussian distributions, respectively. The model parameters of the IGG distribution are directly related to atmospheric parameters. The accuracy of the fit among the IGG, log-normal, and GG distributions with the experimental probability density functions in moderate-to-strong turbulence are compared, and results indicate that the newly proposed IGG model provides an excellent fit to the experimental data. As the receiving diameter is comparable with the atmospheric coherence radius, the proposed IGG model can reproduce the shape of the experimental data, whereas the GG and LN models fail to match the experimental data. The fundamental channel statistics of a free-space optical communication system are also investigated in an IGG-distributed turbulent atmosphere, and a closed-form expression for the outage probability of the system is derived with Meijer's G-function.

Consequences of clonality for sexual fitness: Clonal expansion enhances fitness under spatially restricted dispersal.

PubMed

Van Drunen, Wendy E; van Kleunen, Mark; Dorken, Marcel E

2015-07-21

Clonality is a pervasive feature of sessile organisms, but this form of asexual reproduction is thought to interfere with sexual fitness via the movement of gametes among the modules that comprise the clone. This within-clone movement of gametes is expected to reduce sexual fitness via mate limitation of male reproductive success and, in some cases, via the production of highly inbred (i.e., self-fertilized) offspring. However, clonality also results in the spatial expansion of the genetic individual (i.e., genet), and this should decrease distances gametes and sexually produced offspring must travel to avoid competing with other gametes and offspring from the same clone. The extent to which any negative effects of clonality on mating success might be offset by the positive effects of spatial expansion is poorly understood. Here, we develop spatially explicit models in which fitness was determined by the success of genets through their male and female sex functions. Our results indicate that clonality serves to increase sexual fitness when it is associated with the outward expansion of the genet. Our models further reveal that the main fitness benefit of clonal expansion might occur through the dispersal of offspring over a wider area compared with nonclonal phenotypes. We conclude that, instead of interfering with sexual reproduction, clonal expansion should often serve to enhance sexual fitness.

Linearised and non-linearised isotherm models optimization analysis by error functions and statistical means

PubMed Central

2014-01-01

In adsorption study, to describe sorption process and evaluation of best-fitting isotherm model is a key analysis to investigate the theoretical hypothesis. Hence, numerous statistically analysis have been extensively used to estimate validity of the experimental equilibrium adsorption values with the predicted equilibrium values. Several statistical error analysis were carried out. In the present study, the following statistical analysis were carried out to evaluate the adsorption isotherm model fitness, like the Pearson correlation, the coefficient of determination and the Chi-square test, have been used. The ANOVA test was carried out for evaluating significance of various error functions and also coefficient of dispersion were evaluated for linearised and non-linearised models. The adsorption of phenol onto natural soil (Local name Kalathur soil) was carried out, in batch mode at 30 ± 20 C. For estimating the isotherm parameters, to get a holistic view of the analysis the models were compared between linear and non-linear isotherm models. The result reveled that, among above mentioned error functions and statistical functions were designed to determine the best fitting isotherm. PMID:25018878

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models

PubMed Central

Marias, Kostas; Lambregts, Doenja M. J.; Nikiforaki, Katerina; van Heeswijk, Miriam M.; Bakers, Frans C. H.; Beets-Tan, Regina G. H.

2017-01-01

Purpose The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Material and methods Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. Results All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. Conclusion No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior. PMID:28863161

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models.

PubMed

Manikis, Georgios C; Marias, Kostas; Lambregts, Doenja M J; Nikiforaki, Katerina; van Heeswijk, Miriam M; Bakers, Frans C H; Beets-Tan, Regina G H; Papanikolaou, Nikolaos

2017-01-01

The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.

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

Agostoni, Marco; Lucker, Ben F.; Smith, Matthew A. Y.

Phycobilisomes (PBSs) are pigment-rich super-complexes required for efficient harvest and transfer of light energy to photosynthetic reaction centers of cyanobacteria. The model cyanobacterium Fremyella diplosiphon is able to adjust PBS pigmentation and size in response to the prevailing light spectrum through a process called complementary chromatic acclimation to optimize spectral light absorption, concomitantly optimizing photosynthesis and growth. We explored the fitness costs versus advantages of modulating antennae size and composition under sinusoidal continuous and fluctuating light conditions in F. diplosiphon by comparing growth of wild-type (WT) cells with a mutant strain deficient in PBSs in both monoculture and polyculture conditions.more » Comparative analyses of WT and the PBS-deficient FdCh1 strain under continuous vs. fluctuating sinusoidal light suggest a potential fitness advantage for maintaining PBSs in WT cells during continuous light and a fitness cost during transitions to and acclimation under fluctuating light. Here, we explored the physiological changes correlated with the observed differential growth to understand the dynamics and biochemical bases of comparative fitness of distinct strains under defined growth conditions. Wild-type F. diplosiphon appears to accumulate longer PBS rods and exhibits higher oxidative stress under fluctuating light conditions than continuous sinusoidal light, which may impact responses and the fitness of cells that do not adapt to rapid changes in external light.« less

Comparing of Cox model and parametric models in analysis of effective factors on event time of neuropathy in patients with type 2 diabetes.

PubMed

Kargarian-Marvasti, Sadegh; Rimaz, Shahnaz; Abolghasemi, Jamileh; Heydari, Iraj

2017-01-01

Cox proportional hazard model is the most common method for analyzing the effects of several variables on survival time. However, under certain circumstances, parametric models give more precise estimates to analyze survival data than Cox. The purpose of this study was to investigate the comparative performance of Cox and parametric models in a survival analysis of factors affecting the event time of neuropathy in patients with type 2 diabetes. This study included 371 patients with type 2 diabetes without neuropathy who were registered at Fereydunshahr diabetes clinic. Subjects were followed up for the development of neuropathy between 2006 to March 2016. To investigate the factors influencing the event time of neuropathy, significant variables in univariate model ( P < 0.20) were entered into the multivariate Cox and parametric models ( P < 0.05). In addition, Akaike information criterion (AIC) and area under ROC curves were used to evaluate the relative goodness of fitted model and the efficiency of each procedure, respectively. Statistical computing was performed using R software version 3.2.3 (UNIX platforms, Windows and MacOS). Using Kaplan-Meier, survival time of neuropathy was computed 76.6 ± 5 months after initial diagnosis of diabetes. After multivariate analysis of Cox and parametric models, ethnicity, high-density lipoprotein and family history of diabetes were identified as predictors of event time of neuropathy ( P < 0.05). According to AIC, "log-normal" model with the lowest Akaike's was the best-fitted model among Cox and parametric models. According to the results of comparison of survival receiver operating characteristics curves, log-normal model was considered as the most efficient and fitted model.

AssignFit: a program for simultaneous assignment and structure refinement from solid-state NMR spectra

PubMed Central

Tian, Ye; Schwieters, Charles D.; Opella, Stanley J.; Marassi, Francesca M.

2011-01-01

AssignFit is a computer program developed within the XPLOR-NIH package for the assignment of dipolar coupling (DC) and chemical shift anisotropy (CSA) restraints derived from the solid-state NMR spectra of protein samples with uniaxial order. The method is based on minimizing the difference between experimentally observed solid-state NMR spectra and the frequencies back calculated from a structural model. Starting with a structural model and a set of DC and CSA restraints grouped only by amino acid type, as would be obtained by selective isotopic labeling, AssignFit generates all of the possible assignment permutations and calculates the corresponding atomic coordinates oriented in the alignment frame, together with the associated set of NMR frequencies, which are then compared with the experimental data for best fit. Incorporation of AssignFit in a simulated annealing refinement cycle provides an approach for simultaneous assignment and structure refinement (SASR) of proteins from solid-state NMR orientation restraints. The methods are demonstrated with data from two integral membrane proteins, one α-helical and one β-barrel, embedded in phospholipid bilayer membranes. PMID:22036904

Maximum-likelihood curve-fitting scheme for experiments with pulsed lasers subject to intensity fluctuations.

PubMed

Metz, Thomas; Walewski, Joachim; Kaminski, Clemens F

2003-03-20

Evaluation schemes, e.g., least-squares fitting, are not generally applicable to any types of experiments. If the evaluation schemes were not derived from a measurement model that properly described the experiment to be evaluated, poorer precision or accuracy than attainable from the measured data could result. We outline ways in which statistical data evaluation schemes should be derived for all types of experiment, and we demonstrate them for laser-spectroscopic experiments, in which pulse-to-pulse fluctuations of the laser power cause correlated variations of laser intensity and generated signal intensity. The method of maximum likelihood is demonstrated in the derivation of an appropriate fitting scheme for this type of experiment. Statistical data evaluation contains the following steps. First, one has to provide a measurement model that considers statistical variation of all enclosed variables. Second, an evaluation scheme applicable to this particular model has to be derived or provided. Third, the scheme has to be characterized in terms of accuracy and precision. A criterion for accepting an evaluation scheme is that it have accuracy and precision as close as possible to the theoretical limit. The fitting scheme derived for experiments with pulsed lasers is compared to well-established schemes in terms of fitting power and rational functions. The precision is found to be as much as three timesbetter than for simple least-squares fitting. Our scheme also suppresses the bias on the estimated model parameters that other methods may exhibit if they are applied in an uncritical fashion. We focus on experiments in nonlinear spectroscopy, but the fitting scheme derived is applicable in many scientific disciplines.

Latent Factor Structure of DSM-5 Posttraumatic Stress Disorder

PubMed Central

Gentes, Emily; Dennis, Paul A.; Kimbrel, Nathan A.; Kirby, Angela C.; Hair, Lauren P.; Beckham, Jean C.; Calhoun, Patrick S.

2015-01-01

The current study examined the latent factor structure of posttraumatic stress disorder (PTSD) based on DSM-5 criteria in a sample of participants (N = 374) recruited for studies on trauma and health. Confirmatory factor analyses (CFA) were used to compare the fit of the previous 3-factor DSM-IV model of PTSD to the 4-factor model specified in DSM-5 as well as to a competing 4-factor “dysphoria” model (Simms, Watson, & Doebbeling, 2002) and a 5-factor (Elhai et al., 2011) model of PTSD. Results indicated that the Elhai 5-factor model (re-experiencing, active avoidance, emotional numbing, dysphoric arousal, anxious arousal) provided the best fit to the data, although substantial support was demonstrated for the DSM-5 4-factor model. Low factor loadings were noted for two of the symptoms in the DSM-5 model (psychogenic amnesia and reckless/self-destructive behavior), which raises questions regarding the adequacy of fit of these symptoms with other core features of the disorder. Overall, the findings from the present research suggest the DSM-5 model of PTSD is a significant improvement over the previous DSM-IV model of PTSD. PMID:26366290

Model-based estimates of long-term persistence of induced HPV antibodies: a flexible subject-specific approach.

PubMed

Aregay, Mehreteab; Shkedy, Ziv; Molenberghs, Geert; David, Marie-Pierre; Tibaldi, Fabián

2013-01-01

In infectious diseases, it is important to predict the long-term persistence of vaccine-induced antibodies and to estimate the time points where the individual titers are below the threshold value for protection. This article focuses on HPV-16/18, and uses a so-called fractional-polynomial model to this effect, derived in a data-driven fashion. Initially, model selection was done from among the second- and first-order fractional polynomials on the one hand and from the linear mixed model on the other. According to a functional selection procedure, the first-order fractional polynomial was selected. Apart from the fractional polynomial model, we also fitted a power-law model, which is a special case of the fractional polynomial model. Both models were compared using Akaike's information criterion. Over the observation period, the fractional polynomials fitted the data better than the power-law model; this, of course, does not imply that it fits best over the long run, and hence, caution ought to be used when prediction is of interest. Therefore, we point out that the persistence of the anti-HPV responses induced by these vaccines can only be ascertained empirically by long-term follow-up analysis.

A TCP model for external beam treatment of intermediate-risk prostate cancer

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

Walsh, Sean; Putten, Wil van der

2013-03-15

Purpose: Biological models offer the ability to predict clinical outcomes. The authors describe a model to predict the clinical response of intermediate-risk prostate cancer to external beam radiotherapy for a variety of fractionation regimes. Methods: A fully heterogeneous population averaged tumor control probability model was fit to clinical outcome data for hyper, standard, and hypofractionated treatments. The tumor control probability model was then employed to predict the clinical outcome of extreme hypofractionation regimes, as utilized in stereotactic body radiotherapy. Results: The tumor control probability model achieves an excellent level of fit, R{sup 2} value of 0.93 and a root meanmore » squared error of 1.31%, to the clinical outcome data for hyper, standard, and hypofractionated treatments using realistic values for biological input parameters. Residuals Less-Than-Or-Slanted-Equal-To 1.0% are produced by the tumor control probability model when compared to clinical outcome data for stereotactic body radiotherapy. Conclusions: The authors conclude that this tumor control probability model, used with the optimized radiosensitivity values obtained from the fit, is an appropriate mechanistic model for the analysis and evaluation of external beam RT plans with regard to tumor control for these clinical conditions.« less

WIMPs at the galactic center

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

Agrawal, Prateek; Fox, Patrick J.; Harnik, Roni

2015-05-01

Simple models of weakly interacting massive particles (WIMPs) predict dark matter annihilations into pairs of electroweak gauge bosons, Higgses or tops, which through their subsequent cascade decays produce a spectrum of gamma rays. Intriguingly, an excess in gamma rays coming from near the Galactic center has been consistently observed in Fermi data. A recent analysis by the Fermi collaboration confirms these earlier results. Taking into account the systematic uncertainties in the modelling of the gamma ray backgrounds, we show for the first time that this excess can be well fit by these final states. In particular, for annihilations to (WW,more » ZZ, hh, t t-bar ), dark matter with mass between threshold and approximately (165, 190, 280, 310) GeV gives an acceptable fit. The fit range for b b-bar is also enlarged to 35 GeV ∼< m{sub χ} ∼< 165 GeV. These are to be compared to previous fits that concluded only much lighter dark matter annihilating into b, τ, and light quark final states could describe the excess. We demonstrate that simple, well-motivated models of WIMP dark matter including a thermal-relic neutralino of the MSSM, Higgs portal models, as well as other simplified models can explain the excess.« less

eDNAoccupancy: An R package for multi-scale occupancy modeling of environmental DNA data

USGS Publications Warehouse

Dorazio, Robert; Erickson, Richard A.

2017-01-01

In this article we describe eDNAoccupancy, an R package for fitting Bayesian, multi-scale occupancy models. These models are appropriate for occupancy surveys that include three, nested levels of sampling: primary sample units within a study area, secondary sample units collected from each primary unit, and replicates of each secondary sample unit. This design is commonly used in occupancy surveys of environmental DNA (eDNA). eDNAoccupancy allows users to specify and fit multi-scale occupancy models with or without covariates, to estimate posterior summaries of occurrence and detection probabilities, and to compare different models using Bayesian model-selection criteria. We illustrate these features by analyzing two published data sets: eDNA surveys of a fungal pathogen of amphibians and eDNA surveys of an endangered fish species.

Impact of Pathogen Population Heterogeneity and Stress-Resistant Variants on Food Safety.

PubMed

Abee, T; Koomen, J; Metselaar, K I; Zwietering, M H; den Besten, H M W

2016-01-01

This review elucidates the state-of-the-art knowledge about pathogen population heterogeneity and describes the genotypic and phenotypic analyses of persister subpopulations and stress-resistant variants. The molecular mechanisms underlying the generation of persister phenotypes and genetic variants are identified. Zooming in on Listeria monocytogenes, a comparative whole-genome sequence analysis of wild types and variants that enabled the identification of mutations in variants obtained after a single exposure to lethal food-relevant stresses is described. Genotypic and phenotypic features are compared to those for persistent strains isolated from food processing environments. Inactivation kinetics, models used for fitting, and the concept of kinetic modeling-based schemes for detection of variants are presented. Furthermore, robustness and fitness parameters of L. monocytogenes wild type and variants are used to model their performance in food chains. Finally, the impact of stress-resistant variants and persistence in food processing environments on food safety is discussed.

An update on modeling dose-response relationships: Accounting for correlated data structure and heterogeneous error variance in linear and nonlinear mixed models.

PubMed

Gonçalves, M A D; Bello, N M; Dritz, S S; Tokach, M D; DeRouchey, J M; Woodworth, J C; Goodband, R D

2016-05-01

Advanced methods for dose-response assessments are used to estimate the minimum concentrations of a nutrient that maximizes a given outcome of interest, thereby determining nutritional requirements for optimal performance. Contrary to standard modeling assumptions, experimental data often present a design structure that includes correlations between observations (i.e., blocking, nesting, etc.) as well as heterogeneity of error variances; either can mislead inference if disregarded. Our objective is to demonstrate practical implementation of linear and nonlinear mixed models for dose-response relationships accounting for correlated data structure and heterogeneous error variances. To illustrate, we modeled data from a randomized complete block design study to evaluate the standardized ileal digestible (SID) Trp:Lys ratio dose-response on G:F of nursery pigs. A base linear mixed model was fitted to explore the functional form of G:F relative to Trp:Lys ratios and assess model assumptions. Next, we fitted 3 competing dose-response mixed models to G:F, namely a quadratic polynomial (QP) model, a broken-line linear (BLL) ascending model, and a broken-line quadratic (BLQ) ascending model, all of which included heteroskedastic specifications, as dictated by the base model. The GLIMMIX procedure of SAS (version 9.4) was used to fit the base and QP models and the NLMIXED procedure was used to fit the BLL and BLQ models. We further illustrated the use of a grid search of initial parameter values to facilitate convergence and parameter estimation in nonlinear mixed models. Fit between competing dose-response models was compared using a maximum likelihood-based Bayesian information criterion (BIC). The QP, BLL, and BLQ models fitted on G:F of nursery pigs yielded BIC values of 353.7, 343.4, and 345.2, respectively, thus indicating a better fit of the BLL model. The BLL breakpoint estimate of the SID Trp:Lys ratio was 16.5% (95% confidence interval [16.1, 17.0]). Problems with the estimation process rendered results from the BLQ model questionable. Importantly, accounting for heterogeneous variance enhanced inferential precision as the breadth of the confidence interval for the mean breakpoint decreased by approximately 44%. In summary, the article illustrates the use of linear and nonlinear mixed models for dose-response relationships accounting for heterogeneous residual variances, discusses important diagnostics and their implications for inference, and provides practical recommendations for computational troubleshooting.

LGALS4, CEACAM6, TSPAN8, and COL1A2: Blood Markers for Colorectal Cancer-Validation in a Cohort of Subjects With Positive Fecal Immunochemical Test Result.

PubMed

Rodia, Maria Teresa; Solmi, Rossella; Pasini, Francesco; Nardi, Elena; Mattei, Gabriella; Ugolini, Giampaolo; Ricciardiello, Luigi; Strippoli, Pierluigi; Miglio, Rossella; Lauriola, Mattia

2018-06-01

A noninvasive blood test for the early detection of colorectal cancer (CRC) is highly required. We evaluated a panel of 4 mRNAs as putative markers of CRC. We tested LGALS4, CEACAM6, TSPAN8, and COL1A2, referred to as the CELTiC panel, using quantitative reverse transcription polymerase chain reaction, on subjects with positive fecal immunochemical test (FIT) results and undergoing colonoscopy. Using a nonparametric test and multinomial logistic model, FIT-positive subjects were compared with CRC patients and healthy individuals. All the genes of the CELTiC panel displayed statistically significant differences between the healthy subjects (n = 67), both low-risk (n = 36) and high-risk/CRC (n = 92) subjects, and those in the negative-colonoscopy, FIT-positive group (n = 36). The multinomial logistic model revealed LGALS4 was the most powerful marker discriminating the 4 groups. When assessing the diagnostic values by analysis of the areas under the receiver operating characteristic curves (AUCs), the CELTiC panel reached an AUC of 0.91 (sensitivity, 79%; specificity, 94%) comparing normal subjects to low-risk subjects, and 0.88 (sensitivity, 75%; specificity, 87%) comparing normal and high-risk/CRC subjects. The comparison between the normal subjects and the negative-colonoscopy, FIT-positive group revealed an AUC of 0.93 (sensitivity, 82%; specificity, 97%). The CELTiC panel could represent a useful tool for discriminating subjects with positive FIT findings and for the early detection of precancerous adenomatous lesions and CRC. Copyright © 2017 Elsevier Inc. All rights reserved.

A comparison of fit of CNC-milled titanium and zirconia frameworks to implants.

PubMed

Abduo, Jaafar; Lyons, Karl; Waddell, Neil; Bennani, Vincent; Swain, Michael

2012-05-01

Computer numeric controlled (CNC) milling was proven to be predictable method to fabricate accurately fitting implant titanium frameworks. However, no data are available regarding the fit of CNC-milled implant zirconia frameworks. To compare the precision of fit of implant frameworks milled from titanium and zirconia and relate it to peri-implant strain development after framework fixation. A partially edentulous epoxy resin models received two Branemark implants in the areas of the lower left second premolar and second molar. From this model, 10 identical frameworks were fabricated by mean of CNC milling. Half of them were made from titanium and the other half from zirconia. Strain gauges were mounted close to the implants to qualitatively and quantitatively assess strain development as a result of framework fitting. In addition, the fit of the framework implant interface was measured using an optical microscope, when only one screw was tightened (passive fit) and when all screws were tightened (vertical fit). The data was statistically analyzed using the Mann-Whitney test. All frameworks produced measurable amounts of peri-implant strain. The zirconia frameworks produced significantly less strain than titanium. Combining the qualitative and quantitative information indicates that the implants were under vertical displacement rather than horizontal. The vertical fit was similar for zirconia (3.7 µm) and titanium (3.6 µm) frameworks; however, the zirconia frameworks exhibited a significantly finer passive fit (5.5 µm) than titanium frameworks (13.6 µm). CNC milling produced zirconia and titanium frameworks with high accuracy. The difference between the two materials in terms of fit is expected to be of minimal clinical significance. The strain developed around the implants was more related to the framework fit rather than framework material. © 2011 Wiley Periodicals, Inc.

N* resonances from KΛ amplitudes in sliced bins in energy

NASA Astrophysics Data System (ADS)

Anisovich, A. V.; Burkert, V.; Hadžimehmedović, M.; Ireland, D. G.; Klempt, E.; Nikonov, V. A.; Omerović, R.; Sarantsev, A. V.; Stahov, J.; Švarc, A.; Thoma, U.

2017-12-01

The two reactions γ p→ K+Λ and π- p→ K0Λ are analyzed to determine the leading photoproduction multipoles and the pion-induced partial wave amplitudes in slices of the invariant mass. The multipoles and the partial-wave amplitudes are simultaneously fitted in a multichannel Laurent+Pietarinen model (L+P model), which determines the poles in the complex energy plane on the second Riemann sheet close to the physical axes. The results from the L+P fit are compared with the results of an energy-dependent fit based on the Bonn-Gatchina (BnGa) approach. The study confirms the existence of several poles due to nucleon resonances in the region at about 1.9 GeV with quantum numbers JP = 1/2+, 3/2+, 1/2-, 3/2-, 5/2-.

Validating models of target acquisition performance in the dismounted soldier context

NASA Astrophysics Data System (ADS)

Glaholt, Mackenzie G.; Wong, Rachel K.; Hollands, Justin G.

2018-04-01

The problem of predicting real-world operator performance with digital imaging devices is of great interest within the military and commercial domains. There are several approaches to this problem, including: field trials with imaging devices, laboratory experiments using imagery captured from these devices, and models that predict human performance based on imaging device parameters. The modeling approach is desirable, as both field trials and laboratory experiments are costly and time-consuming. However, the data from these experiments is required for model validation. Here we considered this problem in the context of dismounted soldiering, for which detection and identification of human targets are essential tasks. Human performance data were obtained for two-alternative detection and identification decisions in a laboratory experiment in which photographs of human targets were presented on a computer monitor and the images were digitally magnified to simulate range-to-target. We then compared the predictions of different performance models within the NV-IPM software package: Targeting Task Performance (TTP) metric model and the Johnson model. We also introduced a modification to the TTP metric computation that incorporates an additional correction for target angular size. We examined model predictions using NV-IPM default values for a critical model constant, V50, and we also considered predictions when this value was optimized to fit the behavioral data. When using default values, certain model versions produced a reasonably close fit to the human performance data in the detection task, while for the identification task all models substantially overestimated performance. When using fitted V50 values the models produced improved predictions, though the slopes of the performance functions were still shallow compared to the behavioral data. These findings are discussed in relation to the models' designs and parameters, and the characteristics of the behavioral paradigm.

Examining the Latent Structure of the Delis-Kaplan Executive Function System.

PubMed

Karr, Justin E; Hofer, Scott M; Iverson, Grant L; Garcia-Barrera, Mauricio A

2018-05-04

The current study aimed to determine whether the Delis-Kaplan Executive Function System (D-KEFS) taps into three executive function factors (inhibition, shifting, fluency) and to assess the relationship between these factors and tests of executive-related constructs less often measured in latent variable research: reasoning, abstraction, and problem solving. Participants included 425 adults from the D-KEFS standardization sample (20-49 years old; 50.1% female; 70.1% White). Eight alternative measurement models were compared based on model fit, with test scores assigned a priori to three factors: inhibition (Color-Word Interference, Tower), shifting (Trail Making, Sorting, Design Fluency), and fluency (Verbal/Design Fluency). The Twenty Questions, Word Context, and Proverb Tests were predicted in separate structural models. The three-factor model fit the data well (CFI = 0.938; RMSEA = 0.047), although a two-factor model, with shifting and fluency merged, fit similarly well (CFI = 0.929; RMSEA = 0.048). A bifactor model fit best (CFI = 0.977; RMSEA = 0.032) and explained the most variance in shifting indicators, but rarely converged among 5,000 bootstrapped samples. When the three first-order factors simultaneously predicted the criterion variables, only shifting was uniquely predictive (p < .05; R2 = 0.246-0.408). The bifactor significantly predicted all three criterion variables (p < .001; R2 = 0.141-242). Results supported a three-factor D-KEFS model (i.e., inhibition, shifting, and fluency), although shifting and fluency were highly related (r = 0.696). The bifactor showed superior fit, but converged less often than other models. Shifting best predicted tests of reasoning, abstraction, and problem solving. These findings support the validity of D-KEFS scores for measuring executive-related constructs and provide a framework through which clinicians can interpret D-KEFS results.

Validation of the LOD score compared with APACHE II score in prediction of the hospital outcome in critically ill patients.

PubMed

Khwannimit, Bodin

2008-01-01

The Logistic Organ Dysfunction score (LOD) is an organ dysfunction score that can predict hospital mortality. The aim of this study was to validate the performance of the LOD score compared with the Acute Physiology and Chronic Health Evaluation II (APACHE II) score in a mixed intensive care unit (ICU) at a tertiary referral university hospital in Thailand. The data were collected prospectively on consecutive ICU admissions over a 24 month period from July1, 2004 until June 30, 2006. Discrimination was evaluated by the area under the receiver operating characteristic curve (AUROC). The calibration was assessed by the Hosmer-Lemeshow goodness-of-fit H statistic. The overall fit of the model was evaluated by the Brier's score. Overall, 1,429 patients were enrolled during the study period. The mortality in the ICU was 20.9% and in the hospital was 27.9%. The median ICU and hospital lengths of stay were 3 and 18 days, respectively, for all patients. Both models showed excellent discrimination. The AUROC for the LOD and APACHE II were 0.860 [95% confidence interval (CI) = 0.838-0.882] and 0.898 (95% Cl = 0.879-0.917), respectively. The LOD score had perfect calibration with the Hosmer-Lemeshow goodness-of-fit H chi-2 = 10 (p = 0.44). However, the APACHE II had poor calibration with the Hosmer-Lemeshow goodness-of-fit H chi-2 = 75.69 (p < 0.001). Brier's score showed the overall fit for both models were 0.123 (95%Cl = 0.107-0.141) and 0.114 (0.098-0.132) for the LOD and APACHE II, respectively. Thus, the LOD score was found to be accurate for predicting hospital mortality for general critically ill patients in Thailand.

Fit accuracy of metal partial removable dental prosthesis frameworks fabricated by traditional or light curing modeling material technique: An in vitro study

PubMed Central

Anan, Mohammad Tarek M.; Al-Saadi, Mohannad H.

2015-01-01

Objective The aim of this study was to compare the fit accuracies of metal partial removable dental prosthesis (PRDP) frameworks fabricated by the traditional technique (TT) or the light-curing modeling material technique (LCMT). Materials and methods A metal model of a Kennedy class III modification 1 mandibular dental arch with two edentulous spaces of different spans, short and long, was used for the study. Thirty identical working casts were used to produce 15 PRDP frameworks each by TT and by LCMT. Every framework was transferred to a metal master cast to measure the gap between the metal base of the framework and the crest of the alveolar ridge of the cast. Gaps were measured at three points on each side by a USB digital intraoral camera at ×16.5 magnification. Images were transferred to a graphics editing program. A single examiner performed all measurements. The two-tailed t-test was performed at the 5% significance level. Results The mean gap value was significantly smaller in the LCMT group compared to the TT group. The mean value of the short edentulous span was significantly smaller than that of the long edentulous span in the LCMT group, whereas the opposite result was obtained in the TT group. Conclusion Within the limitations of this study, it can be concluded that the fit of the LCMT-fabricated frameworks was better than the fit of the TT-fabricated frameworks. The framework fit can differ according to the span of the edentate ridge and the fabrication technique for the metal framework. PMID:26236129

Estimating and Comparing Dam Deformation Using Classical and GNSS Techniques.

PubMed

Barzaghi, Riccardo; Cazzaniga, Noemi Emanuela; De Gaetani, Carlo Iapige; Pinto, Livio; Tornatore, Vincenza

2018-03-02

Global Navigation Satellite Systems (GNSS) receivers are nowadays commonly used in monitoring applications, e.g., in estimating crustal and infrastructure displacements. This is basically due to the recent improvements in GNSS instruments and methodologies that allow high-precision positioning, 24 h availability and semiautomatic data processing. In this paper, GNSS-estimated displacements on a dam structure have been analyzed and compared with pendulum data. This study has been carried out for the Eleonora D'Arborea (Cantoniera) dam, which is in Sardinia. Time series of pendulum and GNSS over a time span of 2.5 years have been aligned so as to be comparable. Analytical models fitting these time series have been estimated and compared. Those models were able to properly fit pendulum data and GNSS data, with standard deviation of residuals smaller than one millimeter. These encouraging results led to the conclusion that GNSS technique can be profitably applied to dam monitoring allowing a denser description, both in space and time, of the dam displacements than the one based on pendulum observations.

Development and evaluation of social cognitive measures related to adolescent physical activity.

PubMed

Dewar, Deborah L; Lubans, David Revalds; Morgan, Philip James; Plotnikoff, Ronald C

2013-05-01

This study aimed to develop and evaluate the construct validity and reliability of modernized social cognitive measures relating to physical activity behaviors in adolescents. An instrument was developed based on constructs from Bandura's Social Cognitive Theory and included the following scales: self-efficacy, situation (perceived physical environment), social support, behavioral strategies, and outcome expectations and expectancies. The questionnaire was administered in a sample of 171 adolescents (age = 13.6 ± 1.2 years, females = 61%). Confirmatory factor analysis was employed to examine model-fit for each scale using multiple indices, including chi-square index, comparative-fit index (CFI), goodness-of-fit index (GFI), and the root mean square error of approximation (RMSEA). Reliability properties were also examined (ICC and Cronbach's alpha). Each scale represented a statistically sound measure: fit indices indicated each model to be an adequate-to-exact fit to the data; internal consistency was acceptable to good (α = 0.63-0.79); rank order repeatability was strong (ICC = 0.82-0.91). Results support the validity and reliability of social cognitive scales relating to physical activity among adolescents. As such, the developed scales have utility for the identification of potential social cognitive correlates of youth physical activity, mediators of physical activity behavior changes and the testing of theoretical models based on Social Cognitive Theory.

Comparing tree foliage biomass models fitted to a multispecies, felled-tree biomass dataset for the United States

Treesearch

Brian J. Clough; Matthew B. Russell; Grant M. Domke; Christopher W. Woodall; Philip J. Radtke

2016-01-01

tEstimation of live tree biomass is an important task for both forest carbon accounting and studies of nutri-ent dynamics in forest ecosystems. In this study, we took advantage of an extensive felled-tree database(with 2885 foliage biomass observations) to compare different models and grouping schemes based onphylogenetic and geographic variation for predicting foliage...

A comparative study of generalized linear mixed modelling and artificial neural network approach for the joint modelling of survival and incidence of Dengue patients in Sri Lanka

NASA Astrophysics Data System (ADS)

Hapugoda, J. C.; Sooriyarachchi, M. R.

2017-09-01

Survival time of patients with a disease and the incidence of that particular disease (count) is frequently observed in medical studies with the data of a clustered nature. In many cases, though, the survival times and the count can be correlated in a way that, diseases that occur rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these two variables will provide interesting and certainly improved results than modelling these separately. Authors have previously proposed a methodology using Generalized Linear Mixed Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a most powerful computational tool to model complex non-linear systems, it was proposed to develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that approach. Thus, the objective of this study is to develop a model using ANN approach and compare the results with the previously developed GLMM model. As the response variables are continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted to model the data. To compare the model fit, measures such as root mean square error (RMSE), absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the GRNN model fits the data better than the GLMM model.

Experimental study of the γ p →π0η p reaction with the A2 setup at the Mainz Microtron

NASA Astrophysics Data System (ADS)

Sokhoyan, V.; Prakhov, S.; Fix, A.; Abt, S.; Achenbach, P.; Adlarson, P.; Afzal, F.; Aguar-Bartolomé, P.; Ahmed, Z.; Ahrens, J.; Annand, J. R. M.; Arends, H. J.; Bantawa, K.; Bashkanov, M.; Beck, R.; Biroth, M.; Borisov, N. S.; Braghieri, A.; Briscoe, W. J.; Cherepnya, S.; Cividini, F.; Collicott, C.; Costanza, S.; Denig, A.; Dieterle, M.; Downie, E. J.; Drexler, P.; Ferretti Bondy, M. I.; Fil'kov, L. V.; Gardner, S.; Garni, S.; Glazier, D. I.; Gorodnov, I.; Gradl, W.; Günther, M.; Gurevich, G. M.; Hamill, C. B.; Heijkenskjöld, L.; Hornidge, D.; Huber, G. M.; Käser, A.; Kashevarov, V. L.; Kay, S.; Keshelashvili, I.; Kondratiev, R.; Korolija, M.; Krusche, B.; Lazarev, A.; Lisin, V.; Livingston, K.; Lutterer, S.; MacGregor, I. J. D.; Manley, D. M.; Martel, P. P.; McGeorge, J. C.; Middleton, D. G.; Miskimen, R.; Mornacchi, E.; Mushkarenkov, A.; Neganov, A.; Neiser, A.; Oberle, M.; Ostrick, M.; Otte, P. B.; Paudyal, D.; Pedroni, P.; Polonski, A.; Ron, G.; Rostomyan, T.; Sarty, A.; Sfienti, C.; Spieker, K.; Steffen, O.; Strakovsky, I. I.; Strandberg, B.; Strub, Th.; Supek, I.; Thiel, A.; Thiel, M.; Thomas, A.; Unverzagt, M.; Usov, Yu. A.; Wagner, S.; Walford, N. K.; Watts, D. P.; Werthmüller, D.; Wettig, J.; Witthauer, L.; Wolfes, M.; Zana, L. A.; A2 Collaboration at MAMI

2018-05-01

The data available from the A2 Collaboration at MAMI were analyzed to select the γ p →π0η p reaction on an event-by-event basis, which allows for partial-wave analyses of three-body final states to obtain more reliable results, compared to fits to measured distributions. These data provide the world's best statistical accuracy in the energy range from threshold to Eγ=1.45 GeV, allowing a finer energy binning in the measurement of all observables needed for understanding the reaction dynamics. The results obtained for the measured observables are compared to existing models, and the impact from the new data is checked by the fit with the revised Mainz model.

Disease Mapping of Zero-excessive Mesothelioma Data in Flanders

PubMed Central

Neyens, Thomas; Lawson, Andrew B.; Kirby, Russell S.; Nuyts, Valerie; Watjou, Kevin; Aregay, Mehreteab; Carroll, Rachel; Nawrot, Tim S.; Faes, Christel

2016-01-01

Purpose To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion. Methods The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero-inflation and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature. Results The results indicate that hurdle models with a random effects term accounting for extra-variance in the Bernoulli zero-inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra-variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra-variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary. Conclusions Models taking into account zero-inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this. PMID:27908590

Disease mapping of zero-excessive mesothelioma data in Flanders.

PubMed

Neyens, Thomas; Lawson, Andrew B; Kirby, Russell S; Nuyts, Valerie; Watjou, Kevin; Aregay, Mehreteab; Carroll, Rachel; Nawrot, Tim S; Faes, Christel

2017-01-01

To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion. The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero inflation, and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion, and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature. The results indicate that hurdle models with a random effects term accounting for extra variance in the Bernoulli zero-inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary. Models taking into account zero inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this. Copyright © 2016 Elsevier Inc. All rights reserved.

Application of screened Coulomb potential in fitting DBV star PG 0112+104

NASA Astrophysics Data System (ADS)

Chen, Y. H.

2018-03-01

With 78.7 d of observations for PG 0112+104, a pulsating DB star, from Campaign 8 of Kepler 2 mission, Hermes et al. made a detailed mode identification. A reliable mode identification, with 5 l = 1 modes, 3 l = 2 modes, and 3 l = 1 or 2 modes, was identified. Grids of DBV star models are evolved by WDEC with element diffusion effect of pure Coulomb potential and screened Coulomb potential. Fitting the identified modes of PG 0112+104 by the calculated ones, we studied the difference of element diffusion effect between adopting pure Coulomb potential and screened Coulomb potential. Our aim is to reduce the fitting error by studying new input physics. The starting models including their chemical composition profile are from white dwarf models evolved by MESA. They were calculated following the stellar evolution from the main sequence to the start of the white dwarf cooling sequences. The optimal parameters are basically consistent with that of previous spectroscopic and asteroseismological studies. The pure and screened Coulomb potential lead to different composition profiles of the C/O-He interface area. High k modes are very sensitive to the area. However, most of the observed modes for PG 0112+104 are low k modes. The σRMS taking the screened Coulomb potential is reduced by 4 per cent compared with taking the pure Coulomb potential when fitting the identified low k modes of PG 0112+104. Fitting the Kepler 2 data with our models improved the σRMS of the fit by 27 per cent.

Performance of the score systems Acute Physiology and Chronic Health Evaluation II and III at an interdisciplinary intensive care unit, after customization

PubMed Central

Markgraf, Rainer; Deutschinoff, Gerd; Pientka, Ludger; Scholten, Theo; Lorenz, Cristoph

2001-01-01

Background: Mortality predictions calculated using scoring scales are often not accurate in populations other than those in which the scales were developed because of differences in case-mix. The present study investigates the effect of first-level customization, using a logistic regression technique, on discrimination and calibration of the Acute Physiology and Chronic Health Evaluation (APACHE) II and III scales. Method: Probabilities of hospital death for patients were estimated by applying APACHE II and III and comparing these with observed outcomes. Using the split sample technique, a customized model to predict outcome was developed by logistic regression. The overall goodness-of-fit of the original and the customized models was assessed. Results: Of 3383 consecutive intensive care unit (ICU) admissions over 3 years, 2795 patients could be analyzed, and were split randomly into development and validation samples. The discriminative powers of APACHE II and III were unchanged by customization (areas under the receiver operating characteristic [ROC] curve 0.82 and 0.85, respectively). Hosmer-Lemeshow goodness-of-fit tests showed good calibration for APACHE II, but insufficient calibration for APACHE III. Customization improved calibration for both models, with a good fit for APACHE III as well. However, fit was different for various subgroups. Conclusions: The overall goodness-of-fit of APACHE III mortality prediction was improved significantly by customization, but uniformity of fit in different subgroups was not achieved. Therefore, application of the customized model provides no advantage, because differences in case-mix still limit comparisons of quality of care. PMID:11178223

Efficient Workflows for Curation of Heterogeneous Data Supporting Modeling of U-Nb Alloy Aging

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

Ward, Logan Timothy; Hackenberg, Robert Errol

These are slides from a presentation summarizing a graduate research associate's summer project. The following topics are covered in these slides: data challenges in materials, aging in U-Nb Alloys, Building an Aging Model, Different Phase Trans. in U-Nb, the Challenge, Storing Materials Data, Example Data Source, Organizing Data: What is a Schema?, What does a "XML Schema" look like?, Our Data Schema: Nice and Simple, Storing Data: Materials Data Curation System (MDCS), Problem with MDCS: Slow Data Entry, Getting Literature into MDCS, Staging Data in Excel Document, Final Result: MDCS Records, Analyzing Image Data, Process for Making TTT Diagram, Bottleneckmore » Number 1: Image Analysis, Fitting a TTP Boundary, Fitting a TTP Curve: Comparable Results, How Does it Compare to Our Data?, Image Analysis Workflow, Curating Hardness Records, Hardness Data: Two Key Decisions, Before Peak Age? - Automation, Interactive Viz, Which Transformation?, Microstructure-Informed Model, Tracking the Entire Process, General Problem with Property Models, Pinyon: Toolkit for Managing Model Creation, Tracking Individual Decisions, Jupyter: Docs and Code in One File, Hardness Analysis Workflow, Workflow for Aging Models, and conclusions.« less

Resolution, Scales and Predictability: Is High Resolution Detrimental To Predictability At Extended Forecast Times?

NASA Astrophysics Data System (ADS)

Mesinger, F.

The traditional views hold that high-resolution limited area models (LAMs) down- scale large-scale lateral boundary information, and that predictability of small scales is short. Inspection of various rms fits/errors has contributed to these views. It would follow that the skill of LAMs should visibly deteriorate compared to that of their driver models at more extended forecast times. The limited area Eta Model at NCEP has an additional handicap of being driven by LBCs of the previous Avn global model run, at 0000 and 1200 UTC estimated to amount to about an 8 h loss in accuracy. This should make its relative skill compared to that of the Avn deteriorate even faster. These views are challenged by various Eta results including rms fits to raobs out to 84 h. It is argued that it is the largest scales that contribute the most to the skill of the Eta relative to that of the Avn.

The atmospheric emission method of calculating the neutral atmosphere and charged particle densities in the upper atmosphere

NASA Astrophysics Data System (ADS)

McElroy, Kenneth L., Jr.

1992-12-01

A method is presented for the determination of neutral gas densities in the ionosphere from rocket-borne measurements of UV atmospheric emissions. Computer models were used to calculate an initial guess for the neutral atmosphere. Using this neutral atmosphere, intensity profiles for the N2 (0,5) Vegard-Kaplan band, the N2 Lyman-Birge-Hopfield band system, and the OI2972 A line were calculated and compared with the March 1990 NPS MUSTANG data. The neutral atmospheric model was modified and the intensity profiles recalculated until a fit with the data was obtained. The neutral atmosphere corresponding to the intensity profile that fit the data was assumed to be the atmospheric composition prevailing at the time of the observation. The ion densities were then calculated from the neutral atmosphere using a photochemical model. The electron density profile calculated by this model was compared with the electron density profile measured by the U.S. Air Force Geophysics Laboratory at a nearby site.

Examining the Process of Responding to Circumplex Scales of Interpersonal Values Items: Should Ideal Point Scoring Methods Be Considered?

PubMed

Ling, Ying; Zhang, Minqiang; Locke, Kenneth D; Li, Guangming; Li, Zonglong

2016-01-01

The Circumplex Scales of Interpersonal Values (CSIV) is a 64-item self-report measure of goals from each octant of the interpersonal circumplex. We used item response theory methods to compare whether dominance models or ideal point models best described how people respond to CSIV items. Specifically, we fit a polytomous dominance model called the generalized partial credit model and an ideal point model of similar complexity called the generalized graded unfolding model to the responses of 1,893 college students. The results of both graphical comparisons of item characteristic curves and statistical comparisons of model fit suggested that an ideal point model best describes the process of responding to CSIV items. The different models produced different rank orderings of high-scoring respondents, but overall the models did not differ in their prediction of criterion variables (agentic and communal interpersonal traits and implicit motives).

Emitting electron spectra and acceleration processes in the jet of PKS 0447-439

NASA Astrophysics Data System (ADS)

Zhou, Yao; Yan, Dahai; Dai, Benzhong; Zhang, Li

2014-02-01

We investigate the electron energy distributions (EEDs) and the corresponding acceleration processes in the jet of PKS 0447-439, and estimate its redshift through modeling its observed spectral energy distribution (SED) in the frame of a one-zone synchrotron-self Compton (SSC) model. Three EEDs formed in different acceleration scenarios are assumed: the power-law with exponential cut-off (PLC) EED (shock-acceleration scenario or the case of the EED approaching equilibrium in the stochastic-acceleration scenario), the log-parabolic (LP) EED (stochastic-acceleration scenario and the acceleration dominating), and the broken power-law (BPL) EED (no acceleration scenario). The corresponding fluxes of both synchrotron and SSC are then calculated. The model is applied to PKS 0447-439, and modeled SEDs are compared to the observed SED of this object by using the Markov Chain Monte Carlo method. The results show that the PLC model fails to fit the observed SED well, while the LP and BPL models give comparably good fits for the observed SED. The results indicate that it is possible that a stochastic acceleration process acts in the emitting region of PKS 0447-439 and the EED is far from equilibrium (acceleration dominating) or no acceleration process works (in the emitting region). The redshift of PKS 0447-439 is also estimated in our fitting: z = 0.16 ± 0.05 for the LP case and z = 0.17 ± 0.04 for BPL case.

Protein Dynamics from NMR: The Slowly Relaxing Local Structure Analysis Compared with Model-Free Analysis

PubMed Central

Meirovitch, Eva; Shapiro, Yury E.; Polimeno, Antonino; Freed, Jack H.

2009-01-01

15N-1H spin relaxation is a powerful method for deriving information on protein dynamics. The traditional method of data analysis is model-free (MF), where the global and local N-H motions are independent and the local geometry is simplified. The common MF analysis consists of fitting single-field data. The results are typically field-dependent, and multi-field data cannot be fit with standard fitting schemes. Cases where known functional dynamics has not been detected by MF were identified by us and others. Recently we applied to spin relaxation in proteins the Slowly Relaxing Local Structure (SRLS) approach which accounts rigorously for mode-mixing and general features of local geometry. SRLS was shown to yield MF in appropriate asymptotic limits. We found that the experimental spectral density corresponds quite well to the SRLS spectral density. The MF formulae are often used outside of their validity ranges, allowing small data sets to be force-fitted with good statistics but inaccurate best-fit parameters. This paper focuses on the mechanism of force-fitting and its implications. It is shown that MF force-fits the experimental data because mode-mixing, the rhombic symmetry of the local ordering and general features of local geometry are not accounted for. Combined multi-field multi-temperature data analyzed by MF may lead to the detection of incorrect phenomena, while conformational entropy derived from MF order parameters may be highly inaccurate. On the other hand, fitting to more appropriate models can yield consistent physically insightful information. This requires that the complexity of the theoretical spectral densities matches the integrity of the experimental data. As shown herein, the SRLS densities comply with this requirement. PMID:16821820

Galactic cosmic-ray model in the light of AMS-02 nuclei data

NASA Astrophysics Data System (ADS)

Niu, Jia-Shu; Li, Tianjun

2018-01-01

Cosmic ray (CR) physics has entered a precision-driven era. With the latest AMS-02 nuclei data (boron-to-carbon ratio, proton flux, helium flux, and antiproton-to-proton ratio), we perform a global fitting and constrain the primary source and propagation parameters of cosmic rays in the Milky Way by considering 3 schemes with different data sets (with and without p ¯ /p data) and different propagation models (diffusion-reacceleration and diffusion-reacceleration-convection models). We find that the data set with p ¯/p data can remove the degeneracy between the propagation parameters effectively and it favors the model with a very small value of convection (or disfavors the model with convection). The separated injection spectrum parameters are used for proton and other nucleus species, which reveal the different breaks and slopes among them. Moreover, the helium abundance, antiproton production cross sections, and solar modulation are parametrized in our global fitting. Benefited from the self-consistence of the new data set, the fitting results show a little bias, and thus the disadvantages and limitations of the existed propagation models appear. Comparing to the best fit results for the local interstellar spectra (ϕ =0 ) with the VOYAGER-1 data, we find that the primary sources or propagation mechanisms should be different between proton and helium (or other heavier nucleus species). Thus, how to explain these results properly is an interesting and challenging question.

Parameterizing sorption isotherms using a hybrid global-local fitting procedure.

PubMed

Matott, L Shawn; Singh, Anshuman; Rabideau, Alan J

2017-05-01

Predictive modeling of the transport and remediation of groundwater contaminants requires an accurate description of the sorption process, which is usually provided by fitting an isotherm model to site-specific laboratory data. Commonly used calibration procedures, listed in order of increasing sophistication, include: trial-and-error, linearization, non-linear regression, global search, and hybrid global-local search. Given the considerable variability in fitting procedures applied in published isotherm studies, we investigated the importance of algorithm selection through a series of numerical experiments involving 13 previously published sorption datasets. These datasets, considered representative of state-of-the-art for isotherm experiments, had been previously analyzed using trial-and-error, linearization, or non-linear regression methods. The isotherm expressions were re-fit using a 3-stage hybrid global-local search procedure (i.e. global search using particle swarm optimization followed by Powell's derivative free local search method and Gauss-Marquardt-Levenberg non-linear regression). The re-fitted expressions were then compared to previously published fits in terms of the optimized weighted sum of squared residuals (WSSR) fitness function, the final estimated parameters, and the influence on contaminant transport predictions - where easily computed concentration-dependent contaminant retardation factors served as a surrogate measure of likely transport behavior. Results suggest that many of the previously published calibrated isotherm parameter sets were local minima. In some cases, the updated hybrid global-local search yielded order-of-magnitude reductions in the fitness function. In particular, of the candidate isotherms, the Polanyi-type models were most likely to benefit from the use of the hybrid fitting procedure. In some cases, improvements in fitness function were associated with slight (<10%) changes in parameter values, but in other cases significant (>50%) changes in parameter values were noted. Despite these differences, the influence of isotherm misspecification on contaminant transport predictions was quite variable and difficult to predict from inspection of the isotherms. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparing a thermo-mechanical Weichselian Ice Sheet reconstruction to reconstructions based on the sea level equation: aspects of ice configurations and glacial isostatic adjustment

NASA Astrophysics Data System (ADS)

Schmidt, P.; Lund, B.; Näslund, J.-O.; Fastook, J.

2014-05-01

In this study we compare a recent reconstruction of the Weichselian Ice Sheet as simulated by the University of Maine ice sheet model (UMISM) to two reconstructions commonly used in glacial isostatic adjustment (GIA) modelling: ICE-5G and ANU (Australian National University, also known as RSES). The UMISM reconstruction is carried out on a regional scale based on thermo-mechanical modelling, whereas ANU and ICE-5G are global models based on the sea level equation. The three models of the Weichselian Ice Sheet are compared directly in terms of ice volume, extent and thickness, as well as in terms of predicted glacial isostatic adjustment in Fennoscandia. The three reconstructions display significant differences. Whereas UMISM and ANU includes phases of pronounced advance and retreat prior to the last glacial maximum (LGM), the thickness and areal extent of the ICE-5G ice sheet is more or less constant up until the LGM. During the post-LGM deglaciation phase ANU and ICE-5G melt relatively uniformly over the entire ice sheet in contrast to UMISM, which melts preferentially from the edges, thus reflecting the fundamental difference in the reconstruction scheme. We find that all three reconstructions fit the present-day uplift rates over Fennoscandia equally well, albeit with different optimal earth model parameters. Given identical earth models, ICE-5G predicts the fastest present-day uplift rates, and ANU the slowest. Moreover, only for ANU can a unique best-fit model be determined. For UMISM and ICE-5G there is a range of earth models that can reproduce the present-day uplift rates equally well. This is understood from the higher present-day uplift rates predicted by ICE-5G and UMISM, which result in bifurcations in the best-fit upper- and lower-mantle viscosities. We study the areal distributions of present-day residual surface velocities in Fennoscandia and show that all three reconstructions generally over-predict velocities in southwestern Fennoscandia and that there are large differences in the fit to the observational data in Finland and northernmost Sweden and Norway. These difference may provide input to further enhancements of the ice sheet reconstructions.

Chronic Ankle Instability: Evolution of the Model

PubMed Central

Hiller, Claire E.; Kilbreath, Sharon L.; Refshauge, Kathryn M.

2011-01-01

Abstract Context: The Hertel model of chronic ankle instability (CAI) is commonly used in research but may not be sufficiently comprehensive. Mechanical instability and functional instability are considered part of a continuum, and recurrent sprain occurs when both conditions are present. A modification of the Hertel model is proposed whereby these 3 components can exist independently or in combination. Objective: To examine the fit of data from people with CAI to 2 CAI models and to explore whether the different subgroups display impairments when compared with a control group. Design: Cross-sectional study. Patients or Other Participants: Community-dwelling adults and adolescent dancers were recruited: 137 ankles with ankle sprain for objective 1 and 81 with CAI and 43 controls for objective 2. Intervention(s): Two balance tasks and time to recover from an inversion perturbation were assessed to determine if the subgroups demonstrated impairments when compared with a control group (objective 2). Main Outcome Measure(s): For objective 1 (fit to the 2 models), outcomes were Cumberland Ankle Instability Tool score, anterior drawer test results, and number of sprains. For objective 2, outcomes were 2 balance tasks (number of foot lifts in 30 seconds, ability to balance on the ball of the foot) and time to recover from an inversion perturbation. The Cohen d was calculated to compare each subgroup with the control group. Results: A total of 56.5% of ankles (n = 61) fit the Hertel model, whereas all ankles (n = 108) fit the proposed model. In the proposed model, 42.6% of ankles were classified as perceived instability, 30.5% as recurrent sprain and perceived instability, and 26.9% as among the remaining groups. All CAI subgroups performed more poorly on the balance and inversion-perturbation tasks than the control group. Subgroups with perceived instability had greater impairment in single-leg stance, whereas participants with recurrent sprain performed more poorly than the other subgroups when balancing on the ball of the foot. Only individuals with hypomobility appeared unimpaired when recovering from an inversion perturbation. Conclusions: The new model of CAI is supported by the available data. Perceived instability alone and in combination characterized the majority of participants. Several impairments distinguished the sprain groups from the control group. PMID:21391798

Univariate and multivariate spatial models of health facility utilisation for childhood fevers in an area on the coast of Kenya.

PubMed

Ouma, Paul O; Agutu, Nathan O; Snow, Robert W; Noor, Abdisalan M

2017-09-18

Precise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors. A theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test. The best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R 2  = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values. Our study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time.

Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.

PubMed

Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J

2010-12-01

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies conservation planning. Journal compilation © 2010 Society for Conservation Biology. No claim to original US government works.

Conditional statistical inference with multistage testing designs.

PubMed

Zwitser, Robert J; Maris, Gunter

2015-03-01

In this paper it is demonstrated how statistical inference from multistage test designs can be made based on the conditional likelihood. Special attention is given to parameter estimation, as well as the evaluation of model fit. Two reasons are provided why the fit of simple measurement models is expected to be better in adaptive designs, compared to linear designs: more parameters are available for the same number of observations; and undesirable response behavior, like slipping and guessing, might be avoided owing to a better match between item difficulty and examinee proficiency. The results are illustrated with simulated data, as well as with real data.

Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network.

PubMed

Wang, K W; Deng, C; Li, J P; Zhang, Y Y; Li, X Y; Wu, M C

2017-04-01

Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

Turkish version of the Academic Motivation Scale.

PubMed

Can, Gürhan

2015-04-01

The purpose of this study was to adapt the college version of the Academic Motivation Scale (AMS) into Turkish. The participants were 797 college students (437 men, 360 women) with a mean age of 20.1 yr. A seven-factor model of the scale, as well as alternative models (five-, three-, two-, and one-factor models) were investigated and compared through confirmatory factor analysis. The seven-factor model demonstrated adequate fit to the data. The fit indices obtained from the five-factor model were acceptable also. Hancock's coefficient H values and test-retest correlation coefficients of the subscales indicated that reliability of the scale was adequate except for the identified regulation subscale. The CFA conducted for the groups of men and women produced more acceptable fit indices values for men than women, but women obtained significantly higher scores from the AMS subscales. Correlations among the seven subscales partially supported the simplex pattern which claims that the neighboring subscales should have stronger positive correlations than the non-neighboring subscales and that the subscales which are the farthest apart should have the strongest negative relationships.

Population patterns in World’s administrative units

PubMed Central

Miramontes, Pedro; Cocho, Germinal

2017-01-01

Whereas there has been an extended discussion concerning city population distribution, little has been said about that of administrative divisions. In this work, we investigate the population distribution of second-level administrative units of 150 countries and territories and propose the discrete generalized beta distribution (DGBD) rank-size function to describe the data. After testing the balance between the goodness of fit and number of parameters of this function compared with a power law, which is the most common model for city population, the DGBD is a good statistical model for 96% of our datasets and preferred over a power law in almost every case. Moreover, the DGBD is preferred over a power law for fitting country population data, which can be seen as the zeroth-level administrative unit. We present a computational toy model to simulate the formation of administrative divisions in one dimension and give numerical evidence that the DGBD arises from a particular case of this model. This model, along with the fitting of the DGBD, proves adequate in reproducing and describing local unit evolution and its effect on the population distribution. PMID:28791153

Improving RNA nearest neighbor parameters for helices by going beyond the two-state model.

PubMed

Spasic, Aleksandar; Berger, Kyle D; Chen, Jonathan L; Seetin, Matthew G; Turner, Douglas H; Mathews, David H

2018-06-01

RNA folding free energy change nearest neighbor parameters are widely used to predict folding stabilities of secondary structures. They were determined by linear regression to datasets of optical melting experiments on small model systems. Traditionally, the optical melting experiments are analyzed assuming a two-state model, i.e. a structure is either complete or denatured. Experimental evidence, however, shows that structures exist in an ensemble of conformations. Partition functions calculated with existing nearest neighbor parameters predict that secondary structures can be partially denatured, which also directly conflicts with the two-state model. Here, a new approach for determining RNA nearest neighbor parameters is presented. Available optical melting data for 34 Watson-Crick helices were fit directly to a partition function model that allows an ensemble of conformations. Fitting parameters were the enthalpy and entropy changes for helix initiation, terminal AU pairs, stacks of Watson-Crick pairs and disordered internal loops. The resulting set of nearest neighbor parameters shows a 38.5% improvement in the sum of residuals in fitting the experimental melting curves compared to the current literature set.

Less is more? Assessing the validity of the ICD-11 model of PTSD across multiple trauma samples

PubMed Central

Hansen, Maj; Hyland, Philip; Armour, Cherie; Shevlin, Mark; Elklit, Ask

2015-01-01

Background In the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the symptom profile of posttraumatic stress disorder (PTSD) was expanded to include 20 symptoms. An alternative model of PTSD is outlined in the proposed 11th edition of the International Classification of Diseases (ICD-11) that includes just six symptoms. Objectives and method The objectives of the current study are: 1) to independently investigate the fit of the ICD-11 model of PTSD, and three DSM-5-based models of PTSD, across seven different trauma samples (N=3,746) using confirmatory factor analysis; 2) to assess the concurrent validity of the ICD-11 model of PTSD; and 3) to determine if there are significant differences in diagnostic rates between the ICD-11 guidelines and the DSM-5 criteria. Results The ICD-11 model of PTSD was found to provide excellent model fit in six of the seven trauma samples, and tests of factorial invariance showed that the model performs equally well for males and females. DSM-5 models provided poor fit of the data. Concurrent validity was established as the ICD-11 PTSD factors were all moderately to strongly correlated with scores of depression, anxiety, dissociation, and aggression. Levels of association were similar for ICD-11 and DSM-5 suggesting that explanatory power is not affected due to the limited number of items included in the ICD-11 model. Diagnostic rates were significantly lower according to ICD-11 guidelines compared to the DSM-5 criteria. Conclusions The proposed factor structure of the ICD-11 model of PTSD appears valid across multiple trauma types, possesses good concurrent validity, and is more stringent in terms of diagnosis compared to the DSM-5 criteria. PMID:26450830

Less is more? Assessing the validity of the ICD-11 model of PTSD across multiple trauma samples.

PubMed

Hansen, Maj; Hyland, Philip; Armour, Cherie; Shevlin, Mark; Elklit, Ask

2015-01-01

In the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the symptom profile of posttraumatic stress disorder (PTSD) was expanded to include 20 symptoms. An alternative model of PTSD is outlined in the proposed 11th edition of the International Classification of Diseases (ICD-11) that includes just six symptoms. The objectives of the current study are: 1) to independently investigate the fit of the ICD-11 model of PTSD, and three DSM-5-based models of PTSD, across seven different trauma samples (N=3,746) using confirmatory factor analysis; 2) to assess the concurrent validity of the ICD-11 model of PTSD; and 3) to determine if there are significant differences in diagnostic rates between the ICD-11 guidelines and the DSM-5 criteria. The ICD-11 model of PTSD was found to provide excellent model fit in six of the seven trauma samples, and tests of factorial invariance showed that the model performs equally well for males and females. DSM-5 models provided poor fit of the data. Concurrent validity was established as the ICD-11 PTSD factors were all moderately to strongly correlated with scores of depression, anxiety, dissociation, and aggression. Levels of association were similar for ICD-11 and DSM-5 suggesting that explanatory power is not affected due to the limited number of items included in the ICD-11 model. Diagnostic rates were significantly lower according to ICD-11 guidelines compared to the DSM-5 criteria. The proposed factor structure of the ICD-11 model of PTSD appears valid across multiple trauma types, possesses good concurrent validity, and is more stringent in terms of diagnosis compared to the DSM-5 criteria.

Vastly accelerated linear least-squares fitting with numerical optimization for dual-input delay-compensated quantitative liver perfusion mapping.

PubMed

Jafari, Ramin; Chhabra, Shalini; Prince, Martin R; Wang, Yi; Spincemaille, Pascal

2018-04-01

To propose an efficient algorithm to perform dual input compartment modeling for generating perfusion maps in the liver. We implemented whole field-of-view linear least squares (LLS) to fit a delay-compensated dual-input single-compartment model to very high temporal resolution (four frames per second) contrast-enhanced 3D liver data, to calculate kinetic parameter maps. Using simulated data and experimental data in healthy subjects and patients, whole-field LLS was compared with the conventional voxel-wise nonlinear least-squares (NLLS) approach in terms of accuracy, performance, and computation time. Simulations showed good agreement between LLS and NLLS for a range of kinetic parameters. The whole-field LLS method allowed generating liver perfusion maps approximately 160-fold faster than voxel-wise NLLS, while obtaining similar perfusion parameters. Delay-compensated dual-input liver perfusion analysis using whole-field LLS allows generating perfusion maps with a considerable speedup compared with conventional voxel-wise NLLS fitting. Magn Reson Med 79:2415-2421, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Paleotemperature reconstruction from mammalian phosphate δ18O records - an alternative view on data processing

NASA Astrophysics Data System (ADS)

Skrzypek, Grzegorz; Sadler, Rohan; Wiśniewski, Andrzej

2017-04-01

The stable oxygen isotope composition of phosphates (δ18O) extracted from mammalian bone and teeth material is commonly used as a proxy for paleotemperature. Historically, several different analytical and statistical procedures for determining air paleotemperatures from the measured δ18O of phosphates have been applied. This inconsistency in both stable isotope data processing and the application of statistical procedures has led to large and unwanted differences between calculated results. This study presents the uncertainty associated with two of the most commonly used regression methods: least squares inverted fit and transposed fit. We assessed the performance of these methods by designing and applying calculation experiments to multiple real-life data sets, calculating in reverse temperatures, and comparing them with true recorded values. Our calculations clearly show that the mean absolute errors are always substantially higher for the inverted fit (a causal model), with the transposed fit (a predictive model) returning mean values closer to the measured values (Skrzypek et al. 2015). The predictive models always performed better than causal models, with 12-65% lower mean absolute errors. Moreover, the least-squares regression (LSM) model is more appropriate than Reduced Major Axis (RMA) regression for calculating the environmental water stable oxygen isotope composition from phosphate signatures, as well as for calculating air temperature from the δ18O value of environmental water. The transposed fit introduces a lower overall error than the inverted fit for both the δ18O of environmental water and Tair calculations; therefore, the predictive models are more statistically efficient than the causal models in this instance. The direct comparison of paleotemperature results from different laboratories and studies may only be achieved if a single method of calculation is applied. Reference Skrzypek G., Sadler R., Wiśniewski A., 2016. Reassessment of recommendations for processing mammal phosphate δ18O data for paleotemperature reconstruction. Palaeogeography, Palaeoclimatology, Palaeoecology 446, 162-167.

Level-Specific Evaluation of Model Fit in Multilevel Structural Equation Modeling

ERIC Educational Resources Information Center

Ryu, Ehri; West, Stephen G.

2009-01-01

In multilevel structural equation modeling, the "standard" approach to evaluating the goodness of model fit has a potential limitation in detecting the lack of fit at the higher level. Level-specific model fit evaluation can address this limitation and is more informative in locating the source of lack of model fit. We proposed level-specific test…

A simple computational algorithm of model-based choice preference.

PubMed

Toyama, Asako; Katahira, Kentaro; Ohira, Hideki

2017-08-01

A broadly used computational framework posits that two learning systems operate in parallel during the learning of choice preferences-namely, the model-free and model-based reinforcement-learning systems. In this study, we examined another possibility, through which model-free learning is the basic system and model-based information is its modulator. Accordingly, we proposed several modified versions of a temporal-difference learning model to explain the choice-learning process. Using the two-stage decision task developed by Daw, Gershman, Seymour, Dayan, and Dolan (2011), we compared their original computational model, which assumes a parallel learning process, and our proposed models, which assume a sequential learning process. Choice data from 23 participants showed a better fit with the proposed models. More specifically, the proposed eligibility adjustment model, which assumes that the environmental model can weight the degree of the eligibility trace, can explain choices better under both model-free and model-based controls and has a simpler computational algorithm than the original model. In addition, the forgetting learning model and its variation, which assume changes in the values of unchosen actions, substantially improved the fits to the data. Overall, we show that a hybrid computational model best fits the data. The parameters used in this model succeed in capturing individual tendencies with respect to both model use in learning and exploration behavior. This computational model provides novel insights into learning with interacting model-free and model-based components.

FUNGIBILITY AND CONSUMER CHOICE: EVIDENCE FROM COMMODITY PRICE SHOCKS.

PubMed

Hastings, Justine S; Shapiro, Jesse M

2013-11-01

We formulate a test of the fungibility of money based on parallel shifts in the prices of different quality grades of a commodity. We embed the test in a discrete-choice model of product quality choice and estimate the model using panel microdata on gasoline purchases. We find that when gasoline prices rise consumers substitute to lower octane gasoline, to an extent that cannot be explained by income effects. Across a wide range of specifications, we consistently reject the null hypothesis that households treat "gas money" as fungible with other income. We compare the empirical fit of three psychological models of decision-making. A simple model of category budgeting fits the data well, with models of loss aversion and salience both capturing important features of the time series.

FUNGIBILITY AND CONSUMER CHOICE: EVIDENCE FROM COMMODITY PRICE SHOCKS*

PubMed Central

Hastings, Justine S.; Shapiro, Jesse M.

2015-01-01

We formulate a test of the fungibility of money based on parallel shifts in the prices of different quality grades of a commodity. We embed the test in a discrete-choice model of product quality choice and estimate the model using panel microdata on gasoline purchases. We find that when gasoline prices rise consumers substitute to lower octane gasoline, to an extent that cannot be explained by income effects. Across a wide range of specifications, we consistently reject the null hypothesis that households treat “gas money” as fungible with other income. We compare the empirical fit of three psychological models of decision-making. A simple model of category budgeting fits the data well, with models of loss aversion and salience both capturing important features of the time series. PMID:26937053

A longitudinal examination of the link between youth physical fitness and academic achievement.

PubMed

London, Rebecca A; Castrechini, Sebastian

2011-07-01

Childhood obesity has been linked with other persistent health problems, but research is just beginning to examine its relationship with academic performance. This article tracks students longitudinally to examine the ways student physical fitness and changes in fitness align with school performance. Using matched administrative data and individual growth modeling, we examine the relationship between academic achievement and overall physical fitness longitudinally from fourth to seventh and sixth to ninth grades for students in a California community. Comparing those who are persistently fit to those who are persistently unfit, we find disparities in both math and English language arts test scores. These academic disparities begin even before students begin fitness testing in fifth grade and are larger for girls and Latinos. Overall physical fitness is a better predictor of academic achievement than obesity as measured by body mass index. Socioeconomic status acts as a buffer for those who have poor physical fitness but strong academic performance. The findings indicate the presence of a physical fitness achievement gap that has consequences for potential students' future educational and health outcomes. This gap begins as early as fourth grade, which is before physical fitness testing begins in California. © 2011, American School Health Association.

Adiposity as a full mediator of the influence of cardiorespiratory fitness and inflammation in schoolchildren: The FUPRECOL Study.

PubMed

Garcia-Hermoso, A; Agostinis-Sobrinho, C; Mota, J; Santos, R M; Correa-Bautista, J E; Ramírez-Vélez, R

2017-06-01

Studies in the paediatric population have shown inconsistent associations between cardiorespiratory fitness and inflammation independently of adiposity. The purpose of this study was (i) to analyse the combined association of cardiorespiratory fitness and adiposity with high-sensitivity C-reactive protein (hs-CRP), and (ii) to determine whether adiposity acts as a mediator on the association between cardiorespiratory fitness and hs-CRP in children and adolescents. This cross-sectional study included 935 (54.7% girls) healthy children and adolescents from Bogotá, Colombia. The 20 m shuttle run test was used to estimate cardiorespiratory fitness. We assessed the following adiposity parameters: body mass index, waist circumference, and fat mass index and the sum of subscapular and triceps skinfold thickness. High sensitivity assays were used to obtain hs-CRP. Linear regression models were fitted for mediation analyses examined whether the association between cardiorespiratory fitness and hs-CRP was mediated by each of adiposity parameters according to Baron and Kenny procedures. Lower levels of hs-CRP were associated with the best schoolchildren profiles (high cardiorespiratory fitness + low adiposity) (p for trend <0.001 in the four adiposity parameters), compared with unfit and overweight (low cardiorespiratory fitness + high adiposity) counterparts. Linear regression models suggest a full mediation of adiposity on the association between cardiorespiratory fitness and hs-CRP levels. Our findings seem to emphasize the importance of obesity prevention in childhood, suggesting that having high levels of cardiorespiratory fitness may not counteract the negative consequences ascribed to adiposity on hs-CRP. Copyright © 2017 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

Right-Sizing Statistical Models for Longitudinal Data

PubMed Central

Wood, Phillip K.; Steinley, Douglas; Jackson, Kristina M.

2015-01-01

Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to “right-size” the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting overly parsimonious models to more complex better fitting alternatives, and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically under-identified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A three-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation/covariation patterns. The orthogonal, free-curve slope-intercept (FCSI) growth model is considered as a general model which includes, as special cases, many models including the Factor Mean model (FM, McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, Hierarchical Linear Models (HLM), Repeated Measures MANOVA, and the Linear Slope Intercept (LinearSI) Growth Model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparison of several candidate parametric growth and chronometric models in a Monte Carlo study. PMID:26237507

The adventure of carbon stars. Observations and modeling of a set of C-rich AGB stars

NASA Astrophysics Data System (ADS)

Rau, G.; Hron, J.; Paladini, C.; Aringer, B.; Eriksson, K.; Marigo, P.; Nowotny, W.; Grellmann, R.

2017-04-01

Context. Modeling stellar atmospheres is a complex and intriguing task in modern astronomy. A systematic comparison of models with multi-technique observations is the only efficient way to constrain the models. Aims: We intend to perform self-consistent modeling of the atmospheres of six carbon-rich AGB stars (R Lep, R Vol, Y Pav, AQ Sgr, U Hya, and X TrA) with the aim of enlarging the knowledge of the dynamic processes occurring in their atmospheres. Methods: We used VLTI/MIDI interferometric observations, in combination with spectro-photometric data, and compared them with self-consistent, dynamic model atmospheres. Results: We found that the models can reproduce spectral energy distribution (SED) data well at wavelengths longer than 1 μm, and the interferometric observations between 8 μm and 10 μm. Discrepancies observed at wavelengths shorter than 1 μm in the SED, and longer than 10 μm in the visibilities, could be due to a combination of data- and model-related effects. The models best fitting the Miras are significantly extended, and have a prominent shell-like structure. On the contrary, the models best fitting the non-Miras are more compact, showing lower average mass loss. The mass loss is of episodic or multi-periodic nature but causes the visual amplitudes to be notably larger than the observed ones. A number of stellar parameters were derived from the model fitting: TRoss, LRoss, M, C/O, and Ṁ. Our findings agree well with literature values within the uncertainties. TRoss, and LRoss are also in good agreement with the temperature derived from the angular diameter T(θ(V-K)) and the bolometric luminosity from the SED fitting Lbol, except for AQ Sgr. The possible reasons are discussed in the text. Finally, θRoss and θ(V-K) agree with one another better for the Miras than for the non-Miras targets, which is probably connected to the episodic nature of the latter models. We also located the stars in the H-R diagram, comparing them with evolutionary tracks. We found that the main derived properties (L, Teff, C/O ratios and stellar masses) from the model fitting are in good agreement with TP-AGB evolutionary calculations for carbon stars carried out with the COLIBRI code. Based on observations made with ESO telescopes at La Silla Paranal Observatory under program IDs: 090.D-0410, 086.D-899, 187.D-0924, 081.D-0021, 086.D-0899.

SU-E-I-07: Response Characteristics and Signal Conversion Modeling of KV Flat-Panel Detector in Cone Beam CT System

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

Wang, Yu; Cao, Ruifen; Pei, Xi

2015-06-15

Purpose: The flat-panel detector response characteristics are investigated to optimize the scanning parameter considering the image quality and less radiation dose. The signal conversion model is also established to predict the tumor shape and physical thickness changes. Methods: With the ELEKTA XVI system, the planar images of 10cm water phantom were obtained under different image acquisition conditions, including tube voltage, electric current, exposure time and frames. The averaged responses of square area in center were analyzed using Origin8.0. The response characteristics for each scanning parameter were depicted by different fitting types. The transmission measured for 10cm water was compared tomore » Monte Carlo simulation. Using the quadratic calibration method, a series of variable-thickness water phantoms images were acquired to derive the signal conversion model. A 20cm wedge water phantom with 2cm step thickness was used to verify the model. At last, the stability and reproducibility of the model were explored during a four week period. Results: The gray values of image center all decreased with the increase of different image acquisition parameter presets. The fitting types adopted were linear fitting, quadratic polynomial fitting, Gauss fitting and logarithmic fitting with the fitting R-Square 0.992, 0.995, 0.997 and 0.996 respectively. For 10cm water phantom, the transmission measured showed better uniformity than Monte Carlo simulation. The wedge phantom experiment show that the radiological thickness changes prediction error was in the range of (-4mm, 5mm). The signal conversion model remained consistent over a period of four weeks. Conclusion: The flat-panel response decrease with the increase of different scanning parameters. The preferred scanning parameter combination was 100kV, 10mA, 10ms, 15frames. It is suggested that the signal conversion model could effectively be used for tumor shape change and radiological thickness prediction. Supported by National Natural Science Foundation of China (81101132, 11305203) and Natural Science Foundation of Anhui Province (11040606Q55, 1308085QH138)« less

The validation of a generalized Hooke's law for coronary arteries.

PubMed

Wang, Chong; Zhang, Wei; Kassab, Ghassan S

2008-01-01

The exponential form of constitutive model is widely used in biomechanical studies of blood vessels. There are two main issues, however, with this model: 1) the curve fits of experimental data are not always satisfactory, and 2) the material parameters may be oversensitive. A new type of strain measure in a generalized Hooke's law for blood vessels was recently proposed by our group to address these issues. The new model has one nonlinear parameter and six linear parameters. In this study, the stress-strain equation is validated by fitting the model to experimental data of porcine coronary arteries. Material constants of left anterior descending artery and right coronary artery for the Hooke's law were computed with a separable nonlinear least-squares method with an excellent goodness of fit. A parameter sensitivity analysis shows that the stability of material constants is improved compared with the exponential model and a biphasic model. A boundary value problem was solved to demonstrate that the model prediction can match the measured arterial deformation under experimental loading conditions. The validated constitutive relation will serve as a basis for the solution of various boundary value problems of cardiovascular biomechanics.

Evaluation of Model Fit in Cognitive Diagnosis Models

ERIC Educational Resources Information Center

Hu, Jinxiang; Miller, M. David; Huggins-Manley, Anne Corinne; Chen, Yi-Hsin

2016-01-01

Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC),…

Adaptive and non-adaptive models of depression: A comparison using register data on antidepressant medication during divorce

PubMed Central

Fawcett, Tim W.; Higginson, Andrew D.; Metsä-Simola, Niina; Hagen, Edward H.; Houston, Alasdair I.; Martikainen, Pekka

2017-01-01

Divorce is associated with an increased probability of a depressive episode, but the causation of events remains unclear. Adaptive models of depression propose that depression is a social strategy in part, whereas non-adaptive models tend to propose a diathesis-stress mechanism. We compare an adaptive evolutionary model of depression to three alternative non-adaptive models with respect to their ability to explain the temporal pattern of depression around the time of divorce. Register-based data (304,112 individuals drawn from a random sample of 11% of Finnish people) on antidepressant purchases is used as a proxy for depression. This proxy affords an unprecedented temporal resolution (a 3-monthly prevalence estimates over 10 years) without any bias from non-compliance, and it can be linked with underlying episodes via a statistical model. The evolutionary-adaptation model (all time periods with risk of divorce are depressogenic) was the best quantitative description of the data. The non-adaptive stress-relief model (period before divorce is depressogenic and period afterwards is not) provided the second best quantitative description of the data. The peak-stress model (periods before and after divorce can be depressogenic) fit the data less well, and the stress-induction model (period following divorce is depressogenic and the preceding period is not) did not fit the data at all. The evolutionary model was the most detailed mechanistic description of the divorce-depression link among the models, and the best fit in terms of predicted curvature; thus, it offers most rigorous hypotheses for further study. The stress-relief model also fit very well and was the best model in a sensitivity analysis, encouraging development of more mechanistic models for that hypothesis. PMID:28614385

Adaptive and non-adaptive models of depression: A comparison using register data on antidepressant medication during divorce.

PubMed

Rosenström, Tom; Fawcett, Tim W; Higginson, Andrew D; Metsä-Simola, Niina; Hagen, Edward H; Houston, Alasdair I; Martikainen, Pekka

2017-01-01

Divorce is associated with an increased probability of a depressive episode, but the causation of events remains unclear. Adaptive models of depression propose that depression is a social strategy in part, whereas non-adaptive models tend to propose a diathesis-stress mechanism. We compare an adaptive evolutionary model of depression to three alternative non-adaptive models with respect to their ability to explain the temporal pattern of depression around the time of divorce. Register-based data (304,112 individuals drawn from a random sample of 11% of Finnish people) on antidepressant purchases is used as a proxy for depression. This proxy affords an unprecedented temporal resolution (a 3-monthly prevalence estimates over 10 years) without any bias from non-compliance, and it can be linked with underlying episodes via a statistical model. The evolutionary-adaptation model (all time periods with risk of divorce are depressogenic) was the best quantitative description of the data. The non-adaptive stress-relief model (period before divorce is depressogenic and period afterwards is not) provided the second best quantitative description of the data. The peak-stress model (periods before and after divorce can be depressogenic) fit the data less well, and the stress-induction model (period following divorce is depressogenic and the preceding period is not) did not fit the data at all. The evolutionary model was the most detailed mechanistic description of the divorce-depression link among the models, and the best fit in terms of predicted curvature; thus, it offers most rigorous hypotheses for further study. The stress-relief model also fit very well and was the best model in a sensitivity analysis, encouraging development of more mechanistic models for that hypothesis.

NEUTRON STAR MASS–RADIUS CONSTRAINTS USING EVOLUTIONARY OPTIMIZATION

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

Stevens, A. L.; Morsink, S. M.; Fiege, J. D.

The equation of state of cold supra-nuclear-density matter, such as in neutron stars, is an open question in astrophysics. A promising method for constraining the neutron star equation of state is modeling pulse profiles of thermonuclear X-ray burst oscillations from hot spots on accreting neutron stars. The pulse profiles, constructed using spherical and oblate neutron star models, are comparable to what would be observed by a next-generation X-ray timing instrument like ASTROSAT , NICER , or a mission similar to LOFT . In this paper, we showcase the use of an evolutionary optimization algorithm to fit pulse profiles to determinemore » the best-fit masses and radii. By fitting synthetic data, we assess how well the optimization algorithm can recover the input parameters. Multiple Poisson realizations of the synthetic pulse profiles, constructed with 1.6 million counts and no background, were fitted with the Ferret algorithm to analyze both statistical and degeneracy-related uncertainty and to explore how the goodness of fit depends on the input parameters. For the regions of parameter space sampled by our tests, the best-determined parameter is the projected velocity of the spot along the observer’s line of sight, with an accuracy of ≤3% compared to the true value and with ≤5% statistical uncertainty. The next best determined are the mass and radius; for a neutron star with a spin frequency of 600 Hz, the best-fit mass and radius are accurate to ≤5%, with respective uncertainties of ≤7% and ≤10%. The accuracy and precision depend on the observer inclination and spot colatitude, with values of ∼1% achievable in mass and radius if both the inclination and colatitude are ≳60°.« less

Investigating the Differential Emission Measure and Energetics of Microflares with Combined SDO/AIA and RHESSI Observations

NASA Technical Reports Server (NTRS)

Inglis, A. R.; Christe, S.

2014-01-01

An important question in solar physics is whether solar microflares, the smallest currently observable flare events in X-rays, possess the same energetic properties as large flares. Recent surveys have suggested that microflares may be less efficient particle accelerators than large flares, and hence contribute less non-thermal energy, which may have implications for coronal heating mechanisms. We therefore explore the energetic properties of microflares by combining EUV and X-ray measurements. We present forward-fitting differential emission measure (DEM) analysis of 10 microflares. The fitting is constrained by combining, for the first time, high-temperature Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) observations and flux data from the Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA). Two fitting models are tested for the DEM; a Gaussian distribution and a uniform DEM profile. A Gaussian fit proved unable to explain the observations for any of the studied microflares. However, 8 of 10 events studied were reasonably fit by a uniform DEM profile. Hence microflare plasma can be considered to be significantly multi-thermal, and may not be significantly peaked or contain resolvable fine structure, within the uncertainties of the observational instruments. The thermal and non-thermal energy is estimated for each microflare, comparing the energy budget with an isothermal plasma assumption. From the multi-thermal fits the minimum non-thermal energy content was found to average approximately 30% of the estimated thermal energy. By comparison, under an isothermal model the non-thermal and thermal energy estimates were generally comparable. Hence, multi-thermal plasma is an important consideration for solar microflares that substantially alters their thermal and non-thermal energy content.

Relationship of physical fitness test results and hockey playing potential in elite-level ice hockey players.

PubMed

Burr, Jaime F; Jamnik, Roni K; Baker, Joseph; Macpherson, Alison; Gledhill, Norman; McGuire, E J

2008-09-01

The primary purpose of this study was to determine the fitness variables with the highest capability for predicting hockey playing potential at the elite level as determined by entry draft selection order. We also examined the differences associated with the predictive abilities of the test components among playing positions. The secondary purpose of this study was to update the physiological profile of contemporary hockey players including positional differences. Fitness test results conducted by our laboratory at the National Hockey League Entry Draft combine were compared with draft selection order on a total of 853 players. Regression models revealed peak anaerobic power output to be important for higher draft round selection in all positions; however, the degree of importance of this measurement varied with playing position. The body index, which is a composite score of height, lean mass, and muscular development, was similarly important in all models, with differing influence by position. Removal of the goalies' data increased predictive capacity, suggesting that talent identification using physical fitness testing of this sort may be more appropriate for skating players. Standing long jump was identified as a significant predictor variable for forwards and defense and could be a useful surrogate for assessing overall hockey potential. Significant differences exist between the physiological profiles of current players based on playing position. There are also positional differences in the relative importance of anthropometric and fitness measures of off-ice hockey tests in relation to draft order. Physical fitness measures and anthropometric data are valuable in helping predict hockey playing potential. Emphasis on anthropometry should be used when comparing elite-level forwards, whereas peak anaerobic power and fatigue rate are more useful for differentiating between defense.

Studying the evolutionary significance of thermal adaptation in ectotherms: The diversification of amphibians' energetics.

PubMed

Nespolo, Roberto F; Figueroa, Julio; Solano-Iguaran, Jaiber J

2017-08-01

A fundamental problem in evolutionary biology is the understanding of the factors that promote or constrain adaptive evolution, and assessing the role of natural selection in this process. Here, comparative phylogenetics, that is, using phylogenetic information and traits to infer evolutionary processes has been a major paradigm . In this study, we discuss Ornstein-Uhlenbeck models (OU) in the context of thermal adaptation in ectotherms. We specifically applied this approach to study amphibians's evolution and energy metabolism. It has been hypothesized that amphibians exploit adaptive zones characterized by low energy expenditure, which generate specific predictions in terms of the patterns of diversification in standard metabolic rate (SMR). We complied whole-animal metabolic rates for 122 species of amphibians, and adjusted several models of diversification. According to the adaptive zone hypothesis, we expected: (1) to find "accelerated evolution" in SMR (i.e., diversification above Brownian Motion expectations, BM), (2) that a model assuming evolutionary optima (i.e., an OU model) fits better than a white-noise model and (3) that a model assuming multiple optima (according to the three amphibians's orders) fits better than a model assuming a single optimum. As predicted, we found that the diversification of SMR occurred most of the time, above BM expectations. Also, we found that a model assuming an optimum explained the data in a better way than a white-noise model. However, we did not find evidence that an OU model with multiple optima fits the data better, suggesting a single optimum in SMR for Anura, Caudata and Gymnophiona. These results show how comparative phylogenetics could be applied for testing adaptive hypotheses regarding history and physiological performance in ectotherms. Copyright © 2016 Elsevier Ltd. All rights reserved.

Radiative Transfer Modeling and Retrievals for Advanced Hyperspectral Sensors

NASA Technical Reports Server (NTRS)

Liu, Xu; Zhou, Daniel K.; Larar, Allen M.; Smith, William L., Sr.; Mango, Stephen A.

2009-01-01

A novel radiative transfer model and a physical inversion algorithm based on principal component analysis will be presented. Instead of dealing with channel radiances, the new approach fits principal component scores of these quantities. Compared to channel-based radiative transfer models, the new approach compresses radiances into a much smaller dimension making both forward modeling and inversion algorithm more efficient.

A comparative modeling study of a dual tracer experiment in a large lysimeter under atmospheric conditions

NASA Astrophysics Data System (ADS)

Stumpp, C.; Nützmann, G.; Maciejewski, S.; Maloszewski, P.

2009-09-01

SummaryIn this paper, five model approaches with different physical and mathematical concepts varying in their model complexity and requirements were applied to identify the transport processes in the unsaturated zone. The applicability of these model approaches were compared and evaluated investigating two tracer breakthrough curves (bromide, deuterium) in a cropped, free-draining lysimeter experiment under natural atmospheric boundary conditions. The data set consisted of time series of water balance, depth resolved water contents, pressure heads and resident concentrations measured during 800 days. The tracer transport parameters were determined using a simple stochastic (stream tube model), three lumped parameter (constant water content model, multi-flow dispersion model, variable flow dispersion model) and a transient model approach. All of them were able to fit the tracer breakthrough curves. The identified transport parameters of each model approach were compared. Despite the differing physical and mathematical concepts the resulting parameters (mean water contents, mean water flux, dispersivities) of the five model approaches were all in the same range. The results indicate that the flow processes are also describable assuming steady state conditions. Homogeneous matrix flow is dominant and a small pore volume with enhanced flow velocities near saturation was identified with variable saturation flow and transport approach. The multi-flow dispersion model also identified preferential flow and additionally suggested a third less mobile flow component. Due to high fitting accuracy and parameter similarity all model approaches indicated reliable results.

3D spherical-cap fitting procedure for (truncated) sessile nano- and micro-droplets & -bubbles.

PubMed

Tan, Huanshu; Peng, Shuhua; Sun, Chao; Zhang, Xuehua; Lohse, Detlef

2016-11-01

In the study of nanobubbles, nanodroplets or nanolenses immobilised on a substrate, a cross-section of a spherical cap is widely applied to extract geometrical information from atomic force microscopy (AFM) topographic images. In this paper, we have developed a comprehensive 3D spherical-cap fitting procedure (3D-SCFP) to extract morphologic characteristics of complete or truncated spherical caps from AFM images. Our procedure integrates several advanced digital image analysis techniques to construct a 3D spherical-cap model, from which the geometrical parameters of the nanostructures are extracted automatically by a simple algorithm. The procedure takes into account all valid data points in the construction of the 3D spherical-cap model to achieve high fidelity in morphology analysis. We compare our 3D fitting procedure with the commonly used 2D cross-sectional profile fitting method to determine the contact angle of a complete spherical cap and a truncated spherical cap. The results from 3D-SCFP are consistent and accurate, while 2D fitting is unavoidably arbitrary in the selection of the cross-section and has a much lower number of data points on which the fitting can be based, which in addition is biased to the top of the spherical cap. We expect that the developed 3D spherical-cap fitting procedure will find many applications in imaging analysis.

Finding the optimal lengths for three branches at a junction.

PubMed

Woldenberg, M J; Horsfield, K

1983-09-21

This paper presents an exact analytical solution to the problem of locating the junction point between three branches so that the sum of the total costs of the branches is minimized. When the cost per unit length of each branch is known the angles between each pair of branches can be deduced following reasoning first introduced to biology by Murray. Assuming the outer ends of each branch are fixed, the location of the junction and the length of each branch are then deduced using plane geometry and trigonometry. The model has applications in determining the optimal cost of a branch or branches at a junction. Comparing the optimal to the actual cost of a junction is a new way to compare cost models for goodness of fit to actual junction geometry. It is an unambiguous measure and is superior to comparing observed and optimal angles between each daughter and the parent branch. We present data for 199 junctions in the pulmonary arteries of two human lungs. For the branches at each junction we calculated the best fitting value of x from the relationship that flow alpha (radius)x. We found that the value of x determined whether a junction was best fitted by a surface, volume, drag or power minimization model. While economy of explanation casts doubt that four models operate simultaneously, we found that optimality may still operate, since the angle to the major daughter is less than the angle to the minor daughter. Perhaps optimality combined with a space filling branching pattern governs the branching geometry of the pulmonary artery.

The Model Human Processor and the Older Adult: Parameter Estimation and Validation Within a Mobile Phone Task

PubMed Central

Jastrzembski, Tiffany S.; Charness, Neil

2009-01-01

The authors estimate weighted mean values for nine information processing parameters for older adults using the Card, Moran, and Newell (1983) Model Human Processor model. The authors validate a subset of these parameters by modeling two mobile phone tasks using two different phones and comparing model predictions to a sample of younger (N = 20; Mage = 20) and older (N = 20; Mage = 69) adults. Older adult models fit keystroke-level performance at the aggregate grain of analysis extremely well (R = 0.99) and produced equivalent fits to previously validated younger adult models. Critical path analyses highlighted points of poor design as a function of cognitive workload, hardware/software design, and user characteristics. The findings demonstrate that estimated older adult information processing parameters are valid for modeling purposes, can help designers understand age-related performance using existing interfaces, and may support the development of age-sensitive technologies. PMID:18194048

The Model Human Processor and the older adult: parameter estimation and validation within a mobile phone task.

PubMed

Jastrzembski, Tiffany S; Charness, Neil

2007-12-01

The authors estimate weighted mean values for nine information processing parameters for older adults using the Card, Moran, and Newell (1983) Model Human Processor model. The authors validate a subset of these parameters by modeling two mobile phone tasks using two different phones and comparing model predictions to a sample of younger (N = 20; M-sub(age) = 20) and older (N = 20; M-sub(age) = 69) adults. Older adult models fit keystroke-level performance at the aggregate grain of analysis extremely well (R = 0.99) and produced equivalent fits to previously validated younger adult models. Critical path analyses highlighted points of poor design as a function of cognitive workload, hardware/software design, and user characteristics. The findings demonstrate that estimated older adult information processing parameters are valid for modeling purposes, can help designers understand age-related performance using existing interfaces, and may support the development of age-sensitive technologies.

Retrospective Correction of Physiological Noise in DTI Using an Extended Tensor Model and Peripheral Measurements

PubMed Central

Mohammadi, Siawoosh; Hutton, Chloe; Nagy, Zoltan; Josephs, Oliver; Weiskopf, Nikolaus

2013-01-01

Diffusion tensor imaging is widely used in research and clinical applications, but this modality is highly sensitive to artefacts. We developed an easy-to-implement extension of the original diffusion tensor model to account for physiological noise in diffusion tensor imaging using measures of peripheral physiology (pulse and respiration), the so-called extended tensor model. Within the framework of the extended tensor model two types of regressors, which respectively modeled small (linear) and strong (nonlinear) variations in the diffusion signal, were derived from peripheral measures. We tested the performance of four extended tensor models with different physiological noise regressors on nongated and gated diffusion tensor imaging data, and compared it to an established data-driven robust fitting method. In the brainstem and cerebellum the extended tensor models reduced the noise in the tensor-fit by up to 23% in accordance with previous studies on physiological noise. The extended tensor model addresses both large-amplitude outliers and small-amplitude signal-changes. The framework of the extended tensor model also facilitates further investigation into physiological noise in diffusion tensor imaging. The proposed extended tensor model can be readily combined with other artefact correction methods such as robust fitting and eddy current correction. PMID:22936599

Comparing the dimensional structure and diagnostic algorithms between DSM-5 and ICD-11 PTSD in children and adolescents.

PubMed

Sachser, Cedric; Berliner, Lucy; Holt, Tonje; Jensen, Tine; Jungbluth, Nathaniel; Risch, Elizabeth; Rosner, Rita; Goldbeck, Lutz

2018-02-01

In contrast to the DSM-5, which expanded the posttraumatic stress disorder (PTSD) symptom profile to 20 symptoms, a workgroup of the upcoming ICD-11 suggested a reduced symptom profile with six symptoms for PTSD. Therefore, the objective of the study was to investigate the dimensional structure of DSM-5 and ICD-11 PTSD in a clinical sample of trauma-exposed children and adolescents and to compare the diagnostic rates of PTSD between diagnostic systems. The study sample consisted of 475 self-reports and 424 caregiver-reports on the child and adolescent trauma screen (CATS), which were collected at pediatric mental health clinics in the US, Norway and Germany. The factor structure of the PTSD construct as defined in the DSM-5 and in alternative models of both DSM-5 and ICD-11 was investigated using confirmatory factor analyses (CFA). To evaluate differences in PTSD prevalence, McNemar's tests for correlated proportions were used. CFA results demonstrated excellent model fit for the proposed ICD-11 model of PTSD. For the DSM-5 models we found the best fit for the hybrid model. Diagnostic rates were significantly lower according to ICD-11 (self-report: 23.4%; caregiver-report: 16.5%) compared with the DSM-5 (self-report: 37.8%; caregiver-report: 31.8%). Agreement was low between diagnostic systems. Study findings provide support for an alternative latent dimensionality of DSM-5 PTSD in children and adolescents. The conceptualization of ICD-11 PTSD shows an excellent fit. Inconsistent PTSD constructs and significantly diverging diagnostic rates between DSM-5 and the ICD-11 will result in major challenges for researchers and clinicians in the field of psychotraumatology.

NAT2, meat consumption and colorectal cancer incidence: an ecological study among 27 countries.

PubMed

Ognjanovic, Simona; Yamamoto, Jennifer; Maskarinec, Gertraud; Le Marchand, Loïc

2006-11-01

The polymorphic gene NAT2 is a major determinant of N-acetyltransferase activity and, thus, may be responsible for differences in one's ability to bioactivate heterocyclic amines, a class of procarcinogens in cooked meat. An unusually marked geographic variation in enzyme activity has been described for NAT2. The present study re-examines the international direct correlation reported for meat intake and colorectal cancer (CRC) incidence, and evaluates the potential modifying effects of NAT2 phenotype and other lifestyle factors on this correlation. Country-specific CRC incidence data, per capita consumption data for meat and other dietary factors, prevalence of the rapid/intermediate NAT2 phenotype, and prevalence of smoking for 27 countries were used. Multiple linear regression models were fit and partial correlation coefficients (PCCs) were computed for men and women separately. Inclusion of the rapid/intermediate NAT2 phenotype with meat consumption improved the fit of the regression model for CRC incidence in both sexes (males-R (2) = 0.78, compared to 0.70 for meat alone; p for difference in model fit-0.009; females-R (2) = 0.76 compared to 0.69 for meat alone; p = 0.02). Vegetable consumption (inversely and in both sexes) and fish consumption (directly and in men only) were also weakly correlated with CRC, whereas smoking prevalence and alcohol consumption had no effects on the models. The PCC between NAT2 and CRC incidence was 0.46 in males and 0.48 in females when meat consumption was included in the model, compared to 0.14 and 0.15, respectively, when it was not. These data suggest that, in combination with meat intake, some proportion of the international variability in CRC incidence may be attributable to genetic susceptibility to heterocyclic amines, as determined by NAT2 genotype.

An accurate halo model for fitting non-linear cosmological power spectra and baryonic feedback models

NASA Astrophysics Data System (ADS)

Mead, A. J.; Peacock, J. A.; Heymans, C.; Joudaki, S.; Heavens, A. F.

2015-12-01

We present an optimized variant of the halo model, designed to produce accurate matter power spectra well into the non-linear regime for a wide range of cosmological models. To do this, we introduce physically motivated free parameters into the halo-model formalism and fit these to data from high-resolution N-body simulations. For a variety of Λ cold dark matter (ΛCDM) and wCDM models, the halo-model power is accurate to ≃ 5 per cent for k ≤ 10h Mpc-1 and z ≤ 2. An advantage of our new halo model is that it can be adapted to account for the effects of baryonic feedback on the power spectrum. We demonstrate this by fitting the halo model to power spectra from the OWLS (OverWhelmingly Large Simulations) hydrodynamical simulation suite via parameters that govern halo internal structure. We are able to fit all feedback models investigated at the 5 per cent level using only two free parameters, and we place limits on the range of these halo parameters for feedback models investigated by the OWLS simulations. Accurate predictions to high k are vital for weak-lensing surveys, and these halo parameters could be considered nuisance parameters to marginalize over in future analyses to mitigate uncertainty regarding the details of feedback. Finally, we investigate how lensing observables predicted by our model compare to those from simulations and from HALOFIT for a range of k-cuts and feedback models and quantify the angular scales at which these effects become important. Code to calculate power spectra from the model presented in this paper can be found at https://github.com/alexander-mead/hmcode.

Why "suboptimal" is optimal: Jensen's inequality and ectotherm thermal preferences.

PubMed

Martin, Tara Laine; Huey, Raymond B

2008-03-01

Body temperature (T(b)) profoundly affects the fitness of ectotherms. Many ectotherms use behavior to control T(b) within narrow levels. These temperatures are assumed to be optimal and therefore to match body temperatures (Trmax) that maximize fitness (r). We develop an optimality model and find that optimal body temperature (T(o)) should not be centered at Trmax but shifted to a lower temperature. This finding seems paradoxical but results from two considerations relating to Jensen's inequality, which deals with how variance and skew influence integrals of nonlinear functions. First, ectotherms are not perfect thermoregulators and so experience a range of T(b). Second, temperature-fitness curves are asymmetric, such that a T(b) higher than Trmax depresses fitness more than will a T(b) displaced an equivalent amount below Trmax. Our model makes several predictions. The magnitude of the optimal shift (Trmax - To) should increase with the degree of asymmetry of temperature-fitness curves and with T(b) variance. Deviations should be relatively large for thermal specialists but insensitive to whether fitness increases with Trmax ("hotter is better"). Asymmetric (left-skewed) T(b) distributions reduce the magnitude of the optimal shift but do not eliminate it. Comparative data (insects, lizards) support key predictions. Thus, "suboptimal" is optimal.

Computational assessment of press-fit acetabular implant fixation: the effect of implant design, interference fit, bone quality, and frictional properties.

PubMed

Janssen, D; Zwartelé, R E; Doets, H C; Verdonschot, N

2010-01-01

Patients suffering from rheumatoid arthritis typically have a poor subchondral bone quality, endangering implant fixation. Using finite element analysis (FEA) an investigation was made to find whether a press-fit acetabular implant with a polar clearance would reduce interfacial micromotions and improve fixation compared with a standard hemispherical design. In addition, the effects of interference fit, friction, and implant material were analysed. Cups were introduced into an FEA model of a human pelvis with simulated subchondral bone plasticity. The models were loaded with a loading configuration simulating two cycles of normal walking, during which contact stresses and interfacial micromotions were monitored. Subsequently, a lever-out simulation was performed to assess the fixation strength of the various cases. A flattened cup with good bone quality produced the lowest interfacial micromotions. Poor bone decreased the fixation strength regardless of the geometry of the cup. Increasing the interference fit of the flattened cup compensated for the loss of fixation strength caused by poor bone quality. In conclusion, a flattened cup did not significantly improve implant fixation over a hemispherical cup in the case of poor bone quality. However, implant fixation can be optimized by increasing interference fit and avoiding inferior frictional properties and low-stiffness implants.

Estimating the age-specific duration of herpes zoster vaccine protection: a matter of model choice?

PubMed

Bilcke, Joke; Ogunjimi, Benson; Hulstaert, Frank; Van Damme, Pierre; Hens, Niel; Beutels, Philippe

2012-04-05

The estimation of herpes zoster (HZ) vaccine efficacy by time since vaccination and age at vaccination is crucial to assess the effectiveness and cost-effectiveness of HZ vaccination. Published estimates for the duration of protection from the vaccine diverge substantially, although based on data from the same trial for a follow-up period of 5 years. Different models were used to obtain these estimates, but it is unclear which of these models is most appropriate (if any). Only one study estimated vaccine efficacy by age at vaccination and time since vaccination combined. Recently, data became available from the same trial for a follow-up period of 7 years. We aim to elaborate on estimating HZ vaccine efficacy (1) by estimating it as a function of time since vaccination and age at vaccination, (2) by comparing the fits of a range of models, and (3) by fitting these models on data for a follow-up period of 5 and 7 years. Although the models' fit to data are very comparable, they differ substantially in how they estimate vaccine efficacy to change as a function of time since vaccination and age at vaccination. An accurate estimation of HZ vaccine efficacy by time since vaccination and age at vaccination is hampered by the lack of insight in the biological processes underlying HZ vaccine protection, and by the fact that such data are currently not available in sufficient detail. Uncertainty about the choice of model to estimate this important parameter should be acknowledged in cost-effectiveness analyses. Copyright © 2011 Elsevier Ltd. All rights reserved.

Perceptions and receptivity of non-spousal family support: A mixed methods study of psychological distress among older, church-going African American men

PubMed Central

Watkins, Daphne C.; Wharton, Tracy; Mitchell, Jamie A.; Matusko, Niki; Kales, Helen

2016-01-01

The purpose of this study was to explore the role of non-spousal family support on mental health among older, church-going African American men. The mixed methods objective was to employ a design that used existing qualitative and quantitative data to explore the interpretive context within which social and cultural experiences occur. Qualitative data (n=21) were used to build a conceptual model that was tested using quantitative data (n= 401). Confirmatory factor analysis indicated an inverse association between non-spousal family support and distress. The comparative fit index, Tucker-Lewis fit index, and root mean square error of approximation indicated good model fit. This study offers unique methodological approaches to using existing, complementary data sources to understand the health of African American men. PMID:28943829

COMPARISON OF CORONAL EXTRAPOLATION METHODS FOR CYCLE 24 USING HMI DATA

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

Arden, William M.; Norton, Aimee A.; Sun, Xudong

2016-05-20

Two extrapolation models of the solar coronal magnetic field are compared using magnetogram data from the Solar Dynamics Observatory /Helioseismic and Magnetic Imager instrument. The two models, a horizontal current–current sheet–source surface (HCCSSS) model and a potential field–source surface (PFSS) model, differ in their treatment of coronal currents. Each model has its own critical variable, respectively, the radius of a cusp surface and a source surface, and it is found that adjusting these heights over the period studied allows for a better fit between the models and the solar open flux at 1 au as calculated from the Interplanetary Magneticmore » Field (IMF). The HCCSSS model provides the better fit for the overall period from 2010 November to 2015 May as well as for two subsets of the period: the minimum/rising part of the solar cycle and the recently identified peak in the IMF from mid-2014 to mid-2015 just after solar maximum. It is found that an HCCSSS cusp surface height of 1.7 R {sub ⊙} provides the best fit to the IMF for the overall period, while 1.7 and 1.9 R {sub ⊙} give the best fits for the two subsets. The corresponding values for the PFSS source surface height are 2.1, 2.2, and 2.0 R {sub ⊙} respectively. This means that the HCCSSS cusp surface rises as the solar cycle progresses while the PFSS source surface falls.« less

Applications of Brazilian pine-fruit shell in natural and carbonized forms as adsorbents to removal of methylene blue from aqueous solutions--kinetic and equilibrium study.

PubMed

Royer, Betina; Cardoso, Natali F; Lima, Eder C; Vaghetti, Julio C P; Simon, Nathalia M; Calvete, Tatiana; Veses, Renato Cataluña

2009-05-30

The Brazilian pine-fruit shell (Araucaria angustifolia) is a food residue, which was used in natural and carbonized forms, as low-cost adsorbents for the removal of methylene blue (MB) from aqueous solutions. Chemical treatment of Brazilian pine-fruit shell (PW), with sulfuric acid produced a non-activated carbonaceous material (C-PW). Both PW and C-PW were tested as low-cost adsorbents for the removal of MB from aqueous effluents. It was observed that C-PW leaded to a remarkable increase in the specific surface area, average porous volume, and average porous diameter of the adsorbent when compared to PW. The effects of shaking time, adsorbent dosage and pH on adsorption capacity were studied. In basic pH region (pH 8.5) the adsorption of MB was favorable. The contact time required to obtain the equilibrium was 6 and 4h at 25 degrees C, using PW and C-PW as adsorbents, respectively. Based on error function values (F(error)) the kinetic data were better fitted to fractionary-order kinetic model when compared to pseudo-first order, pseudo-second order, and chemisorption kinetic models. The equilibrium data were fitted to Langmuir, Freundlich, Sips and Redlich-Peterson isotherm models. For MB dye the equilibrium data were better fitted to the Sips isotherm model using PW and C-PW as adsorbents.

A Simulated Annealing based Optimization Algorithm for Automatic Variogram Model Fitting

NASA Astrophysics Data System (ADS)

Soltani-Mohammadi, Saeed; Safa, Mohammad

2016-09-01

Fitting a theoretical model to an experimental variogram is an important issue in geostatistical studies because if the variogram model parameters are tainted with uncertainty, the latter will spread in the results of estimations and simulations. Although the most popular fitting method is fitting by eye, in some cases use is made of the automatic fitting method on the basis of putting together the geostatistical principles and optimization techniques to: 1) provide a basic model to improve fitting by eye, 2) fit a model to a large number of experimental variograms in a short time, and 3) incorporate the variogram related uncertainty in the model fitting. Effort has been made in this paper to improve the quality of the fitted model by improving the popular objective function (weighted least squares) in the automatic fitting. Also, since the variogram model function (£) and number of structures (m) too affect the model quality, a program has been provided in the MATLAB software that can present optimum nested variogram models using the simulated annealing method. Finally, to select the most desirable model from among the single/multi-structured fitted models, use has been made of the cross-validation method, and the best model has been introduced to the user as the output. In order to check the capability of the proposed objective function and the procedure, 3 case studies have been presented.

Optimization of isotherm models for pesticide sorption on biopolymer-nanoclay composite by error analysis.

PubMed

Narayanan, Neethu; Gupta, Suman; Gajbhiye, V T; Manjaiah, K M

2017-04-01

A carboxy methyl cellulose-nano organoclay (nano montmorillonite modified with 35-45 wt % dimethyl dialkyl (C 14 -C 18 ) amine (DMDA)) composite was prepared by solution intercalation method. The prepared composite was characterized by infrared spectroscopy (FTIR), X-Ray diffraction spectroscopy (XRD) and scanning electron microscopy (SEM). The composite was utilized for its pesticide sorption efficiency for atrazine, imidacloprid and thiamethoxam. The sorption data was fitted into Langmuir and Freundlich isotherms using linear and non linear methods. The linear regression method suggested best fitting of sorption data into Type II Langmuir and Freundlich isotherms. In order to avoid the bias resulting from linearization, seven different error parameters were also analyzed by non linear regression method. The non linear error analysis suggested that the sorption data fitted well into Langmuir model rather than in Freundlich model. The maximum sorption capacity, Q 0 (μg/g) was given by imidacloprid (2000) followed by thiamethoxam (1667) and atrazine (1429). The study suggests that the degree of determination of linear regression alone cannot be used for comparing the best fitting of Langmuir and Freundlich models and non-linear error analysis needs to be done to avoid inaccurate results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modeling Phase-Aligned Gamma-Ray and Radio Millisecond Pulsar Light Curves

NASA Technical Reports Server (NTRS)

Venter, C.; Johnson, T.; Harding, A.

2012-01-01

Since the discovery of the first eight gamma-ray millisecond pulsars (MSPs) by the Fermi Large Area Telescope, this population has been steadily expanding. Four of the more recent detections, PSR J00340534, PSR J1939+2134 (B1937+21; the first MSP ever discovered), PSR J1959+2048 (B1957+20; the first discovery of a black widow system), and PSR J2214+3000, exhibit a phenomenon not present in the original discoveries: nearly phase-aligned radio and gamma-ray light curves (LCs). To account for the phase alignment, we explore models where both the radio and gamma-ray emission originate either in the outer magnetosphere near the light cylinder or near the polar caps. Using a Markov Chain Monte Carlo technique to search for best-fit model parameters, we obtain reasonable LC fits for the first three of these MSPs in the context of altitude-limited outer gap (alOG) and two-pole caustic (alTPC) geometries (for both gamma-ray and radio emission). These models differ from the standard outer gap (OG)/two-pole caustic (TPC) models in two respects: the radio emission originates in caustics at relatively high altitudes compared to the usual conal radio beams, and we allow both the minimum and maximum altitudes of the gamma-ray and radio emission regions to vary within a limited range (excluding the minimum gamma-ray altitude of the alTPC model, which is kept constant at the stellar radius, and that of the alOG model, which is set to the position-dependent null charge surface altitude). Alternatively, phase-aligned solutions also exist for emission originating near the stellar surface in a slot gap scenario (low-altitude slot gap (laSG) models). We find that the alTPC models provide slightly better LC fits than the alOG models, and both of these give better fits than the laSG models (for the limited range of parameters considered in the case of the laSG models). Thus, our fits imply that the phase-aligned LCs are likely of caustic origin, produced in the outer magnetosphere, and that the radio emission for these pulsars may come from close to the light cylinder. In addition, we were able to constrain the minimum and maximum emission altitudes with typical uncertainties of 30% of the light cylinder radius. Our results therefore describe a third gamma-ray MSP subclass, in addition to the two previously found by Venter et al.: those with LCs fit by standard OG/TPC models and those with LCs fit by pair-starved polar cap models.

Calibration and accuracy analysis of a focused plenoptic camera

NASA Astrophysics Data System (ADS)

Zeller, N.; Quint, F.; Stilla, U.

2014-08-01

In this article we introduce new methods for the calibration of depth images from focused plenoptic cameras and validate the results. We start with a brief description of the concept of a focused plenoptic camera and how from the recorded raw image a depth map can be estimated. For this camera, an analytical expression of the depth accuracy is derived for the first time. In the main part of the paper, methods to calibrate a focused plenoptic camera are developed and evaluated. The optical imaging process is calibrated by using a method which is already known from the calibration of traditional cameras. For the calibration of the depth map two new model based methods, which make use of the projection concept of the camera are developed. These new methods are compared to a common curve fitting approach, which is based on Taylor-series-approximation. Both model based methods show significant advantages compared to the curve fitting method. They need less reference points for calibration than the curve fitting method and moreover, supply a function which is valid in excess of the range of calibration. In addition the depth map accuracy of the plenoptic camera was experimentally investigated for different focal lengths of the main lens and is compared to the analytical evaluation.

College Students' Physical Activity and Health-Related Quality of Life: An Achievement Goal Perspective.

PubMed

Zhang, Tao; Xiang, Ping; Gu, Xiangli; Rose, Melanie

2016-06-01

The 2 × 2 achievement goal model, including the mastery-approach, mastery-avoidance, performance-approach, and performance-avoidance goal orientations, has recently been used to explain motivational outcomes in physical activity. This study attempted to examine the relationships among 2 × 2 achievement goal orientations, physical activity, and health-related quality of life (HRQOL) in college students. Participants were 325 students (130 men and 195 women; Mage = 21.4 years) enrolled in physical activity classes at a Southern university. They completed surveys validated in previous research assessing achievement goal orientations, physical activity, and HRQOL. Path analyses revealed a good fit between the model and data (root mean square error of approximation = .06; Comparative Fit Index = .99; Bentler-Bonett Nonnormed Fit Index = .98; Incremental Fit Index = .99), but the model explained small variances in the current study. Mastery-approach and performance-approach goal orientations only had low or no relationships with physical activity. Mastery-approach goal orientation and physical activity also had low positive relationships with HRQOL, but mastery-avoidance and performance-avoidance goal orientations had low negative relationships with HRQOL. The hypothesized mediational role of physical activity in the relationship between mastery-approach and performance-approach goal orientations and HRQOL was not supported in this study. Although the data fit the proposed model well, only small variance was explained by the model. The relationship between physical activity and HRQOL of the college students and other related correlates should be further studied.

Metafitting: Weight optimization for least-squares fitting of PTTI data

NASA Technical Reports Server (NTRS)

Douglas, Rob J.; Boulanger, J.-S.

1995-01-01

For precise time intercomparisons between a master frequency standard and a slave time scale, we have found it useful to quantitatively compare different fitting strategies by examining the standard uncertainty in time or average frequency. It is particularly useful when designing procedures which use intermittent intercomparisons, with some parameterized fit used to interpolate or extrapolate from the calibrating intercomparisons. We use the term 'metafitting' for the choices that are made before a fitting procedure is operationally adopted. We present methods for calculating the standard uncertainty for general, weighted least-squares fits and a method for optimizing these weights for a general noise model suitable for many PTTI applications. We present the results of the metafitting of procedures for the use of a regular schedule of (hypothetical) high-accuracy frequency calibration of a maser time scale. We have identified a cumulative series of improvements that give a significant reduction of the expected standard uncertainty, compared to the simplest procedure of resetting the maser synthesizer after each calibration. The metafitting improvements presented include the optimum choice of weights for the calibration runs, optimized over a period of a week or 10 days.

Analysing model fit of psychometric process models: An overview, a new test and an application to the diffusion model.

PubMed

Ranger, Jochen; Kuhn, Jörg-Tobias; Szardenings, Carsten

2017-05-01

Cognitive psychometric models embed cognitive process models into a latent trait framework in order to allow for individual differences. Due to their close relationship to the response process the models allow for profound conclusions about the test takers. However, before such a model can be used its fit has to be checked carefully. In this manuscript we give an overview over existing tests of model fit and show their relation to the generalized moment test of Newey (Econometrica, 53, 1985, 1047) and Tauchen (J. Econometrics, 30, 1985, 415). We also present a new test, the Hausman test of misspecification (Hausman, Econometrica, 46, 1978, 1251). The Hausman test consists of a comparison of two estimates of the same item parameters which should be similar if the model holds. The performance of the Hausman test is evaluated in a simulation study. In this study we illustrate its application to two popular models in cognitive psychometrics, the Q-diffusion model and the D-diffusion model (van der Maas, Molenaar, Maris, Kievit, & Boorsboom, Psychol Rev., 118, 2011, 339; Molenaar, Tuerlinckx, & van der Maas, J. Stat. Softw., 66, 2015, 1). We also compare the performance of the test to four alternative tests of model fit, namely the M 2 test (Molenaar et al., J. Stat. Softw., 66, 2015, 1), the moment test (Ranger et al., Br. J. Math. Stat. Psychol., 2016) and the test for binned time (Ranger & Kuhn, Psychol. Test. Asess. , 56, 2014b, 370). The simulation study indicates that the Hausman test is superior to the latter tests. The test closely adheres to the nominal Type I error rate and has higher power in most simulation conditions. © 2017 The British Psychological Society.

A New Stellar Atmosphere Grid and Comparisons with HST /STIS CALSPEC Flux Distributions

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

Bohlin, Ralph C.; Fleming, Scott W.; Gordon, Karl D.

The Space Telescope Imaging Spectrograph has measured the spectral energy distributions for several stars of types O, B, A, F, and G. These absolute fluxes from the CALSPEC database are fit with a new spectral grid computed from the ATLAS-APOGEE ATLAS9 model atmosphere database using a chi-square minimization technique in four parameters. The quality of the fits are compared for complete LTE grids by Castelli and Kurucz (CK04) and our new comprehensive LTE grid (BOSZ). For the cooler stars, the fits with the MARCS LTE grid are also evaluated, while the hottest stars are also fit with the NLTE Lanzmore » and Hubeny OB star grids. Unfortunately, these NLTE models do not transition smoothly in the infrared to agree with our new BOSZ LTE grid at the NLTE lower limit of T {sub eff} = 15,000 K. The new BOSZ grid is available via the Space Telescope Institute MAST archive and has a much finer sampled IR wavelength scale than CK04, which will facilitate the modeling of stars observed by the James Webb Space Telescope . Our result for the angular diameter of Sirius agrees with the ground-based interferometric value.« less

Understanding the nature of wealth and its effects on human fitness

PubMed Central

Mulder, Monique Borgerhoff; Beheim, Bret A.

2011-01-01

Studying fitness consequences of variable behavioural, physiological and cognitive traits in contemporary populations constitutes the specific contribution of human behavioural ecology to the study of human diversity. Yet, despite 30 years of evolutionary anthropological interest in the determinants of fitness, there exist few principled investigations of the diverse sources of wealth that might reveal selective forces during recent human history. To develop a more holistic understanding of how selection shapes human phenotypic traits, be these transmitted by genetic or cultural means, we expand the conventional focus on associations between socioeconomic status and fitness to three distinct types of wealth—embodied, material and relational. Using a model selection approach to the study of women's success in raising offspring in an African horticultural population (the Tanzanian Pimbwe), we find that the top performing models consistently include relational and material wealth, with embodied wealth as a less reliable predictor. Specifically, child mortality risk is increased with few household assets, parent nonresidency, child legitimacy, and one or more parents having been accused of witchcraft. The use of multiple models to test various hypotheses greatly facilitates systematic comparative analyses of human behavioural diversity in wealth accrual and investment across different kinds of societies. PMID:21199839

A New Stellar Atmosphere Grid and Comparisons with HST/STIS CALSPEC Flux Distributions

NASA Astrophysics Data System (ADS)

Bohlin, Ralph C.; Mészáros, Szabolcs; Fleming, Scott W.; Gordon, Karl D.; Koekemoer, Anton M.; Kovács, József

2017-05-01

The Space Telescope Imaging Spectrograph has measured the spectral energy distributions for several stars of types O, B, A, F, and G. These absolute fluxes from the CALSPEC database are fit with a new spectral grid computed from the ATLAS-APOGEE ATLAS9 model atmosphere database using a chi-square minimization technique in four parameters. The quality of the fits are compared for complete LTE grids by Castelli & Kurucz (CK04) and our new comprehensive LTE grid (BOSZ). For the cooler stars, the fits with the MARCS LTE grid are also evaluated, while the hottest stars are also fit with the NLTE Lanz & Hubeny OB star grids. Unfortunately, these NLTE models do not transition smoothly in the infrared to agree with our new BOSZ LTE grid at the NLTE lower limit of T eff = 15,000 K. The new BOSZ grid is available via the Space Telescope Institute MAST archive and has a much finer sampled IR wavelength scale than CK04, which will facilitate the modeling of stars observed by the James Webb Space Telescope. Our result for the angular diameter of Sirius agrees with the ground-based interferometric value.

Modelling local GPS/levelling geoid undulations using artificial neural networks

NASA Astrophysics Data System (ADS)

Kavzoglu, T.; Saka, M. H.

2005-04-01

The use of GPS for establishing height control in an area where levelling data are available can involve the so-called GPS/levelling technique. Modelling of the GPS/levelling geoid undulations has usually been carried out using polynomial surface fitting, least-squares collocation (LSC) and finite-element methods. Artificial neural networks (ANNs) have recently been used for many investigations, and proven to be effective in solving complex problems represented by noisy and missing data. In this study, a feed-forward ANN structure, learning the characteristics of the training data through the back-propagation algorithm, is employed to model the local GPS/levelling geoid surface. The GPS/levelling geoid undulations for Istanbul, Turkey, were estimated from GPS and precise levelling measurements obtained during a field study in the period 1998-99. The results are compared to those produced by two well-known conventional methods, namely polynomial fitting and LSC, in terms of root mean square error (RMSE) that ranged from 3.97 to 5.73 cm. The results show that ANNs can produce results that are comparable to polynomial fitting and LSC. The main advantage of the ANN-based surfaces seems to be the low deviations from the GPS/levelling data surface, which is particularly important for distorted levelling networks.

Zero-inflated Conway-Maxwell Poisson Distribution to Analyze Discrete Data.

PubMed

Sim, Shin Zhu; Gupta, Ramesh C; Ong, Seng Huat

2018-01-09

In this paper, we study the zero-inflated Conway-Maxwell Poisson (ZICMP) distribution and develop a regression model. Score and likelihood ratio tests are also implemented for testing the inflation/deflation parameter. Simulation studies are carried out to examine the performance of these tests. A data example is presented to illustrate the concepts. In this example, the proposed model is compared to the well-known zero-inflated Poisson (ZIP) and the zero- inflated generalized Poisson (ZIGP) regression models. It is shown that the fit by ZICMP is comparable or better than these models.

Casting wider nets for anxiety and depression: disability-driven cross-diagnostic subtypes in a large cohort.

PubMed

Wanders, R B K; van Loo, H M; Vermunt, J K; Meijer, R R; Hartman, C A; Schoevers, R A; Wardenaar, K J; de Jonge, P

2016-12-01

In search of empirical classifications of depression and anxiety, most subtyping studies focus solely on symptoms and do so within a single disorder. This study aimed to identify and validate cross-diagnostic subtypes by simultaneously considering symptoms of depression and anxiety, and disability measures. A large cohort of adults (Lifelines, n = 73 403) had a full assessment of 16 symptoms of mood and anxiety disorders, and measurement of physical, social and occupational disability. The best-fitting subtyping model was identified by comparing different hybrid mixture models with and without disability covariates on fit criteria in an independent test sample. The best model's classes were compared across a range of external variables. The best-fitting Mixed Measurement Item Response Theory model with disability covariates identified five classes. Accounting for disability improved differentiation between people reporting isolated non-specific symptoms ['Somatic' (13.0%), and 'Worried' (14.0%)] and psychopathological symptoms ['Subclinical' (8.8%), and 'Clinical' (3.3%)]. Classes showed distinct associations with clinically relevant external variables [e.g. somatization: odds ratio (OR) 8.1-12.3, and chronic stress: OR 3.7-4.4]. The Subclinical class reported symptomatology at subthreshold levels while experiencing disability. No pure depression or anxiety, but only mixed classes were found. An empirical classification model, incorporating both symptoms and disability identified clearly distinct cross-diagnostic subtypes, indicating that diagnostic nets should be cast wider than current phenomenology-based categorical systems.

An investigation of PTSD's core dimensions and relations with anxiety and depression.

PubMed

Byllesby, Brianna M; Durham, Tory A; Forbes, David; Armour, Cherie; Elhai, Jon D

2016-03-01

Posttraumatic stress disorder (PTSD) is highly comorbid with anxiety and depressive disorders, which is suggestive of shared variance or common underlying dimensions. The purpose of the present study was to examine the relationship between the latent factors of PTSD with the constructs of anxiety and depression in order to increase understanding of the co-occurrence of these disorders. Data were collected from a nonclinical sample of 186 trauma-exposed participants using the PTSD Checklist and Hospital Anxiety and Depression Scale. Confirmatory factor analyses were conducted to determine model fit comparing 3 PTSD factor structure models, followed by Wald tests comparing the relationships between PTSD factors and the core dimensions of anxiety and depression. In model comparisons, the 5-factor dysphoric arousal model of PTSD provided the best fit for the data, compared to the emotional numbing and dysphoria models of PTSD. Compared to anxious arousal, the dysphoric arousal and numbing factors of PTSD were more related to depression severity. Numbing, anxious arousal, and dysphoric arousal were not differentially related to the latent anxiety factor. The underlying factors of PTSD contain aspects of the core dimensions of both anxiety and depression. The heterogeneity of PTSD's associations with anxiety and depressive constructs requires additional empirical exploration because clarification regarding these relationships will impact diagnostic classification as well as clinical practice. (c) 2016 APA, all rights reserved).

Understanding the early dynamics of the 2014 porcine epidemic diarrhea virus (PEDV) outbreak in Ontario using the incidence decay and exponential adjustment (IDEA) model.

PubMed

Greer, Amy L; Spence, Kelsey; Gardner, Emma

2017-01-05

The United States swine industry was first confronted with porcine epidemic diarrhea virus (PEDV) in 2013. In young pigs, the virus is highly pathogenic and the associated morbidity and mortality has a significant negative impact on the swine industry. We have applied the IDEA model to better understand the 2014 PEDV outbreak in Ontario, Canada. Using our simple, 2-parameter IDEA model, we have evaluated the early epidemic dynamics of PEDV on Ontario swine farms. We estimated the best-fit R 0 and control parameter (d) for the between farm transmission component of the outbreak by fitting the model to publically available cumulative incidence data. We used maximum likelihood to compare model fit estimates for different combinations of the R 0 and d parameters. Using our initial findings from the iterative fitting procedure, we projected the time course of the epidemic using only a subset of the early epidemic data. The IDEA model projections showed excellent agreement with the observed data based on a 7-day generation time estimate. The best-fit estimate for R 0 was 1.87 (95% CI: 1.52 - 2.34) and for the control parameter (d) was 0.059 (95% CI: 0.022 - 0.117). Using data from the first three generations of the outbreak, our iterative fitting procedure suggests that R 0 and d had stabilized sufficiently to project the time course of the outbreak with reasonable accuracy. The emergence and spread of PEDV represents an important agricultural emergency. The virus presents a significant ongoing threat to the Canadian swine industry. Developing an understanding of the important epidemiological characteristics and disease transmission dynamics of a novel pathogen such as PEDV is critical for helping to guide the implementation of effective, efficient, and economically feasible disease control and prevention strategies that are able to help decrease the impact of an outbreak.

Estimation of polydispersity in aggregating red blood cells by quantitative ultrasound backscatter analysis.

PubMed

de Monchy, Romain; Rouyer, Julien; Destrempes, François; Chayer, Boris; Cloutier, Guy; Franceschini, Emilie

2018-04-01

Quantitative ultrasound techniques based on the backscatter coefficient (BSC) have been commonly used to characterize red blood cell (RBC) aggregation. Specifically, a scattering model is fitted to measured BSC and estimated parameters can provide a meaningful description of the RBC aggregates' structure (i.e., aggregate size and compactness). In most cases, scattering models assumed monodisperse RBC aggregates. This study proposes the Effective Medium Theory combined with the polydisperse Structure Factor Model (EMTSFM) to incorporate the polydispersity of aggregate size. From the measured BSC, this model allows estimating three structural parameters: the mean radius of the aggregate size distribution, the width of the distribution, and the compactness of the aggregates. Two successive experiments were conducted: a first experiment on blood sheared in a Couette flow device coupled with an ultrasonic probe, and a second experiment, on the same blood sample, sheared in a plane-plane rheometer coupled to a light microscope. Results demonstrated that the polydisperse EMTSFM provided the best fit to the BSC data when compared to the classical monodisperse models for the higher levels of aggregation at hematocrits between 10% and 40%. Fitting the polydisperse model yielded aggregate size distributions that were consistent with direct light microscope observations at low hematocrits.

Evaluating Model Fit for Growth Curve Models: Integration of Fit Indices from SEM and MLM Frameworks

ERIC Educational Resources Information Center

Wu, Wei; West, Stephen G.; Taylor, Aaron B.

2009-01-01

Evaluating overall model fit for growth curve models involves 3 challenging issues. (a) Three types of longitudinal data with different implications for model fit may be distinguished: balanced on time with complete data, balanced on time with data missing at random, and unbalanced on time. (b) Traditional work on fit from the structural equation…

Experimental study of the γ p → π 0 η p reaction with the A2 setup at the Mainz Microtron

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

Sokhoyan, V.; Prakhov, S.; Fix, A.

2018-05-29

Here, the data available from the A2 Collaboration at MAMI were analyzed to select the γp → π 0ηp reaction on an event-by-event basis, which allows for partial-wave analyses of three-body final states to obtain more reliable results, compared to fits to measured distributions. These data provide the world’s best statistical accuracy in the energy range from threshold to E γ = 1.45 GeV, allowing a finer energy binning in the measurement of all observables needed for understanding the reaction dynamics. The results obtained for the measured observables are compared to existing models, and the impact from the new datamore » is checked by the fit with the revised Mainz model.« less

Accounting for nonsampling error in estimates of HIV epidemic trends from antenatal clinic sentinel surveillance

PubMed Central

Eaton, Jeffrey W.; Bao, Le

2017-01-01

Objectives The aim of the study was to propose and demonstrate an approach to allow additional nonsampling uncertainty about HIV prevalence measured at antenatal clinic sentinel surveillance (ANC-SS) in model-based inferences about trends in HIV incidence and prevalence. Design Mathematical model fitted to surveillance data with Bayesian inference. Methods We introduce a variance inflation parameter σinfl2 that accounts for the uncertainty of nonsampling errors in ANC-SS prevalence. It is additive to the sampling error variance. Three approaches are tested for estimating σinfl2 using ANC-SS and household survey data from 40 subnational regions in nine countries in sub-Saharan, as defined in UNAIDS 2016 estimates. Methods were compared using in-sample fit and out-of-sample prediction of ANC-SS data, fit to household survey prevalence data, and the computational implications. Results Introducing the additional variance parameter σinfl2 increased the error variance around ANC-SS prevalence observations by a median of 2.7 times (interquartile range 1.9–3.8). Using only sampling error in ANC-SS prevalence ( σinfl2=0), coverage of 95% prediction intervals was 69% in out-of-sample prediction tests. This increased to 90% after introducing the additional variance parameter σinfl2. The revised probabilistic model improved model fit to household survey prevalence and increased epidemic uncertainty intervals most during the early epidemic period before 2005. Estimating σinfl2 did not increase the computational cost of model fitting. Conclusions: We recommend estimating nonsampling error in ANC-SS as an additional parameter in Bayesian inference using the Estimation and Projection Package model. This approach may prove useful for incorporating other data sources such as routine prevalence from Prevention of mother-to-child transmission testing into future epidemic estimates. PMID:28296801

Pleiotropic Models of Polygenic Variation, Stabilizing Selection, and Epistasis

PubMed Central

Gavrilets, S.; de-Jong, G.

1993-01-01

We show that in polymorphic populations many polygenic traits pleiotropically related to fitness are expected to be under apparent ``stabilizing selection'' independently of the real selection acting on the population. This occurs, for example, if the genetic system is at a stable polymorphic equilibrium determined by selection and the nonadditive contributions of the loci to the trait value either are absent, or are random and independent of those to fitness. Stabilizing selection is also observed if the polygenic system is at an equilibrium determined by a balance between selection and mutation (or migration) when both additive and nonadditive contributions of the loci to the trait value are random and independent of those to fitness. We also compare different viability models that can maintain genetic variability at many loci with respect to their ability to account for the strong stabilizing selection on an additive trait. Let V(m) be the genetic variance supplied by mutation (or migration) each generation, V(g) be the genotypic variance maintained in the population, and n be the number of the loci influencing fitness. We demonstrate that in mutation (migration)-selection balance models the strength of apparent stabilizing selection is order V(m)/V(g). In the overdominant model and in the symmetric viability model the strength of apparent stabilizing selection is approximately 1/(2n) that of total selection on the whole phenotype. We show that a selection system that involves pairwise additive by additive epistasis in maintaining variability can lead to a lower genetic load and genetic variance in fitness (approximately 1/(2n) times) than an equivalent selection system that involves overdominance. We show that, in the epistatic model, the apparent stabilizing selection on an additive trait can be as strong as the total selection on the whole phenotype. PMID:8325491

Comparative Analyses of Creep Models of a Solid Propellant

NASA Astrophysics Data System (ADS)

Zhang, J. B.; Lu, B. J.; Gong, S. F.; Zhao, S. P.

2018-05-01

The creep experiments of a solid propellant samples under five different stresses are carried out at 293.15 K and 323.15 K. In order to express the creep properties of this solid propellant, the viscoelastic model i.e. three Parameters solid, three Parameters fluid, four Parameters solid, four Parameters fluid and exponential model are involved. On the basis of the principle of least squares fitting, and different stress of all the parameters for the models, the nonlinear fitting procedure can be used to analyze the creep properties. The study shows that the four Parameters solid model can best express the behavior of creep properties of the propellant samples. However, the three Parameters solid and exponential model cannot very well reflect the initial value of the creep process, while the modified four Parameters models are found to agree well with the acceleration characteristics of the creep process.

THE IMPACT OF ACCURATE EXTINCTION MEASUREMENTS FOR X-RAY SPECTRAL MODELS

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

Smith, Randall K.; Valencic, Lynne A.; Corrales, Lia, E-mail: lynne.a.valencic@nasa.gov

Interstellar extinction includes both absorption and scattering of photons from interstellar gas and dust grains, and it has the effect of altering a source's spectrum and its total observed intensity. However, while multiple absorption models exist, there are no useful scattering models in standard X-ray spectrum fitting tools, such as XSPEC. Nonetheless, X-ray halos, created by scattering from dust grains, are detected around even moderately absorbed sources, and the impact on an observed source spectrum can be significant, if modest, compared to direct absorption. By convolving the scattering cross section with dust models, we have created a spectral model asmore » a function of energy, type of dust, and extraction region that can be used with models of direct absorption. This will ensure that the extinction model is consistent and enable direct connections to be made between a source's X-ray spectral fits and its UV/optical extinction.« less

Modeling growth kinetics of thin films made by atomic layer deposition in lateral high-aspect-ratio structures

NASA Astrophysics Data System (ADS)

Ylilammi, Markku; Ylivaara, Oili M. E.; Puurunen, Riikka L.

2018-05-01

The conformality of thin films grown by atomic layer deposition (ALD) is studied using all-silicon test structures with long narrow lateral channels. A diffusion model, developed in this work, is used for studying the propagation of ALD growth in narrow channels. The diffusion model takes into account the gas transportation at low pressures, the dynamic Langmuir adsorption model for the film growth and the effect of channel narrowing due to film growth. The film growth is calculated by solving the diffusion equation with surface reactions. An efficient analytic approximate solution of the diffusion equation is developed for fitting the model to the measured thickness profile. The fitting gives the equilibrium constant of adsorption and the sticking coefficient. This model and Gordon's plug flow model are compared. The simulations predict the experimental measurement results quite well for Al2O3 and TiO2 ALD processes.

Calibrating the Abaqus Crushable Foam Material Model using UNM Data

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

Schembri, Philip E.; Lewis, Matthew W.

Triaxial test data from the University of New Mexico and uniaxial test data from W-14 is used to calibrate the Abaqus crushable foam material model to represent the syntactic foam comprised of APO-BMI matrix and carbon microballoons used in the W76. The material model is an elasto-plasticity model in which the yield strength depends on pressure. Both the elastic properties and the yield stress are estimated by fitting a line to the elastic region of each test response. The model parameters are fit to the data (in a non-rigorous way) to provide both a conservative and not-conservative material model. Themore » model is verified to perform as intended by comparing the values of pressure and shear stress at yield, as well as the shear and volumetric stress-strain response, to the test data.« less

A bridge role metric model for nodes in software networks.

PubMed

Li, Bo; Feng, Yanli; Ge, Shiyu; Li, Dashe

2014-01-01

A bridge role metric model is put forward in this paper. Compared with previous metric models, our solution of a large-scale object-oriented software system as a complex network is inherently more realistic. To acquire nodes and links in an undirected network, a new model that presents the crucial connectivity of a module or the hub instead of only centrality as in previous metric models is presented. Two previous metric models are described for comparison. In addition, it is obvious that the fitting curve between the Bre results and degrees can well be fitted by a power law. The model represents many realistic characteristics of actual software structures, and a hydropower simulation system is taken as an example. This paper makes additional contributions to an accurate understanding of module design of software systems and is expected to be beneficial to software engineering practices.

A Bridge Role Metric Model for Nodes in Software Networks

PubMed Central

Li, Bo; Feng, Yanli; Ge, Shiyu; Li, Dashe

2014-01-01

A bridge role metric model is put forward in this paper. Compared with previous metric models, our solution of a large-scale object-oriented software system as a complex network is inherently more realistic. To acquire nodes and links in an undirected network, a new model that presents the crucial connectivity of a module or the hub instead of only centrality as in previous metric models is presented. Two previous metric models are described for comparison. In addition, it is obvious that the fitting curve between the results and degrees can well be fitted by a power law. The model represents many realistic characteristics of actual software structures, and a hydropower simulation system is taken as an example. This paper makes additional contributions to an accurate understanding of module design of software systems and is expected to be beneficial to software engineering practices. PMID:25364938

An introduction to multidimensional measurement using Rasch models.

PubMed

Briggs, Derek C; Wilson, Mark

2003-01-01

The act of constructing a measure requires a number of important assumptions. Principle among these assumptions is that the construct is unidimensional. In practice there are many instances when the assumption of unidimensionality does not hold, and where the application of a multidimensional measurement model is both technically appropriate and substantively advantageous. In this paper we illustrate the usefulness of a multidimensional approach to measurement with the Multidimensional Random Coefficient Multinomial Logit (MRCML) model, an extension of the unidimensional Rasch model. An empirical example is taken from a collection of embedded assessments administered to 541 students enrolled in middle school science classes with a hands-on science curriculum. Student achievement on these assessments are multidimensional in nature, but can also be treated as consecutive unidimensional estimates, or as is most common, as a composite unidimensional estimate. Structural parameters are estimated for each model using ConQuest, and model fit is compared. Student achievement in science is also compared across models. The multidimensional approach has the best fit to the data, and provides more reliable estimates of student achievement than under the consecutive unidimensional approach. Finally, at an interpretational level, the multidimensional approach may well provide richer information to the classroom teacher about the nature of student achievement.

Fuzzy Analytic Hierarchy Process-based Chinese Resident Best Fitness Behavior Method Research.

PubMed

Wang, Dapeng; Zhang, Lan

2015-01-01

With explosive development in Chinese economy and science and technology, people's pursuit of health becomes more and more intense, therefore Chinese resident sports fitness activities have been rapidly developed. However, different fitness events popularity degrees and effects on body energy consumption are different, so bases on this, the paper researches on fitness behaviors and gets Chinese residents sports fitness behaviors exercise guide, which provides guidance for propelling to national fitness plan's implementation and improving Chinese resident fitness scientization. The paper starts from the perspective of energy consumption, it mainly adopts experience method, determines Chinese resident favorite sports fitness event energy consumption through observing all kinds of fitness behaviors energy consumption, and applies fuzzy analytic hierarchy process to make evaluation on bicycle riding, shadowboxing practicing, swimming, rope skipping, jogging, running, aerobics these seven fitness events. By calculating fuzzy rate model's membership and comparing their sizes, it gets fitness behaviors that are more helpful for resident health, more effective and popular. Finally, it gets conclusions that swimming is a best exercise mode and its membership is the highest. Besides, the memberships of running, rope skipping and shadowboxing practicing are also relative higher. It should go in for bodybuilding by synthesizing above several kinds of fitness events according to different physical conditions; different living conditions so that can better achieve the purpose of fitness exercises.

M dwarf spectra from 0.6 to 1.5 micron - A spectral sequence, model atmosphere fitting, and the temperature scale

NASA Technical Reports Server (NTRS)

Kirkpatrick, J. D.; Kelly, Douglas M.; Rieke, George H.; Liebert, James; Allard, France; Wehrse, Rainer

1993-01-01

Red/infrared (0.6-1.5 micron) spectra are presented for a sequence of well-studied M dwarfs ranging from M2 through M9. A variety of temperature-sensitive features useful for spectral classification are identified. Using these features, the spectral data are compared to recent theoretical models, from which a temperature scale is assigned. The red portion of the model spectra provide reasonably good fits for dwarfs earlier than M6. For layer types, the infrared region provides a more reliable fit to the observations. In each case, the wavelength region used includes the broad peak of the energy distribution. For a given spectral type, the derived temperature sequence assigns higher temperatures than have earlier studies - the difference becoming more pronounced at lower luminosities. The positions of M dwarfs on the H-R diagram are, as a result, in closer agreement with theoretical tracks of the lower main sequence.

Method development estimating ambient mercury concentration from monitored mercury wet deposition

NASA Astrophysics Data System (ADS)

Chen, S. M.; Qiu, X.; Zhang, L.; Yang, F.; Blanchard, P.

2013-05-01

Speciated atmospheric mercury data have recently been monitored at multiple locations in North America; but the spatial coverage is far less than the long-established mercury wet deposition network. The present study describes a first attempt linking ambient concentration with wet deposition using Beta distribution fitting of a ratio estimate. The mean, median, mode, standard deviation, and skewness of the fitted Beta distribution parameters were generated using data collected in 2009 at 11 monitoring stations. Comparing the normalized histogram and the fitted density function, the empirical and fitted Beta distribution of the ratio shows a close fit. The estimated ambient mercury concentration was further partitioned into reactive gaseous mercury and particulate bound mercury using linear regression model developed by Amos et al. (2012). The method presented here can be used to roughly estimate mercury ambient concentration at locations and/or times where such measurement is not available but where wet deposition is monitored.

Galaxy and mass assembly (GAMA): the consistency of GAMA and WISE derived mass-to-light ratios

NASA Astrophysics Data System (ADS)

Kettlety, T.; Hesling, J.; Phillipps, S.; Bremer, M. N.; Cluver, M. E.; Taylor, E. N.; Bland-Hawthorn, J.; Brough, S.; De Propris, R.; Driver, S. P.; Holwerda, B. W.; Kelvin, L. S.; Sutherland, W.; Wright, A. H.

2018-01-01

Recent work has suggested that mid-IR wavelengths are optimal for estimating the mass-to-light ratios of stellar populations and hence the stellar masses of galaxies. We compare stellar masses deduced from spectral energy distribution (SED) models, fitted to multiwavelength optical-NIR photometry, to luminosities derived from WISE photometry in the W1 and W2 bands at 3.6 and 4.5 μm for non-star forming galaxies. The SED-derived masses for a carefully selected sample of low-redshift (z ≤ 0.15) passive galaxies agree with the prediction from stellar population synthesis models such that M*/LW1 ≃ 0.6 for all such galaxies, independent of other stellar population parameters. The small scatter between masses predicted from the optical SED and from the WISE measurements implies that random errors (as opposed to systematic ones such as the use of different initial mass functions) are smaller than previous, deliberately conservative, estimates for the SED fits. This test is subtly different from simultaneously fitting at a wide range of optical and mid-IR wavelengths, which may just generate a compromised fit: we are directly checking that the best-fitting model to the optical data generates an SED whose M*/LW1 is also consistent with separate mid-IR data. We confirm that for passive low-redshift galaxies a fixed M*/LW1 = 0.65 can generate masses at least as accurate as those obtained from more complex methods. Going beyond the mean value, in agreement with expectations from the models, we see a modest change in M*/LW1 with SED fitted stellar population age but an insignificant one with metallicity.

Fast and accurate fitting and filtering of noisy exponentials in Legendre space.

PubMed

Bao, Guobin; Schild, Detlev

2014-01-01

The parameters of experimentally obtained exponentials are usually found by least-squares fitting methods. Essentially, this is done by minimizing the mean squares sum of the differences between the data, most often a function of time, and a parameter-defined model function. Here we delineate a novel method where the noisy data are represented and analyzed in the space of Legendre polynomials. This is advantageous in several respects. First, parameter retrieval in the Legendre domain is typically two orders of magnitude faster than direct fitting in the time domain. Second, data fitting in a low-dimensional Legendre space yields estimates for amplitudes and time constants which are, on the average, more precise compared to least-squares-fitting with equal weights in the time domain. Third, the Legendre analysis of two exponentials gives satisfactory estimates in parameter ranges where least-squares-fitting in the time domain typically fails. Finally, filtering exponentials in the domain of Legendre polynomials leads to marked noise removal without the phase shift characteristic for conventional lowpass filters.

Synonymous Mutations at the Beginning of the Influenza A Virus Hemagglutinin Gene Impact Experimental Fitness.

PubMed

Canale, Aneth S; Venev, Sergey V; Whitfield, Troy W; Caffrey, Daniel R; Marasco, Wayne A; Schiffer, Celia A; Kowalik, Timothy F; Jensen, Jeffrey D; Finberg, Robert W; Zeldovich, Konstantin B; Wang, Jennifer P; Bolon, Daniel N A

2018-04-13

The fitness effects of synonymous mutations can provide insights into biological and evolutionary mechanisms. We analyzed the experimental fitness effects of all single-nucleotide mutations, including synonymous substitutions, at the beginning of the influenza A virus hemagglutinin (HA) gene. Many synonymous substitutions were deleterious both in bulk competition and for individually isolated clones. Investigating protein and RNA levels of a subset of individually expressed HA variants revealed that multiple biochemical properties contribute to the observed experimental fitness effects. Our results indicate that a structural element in the HA segment viral RNA may influence fitness. Examination of naturally evolved sequences in human hosts indicates a preference for the unfolded state of this structural element compared to that found in swine hosts. Our overall results reveal that synonymous mutations may have greater fitness consequences than indicated by simple models of sequence conservation, and we discuss the implications of this finding for commonly used evolutionary tests and analyses. Copyright © 2018. Published by Elsevier Ltd.

Observational evidence of dust evolution in galactic extinction curves

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

Cecchi-Pestellini, Cesare; Casu, Silvia; Mulas, Giacomo

Although structural and optical properties of hydrogenated amorphous carbons are known to respond to varying physical conditions, most conventional extinction models are basically curve fits with modest predictive power. We compare an evolutionary model of the physical properties of carbonaceous grain mantles with their determination by homogeneously fitting observationally derived Galactic extinction curves with the same physically well-defined dust model. We find that a large sample of observed Galactic extinction curves are compatible with the evolutionary scenario underlying such a model, requiring physical conditions fully consistent with standard density, temperature, radiation field intensity, and average age of diffuse interstellar clouds.more » Hence, through the study of interstellar extinction we may, in principle, understand the evolutionary history of the diffuse interstellar clouds.« less

Radar cross section models for limited aspect angle windows

NASA Astrophysics Data System (ADS)

Robinson, Mark C.

1992-12-01

This thesis presents a method for building Radar Cross Section (RCS) models of aircraft based on static data taken from limited aspect angle windows. These models statistically characterize static RCS. This is done to show that a limited number of samples can be used to effectively characterize static aircraft RCS. The optimum models are determined by performing both a Kolmogorov and a Chi-Square goodness-of-fit test comparing the static RCS data with a variety of probability density functions (pdf) that are known to be effective at approximating the static RCS of aircraft. The optimum parameter estimator is also determined by the goodness of-fit tests if there is a difference in pdf parameters obtained by the Maximum Likelihood Estimator (MLE) and the Method of Moments (MoM) estimators.

T- P Phase Diagram of Nitrogen at High Pressures

NASA Astrophysics Data System (ADS)

Algul, G.; Enginer, Y.; Yurtseven, H.

2018-05-01

By employing a mean field model, calculation of the T- P phase diagram of molecular nitrogen is performed at high pressures up to 200 GPa. Experimental data from the literature are used to fit a quadratic function in T and P, describing the phase line equations which have been derived using the mean field model studied here for N 2, and the fitted parameters are determined. Our model study gives that the observed T- P phase diagram can be described satisfactorily for the first-order transitions between the phases at low as well as high pressures in nitrogen. Some thermodynamic quantities can also be predicted as functions of temperature and pressure from the mean field model studied here and they can be compared with the experimental data.

Effective learning among elite football players: the development of a football-specific self-regulated learning questionnaire.

PubMed

Toering, Tynke; Jordet, Geir; Ripegutu, Anders

2013-01-01

The present study aimed to develop a football-specific self-report instrument measuring self-regulated learning in the context of daily practice, which can be used to monitor the extent to which players take responsibility for their own learning. Development of the instrument involved six steps: 1. Literature review based on Zimmerman's (2006) theory of self-regulated learning, 2. Item generation, 3. Item validation, 4. Pilot studies, 5. Exploratory factor analysis (EFA), and 6. Confirmatory factor analysis (CFA). The instrument was tested for reliability and validity among 204 elite youth football players aged 13-16 years (Mage = 14.6; s = 0.60; 123 boys, 81 girls). The EFA indicated that a five-factor model fitted the observed data best (reflection, evaluation, planning, speaking up, and coaching). However, the CFA showed that a three-factor structure including 22 items produced a satisfactory model fit (reflection, evaluation, and planning; non-normed fit index [NNFI] = 0.96, comparative fit index [CFI] = 0.95, root mean square error of approximation [RMSEA] = 0.067). While the self-regulation processes of reflection, evaluation, and planning are strongly related and fit well into one model, other self-regulated learning processes seem to be more individually determined. In conclusion, the questionnaire developed in this study is considered a reliable and valid instrument to measure self-regulated learning among elite football players.

Deriving sulfamethoxazole dissipation endpoints in pasture soils using first order and biphasic kinetic models.

PubMed

Srinivasan, Prakash; Sarmah, Ajit K; Rohan, Maheswaran

2014-08-01

Single first-order (SFO) kinetic model is often used to derive the dissipation endpoints of an organic chemical in soil. This model is used due to its simplicity and requirement by regulatory agencies. However, using the SFO model for all types of decay pattern could lead to under- or overestimation of dissipation endpoints when the deviation from first-order is significant. In this study the performance of three biphasic kinetic models - bi-exponential decay (BEXP), first-order double exponential decay (FODED), and first-order two-compartment (FOTC) models was evaluated using dissipation datasets of sulfamethoxazole (SMO) antibiotic in three different soils under varying concentration, depth, temperature, and sterile conditions. Corresponding 50% (DT50) and 90% (DT90) dissipation times for the antibiotics were numerically obtained and compared against those obtained using the SFO model. The fit of each model to the measured values was evaluated based on an array of statistical measures such as coefficient of determination (R(2)adj), root mean square error (RMSE), chi-square (χ(2)) test at 1% significance, Bayesian Information Criteria (BIC) and % model error. Box-whisker residual plots were also used to compare the performance of each model to the measured datasets. The antibiotic dissipation was successfully predicted by all four models. However, the nonlinear biphasic models improved the goodness-of-fit parameters for all datasets. Deviations from datasets were also often less evident with the biphasic models. The fits of FOTC and FODED models for SMO dissipation datasets were identical in most cases, and were found to be superior to the BEXP model. Among the biphasic models, the FOTC model was found to be the most suitable for obtaining the endpoints and could provide a mechanistic explanation for SMO dissipation in the soils. Copyright © 2014 Elsevier B.V. All rights reserved.

Dual learning processes underlying human decision-making in reversal learning tasks: functional significance and evidence from the model fit to human behavior

PubMed Central

Bai, Yu; Katahira, Kentaro; Ohira, Hideki

2014-01-01

Humans are capable of correcting their actions based on actions performed in the past, and this ability enables them to adapt to a changing environment. The computational field of reinforcement learning (RL) has provided a powerful explanation for understanding such processes. Recently, the dual learning system, modeled as a hybrid model that incorporates value update based on reward-prediction error and learning rate modulation based on the surprise signal, has gained attention as a model for explaining various neural signals. However, the functional significance of the hybrid model has not been established. In the present study, we used computer simulation in a reversal learning task to address functional significance in a probabilistic reversal learning task. The hybrid model was found to perform better than the standard RL model in a large parameter setting. These results suggest that the hybrid model is more robust against the mistuning of parameters compared with the standard RL model when decision-makers continue to learn stimulus-reward contingencies, which can create abrupt changes. The parameter fitting results also indicated that the hybrid model fit better than the standard RL model for more than 50% of the participants, which suggests that the hybrid model has more explanatory power for the behavioral data than the standard RL model. PMID:25161635

Bright high z SnIa: A challenge for ΛCDM

NASA Astrophysics Data System (ADS)

Perivolaropoulos, L.; Shafieloo, A.

2009-06-01

It has recently been pointed out by Kowalski et. al. [Astrophys. J. 686, 749 (2008).ASJOAB0004-637X10.1086/589937] that there is “an unexpected brightness of the SnIa data at z>1.” We quantify this statement by constructing a new statistic which is applicable directly on the type Ia supernova (SnIa) distance moduli. This statistic is designed to pick up systematic brightness trends of SnIa data points with respect to a best fit cosmological model at high redshifts. It is based on binning the normalized differences between the SnIa distance moduli and the corresponding best fit values in the context of a specific cosmological model (e.g. ΛCDM). These differences are normalized by the standard errors of the observed distance moduli. We then focus on the highest redshift bin and extend its size toward lower redshifts until the binned normalized difference (BND) changes sign (crosses 0) at a redshift zc (bin size Nc). The bin size Nc of this crossing (the statistical variable) is then compared with the corresponding crossing bin size Nmc for Monte Carlo data realizations based on the best fit model. We find that the crossing bin size Nc obtained from the Union08 and Gold06 data with respect to the best fit ΛCDM model is anomalously large compared to Nmc of the corresponding Monte Carlo data sets obtained from the best fit ΛCDM in each case. In particular, only 2.2% of the Monte Carlo ΛCDM data sets are consistent with the Gold06 value of Nc while the corresponding probability for the Union08 value of Nc is 5.3%. Thus, according to this statistic, the probability that the high redshift brightness bias of the Union08 and Gold06 data sets is realized in the context of a (w0,w1)=(-1,0) model (ΛCDM cosmology) is less than 6%. The corresponding realization probability in the context of a (w0,w1)=(-1.4,2) model is more than 30% for both the Union08 and the Gold06 data sets indicating a much better consistency for this model with respect to the BND statistic.

Comparison of a layered slab and an atlas head model for Monte Carlo fitting of time-domain near-infrared spectroscopy data of the adult head

PubMed Central

Selb, Juliette; Ogden, Tyler M.; Dubb, Jay; Fang, Qianqian; Boas, David A.

2014-01-01

Abstract. Near-infrared spectroscopy (NIRS) estimations of the adult brain baseline optical properties based on a homogeneous model of the head are known to introduce significant contamination from extracerebral layers. More complex models have been proposed and occasionally applied to in vivo data, but their performances have never been characterized on realistic head structures. Here we implement a flexible fitting routine of time-domain NIRS data using graphics processing unit based Monte Carlo simulations. We compare the results for two different geometries: a two-layer slab with variable thickness of the first layer and a template atlas head registered to the subject’s head surface. We characterize the performance of the Monte Carlo approaches for fitting the optical properties from simulated time-resolved data of the adult head. We show that both geometries provide better results than the commonly used homogeneous model, and we quantify the improvement in terms of accuracy, linearity, and cross-talk from extracerebral layers. PMID:24407503

Fitting mechanistic epidemic models to data: A comparison of simple Markov chain Monte Carlo approaches.

PubMed

Li, Michael; Dushoff, Jonathan; Bolker, Benjamin M

2018-07-01

Simple mechanistic epidemic models are widely used for forecasting and parameter estimation of infectious diseases based on noisy case reporting data. Despite the widespread application of models to emerging infectious diseases, we know little about the comparative performance of standard computational-statistical frameworks in these contexts. Here we build a simple stochastic, discrete-time, discrete-state epidemic model with both process and observation error and use it to characterize the effectiveness of different flavours of Bayesian Markov chain Monte Carlo (MCMC) techniques. We use fits to simulated data, where parameters (and future behaviour) are known, to explore the limitations of different platforms and quantify parameter estimation accuracy, forecasting accuracy, and computational efficiency across combinations of modeling decisions (e.g. discrete vs. continuous latent states, levels of stochasticity) and computational platforms (JAGS, NIMBLE, Stan).

Simulation on Poisson and negative binomial models of count road accident modeling

NASA Astrophysics Data System (ADS)

Sapuan, M. S.; Razali, A. M.; Zamzuri, Z. H.; Ibrahim, K.

2016-11-01

Accident count data have often been shown to have overdispersion. On the other hand, the data might contain zero count (excess zeros). The simulation study was conducted to create a scenarios which an accident happen in T-junction with the assumption the dependent variables of generated data follows certain distribution namely Poisson and negative binomial distribution with different sample size of n=30 to n=500. The study objective was accomplished by fitting Poisson regression, negative binomial regression and Hurdle negative binomial model to the simulated data. The model validation was compared and the simulation result shows for each different sample size, not all model fit the data nicely even though the data generated from its own distribution especially when the sample size is larger. Furthermore, the larger sample size indicates that more zeros accident count in the dataset.

Clinical and multiple gene expression variables in survival analysis of breast cancer: Analysis with the hypertabastic survival model

PubMed Central

2012-01-01

Background We explore the benefits of applying a new proportional hazard model to analyze survival of breast cancer patients. As a parametric model, the hypertabastic survival model offers a closer fit to experimental data than Cox regression, and furthermore provides explicit survival and hazard functions which can be used as additional tools in the survival analysis. In addition, one of our main concerns is utilization of multiple gene expression variables. Our analysis treats the important issue of interaction of different gene signatures in the survival analysis. Methods The hypertabastic proportional hazards model was applied in survival analysis of breast cancer patients. This model was compared, using statistical measures of goodness of fit, with models based on the semi-parametric Cox proportional hazards model and the parametric log-logistic and Weibull models. The explicit functions for hazard and survival were then used to analyze the dynamic behavior of hazard and survival functions. Results The hypertabastic model provided the best fit among all the models considered. Use of multiple gene expression variables also provided a considerable improvement in the goodness of fit of the model, as compared to use of only one. By utilizing the explicit survival and hazard functions provided by the model, we were able to determine the magnitude of the maximum rate of increase in hazard, and the maximum rate of decrease in survival, as well as the times when these occurred. We explore the influence of each gene expression variable on these extrema. Furthermore, in the cases of continuous gene expression variables, represented by a measure of correlation, we were able to investigate the dynamics with respect to changes in gene expression. Conclusions We observed that use of three different gene signatures in the model provided a greater combined effect and allowed us to assess the relative importance of each in determination of outcome in this data set. These results point to the potential to combine gene signatures to a greater effect in cases where each gene signature represents some distinct aspect of the cancer biology. Furthermore we conclude that the hypertabastic survival models can be an effective survival analysis tool for breast cancer patients. PMID:23241496

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle

PubMed Central

Cho, C. I.; Alam, M.; Choi, T. J.; Choy, Y. H.; Choi, J. G.; Lee, S. S.; Cho, K. H.

2016-01-01

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3–L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of polynomials×3 types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea. PMID:26954184

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle.

PubMed

Cho, C I; Alam, M; Choi, T J; Choy, Y H; Choi, J G; Lee, S S; Cho, K H

2016-05-01

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3-L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of polynomials×3 types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea.

Function approximation and documentation of sampling data using artificial neural networks.

PubMed

Zhang, Wenjun; Barrion, Albert

2006-11-01

Biodiversity studies in ecology often begin with the fitting and documentation of sampling data. This study is conducted to make function approximation on sampling data and to document the sampling information using artificial neural network algorithms, based on the invertebrate data sampled in the irrigated rice field. Three types of sampling data, i.e., the curve species richness vs. the sample size, the curve rarefaction, and the curve mean abundance of newly sampled species vs.the sample size, are fitted and documented using BP (Backpropagation) network and RBF (Radial Basis Function) network. As the comparisons, The Arrhenius model, and rarefaction model, and power function are tested for their ability to fit these data. The results show that the BP network and RBF network fit the data better than these models with smaller errors. BP network and RBF network can fit non-linear functions (sampling data) with specified accuracy and don't require mathematical assumptions. In addition to the interpolation, BP network is used to extrapolate the functions and the asymptote of the sampling data can be drawn. BP network cost a longer time to train the network and the results are always less stable compared to the RBF network. RBF network require more neurons to fit functions and generally it may not be used to extrapolate the functions. The mathematical function for sampling data can be exactly fitted using artificial neural network algorithms by adjusting the desired accuracy and maximum iterations. The total numbers of functional species of invertebrates in the tropical irrigated rice field are extrapolated as 140 to 149 using trained BP network, which are similar to the observed richness.

Evaluating the relationship between job stress and job satisfaction among female hospital nurses in Babol: An application of structural equation modeling.

PubMed

Bagheri Hosseinabadi, Majid; Etemadinezhad, Siavash; Khanjani, Narges; Ahmadi, Omran; Gholinia, Hemat; Galeshi, Mina; Samaei, Seyed Ehsan

2018-01-01

Background: This study was designed to investigate job satisfaction and its relation to perceived job stress among hospital nurses in Babol County, Iran. Methods: This cross-sectional study was conducted on 406 female nurses in 6 Babol hospitals. Respondents completed the Minnesota Satisfaction Questionnaire (MSQ), the health and safety executive (HSE) indicator tool and a demographic questionnaire. Descriptive, analytical and structural equation modeling (SEM) analyses were carried out applying SPSS v. 22 and AMOS v. 22. Results: The Normed Fit Index (NFI), Non-normed Fit Index (NNFI), Incremental Fit Index (IFI)and Comparative Fit Index (CFI) were greater than 0.9. Also, goodness of fit index (GFI=0.99)and adjusted goodness of fit index (AGFI) were greater than 0.8, and root mean square error of approximation (RMSEA) were 0.04, The model was found to be with an appropriate fit. The R-squared was 0.42 for job satisfaction, and all its dimensions were related to job stress. The dimensions of job stress explained 42% of changes in the variance of job satisfaction. There was a significant relationship between the dimensions of job stress such as demand (β =0.173,CI =0.095 - 0.365, P≤0.001), control (β =0.135, CI =0.062 - 0.404, P =0.008), relationships(β =-0.208, CI =-0.637- -0.209; P≤0.001) and changes (β =0.247, CI =0.360 - 1.026, P≤0.001)with job satisfaction. Conclusion: One of the important interventions to increase job satisfaction among nurses maybe improvement in the workplace. Reducing the level of workload in order to improve job demand and minimizing role conflict through reducing conflicting demands are recommended.

Evaluating the relationship between job stress and job satisfaction among female hospital nurses in Babol: An application of structural equation modeling

PubMed Central

Bagheri Hosseinabadi, Majid; Etemadinezhad, Siavash; khanjani, Narges; Ahmadi, Omran; Gholinia, Hemat; Galeshi, Mina; Samaei, Seyed Ehsan

2018-01-01

Background: This study was designed to investigate job satisfaction and its relation to perceived job stress among hospital nurses in Babol County, Iran. Methods: This cross-sectional study was conducted on 406 female nurses in 6 Babol hospitals. Respondents completed the Minnesota Satisfaction Questionnaire (MSQ), the health and safety executive (HSE) indicator tool and a demographic questionnaire. Descriptive, analytical and structural equation modeling (SEM) analyses were carried out applying SPSS v. 22 and AMOS v. 22. Results: The Normed Fit Index (NFI), Non-normed Fit Index (NNFI), Incremental Fit Index (IFI)and Comparative Fit Index (CFI) were greater than 0.9. Also, goodness of fit index (GFI=0.99)and adjusted goodness of fit index (AGFI) were greater than 0.8, and root mean square error of approximation (RMSEA) were 0.04, The model was found to be with an appropriate fit. The R-squared was 0.42 for job satisfaction, and all its dimensions were related to job stress. The dimensions of job stress explained 42% of changes in the variance of job satisfaction. There was a significant relationship between the dimensions of job stress such as demand (β =0.173,CI =0.095 - 0.365, P≤0.001), control (β =0.135, CI =0.062 - 0.404, P =0.008), relationships(β =-0.208, CI =-0.637– -0.209; P≤0.001) and changes (β =0.247, CI =0.360 - 1.026, P≤0.001)with job satisfaction. Conclusion: One of the important interventions to increase job satisfaction among nurses maybe improvement in the workplace. Reducing the level of workload in order to improve job demand and minimizing role conflict through reducing conflicting demands are recommended. PMID:29744305

Rates and risk factors of injury in CrossFitTM: a prospective cohort study.

PubMed

Moran, Sebastian; Booker, Harry; Staines, Jacob; Williams, Sean

2017-09-01

CrossFitTM is a strength and conditioning program that has gained widespread popularity since its inception approximately 15 years ago. However, at present little is known about the level of injury risk associated with this form of training. Movement competency, assessed using the Functional Movement ScreenTM (FMS), has been identified as a risk factor for injury in numerous athletic populations, but its role in CrossFit participants is currently unclear. The aim of this study was to evaluate the level of injury risk associated with CrossFit training, and examine the influence of a number of potential risk factors (including movement competency). A cohort of 117 CrossFit participants were followed prospectively for 12 weeks. Participants' characteristics, previous injury history and training experience were recorded at baseline, and an FMS assessment was conducted. The overall injury incidence rate was 2.10 per 1000 training hours (90% confidence limits: 1.32-3.33). A multivariate Poisson regression model identified males (rate ratio [RR]: 4.44 ×/÷ 3.30, very likely harmful) and those with previous injuries (RR: 2.35 ×/÷ 2.37, likely harmful) as having a higher injury risk. Inferences relating to FMS variables were unclear in the multivariate model, although number of asymmetries was a clear risk factor in a univariate model (RR per two additional asymmetries: 2.62 ×/÷ 1.53, likely harmful). The injury incidence rate associated with CrossFit training was low, and comparable to other forms of recreational fitness activities. Previous injury and gender were identified as risk factors for injury, whilst the role of movement competency in this setting warrants further investigation.

The Hierarchical Rater Model for Rated Test Items and Its Application to Large-Scale Educational Assessment Data.

ERIC Educational Resources Information Center

Patz, Richard J.; Junker, Brian W.; Johnson, Matthew S.; Mariano, Louis T.

2002-01-01

Discusses the hierarchical rater model (HRM) of R. Patz (1996) and shows how it can be used to scale examinees and items, model aspects of consensus among raters, and model individual rater severity and consistency effects. Also shows how the HRM fits into the generalizability theory framework. Compares the HRM to the conventional item response…

Factorial invariance of child self-report across age subgroups: a confirmatory factor analysis of ages 5 to 16 years utilizing the PedsQL 4.0 Generic Core Scales.

PubMed

Limbers, Christine A; Newman, Daniel A; Varni, James W

2008-01-01

The utilization of health-related quality of life (HRQOL) measurement in an effort to improve pediatric health and well-being and determine the value of health care services has grown dramatically over the past decade. The paradigm shift toward patient-reported outcomes (PROs) in clinical trials has provided the opportunity to emphasize the value and essential need for pediatric patient self-report. In order for HRQOL/PRO comparisons to be meaningful for subgroup analyses, it is essential to demonstrate factorial invariance. This study examined age subgroup factorial invariance of child self-report for ages 5 to 16 years on more than 8,500 children utilizing the PedsQL 4.0 Generic Core Scales. Multigroup Confirmatory Factor Analysis (MGCFA) was performed specifying a five-factor model. Two multigroup structural equation models, one with constrained parameters and the other with unconstrained parameters, were proposed to compare the factor loadings across the age subgroups. Metric invariance (i.e., equal factor loadings) across the age subgroups was demonstrated based on stability of the Comparative Fit Index between the two models, and several additional indices of practical fit including the Root Mean Squared Error of Approximation, the Non-Normed Fit Index, and the Parsimony Normed Fit Index. The findings support an equivalent five-factor structure across the age subgroups. Based on these data, it can be concluded that children across the age subgroups in this study interpreted items on the PedsQL 4.0 Generic Core Scales in a similar manner regardless of their age.

Could the differences in the biochemistry of prostate carcinoma compared to benign prostate tissue biopsy fragments be evaluated through Raman spectroscopy?

NASA Astrophysics Data System (ADS)

Silveira, Landulfo; Leite, Kátia Ramos M.; Srougi, Miguel; Silveira, Fabrício L.; Pacheco, Marcos Tadeu T.; Zângaro, Renato A.; Pasqualucci, Carlos A.

2013-03-01

It has been proposed a spectral model to evaluate the biochemical differences between prostate carcinoma and benign fragments using dispersive Raman spectroscopy. We have examined 51 prostate fragments from surgically removed PrCa; each fragment was snap-frozen and stored (-80°C) prior spectral analysis. Raman spectrum was measured using a Raman spectrometer (830 nm excitation) coupled to a fiber-optic probe. Integration time and laser power were set to 50 s and 300 mW, respectively. It has been collected triplicate spectra from each fragment (total 153 spectra). Some samples exhibited a strong fluorescence, which was removed by a 7th order polynomial fitting. It has been developed a spectral model based on the least-squares fitting of the spectra of pure biochemicals (actin, collagen, elastin, carotene, glycogen, phosphatidylcholine, hemoglobin, and water) with the spectra of tissues, where the fitting parameters are the relative contribution of the compounds to the tissue spectrum. The spectra (600-1800 cm-1 range) are dominated by bands of proteins; it has been found a small difference in the mean spectra of PrCa compared to the benign tissue, mainly in the 1000-1400 cm-1 region, indicating similar biochemical constitution. The spectral fitting model revealed that elastin and phosphatidylcholine were increased in PrCa, whereas blood and water were reduced in malignant lesions (p < 0.05). A discrimination of PrCa from benign tissue using Mahalanobis distance applied to the contribution of elastin, hemoglobin and phosphatidylcholine resulted in sensitivity of 72% and specificity of 70%.

Model fit evaluation in multilevel structural equation models

PubMed Central

Ryu, Ehri

2014-01-01

Assessing goodness of model fit is one of the key questions in structural equation modeling (SEM). Goodness of fit is the extent to which the hypothesized model reproduces the multivariate structure underlying the set of variables. During the earlier development of multilevel structural equation models, the “standard” approach was to evaluate the goodness of fit for the entire model across all levels simultaneously. The model fit statistics produced by the standard approach have a potential problem in detecting lack of fit in the higher-level model for which the effective sample size is much smaller. Also when the standard approach results in poor model fit, it is not clear at which level the model does not fit well. This article reviews two alternative approaches that have been proposed to overcome the limitations of the standard approach. One is a two-step procedure which first produces estimates of saturated covariance matrices at each level and then performs single-level analysis at each level with the estimated covariance matrices as input (Yuan and Bentler, 2007). The other level-specific approach utilizes partially saturated models to obtain test statistics and fit indices for each level separately (Ryu and West, 2009). Simulation studies (e.g., Yuan and Bentler, 2007; Ryu and West, 2009) have consistently shown that both alternative approaches performed well in detecting lack of fit at any level, whereas the standard approach failed to detect lack of fit at the higher level. It is recommended that the alternative approaches are used to assess the model fit in multilevel structural equation model. Advantages and disadvantages of the two alternative approaches are discussed. The alternative approaches are demonstrated in an empirical example. PMID:24550882

Confirmatory factor analysis of the Oral Health Impact Profile.

PubMed

John, M T; Feuerstahler, L; Waller, N; Baba, K; Larsson, P; Celebić, A; Kende, D; Rener-Sitar, K; Reissmann, D R

2014-09-01

Previous exploratory analyses suggest that the Oral Health Impact Profile (OHIP) consists of four correlated dimensions and that individual differences in OHIP total scores reflect an underlying higher-order factor. The aim of this report is to corroborate these findings in the Dimensions of Oral Health-Related Quality of Life (DOQ) Project, an international study of general population subjects and prosthodontic patients. Using the project's Validation Sample (n = 5022), we conducted confirmatory factor analyses in a sample of 4993 subjects with sufficiently complete data. In particular, we compared the psychometric performance of three models: a unidimensional model, a four-factor model and a bifactor model that included one general factor and four group factors. Using model-fit criteria and factor interpretability as guides, the four-factor model was deemed best in terms of strong item loadings, model fit (RMSEA = 0·05, CFI = 0·99) and interpretability. These results corroborate our previous findings that four highly correlated factors - which we have named Oral Function, Oro-facial Pain, Oro-facial Appearance and Psychosocial Impact - can be reliably extracted from the OHIP item pool. However, the good fit of the unidimensional model and the high interfactor correlations in the four-factor solution suggest that OHRQoL can also be sufficiently described with one score. © 2014 John Wiley & Sons Ltd.

History, Epidemic Evolution, and Model Burn-In for a Network of Annual Invasion: Soybean Rust.

PubMed

Sanatkar, M R; Scoglio, C; Natarajan, B; Isard, S A; Garrett, K A

2015-07-01

Ecological history may be an important driver of epidemics and disease emergence. We evaluated the role of history and two related concepts, the evolution of epidemics and the burn-in period required for fitting a model to epidemic observations, for the U.S. soybean rust epidemic (caused by Phakopsora pachyrhizi). This disease allows evaluation of replicate epidemics because the pathogen reinvades the United States each year. We used a new maximum likelihood estimation approach for fitting the network model based on observed U.S. epidemics. We evaluated the model burn-in period by comparing model fit based on each combination of other years of observation. When the miss error rates were weighted by 0.9 and false alarm error rates by 0.1, the mean error rate did decline, for most years, as more years were used to construct models. Models based on observations in years closer in time to the season being estimated gave lower miss error rates for later epidemic years. The weighted mean error rate was lower in backcasting than in forecasting, reflecting how the epidemic had evolved. Ongoing epidemic evolution, and potential model failure, can occur because of changes in climate, host resistance and spatial patterns, or pathogen evolution.

Genome-Wide Gene Expression Analysis of Bordetella pertussis Isolates Associated with a Resurgence in Pertussis: Elucidation of Factors Involved in the Increased Fitness of Epidemic Strains

PubMed Central

King, Audrey J.; van der Lee, Saskia; Mohangoo, Archena; van Gent, Marjolein; van der Ark, Arno; van de Waterbeemd, Bas

2013-01-01

Bordetella pertussis (B. pertussis) is the causative agent of whooping cough, which is a highly contagious disease in the human respiratory tract. Despite vaccination since the 1950s, pertussis remains the most prevalent vaccine-preventable disease in developed countries. A recent resurgence pertussis is associated with the expansion of B. pertussis strains with a novel allele for the pertussis toxin (ptx) promoter ptxP3 in place of resident ptxP1 strains. The recent expansion of ptxP3 strains suggests that these strains carry mutations that have increased their fitness. Compared to the ptxP1 strains, ptxP3 strains produce more Ptx, which results in increased virulence and immune suppression. In this study, we investigated the contribution of gene expression changes of various genes on the increased fitness of the ptxP3 strains. Using genome-wide gene expression profiling, we show that several virulence genes had higher expression levels in the ptxP3 strains compared to the ptxP1 strains. We provide the first evidence that wildtype ptxP3 strains are better colonizers in an intranasal mouse infection model. This study shows that the ptxP3 mutation and the genetic background of ptxP3 strains affect fitness by contributing to the ability to colonize in a mouse infection model. These results show that the genetic background of ptxP3 strains with a higher expression of virulence genes contribute to increased fitness. PMID:23776625

The classification of body dysmorphic disorder symptoms in male and female adolescents.

PubMed

Schneider, Sophie C; Baillie, Andrew J; Mond, Jonathan; Turner, Cynthia M; Hudson, Jennifer L

2018-01-01

Body dysmorphic disorder (BDD) was categorised in DSM-5 within the newly created 'obsessive-compulsive and related disorders' chapter, however this classification remains subject to debate. Confirmatory factor analysis was used to test competing models of the co-occurrence of symptoms of BDD, obsessive-compulsive disorder, unipolar depression, anxiety, and eating disorders in a community sample of adolescents, and to explore potential sex differences in these models. Self-report questionnaires assessing disorder symptoms were completed by 3149 Australian adolescents. The fit of correlated factor models was calculated separately in males and females, and measurement invariance testing compared parameters of the best-fitting model between males and females. All theoretical models of the classification of BDD had poor fit to the data. Good fit was found for a novel model where BDD symptoms formed a distinct latent factor, correlated with affective disorder and eating disorder latent factors. Metric non-invariance was found between males and females, and the majority of factor loadings differed between males and females. Correlations between some latent factors also differed by sex. Only cross-sectional data were collected, and the study did not assess a broad range of DSM-5 defined eating disorder symptoms or other disorders in the DSM-5 obsessive-compulsive and related disorders chapter. This study is the first to statistically evaluate competing models of BDD classification. The findings highlight the unique features of BDD and its associations with affective and eating disorders. Future studies examining the classification of BDD should consider developmental and sex differences in their models. Copyright © 2017. Published by Elsevier B.V.

Improved accuracy and precision of tracer kinetic parameters by joint fitting to variable flip angle and dynamic contrast enhanced MRI data.

PubMed

Dickie, Ben R; Banerji, Anita; Kershaw, Lucy E; McPartlin, Andrew; Choudhury, Ananya; West, Catharine M; Rose, Chris J

2016-10-01

To improve the accuracy and precision of tracer kinetic model parameter estimates for use in dynamic contrast enhanced (DCE) MRI studies of solid tumors. Quantitative DCE-MRI requires an estimate of precontrast T1 , which is obtained prior to fitting a tracer kinetic model. As T1 mapping and tracer kinetic signal models are both a function of precontrast T1 it was hypothesized that its joint estimation would improve the accuracy and precision of both precontrast T1 and tracer kinetic model parameters. Accuracy and/or precision of two-compartment exchange model (2CXM) parameters were evaluated for standard and joint fitting methods in well-controlled synthetic data and for 36 bladder cancer patients. Methods were compared under a number of experimental conditions. In synthetic data, joint estimation led to statistically significant improvements in the accuracy of estimated parameters in 30 of 42 conditions (improvements between 1.8% and 49%). Reduced accuracy was observed in 7 of the remaining 12 conditions. Significant improvements in precision were observed in 35 of 42 conditions (between 4.7% and 50%). In clinical data, significant improvements in precision were observed in 18 of 21 conditions (between 4.6% and 38%). Accuracy and precision of DCE-MRI parameter estimates are improved when signal models are fit jointly rather than sequentially. Magn Reson Med 76:1270-1281, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

How rapidly does the excess risk of lung cancer decline following quitting smoking? A quantitative review using the negative exponential model.

PubMed

Fry, John S; Lee, Peter N; Forey, Barbara A; Coombs, Katharine J

2013-10-01

The excess lung cancer risk from smoking declines with time quit, but the shape of the decline has never been precisely modelled, or meta-analyzed. From a database of studies of at least 100 cases, we extracted 106 blocks of RRs (from 85 studies) comparing current smokers, former smokers (by time quit) and never smokers. Corresponding pseudo-numbers of cases and controls (or at-risk) formed the data for fitting the negative exponential model. We estimated the half-life (H, time in years when the excess risk becomes half that for a continuing smoker) for each block, investigated model fit, and studied heterogeneity in H. We also conducted sensitivity analyses allowing for reverse causation, either ignoring short-term quitters (S1) or considering them smokers (S2). Model fit was poor ignoring reverse causation, but much improved for both sensitivity analyses. Estimates of H were similar for all three analyses. For the best-fitting analysis (S1), H was 9.93 (95% CI 9.31-10.60), but varied by sex (females 7.92, males 10.71), and age (<50years 6.98, 70+years 12.99). Given that reverse causation is taken account of, the model adequately describes the decline in excess risk. However, estimates of H may be biased by factors including misclassification of smoking status. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Development of Evaluation Indicators for Hospice and Palliative Care Professionals Training Programs in Korea.

PubMed

Kang, Jina; Park, Kyoung-Ok

2017-01-01

The importance of training for Hospice and Palliative Care (HPC) professionals has been increasing with the systemization of HPC in Korea. Hence, the need and importance of training quality for HPC professionals are growing. This study evaluated the construct validity and reliability of the Evaluation Indicators for standard Hospice and Palliative Care Training (EIHPCT) program. As a framework to develop evaluation indicators, an invented theoretical model combining Stufflebeam's CIPP (Context-Input-Process-Product) evaluation model with PRECEDE-PROCEED model was used. To verify the construct validity of the EIHPCT program, a structured survey was performed with 169 professionals who were the HPC training program administrators, trainers, and trainees. To examine the validity of the areas of the EIHPCT program, exploratory factor analysis and confirmatory factor analysis were conducted. First, in the exploratory factor analysis, the indicators with factor loadings above 0.4 were chosen as desirable items, and some cross-loaded items that loaded at 0.4 or higher on two or more factors were adjusted as the higher factor. Second, the model fit of the modified EIHPCT program was quite good in the confirmatory factor analysis (Goodness-of-Fit Index > 0.70, Comparative Fit Index > 0.80, Normed Fit Index > 0.80, Root Mean square of Residuals < 0.05). The modified model of the EIHPCT comprised 4 areas, 13 subdomains, and 61 indicators. The evaluation indicators of the modified model will be valuable references for improving the HPC professional training program.

Connecting clinical and actuarial prediction with rule-based methods.

PubMed

Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

2015-06-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

Isolating the cow-specific part of residual energy intake in lactating dairy cows using random regressions.

PubMed

Fischer, A; Friggens, N C; Berry, D P; Faverdin, P

2018-07-01

The ability to properly assess and accurately phenotype true differences in feed efficiency among dairy cows is key to the development of breeding programs for improving feed efficiency. The variability among individuals in feed efficiency is commonly characterised by the residual intake approach. Residual feed intake is represented by the residuals of a linear regression of intake on the corresponding quantities of the biological functions that consume (or release) energy. However, the residuals include both, model fitting and measurement errors as well as any variability in cow efficiency. The objective of this study was to isolate the individual animal variability in feed efficiency from the residual component. Two separate models were fitted, in one the standard residual energy intake (REI) was calculated as the residual of a multiple linear regression of lactation average net energy intake (NEI) on lactation average milk energy output, average metabolic BW, as well as lactation loss and gain of body condition score. In the other, a linear mixed model was used to simultaneously fit fixed linear regressions and random cow levels on the biological traits and intercept using fortnight repeated measures for the variables. This method split the predicted NEI in two parts: one quantifying the population mean intercept and coefficients, and one quantifying cow-specific deviations in the intercept and coefficients. The cow-specific part of predicted NEI was assumed to isolate true differences in feed efficiency among cows. NEI and associated energy expenditure phenotypes were available for the first 17 fortnights of lactation from 119 Holstein cows; all fed a constant energy-rich diet. Mixed models fitting cow-specific intercept and coefficients to different combinations of the aforementioned energy expenditure traits, calculated on a fortnightly basis, were compared. The variance of REI estimated with the lactation average model represented only 8% of the variance of measured NEI. Among all compared mixed models, the variance of the cow-specific part of predicted NEI represented between 53% and 59% of the variance of REI estimated from the lactation average model or between 4% and 5% of the variance of measured NEI. The remaining 41% to 47% of the variance of REI estimated with the lactation average model may therefore reflect model fitting errors or measurement errors. In conclusion, the use of a mixed model framework with cow-specific random regressions seems to be a promising method to isolate the cow-specific component of REI in dairy cows.

Comparative evaluation of shock absorption ability of custom-fit mouthguards with new-generation polyolefin self-adapting mouthguards in three different maxillary anterior teeth alignments using Fiber Bragg Grating (FBG) sensors.

PubMed

Grewal, Navneet; Kumari, Foolan; Tiwari, Umesh

2015-08-01

Prevention of orofacial injuries is one of the biggest pre-occupations in sports dentistry. The custom-fitted mouthguard is considered the best choice for fit and protection when compared to over-the-counter commercial mouthguards. However, cost and time prohibit their mass production. It is therefore imperative to have an over-the-counter true mouth-formed mouthguard with comparable properties. The present in vitro experimental study was carried out to compare the shock absorption ability of EVA laminate mouthguards with self-adapting polyolefin material mouthguards in three different anterior teeth alignments. Finite element analysis (FEA) was performed to simulate the stress distribution due to impact on the respective mouthguards. Customized pendulum device with three interchangeable standard size impact objects was used. Response of grating was monitored using a FBG interrogation system. Shift in wavelength for each impact was measured. Three standardized jaw models were subjected to a total of 72 impact strikes with three different balls on two specified sites by releasing the objects from two different heights H1 24 cm and H2 48 cm. Two-way ANOVA was applied and comparative values computed. It was found that the percentage shock absorption ability of self-adapted polyolefin mouthguard was highly significant at <0.001 level in both regions. The influence of height on the shock absorption ability of both mouthguards was highly significant at P < 0.001. It was concluded that self-adapting polyolefin mouthguards fulfill similar protection requirement as custom-fit mouthguards and can be used for millions of athletes if properly fitted chairside by a dentist without requiring laboratory fabrication. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Casas Muertas and Oficina No. 1: internal migrations and malaria trends in Venezuela 1905-1945.

PubMed

Chaves, Luis Fernando

2007-06-01

To compare internal migration and temperature as factors behind the decreasing trend in malaria deaths observed in Venezuela from 1905 to 1945, linear autoregressive models are fitted to a historical dataset. The model that only incorporates internal migration is the one with the best fit. The decreasing trend in malaria deaths in Venezuela, from 1905 to 1945, is not explained by a trend in mean annual temperature, but it is associated with an increase in the proportion of population in the Capital District, during a time period when the area was the principal attractor of migrations within the country.

Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects.

PubMed

Dong, Ni; Huang, Helai; Zheng, Liang

2015-09-01

In zone-level crash prediction, accounting for spatial dependence has become an extensively studied topic. This study proposes Support Vector Machine (SVM) model to address complex, large and multi-dimensional spatial data in crash prediction. Correlation-based Feature Selector (CFS) was applied to evaluate candidate factors possibly related to zonal crash frequency in handling high-dimension spatial data. To demonstrate the proposed approaches and to compare them with the Bayesian spatial model with conditional autoregressive prior (i.e., CAR), a dataset in Hillsborough county of Florida was employed. The results showed that SVM models accounting for spatial proximity outperform the non-spatial model in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-zonal spatial correlations. The best model predictive capability, relatively, is associated with the model considering proximity of the centroid distance by choosing the RBF kernel and setting the 10% of the whole dataset as the testing data, which further exhibits SVM models' capacity for addressing comparatively complex spatial data in regional crash prediction modeling. Moreover, SVM models exhibit the better goodness-of-fit compared with CAR models when utilizing the whole dataset as the samples. A sensitivity analysis of the centroid-distance-based spatial SVM models was conducted to capture the impacts of explanatory variables on the mean predicted probabilities for crash occurrence. While the results conform to the coefficient estimation in the CAR models, which supports the employment of the SVM model as an alternative in regional safety modeling. Copyright © 2015 Elsevier Ltd. All rights reserved.

A BRDF statistical model applying to space target materials modeling

NASA Astrophysics Data System (ADS)

Liu, Chenghao; Li, Zhi; Xu, Can; Tian, Qichen

2017-10-01

In order to solve the problem of poor effect in modeling the large density BRDF measured data with five-parameter semi-empirical model, a refined statistical model of BRDF which is suitable for multi-class space target material modeling were proposed. The refined model improved the Torrance-Sparrow model while having the modeling advantages of five-parameter model. Compared with the existing empirical model, the model contains six simple parameters, which can approximate the roughness distribution of the material surface, can approximate the intensity of the Fresnel reflectance phenomenon and the attenuation of the reflected light's brightness with the azimuth angle changes. The model is able to achieve parameter inversion quickly with no extra loss of accuracy. The genetic algorithm was used to invert the parameters of 11 different samples in the space target commonly used materials, and the fitting errors of all materials were below 6%, which were much lower than those of five-parameter model. The effect of the refined model is verified by comparing the fitting results of the three samples at different incident zenith angles in 0° azimuth angle. Finally, the three-dimensional modeling visualizations of these samples in the upper hemisphere space was given, in which the strength of the optical scattering of different materials could be clearly shown. It proved the good describing ability of the refined model at the material characterization as well.

Fit Assessment of N95 Filtering-Facepiece Respirators in the U.S. Centers for Disease Control and Prevention Strategic National Stockpile.

PubMed

Bergman, Michael; Zhuang, Ziqing; Brochu, Elizabeth; Palmiero, Andrew

National Institute for Occupational Safety and Health (NIOSH)-approved N95 filtering-facepiece respirators (FFR) are currently stockpiled by the U.S. Centers for Disease Control and Prevention (CDC) for emergency deployment to healthcare facilities in the event of a widespread emergency such as an influenza pandemic. This study assessed the fit of N95 FFRs purchased for the CDC Strategic National Stockpile. The study addresses the question of whether the fit achieved by specific respirator sizes relates to facial size categories as defined by two NIOSH fit test panels. Fit test data were analyzed from 229 test subjects who performed a nine-donning fit test on seven N95 FFR models using a quantitative fit test protocol. An initial respirator model selection process was used to determine if the subject could achieve an adequate fit on a particular model; subjects then tested the adequately fitting model for the nine-donning fit test. Only data for models which provided an adequate initial fit (through the model selection process) for a subject were analyzed for this study. For the nine-donning fit test, six of the seven respirator models accommodated the fit of subjects (as indicated by geometric mean fit factor > 100) for not only the intended NIOSH bivariate and PCA panel sizes corresponding to the respirator size, but also for other panel sizes which were tested for each model. The model which showed poor performance may not be accurately represented because only two subjects passed the initial selection criteria to use this model. Findings are supportive of the current selection of facial dimensions for the new NIOSH panels. The various FFR models selected for the CDC Strategic National Stockpile provide a range of sizing options to fit a variety of facial sizes.

Fit Assessment of N95 Filtering-Facepiece Respirators in the U.S. Centers for Disease Control and Prevention Strategic National Stockpile

PubMed Central

Bergman, Michael; Zhuang, Ziqing; Brochu, Elizabeth; Palmiero, Andrew

2016-01-01

National Institute for Occupational Safety and Health (NIOSH)-approved N95 filtering-facepiece respirators (FFR) are currently stockpiled by the U.S. Centers for Disease Control and Prevention (CDC) for emergency deployment to healthcare facilities in the event of a widespread emergency such as an influenza pandemic. This study assessed the fit of N95 FFRs purchased for the CDC Strategic National Stockpile. The study addresses the question of whether the fit achieved by specific respirator sizes relates to facial size categories as defined by two NIOSH fit test panels. Fit test data were analyzed from 229 test subjects who performed a nine-donning fit test on seven N95 FFR models using a quantitative fit test protocol. An initial respirator model selection process was used to determine if the subject could achieve an adequate fit on a particular model; subjects then tested the adequately fitting model for the nine-donning fit test. Only data for models which provided an adequate initial fit (through the model selection process) for a subject were analyzed for this study. For the nine-donning fit test, six of the seven respirator models accommodated the fit of subjects (as indicated by geometric mean fit factor > 100) for not only the intended NIOSH bivariate and PCA panel sizes corresponding to the respirator size, but also for other panel sizes which were tested for each model. The model which showed poor performance may not be accurately represented because only two subjects passed the initial selection criteria to use this model. Findings are supportive of the current selection of facial dimensions for the new NIOSH panels. The various FFR models selected for the CDC Strategic National Stockpile provide a range of sizing options to fit a variety of facial sizes. PMID:26877587

Finite mixture model: A maximum likelihood estimation approach on time series data

NASA Astrophysics Data System (ADS)

Yen, Phoong Seuk; Ismail, Mohd Tahir; Hamzah, Firdaus Mohamad

2014-09-01

Recently, statistician emphasized on the fitting of finite mixture model by using maximum likelihood estimation as it provides asymptotic properties. In addition, it shows consistency properties as the sample sizes increases to infinity. This illustrated that maximum likelihood estimation is an unbiased estimator. Moreover, the estimate parameters obtained from the application of maximum likelihood estimation have smallest variance as compared to others statistical method as the sample sizes increases. Thus, maximum likelihood estimation is adopted in this paper to fit the two-component mixture model in order to explore the relationship between rubber price and exchange rate for Malaysia, Thailand, Philippines and Indonesia. Results described that there is a negative effect among rubber price and exchange rate for all selected countries.

Physical fitness reference standards in fibromyalgia: The al-Ándalus project.

PubMed

Álvarez-Gallardo, I C; Carbonell-Baeza, A; Segura-Jiménez, V; Soriano-Maldonado, A; Intemann, T; Aparicio, V A; Estévez-López, F; Camiletti-Moirón, D; Herrador-Colmenero, M; Ruiz, J R; Delgado-Fernández, M; Ortega, F B

2017-11-01

We aimed (1) to report age-specific physical fitness levels in people with fibromyalgia of a representative sample from Andalusia; and (2) to compare the fitness levels of people with fibromyalgia with non-fibromyalgia controls. This cross-sectional study included 468 (21 men) patients with fibromyalgia and 360 (55 men) controls. The fibromyalgia sample was geographically representative from southern Spain. Physical fitness was assessed with the Senior Fitness Test battery plus the handgrip test. We applied the Generalized Additive Model for Location, Scale and Shape to calculate percentile curves for women and fitted mean curves using a linear regression for men. Our results show that people with fibromyalgia reached worse performance in all fitness tests than controls (P < 0.001) in all age ranges (P < 0.001). This study provides a comprehensive description of age-specific physical fitness levels among patients with fibromyalgia and controls in a large sample of patients with fibromyalgia from southern of Spain. Physical fitness levels of people with fibromyalgia from Andalusia are very low in comparison with age-matched healthy controls. This information could be useful to correctly interpret physical fitness assessments and helping health care providers to identify individuals at risk for losing physical independence. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The influence of prototype testing in three-dimensional aortic models on fenestrated endograft design.

PubMed

Taher, Fadi; Falkensammer, Juergen; McCarte, Jamie; Strassegger, Johann; Uhlmann, Miriam; Schuch, Philipp; Assadian, Afshin

2017-06-01

The fenestrated Anaconda endograft (Vascutek/Terumo, Inchinnan, UK) is intended for the treatment of abdominal aortic aneurysms with an insufficient infrarenal landing zone. The endografts are custom-made with use of high-resolution, 1-mm-slice computed tomography angiography images. For every case, a nonsterile prototype and a three-dimensional (3D) model of the patient's aorta are constructed to allow the engineers as well as the physician to test-implant the device and to review the fit of the graft. The aim of this investigation was to assess the impact of 3D model construction and prototype testing on the design of the final sterile endograft. A prospectively held database on fenestrated endovascular aortic repair patients treated at a single institution was completed with data from the Vascutek engineers' prototype test results as well as the product request forms. Changes to endograft design based on prototype testing were assessed and are reported for all procedures. Between April 1, 2013, and August 18, 2015, 60 fenestrated Anaconda devices were implanted. Through prototype testing, engineers were able to identify and report potential risks to technical success related to use of the custom device for the respective patient. Theoretical concerns about endograft fit in the rigid model were expressed in 51 cases (85.0%), and the engineers suggested potential changes to the design of 21 grafts (35.0%). Thirteen cases (21.7%) were eventually modified after the surgeon's testing of the prototype. A second prototype was ordered in three cases (5.0%) because of extensive changes to endograft design, such as inclusion of an additional fenestration. Technical success rates were comparable for grafts that showed a perfect fit from the beginning and cases in which prototype testing resulted in a modification of graft design. Planning and construction of fenestrated endografts for complex aortic anatomies where exact fit and positioning of the graft are paramount to allow cannulation of the aortic branches are challenging. In the current series, approximately one in five endografts was modified after prototype testing in an aortic model. Eventually, success rates were comparable between the groups of patients with a good primary prototype fit and those in which the endograft design was altered. Prototype testing in 3D aortic models is a valuable tool to test the fit of a custom-made endograft before implantation. This may help avoid potentially debilitating adverse events associated with misaligned fenestrations and unconnected aortic branches. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Shape dependence of two-cylinder Rényi entropies for free bosons on a lattice

NASA Astrophysics Data System (ADS)

Chojnacki, Leilee; Cook, Caleb Q.; Dalidovich, Denis; Hayward Sierens, Lauren E.; Lantagne-Hurtubise, Étienne; Melko, Roger G.; Vlaar, Tiffany J.

2016-10-01

Universal scaling terms occurring in Rényi entanglement entropies have the potential to bring new understanding to quantum critical points in free and interacting systems. Quantitative comparisons between analytical continuum theories and numerical calculations on lattice models play a crucial role in advancing such studies. In this paper, we exactly calculate the universal two-cylinder shape dependence of entanglement entropies for free bosons on finite-size square lattices, and compare to approximate functions derived in the continuum using several different Ansätze. Although none of these Ansätze are exact in the thermodynamic limit, we find that numerical fits are in good agreement with continuum functions derived using the anti-de Sitter/conformal field theory correspondence, an extensive mutual information model, and a quantum Lifshitz model. We use fits of our lattice data to these functions to calculate universal scalars defined in the thin-cylinder limit, and compare to values previously obtained for the free boson field theory in the continuum.

A Study of the Optimal Model of the Flotation Kinetics of Copper Slag from Copper Mine BOR

NASA Astrophysics Data System (ADS)

Stanojlović, Rodoljub D.; Sokolović, Jovica M.

2014-10-01

In this study the effect of mixtures of copper slag and flotation tailings from copper mine Bor, Serbia on the flotation results of copper recovery and flotation kinetics parameters in a batch flotation cell has been investigated. By simultaneous adding old flotation tailings in the ball mill at the rate of 9%, it is possible to increase copper recovery for about 20%. These results are compared with obtained copper recovery of pure copper slag. The results of batch flotation test were fitted by MatLab software for modeling the first-order flotation kinetics in order to determine kinetics parameters and define an optimal model of the flotation kinetics. Six kinetic models are tested on the batch flotation copper recovery against flotation time. All models showed good correlation, however the modified Kelsall model provided the best fit.

Bayesian analysis of CCDM models

NASA Astrophysics Data System (ADS)

Jesus, J. F.; Valentim, R.; Andrade-Oliveira, F.

2017-09-01

Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3αH0 model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.

Estimating and Comparing Dam Deformation Using Classical and GNSS Techniques

PubMed Central

Barzaghi, Riccardo; De Gaetani, Carlo Iapige

2018-01-01

Global Navigation Satellite Systems (GNSS) receivers are nowadays commonly used in monitoring applications, e.g., in estimating crustal and infrastructure displacements. This is basically due to the recent improvements in GNSS instruments and methodologies that allow high-precision positioning, 24 h availability and semiautomatic data processing. In this paper, GNSS-estimated displacements on a dam structure have been analyzed and compared with pendulum data. This study has been carried out for the Eleonora D’Arborea (Cantoniera) dam, which is in Sardinia. Time series of pendulum and GNSS over a time span of 2.5 years have been aligned so as to be comparable. Analytical models fitting these time series have been estimated and compared. Those models were able to properly fit pendulum data and GNSS data, with standard deviation of residuals smaller than one millimeter. These encouraging results led to the conclusion that GNSS technique can be profitably applied to dam monitoring allowing a denser description, both in space and time, of the dam displacements than the one based on pendulum observations. PMID:29498650

The Sheperd equation and chaos identification.

PubMed

Gregson, Robert A M

2010-04-01

An equation created by Sheperd (1982) to model stability in exploited fish populations has been found to have a wider application, and it exhibits complicated internal dynamics, including phases of strict periodicity and of chaos. It may be potentially applicable to other psychophysiological contexts. The problems of determining goodness-of fit, and the comparative performance of alternative models including the Shephed model, are briefly addressed.

In silico modelling of directed evolution: Implications for experimental design and stepwise evolution.

PubMed

Wedge, David C; Rowe, William; Kell, Douglas B; Knowles, Joshua

2009-03-07

We model the process of directed evolution (DE) in silico using genetic algorithms. Making use of the NK fitness landscape model, we analyse the effects of mutation rate, crossover and selection pressure on the performance of DE. A range of values of K, the epistatic interaction of the landscape, are considered, and high- and low-throughput modes of evolution are compared. Our findings suggest that for runs of or around ten generations' duration-as is typical in DE-there is little difference between the way in which DE needs to be configured in the high- and low-throughput regimes, nor across different degrees of landscape epistasis. In all cases, a high selection pressure (but not an extreme one) combined with a moderately high mutation rate works best, while crossover provides some benefit but only on the less rugged landscapes. These genetic algorithms were also compared with a "model-based approach" from the literature, which uses sequential fixing of the problem parameters based on fitting a linear model. Overall, we find that purely evolutionary techniques fare better than do model-based approaches across all but the smoothest landscapes.

ADHD and the externalizing spectrum: direct comparison of categorical, continuous, and hybrid models of liability in a nationally representative sample.

PubMed

Carragher, Natacha; Krueger, Robert F; Eaton, Nicholas R; Markon, Kristian E; Keyes, Katherine M; Blanco, Carlos; Saha, Tulshi D; Hasin, Deborah S

2014-08-01

Alcohol use disorders, substance use disorders, and antisocial personality disorder share a common externalizing liability, which may also include attention-deficit hyperactivity disorder (ADHD). However, few studies have compared formal quantitative models of externalizing liability, with the aim of delineating the categorical and/or continuous nature of this liability in the community. This study compares categorical, continuous, and hybrid models of externalizing liability. Data were derived from the 2004-2005 National Epidemiologic Survey on Alcohol and Related Conditions (N = 34,653). Seven disorders were modeled: childhood ADHD and lifetime diagnoses of antisocial personality disorder (ASPD), nicotine dependence, alcohol dependence, marijuana dependence, cocaine dependence, and other substance dependence. The continuous latent trait model provided the best fit to the data. Measurement invariance analyses supported the fit of the model across genders, with females displaying a significantly lower probability of experiencing externalizing disorders. Cocaine dependence, marijuana dependence, other substance dependence, alcohol dependence, ASPD, nicotine dependence, and ADHD provided the greatest information, respectively, about the underlying externalizing continuum. Liability to externalizing disorders is continuous and dimensional in severity. The findings have important implications for the organizational structure of externalizing psychopathology in psychiatric nomenclatures.

Comparing a thermo-mechanical Weichselian ice sheet reconstruction to GIA driven reconstructions: aspects of earth response and ice configuration

NASA Astrophysics Data System (ADS)

Schmidt, P.; Lund, B.; Näslund, J.-O.

2013-12-01

In this study we compare a recent reconstruction of the Weichselian ice-sheet as simulated by the University of Main ice-sheet model (UMISM) to two reconstructions commonly used in glacial isostatic adjustment (GIA) modeling: ICE-5G and ANU (also known as RSES). The UMISM reconstruction is carried out on a regional scale based on thermo-mechanical modelling whereas ANU and ICE-5G are global models based on the sea-level equation. The Weichselian ice-sheet in the three models are compared directly in terms of ice volume, extent and thickness, as well as in terms of predicted glacial isostatic adjustment in Fennoscandia. The three reconstructions display significant differences. UMISM and ANU includes phases of pronounced advance and retreat prior to the last glacial maximum (LGM), whereas the thickness and areal extent of the ICE-5G ice-sheet is more or less constant up until LGM. The final retreat of the ice-sheet initiates at earliest time in ICE-5G and latest in UMISM, while ice free conditions are reached earliest in UMISM and latest in ICE-5G. The post-LGM deglaciation style also differs notably between the ice models. While the UMISM simulation includes two temporary halts in the deglaciation, the later during the Younger Dryas, ANU only includes a decreased deglaciation rate during Younger Dryas and ICE-5G retreats at a relatively constant pace after an initial slow phase. Moreover, ANU and ICE-5G melt relatively uniformly over the entire ice-sheet in contrast to UMISM which melts preferentially from the edges. We find that all three reconstructions fit the present day uplift rates over Fennoscandia and the observed relative sea-level curve along the Ångerman river equally well, albeit with different optimal earth model parameters. Given identical earth models, ICE-5G predicts the fastest present day uplift rates and ANU the slowest, ANU also prefers the thinnest lithosphere. Moreover, only for ANU can a unique best fit model be determined. For UMISM and ICE-5G there is a range of earth models that can reproduce the present day uplift rates equally well. This is understood from the higher present day uplift rates predicted by ICE-5G and UMISM, which results in a bifurcation in the best fit mantle viscosity. Comparison of the uplift histories predicted by the ice-sheets indicate that inclusion of relative sea-level data in the data fit can reduce the observed ambiguity. We study the areal distributions of present day residual surface velocities in Fennoscandia and show that all three reconstructions generally over-predict velocities in southwestern Fennoscandia and that there are large differences in the fit to the observational data in Finland and northernmost Sweden and Norway. These difference may provide input to further enhancements of the ice-sheet reconstructions.

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit

PubMed Central

Hoover, Stephen; Jackson, Eric V.; Paul, David; Locke, Robert

2016-01-01

Summary Background Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Objective Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. Methods We used five years of retrospective daily NICU census data for model development (January 2008 – December 2012, N=1827 observations) and one year of data for validation (January – December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. Results The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Conclusions Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning. PMID:27437040

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit.

PubMed

Capan, Muge; Hoover, Stephen; Jackson, Eric V; Paul, David; Locke, Robert

2016-01-01

Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. We used five years of retrospective daily NICU census data for model development (January 2008 - December 2012, N=1827 observations) and one year of data for validation (January - December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning.

Faecal immunochemical tests to triage patients with lower abdominal symptoms for suspected colorectal cancer referrals in primary care: a systematic review and cost-effectiveness analysis.

PubMed

Westwood, Marie; Corro Ramos, Isaac; Lang, Shona; Luyendijk, Marianne; Zaim, Remziye; Stirk, Lisa; Al, Maiwenn; Armstrong, Nigel; Kleijnen, Jos

2017-05-01

Colorectal cancer (CRC) is the third most common cancer in the UK. Presenting symptoms that can be associated with CRC usually have another explanation. Faecal immunochemical tests (FITs) detect blood that is not visible to the naked eye and may help to select patients who are likely to benefit from further investigation. To assess the effectiveness of FITs [OC-Sensor (Eiken Chemical Co./MAST Diagnostics, Tokyo, Japan), HM-JACKarc (Kyowa Medex/Alpha Laboratories Ltd, Tokyo, Japan), FOB Gold (Sentinel/Sysmex, Sentinel Diagnostics, Milan, Italy), RIDASCREEN Hb or RIDASCREEN Hb/Hp complex (R-Biopharm, Darmstadt, Germany)] for primary care triage of people with low-risk symptoms. Twenty-four resources were searched to March 2016. Review methods followed published guidelines. Summary estimates were calculated using a bivariate model or a random-effects logistic regression model. The cost-effectiveness analysis considered long-term costs and quality-adjusted life-years (QALYs) that were associated with different faecal occult blood tests and direct colonoscopy referral. Modelling comprised a diagnostic decision model, a Markov model for long-term costs and QALYs that were associated with CRC treatment and progression, and a Markov model for QALYs that were associated with no CRC. We included 10 studies. Using a single sample and 10 µg Hb/g faeces threshold, sensitivity estimates for OC-Sensor [92.1%, 95% confidence interval (CI) 86.9% to 95.3%] and HM-JACKarc (100%, 95% CI 71.5% to 100%) indicated that both may be useful to rule out CRC. Specificity estimates were 85.8% (95% CI 78.3% to 91.0%) and 76.6% (95% CI 72.6% to 80.3%). Triage using FITs could rule out CRC and avoid colonoscopy in approximately 75% of symptomatic patients. Data from our systematic review suggest that 22.5-93% of patients with a positive FIT and no CRC have other significant bowel pathologies. The results of the base-case analysis suggested minimal difference in QALYs between all of the strategies; no triage (referral straight to colonoscopy) is the most expensive. Faecal immunochemical testing was cost-effective (cheaper and more, or only slightly less, effective) compared with no triage. Faecal immunochemical testing was more effective and costly than guaiac faecal occult blood testing, but remained cost-effective at a threshold incremental cost-effectiveness ratio of £30,000. The results of scenario analyses did not differ substantively from the base-case. Results were better for faecal immunochemical testing when accuracy of the guaiac faecal occult blood test (gFOBT) was based on studies that were more representative of the correct population. Only one included study evaluated faecal immunochemical testing in primary care; however, all of the other studies evaluated faecal immunochemical testing at the point of referral. Further, validation data for the Faecal haemoglobin, Age and Sex Test (FAST) score, which includes faecal immunochemical testing, showed no significant difference in performance between primary and secondary care. There were insufficient data to adequately assess FOB Gold, RIDASCREEN Hb or RIDASCREEN Hb/Hp complex. No study compared FIT assays, or FIT assays versus gFOBT; all of the data included in this assessment refer to the clinical effectiveness of individual FIT methods and not their comparative effectiveness. Faecal immunochemical testing is likely to be a clinically effective and cost-effective strategy for triaging people who are presenting, in primary care settings, with lower abdominal symptoms and who are at low risk for CRC. Further research is required to confirm the effectiveness of faecal immunochemical testing in primary care practice and to compare the performance of different FIT assays. This study is registered as PROSPERO CRD42016037723. The National Institute for Health Research Health Technology Assessment programme.

[Radiance Simulation of BUV Hyperspectral Sensor on Multi Angle Observation, and Improvement to Initial Total Ozone Estimating Model of TOMS V8 Total Ozone Algorithm].

PubMed

Lü, Chun-guang; Wang, Wei-he; Yang, Wen-bo; Tian, Qing-iju; Lu, Shan; Chen, Yun

2015-11-01

New hyperspectral sensor to detect total ozone is considered to be carried on geostationary orbit platform in the future, because local troposphere ozone pollution and diurnal variation of ozone receive more and more attention. Sensors carried on geostationary satellites frequently obtain images on the condition of larger observation angles so that it has higher requirements of total ozone retrieval on these observation geometries. TOMS V8 algorithm is developing and widely used in low orbit ozone detecting sensors, but it still lack of accuracy on big observation geometry, therefore, how to improve the accuracy of total ozone retrieval is still an urgent problem that demands immediate solution. Using moderate resolution atmospheric transmission, MODT-RAN, synthetic UV backscatter radiance in the spectra region from 305 to 360 nm is simulated, which refers to clear sky, multi angles (12 solar zenith angles and view zenith angles) and 26 standard profiles, moreover, the correlation and trends between atmospheric total ozone and backward scattering of the earth UV radiation are analyzed based on the result data. According to these result data, a new modified initial total ozone estimation model in TOMS V8 algorithm is considered to be constructed in order to improve the initial total ozone estimating accuracy on big observation geometries. The analysis results about total ozone and simulated UV backscatter radiance shows: Radiance in 317.5 nm (R₃₁₇.₅) decreased as the total ozone rise. Under the small solar zenith Angle (SZA) and the same total ozone, R₃₁₇.₅ decreased with the increase of view zenith Angle (VZA) but increased on the large SZA. Comparison of two fit models shows: without the condition that both SZA and VZA are large (> 80°), exponential fitting model and logarithm fitting model all show high fitting precision (R² > 0.90), and precision of the two decreased as the SZA and VZA rise. In most cases, the precision of logarithm fitting mode is about 0.9% higher than exponential fitting model. With the increasing of VZA or SZA, the fitting precision gradually lower, and the fall is more in the larger VZA or SZA. In addition, the precision of fitting mode exist a plateau in the small SZA range. The modified initial total ozone estimating model (ln(I) vs. Ω) is established based on logarithm fitting mode, and compared with traditional estimating model (I vs. ln(Ω)), that shows: the RMSE of ln(I) vs. Ω and I vs. ln(Ω) all have the down trend with the rise of total ozone. In the low region of total ozone (175-275 DU), the RMSE is obvious higher than high region (425-525 DU), moreover, a RMSE peak and a trough exist in 225 and 475 DU respectively. With the increase of VZA and SZA, the RMSE of two initial estimating models are overall rise, and the upraising degree is ln(I) vs. Ω obvious with the growing of SZA and VZA. The estimating result by modified model is better than traditional model on the whole total ozone range (RMSE is 0.087%-0.537% lower than traditional model), especially on lower total ozone region and large observation geometries. Traditional estimating model relies on the precision of exponential fitting model, and modified estimating model relies on the precision of logarithm fitting model. The improvement of the estimation accuracy by modified initial total ozone estimating model expand the application range of TOMS V8 algorithm. For sensor carried on geostationary orbit platform, there is no doubt that the modified estimating model can help improve the inversion accuracy on wide spatial and time range This modified model could give support and reference to TOMS algorithm update in the future.

High intensity training in obesity: a Meta‐analysis

PubMed Central

Theel, W.; Kasteleyn, M. J.; Franssen, F. M. E.; Hiemstra, P. S.; Rudolphus, A.; Taube, C.; Braunstahl, G. J.

2017-01-01

Summary Introduction High Intensity training (HIT) is a time‐effective alternative to traditional exercise programs in adults with obesity, but the superiority in terms of improving cardiopulmonary fitness and weight loss has not been demonstrated. Objective to determine the effectiveness of HIT on cardiopulmonary fitness and body composition in adults with obesity compared to traditional (high volume continuous) exercise. Methods A systematic search of the main health science databases was conducted for randomized controlled trials comparing HIT with traditional forms of exercise in people with obesity. Eighteen studies were included in the meta‐analysis. The (unstandardized) mean difference of each outcome parameters was calculated and pooled with the random effects model. Results HIT resulted in greater improvement of cardiopulmonary fitness (VO2max) (MD 1.83, 95% CI 0.70, 2.96, p<0.005; I2=31%) and a greater reduction of %body fat (MD ‐1.69, 95% CI ‐3.10, ‐0.27, p=0.02, I2=30%) compared to traditional exercise. Overall effect for BMI was not different between HIT and traditional exercise. Conclusion Training at high intensity is superior to improve cardiopulmonary fitness and to reduce %body fat in adults with obesity compared to traditional exercise. Future studies are needed to design specific HIT programs for the obese with regard to optimal effect and long‐term adherence. PMID:29071102

N* resonances from K $$\\Lambda$$ Λ amplitudes in sliced bins in energy

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

Anisovich, A. V.; Burkert, V.; Hadžimehmedović, M.

The two reactionsmore » $$\\gamma p\\to K^+\\Lambda$$ and $$\\pi^-p\\to K^0\\Lambda$$ are analyzed to determine the leading photoproduction multipoles and the pion-induced partial wave amplitudes in slices of the invariant mass. The multipoles and the partial-wave amplitudes are simultaneously fitted in a multichannel Laurent+Pietarinen model (L+P model), which determines the poles in the complex energy plane on the second Riemann sheet close to the physical axes. The results from the L+P fit are compared with the results of an energy-dependent fit based on the Bonn-Gatchina (BnGa) approach. The study confirms the existence of several poles due to nucleon resonances in the region at about 1.9\\,GeV with quantum numbers $J^P = 1/2^+$, $3/2^+, 1/2^-, 3/2^-, 5/2^-$.« less

Water sorption equilibria and kinetics of henna leaves

NASA Astrophysics Data System (ADS)

Sghaier, Khamsa; Peczalski, Roman; Bagane, Mohamed

2018-05-01

In this work, firstly the sorption isotherms of henna leaves were determined using a dynamic vapor sorption ( DVS) device at 3 temperatures (30, 40, 50 °C). The equilibrium data were well fitted by the GAB model. Secondly, drying kinetics were measured using a pilot convective dryer for 3 air temperatures (same as above), 3 velocities (0.5, 1, 1.42 m/s) and 4 relative humidities (20, 30, 35, 40%). The drying kinetic coefficients were identified by fitting the DVS and pilot dryer data by Lewis semi-empirical model. In order to compare the obtained kinetic parameters with literature, the water diffusivities were also identified by fitting the data by the simplified solution of fickian diffusion equation. The identified kinetic coefficient was mainly dependent on air temperature and velocity what proved that it represented rather the external transfer and not the internal one.

N* resonances from K $$\\Lambda$$ Λ amplitudes in sliced bins in energy

DOE PAGES

Anisovich, A. V.; Burkert, V.; Hadžimehmedović, M.; ...

2017-12-22

The two reactionsmore » $$\\gamma p\\to K^+\\Lambda$$ and $$\\pi^-p\\to K^0\\Lambda$$ are analyzed to determine the leading photoproduction multipoles and the pion-induced partial wave amplitudes in slices of the invariant mass. The multipoles and the partial-wave amplitudes are simultaneously fitted in a multichannel Laurent+Pietarinen model (L+P model), which determines the poles in the complex energy plane on the second Riemann sheet close to the physical axes. The results from the L+P fit are compared with the results of an energy-dependent fit based on the Bonn-Gatchina (BnGa) approach. The study confirms the existence of several poles due to nucleon resonances in the region at about 1.9\\,GeV with quantum numbers $J^P = 1/2^+$, $3/2^+, 1/2^-, 3/2^-, 5/2^-$.« less

Comparison of marginal and internal fit of 3-unit ceramic fixed dental prostheses made with either a conventional or digital impression.

PubMed

Su, Ting-Shu; Sun, Jian

2016-09-01

For 20 years, the intraoral digital impression technique has been applied to the fabrication of computer aided design and computer aided manufacturing (CAD-CAM) fixed dental prostheses (FDPs). Clinical fit is one of the main determinants of the success of an FDP. Studies of the clinical fit of 3-unit ceramic FDPs made by means of a conventional impression versus a digital impression technology are limited. The purpose of this in vitro study was to evaluate and compare the internal fit and marginal fit of CAD-CAM, 3-unit ceramic FDP frameworks fabricated from an intraoral digital impression and a conventional impression. A standard model was designed for a prepared maxillary left canine and second premolar and missing first premolar. The model was scanned with an intraoral digital scanner, exporting stereolithography (STL) files as the experimental group (digital group). The model was used to fabricate 10 stone casts that were scanned with an extraoral scanner, exporting STL files to a computer connected to the scanner as the control group (conventional group). The STL files were used to produce zirconia FDP frameworks with CAD-CAM. These frameworks were seated on the standard model and evaluated for marginal and internal fit. Each framework was segmented into 4 sections per abutment teeth, resulting in 8 sections per framework, and was observed using optical microscopy with ×50 magnification. Four measurement points were selected on each section as marginal discrepancy (P1), mid-axial wall (P2), axio-occusal edge (P3), and central-occlusal point (P4). Mean marginal fit values of the digital group (64 ±16 μm) were significantly smaller than those of the conventional group (76 ±18 μm) (P<.05). The mean internal fit values of the digital group (111 ±34 μm) were significantly smaller than those of the conventional group (132 ±44 μm) (P<.05). CAD-CAM 3-unit zirconia FDP frameworks fabricated from intraoral digital and conventional impressions showed clinically acceptable marginal and internal fit. The marginal and internal fit of frameworks fabricated from the intraoral digital impression system were better than those fabricated from conventional impressions. Copyright © 2016 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

An Investigation of Item Fit Statistics for Mixed IRT Models

ERIC Educational Resources Information Center

Chon, Kyong Hee

2009-01-01

The purpose of this study was to investigate procedures for assessing model fit of IRT models for mixed format data. In this study, various IRT model combinations were fitted to data containing both dichotomous and polytomous item responses, and the suitability of the chosen model mixtures was evaluated based on a number of model fit procedures.…

Effect of hunter selectivity on harvest rates of radio-collared white-tailed deer in Pennsylvania

USGS Publications Warehouse

Buderman, Frances E.; Diefenbach, Duane R.; Rosenberry, C.S.; Wallingford, Bret D.; Long, Eric S.

2014-01-01

Radio transmitters are a commonly used tool for monitoring the fates of harvested species, although little research has been devoted to whether a visible radio transmitter changes a hunters' willingness to harvest that animal. We initially surveyed deer hunters to assess their willingness to harvest radio-collared deer and predicted radio collars were unlikely to affect the harvest of antlerless deer, but hunters may be less willing to harvest small-antlered males with radio collars compared to large-antlered males. We fitted white-tailed deer (Odocoileus virginianus) with radio collars that were visible to hunters or with ear-tag transmitters or ear-tags that were difficult to detect visually and estimated if harvest rates differed among marking methods. For females, the best model failed to detect an effect of radio collars on harvest rates. Also, we failed to detect a difference between male deer fitted with radio collars and ear-tag transmitters. When we compared males fitted with radio collars versus ear tags, we found harvest rate patterns were opposite to our predictions, with lower harvest rates for adult males fitted with radio collars and higher harvest rates for yearling males fitted with radio collars. Our study suggests that harvest rate estimates generated from a sample of deer fitted with visible radio collars can be representative of the population of inference. 

Hydration and conformational equilibria of simple hydrophobic and amphiphilic solutes.

PubMed Central

Ashbaugh, H S; Kaler, E W; Paulaitis, M E

1998-01-01

We consider whether the continuum model of hydration optimized to reproduce vacuum-to-water transfer free energies simultaneously describes the hydration free energy contributions to conformational equilibria of the same solutes in water. To this end, transfer and conformational free energies of idealized hydrophobic and amphiphilic solutes in water are calculated from explicit water simulations and compared to continuum model predictions. As benchmark hydrophobic solutes, we examine the hydration of linear alkanes from methane through hexane. Amphiphilic solutes were created by adding a charge of +/-1e to a terminal methyl group of butane. We find that phenomenological continuum parameters fit to transfer free energies are significantly different from those fit to conformational free energies of our model solutes. This difference is attributed to continuum model parameters that depend on solute conformation in water, and leads to effective values for the free energy/surface area coefficient and Born radii that best describe conformational equilibrium. In light of these results, we believe that continuum models of hydration optimized to fit transfer free energies do not accurately capture the balance between hydrophobic and electrostatic contributions that determines the solute conformational state in aqueous solution. PMID:9675177

On-Ground Processing of Yaogan-24 Remote Sensing Satellite Attitude Data and Verification Using Geometric Field Calibration

PubMed Central

Wang, Mi; Fan, Chengcheng; Yang, Bo; Jin, Shuying; Pan, Jun

2016-01-01

Satellite attitude accuracy is an important factor affecting the geometric processing accuracy of high-resolution optical satellite imagery. To address the problem whereby the accuracy of the Yaogan-24 remote sensing satellite’s on-board attitude data processing is not high enough and thus cannot meet its image geometry processing requirements, we developed an approach involving on-ground attitude data processing and digital orthophoto (DOM) and the digital elevation model (DEM) verification of a geometric calibration field. The approach focuses on three modules: on-ground processing based on bidirectional filter, overall weighted smoothing and fitting, and evaluation in the geometric calibration field. Our experimental results demonstrate that the proposed on-ground processing method is both robust and feasible, which ensures the reliability of the observation data quality, convergence and stability of the parameter estimation model. In addition, both the Euler angle and quaternion could be used to build a mathematical fitting model, while the orthogonal polynomial fitting model is more suitable for modeling the attitude parameter. Furthermore, compared to the image geometric processing results based on on-board attitude data, the image uncontrolled and relative geometric positioning result accuracy can be increased by about 50%. PMID:27483287

Longitudinal Models of Reading Achievement of Students with Learning Disabilities and without Disabilities

ERIC Educational Resources Information Center

Sullivan, Amanda L.; Kohli, Nidhi; Farnsworth, Elyse M.; Sadeh, Shanna; Jones, Leila

2017-01-01

Objective: Accurate estimation of developmental trajectories can inform instruction and intervention. We compared the fit of linear, quadratic, and piecewise mixed-effects models of reading development among students with learning disabilities relative to their typically developing peers. Method: We drew an analytic sample of 1,990 students from…

Fitting the Mixed Rasch Model to a Reading Comprehension Test: Identifying Reader Types

ERIC Educational Resources Information Center

Baghaei, Purya; Carstensen, Claus H.

2013-01-01

Standard unidimensional Rasch models assume that persons with the same ability parameters are comparable. That is, the same interpretation applies to persons with identical ability estimates as regards the underlying mental processes triggered by the test. However, research in cognitive psychology shows that persons at the same trait level may…

A comparative study between nonlinear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

USDA-ARS?s Scientific Manuscript database

Non-linear regression techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks present interesting and alternative features for such modeling purposes. In this work, a univariate hydrothermal-time based Weibull m...

Weakly Informative Prior for Point Estimation of Covariance Matrices in Hierarchical Models

ERIC Educational Resources Information Center

Chung, Yeojin; Gelman, Andrew; Rabe-Hesketh, Sophia; Liu, Jingchen; Dorie, Vincent

2015-01-01

When fitting hierarchical regression models, maximum likelihood (ML) estimation has computational (and, for some users, philosophical) advantages compared to full Bayesian inference, but when the number of groups is small, estimates of the covariance matrix (S) of group-level varying coefficients are often degenerate. One can do better, even from…

Accuracy of templates for navigated implantation made by rapid prototyping with DICOM datasets of cone beam computer tomography (CBCT).

PubMed

Weitz, Jochen; Deppe, Herbert; Stopp, Sebastian; Lueth, Tim; Mueller, Steffen; Hohlweg-Majert, Bettina

2011-12-01

The aim of this study is to evaluate the accuracy of a surgical template-aided implant placement produced by rapid prototyping using a DICOM dataset from cone beam computer tomography (CBCT). On the basis of CBCT scans (Sirona® Galileos), a total of ten models were produced using a rapid-prototyping three-dimensional printer. On the same patients, impressions were performed to compare fitting accuracy of both methods. From the models made by impression, templates were produced and accuracy was compared and analyzed with the rapid-prototyping model. Whereas templates made by conventional procedure had an excellent accuracy, the fitting accuracy of those produced by DICOM datasets was not sufficient. Deviations ranged between 2.0 and 3.5 mm, after modification of models between 1.4 and 3.1 mm. The findings of this study suggest that the accuracy of the low-dose Sirona Galileos® DICOM dataset seems to show a high deviation, which is not useable for accurate surgical transfer for example in implant surgery.

A rigorous multiple independent binding site model for determining cell-based equilibrium dissociation constants.

PubMed

Drake, Andrew W; Klakamp, Scott L

2007-01-10

A new 4-parameter nonlinear equation based on the standard multiple independent binding site model (MIBS) is presented for fitting cell-based ligand titration data in order to calculate the ligand/cell receptor equilibrium dissociation constant and the number of receptors/cell. The most commonly used linear (Scatchard Plot) or nonlinear 2-parameter model (a single binding site model found in commercial programs like Prism(R)) used for analysis of ligand/receptor binding data assumes only the K(D) influences the shape of the titration curve. We demonstrate using simulated data sets that, depending upon the cell surface receptor expression level, the number of cells titrated, and the magnitude of the K(D) being measured, this assumption of always being under K(D)-controlled conditions can be erroneous and can lead to unreliable estimates for the binding parameters. We also compare and contrast the fitting of simulated data sets to the commonly used cell-based binding equation versus our more rigorous 4-parameter nonlinear MIBS model. It is shown through these simulations that the new 4-parameter MIBS model, when used for cell-based titrations under optimal conditions, yields highly accurate estimates of all binding parameters and hence should be the preferred model to fit cell-based experimental nonlinear titration data.