Cardiorespiratory fitness and death from cancer
DEFF Research Database (Denmark)
Jensen, Magnus Thorsten; Holtermann, Andreas; Bay, Hans
2016-01-01
OBJECTIVES: Poor cardiorespiratory fitness (CRF) is associated with death from cancer. If follow-up time is short, this association may be confounded by subclinical disease already present at the time of CRF assessment. This study investigates the association between CRF and death from cancer...... using a bicycle ergometer test and analysed in multivariable Cox models including conventional risk factors, social class and self-reported physical activity. Death from cancer and all-cause mortality was assessed using Danish national registers. Follow-up was 100% complete. RESULTS: In total, 5131 men...... were included, mean (SD) age 48.8 (5.4) years. During 44 years of follow-up, 4486 subjects died (87.4%), 1527 (29.8%) from cancer. In multivariable models, CRF was highly significantly inversely associated with death from cancer and all-cause mortality ((HR (95% CI)) 0.83 (0.77 to 0.90) and 0.89 (0...
Selection in spatial stochastic models of cancer: Migration as a key modulator of fitness
Directory of Open Access Journals (Sweden)
Stupack Dwayne
2010-04-01
Full Text Available Abstract Background We study the selection dynamics in a heterogeneous spatial colony of cells. We use two spatial generalizations of the Moran process, which include cell divisions, death and migration. In the first model, migration is included explicitly as movement to a proximal location. In the second, migration is implicit, through the varied ability of cell types to place their offspring a distance away, in response to another cell's death. Results In both models, we find that migration has a direct positive impact on the ability of a single mutant cell to invade a pre-existing colony. Thus, a decrease in the growth potential can be compensated by an increase in cell migration. We further find that the neutral ridges (the set of all types with the invasion probability equal to that of the host cells remain invariant under the increase of system size (for large system sizes, thus making the invasion probability a universal characteristic of the cells selection status. We find that repeated instances of large scale cell-death, such as might arise during therapeutic intervention or host response, strongly select for the migratory phenotype. Conclusions These models can help explain the many examples in the biological literature, where genes involved in cell's migratory and invasive machinery are also associated with increased cellular fitness, even though there is no known direct effect of these genes on the cellular reproduction. The models can also help to explain how chemotherapy may provide a selection mechanism for highly invasive phenotypes. Reviewers This article was reviewed by Marek Kimmel and Glenn Webb.
The Model Characteristics of Physical Fitness in CrossFit
Directory of Open Access Journals (Sweden)
Vasilii V. Volkov
2014-06-01
Full Text Available The aim of the study is to work out the model characteristics of the physical fitness of CrossFit athletes based on laboratory functional testing (n=10. The analysis of the body composition was conducted using the dual-energy absorptiometry method. The morpho-functional characteristics of the heart were explored using a high-resolution ultrasound scanner. Oxygen consumption at the aerobic-anaerobic threshold and maximum oxygen consumption were determined in a step test on arm and leg cycle ergometers using a gas-analyzer. The level of the physical fitness of leg muscles in the males and females who took part in the study was satisfactory. However, it was considerably higher than the norm for untrained people. The level of the physical fitness of arm muscles was higher than the average and matched the Master of Sport of International Class standards. The productivity of the cardio-vascular system was much higher than in healthy males and females who do not work out and comparable to the standards for advanced soccer players.
Evaluation of Model Fit in Cognitive Diagnosis Models
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),…
Are Physical Education Majors Models for Fitness?
Kamla, James; Snyder, Ben; Tanner, Lori; Wash, Pamela
2012-01-01
The National Association of Sport and Physical Education (NASPE) (2002) has taken a firm stance on the importance of adequate fitness levels of physical education teachers stating that they have the responsibility to model an active lifestyle and to promote fitness behaviors. Since the NASPE declaration, national initiatives like Let's Move…
Fitting Neuron Models to Spike Trains
Rossant, Cyrille; Goodman, Dan F. M.; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K.; Brette, Romain
2011-01-01
Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input–output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model. PMID:21415925
Contrast Gain Control Model Fits Masking Data
Watson, Andrew B.; Solomon, Joshua A.; Null, Cynthia H. (Technical Monitor)
1994-01-01
We studied the fit of a contrast gain control model to data of Foley (JOSA 1994), consisting of thresholds for a Gabor patch masked by gratings of various orientations, or by compounds of two orientations. Our general model includes models of Foley and Teo & Heeger (IEEE 1994). Our specific model used a bank of Gabor filters with octave bandwidths at 8 orientations. Excitatory and inhibitory nonlinearities were power functions with exponents of 2.4 and 2. Inhibitory pooling was broad in orientation, but narrow in spatial frequency and space. Minkowski pooling used an exponent of 4. All of the data for observer KMF were well fit by the model. We have developed a contrast gain control model that fits masking data. Unlike Foley's, our model accepts images as inputs. Unlike Teo & Heeger's, our model did not require multiple channels for different dynamic ranges.
Fitting Hidden Markov Models to Psychological Data
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Ingmar Visser
2002-01-01
Full Text Available Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive statistics for model selection and model assessment are lacking in the psychological literature. We present model selection and model assessment statistics that are particularly useful in applying hidden Markov models in psychology. These statistics are presented and evaluated by simulation studies for a toy example. We compare AIC, BIC and related criteria and introduce a prediction error measure for assessing goodness-of-fit. In a simulation study, two methods of fitting equality constraints are compared. In two illustrative examples with experimental data we apply selection criteria, fit models with constraints and assess goodness-of-fit. First, data from a concept identification task is analyzed. Hidden Markov models provide a flexible approach to analyzing such data when compared to other modeling methods. Second, a novel application of hidden Markov models in implicit learning is presented. Hidden Markov models are used in this context to quantify knowledge that subjects express in an implicit learning task. This method of analyzing implicit learning data provides a comprehensive approach for addressing important theoretical issues in the field.
Screening for colorectal cancer: what fits best?
LENUS (Irish Health Repository)
Lee, Chun Seng
2012-06-01
Colorectal cancer (CRC) screening has been shown to be effective in reducing CRC incidence and mortality. There are currently a number of screening modalities available for implementation into a population-based CRC screening program. Each screening method offers different strengths but also possesses its own limitations as a population-based screening strategy. We review the current evidence base for accepted CRC screening tools and evaluate their merits alongside their challenges in fulfilling their role in the detection of CRC. We also aim to provide an outlook on the demands of a low-risk population-based CRC screening program with a view to providing insight as to which modality would best suit current and future needs.
Modeling and Fitting Exoplanet Transit Light Curves
Millholland, Sarah; Ruch, G. T.
2013-01-01
We present a numerical model along with an original fitting routine for the analysis of transiting extra-solar planet light curves. Our light curve model is unique in several ways from other available transit models, such as the analytic eclipse formulae of Mandel & Agol (2002) and Giménez (2006), the modified Eclipsing Binary Orbit Program (EBOP) model implemented in Southworth’s JKTEBOP code (Popper & Etzel 1981; Southworth et al. 2004), or the transit model developed as a part of the EXOFAST fitting suite (Eastman et al. in prep.). Our model employs Keplerian orbital dynamics about the system’s center of mass to properly account for stellar wobble and orbital eccentricity, uses a unique analytic solution derived from Kepler’s Second Law to calculate the projected distance between the centers of the star and planet, and calculates the effect of limb darkening using a simple technique that is different from the commonly used eclipse formulae. We have also devised a unique Monte Carlo style optimization routine for fitting the light curve model to observed transits. We demonstrate that, while the effect of stellar wobble on transit light curves is generally small, it becomes significant as the planet to stellar mass ratio increases and the semi-major axes of the orbits decrease. We also illustrate the appreciable effects of orbital ellipticity on the light curve and the necessity of accounting for its impacts for accurate modeling. We show that our simple limb darkening calculations are as accurate as the analytic equations of Mandel & Agol (2002). Although our Monte Carlo fitting algorithm is not as mathematically rigorous as the Markov Chain Monte Carlo based algorithms most often used to determine exoplanetary system parameters, we show that it is straightforward and returns reliable results. Finally, we show that analyses performed with our model and optimization routine compare favorably with exoplanet characterizations published by groups such as the
Model-based estimation of individual fitness
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).
Hayduk, Leslie
2014-01-01
Researchers using factor analysis tend to dismiss the significant ill fit of factor models by presuming that if their factor model is close-to-fitting, it is probably close to being properly causally specified. Close fit may indeed result from a model being close to properly causally specified, but close-fitting factor models can also be seriously…
Survival model construction guided by fit and predictive strength.
Chauvel, Cécile; O'Quigley, John
2016-10-05
Survival model construction can be guided by goodness-of-fit techniques as well as measures of predictive strength. Here, we aim to bring together these distinct techniques within the context of a single framework. The goal is how to best characterize and code the effects of the variables, in particular time dependencies, when taken either singly or in combination with other related covariates. Simple graphical techniques can provide an immediate visual indication as to the goodness-of-fit but, in cases of departure from model assumptions, will point in the direction of a more involved and richer alternative model. These techniques appear to be intuitive. This intuition is backed up by formal theorems that underlie the process of building richer models from simpler ones. Measures of predictive strength are used in conjunction with these goodness-of-fit techniques and, again, formal theorems show that these measures can be used to help identify models closest to the unknown non-proportional hazards mechanism that we can suppose generates the observations. Illustrations from studies in breast cancer show how these tools can be of help in guiding the practical problem of efficient model construction for survival data.
Evaluation of model fit in nonlinear multilevel structural equation modeling
Directory of Open Access Journals (Sweden)
Karin eSchermelleh-Engel
2014-03-01
Full Text Available Evaluating model fit in nonlinear multilevel structural equation models (MSEM presents a challenge as no adequate test statistic is available. Nevertheless, using a product indicator approach a likelihood ratio test for linear models is provided which may also be useful for nonlinear MSEM. The main problem with nonlinear models is that product variables are nonnormally distributed. Although robust test statistics have been developed for linear SEM to ensure valid results under the condition of nonnormality, they were not yet investigated for nonlinear MSEM. In a Monte Carlo study, the performance of the robust likelihood ratio test was investigated for models with single-level latent interaction effects using the unconstrained product indicator approach. As overall model fit evaluation has a potential limitation in detecting the lack of fit at a single level even for linear models, level-specific model fit evaluation was also investigated using partially saturated models. Four population models were considered: a model with interaction effects at both levels, an interaction effect at the within-group level, an interaction effect at the between-group level, and a model with no interaction effects at both levels. For these models the number of groups, predictor correlation, and model misspecification was varied. The results indicate that the robust test statistic performed sufficiently well. Advantages of level-specific model fit evaluation for the detection of model misfit are demonstrated.
Evaluation of model fit in nonlinear multilevel structural equation modeling.
Schermelleh-Engel, Karin; Kerwer, Martin; Klein, Andreas G
2014-01-01
Evaluating model fit in nonlinear multilevel structural equation models (MSEM) presents a challenge as no adequate test statistic is available. Nevertheless, using a product indicator approach a likelihood ratio test for linear models is provided which may also be useful for nonlinear MSEM. The main problem with nonlinear models is that product variables are non-normally distributed. Although robust test statistics have been developed for linear SEM to ensure valid results under the condition of non-normality, they have not yet been investigated for nonlinear MSEM. In a Monte Carlo study, the performance of the robust likelihood ratio test was investigated for models with single-level latent interaction effects using the unconstrained product indicator approach. As overall model fit evaluation has a potential limitation in detecting the lack of fit at a single level even for linear models, level-specific model fit evaluation was also investigated using partially saturated models. Four population models were considered: a model with interaction effects at both levels, an interaction effect at the within-group level, an interaction effect at the between-group level, and a model with no interaction effects at both levels. For these models the number of groups, predictor correlation, and model misspecification was varied. The results indicate that the robust test statistic performed sufficiently well. Advantages of level-specific model fit evaluation for the detection of model misfit are demonstrated.
An Investigation of Item Fit Statistics for Mixed IRT Models
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.…
Fitting Additive Binomial Regression Models with the R Package blm
Directory of Open Access Journals (Sweden)
Stephanie Kovalchik
2013-09-01
Full Text Available The R package blm provides functions for fitting a family of additive regression models to binary data. The included models are the binomial linear model, in which all covariates have additive effects, and the linear-expit (lexpit model, which allows some covariates to have additive effects and other covariates to have logisitc effects. Additive binomial regression is a model of event probability, and the coefficients of linear terms estimate covariate-adjusted risk differences. Thus, in contrast to logistic regression, additive binomial regression puts focus on absolute risk and risk differences. In this paper, we give an overview of the methodology we have developed to fit the binomial linear and lexpit models to binary outcomes from cohort and population-based case-control studies. We illustrate the blm packages methods for additive model estimation, diagnostics, and inference with risk association analyses of a bladder cancer nested case-control study in the NIH-AARP Diet and Health Study.
The best-fit universe. [cosmological models
Turner, Michael S.
1991-01-01
Inflation provides very strong motivation for a flat Universe, Harrison-Zel'dovich (constant-curvature) perturbations, and cold dark matter. However, there are a number of cosmological observations that conflict with the predictions of the simplest such model: one with zero cosmological constant. They include the age of the Universe, dynamical determinations of Omega, galaxy-number counts, and the apparent abundance of large-scale structure in the Universe. While the discrepancies are not yet serious enough to rule out the simplest and most well motivated model, the current data point to a best-fit model with the following parameters: Omega(sub B) approximately equal to 0.03, Omega(sub CDM) approximately equal to 0.17, Omega(sub Lambda) approximately equal to 0.8, and H(sub 0) approximately equal to 70 km/(sec x Mpc) which improves significantly the concordance with observations. While there is no good reason to expect such a value for the cosmological constant, there is no physical principle that would rule out such.
Blanquart, François; Bataillon, Thomas
2016-06-01
The fitness landscape defines the relationship between genotypes and fitness in a given environment and underlies fundamental quantities such as the distribution of selection coefficient and the magnitude and type of epistasis. A better understanding of variation in landscape structure across species and environments is thus necessary to understand and predict how populations will adapt. An increasing number of experiments investigate the properties of fitness landscapes by identifying mutations, constructing genotypes with combinations of these mutations, and measuring the fitness of these genotypes. Yet these empirical landscapes represent a very small sample of the vast space of all possible genotypes, and this sample is often biased by the protocol used to identify mutations. Here we develop a rigorous statistical framework based on Approximate Bayesian Computation to address these concerns and use this flexible framework to fit a broad class of phenotypic fitness models (including Fisher's model) to 26 empirical landscapes representing nine diverse biological systems. Despite uncertainty owing to the small size of most published empirical landscapes, the inferred landscapes have similar structure in similar biological systems. Surprisingly, goodness-of-fit tests reveal that this class of phenotypic models, which has been successful so far in interpreting experimental data, is a plausible in only three of nine biological systems. More precisely, although Fisher's model was able to explain several statistical properties of the landscapes-including the mean and SD of selection and epistasis coefficients-it was often unable to explain the full structure of fitness landscapes.
Curve fitting methods for solar radiation data modeling
Energy Technology Data Exchange (ETDEWEB)
Karim, Samsul Ariffin Abdul, E-mail: samsul-ariffin@petronas.com.my, E-mail: balbir@petronas.com.my; Singh, Balbir Singh Mahinder, E-mail: samsul-ariffin@petronas.com.my, E-mail: balbir@petronas.com.my [Department of Fundamental and Applied Sciences, Faculty of Sciences and Information Technology, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak Darul Ridzuan (Malaysia)
2014-10-24
This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R{sup 2}. The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both with two terms) gives better results as compare with the other fitting methods.
Curve fitting methods for solar radiation data modeling
Karim, Samsul Ariffin Abdul; Singh, Balbir Singh Mahinder
2014-10-01
This paper studies the use of several type of curve fitting method to smooth the global solar radiation data. After the data have been fitted by using curve fitting method, the mathematical model of global solar radiation will be developed. The error measurement was calculated by using goodness-fit statistics such as root mean square error (RMSE) and the value of R2. The best fitting methods will be used as a starting point for the construction of mathematical modeling of solar radiation received in Universiti Teknologi PETRONAS (UTP) Malaysia. Numerical results indicated that Gaussian fitting and sine fitting (both with two terms) gives better results as compare with the other fitting methods.
A Comparison of Item Fit Statistics for Mixed IRT Models
Chon, Kyong Hee; Lee, Won-Chan; Dunbar, Stephen B.
2010-01-01
In this study we examined procedures for assessing model-data fit of item response theory (IRT) models for mixed format data. The model fit indices used in this study include PARSCALE's G[superscript 2], Orlando and Thissen's S-X[superscript 2] and S-G[superscript 2], and Stone's chi[superscript 2*] and G[superscript 2*]. To investigate the…
Hyper-Fit: Fitting Linear Models to Multidimensional Data with Multivariate Gaussian Uncertainties
Robotham, A S G
2015-01-01
Astronomical data is often uncertain with errors that are heteroscedastic (different for each data point) and covariant between different dimensions. Assuming that a set of D-dimensional data points can be described by a (D - 1)-dimensional plane with intrinsic scatter, we derive the general likelihood function to be maximised to recover the best fitting model. Alongside the mathematical description, we also release the hyper-fit package for the R statistical language (github.com/asgr/hyper.fit) and a user-friendly web interface for online fitting (hyperfit.icrar.org). The hyper-fit package offers access to a large number of fitting routines, includes visualisation tools, and is fully documented in an extensive user manual. Most of the hyper-fit functionality is accessible via the web interface. In this paper we include applications to toy examples and to real astronomical data from the literature: the mass-size, Tully-Fisher, Fundamental Plane, and mass-spin-morphology relations. In most cases the hyper-fit ...
Model-Free CUSUM Methods for Person Fit
Armstrong, Ronald D.; Shi, Min
2009-01-01
This article demonstrates the use of a new class of model-free cumulative sum (CUSUM) statistics to detect person fit given the responses to a linear test. The fundamental statistic being accumulated is the likelihood ratio of two probabilities. The detection performance of this CUSUM scheme is compared to other model-free person-fit statistics…
Automated Model Fit Method for Diesel Engine Control Development
Seykens, X.; Willems, F.P.T.; Kuijpers, B.; Rietjens, C.
2014-01-01
This paper presents an automated fit for a control-oriented physics-based diesel engine combustion model. This method is based on the combination of a dedicated measurement procedure and structured approach to fit the required combustion model parameters. Only a data set is required that is consider
topicmodels: An R Package for Fitting Topic Models
Directory of Open Access Journals (Sweden)
Bettina Grun
2011-05-01
Full Text Available Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure for fitting topic models based on data structures from the text mining package tm. The package includes interfaces to two algorithms for fitting topic models: the variational expectation-maximization algorithm provided by David M. Blei and co-authors and an algorithm using Gibbs sampling by Xuan-Hieu Phan and co-authors.
An R package for fitting age, period and cohort models
Directory of Open Access Journals (Sweden)
Adriano Decarli
2014-11-01
Full Text Available In this paper we present the R implementation of a GLIM macro which fits age-period-cohort model following Osmond and Gardner. In addition to the estimates of the corresponding model, owing to the programming capability of R as an object oriented language, methods for printing, plotting and summarizing the results are provided. Furthermore, the researcher has fully access to the output of the main function (apc which returns all the models fitted within the function. It is so possible to critically evaluate the goodness of fit of the resulting model.
Robust discriminative response map fitting with constrained local models
Asthana, Akshay; Zafeiriou, Stefanos; Cheng, Shiyang; Pantic, Maja
2013-01-01
We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario. The motivation behind this approach is that, u
Bayesian item fit analysis for unidimensional item response theory models.
Sinharay, Sandip
2006-11-01
Assessing item fit for unidimensional item response theory models for dichotomous items has always been an issue of enormous interest, but there exists no unanimously agreed item fit diagnostic for these models, and hence there is room for further investigation of the area. This paper employs the posterior predictive model-checking method, a popular Bayesian model-checking tool, to examine item fit for the above-mentioned models. An item fit plot, comparing the observed and predicted proportion-correct scores of examinees with different raw scores, is suggested. This paper also suggests how to obtain posterior predictive p-values (which are natural Bayesian p-values) for the item fit statistics of Orlando and Thissen that summarize numerically the information in the above-mentioned item fit plots. A number of simulation studies and a real data application demonstrate the effectiveness of the suggested item fit diagnostics. The suggested techniques seem to have adequate power and reasonable Type I error rate, and psychometricians will find them promising.
Fitting polytomous Rasch models in SAS
DEFF Research Database (Denmark)
Christensen, Karl Bang
2006-01-01
The item parameters of a polytomous Rasch model can be estimated using marginal and conditional approaches. This paper describes how this can be done in SAS (V8.2) for three item parameter estimation procedures: marginal maximum likelihood estimation, conditional maximum likelihood estimation......, and pairwise conditional estimation. The use of the procedures for extensions of the Rasch model is also discussed. The accuracy of the methods are evaluated using a simulation study....
FITTING PHOTOIONIZATION MODELS TO PLANETARY NEBULAE
Directory of Open Access Journals (Sweden)
J. Bohigas
2009-01-01
Full Text Available Good to excellent photoionization models based on the Cloudy code were obtained for 13 out of 19 spectra of planetary nebulae. The two most important assumptions are that the photoionizing continuum is a Rauch model star, with gravity set by the condition that the stellar mass must be 1 M , and density is constant and determined from the observed [S II]6717/6731 ratio. The temperature and luminosity of the central star, the inner radius of the nebula and the abundance of carbon are treated as free parameters in each model run, destined to obtain the best possible t to the relative intensities of He II 4686, [O III]5007 and [N II]6584. Observed and modeled nebular temperatures derived from [N II] (6548+6584 /5755 agree within 10%, but models usually underestimate temperatures found from [O III] (4959+5007 /4363, more so when the slit does not cover the in-depth extent of the ionized region. Helium, nitrogen, oxygen, neon, sulfur and argon model abundances are uncertain at the 15%, 15%, 10%, 7%, 30% and 7% level. It is shown that neon abundance in PNe has been consistently overestimated, and an alternative ionization correction factor is recommended.
How Good Are Statistical Models at Approximating Complex Fitness Landscapes?
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
How Good Are Statistical Models at Approximating Complex Fitness Landscapes?
du Plessis, Louis; Leventhal, Gabriel E; Bonhoeffer, Sebastian
2016-09-01
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.
An Algorithm for Optimally Fitting a Wiener Model
Directory of Open Access Journals (Sweden)
Lucas P. Beverlin
2011-01-01
Full Text Available The purpose of this work is to present a new methodology for fitting Wiener networks to datasets with a large number of variables. Wiener networks have the ability to model a wide range of data types, and their structures can yield parameters with phenomenological meaning. There are several challenges to fitting such a model: model stiffness, the nonlinear nature of a Wiener network, possible overfitting, and the large number of parameters inherent with large input sets. This work describes a methodology to overcome these challenges by using several iterative algorithms under supervised learning and fitting subsets of the parameters at a time. This methodology is applied to Wiener networks that are used to predict blood glucose concentrations. The predictions of validation sets from models fit to four subjects using this methodology yielded a higher correlation between observed and predicted observations than other algorithms, including the Gauss-Newton and Levenberg-Marquardt algorithms.
Fitting ARMA Time Series by Structural Equation Models.
van Buuren, Stef
1997-01-01
This paper outlines how the stationary ARMA (p,q) model (G. Box and G. Jenkins, 1976) can be specified as a structural equation model. Maximum likelihood estimates for the parameters in the ARMA model can be obtained by software for fitting structural equation models. The method is applied to three problem types. (SLD)
Relative and Absolute Fit Evaluation in Cognitive Diagnosis Modeling
Chen, Jinsong; de la Torre, Jimmy; Zhang, Zao
2013-01-01
As with any psychometric models, the validity of inferences from cognitive diagnosis models (CDMs) determines the extent to which these models can be useful. For inferences from CDMs to be valid, it is crucial that the fit of the model to the data is ascertained. Based on a simulation study, this study investigated the sensitivity of various fit…
Critical elements on fitting the Bayesian multivariate Poisson Lognormal model
Zamzuri, Zamira Hasanah binti
2015-10-01
Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.
Deisboeck, Thomas S; Wang, Zhihui; Macklin, Paul; Cristini, Vittorio
2011-08-15
Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insights in the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community.
Mouse models of pancreatic cancer
Institute of Scientific and Technical Information of China (English)
Marta Herreros-Villanueva; Elizabeth Hijona; Angel Cosme; Luis Bujanda
2012-01-01
Pancreatic cancer is one of the most lethal of human malignancies ranking 4th among cancer-related death in the western world and in the United States,and potent therapeutic options are lacking.Although during the last few years there have been important advances in the understanding of the molecular events responsible for the development of pancreatic cancer,currently specific mechanisms of treatment resistance remain poorly understood and new effective systemic drugs need to be developed and probed.In vivo models to study pancreatic cancer and approach this issue remain limited and present different molecular features that must be considered in the studies depending on the purpose to fit special research themes.In the last few years,several genetically engineered mouse models of pancreatic exocrine neoplasia have been developed.These models mimic the disease as they reproduce genetic alterations implicated in the progression of pancreatic cancer.Genetic alterations such as activating mutations in KRas,or TGFb and/or inactivation of tumoral suppressors such as p53,INK4A/ARF BRCA2 and Smad4 are the most common drivers to pancreatic carcinogenesis and have been used to create transgenic mice.These mouse models have a spectrum of pathologic changes,from pancreatic intraepithelial neoplasia to lesions that progress histologically culminating in fully invasive and metastatic disease and represent the most useful preclinical model system.These models can characterize the cellular and molecular pathology of pancreatic neoplasia and cancer and constitute the best tool to investigate new therapeutic approaches,chemopreventive and/or anticancer treatments.Here,we review and update the current mouse models that reproduce different stages of human pancreatic ductal adenocarcinoma and will have clinical relevance in future pancreatic cancer developments.
Automatic fitting of spiking neuron models to electrophysiological recordings
Directory of Open Access Journals (Sweden)
Cyrille Rossant
2010-03-01
Full Text Available 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.
HDFITS: porting the FITS data model to HDF5
Price, D C; Greenhill, L J
2015-01-01
The FITS (Flexible Image Transport System) data format has been the de facto data format for astronomy-related data products since its inception in the late 1970s. While the FITS file format is widely supported, it lacks many of the features of more modern data serialization, such as the Hierarchical Data Format (HDF5). The HDF5 file format offers considerable advantages over FITS, such as improved I/O speed and compression, but has yet to gain widespread adoption within astronomy. One of the major holdbacks is that HDF5 is not well supported by data reduction software packages and image viewers. Here, we present a comparison of FITS and HDF5 as a format for storage of astronomy datasets. We show that the underlying data model of FITS can be ported to HDF5 in a straightforward manner, and that by doing so the advantages of the HDF5 file format can be leveraged immediately. In addition, we present a software tool, fits2hdf, for converting between FITS and a new `HDFITS' format, where data are stored in HDF5 in...
Fitting Equilibrium Search Models to Labour Market Data
DEFF Research Database (Denmark)
Bowlus, Audra J.; Kiefer, Nicholas M.; Neumann, George R.
1996-01-01
Specification and estimation of a Burdett-Mortensen type equilibrium search model is considered. The estimation is nonstandard. An estimation strategy asymptotically equivalent to maximum likelihood is proposed and applied. The results indicate that specifications with a small number of productiv...... of productivity types fit the data well compared to the homogeneous model....
Curve Fitting And Interpolation Model Applied In Nonel Dosage Detection
Directory of Open Access Journals (Sweden)
Jiuling Li
2013-06-01
Full Text Available The Curve Fitting and Interpolation Model are applied in Nonel dosage detection in this paper firstly, and the gray of continuous explosive in the Nonel has been forecasted. Although the traditional infrared equipment establishes the relationship of explosive dosage and light intensity, but the forecast accuracy is very low. Therefore, gray prediction models based on curve fitting and interpolation are framed separately, and the deviations from the different models are compared. Simultaneously, combining on the sample library features, the cubic polynomial fitting curve of the higher precision is used to predict grays, and 5mg-28mg Nonel gray values are calculated by MATLAB. Through the predictive values, the dosage detection operations are simplified, and the defect missing rate of the Nonel are reduced. Finally, the quality of Nonel is improved.
Fan, Xitao; Wang, Lin; Thompson, Bruce
1999-01-01
A Monte Carlo simulation study investigated the effects on 10 structural equation modeling fit indexes of sample size, estimation method, and model specification. Some fit indexes did not appear to be comparable, and it was apparent that estimation method strongly influenced almost all fit indexes examined, especially for misspecified models. (SLD)
Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS
DEFF Research Database (Denmark)
Bolker, B.M.; Gardner, B.; Maunder, M.
2013-01-01
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. R is convenient and (relatively) easy ...
Time-domain fitting of battery electrochemical impedance models
Alavi, S. M. M.; Birkl, C. R.; Howey, D. A.
2015-08-01
Electrochemical impedance spectroscopy (EIS) is an effective technique for diagnosing the behaviour of electrochemical devices such as batteries and fuel cells, usually by fitting data to an equivalent circuit model (ECM). The common approach in the laboratory is to measure the impedance spectrum of a cell in the frequency domain using a single sine sweep signal, then fit the ECM parameters in the frequency domain. This paper focuses instead on estimation of the ECM parameters directly from time-domain data. This may be advantageous for parameter estimation in practical applications such as automotive systems including battery-powered vehicles, where the data may be heavily corrupted by noise. The proposed methodology is based on the simplified refined instrumental variable for continuous-time fractional systems method ('srivcf'), provided by the Crone toolbox [1,2], combined with gradient-based optimisation to estimate the order of the fractional term in the ECM. The approach was tested first on synthetic data and then on real data measured from a 26650 lithium-ion iron phosphate cell with low-cost equipment. The resulting Nyquist plots from the time-domain fitted models match the impedance spectrum closely (much more accurately than when a Randles model is assumed), and the fitted parameters as separately determined through a laboratory potentiostat with frequency domain fitting match to within 13%.
Physical Inactivity And Low Fitness Deserve More Attention To Alter Cancer Risk And Prognosis
Sanchis-Gomar, Fabian; Lucia, Alejandro; Yvert, Thomas; Ruiz-Casado, Ana; Pareja-Galeano, Helios; Santos-Lozano, Alejandro; Fiuza-Luces, Carmen; Garatachea, Nuria; Lippi, Giuseppe; Bouchard, Claude; Berger, Nathan A.
2015-01-01
Sedentary lifestyle is associated with elevated cancer risk whereas regular physical activity (PA) and high cardiorespiratory fitness (CRF) have the opposite effect, with several biological mechanisms mediating such associations. There is a need for lifestyle interventions aimed at increasing the PA levels and CRF of the general population and particularly cancer survivors. Further, provocative data suggest a dose-dependent benefit of increasing levels of PA and/or CRF against cancer risk or mortality. Thus, current PA guidelines (≥150 min/week of moderate-to-vigorous PA) may not be sufficiently rigorous for preventing cancer nor for extending cancer survivorship. Research targeting this issue is urgently needed. Promoting regular PA along with monitoring indicators of CRF and adiposity may provide powerful strategies to prevent cancer in populations, help cancer patients more effectively deal with their disease and enhance secondary prevention programs in those who are affected by cancer. PMID:25416409
Kompaneets Model Fitting of the Orion-Eridanus Superbubble
Pon, Andy; Bally, John; Heiles, Carl
2014-01-01
Winds and supernovae from OB associations create large cavities in the interstellar medium referred to as superbubbles. The Orion molecular clouds are the nearest high mass star-forming region and have created a highly elongated, 20 degree x 45 degree, superbubble. We fit Kompaneets models to the Orion-Eridanus superbubble and find that a model where the Eridanus side of the superbubble is oriented away from the Sun provides a marginal fit. Because this model requires an unusually small scale height of 40 pc and has the superbubble inclined 35 degrees from the normal to the Galactic plane, we propose that this model should be treated as a general framework for modeling the Orion-Eridanus superbubble, with a secondary physical mechanism not included in the Kompaneets model required to fully account for the orientation and elongation of the superbubble.
Ongoing Processes in a Fitness Network Model under Restricted Resources.
Directory of Open Access Journals (Sweden)
Takayuki Niizato
Full Text Available In real networks, the resources that make up the nodes and edges are finite. This constraint poses a serious problem for network modeling, namely, the compatibility between robustness and efficiency. However, these concepts are generally in conflict with each other. In this study, we propose a new fitness-driven network model for finite resources. In our model, each individual has its own fitness, which it tries to increase. The main assumption in fitness-driven networks is that incomplete estimation of fitness results in a dynamical growing network. By taking into account these internal dynamics, nodes and edges emerge as a result of exchanges between finite resources. We show that our network model exhibits exponential distributions in the in- and out-degree distributions and a power law distribution of edge weights. Furthermore, our network model resolves the trade-off relationship between robustness and efficiency. Our result suggests that growing and anti-growing networks are the result of resolving the trade-off problem itself.
[How to fit and interpret multilevel models using SPSS].
Pardo, Antonio; Ruiz, Miguel A; San Martín, Rafael
2007-05-01
Hierarchic or multilevel models are used to analyse data when cases belong to known groups and sample units are selected both from the individual level and from the group level. In this work, the multilevel models most commonly discussed in the statistic literature are described, explaining how to fit these models using the SPSS program (any version as of the 11 th ) and how to interpret the outcomes of the analysis. Five particular models are described, fitted, and interpreted: (1) one-way analysis of variance with random effects, (2) regression analysis with means-as-outcomes, (3) one-way analysis of covariance with random effects, (4) regression analysis with random coefficients, and (5) regression analysis with means- and slopes-as-outcomes. All models are explained, trying to make them understandable to researchers in health and behaviour sciences.
A neutrino model fit to the CMB power spectrum
Shanks, T.; Johnson, R. W. F.; Schewtschenko, J. A.; Whitbourn, J. R.
2014-12-01
The standard cosmological model, Λ cold dark matter (ΛCDM), provides an excellent fit to cosmic microwave background (CMB) data. However, the model has well-known problems. For example, the cosmological constant, Λ, is fine-tuned to 1 part in 10100 and the CDM particle is not yet detected in the laboratory. Shanks previously investigated a model which assumed neither exotic particles nor a cosmological constant but instead postulated a low Hubble constant (H0) to allow a baryon density compatible with inflation and zero spatial curvature. However, recent Planck results make it more difficult to reconcile such a model with CMB power spectra. Here, we relax the previous assumptions to assess the effects of assuming three active neutrinos of mass ≈5 eV. If we assume a low H0 ≈ 45 km s-1 Mpc-1 then, compared to the previous purely baryonic model, we find a significantly improved fit to the first three peaks of the Planck power spectrum. Nevertheless, the goodness of fit is still significantly worse than for ΛCDM and would require appeal to unknown systematic effects for the fit ever to be considered acceptable. A further serious problem is that the amplitude of fluctuations is low (σ8 ≈ 0.2), making it difficult to form galaxies by the present day. This might then require seeds, perhaps from a primordial magnetic field, to be invoked for galaxy formation. These and other problems demonstrate the difficulties faced by models other than ΛCDM in fitting ever more precise cosmological data.
Fuzzy Partition Models for Fitting a Set of Partitions.
Gordon, A. D.; Vichi, M.
2001-01-01
Describes methods for fitting a fuzzy consensus partition to a set of partitions of the same set of objects. Describes and illustrates three models defining median partitions and compares these methods to an alternative approach to obtaining a consensus fuzzy partition. Discusses interesting differences in the results. (SLD)
Assessing fit in Bayesian models for spatial processes
Jun, M.
2014-09-16
© 2014 John Wiley & Sons, Ltd. Gaussian random fields are frequently used to model spatial and spatial-temporal data, particularly in geostatistical settings. As much of the attention of the statistics community has been focused on defining and estimating the mean and covariance functions of these processes, little effort has been devoted to developing goodness-of-fit tests to allow users to assess the models\\' adequacy. We describe a general goodness-of-fit test and related graphical diagnostics for assessing the fit of Bayesian Gaussian process models using pivotal discrepancy measures. Our method is applicable for both regularly and irregularly spaced observation locations on planar and spherical domains. The essential idea behind our method is to evaluate pivotal quantities defined for a realization of a Gaussian random field at parameter values drawn from the posterior distribution. Because the nominal distribution of the resulting pivotal discrepancy measures is known, it is possible to quantitatively assess model fit directly from the output of Markov chain Monte Carlo algorithms used to sample from the posterior distribution on the parameter space. We illustrate our method in a simulation study and in two applications.
The Gold Medal Fitness Program: A Model for Teacher Change
Wright, Jan; Konza, Deslea; Hearne, Doug; Okely, Tony
2008-01-01
Background: Following the 2000 Sydney Olympics, the NSW Premier, Mr Bob Carr, launched a school-based initiative in NSW government primary schools called the "Gold Medal Fitness Program" to encourage children to be fitter and more active. The Program was introduced into schools through a model of professional development, "Quality…
Raindrop size distribution: Fitting performance of common theoretical models
Adirosi, E.; Volpi, E.; Lombardo, F.; Baldini, L.
2016-10-01
Modelling raindrop size distribution (DSD) is a fundamental issue to connect remote sensing observations with reliable precipitation products for hydrological applications. To date, various standard probability distributions have been proposed to build DSD models. Relevant questions to ask indeed are how often and how good such models fit empirical data, given that the advances in both data availability and technology used to estimate DSDs have allowed many of the deficiencies of early analyses to be mitigated. Therefore, we present a comprehensive follow-up of a previous study on the comparison of statistical fitting of three common DSD models against 2D-Video Distrometer (2DVD) data, which are unique in that the size of individual drops is determined accurately. By maximum likelihood method, we fit models based on lognormal, gamma and Weibull distributions to more than 42.000 1-minute drop-by-drop data taken from the field campaigns of the NASA Ground Validation program of the Global Precipitation Measurement (GPM) mission. In order to check the adequacy between the models and the measured data, we investigate the goodness of fit of each distribution using the Kolmogorov-Smirnov test. Then, we apply a specific model selection technique to evaluate the relative quality of each model. Results show that the gamma distribution has the lowest KS rejection rate, while the Weibull distribution is the most frequently rejected. Ranking for each minute the statistical models that pass the KS test, it can be argued that the probability distributions whose tails are exponentially bounded, i.e. light-tailed distributions, seem to be adequate to model the natural variability of DSDs. However, in line with our previous study, we also found that frequency distributions of empirical DSDs could be heavy-tailed in a number of cases, which may result in severe uncertainty in estimating statistical moments and bulk variables.
MNP: R Package for Fitting the Multinomial Probit Model
Directory of Open Access Journals (Sweden)
Kosuke Imai
2005-05-01
Full Text Available MNP is a publicly available R package that fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP software can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005.
Supersymmetry with prejudice: Fitting the wrong model to LHC data
Allanach, B. C.; Dolan, Matthew J.
2012-09-01
We critically examine interpretations of hypothetical supersymmetric LHC signals, fitting to alternative wrong models of supersymmetry breaking. The signals we consider are some of the most constraining on the sparticle spectrum: invariant mass distributions with edges and endpoints from the golden decay chain q˜→qχ20(→l˜±l∓q)→χ10l+l-q. We assume a constrained minimal supersymmetric standard model (CMSSM) point to be the ‘correct’ one, but fit the signals instead with minimal gauge mediated supersymmetry breaking models (mGMSB) with a neutralino quasistable lightest supersymmetric particle, minimal anomaly mediation and large volume string compactification models. Minimal anomaly mediation and large volume scenario can be unambiguously discriminated against the CMSSM for the assumed signal and 1fb-1 of LHC data at s=14TeV. However, mGMSB would not be discriminated on the basis of the kinematic endpoints alone. The best-fit point spectra of mGMSB and CMSSM look remarkably similar, making experimental discrimination at the LHC based on the edges or Higgs properties difficult. However, using rate information for the golden chain should provide the additional separation required.
Geometrical model fitting for interferometric data: GEM-FIND
Klotz, D; Paladini, C; Hron, J; Wachter, G
2012-01-01
We developed the tool GEM-FIND that allows to constrain the morphology and brightness distribution of objects. The software fits geometrical models to spectrally dispersed interferometric visibility measurements in the N-band using the Levenberg-Marquardt minimization method. Each geometrical model describes the brightness distribution of the object in the Fourier space using a set of wavelength-independent and/or wavelength-dependent parameters. In this contribution we numerically analyze the stability of our nonlinear fitting approach by applying it to sets of synthetic visibilities with statistically applied errors, answering the following questions: How stable is the parameter determination with respect to (i) the number of uv-points, (ii) the distribution of points in the uv-plane, (iii) the noise level of the observations?
Fitting and Comparison of Models of Radio Spectra
Nikolic, Bojan
2009-01-01
I describe an approach to fitting and comparison of radio spectra based on Bayesian analysis and realised using a new implementation of the nested sampling algorithm. Such an approach improves on the commonly used maximum-likelihood fitting of radio spectra by allowing objective model selection, calculation of the full probability distributions of the model parameters and provides a natural mechanism for including information other than the measured spectra through priors. In this paper I cover the theoretical background, the algorithms used and the implementation details of the computer code. I also briefly illustrate the method with some previously published data for three near-by galaxies. In forthcoming papers we will present the results of applying this analysis larger data sets, including some new observations, and the physical conclusions that can be made. The computer code as well as the overall approach described here may also be useful for analysis of other multi-chromatic broad-band observations an...
Atmospheric Turbulence Modeling for Aerospace Vehicles: Fractional Order Fit
Kopasakis, George (Inventor)
2015-01-01
An improved model for simulating atmospheric disturbances is disclosed. A scale Kolmogorov spectral may be scaled to convert the Kolmogorov spectral into a finite energy von Karman spectral and a fractional order pole-zero transfer function (TF) may be derived from the von Karman spectral. Fractional order atmospheric turbulence may be approximated with an integer order pole-zero TF fit, and the approximation may be stored in memory.
Thissen, David
2013-01-01
In this commentary, David Thissen states that "Goodness-of-fit assessment for IRT models is maturing; it has come a long way from zero." Thissen then references prior works on "goodness of fit" in the index of Lord and Novick's (1968) classic text; Yen (1984); Drasgow, Levine, Tsien, Williams, and Mead (1995); Chen and…
Supersymmetry With Prejudice: Fitting the Wrong Model to LHC Data
Allanach, B C
2011-01-01
We critically examine interpretations of hypothetical supersymmetric LHC signals, fitting to alternative wrong models of supersymmetry breaking. The signals we consider are some of the most constraining on the sparticle spectrum: invariant mass distributions with edges and end-points from the golden cascade decay chain \\tilde{q}_L -> q \\chi_2^0 (-> \\tilde{l}^{\\pm} l^{\\mp} q) -> \\chi_1^0 l^+ l^- q. We assume a CMSSM point to be the `correct' one, and fit the signals instead to minimal gauge mediated supersymmetry breaking models (mGMSB) with a neutralino quasi-stable lightest supersymmetric particle, minimal anomaly mediation (mAMSB) and large volume string compactification models (LVS). mAMSB and LVS can be unambiguously discriminated against the CMSSM for the parameter point assumed and 1 inverse femtobarn of LHC data at 14 TeV. However, mGMSB would not be discriminated on the basis of the kinematic end-points alone, and would require further, more detailed investigation. The best-fit points of mGMSB and CMS...
Bayesian Data-Model Fit Assessment for Structural Equation Modeling
Levy, Roy
2011-01-01
Bayesian approaches to modeling are receiving an increasing amount of attention in the areas of model construction and estimation in factor analysis, structural equation modeling (SEM), and related latent variable models. However, model diagnostics and model criticism remain relatively understudied aspects of Bayesian SEM. This article describes…
Broadband distortion modeling in Lyman-$\\alpha$ forest BAO fitting
Blomqvist, Michael; Bautista, Julian E; Ariño, Andreu; Busca, Nicolás G; Miralda-Escudé, Jordi; Slosar, Anže; Font-Ribera, Andreu; Margala, Daniel; Schneider, Donald P; Vazquez, Jose A
2015-01-01
In recent years, the Lyman-$\\alpha$ absorption observed in the spectra of high-redshift quasars has been used as a tracer of large-scale structure by means of the three-dimensional Lyman-$\\alpha$ forest auto-correlation function at redshift $z\\simeq 2.3$, but the need to fit the quasar continuum in every absorption spectrum introduces a broadband distortion that is difficult to correct and causes a systematic error for measuring any broadband properties. We describe a $k$-space model for this broadband distortion based on a multiplicative correction to the power spectrum of the transmitted flux fraction that suppresses power on scales corresponding to the typical length of a Lyman-$\\alpha$ forest spectrum. Implementing the distortion model in fits for the baryon acoustic oscillation (BAO) peak position in the Lyman-$\\alpha$ forest auto-correlation, we find that the fitting method recovers the input values of the linear bias parameter $b_{F}$ and the redshift-space distortion parameter $\\beta_{F}$ for mock dat...
Bosone, Lucia; Martinez, Frédéric; Kalampalikis, Nikos
2015-04-01
In health-promotional campaigns, positive and negative role models can be deployed to illustrate the benefits or costs of certain behaviors. The main purpose of this article is to investigate why, how, and when exposure to role models strengthens the persuasiveness of a message, according to regulatory fit theory. We argue that exposure to a positive versus a negative model activates individuals' goals toward promotion rather than prevention. By means of two experiments, we demonstrate that high levels of persuasion occur when a message advertising healthy dietary habits offers a regulatory fit between its framing and the described role model. Our data also establish that the effects of such internal regulatory fit by vicarious experience depend on individuals' perceptions of response-efficacy and self-efficacy. Our findings constitute a significant theoretical complement to previous research on regulatory fit and contain valuable practical implications for health-promotional campaigns.
The effectiveness of FOBT vs. FIT: A meta-analysis on colorectal cancer screening test
Mousavinezhad, Maryam; Majdzadeh, Reza; Akbari Sari, Ali; Delavari, Alireza; Mohtasham, Farideh
2016-01-01
Background: After lung and prostate cancers, colorectal cancer (CRC) is the third most common cancer in men and the second most common cancer in women after breast cancer worldwide. Every year, more than one million people are diagnosed with colorectal cancer worldwide and half of these patients die from this disease, making it the fourth leading cause of death in the world. This systematic review aimed to assess the effectiveness of the two colorectal diagnostic tests of FOBT (fecal occult blood test) and FIT (fecal immunochemical test)) in terms of technical performance. Methods: To retrieve the relevant evidence, appropriate medical databases such as Cochrane library, NHSEED, Scopus and Google scholar were searched from February 2013 to July 2014, using free-texts and Mesh. In this study, inclusion/exclusion criteria of the papers, randomized controlled trials, economic evaluations, systematic reviews, meta-analyses and meta-syntheses of the effectiveness of FIT versus FOBT tests in moderate-risk populations (age: 50 to 70 years), which had reported the least of such outcomes as sensitivity, specificity and clinical outcomes were reviewed. The analyses of the effectiveness outcomes were performed in the form of meta-analysis. Results: Five papers were eligible to be included in the final phase of the study for synthesis. FIT showed a better performance in participation and positivity rate. Moreover, in terms of false positive and negative rate, FIT showed fewer rates compared to FOBT (RR:-4.06; 95% CI (-7.89-0.24), and NN-scope (Number need to scope) (2.2% vs. 1.6%), and NN-screen (Number need to screen) (84% vs. 31-49% in different cut off levels) showed significant differences in FOBT vs. FIT, respectively. Conclusion: In the five included studies (3, 11-14), the acceptability of FIT was more than FOBT. However, in our meta-analysis, no difference was found between the two tests. FIT was significant in positivity rate and had a better performance in
Fitting Latent Cluster Models for Networks with latentnet
Directory of Open Access Journals (Sweden)
Pavel N. Krivitsky
2007-12-01
Full Text Available latentnet is a package to fit and evaluate statistical latent position and cluster models for networks. Hoﬀ, Raftery, and Handcock (2002 suggested an approach to modeling networks based on positing the existence of an latent space of characteristics of the actors. Relationships form as a function of distances between these characteristics as well as functions of observed dyadic level covariates. In latentnet social distances are represented in a Euclidean space. It also includes a variant of the extension of the latent position model to allow for clustering of the positions developed in Handcock, Raftery, and Tantrum (2007.The package implements Bayesian inference for the models based on an Markov chain Monte Carlo algorithm. It can also compute maximum likelihood estimates for the latent position model and a two-stage maximum likelihood method for the latent position cluster model. For latent position cluster models, the package provides a Bayesian way of assessing how many groups there are, and thus whether or not there is any clustering (since if the preferred number of groups is 1, there is little evidence for clustering. It also estimates which cluster each actor belongs to. These estimates are probabilistic, and provide the probability of each actor belonging to each cluster. It computes four types of point estimates for the coefficients and positions: maximum likelihood estimate, posterior mean, posterior mode and the estimator which minimizes Kullback-Leibler divergence from the posterior. You can assess the goodness-of-fit of the model via posterior predictive checks. It has a function to simulate networks from a latent position or latent position cluster model.
Rapid world modeling: Fitting range data to geometric primitives
Energy Technology Data Exchange (ETDEWEB)
Feddema, J.; Little, C.
1996-12-31
For the past seven years, Sandia National Laboratories has been active in the development of robotic systems to help remediate DOE`s waste sites and decommissioned facilities. Some of these facilities have high levels of radioactivity which prevent manual clean-up. Tele-operated and autonomous robotic systems have been envisioned as the only suitable means of removing the radioactive elements. World modeling is defined as the process of creating a numerical geometric model of a real world environment or workspace. This model is often used in robotics to plan robot motions which perform a task while avoiding obstacles. In many applications where the world model does not exist ahead of time, structured lighting, laser range finders, and even acoustical sensors have been used to create three dimensional maps of the environment. These maps consist of thousands of range points which are difficult to handle and interpret. This paper presents a least squares technique for fitting range data to planar and quadric surfaces, including cylinders and ellipsoids. Once fit to these primitive surfaces, the amount of data associated with a surface is greatly reduced up to three orders of magnitude, thus allowing for more rapid handling and analysis of world data.
Direct model fitting to combine dithered ACS images
Mahmoudian, Haniyeh
2013-01-01
The information lost in images of undersampled CCD cameras can be recovered with the technique of `dithering'. A number of subexposures is taken with sub-pixel shifts in order to record structures on scales smaller than a pixel. The standard method to combine such exposures, `Drizzle', averages after reversing the displacements, including rotations and distortions. More sophisticated methods are available to produce, e.g., Nyquist sampled representations of band-limited inputs. While the combined images produced by these methods can be of high quality, their use as input for forward-modelling techniques in gravitational lensing is still not optimal, because the residual artefacts still affect the modelling results in unpredictable ways. In this paper we argue for an overall modelling approach that takes into account the dithering and the lensing without the intermediate product of a combined image. As one building block we introduce an alternative approach to combine dithered images by direct model fitting wi...
Issues in Evaluating Model Fit With Missing Data
Davey, Adam
2005-01-01
Effects of incomplete data on fit indexes remain relatively unexplored. We evaluate a wide set of fit indexes (?[squared], root mean squared error of appproximation, Normed Fit Index [NFI], Tucker-Lewis Index, comparative fit index, gamma-hat, and McDonald's Centrality Index) varying conditions of sample size (100-1,000 in increments of 50),…
Mechanical Response of Polycarbonate with Strength Model Fits
2012-02-01
is used as free -parameter to improve the quality of the fit. ̇ is the strain rate and ?̇? is the reference strain rate for which 1/s was used...experimental data. Table 3. ZA model parameters. Bo= 0.006715948 1/K B1= 0.00009503 1/K Bpa = 550 MPa Bopa= 48 MPa ωa= -8 ▬ ωb= -0.01 ▬ β= 0.5...Hybrid Hard/Ductile All-Plastic-and Glass-Plastic-Based Composites ; ARL-TR-3155; U.S. Army Research Laboratory: Aberdeen Proving Ground, MD, February
Directory of Open Access Journals (Sweden)
Stegeman Inge
2012-06-01
Full Text Available Abstract Background Colorectal cancer (CRC is the most common cancer in Europe with a mortality rate of almost 50%. The prognosis of patients is largely determined by the clinical and pathological stage at the time of diagnosis. Population screening has been shown to reduce CRC-related mortality rate. Most screening programs worldwide rely on fecal immunochemical testing (FIT. The effectiveness of a FIT screening program is not only influenced by initial participation rate, but also by program adherence during consecutive screening rounds. We aim to evaluate the participation rate in and yield of a third CRC screening round using FIT. Methods and design Four years after the first screening round and two years after the second round, a total number of approximately 11,000 average risk individuals (50 to 75 years of age will be invited to participate in a third round of FIT-based CRC screening. We will select individuals in the same target area as in the previous screening rounds, using the electronic database of the regional municipal administration registrations. We will invite all FIT-negatives and all non-participants in previous screening rounds, as well as eligible first time invitees who have moved into the area or have become 50 years of age. FITs will be analyzed in the special technique laboratory of the Academic Medical Center of the University of Amsterdam. All FIT-positives will be invited for a consultation at the outpatient clinic. In the absence of contra-indications, a colonoscopy will follow at the Academic Medical Center or at the Flevohospital. The primary outcome measures are the participation rate, defined as the proportion of invitees that return a FIT in this third round of FIT-screening, and the diagnostic yield of the program. Implications This study will provide precise data on the participation in later FIT screening rounds. This enables to estimate the effectiveness of CRC screening programs that rely on repeated
Raykov, Tenko; Lee, Chun-Lung; Marcoulides, George A.; Chang, Chi
2013-01-01
The relationship between saturated path-analysis models and their fit to data is revisited. It is demonstrated that a saturated model need not fit perfectly or even well a given data set when fit to the raw data is examined, a criterion currently frequently overlooked by researchers utilizing path analysis modeling techniques. The potential of…
Assessing Model Data Fit of Unidimensional Item Response Theory Models in Simulated Data
Kose, Ibrahim Alper
2014-01-01
The purpose of this paper is to give an example of how to assess the model-data fit of unidimensional IRT models in simulated data. Also, the present research aims to explain the importance of fit and the consequences of misfit by using simulated data sets. Responses of 1000 examinees to a dichotomously scoring 20 item test were simulated with 25…
von Davier, M; Molenaar, IW
2003-01-01
A normally distributed person-fit index is proposed for detecting aberrant response patterns in latent class models and mixture distribution IRT models for dichotomous and polytomous data. This article extends previous work on the null distribution of person-fit indices for the dichotomous Rasch mod
An NCME Instructional Module on Item-Fit Statistics for Item Response Theory Models
Ames, Allison J.; Penfield, Randall D.
2015-01-01
Drawing valid inferences from item response theory (IRT) models is contingent upon a good fit of the data to the model. Violations of model-data fit have numerous consequences, limiting the usefulness and applicability of the model. This instructional module provides an overview of methods used for evaluating the fit of IRT models. Upon completing…
Cavity approach for modeling and fitting polymer stretching
Massucci, Francesco Alessandro; Vicente, Conrad J Pérez
2014-01-01
The mechanical properties of molecules are today captured by single molecule manipulation experiments, so that polymer features are tested at a nanometric scale. Yet devising mathematical models to get further insight beyond the commonly studied force--elongation relation is typically hard. Here we draw from techniques developed in the context of disordered systems to solve models for single and double--stranded DNA stretching in the limit of a long polymeric chain. Since we directly derive the marginals for the molecule local orientation, our approach allows us to readily calculate the experimental elongation as well as other observables at wish. As an example, we evaluate the correlation length as a function of the stretching force. Furthermore, we are able to fit successfully our solution to real experimental data. Although the model is admittedly phenomenological, our findings are very sound. For single--stranded DNA our solution yields the correct (monomer) scale and, yet more importantly, the right pers...
Empirical fitness models for hepatitis C virus immunogen design
Hart, Gregory R.; Ferguson, Andrew L.
2015-12-01
Hepatitis C virus (HCV) afflicts 170 million people worldwide, 2%-3% of the global population, and kills 350 000 each year. Prophylactic vaccination offers the most realistic and cost effective hope of controlling this epidemic in the developing world where expensive drug therapies are not available. Despite 20 years of research, the high mutability of the virus and lack of knowledge of what constitutes effective immune responses have impeded development of an effective vaccine. Coupling data mining of sequence databases with spin glass models from statistical physics, we have developed a computational approach to translate clinical sequence databases into empirical fitness landscapes quantifying the replicative capacity of the virus as a function of its amino acid sequence. These landscapes explicitly connect viral genotype to phenotypic fitness, and reveal vulnerable immunological targets within the viral proteome that can be exploited to rationally design vaccine immunogens. We have recovered the empirical fitness landscape for the HCV RNA-dependent RNA polymerase (protein NS5B) responsible for viral genome replication, and validated the predictions of our model by demonstrating excellent accord with experimental measurements and clinical observations. We have used our landscapes to perform exhaustive in silico screening of 16.8 million T-cell immunogen candidates to identify 86 optimal formulations. By reducing the search space of immunogen candidates by over five orders of magnitude, our approach can offer valuable savings in time, expense, and labor for experimental vaccine development and accelerate the search for a HCV vaccine. Abbreviations: HCV—hepatitis C virus, HLA—human leukocyte antigen, CTL—cytotoxic T lymphocyte, NS5B—nonstructural protein 5B, MSA—multiple sequence alignment, PEG-IFN—pegylated interferon.
a Simple Evolutionary Model for Cancer Cell Population and its Implications on Cancer Therapy
Yao, Peng; Wen, Shutang; Li, Baoshun; Li, Yuxiao
We established a simple evolutionary model based on the cancer stem cell hypothesis. By taking cellular interactions into consideration, we introduced the evolutionary games theory into the quasispecies model. The fitness values are determined by both genotypes and cellular interactions. In the evolutionary model, a cancer cell population can evolve in different patterns. For single peak intrinsic fitness landscape, the evolution pattern can transit with increasing differentiation probability from malignant cells to benign cells in four different modes. For a large enough value of differentiation probability, the evolution is always the case that the malignant cells extinct ultimately, which might give some implications on cancer therapy.
Robust goodness-of-fit tests for AR（p） models based on L1-norm fitting
Institute of Scientific and Technical Information of China (English)
蒋建成; 郑忠国
1999-01-01
A robustified residual autocorrelation is defined based on L1-regression. Under very general conditions,the asymptotic distribution of the robust residual autocorrelation is obtained. A robustified portmanteau statistic is then constructed which can be used in checking the goodness-of-fit of AR（p） models when using L1-norm fitting. Empirical results show that L1-norm estimators and the proposed portmanteau statistic are robust against outliers, error distributions, and accuracy for a given finite sample.
Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS
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.
A neutrino model fit to the CMB power spectrum
Shanks, T; Schewtschenko, J A; Whitbourn, J R
2014-01-01
The current standard cosmological model, LCDM, provides an excellent fit to the WMAP and Planck CMB data. However, the model has well known problems. For example, the cosmological constant is fine tuned to 1 part in 10^100 and the cold dark matter (CDM) particle is not yet detected in the laboratory. Here we seek an alternative model to LCDM which makes minimal assumptions about new physics. This is based on previous work by Shanks who investigated a model which assumed neither exotic particles nor a cosmological constant but instead postulated a low Hubble constant (H_0) to help allow a baryon density which was compatible with an inflationary model with zero spatial curvature. However, the recent Planck results make it more difficult to reconcile such a model with the cosmic microwave background (CMB) temperature fluctuations. Here we relax the previous assumptions to assess the effects of assuming standard model neutrinos of moderate mass (~5eV) but with no CDM and no cosmological constant. If we assume a l...
3D Building Model Fitting Using A New Kinetic Framework
Brédif, Mathieu; Pierrot-Deseilligny, Marc; Maître, Henri
2008-01-01
We describe a new approach to fit the polyhedron describing a 3D building model to the point cloud of a Digital Elevation Model (DEM). We introduce a new kinetic framework that hides to its user the combinatorial complexity of determining or maintaining the polyhedron topology, allowing the design of a simple variational optimization. This new kinetic framework allows the manipulation of a bounded polyhedron with simple faces by specifying the target plane equations of each of its faces. It proceeds by evolving continuously from the polyhedron defined by its initial topology and its initial plane equations to a polyhedron that is as topologically close as possible to the initial polyhedron but with the new plane equations. This kinetic framework handles internally the necessary topological changes that may be required to keep the faces simple and the polyhedron bounded. For each intermediate configurations where the polyhedron looses the simplicity of its faces or its boundedness, the simplest topological mod...
The FIT Model - Fuel-cycle Integration and Tradeoffs
Energy Technology Data Exchange (ETDEWEB)
Steven J. Piet; Nick R. Soelberg; Samuel E. Bays; Candido Pereira; Layne F. Pincock; Eric L. Shaber; Meliisa C Teague; Gregory M Teske; Kurt G Vedros
2010-09-01
All mass streams from fuel separation and fabrication are products that must meet some set of product criteria – fuel feedstock impurity limits, waste acceptance criteria (WAC), material storage (if any), or recycle material purity requirements such as zirconium for cladding or lanthanides for industrial use. These must be considered in a systematic and comprehensive way. The FIT model and the “system losses study” team that developed it [Shropshire2009, Piet2010] are an initial step by the FCR&D program toward a global analysis that accounts for the requirements and capabilities of each component, as well as major material flows within an integrated fuel cycle. This will help the program identify near-term R&D needs and set longer-term goals. The question originally posed to the “system losses study” was the cost of separation, fuel fabrication, waste management, etc. versus the separation efficiency. In other words, are the costs associated with marginal reductions in separations losses (or improvements in product recovery) justified by the gains in the performance of other systems? We have learned that that is the wrong question. The right question is: how does one adjust the compositions and quantities of all mass streams, given uncertain product criteria, to balance competing objectives including cost? FIT is a method to analyze different fuel cycles using common bases to determine how chemical performance changes in one part of a fuel cycle (say used fuel cooling times or separation efficiencies) affect other parts of the fuel cycle. FIT estimates impurities in fuel and waste via a rough estimate of physics and mass balance for a set of technologies. If feasibility is an issue for a set, as it is for “minimum fuel treatment” approaches such as melt refining and AIROX, it can help to make an estimate of how performances would have to change to achieve feasibility.
Methodical fitting for mathematical models of rubber-like materials
Destrade, Michel; Saccomandi, Giuseppe; Sgura, Ivonne
2017-02-01
A great variety of models can describe the nonlinear response of rubber to uniaxial tension. Yet an in-depth understanding of the successive stages of large extension is still lacking. We show that the response can be broken down in three steps, which we delineate by relying on a simple formatting of the data, the so-called Mooney plot transform. First, the small-to-moderate regime, where the polymeric chains unfold easily and the Mooney plot is almost linear. Second, the strain-hardening regime, where blobs of bundled chains unfold to stiffen the response in correspondence to the `upturn' of the Mooney plot. Third, the limiting-chain regime, with a sharp stiffening occurring as the chains extend towards their limit. We provide strain-energy functions with terms accounting for each stage that (i) give an accurate local and then global fitting of the data; (ii) are consistent with weak nonlinear elasticity theory and (iii) can be interpreted in the framework of statistical mechanics. We apply our method to Treloar's classical experimental data and also to some more recent data. Our method not only provides models that describe the experimental data with a very low quantitative relative error, but also shows that the theory of nonlinear elasticity is much more robust that seemed at first sight.
A Simulated Annealing based Optimization Algorithm for Automatic Variogram Model Fitting
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.
Engineered Swine Models of Cancer
Directory of Open Access Journals (Sweden)
Adrienne L. Watson
2016-05-01
Full Text Available Over the past decade, the technology to engineer genetically modified swine has seen many advancements, and because their physiology is remarkably similar to that of humans, swine models of cancer may be extremely valuable for preclinical safety studies as well as toxicity testing of pharmaceuticals prior to the start of human clinical trials. Hence, the benefits of using swine as a large animal model in cancer research and the potential applications and future opportunities of utilizing pigs in cancer modeling are immense. In this review, we discuss how pigs have been and can be used as a biomedical models for cancer research, with an emphasis on current technologies. We have focused on applications of precision genetics that can provide models that mimic human cancer predisposition syndromes. In particular, we describe the advantages of targeted gene-editing using custom endonucleases, specifically TALENs and CRISPRs, and transposon systems, to make novel pig models of cancer with broad preclinical applications.
RNA virus evolution via a fitness-space model
Energy Technology Data Exchange (ETDEWEB)
Tsimring, L.S.; Levine, H. [Institute for Nonlinear Science, University of California, San Diego, La Jolla, California 92093-0402 (United States); Kessler, D.A. [Department of Physics, Bar-Ilan University, Ramat Gan 52900 (Israel)
1996-06-01
We present a mean-field theory for the evolution of RNA virus populations. The theory operates with a distribution of the population in a one-dimensional fitness space, and is valid for sufficiently smooth fitness landscapes. Our approach explains naturally the recent experimental observation [I. S. Novella {ital et} {ital al}., Proc. Natl. Acad. Sci. U.S.A. {bold 92}, 5841{endash}5844 (1995)] of two distinct stages in the growth of virus fitness. {copyright} {ital 1995 The American Physical Society.}
May, Anne M.; Van Weert, Ellen; Korstjens, Irene; Hoekstra-Weebers, Josette E. H. M.; Van Der Schans, Cees P.; Zonderland, Maria L.; Mesters, Ilse; Van Den Borne, Bart; Ros, Wynand J. G.
2008-01-01
We compared the effect of a group-based 12-week supervised exercise programme, i.e. aerobic and resistance exercise, and group sports, with that of the same programme combined with cognitive-behavioural training on physical fitness and activity of cancer survivors. One hundred and forty seven cancer
Mouse models for cancer research
Institute of Scientific and Technical Information of China (English)
Wei Zhang; Lynette Moore; Ping Ji
2011-01-01
Mouse models of cancer enable researchers to leamn about tumor biology in complicated and dynamic physiological systems. Since the development of gene targeting in mice, cancer biologists have been among the most frequent users of transgenic mouse models, which have dramatically increased knowledge about how cancers form and grow. The Chinese Joumnal of Cancer will publish a series of papers reporting the use of mouse models in studying genetic events in cancer cases. This editorial is an overview of the development and applications of mouse models of cancer and directs the reader to upcoming papers describing the use of these models to be published in coming issues, beginning with three articles in the current issue.
Convergence, Admissibility, and Fit of Alternative Confirmatory Factor Analysis Models for MTMM Data
Lance, Charles E.; Fan, Yi
2016-01-01
We compared six different analytic models for multitrait-multimethod (MTMM) data in terms of convergence, admissibility, and model fit to 258 samples of previously reported data. Two well-known models, the correlated trait-correlated method (CTCM) and the correlated trait-correlated uniqueness (CTCU) models, were fit for reference purposes in…
An Application of M[subscript 2] Statistic to Evaluate the Fit of Cognitive Diagnostic Models
Liu, Yanlou; Tian, Wei; Xin, Tao
2016-01-01
The fit of cognitive diagnostic models (CDMs) to response data needs to be evaluated, since CDMs might yield misleading results when they do not fit the data well. Limited-information statistic M[subscript 2] and the associated root mean square error of approximation (RMSEA[subscript 2]) in item factor analysis were extended to evaluate the fit of…
NUTRITION AND FITNESS (PART 1: OBESITY, THE METABOLIC SYNDROME, CARDIOVASCULAR DISEASE, AND CANCER
Directory of Open Access Journals (Sweden)
Artemis P. Simopoulos
2005-12-01
Full Text Available The proceedings of the Fifth International Conference on Nutrition and Fitness held in Athens, Greece, on June 91-2, 2004 are presented in the book as the first volume of the series. The objectives of the book are to review/discuss the latest information on nutrition and fitness by taking into consideration i genetic endowment, ii adaptation to the nutritional factors and the effect of various resources of energy on exercise and performance, iii the epidemiology of obesity, iv the relationship of nutrition and fitness to chronic diseases (cardiovascular diseases, syndrome X, obesity, osteoporosis, diabetes, cancer. The book also discusses the classification system of obesity in several countries and compares the diets used in several regions/countries. FEATURES A common, uniform strategy and evidence-based approach to organizing and interpreting the literature is used in all chapters. This textbook is composed of three parts with sub-sections in three of them. The topics of the parts are: i Obesity and Metabolic Syndrome, ii Coronary Heart Disease and iii Cancer. In each specific chapter, an epidemiological picture has been systematically developed from the data available in prospective, retrospective, case-control, and cross-sectional studies. The tables and figures are numerous, helpful and very useful. AUDIENCE This book is almost a compulsory reading for anyone interested in cardiovascular system, nutrition, metabolism, social and preventive medicine, clinical nutrition, diabetics, genetics, obesity, public health, sports medicine and for those wishing to run comprehensive research in this and relevant areas. The fact that the contributors are leading international researchers in this field makes this book more welcome. ASSESSMENT This book is almost a compulsory reading for anyone interested in pediatric injuries and for those wishing to run comprehensive research in this and relevant areas. The fact that the contributors are leading
Albanese, A; Urso, R; Bianciardi, L; Rigato, M; Battisti, E
2009-11-01
With reference to experimental data in the literature, we present a model consisting of two elastic elements, conceived to simulate resistance to stretching, at constant velocity of elongation, of corneal tissue affected by keratoconus, treated with riboflavin and ultraviolet irradiation to induce cross-linking. The function describing model behaviour adapted to stress and strain values. It was found that the Young's moduli of the two elastic elements increased in cross-linked tissues and that cross-linking treatment therefore increased corneal rigidity. It is recognized that this observation is substantially in line with the conclusion reported in the literature, obtained using an exponential fitting function. It is observed, however, that the latter function implies a condition of non-zero stresses without strain, and does not provide interpretative insights for lack of any biomechanical basis. Above all, the function fits a singular trend, inexplicably claimed to be viscoelastic, with surprising perfection. In any case, using the reported data, the study demonstrates that a fitting equation obtained by a modelling approach not only shows the evident efficacy of the treatment, but also provides orientations for studying modifications induced in cross-linked fibres.
Kinetics Modeling of Cancer Immunology.
1986-05-09
CANCER IMMUNOLOGY -1 DTICS ELECTED SEP 9 8 UNITED STATES NAVAL ACADEMY ANNAPOLIS, MARYLAND V ,1986 %,e docment ha le approved for public A." I and sale...1986 4. TITLE (and Subtitle) S. TYPE OF REPORT & PERIOD COVERED KINETICS MODELING OF CANCER IMMUNOLOGY Final: 1985/1986 6. PERFORMING ORG. REPORT...137 (1986) "Kinetics Modeling of Cancer Immunology " A Trident Scholar Project Report by Midn I/C Scott Helmers, Class of 1986 United States Naval
Finch, W. Holmes; Finch, Maria E. Hernandez
2016-01-01
Researchers and data analysts are sometimes faced with the problem of very small samples, where the number of variables approaches or exceeds the overall sample size; i.e. high dimensional data. In such cases, standard statistical models such as regression or analysis of variance cannot be used, either because the resulting parameter estimates…
Directory of Open Access Journals (Sweden)
W. Holmes Finch
2016-05-01
Full Text Available Researchers and data analysts are sometimes faced with the problem of very small samples, where the number of variables approaches or exceeds the overall sample size; i.e. high dimensional data. In such cases, standard statistical models such as regression or analysis of variance cannot be used, either because the resulting parameter estimates exhibit very high variance and can therefore not be trusted, or because the statistical algorithm cannot converge on parameter estimates at all. There exist an alternative set of model estimation procedures, known collectively as regularization methods, which can be used in such circumstances, and which have been shown through simulation research to yield accurate parameter estimates. The purpose of this paper is to describe, for those unfamiliar with them, the most popular of these regularization methods, the lasso, and to demonstrate its use on an actual high dimensional dataset involving adults with autism, using the R software language. Results of analyses involving relating measures of executive functioning with a full scale intelligence test score are presented, and implications of using these models are discussed.
A fitted neoprene garment to cover dressings in swine models.
Mino, Matthew J; Mauskar, Neil A; Matt, Sara E; Pavlovich, Anna R; Prindeze, Nicholas J; Moffatt, Lauren T; Shupp, Jeffrey W
2012-12-17
Domesticated porcine species are commonly used in studies of wound healing, owing to similarities between porcine skin and human skin. Such studies often involve wound dressings, and keeping these dressings intact on the animal can be a challenge. The authors describe a novel and simple technique for constructing a fitted neoprene garment for pigs that covers dressings and maintains their integrity during experiments.
Fitting Item Response Theory Models to Two Personality Inventories: Issues and Insights.
Chernyshenko, Oleksandr S.; Stark, Stephen; Chan, Kim-Yin; Drasgow, Fritz; Williams, Bruce
2001-01-01
Compared the fit of several Item Response Theory (IRT) models to two personality assessment instruments using data from 13,059 individuals responding to one instrument and 1,770 individuals responding to the other. Two- and three-parameter logistic models fit some scales reasonably well, but not others, and the graded response model generally did…
Mead, Alexander; Heymans, Catherine; Joudaki, Shahab; Heavens, Alan
2015-01-01
We present an optimised 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 $\\Lambda$CDM and $w$CDM models the halo-model power is accurate to $\\simeq 5$ per cent for $k\\leq 10h\\,\\mathrm{Mpc}^{-1}$ and $z\\leq 2$. We compare our results with recent revisions of the popular HALOFIT model and show that our predictions are more accurate. 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 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 limi...
Modelling population dynamics model formulation, fitting and assessment using state-space methods
Newman, K B; Morgan, B J T; King, R; Borchers, D L; Cole, D J; Besbeas, P; Gimenez, O; Thomas, L
2014-01-01
This book gives a unifying framework for estimating the abundance of open populations: populations subject to births, deaths and movement, given imperfect measurements or samples of the populations. The focus is primarily on populations of vertebrates for which dynamics are typically modelled within the framework of an annual cycle, and for which stochastic variability in the demographic processes is usually modest. Discrete-time models are developed in which animals can be assigned to discrete states such as age class, gender, maturity, population (within a metapopulation), or species (for multi-species models). The book goes well beyond estimation of abundance, allowing inference on underlying population processes such as birth or recruitment, survival and movement. This requires the formulation and fitting of population dynamics models. The resulting fitted models yield both estimates of abundance and estimates of parameters characterizing the underlying processes.
A New Finite Interval Lifetime Distribution Model for Fitting Bathtub-Shaped Failure Rate Curve
Directory of Open Access Journals (Sweden)
Xiaohong Wang
2015-01-01
Full Text Available This paper raised a new four-parameter fitting model to describe bathtub curve, which is widely used in research on components’ life analysis, then gave explanation of model parameters, and provided parameter estimation method as well as application examples utilizing some well-known lifetime data. By comparative analysis between the new model and some existing bathtub curve fitting model, we can find that the new fitting model is very convenient and its parameters are clear; moreover, this model is of universal applicability which is not only suitable for bathtub-shaped failure rate curves but also applicable for the constant, increasing, and decreasing failure rate curves.
Bauer, Daniel J.; Sterba, Sonya K.
2011-01-01
Previous research has compared methods of estimation for fitting multilevel models to binary data, but there are reasons to believe that the results will not always generalize to the ordinal case. This article thus evaluates (a) whether and when fitting multilevel linear models to ordinal outcome data is justified and (b) which estimator to employ…
Using the PLUM procedure of SPSS to fit unequal variance and generalized signal detection models.
DeCarlo, Lawrence T
2003-02-01
The recent addition of aprocedure in SPSS for the analysis of ordinal regression models offers a simple means for researchers to fit the unequal variance normal signal detection model and other extended signal detection models. The present article shows how to implement the analysis and how to interpret the SPSS output. Examples of fitting the unequal variance normal model and other generalized signal detection models are given. The approach offers a convenient means for applying signal detection theory to a variety of research.
Van Puymbroeck, Marieke; Schmid, Arlene; Shinew, Kimberly J; Hsieh, Pei-Chun
2011-01-01
Breast cancer survivors often experience changes in their perception of their bodies following surgical treatment. These changes in body image may increase self-consciousness and perceptions of physical activity constraints and reduce participation in physical activity. While the number of studies examining different types of yoga targeting women with breast cancer has increased, studies thus far have not studied the influence that Hatha yoga has on body image and physical activity constraints. The objective of this study was to explore the changes that occur in breast cancer survivors in terms of body image, perceived constraints, and physical fitness following an 8-week Hatha yoga intervention. This study used a nonrandomized two-group pilot study, comparing an 8-week Hatha yoga intervention with a light exercise group, both designed for women who were at least nine months post-treatment for breast cancer. Both quantitative and qualitative data were collected in the areas of body image, physical activity constraints, and physical fitness. Findings indicated that quantitatively, yoga participants experienced reductions in physical activity constraints and improvements in lower- and upper-body strength and flexibility, while control participants experienced improvements in abdominal strength and lower-body strength. Qualitative findings support changes in body image, physical activity constraints, and physical fitness for the participants in the yoga group. In conclusion, Hatha yoga may reduce constraints to physical activity and improve fitness in breast cancer survivors. More research is needed to explore the relationship between Hatha yoga and improvements in body image.
Cervical Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Breast Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Liver Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Ovarian Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Prostate Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Pancreatic Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Colorectal Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Bladder Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Esophageal Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Lung Cancer Risk Prediction Models
Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Testicular Cancer Risk Prediction Models
Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.
Roe, Byron
2013-01-01
The effect of correlations between model parameters and nuisance parameters is discussed, in the context of fitting model parameters to data. Modifications to the usual $\\chi^2$ method are required. Fake data studies, as used at present, will not be optimum. Problems will occur for applications of the Maltoni-Schwetz \\cite{ms} theorem. Neutrino oscillations are used as examples, but the problems discussed here are general ones, which are often not addressed.
Refractive Index of Humid Air in the Infrared: Model Fits
Mathar, R J
2006-01-01
The theory of summation of electromagnetic line transitions is used to tabulate the Taylor expansion of the refractive index of humid air over the basic independent parameters (temperature, pressure, humidity, wavelength) in five separate infrared regions from the H to the Q band at a fixed percentage of Carbon Dioxide. These are least-squares fits to raw, highly resolved spectra for a set of temperatures from 10 to 25 C, a set of pressures from 500 to 1023 hPa, and a set of relative humidities from 5 to 60%. These choices reflect the prospective application to characterize ambient air at mountain altitudes of astronomical telescopes.
A Gompertzian model with random effects to cervical cancer growth
Energy Technology Data Exchange (ETDEWEB)
Mazlan, Mazma Syahidatul Ayuni; Rosli, Norhayati [Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang (Malaysia)
2015-05-15
In this paper, a Gompertzian model with random effects is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via maximum likehood estimation. We apply 4-stage Runge-Kutta (SRK4) for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of the cervical cancer growth. Low values of root mean-square error (RMSE) of Gompertzian model with random effect indicate good fits.
Gompertzian stochastic model with delay effect to cervical cancer growth
Energy Technology Data Exchange (ETDEWEB)
Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti [Faculty of Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300 Gambang, Pahang (Malaysia); Bahar, Arifah [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor and UTM Centre for Industrial and Applied Mathematics (UTM-CIAM), Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)
2015-02-03
In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits.
Gompertzian stochastic model with delay effect to cervical cancer growth
Mazlan, Mazma Syahidatul Ayuni binti; Rosli, Norhayati binti; Bahar, Arifah
2015-02-01
In this paper, a Gompertzian stochastic model with time delay is introduced to describe the cervical cancer growth. The parameters values of the mathematical model are estimated via Levenberg-Marquardt optimization method of non-linear least squares. We apply Milstein scheme for solving the stochastic model numerically. The efficiency of mathematical model is measured by comparing the simulated result and the clinical data of cervical cancer growth. Low values of Mean-Square Error (MSE) of Gompertzian stochastic model with delay effect indicate good fits.
Mouse Models of Gastric Cancer
Directory of Open Access Journals (Sweden)
Timothy C. Wang
2013-01-01
Full Text Available Animal models have greatly enriched our understanding of the molecular mechanisms of numerous types of cancers. Gastric cancer is one of the most common cancers worldwide, with a poor prognosis and high incidence of drug-resistance. However, most inbred strains of mice have proven resistant to gastric carcinogenesis. To establish useful models which mimic human gastric cancer phenotypes, investigators have utilized animals infected with Helicobacter species and treated with carcinogens. In addition, by exploiting genetic engineering, a variety of transgenic and knockout mouse models of gastric cancer have emerged, such as INS-GAS mice and TFF1 knockout mice. Investigators have used the combination of carcinogens and gene alteration to accelerate gastric cancer development, but rarely do mouse models show an aggressive and metastatic gastric cancer phenotype that could be relevant to preclinical studies, which may require more specific targeting of gastric progenitor cells. Here, we review current gastric carcinogenesis mouse models and provide our future perspectives on this field.
The issue of statistical power for overall model fit in evaluating structural equation models
Directory of Open Access Journals (Sweden)
Richard HERMIDA
2015-06-01
Full Text Available Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA index of overall model fit in structural equation modeling. By way of example, we examine the current state of power in the literature by reviewing studies in top Industrial-Organizational (I/O Psychology journals using SEMs. Results indicate that in many studies, power is very low, which implies acceptance of invalid models. Additionally, we examined methodological situations which may have an influence on statistical power of SEMs. Results showed that power varies significantly as a function of model type and whether or not the model is the main model for the study. Finally, results indicated that power is significantly related to model fit statistics used in evaluating SEMs. The results from this quantitative review imply that researchers should be more vigilant with respect to power in structural equation modeling. We therefore conclude by offering methodological best practices to increase confidence in the interpretation of structural equation modeling results with respect to statistical power issues.
Adams, Matthew P.; Collier, Catherine J.; Uthicke, Sven; Ow, Yan X.; Langlois, Lucas; O’Brien, Katherine R.
2017-01-01
When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (Topt) for maximum photosynthetic rate (Pmax). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike.
Assessing Fit of Cognitive Diagnostic Models: A Case Study
Sinharay, Sandip; Almond, Russell G.
2007-01-01
A cognitive diagnostic model uses information from educational experts to describe the relationships between item performances and posited proficiencies. When the cognitive relationships can be described using a fully Bayesian model, Bayesian model checking procedures become available. Checking models tied to cognitive theory of the domains…
DEFF Research Database (Denmark)
Stein, Wilfred D; Litman, Thomas
2006-01-01
We successfully modeled the recurrence of tumors in breast cancer patients, assuming that: (i) A breast cancer patient is likely to have some circulating metastatic cells, even after initial surgery. (ii) These metastatic cells are dormant. (iii) The dormant cells are subject to attrition...... by the body's immune system, or by random apoptosis or senescence. (iv) Recurrence suppressor mechanisms exist. (v) When such genes are disabled by random mutations, the dormant metastatic cell is activated, and will develop to a cancer recurrence. The model was also fitted to data on the survival...
Basch, Corey Hannah; Ethan, Danna; Hillyer, Grace Clarke; Berdnik, Alyssa
2014-04-02
The desire to be tan is a phenomenon that public health researchers have investigated, as exposure to UV radiation increases the chances of developing skin cancer. Media messages in women's magazines have been shown to contribute to this problem. Much less is known about the prevalence of skin cancer prevention messages in these magazines. This study's aim was to identify the number and type of articles and advertised products devoted to skin health (sun protection and skin cancer prevention in particular) within five popular U.S. greater than women's health and fitness magazines. We analyzed articles and advertisements over seven months of issues of the following popular women's health and fitness magazines: Fitness, Health, Self, Shape, and Women's Health, March 2013 through September 2013. Overall, 31 issues of the five magazines with a total of 780 articles and 1,986 advertisements were analyzed. Of the 780 articles, a mere 2.9% (n=23) were devoted to skin. Of the 258 skin product advertisements, less than 20% of the products contained sun protection factor (SPF). These findings suggest that women's health and fitness magazines can improve their efforts in informing women of skin cancer risks and preventive measures to minimize these risks. The role of these magazines in building health literacy among their readers is also discussed.
Sellwood, J A
2015-01-01
This posting announces public availability of version 1.2 of the DiskFit software package developed by the authors, which may be used to fit simple non-axisymmetric models either to images or to velocity fields of disk galaxies. Here we give an outline of the capability of the code and provide the link to downloading executables, the source code, and a comprehensive on-line manual. We argue that in important respects the code is superior to rotcur for fitting kinematic maps and to galfit for fitting multi-component models to photometric images.
Cai, Li; Lee, Taehun
2009-01-01
We apply the Supplemented EM algorithm (Meng & Rubin, 1991) to address a chronic problem with the "two-stage" fitting of covariance structure models in the presence of ignorable missing data: the lack of an asymptotically chi-square distributed goodness-of-fit statistic. We show that the Supplemented EM algorithm provides a…
Directory of Open Access Journals (Sweden)
Courneya Kerry S
2012-11-01
Full Text Available Abstract Background Limited research has examined the association between physical activity, health-related fitness, and disease outcomes in breast cancer survivors. Here, we present the rationale and design of the Alberta Moving Beyond Breast Cancer (AMBER Study, a prospective cohort study designed specifically to examine the role of physical activity and health-related fitness in breast cancer survivorship from the time of diagnosis and for the balance of life. The AMBER Study will examine the role of physical activity and health-related fitness in facilitating treatment completion, alleviating treatment side effects, hastening recovery after treatments, improving long term quality of life, and reducing the risks of disease recurrence, other chronic diseases, and premature death. Methods/Design The AMBER Study will enroll 1500 newly diagnosed, incident, stage I-IIIc breast cancer survivors in Alberta, Canada over a 5 year period. Assessments will be made at baseline (within 90 days of surgery, 1 year, and 3 years consisting of objective and self-reported measurements of physical activity, health-related fitness, blood collection, lymphedema, patient-reported outcomes, and determinants of physical activity. A final assessment at 5 years will measure patient-reported data only. The cohort members will be followed for an additional 5 years for disease outcomes. Discussion The AMBER cohort will answer key questions related to physical activity and health-related fitness in breast cancer survivors including: (1 the independent and interactive associations of physical activity and health-related fitness with disease outcomes (e.g., recurrence, breast cancer-specific mortality, overall survival, treatment completion rates, symptoms and side effects (e.g., pain, lymphedema, fatigue, neuropathy, quality of life, and psychosocial functioning (e.g., anxiety, depression, self-esteem, happiness, (2 the determinants of physical activity and
A model for programmatic assessment fit for purpose.
Vleuten, C.P.M. van der; Schuwirth, L.W.; Driessen, E.W.; Dijkstra, J.; Tigelaar, D.; Baartman, L.K.; Tartwijk, J. van
2012-01-01
We propose a model for programmatic assessment in action, which simultaneously optimises assessment for learning and assessment for decision making about learner progress. This model is based on a set of assessment principles that are interpreted from empirical research. It specifies cycles of train
Atmospheric Turbulence Modeling for Aero Vehicles: Fractional Order Fits
Kopasakis, George
2015-01-01
Atmospheric turbulence models are necessary for the design of both inlet/engine and flight controls, as well as for studying coupling between the propulsion and the vehicle structural dynamics for supersonic vehicles. Models based on the Kolmogorov spectrum have been previously utilized to model atmospheric turbulence. In this paper, a more accurate model is developed in its representative fractional order form, typical of atmospheric disturbances. This is accomplished by first scaling the Kolmogorov spectral to convert them into finite energy von Karman forms and then by deriving an explicit fractional circuit-filter type analog for this model. This circuit model is utilized to develop a generalized formulation in frequency domain to approximate the fractional order with the products of first order transfer functions, which enables accurate time domain simulations. The objective of this work is as follows. Given the parameters describing the conditions of atmospheric disturbances, and utilizing the derived formulations, directly compute the transfer function poles and zeros describing these disturbances for acoustic velocity, temperature, pressure, and density. Time domain simulations of representative atmospheric turbulence can then be developed by utilizing these computed transfer functions together with the disturbance frequencies of interest.
Fitting the Two-Higgs-Doublet model of type II
Eberhardt, Otto
2014-01-01
We present the current status of the Two-Higgs-Doublet model of type II. Taking into account all available relevant information, we exclude at $95$% CL sizeable deviations of the so-called alignment limit, in which all couplings of the light CP-even Higgs boson $h$ are Standard-Model-like. While we can set a lower limit of $240$ GeV on the mass of the pseudoscalar Higgs boson at $95$% CL, the mass of the heavy CP-even Higgs boson $H$ can be even lighter than $200$ GeV. The strong constraints on the model parameters also set limits on the triple Higgs couplings: the $hhh$ coupling in the Two-Higgs-Doublet model of type II cannot be larger than in the Standard Model, while the $hhH$ coupling can maximally be $2.5$ times the size of the Standard Model $hhh$ coupling, assuming an $H$ mass below $1$ TeV. The selection of benchmark scenarios which maximize specific effects within the allowed regions for further collider studies is illustrated for the $H$ branching fraction to fermions and gauge bosons. As an exampl...
Fitness model for the Italian interbank money market
de Masi, G.; Iori, G.; Caldarelli, G.
2006-12-01
We use the theory of complex networks in order to quantitatively characterize the formation of communities in a particular financial market. The system is composed by different banks exchanging on a daily basis loans and debts of liquidity. Through topological analysis and by means of a model of network growth we can determine the formation of different group of banks characterized by different business strategy. The model based on Pareto’s law makes no use of growth or preferential attachment and it reproduces correctly all the various statistical properties of the system. We believe that this network modeling of the market could be an efficient way to evaluate the impact of different policies in the market of liquidity.
A no-scale inflationary model to fit them all
Energy Technology Data Exchange (ETDEWEB)
Ellis, John [Theoretical Particle Physics and Cosmology Group, Department of Physics, King' s College London, WC2R 2LS London (United Kingdom); García, Marcos A.G.; Olive, Keith A. [William I. Fine Theoretical Physics Institute, School of Physics and Astronomy, University of Minnesota, 116 Church Street SE, Minneapolis, MN 55455 (United States); Nanopoulos, Dimitri V., E-mail: john.ellis@cern.ch, E-mail: garciagarcia@physics.umn.edu, E-mail: dimitri@physics.tamu.edu, E-mail: olive@physics.umn.edu [George P. and Cynthia W. Mitchell Institute for Fundamental Physics and Astronomy, Texas A and M University, College Station, 77843 Texas (United States)
2014-08-01
The magnitude of B-mode polarization in the cosmic microwave background as measured by BICEP2 favours models of chaotic inflation with a quadratic m{sup 2} φ{sup 2}/2 potential, whereas data from the Planck satellite favour a small value of the tensor-to-scalar perturbation ratio r that is highly consistent with the Starobinsky R +R{sup 2} model. Reality may lie somewhere between these two scenarios. In this paper we propose a minimal two-field no-scale supergravity model that interpolates between quadratic and Starobinsky-like inflation as limiting cases, while retaining the successful prediction n{sub s} ≅ 0.96.
A fitness model for the Italian Interbank Money Market
De Masi, G; Iori, G
2006-01-01
We use the theory of complex networks in order to quantitatively characterize the formation of communities in a particular financial market. The system is composed by different banks exchanging on a daily basis loans and debts of liquidity. Through topological analysis and by means of a model of network growth we can determine the formation of different group of banks characterized by different business strategy. The model based on Pareto's Law makes no use of growth or preferential attachment and it reproduces correctly all the various statistical properties of the system. We believe that this network modeling of the market could be an efficient way to evaluate the impact of different policies in the market of liquidity.
Fitness model for the Italian interbank money market.
De Masi, G; Iori, G; Caldarelli, G
2006-12-01
We use the theory of complex networks in order to quantitatively characterize the formation of communities in a particular financial market. The system is composed by different banks exchanging on a daily basis loans and debts of liquidity. Through topological analysis and by means of a model of network growth we can determine the formation of different group of banks characterized by different business strategy. The model based on Pareto's law makes no use of growth or preferential attachment and it reproduces correctly all the various statistical properties of the system. We believe that this network modeling of the market could be an efficient way to evaluate the impact of different policies in the market of liquidity.
SPSS macros to compare any two fitted values from a regression model.
Weaver, Bruce; Dubois, Sacha
2012-12-01
In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.
BOUSSINESQ MODELLING OF NEARSHORE WAVES UNDER BODY FITTED COORDINATE
Institute of Scientific and Technical Information of China (English)
FANG Ke-zhao; ZOU Zhi-li; LIU Zhong-bo; YIN Ji-wei
2012-01-01
A set of nonlinear Boussinesq equations with fully nonlinearity property is solved numerically in generalized coordinates,to develop a Boussinesq-type wave model in dealing with irregular computation boundaries in complex nearshore regions and to facilitate the grid refinements in simulations.The governing equations expressed in contravariant components of velocity vectors under curv ilinear coordinates are derived and a high order finite difference scheme on a staggered grid is employed for the numerical implementation.The developed model is used to simulate nearshore wave propagations under curvilinear coordinates,the numerical results are compared against analytical or experimental data with a good agreement.
Using proper regression methods for fitting the Langmuir model to sorption data
The Langmuir model, originally developed for the study of gas sorption to surfaces, is one of the most commonly used models for fitting phosphorus sorption data. There are good theoretical reasons, however, against applying this model to describe P sorption to soils. Nevertheless, the Langmuir model...
The empirical likelihood goodness-of-fit test for regression model
Institute of Scientific and Technical Information of China (English)
Li-xing ZHU; Yong-song QIN; Wang-li XU
2007-01-01
Goodness-of-fit test for regression modes has received much attention in literature. In this paper, empirical likelihood (EL) goodness-of-fit tests for regression models including classical parametric and autoregressive (AR) time series models are proposed. Unlike the existing locally smoothing and globally smoothing methodologies, the new method has the advantage that the tests are self-scale invariant and that the asymptotic null distribution is chi-squared. Simulations are carried out to illustrate the methodology.
Zhu, Xiang; Zhang, Dianwen
2013-01-01
We present a fast, accurate and robust parallel Levenberg-Marquardt minimization optimizer, GPU-LMFit, which is implemented on graphics processing unit for high performance scalable parallel model fitting processing. GPU-LMFit can provide a dramatic speed-up in massive model fitting analyses to enable real-time automated pixel-wise parametric imaging microscopy. We demonstrate the performance of GPU-LMFit for the applications in superresolution localization microscopy and fluorescence lifetime imaging microscopy.
Xiang Zhu; Dianwen Zhang
2013-01-01
We present a fast, accurate and robust parallel Levenberg-Marquardt minimization optimizer, GPU-LMFit, which is implemented on graphics processing unit for high performance scalable parallel model fitting processing. GPU-LMFit can provide a dramatic speed-up in massive model fitting analyses to enable real-time automated pixel-wise parametric imaging microscopy. We demonstrate the performance of GPU-LMFit for the applications in superresolution localization microscopy and fluorescence lifetim...
Design of spatial experiments: Model fitting and prediction
Energy Technology Data Exchange (ETDEWEB)
Fedorov, V.V.
1996-03-01
The main objective of the paper is to describe and develop model oriented methods and algorithms for the design of spatial experiments. Unlike many other publications in this area, the approach proposed here is essentially based on the ideas of convex design theory.
Goodness-of-fit tests in mixed models
Claeskens, Gerda
2009-05-12
Mixed models, with both random and fixed effects, are most often estimated on the assumption that the random effects are normally distributed. In this paper we propose several formal tests of the hypothesis that the random effects and/or errors are normally distributed. Most of the proposed methods can be extended to generalized linear models where tests for non-normal distributions are of interest. Our tests are nonparametric in the sense that they are designed to detect virtually any alternative to normality. In case of rejection of the null hypothesis, the nonparametric estimation method that is used to construct a test provides an estimator of the alternative distribution. © 2009 Sociedad de Estadística e Investigación Operativa.
On assessing model fit for distribution-free longitudinal models under missing data.
Wu, P; Tu, X M; Kowalski, J
2014-01-15
The generalized estimating equation (GEE), a distribution-free, or semi-parametric, approach for modeling longitudinal data, is used in a wide range of behavioral, psychotherapy, pharmaceutical drug safety, and healthcare-related research studies. Most popular methods for assessing model fit are based on the likelihood function for parametric models, rendering them inappropriate for distribution-free GEE. One rare exception is a score statistic initially proposed by Tsiatis for logistic regression (1980) and later extended by Barnhart and Willamson to GEE (1998). Because GEE only provides valid inference under the missing completely at random assumption and missing values arising in most longitudinal studies do not follow such a restricted mechanism, this GEE-based score test has very limited applications in practice. We propose extensions of this goodness-of-fit test to address missing data under the missing at random assumption, a more realistic model that applies to most studies in practice. We examine the performance of the proposed tests using simulated data and demonstrate the utilities of such tests with data from a real study on geriatric depression and associated medical comorbidities.
Network growth models: A behavioural basis for attachment proportional to fitness
Bell, Michael; Perera, Supun; Piraveenan, Mahendrarajah; Bliemer, Michiel; Latty, Tanya; Reid, Chris
2017-01-01
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. PMID:28205599
Model independent analysis of dark energy I: Supernova fitting result
Gong, Y
2004-01-01
The nature of dark energy is a mystery to us. This paper uses the supernova data to explore the property of dark energy by some model independent methods. We first Talyor expanded the scale factor $a(t)$ to find out the deceleration parameter $q_0<0$. This result just invokes the Robertson-Walker metric. Then we discuss several different parameterizations used in the literature. We find that $\\Omega_{\\rm DE0}$ is almost less than -1 at $1\\sigma$ level. We also find that the transition redshift from deceleration phase to acceleration phase is $z_{\\rm T}\\sim 0.3$.
Adaptation in tunably rugged fitness landscapes: the rough Mount Fuji model.
Neidhart, Johannes; Szendro, Ivan G; Krug, Joachim
2014-10-01
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.
Gfitter - Revisiting the global electroweak fit of the Standard Model and beyond
Energy Technology Data Exchange (ETDEWEB)
Flaecher, H.; Hoecker, A. [European Organization for Nuclear Research (CERN), Geneva (Switzerland); Goebel, M. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)]|[Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany)]|[Hamburg Univ. (Germany). Inst. fuer Experimentalphysik; Haller, J. [Hamburg Univ. (Germany). Inst. fuer Experimentalphysik; Moenig, K.; Stelzer, J. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)]|[Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany)
2008-11-15
The global fit of the Standard Model to electroweak precision data, routinely performed by the LEP electroweak working group and others, demonstrated impressively the predictive power of electroweak unification and quantum loop corrections. We have revisited this fit in view of (i) the development of the new generic fitting package, Gfitter, allowing flexible and efficient model testing in high-energy physics, (ii) the insertion of constraints from direct Higgs searches at LEP and the Tevatron, and (iii) a more thorough statistical interpretation of the results. Gfitter is a modular fitting toolkit, which features predictive theoretical models as independent plugins, and a statistical analysis of the fit results using toy Monte Carlo techniques. The state-of-the-art electroweak Standard Model is fully implemented, as well as generic extensions to it. Theoretical uncertainties are explicitly included in the fit through scale parameters varying within given error ranges. This paper introduces the Gfitter project, and presents state-of-the-art results for the global electroweak fit in the Standard Model, and for a model with an extended Higgs sector (2HDM). Numerical and graphical results for fits with and without including the constraints from the direct Higgs searches at LEP and Tevatron are given. Perspectives for future colliders are analysed and discussed. Including the direct Higgs searches, we find M{sub H}=116.4{sup +18.3}{sub -1.3} GeV, and the 2{sigma} and 3{sigma} allowed regions [114,145] GeV and [[113,168] and [180,225
Li, Tongyun; Xie, Chao; Jiao, Hong
2016-05-30
This article explored the application of the posterior predictive model checking (PPMC) method in assessing fit for unidimensional polytomous item response theory (IRT) models, specifically the divide-by-total models (e.g., the generalized partial credit model). Previous research has primarily focused on using PPMC in model checking for unidimensional and multidimensional IRT models for dichotomous data, and has paid little attention to polytomous models. A Monte Carlo simulation was conducted to investigate the performance of PPMC in detecting different sources of misfit for the partial credit model family. Results showed that the PPMC method, in combination with appropriate discrepancy measures, had adequate power in detecting different sources of misfit for the partial credit model family. Global odds ratio and item total correlation exhibited specific patterns in detecting the absence of the slope parameter, whereas Yen's Q1 was found to be promising in the detection of misfit caused by the constant category intersection parameter constraint across items. (PsycINFO Database Record
Directory of Open Access Journals (Sweden)
Grant B. Morgan
2015-02-01
Full Text Available Bi-factor confirmatory factor models have been influential in research on cognitive abilities because they often better fit the data than correlated factors and higher-order models. They also instantiate a perspective that differs from that offered by other models. Motivated by previous work that hypothesized an inherent statistical bias of fit indices favoring the bi-factor model, we compared the fit of correlated factors, higher-order, and bi-factor models via Monte Carlo methods. When data were sampled from a true bi-factor structure, each of the approximate fit indices was more likely than not to identify the bi-factor solution as the best fitting. When samples were selected from a true multiple correlated factors structure, approximate fit indices were more likely overall to identify the correlated factors solution as the best fitting. In contrast, when samples were generated from a true higher-order structure, approximate fit indices tended to identify the bi-factor solution as best fitting. There was extensive overlap of fit values across the models regardless of true structure. Although one model may fit a given dataset best relative to the other models, each of the models tended to fit the data well in absolute terms. Given this variability, models must also be judged on substantive and conceptual grounds.
Revisiting a Statistical Shortcoming When Fitting the Langmuir Model to Sorption Data
The Langmuir model is commonly used for describing sorption behavior of reactive solutes to surfaces. Fitting the Langmuir model to sorption data requires either the use of nonlinear regression or, alternatively, linear regression using one of the linearized versions of the model. Statistical limit...
A simple model of group selection that cannot be analyzed with inclusive fitness
M. van Veelen; S. Luo; B. Simon
2014-01-01
A widespread claim in evolutionary theory is that every group selection model can be recast in terms of inclusive fitness. Although there are interesting classes of group selection models for which this is possible, we show that it is not true in general. With a simple set of group selection models,
Development of a program to fit data to a new logistic model for microbial growth.
Fujikawa, Hiroshi; Kano, Yoshihiro
2009-06-01
Recently we developed a mathematical model for microbial growth in food. The model successfully predicted microbial growth at various patterns of temperature. In this study, we developed a program to fit data to the model with a spread sheet program, Microsoft Excel. Users can instantly get curves fitted to the model by inputting growth data and choosing the slope portion of a curve. The program also could estimate growth parameters including the rate constant of growth and the lag period. This program would be a useful tool for analyzing growth data and further predicting microbial growth.
ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks
Directory of Open Access Journals (Sweden)
David R. Hunter
2008-12-01
Full Text Available We describe some of the capabilities of the ergm package and the statistical theory underlying it. This package contains tools for accomplishing three important, and inter-related, tasks involving exponential-family random graph models (ERGMs: estimation, simulation, and goodness of fit. More precisely, ergm has the capability of approximating a maximum likelihood estimator for an ERGM given a network data set; simulating new network data sets from a fitted ERGM using Markov chain Monte Carlo; and assessing how well a fitted ERGM does at capturing characteristics of a particular network data set.
DEFF Research Database (Denmark)
Lerche, Lene; Olsen, Anja; Petersen, Kristina Elin Nielsen
2017-01-01
the Danish step test, the physical activity questionnaire Active-Q and self-rated fitness against directly measured maximal oxygen uptake (VO2 max). A population based subsample (n=125) was included from the 'Diet, Cancer and Health - Next Generations' (DCH-NG) cohort which is under establishment. Validity......). When validating the questionnaire-derived measures of PA, leisure time physical activity was not correlated with VO2 max. Positive correlations were found for sports overall, but these were only significant for men: total hours per week of sports (r=0.26), MET-hours per week of sports (r=0.......28) and vigorous sports (0.28) alone were positively correlated with VO2 max. Finally, the percentage of misclassification was low for self-rated fitness (women: 9% and men: 13%). Thus, self-rated fitness was found to be a superior method to the Danish step test, as well as being less cost prohibitive and more...
Shekhar, Karthik; Ferguson, Andrew L; Barton, John P; Kardar, Mehran; Chakraborty, Arup K
2013-01-01
Mutational escape from vaccine induced immune responses has thwarted the development of a successful vaccine against AIDS, whose causative agent is HIV, a highly mutable virus. Knowing the virus' fitness as a function of its proteomic sequence can enable rational design of potent vaccines, as this information can focus vaccine induced immune responses to target mutational vulnerabilities of the virus. Spin models have been proposed as a means to infer intrinsic fitness landscapes of HIV proteins from patient-derived viral protein sequences. These sequences are the product of non-equilibrium viral evolution driven by patient-specific immune responses, and are subject to phylogenetic constraints. How can such sequence data allow inference of intrinsic fitness landscapes? We combined computer simulations and variational theory \\'{a} la Feynman to show that, in most circumstances, spin models inferred from patient-derived viral sequences reflect the correct rank order of the fitness of mutant viral strains. Our f...
Shekhar, Karthik; Ruberman, Claire F.; Ferguson, Andrew L.; Barton, John P.; Kardar, Mehran; Chakraborty, Arup K.
2013-12-01
Mutational escape from vaccine-induced immune responses has thwarted the development of a successful vaccine against AIDS, whose causative agent is HIV, a highly mutable virus. Knowing the virus' fitness as a function of its proteomic sequence can enable rational design of potent vaccines, as this information can focus vaccine-induced immune responses to target mutational vulnerabilities of the virus. Spin models have been proposed as a means to infer intrinsic fitness landscapes of HIV proteins from patient-derived viral protein sequences. These sequences are the product of nonequilibrium viral evolution driven by patient-specific immune responses and are subject to phylogenetic constraints. How can such sequence data allow inference of intrinsic fitness landscapes? We combined computer simulations and variational theory á la Feynman to show that, in most circumstances, spin models inferred from patient-derived viral sequences reflect the correct rank order of the fitness of mutant viral strains. Our findings are relevant for diverse viruses.
Efficient fitting of multiplanet Keplerian models to radial velocity and astrometry data
Howard, J T Wright A W
2009-01-01
We describe a technique for solving for the orbital elements of multiple planets from radial velocity (RV) and/or astrometric data taken with 1 m/s and microarcsecond precision, appropriate for efforts to detect Earth-massed planets in their stars' habitable zones, such as NASA's proposed Space Interferometry Mission. We include details of calculating analytic derivatives for use in the Levenberg-Marquardt (LM) algorithm for the problems of fitting RV and astrometric data separately and jointly. We also explicate the general method of separating the linear and nonlinear components of a model fit in the context of an LM fit, show how explicit derivatives can be calculated in such a model, and demonstrate the speed up and convergence improvements of such a scheme in the case of a five-planet fit to published radial velocity data for 55 Cnc.
Walsh, Linda; Zhang, Wei
2016-03-01
In the assessment of health risks after nuclear accidents, some health consequences require special attention. For example, in their 2013 report on health risk assessment after the Fukushima nuclear accident, the World Health Organisation (WHO) panel of experts considered risks of breast cancer, thyroid cancer and leukaemia. For these specific cancer types, use was made of already published excess relative risk (ERR) and excess absolute risk (EAR) models for radiation-related cancer incidence fitted to the epidemiological data from the Japanese A-bomb Life Span Study (LSS). However, it was also considered important to assess all other types of solid cancer together and the WHO, in their above-mentioned report, stated "No model to calculate the risk for all other solid cancer excluding breast and thyroid cancer risks is available from the LSS data". Applying the LSS models for all solid cancers along with the models for the specific sites means that some cancers have an overlap in the risk evaluations. Thus, calculating the total solid cancer risk plus the breast cancer risk plus the thyroid cancer risk can overestimate the total risk by several per cent. Therefore, the purpose of this paper was to publish the required models for all other solid cancers, i.e. all solid cancers other than those types of cancer requiring special attention after a nuclear accident. The new models presented here have been fitted to the same LSS data set from which the risks provided by the WHO were derived. Although it is known already that the EAR and ERR effect modifications by sex are statistically significant for the outcome "all solid cancer", it is shown here that sex modification is not statistically significant for the outcome "all solid cancer other than thyroid and breast cancer". It is also shown here that the sex-averaged solid cancer risks with and without the sex modification are very similar once breast and thyroid cancers are factored out. Some other notable model
Directory of Open Access Journals (Sweden)
Y. Kakinami
2009-08-01
Full Text Available Empirical models of Total Electron Content (TEC based on functional fitting over Taiwan (120° E, 24° N have been constructed using data of the Global Positioning System (GPS from 1998 to 2007 during geomagnetically quiet condition (D_{st}>−30 nT. The models provide TEC as functions of local time (LT, day of year (DOY and the solar activity (F, which are represented by 1–162 days mean of F10.7 and EUV. Other models based on median values have been also constructed and compared with the models based on the functional fitting. Under same values of F parameter, the models based on the functional fitting show better accuracy than those based on the median values in all cases. The functional fitting model using daily EUV is the most accurate with 9.2 TECu of root mean square error (RMS than the 15-days running median with 10.4 TECu RMS and the model of International Reference Ionosphere 2007 (IRI2007 with 14.7 TECu RMS. IRI2007 overestimates TEC when the solar activity is low, and underestimates TEC when the solar activity is high. Though average of 81 days centered running mean of F10.7 and daily F10.7 is often used as indicator of EUV, our result suggests that average of F10.7 mean from 1 to 54 day prior and current day is better than the average of 81 days centered running mean for reproduction of TEC. This paper is for the first time comparing the median based model with the functional fitting model. Results indicate the functional fitting model yielding a better performance than the median based one. Meanwhile we find that the EUV radiation is essential to derive an optimal TEC.
Optimisation of Ionic Models to Fit Tissue Action Potentials: Application to 3D Atrial Modelling
Directory of Open Access Journals (Sweden)
Amr Al Abed
2013-01-01
Full Text Available A 3D model of atrial electrical activity has been developed with spatially heterogeneous electrophysiological properties. The atrial geometry, reconstructed from the male Visible Human dataset, included gross anatomical features such as the central and peripheral sinoatrial node (SAN, intra-atrial connections, pulmonary veins, inferior and superior vena cava, and the coronary sinus. Membrane potentials of myocytes from spontaneously active or electrically paced in vitro rabbit cardiac tissue preparations were recorded using intracellular glass microelectrodes. Action potentials of central and peripheral SAN, right and left atrial, and pulmonary vein myocytes were each fitted using a generic ionic model having three phenomenological ionic current components: one time-dependent inward, one time-dependent outward, and one leakage current. To bridge the gap between the single-cell ionic models and the gross electrical behaviour of the 3D whole-atrial model, a simplified 2D tissue disc with heterogeneous regions was optimised to arrive at parameters for each cell type under electrotonic load. Parameters were then incorporated into the 3D atrial model, which as a result exhibited a spontaneously active SAN able to rhythmically excite the atria. The tissue-based optimisation of ionic models and the modelling process outlined are generic and applicable to image-based computer reconstruction and simulation of excitable tissue.
Is Model Fitting Necessary for Model-Based fMRI?
Wilson, Robert C; Niv, Yael
2015-06-01
Model-based analysis of fMRI data is an important tool for investigating the computational role of different brain regions. With this method, theoretical models of behavior can be leveraged to find the brain structures underlying variables from specific algorithms, such as prediction errors in reinforcement learning. One potential weakness with this approach is that models often have free parameters and thus the results of the analysis may depend on how these free parameters are set. In this work we asked whether this hypothetical weakness is a problem in practice. We first developed general closed-form expressions for the relationship between results of fMRI analyses using different regressors, e.g., one corresponding to the true process underlying the measured data and one a model-derived approximation of the true generative regressor. Then, as a specific test case, we examined the sensitivity of model-based fMRI to the learning rate parameter in reinforcement learning, both in theory and in two previously-published datasets. We found that even gross errors in the learning rate lead to only minute changes in the neural results. Our findings thus suggest that precise model fitting is not always necessary for model-based fMRI. They also highlight the difficulty in using fMRI data for arbitrating between different models or model parameters. While these specific results pertain only to the effect of learning rate in simple reinforcement learning models, we provide a template for testing for effects of different parameters in other models.
Soft X-ray spectral fits of Geminga with model neutron star atmospheres
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.
Finite population size effects in quasispecies models with single-peak fitness landscape
Saakian, David B.; Deem, Michael W.; Hu, Chin-Kun
2012-04-01
We consider finite population size effects for Crow-Kimura and Eigen quasispecies models with single-peak fitness landscape. We formulate accurately the iteration procedure for the finite population models, then derive the Hamilton-Jacobi equation (HJE) to describe the dynamic of the probability distribution. The steady-state solution of HJE gives the variance of the mean fitness. Our results are useful for understanding the population sizes of viruses in which the infinite population models can give reliable results for biological evolution problems.
Fit of different linear models to the lactation curve of Italian water buffalo
Directory of Open Access Journals (Sweden)
N.P.P. Macciotta
2010-01-01
Full Text Available Mathematical modelling of lactation curve by suitable functions of time, widely used in the dairy cattle industry, can represent also for buffaloes a fundamental tool for management and breeding decision, where average curves are considered, and for genetic evaluation by random regression models, where individual patterns are fitted.
Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes
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…
Human Cancer Models Initiative | Office of Cancer Genomics
The Human Cancer Models Initiative (HCMI) is an international consortium that is generating novel human tumor-derived culture models, which are annotated with genomic and clinical data. In an effort to advance cancer research and more fully understand how in vitro findings are related to clinical biology, HCMI-developed models and related data will be available as a community resource for cancer research.
Shan, Bonan; Wang, Jiang; Zhang, Lvxia; Deng, Bin; Wei, Xile
2017-02-01
In order to fit neural model’s spiking features to electrophysiological recordings, in this paper, a fitting framework based on particle swarm optimization (PSO) algorithm is proposed to estimate the model parameters in an augmented multi-timescale adaptive threshold (AugMAT) model. PSO algorithm is an advanced evolutionary calculation method based on iteration. Selecting a reasonable criterion function will ensure the effectiveness of PSO algorithm. In this work, firing rate information is used as the main spiking feature and the estimation error of firing rate is selected as the criterion for fitting. A series of simulations are presented to verify the performance of the framework. The first step is model validation; an artificial training data is introduced to test the fitting procedure. Then we talk about the suitable PSO parameters, which exhibit adequate compromise between speediness and accuracy. Lastly, this framework is used to fit the electrophysiological recordings, after three adjustment steps, the features of experimental data are translated into realistic spiking neuron model.
Modelling metabolic evolution on phenotypic fitness landscapes: a case study on C4 photosynthesis.
Heckmann, David
2015-12-01
How did the complex metabolic systems we observe today evolve through adaptive evolution? The fitness landscape is the theoretical framework to answer this question. Since experimental data on natural fitness landscapes is scarce, computational models are a valuable tool to predict landscape topologies and evolutionary trajectories. Careful assumptions about the genetic and phenotypic features of the system under study can simplify the design of such models significantly. The analysis of C4 photosynthesis evolution provides an example for accurate predictions based on the phenotypic fitness landscape of a complex metabolic trait. The C4 pathway evolved multiple times from the ancestral C3 pathway and models predict a smooth 'Mount Fuji' landscape accordingly. The modelled phenotypic landscape implies evolutionary trajectories that agree with data on modern intermediate species, indicating that evolution can be predicted based on the phenotypic fitness landscape. Future directions will have to include structural changes of metabolic fitness landscape structure with changing environments. This will not only answer important evolutionary questions about reversibility of metabolic traits, but also suggest strategies to increase crop yields by engineering the C4 pathway into C3 plants.
Can a first-order exponential decay model fit heart rate recovery after resistance exercise?
Bartels-Ferreira, Rhenan; de Sousa, Élder D; Trevizani, Gabriela A; Silva, Lilian P; Nakamura, Fábio Y; Forjaz, Cláudia L M; Lima, Jorge Roberto P; Peçanha, Tiago
2015-03-01
The time-constant of postexercise heart rate recovery (HRRτ ) obtained by fitting heart rate decay curve by a first-order exponential fitting has being used to assess cardiac autonomic recovery after endurance exercise. The feasibility of this model was not tested after resistance exercise (RE). The aim of this study was to test the goodness of fit of the first-order exponential decay model to fit heart rate recovery (HRR) after RE. Ten healthy subjects participated in the study. The experimental sessions occurred in two separated days and consisted of performance of 1 set of 10 repetitions at 50% or 80% of the load achieved on the one-repetition maximum test [low-intensity (LI) and high-intensity (HI) sessions, respectively]. Heart rate (HR) was continuously registered before and during exercise and also for 10 min of recovery. A monoexponential equation was used to fit the HRR curve during the postexercise period using different time windows (i.e. 30, 60, 90, … 600 s). For each time window, (i) HRRτ was calculated and (ii) variation of HR explained by the model (R(2) goodness of fit index) was assessed. The HRRτ showed stabilization from 360 and 420 s on LI and HI, respectively. Acceptable R(2) values were observed from the 360 s on LI (R(2) > 0.65) and at all tested time windows on HI (R(2) > 0.75). In conclusion, this study showed that using a minimum length of monitoring (~420 s) HRR after RE can be adequately modelled by a first-order exponential fitting.
Does the Foreign Income Shock in a Small Open Economy DSGE Model Fit Croatian Data?
Arčabić, Vladimir; Globan, Tomislav; Nadoveza, Ozana; Rogić Dumančić, Lucija; Tica, Josip
2016-01-01
The paper compares theoretical impulse response functions from a DSGE model for a small open economy with an empirical VAR model estimated for the Croatian economy. The theoretical model fits the data well as long as monetary policy is modelled as a fixed exchange rate regime. The paper considers only a foreign output gap shock. A positive foreign shock increases domestic GDP and prices and decreases terms of trade, which is in compliance with theoretical assumptions. Interest rates behave di...
The Predicting Model of E-commerce Site Based on the Ideas of Curve Fitting
Tao, Zhang; Li, Zhang; Dingjun, Chen
On the basis of the idea of the second multiplication curve fitting, the number and scale of Chinese E-commerce site is analyzed. A preventing increase model is introduced in this paper, and the model parameters are solved by the software of Matlab. The validity of the preventing increase model is confirmed though the numerical experiment. The experimental results show that the precision of preventing increase model is ideal.
A goodness-of-fit test for occupancy models with correlated within-season revisits
Wright, Wilson; Irvine, Kathryn M.; Rodhouse, Thomas J.
2016-01-01
Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection-level component of the model (e.g., first-order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodnessof- fit test using a chi-square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie– Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov-structured detection-level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness-of-fit test and
Mathematical Modeling of Allelopathy. III. A Model for Curve-Fitting Allelochemical Dose Responses
Liu, De Li; An, Min; Johnson, Ian R.; Lovett, John V.
2003-01-01
Bioassay techniques are often used to study the effects of allelochemicals on plant processes, and it is generally observed that the processes are stimulated at low allelochemical concentrations and inhibited as the concentrations increase. A simple empirical model is presented to analyze this type of response. The stimulation-inhibition properties of allelochemical-dose responses can be described by the parameters in the model. The indices, p% reductions, are calculated to assess the allelochemical effects. The model is compared with experimental data for the response of lettuce seedling growth to Centaurepensin, the olfactory response of weevil larvae to α-terpineol, and the responses of annual ryegrass (Lolium multiflorum Lam.), creeping red fescue (Festuca rubra L., cv. Ensylva), Kentucky bluegrass (Poa pratensis L., cv. Kenblue), perennial ryegrass (L. perenne L., cv. Manhattan), and Rebel tall fescue (F. arundinacea Schreb) seedling growth to leachates of Rebel and Kentucky 31 tall fescue. The results show that the model gives a good description to observations and can be used to fit a wide range of dose responses. Assessments of the effects of leachates of Rebel and Kentucky 31 tall fescue clearly differentiate the properties of the allelopathic sources and the relative sensitivities of indicators such as the length of root and leaf. PMID:19330111
Mathematical Modeling of Allelopathy. III. A Model for Curve-Fitting Allelochemical Dose Responses.
Liu, De Li; An, Min; Johnson, Ian R; Lovett, John V
2003-01-01
Bioassay techniques are often used to study the effects of allelochemicals on plant processes, and it is generally observed that the processes are stimulated at low allelochemical concentrations and inhibited as the concentrations increase. A simple empirical model is presented to analyze this type of response. The stimulation-inhibition properties of allelochemical-dose responses can be described by the parameters in the model. The indices, p% reductions, are calculated to assess the allelochemical effects. The model is compared with experimental data for the response of lettuce seedling growth to Centaurepensin, the olfactory response of weevil larvae to alpha-terpineol, and the responses of annual ryegrass (Lolium multiflorum Lam.), creeping red fescue (Festuca rubra L., cv. Ensylva), Kentucky bluegrass (Poa pratensis L., cv. Kenblue), perennial ryegrass (L. perenne L., cv. Manhattan), and Rebel tall fescue (F. arundinacea Schreb) seedling growth to leachates of Rebel and Kentucky 31 tall fescue. The results show that the model gives a good description to observations and can be used to fit a wide range of dose responses. Assessments of the effects of leachates of Rebel and Kentucky 31 tall fescue clearly differentiate the properties of the allelopathic sources and the relative sensitivities of indicators such as the length of root and leaf.
A new analytical edge spread function fitting model for modulation transfer function measurement
Institute of Scientific and Technical Information of China (English)
Tiecheng Li; Huajun Feng; Zhihai Xu
2011-01-01
@@ We propose a new analytical edge spread function (ESF) fitting model to measure the modulation transfer function (MTF).The ESF data obtained from a slanted-edge image are fitted to our model through the non-linear least squares (NLLSQ) method.The differentiation of the ESF yields the line spread function (LSF), the Fourier transform of which gives the profile of two-dimensional MTF.Compared with the previous methods, the MTF estimate determined by our method conforms more closely to the reference.A practical application of our MTF measurement in degraded image restoration also validates the accuracy of our model.%We propose a new analytical edge spread function (ESF) fitting model to measure the modulation transfer function (MTF). The ESF data obtained from a slanted-edge image are fitted to our model through the non-linear least squares (NLLSQ) method. The differentiation of the ESF yields the line spread function (LSF), the Fourier transform of which gives the profile of two-dimensional MTF. Compared with the previous methods, the MTF estimate determined by our method conforms more closely to the reference. A practical application of our MTF measurement in degraded image restoration also validates the accuracy of our model.
Directory of Open Access Journals (Sweden)
Thomas J Matthews
2014-06-01
Full Text Available A species abundance distribution (SAD characterises patterns in the commonness and rarity of all species within an ecological community. As such, the SAD provides the theoretical foundation for a number of other biogeographical and macroecological patterns, such as the species–area relationship, as well as being an interesting pattern in its own right. While there has been resurgence in the study of SADs in the last decade, less focus has been placed on methodology in SAD research, and few attempts have been made to synthesise the vast array of methods which have been employed in SAD model evaluation. As such, our review has two aims. First, we provide a general overview of SADs, including descriptions of the commonly used distributions, plotting methods and issues with evaluating SAD models. Second, we review a number of recent advances in SAD model fitting and comparison. We conclude by providing a list of recommendations for fitting and evaluating SAD models. We argue that it is time for SAD studies to move away from many of the traditional methods available for fitting and evaluating models, such as sole reliance on the visual examination of plots, and embrace statistically rigorous techniques. In particular, we recommend the use of both goodness-of-fit tests and model-comparison analyses because each provides unique information which one can use to draw inferences.
Local and omnibus goodness-of-fit tests in classical measurement error models
Ma, Yanyuan
2010-09-14
We consider functional measurement error models, i.e. models where covariates are measured with error and yet no distributional assumptions are made about the mismeasured variable. We propose and study a score-type local test and an orthogonal series-based, omnibus goodness-of-fit test in this context, where no likelihood function is available or calculated-i.e. all the tests are proposed in the semiparametric model framework. We demonstrate that our tests have optimality properties and computational advantages that are similar to those of the classical score tests in the parametric model framework. The test procedures are applicable to several semiparametric extensions of measurement error models, including when the measurement error distribution is estimated non-parametrically as well as for generalized partially linear models. The performance of the local score-type and omnibus goodness-of-fit tests is demonstrated through simulation studies and analysis of a nutrition data set.
Comparing PyMorph and SDSS photometry. I. Background sky and model fitting effects
Fischer, J.-L.; Bernardi, M.; Meert, A.
2017-01-01
A number of recent estimates of the total luminosities of galaxies in the SDSS are significantly larger than those reported by the SDSS pipeline. This is because of a combination of three effects: one is simply a matter of defining the scale out to which one integrates the fit when defining the total luminosity, and amounts on average to ≤0.1 mags even for the most luminous galaxies. The other two are less trivial and tend to be larger; they are due to differences in how the background sky is estimated and what model is fit to the surface brightness profile. We show that PyMorph sky estimates are fainter than those of the SDSS DR7 or DR9 pipelines, but are in excellent agreement with the estimates of Blanton et al. (2011). Using the SDSS sky biases luminosities by more than a few tenths of a magnitude for objects with half-light radii ≥7 arcseconds. In the SDSS main galaxy sample these are typically luminous galaxies, so they are not necessarily nearby. This bias becomes worse when allowing the model more freedom to fit the surface brightness profile. When PyMorph sky values are used, then two component Sersic-Exponential fits to E+S0s return more light than single component deVaucouleurs fits (up to ˜0.2 mag), but less light than single Sersic fits (0.1 mag). Finally, we show that PyMorph fits of Meert et al. (2015) to DR7 data remain valid for DR9 images. Our findings show that, especially at large luminosities, these PyMorph estimates should be preferred to the SDSS pipeline values.
The FIT 2.0 Model - Fuel-cycle Integration and Tradeoffs
Energy Technology Data Exchange (ETDEWEB)
Steven J. Piet; Nick R. Soelberg; Layne F. Pincock; Eric L. Shaber; Gregory M Teske
2011-06-01
All mass streams from fuel separation and fabrication are products that must meet some set of product criteria – fuel feedstock impurity limits, waste acceptance criteria (WAC), material storage (if any), or recycle material purity requirements such as zirconium for cladding or lanthanides for industrial use. These must be considered in a systematic and comprehensive way. The FIT model and the “system losses study” team that developed it [Shropshire2009, Piet2010b] are steps by the Fuel Cycle Technology program toward an analysis that accounts for the requirements and capabilities of each fuel cycle component, as well as major material flows within an integrated fuel cycle. This will help the program identify near-term R&D needs and set longer-term goals. This report describes FIT 2, an update of the original FIT model.[Piet2010c] FIT is a method to analyze different fuel cycles; in particular, to determine how changes in one part of a fuel cycle (say, fuel burnup, cooling, or separation efficiencies) chemically affect other parts of the fuel cycle. FIT provides the following: Rough estimate of physics and mass balance feasibility of combinations of technologies. If feasibility is an issue, it provides an estimate of how performance would have to change to achieve feasibility. Estimate of impurities in fuel and impurities in waste as function of separation performance, fuel fabrication, reactor, uranium source, etc.
An alternative model of cancer cell growth and metastasis.
Vaidya, Jayant S
2007-04-01
I propose an alternative model of cancer in which metastasis need not all arise out of spread from the "original" tumour. The model assumes that cancer cells arise from stem cells that best grow in the organ of their differentiation. When the internal milieu allows it they also grow at other sites as well, thus complementing the conventional (spreading) metastatic process. Several phenomena in the natural history of cancer, especially breast cancer, that challenge the conventional model, fit well after inclusion of the new model. These are (a) a very modest benefit of screening (b) frequent sparing of lungs from haematogenous metastasis (c) presence of occult cancers in autopsy studies (d) only a modest effect of local treatment (e) relative ineffectiveness of high-dose chemotherapy (f) constant time between surgery and peak of hazard of relapse irrespective of stage of the tumour. All these phenomena are much easier to explain when one rejects the dogma that all metastasis arise only from the primary tumour. This paper is aimed only to suggest an alternative perspective of natural history of solid tumours--to stimulate research on the complex internal milieu that allows cancer cells to develop in new light.
Directory of Open Access Journals (Sweden)
Javier Macias-Guarasa
2012-10-01
Full Text Available This paper presents a novel approach for indoor acoustic source localization using sensor arrays. The proposed solution starts by defining a generative model, designed to explain the acoustic power maps obtained by Steered Response Power (SRP strategies. An optimization approach is then proposed to fit the model to real input SRP data and estimate the position of the acoustic source. Adequately fitting the model to real SRP data, where noise and other unmodelled effects distort the ideal signal, is the core contribution of the paper. Two basic strategies in the optimization are proposed. First, sparse constraints in the parameters of the model are included, enforcing the number of simultaneous active sources to be limited. Second, subspace analysis is used to filter out portions of the input signal that cannot be explained by the model. Experimental results on a realistic speech database show statistically significant localization error reductions of up to 30% when compared with the SRP-PHAT strategies.
Drosophila models for cancer research.
Vidal, Marcos; Cagan, Ross L
2006-02-01
Drosophila is a model system for cancer research. Investigation with fruit flies has facilitated a number of important recent discoveries in the field: the hippo signaling pathway, which coordinates cell proliferation and death to achieve normal tissue size; 'social' behaviors of cells, including cell competition and apoptosis-induced compensatory proliferation, that help ensure normal tissue size; and a growing understanding of how oncogenes and tumor suppressors cooperate to achieve tumor growth and metastasis in situ. In the future, Drosophila models can be extended beyond basic research in the search for human therapeutics.
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.
Wang, Chee Keng John; Pyun, Do Young; Liu, Woon Chia; Lim, Boon San Coral; Li, Fuzhong
2013-01-01
Using a multilevel latent growth curve modeling (LGCM) approach, this study examined longitudinal change in levels of physical fitness performance over time (i.e. four years) in young adolescents aged from 12-13 years. The sample consisted of 6622 students from 138 secondary schools in Singapore. Initial analyses found between-school variation on…
Fit Gap Analysis – The Role of Business Process Reference Models
Directory of Open Access Journals (Sweden)
Dejan Pajk
2013-12-01
Full Text Available Enterprise resource planning (ERP systems support solutions for standard business processes such as financial, sales, procurement and warehouse. In order to improve the understandability and efficiency of their implementation, ERP vendors have introduced reference models that describe the processes and underlying structure of an ERP system. To select and successfully implement an ERP system, the capabilities of that system have to be compared with a company’s business needs. Based on a comparison, all of the fits and gaps must be identified and further analysed. This step usually forms part of ERP implementation methodologies and is called fit gap analysis. The paper theoretically overviews methods for applying reference models and describes fit gap analysis processes in detail. The paper’s first contribution is its presentation of a fit gap analysis using standard business process modelling notation. The second contribution is the demonstration of a process-based comparison approach between a supply chain process and an ERP system process reference model. In addition to its theoretical contributions, the results can also be practically applied to projects involving the selection and implementation of ERP systems.
On Fitting Nonlinear Latent Curve Models to Multiple Variables Measured Longitudinally
Blozis, Shelley A.
2007-01-01
This article shows how nonlinear latent curve models may be fitted for simultaneous analysis of multiple variables measured longitudinally using Mx statistical software. Longitudinal studies often involve observation of several variables across time with interest in the associations between change characteristics of different variables measured…
Impact of Missing Data on Person-Model Fit and Person Trait Estimation
Zhang, Bo; Walker, Cindy M.
2008-01-01
The purpose of this research was to examine the effects of missing data on person-model fit and person trait estimation in tests with dichotomous items. Under the missing-completely-at-random framework, four missing data treatment techniques were investigated including pairwise deletion, coding missing responses as incorrect, hotdeck imputation,…
Small-sample robust estimators of noncentrality-based and incremental model fit
Boomsma, Anne; Herzog, W.
2009-01-01
Traditional estimators of fit measures based on the noncentral chi-square distribution (root mean square error of approximation [RMSEA], Steiger's , etc.) tend to overreject acceptable models when the sample size is small. To handle this problem, it is proposed to employ Bartlett's (1950), Yuan's (2
Checking the Adequacy of Fit of Models from Split-Plot Designs
DEFF Research Database (Denmark)
Almini, A. A.; Kulahci, Murat; Montgomery, D. C.
2009-01-01
One of the main features that distinguish split-plot experiments from other experiments is that they involve two types of experimental errors: the whole-plot (WP) error and the subplot (SP) error. Taking this into consideration is very important when computing measures of adequacy of fit for split......-plot models. In this article, we propose the computation of two R-2, R-2-adjusted, prediction error sums of squares (PRESS), and R-2-prediction statistics to measure the adequacy of fit for the WP and the SP submodels in a split-plot design. This is complemented with the graphical analysis of the two types...... of errors to check for any violation of the underlying assumptions and the adequacy of fit of split-plot models. Using examples, we show how computing two measures of model adequacy of fit for each split-plot design model is appropriate and useful as they reveal whether the correct WP and SP effects have...
Spherical Cancer Models in Tumor Biology
Directory of Open Access Journals (Sweden)
Louis-Bastien Weiswald
2015-01-01
Full Text Available Three-dimensional (3D in vitro models have been used in cancer research as an intermediate model between in vitro cancer cell line cultures and in vivo tumor. Spherical cancer models represent major 3D in vitro models that have been described over the past 4 decades. These models have gained popularity in cancer stem cell research using tumorospheres. Thus, it is crucial to define and clarify the different spherical cancer models thus far described. Here, we focus on in vitro multicellular spheres used in cancer research. All these spherelike structures are characterized by their well-rounded shape, the presence of cancer cells, and their capacity to be maintained as free-floating cultures. We propose a rational classification of the four most commonly used spherical cancer models in cancer research based on culture methods for obtaining them and on subsequent differences in sphere biology: the multicellular tumor spheroid model, first described in the early 70s and obtained by culture of cancer cell lines under nonadherent conditions; tumorospheres, a model of cancer stem cell expansion established in a serum-free medium supplemented with growth factors; tissue-derived tumor spheres and organotypic multicellular spheroids, obtained by tumor tissue mechanical dissociation and cutting. In addition, we describe their applications to and interest in cancer research; in particular, we describe their contribution to chemoresistance, radioresistance, tumorigenicity, and invasion and migration studies. Although these models share a common 3D conformation, each displays its own intrinsic properties. Therefore, the most relevant spherical cancer model must be carefully selected, as a function of the study aim and cancer type.
Directory of Open Access Journals (Sweden)
Rita Yi Man Li
2012-03-01
Full Text Available Entrepreneurs have always born the risk of running their business. They reap a profit in return for their risk taking and work. Housing developers are no different. In many countries, such as Australia, the United Kingdom and the United States, they interpret the tastes of the buyers and provide the dwellings they develop with basic fittings such as floor and wall coverings, bathroom fittings and kitchen cupboards. In mainland China, however, in most of the developments, units or houses are sold without floor or wall coverings, kitchen or bathroom fittings. What is the motive behind this choice? This paper analyses the factors affecting housing developers’ decisions to provide fittings based on 1701 housing developments in Hangzhou, Chongqing and Hangzhou using a Probit model. The results show that developers build a higher proportion of bare units in mainland China when: 1 there is shortage of housing; 2 land costs are high so that the comparative costs of providing fittings become relatively low.
Fitting parametric models of diffusion MRI in regions of partial volume
Eaton-Rosen, Zach; Cardoso, M. J.; Melbourne, Andrew; Orasanu, Eliza; Bainbridge, Alan; Kendall, Giles S.; Robertson, Nicola J.; Marlow, Neil; Ourselin, Sebastien
2016-03-01
Regional analysis is normally done by fitting models per voxel and then averaging over a region, accounting for partial volume (PV) only to some degree. In thin, folded regions such as the cerebral cortex, such methods do not work well, as the partial volume confounds parameter estimation. Instead, we propose to fit the models per region directly with explicit PV modeling. In this work we robustly estimate region-wise parameters whilst explicitly accounting for partial volume effects. We use a high-resolution segmentation from a T1 scan to assign each voxel in the diffusion image a probabilistic membership to each of k tissue classes. We rotate the DW signal at each voxel so that it aligns with the z-axis, then model the signal at each voxel as a linear superposition of a representative signal from each of the k tissue types. Fitting involves optimising these representative signals to best match the data, given the known probabilities of belonging to each tissue type that we obtained from the segmentation. We demonstrate this method improves parameter estimation in digital phantoms for the diffusion tensor (DT) and `Neurite Orientation Dispersion and Density Imaging' (NODDI) models. The method provides accurate parameter estimates even in regions where the normal approach fails completely, for example where partial volume is present in every voxel. Finally, we apply this model to brain data from preterm infants, where the thin, convoluted, maturing cortex necessitates such an approach.
A flexible, interactive software tool for fitting the parameters of neuronal models
Directory of Open Access Journals (Sweden)
Péter eFriedrich
2014-07-01
Full Text Available The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problem of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting
Fitting a mixture model by expectation maximization to discover motifs in biopolymers
Energy Technology Data Exchange (ETDEWEB)
Bailey, T.L.; Elkan, C. [Univ. of California, La Jolla, CA (United States)
1994-12-31
The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expectation maximization to fit a two-component finite mixture model to the set of sequences. Multiple motifs are found by fitting a mixture model to the data, probabilistically erasing the occurrences of the motif thus found, and repeating the process to find successive motifs. The algorithm requires only a set of unaligned sequences and a number specifying the width of the motifs as input. It returns a model of each motif and a threshold which together can be used as a Bayes-optimal classifier for searching for occurrences of the motif in other databases. The algorithm estimates how many times each motif occurs in each sequence in the dataset and outputs an alignment of the occurrences of the motif. The algorithm is capable of discovering several different motifs with differing numbers of occurrences in a single dataset.
Kompaneets Model Fitting of the Orion-Eridanus Superbubble II: Thinking Outside of Barnard's Loop
Pon, Andy; Alves, Joao; Bally, John; Basu, Shantanu; Tielens, Alexander G G M
2016-01-01
The Orion star-forming region is the nearest active high-mass star-forming region and has created a large superbubble, the Orion-Eridanus superbubble. Recent work by Ochsendorf et al. (2015) has extended the accepted boundary of the superbubble. We fit Kompaneets models of superbubbles expanding in exponential atmospheres to the new, larger shape of the Orion-Eridanus superbubble. We find that this larger morphology of the superbubble is consistent with the evolution of the superbubble being primarily controlled by expansion into the exponential Galactic disk ISM if the superbubble is oriented with the Eridanus side farther from the Sun than the Orion side. Unlike previous Kompaneets model fits that required abnormally small scale heights for the Galactic disk (<40 pc), we find morphologically consistent models with scale heights of 80 pc, similar to that expected for the Galactic disk.
Shekhar, Karthik; Ruberman, Claire F.; Ferguson, Andrew L.; Barton, John P.; Kardar, Mehran; Chakraborty, Arup K.
2017-01-01
Mutational escape from vaccine-induced immune responses has thwarted the development of a successful vaccine against AIDS, whose causative agent is HIV, a highly mutable virus. Knowing the virus’ fitness as a function of its proteomic sequence can enable rational design of potent vaccines, as this information can focus vaccine-induced immune responses to target mutational vulnerabilities of the virus. Spin models have been proposed as a means to infer intrinsic fitness landscapes of HIV proteins from patient-derived viral protein sequences. These sequences are the product of nonequilibrium viral evolution driven by patient-specific immune responses and are subject to phylogenetic constraints. How can such sequence data allow inference of intrinsic fitness landscapes? We combined computer simulations and variational theory á la Feynman to show that, in most circumstances, spin models inferred from patient-derived viral sequences reflect the correct rank order of the fitness of mutant viral strains. Our findings are relevant for diverse viruses. PMID:24483484
Double-sigmoid model for fitting fatigue profiles in mouse fast- and slow-twitch muscle.
Cairns, S P; Robinson, D M; Loiselle, D S
2008-07-01
We present a curve-fitting approach that permits quantitative comparisons of fatigue profiles obtained with different stimulation protocols in isolated slow-twitch soleus and fast-twitch extensor digitorum longus (EDL) muscles of mice. Profiles from our usual stimulation protocol (125 Hz for 500 ms, evoked once every second for 100-300 s) could be fitted by single-term functions (sigmoids or exponentials) but not by a double exponential. A clearly superior fit, as confirmed by the Akaiki Information Criterion, was achieved using a double-sigmoid function. Fitting accuracy was exceptional; mean square errors were typically 0.9995. The first sigmoid (early fatigue) involved approximately 10% decline of isometric force to an intermediate plateau in both muscle types; the second sigmoid (late fatigue) involved a reduction of force to a final plateau, the decline being 83% of initial force in EDL and 63% of initial force in soleus. The maximal slope of each sigmoid was seven- to eightfold greater in EDL than in soleus. The general applicability of the model was tested by fitting profiles with a severe force loss arising from repeated tetanic stimulation evoked at different frequencies or rest periods, or with excitation via nerve terminals in soleus. Late fatigue, which was absent at 30 Hz, occurred earlier and to a greater extent at 125 than 50 Hz. The model captured small changes in rate of late fatigue for nerve terminal versus sarcolemmal stimulation. We conclude that a double-sigmoid expression is a useful and accurate model to characterize fatigue in isolated muscle preparations.
Butt, Z.; Haberman, S
2009-01-01
We implement a specialised iterative regression methodology in R for the analysis of age-period mortality data based on a class of generalised Lee-Carter (LC) type modelling structures. The LC-based modelling frameworks is viewed in the current literature as among the most efficient and transparent methods of modelling and projecting mortality improvements. Thus, we make use of the modelling approach discussed in Renshaw and Haberman (2006), which extends the basic LC model and proposes to ma...
Fitting a Bivariate Measurement Error Model for Episodically Consumed Dietary Components
Zhang, Saijuan
2011-01-06
There has been great public health interest in estimating usual, i.e., long-term average, intake of episodically consumed dietary components that are not consumed daily by everyone, e.g., fish, red meat and whole grains. Short-term measurements of episodically consumed dietary components have zero-inflated skewed distributions. So-called two-part models have been developed for such data in order to correct for measurement error due to within-person variation and to estimate the distribution of usual intake of the dietary component in the univariate case. However, there is arguably much greater public health interest in the usual intake of an episodically consumed dietary component adjusted for energy (caloric) intake, e.g., ounces of whole grains per 1000 kilo-calories, which reflects usual dietary composition and adjusts for different total amounts of caloric intake. Because of this public health interest, it is important to have models to fit such data, and it is important that the model-fitting methods can be applied to all episodically consumed dietary components.We have recently developed a nonlinear mixed effects model (Kipnis, et al., 2010), and have fit it by maximum likelihood using nonlinear mixed effects programs and methodology (the SAS NLMIXED procedure). Maximum likelihood fitting of such a nonlinear mixed model is generally slow because of 3-dimensional adaptive Gaussian quadrature, and there are times when the programs either fail to converge or converge to models with a singular covariance matrix. For these reasons, we develop a Monte-Carlo (MCMC) computation of fitting this model, which allows for both frequentist and Bayesian inference. There are technical challenges to developing this solution because one of the covariance matrices in the model is patterned. Our main application is to the National Institutes of Health (NIH)-AARP Diet and Health Study, where we illustrate our methods for modeling the energy-adjusted usual intake of fish and whole
The mathematics of cancer: integrating quantitative models.
Altrock, Philipp M; Liu, Lin L; Michor, Franziska
2015-12-01
Mathematical modelling approaches have become increasingly abundant in cancer research. The complexity of cancer is well suited to quantitative approaches as it provides challenges and opportunities for new developments. In turn, mathematical modelling contributes to cancer research by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research in cancer biology.
Kinetic modelling of RDF pyrolysis: Model-fitting and model-free approaches.
Çepelioğullar, Özge; Haykırı-Açma, Hanzade; Yaman, Serdar
2016-02-01
In this study, refuse derived fuel (RDF) was selected as solid fuel and it was pyrolyzed in a thermal analyzer from room temperature to 900°C at heating rates of 5, 10, 20, and 50°C/min in N2 atmosphere. The obtained thermal data was used to calculate the kinetic parameters using Coats-Redfern, Friedman, Flylnn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods. As a result of Coats-Redfern model, decomposition process was assumed to be four independent reactions with different reaction orders. On the other hand, model free methods demonstrated that activation energy trend had similarities for the reaction progresses of 0.1, 0.2-0.7 and 0.8-0.9. The average activation energies were found between 73-161kJ/mol and it is possible to say that FWO and KAS models produced closer results to the average activation energies compared to Friedman model. Experimental studies showed that RDF may be a sustainable and promising feedstock for alternative processes in terms of waste management strategies.
Tectonic plate under a localized boundary stress: fitting of a zero-range solvable model
Petrova, L
2008-01-01
We suggest a method of fitting of a zero-range model of a tectonic plate under a boundary stress on the basis of comparison of the theoretical formulae for the corresponding eigenfunctions/eigenvalues with the results extraction under monitoring, in the remote zone, of non-random (regular) oscillations of the Earth with periods 0.2-6 hours, on the background seismic process, in case of low seismic activity. Observations of changes of the characteristics of the oscillations (frequency, amplitude and polarization) in course of time, together with the theoretical analysis of the fitted model, would enable us to localize the stressed zone on the boundary of the plate and estimate the risk of a powerful earthquake at the zone.
Balbuena Ortega, A; Arroyo Carrasco, M L; Méndez Otero, M M; Gayou, V L; Delgado Macuil, R; Martínez Gutiérrez, H; Iturbe Castillo, M D
2014-12-12
In this paper, the nonlinear refractive index of colloidal gold nanoparticles under continuous wave illumination is investigated with the z-scan technique. Gold nanoparticles were synthesized using ascorbic acid as reductant, phosphates as stabilizer and cetyltrimethylammonium chloride (CTAC) as surfactant agent. The nanoparticle size was controlled with the CTAC concentration. Experiments changing incident power and sample concentration were done. The experimental z-scan results were fitted with three models: thermal lens, aberrant thermal lens and the nonlocal model. It is shown that the nonlocal model reproduces with exceptionally good agreement; the obtained experimental behaviour.
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.
Effects of new mutations on fitness: insights from models and data.
Bataillon, Thomas; Bailey, Susan F
2014-07-01
The rates and properties of new mutations affecting fitness have implications for a number of outstanding questions in evolutionary biology. Obtaining estimates of mutation rates and effects has historically been challenging, and little theory has been available for predicting the distribution of fitness effects (DFE); however, there have been recent advances on both fronts. Extreme-value theory predicts the DFE of beneficial mutations in well-adapted populations, while phenotypic fitness landscape models make predictions for the DFE of all mutations as a function of the initial level of adaptation and the strength of stabilizing selection on traits underlying fitness. Direct experimental evidence confirms predictions on the DFE of beneficial mutations and favors distributions that are roughly exponential but bounded on the right. A growing number of studies infer the DFE using genomic patterns of polymorphism and divergence, recovering a wide range of DFE. Future work should be aimed at identifying factors driving the observed variation in the DFE. We emphasize the need for further theory explicitly incorporating the effects of partial pleiotropy and heterogeneity in the environment on the expected DFE.
Institute of Scientific and Technical Information of China (English)
Xin; YAO; Min; ZHANG
2014-01-01
The mathematical model is often used for fitting the trend of changes in cultivated land resources in the land use planning,but the fitting effect is different in different study areas. In this paper,we take two geographically adjacent cities with great differences in the economic development model,Xinghua City and Jingjiang City,as the research object. Using logarithmic model( M1),Kuznets model( M2),logistic model( M3) and multivariate linear model( M4),we fit the process of changes in cultivated land resources during the period 1980- 2009,and compare the differences in the fitting effect between different models. In terms of the model fitting effect in Xinghua City,it is in the order of M3 > M4 > M1 > M2,which is related to the fact that the local areas lay great emphasis on agricultural development,and pay close attention to ensuring the cultivated land area; in terms of the model fitting effect in Jingjiang City,it is in the order of M1 > M3 > M4 > M2,and the deep-seated cause is that its development model is dominated by extended trade expansion,and the level of intensive land use is constantly improved. In addition,we discuss the multi-stage characteristics of changes in cultivated land resources,and propose a solution of using the same model to simulate in various phases. The research results in Jingjiang City show that the coefficient of determination in the first phase( R2=0. 958) and the standard error( SE = 0. 261) are both better than those of the original model( R2= 0. 945,SE = 0. 312); the coefficient of determination in the second phase is slightly low( R2= 0. 851),but the standard error is greatly improved( SE = 0. 137). Compared with the research conclusions of other scholars,it can be believed that this method can better solve the problems that the scatter plot of logistic model presents wave-shape and the scatter plot of Kuznets model presents " M"-shape,in order to improve the applicability of mathematical models.
SCAN-based hybrid and double-hybrid density functionals from models without fitted parameters
Hui, Kerwin; Chai, Jeng-Da
2015-01-01
By incorporating the nonempirical SCAN semilocal density functional [Sun, Ruzsinszky, and Perdew, Phys. Rev. Lett. 115, 036402 (2015)] in the underlying expression of four existing hybrid and double-hybrid models, we propose one hybrid (SCAN0) and three double-hybrid (SCAN0-DH, SCAN-QIDH, and SCAN0-2) density functionals, which are free from any fitted parameters. The SCAN-based double-hybrid functionals consistently outperform their parent SCAN semilocal functional for self-interaction probl...
Bereczkei, Tamas; Mesko, Norbert
2007-01-01
Multiple Fitness Model states that attractiveness varies across multiple dimensions, with each feature representing a different aspect of mate value. In the present study, male raters judged the attractiveness of young females with neotenous and mature facial features, with various hair lengths. Results revealed that the physical appearance of long-haired women was rated high, regardless of their facial attractiveness being valued high or low. Women rated as most attractive were those whose f...
Model fitting of kink waves in the solar atmosphere: Gaussian damping and time-dependence
Morton, R J
2016-01-01
{Observations of the solar atmosphere have shown that magnetohydrodynamic waves are ubiquitous throughout. Improvements in instrumentation and the techniques used for measurement of the waves now enables subtleties of competing theoretical models to be compared with the observed waves behaviour. Some studies have already begun to undertake this process. However, the techniques employed for model comparison have generally been unsuitable and can lead to erroneous conclusions about the best model. The aim here is to introduce some robust statistical techniques for model comparison to the solar waves community, drawing on the experiences from other areas of astrophysics. In the process, we also aim to investigate the physics of coronal loop oscillations. } {The methodology exploits least-squares fitting to compare models to observational data. We demonstrate that the residuals between the model and observations contain significant information about the ability for the model to describe the observations, and show...
Cheng, Yuan-Chieh; Chen, Jia-Hong; Chang, Rong-Jie; Wang, Chung-Yen; Hsu, Wei-Yao; Wang, Pei-Jen
2015-09-01
Contact lenses are typically measured by the wet-box method because of the high optical power resulting from the anterior central curvature of cornea, even though the back vertex power of the lenses are small. In this study, an optical measurement system based on the Shack-Hartmann wavefront principle was established to investigate the aberrations of soft contact lenses. Fitting conditions were micmicked to study the optical design of an eye model with various topographical shapes in the anterior cornea. Initially, the contact lenses were measured by the wet-box method, and then by fitting the various topographical shapes of cornea to the eye model. In addition, an optics simulation program was employed to determine the sources of errors and assess the accuracy of the system. Finally, samples of soft contact lenses with various Diopters were measured; and, both simulations and experimental results were compared for resolving the controversies of fitting contact lenses to an eye model for optical measurements. More importantly, the results show that the proposed system can be employed for study of primary aberrations in contact lenses.
Efficient parallel implementation of active appearance model fitting algorithm on GPU.
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
Directory of Open Access Journals (Sweden)
Jinwei Wang
2014-01-01
Full Text Available 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.
Directory of Open Access Journals (Sweden)
Joshua M. Diamond
2016-01-01
Full Text Available The conserved nature of sleep in Drosophila has allowed the fruit fly to emerge in the last decade as a powerful model organism in which to study sleep. Recent sleep studies in Drosophila have focused on the discovery and characterization of hyposomnolent mutants. One common feature of these animals is a change in sleep architecture: sleep bout count tends to be greater, and sleep bout length lower, in hyposomnolent mutants. I propose a mathematical model, produced by least-squares nonlinear regression to fit the form Y = aX∧b, which can explain sleep behavior in the healthy animal as well as previously-reported changes in total sleep and sleep architecture in hyposomnolent mutants. This model, fit to sleep data, yields coefficient of determination R squared, which describes goodness of fit. R squared is lower, as compared to control, in hyposomnolent mutants insomniac and fumin. My findings raise the possibility that low R squared is a feature of all hyposomnolent mutants, not just insomniac and fumin. If this were the case, R squared could emerge as a novel means by which sleep researchers might assess sleep dysfunction.
Cancer progression modeling using static sample data.
Sun, Yijun; Yao, Jin; Nowak, Norma J; Goodison, Steve
2014-01-01
As molecular profiling data continues to accumulate, the design of integrative computational analyses that can provide insights into the dynamic aspects of cancer progression becomes feasible. Here, we present a novel computational method for the construction of cancer progression models based on the analysis of static tumor samples. We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data. Our findings support a linear, branching model for breast cancer progression. An interactive model facilitates the identification of key molecular events in the advance of disease to malignancy.
Cardinal, Bradley J.; Cardinal, Marita K.
2002-01-01
Compared the role modeling attitudes and physical activity and fitness promoting behaviors of undergraduate students majoring in physical education and in elementary education. Student teacher surveys indicated that physical education majors had more positive attitudes toward role modeling physical activity and fitness promoting behaviors and…
Measuring fit of sequence data to phylogenetic model: gain of power using marginal tests.
Waddell, Peter J; Ota, Rissa; Penny, David
2009-10-01
Testing fit of data to model is fundamentally important to any science, but publications in the field of phylogenetics rarely do this. Such analyses discard fundamental aspects of science as prescribed by Karl Popper. Indeed, not without cause, Popper (Unended quest: an intellectual autobiography. Fontana, London, 1976) once argued that evolutionary biology was unscientific as its hypotheses were untestable. Here we trace developments in assessing fit from Penny et al. (Nature 297:197-200, 1982) to the present. We compare the general log-likelihood ratio (the G or G (2) statistic) statistic between the evolutionary tree model and the multinomial model with that of marginalized tests applied to an alignment (using placental mammal coding sequence data). It is seen that the most general test does not reject the fit of data to model (P approximately 0.5), but the marginalized tests do. Tests on pairwise frequency (F) matrices, strongly (P < 0.001) reject the most general phylogenetic (GTR) models commonly in use. It is also clear (P < 0.01) that the sequences are not stationary in their nucleotide composition. Deviations from stationarity and homogeneity seem to be unevenly distributed amongst taxa; not necessarily those expected from examining other regions of the genome. By marginalizing the 4( t ) patterns of the i.i.d. model to observed and expected parsimony counts, that is, from constant sites, to singletons, to parsimony informative characters of a minimum possible length, then the likelihood ratio test regains power, and it too rejects the evolutionary model with P < 0.001. Given such behavior over relatively recent evolutionary time, readers in general should maintain a healthy skepticism of results, as the scale of the systematic errors in published trees may really be far larger than the analytical methods (e.g., bootstrap) report.
Fitting the HIV epidemic in Zambia: a two-sex micro-simulation model.
Directory of Open Access Journals (Sweden)
Pauline M Leclerc
Full Text Available BACKGROUND: In describing and understanding how the HIV epidemic spreads in African countries, previous studies have not taken into account the detailed periods at risk. This study is based on a micro-simulation model (individual-based of the spread of the HIV epidemic in the population of Zambia, where women tend to marry early and where divorces are not frequent. The main target of the model was to fit the HIV seroprevalence profiles by age and sex observed at the Demographic and Health Survey conducted in 2001. METHODS AND FINDINGS: A two-sex micro-simulation model of HIV transmission was developed. Particular attention was paid to precise age-specific estimates of exposure to risk through the modelling of the formation and dissolution of relationships: marriage (stable union, casual partnership, and commercial sex. HIV transmission was exclusively heterosexual for adults or vertical (mother-to-child for children. Three stages of HIV infection were taken into account. All parameters were derived from empirical population-based data. Results show that basic parameters could not explain the dynamics of the HIV epidemic in Zambia. In order to fit the age and sex patterns, several assumptions were made: differential susceptibility of young women to HIV infection, differential susceptibility or larger number of encounters for male clients of commercial sex workers, and higher transmission rate. The model allowed to quantify the role of each type of relationship in HIV transmission, the proportion of infections occurring at each stage of disease progression, and the net reproduction rate of the epidemic (R(0 = 1.95. CONCLUSIONS: The simulation model reproduced the dynamics of the HIV epidemic in Zambia, and fitted the age and sex pattern of HIV seroprevalence in 2001. The same model could be used to measure the effect of changing behaviour in the future.
Computational Software for Fitting Seismic Data to Epidemic-Type Aftershock Sequence Models
Chu, A.
2014-12-01
Modern earthquake catalogs are often analyzed using spatial-temporal point process models such as the epidemic-type aftershock sequence (ETAS) models of Ogata (1998). My work introduces software to implement two of ETAS models described in Ogata (1998). To find the Maximum-Likelihood Estimates (MLEs), my software provides estimates of the homogeneous background rate parameter and the temporal and spatial parameters that govern triggering effects by applying the Expectation-Maximization (EM) algorithm introduced in Veen and Schoenberg (2008). Despite other computer programs exist for similar data modeling purpose, using EM-algorithm has the benefits of stability and robustness (Veen and Schoenberg, 2008). Spatial shapes that are very long and narrow cause difficulties in optimization convergence and problems with flat or multi-modal log-likelihood functions encounter similar issues. My program uses a robust method to preset a parameter to overcome the non-convergence computational issue. In addition to model fitting, the software is equipped with useful tools for examining modeling fitting results, for example, visualization of estimated conditional intensity, and estimation of expected number of triggered aftershocks. A simulation generator is also given with flexible spatial shapes that may be defined by the user. This open-source software has a very simple user interface. The user may execute it on a local computer, and the program also has potential to be hosted online. Java language is used for the software's core computing part and an optional interface to the statistical package R is provided.
Kinetic modeling and fitting software for interconnected reaction schemes: VisKin.
Zhang, Xuan; Andrews, Jared N; Pedersen, Steen E
2007-02-15
Reaction kinetics for complex, highly interconnected kinetic schemes are modeled using analytical solutions to a system of ordinary differential equations. The algorithm employs standard linear algebra methods that are implemented using MatLab functions in a Visual Basic interface. A graphical user interface for simple entry of reaction schemes facilitates comparison of a variety of reaction schemes. To ensure microscopic balance, graph theory algorithms are used to determine violations of thermodynamic cycle constraints. Analytical solutions based on linear differential equations result in fast comparisons of first order kinetic rates and amplitudes as a function of changing ligand concentrations. For analysis of higher order kinetics, we also implemented a solution using numerical integration. To determine rate constants from experimental data, fitting algorithms that adjust rate constants to fit the model to imported data were implemented using the Levenberg-Marquardt algorithm or using Broyden-Fletcher-Goldfarb-Shanno methods. We have included the ability to carry out global fitting of data sets obtained at varying ligand concentrations. These tools are combined in a single package, which we have dubbed VisKin, to guide and analyze kinetic experiments. The software is available online for use on PCs.
Directory of Open Access Journals (Sweden)
Riionheimo Janne
2003-01-01
Full Text Available We describe a technique for estimating control parameters for a plucked string synthesis model using a genetic algorithm. The model has been intensively used for sound synthesis of various string instruments but the fine tuning of the parameters has been carried out with a semiautomatic method that requires some hand adjustment with human listening. An automated method for extracting the parameters from recorded tones is described in this paper. The calculation of the fitness function utilizes knowledge of the properties of human hearing.
A NON-UNIFORM SEDIMENT TRANSPORT MODEL WITH THE BOUNDARY-FITTING ORTHOGONAL COORDINATE SYSTEM
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
A 2-D non-uniform sediment mathmatical model in the boundary-fitting orthogonal coordinate system was developed in this paper. The governing equations, the numerical scheme, the boundary conditions, the movable boundary technique and the numerical solutions were presented. The model was verified by the data of the reach 25km upstream the Jialingjiang estuary and the 44km long main stream of the Chongqing reach of the Yangtze river. The calculated results show that, the water elevation, the velocity distribution and the river bed deformation are in agreement with the measured data.
Fitting the CDO correlation skew: a tractable structural jump-diffusion model
DEFF Research Database (Denmark)
Willemann, Søren
2007-01-01
We extend a well-known structural jump-diffusion model for credit risk to handle both correlations through diffusion of asset values and common jumps in asset value. Through a simplifying assumption on the default timing and efficient numerical techniques, we develop a semi-analytic framework...... allowing for instantaneous calibration to heterogeneous CDS curves and fast computation of CDO tranche spreads. We calibrate the model to CDX and iTraxx data from February 2007 and achieve a satisfactory fit. To price the senior tranches for both indices, we require a risk-neutral probability of a market...
Fitting of different models for water vapour sorption on potato starch granules
Czepirski, L.; Komorowska-Czepirska, E.; Szymońska, J.
2002-08-01
Water vapour adsorption isotherms for native and modified potato starch were investigated. To obtain the best fit for the experimental data several models based on the BET approach were evaluated. The hypothesis that water is adsorbed on the starch granules at the primary and secondary adsorption sites as well as a concept considering the adsorbent fractality were also tested. It was found, that the equilibrium adsorption points in the examined range of relative humidity (0.03-0.90) were most accurately predicted by using a three-parameter model proposed by Kats and Kutarov.
Grishaev, Alexander; Guo, Liang; Irving, Thomas; Bax, Ad
2010-11-10
A new procedure, AXES, is introduced for fitting small-angle X-ray scattering (SAXS) data to macromolecular structures and ensembles of structures. By using explicit water models to account for the effect of solvent, and by restricting the adjustable fitting parameters to those that dominate experimental uncertainties, including sample/buffer rescaling, detector dark current, and, within a narrow range, hydration layer density, superior fits between experimental high resolution structures and SAXS data are obtained. AXES results are found to be more discriminating than standard Crysol fitting of SAXS data when evaluating poorly or incorrectly modeled protein structures. AXES results for ensembles of structures previously generated for ubiquitin show improved fits over fitting of the individual members of these ensembles, indicating these ensembles capture the dynamic behavior of proteins in solution.
Testing Lack-of-fit for a Polynomial Errors-in-variables Model
Institute of Scientific and Technical Information of China (English)
Li-xing Zhu; Wei-xing Song; Heng-jian Gui
2003-01-01
When a regression model is applied as an approximation of underlying model of data, the model checking is important and relevant. In this paper, we investigate the lack-of-fit test for a polynomial errorin-variables model. As the ordinary residuals are biased when there exist measurement errors in covariables,we correct them and then construct a residual-based test of score type. The constructed test is asymptotically chi-squared under null hypotheses. Simulation study shows that the test can maintain the significance level well.The choice of weight functions involved in the test statistic and the related power study are also investigated.The application to two examples is illustrated. The approach can be readily extended to handle more general models.
Model fitting of kink waves in the solar atmosphere: Gaussian damping and time-dependence
Morton, R. J.; Mooroogen, K.
2016-09-01
Aims: Observations of the solar atmosphere have shown that magnetohydrodynamic waves are ubiquitous throughout. Improvements in instrumentation and the techniques used for measurement of the waves now enables subtleties of competing theoretical models to be compared with the observed waves behaviour. Some studies have already begun to undertake this process. However, the techniques employed for model comparison have generally been unsuitable and can lead to erroneous conclusions about the best model. The aim here is to introduce some robust statistical techniques for model comparison to the solar waves community, drawing on the experiences from other areas of astrophysics. In the process, we also aim to investigate the physics of coronal loop oscillations. Methods: The methodology exploits least-squares fitting to compare models to observational data. We demonstrate that the residuals between the model and observations contain significant information about the ability for the model to describe the observations, and show how they can be assessed using various statistical tests. In particular we discuss the Kolmogorov-Smirnoff one and two sample tests, as well as the runs test. We also highlight the importance of including any observational trend line in the model-fitting process. Results: To demonstrate the methodology, an observation of an oscillating coronal loop undergoing standing kink motion is used. The model comparison techniques provide evidence that a Gaussian damping profile provides a better description of the observed wave attenuation than the often used exponential profile. This supports previous analysis from Pascoe et al. (2016, A&A, 585, L6). Further, we use the model comparison to provide evidence of time-dependent wave properties of a kink oscillation, attributing the behaviour to the thermodynamic evolution of the local plasma.
Laboratory animal models for esophageal cancer
Directory of Open Access Journals (Sweden)
Dhanya Venugopalan Nair
2016-11-01
Full Text Available The incidence of esophageal cancer is rapidly increasing especially in developing countries. The major risk factors include unhealthy lifestyle practices such as alcohol consumption, smoking, and chewing tobacco to name a few. Diagnosis at an advanced stage and poor prognosis make esophageal cancer one of the most lethal diseases. These factors have urged further research in understanding the pathophysiology of the disease. Animal models not only aid in understanding the molecular pathogenesis of esophageal cancer but also help in developing therapeutic interventions for the disease. This review throws light on the various recent laboratory animal models for esophageal cancer.
An exactly solvable, spatial model of mutation accumulation in cancer
Paterson, Chay; Nowak, Martin A.; Waclaw, Bartlomiej
2016-12-01
One of the hallmarks of cancer is the accumulation of driver mutations which increase the net reproductive rate of cancer cells and allow them to spread. This process has been studied in mathematical models of well mixed populations, and in computer simulations of three-dimensional spatial models. But the computational complexity of these more realistic, spatial models makes it difficult to simulate realistically large and clinically detectable solid tumours. Here we describe an exactly solvable mathematical model of a tumour featuring replication, mutation and local migration of cancer cells. The model predicts a quasi-exponential growth of large tumours, even if different fragments of the tumour grow sub-exponentially due to nutrient and space limitations. The model reproduces clinically observed tumour growth times using biologically plausible rates for cell birth, death, and migration rates. We also show that the expected number of accumulated driver mutations increases exponentially in time if the average fitness gain per driver is constant, and that it reaches a plateau if the gains decrease over time. We discuss the realism of the underlying assumptions and possible extensions of the model.
Mouse models of anemia of cancer.
Directory of Open Access Journals (Sweden)
Airie Kim
Full Text Available Anemia of cancer (AC may contribute to cancer-related fatigue and impair quality of life. Improved understanding of the pathogenesis of AC could facilitate better treatment, but animal models to study AC are lacking. We characterized four syngeneic C57BL/6 mouse cancers that cause AC. Mice with two different rapidly-growing metastatic lung cancers developed the characteristic findings of anemia of inflammation (AI, with dramatically different degrees of anemia. Mice with rapidly-growing metastatic melanoma also developed a severe anemia by 14 days, with hematologic and inflammatory parameters similar to AI. Mice with a slow-growing peritoneal ovarian cancer developed an iron-deficiency anemia, likely secondary to chronically impaired nutrition and bleeding into the peritoneal cavity. Of the four models, hepcidin mRNA levels were increased only in the milder lung cancer model. Unlike in our model of systemic inflammation induced by heat-killed Brucella abortus, ablation of hepcidin in the ovarian cancer and the milder lung cancer mouse models did not affect the severity of anemia. Hepcidin-independent mechanisms play an important role in these murine models of AC.
A Parametric Model of Shoulder Articulation for Virtual Assessment of Space Suit Fit
Kim, K. Han; Young, Karen S.; Bernal, Yaritza; Boppana, Abhishektha; Vu, Linh Q.; Benson, Elizabeth A.; Jarvis, Sarah; Rajulu, Sudhakar L.
2016-01-01
Shoulder injury is one of the most severe risks that have the potential to impair crewmembers' performance and health in long duration space flight. Overall, 64% of crewmembers experience shoulder pain after extra-vehicular training in a space suit, and 14% of symptomatic crewmembers require surgical repair (Williams & Johnson, 2003). Suboptimal suit fit, in particular at the shoulder region, has been identified as one of the predominant risk factors. However, traditional suit fit assessments and laser scans represent only a single person's data, and thus may not be generalized across wide variations of body shapes and poses. The aim of this work is to develop a software tool based on a statistical analysis of a large dataset of crewmember body shapes. This tool can accurately predict the skin deformation and shape variations for any body size and shoulder pose for a target population, from which the geometry can be exported and evaluated against suit models in commercial CAD software. A preliminary software tool was developed by statistically analyzing 150 body shapes matched with body dimension ranges specified in the Human-Systems Integration Requirements of NASA ("baseline model"). Further, the baseline model was incorporated with shoulder joint articulation ("articulation model"), using additional subjects scanned in a variety of shoulder poses across a pre-specified range of motion. Scan data was cleaned and aligned using body landmarks. The skin deformation patterns were dimensionally reduced and the co-variation with shoulder angles was analyzed. A software tool is currently in development and will be presented in the final proceeding. This tool would allow suit engineers to parametrically generate body shapes in strategically targeted anthropometry dimensions and shoulder poses. This would also enable virtual fit assessments, with which the contact volume and clearance between the suit and body surface can be predictively quantified at reduced time and
Blowout Jets: Hinode X-Ray Jets that Don't Fit the Standard Model
Moore, Ronald L.; Cirtain, Jonathan W.; Sterling, Alphonse C.; Falconer, David A.
2010-01-01
Nearly half of all H-alpha macrospicules in polar coronal holes appear to be miniature filament eruptions. This suggests that there is a large class of X-ray jets in which the jet-base magnetic arcade undergoes a blowout eruption as in a CME, instead of remaining static as in most solar X-ray jets, the standard jets that fit the model advocated by Shibata. Along with a cartoon depicting the standard model, we present a cartoon depicting the signatures expected of blowout jets in coronal X-ray images. From Hinode/XRT movies and STEREO/EUVI snapshots in polar coronal holes, we present examples of (1) X-ray jets that fit the standard model, and (2) X-ray jets that do not fit the standard model but do have features appropriate for blowout jets. These features are (1) a flare arcade inside the jet-base arcade in addition to the small flare arcade (bright point) outside that standard jets have, (2) a filament of cool (T is approximately 80,000K) plasma that erupts from the core of the jetbase arcade, and (3) an extra jet strand that should not be made by the reconnection for standard jets but could be made by reconnection between the ambient unipolar open field and the opposite-polarity leg of the filament-carrying flux-rope core field of the erupting jet-base arcade. We therefore infer that these non-standard jets are blowout jets, jets made by miniature versions of the sheared-core-arcade eruptions that make CMEs
Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J
2016-05-01
Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) .
Ashby, Nathaniel J S; Jekel, Marc; Dickert, Stephan; Glöckner, Andreas
2016-12-01
Recent research makes increasing use of eye-tracking methodologies to generate and test process models. Overall, such research suggests that attention, generally indexed by fixations (gaze duration), plays a critical role in the construction of preference, although the methods used to support this supposition differ substantially. In 2 studies we empirically test prototypical versions of prominent processing assumptions against 1 another and several base models. We find that general evidence accumulation processes provide a good fit to the data. An accumulation process that assumes leakage and temporal variability in evidence weighting (i.e., a primacy effect) fits the aggregate data, both in terms of choices and decision times, and does so across varying types of choices (e.g., charitable giving and hedonic consumption) and numbers of options well. However, when comparing models on the level of the individual, for a majority of participants simpler models capture choice data better. The theoretical and practical implications of these findings are discussed. (PsycINFO Database Record
Implicit Active Contour Model with Local and Global Intensity Fitting Energies
Directory of Open Access Journals (Sweden)
Xiaozeng Xu
2013-01-01
Full Text Available We propose a new active contour model which integrates a local intensity fitting (LIF energy with an auxiliary global intensity fitting (GIF energy. The LIF energy is responsible for attracting the contour toward object boundaries and is dominant near object boundaries, while the GIF energy incorporates global image information to improve the robustness to initialization of the contours. The proposed model not only can provide desirable segmentation results in the presence of intensity inhomogeneity but also allows for more flexible initialization of the contour compared to the RSF and LIF models, and we give a theoretical proof to compute a unique steady state regardless of the initialization; that is, the convergence of the zero-level line is irrespective of the initial function. This means that we can obtain the same zero-level line in the steady state, if we choose the initial function as a bounded function. In particular, our proposed model has the capability of detecting multiple objects or objects with interior holes or blurred edges.
An improved cosmological model fitting of Planck data with a dark energy spike
Park, Chan-Gyung
2015-01-01
The $\\Lambda$ cold dark matter ($\\Lambda\\textrm{CDM}$) model is currently known as the simplest cosmology model that best describes observations with minimal number of parameters. Here we introduce a cosmology model that is preferred over the conventional $\\Lambda\\textrm{CDM}$ one by constructing dark energy as the sum of the cosmological constant $\\Lambda$ and the additional fluid that is designed to have an extremely short transient spike in energy density during the radiation-matter equality era and the early scaling behavior with radiation and matter densities. The density parameter of the additional fluid is defined as a Gaussian function plus a constant in logarithmic scale-factor space. Searching for the best-fit cosmological parameters in the presence of such a dark energy spike gives a far smaller chi-square value by about five times the number of additional parameters introduced and narrower constraints on matter density and Hubble constant compared with the best-fit $\\Lambda\\textrm{CDM}$ model. The...
Furlan, E; Ali, B; Stutz, A M; Stanke, T; Tobin, J J; Megeath, S T; Osorio, M; Hartmann, L; Calvet, N; Poteet, C A; Booker, J; Manoj, P; Watson, D M; Allen, L
2016-01-01
We present key results from the Herschel Orion Protostar Survey (HOPS): spectral energy distributions (SEDs) and model fits of 330 young stellar objects, predominantly protostars, in the Orion molecular clouds. This is the largest sample of protostars studied in a single, nearby star-formation complex. With near-infrared photometry from 2MASS, mid- and far-infrared data from Spitzer and Herschel, and sub-millimeter photometry from APEX, our SEDs cover 1.2-870 $\\mu$m and sample the peak of the protostellar envelope emission at ~100 $\\mu$m. Using mid-IR spectral indices and bolometric temperatures, we classify our sample into 92 Class 0 protostars, 125 Class I protostars, 102 flat-spectrum sources, and 11 Class II pre-main-sequence stars. We implement a simple protostellar model (including a disk in an infalling envelope with outflow cavities) to generate a grid of 30400 model SEDs and use it to determine the best-fit model parameters for each protostar. We argue that far-IR data are essential for accurate cons...
Systematic effects on the size-luminosity relation: dependence on model fitting and morphology
Bernardi, M; Vikram, V; Huertas-Company, M; Mei, S; Shankar, F; Sheth, R K
2012-01-01
We quantify the systematics in the size-luminosity relation of galaxies in the SDSS main sample which arise from fitting different 1- and 2-component model profiles to the images. In objects brighter than L*, fitting a single Sersic profile to what is really a two-component SerExp system leads to biases: the half-light radius is increasingly overestimated as n of the fitted single component increases; it is also overestimated at B/T ~ 0.6. However, the net effect on the R-L relation is small, except for the most luminous tail, where it curves upwards towards larger sizes. We also study how this relation depends on morphological type. Our analysis is one of the first to use Bayesian-classifier derived weights, rather than hard cuts, to define morphology. Crudely, there appear to be only two relations: one for early-types (Es, S0s and Sa's) and another for late-types (Sbs and Scds). However, closer inspection shows that within the early-type sample S0s tend to be 15% smaller than Es of the same luminosity, and,...
The Shape of Dark Matter Haloes II. The Galactus HI Modelling & Fitting Tool
Peters, S P C; Allen, R J; Freeman, K C
2016-01-01
We present a new HI modelling tool called \\textsc{Galactus}. The program has been designed to perform automated fits of disc-galaxy models to observations. It includes a treatment for the self-absorption of the gas. The software has been released into the public domain. We describe the design philosophy and inner workings of the program. After this, we model the face-on galaxy NGC2403, using both self-absorption and optically thin models, showing that self-absorption occurs even in face-on galaxies. It is shown that the maximum surface brightness plateaus seen in Paper I of this series are indeed signs of self-absorption. The apparent HI mass of an edge-on galaxy can be drastically lower compared to that same galaxy seen face-on. The Tully-Fisher relation is found to be relatively free from self-absorption issues.
Adapted strategic plannig model applied to small business: a case study in the fitness area
Directory of Open Access Journals (Sweden)
Eduarda Tirelli Hennig
2012-06-01
Full Text Available The strategic planning is an important management tool in the corporate scenario and shall not be restricted to big Companies. However, this kind of planning process in small business may need special adaptations due to their own characteristics. This paper aims to identify and adapt the existent models of strategic planning to the scenario of a small business in the fitness area. Initially, it is accomplished a comparative study among models of different authors to identify theirs phases and activities. Then, it is defined which of these phases and activities should be present in a model that will be utilized in a small business. That model was applied to a Pilates studio; it involves the establishment of an organizational identity, an environmental analysis as well as the definition of strategic goals, strategies and actions to reach them. Finally, benefits to the organization could be identified, as well as hurdles in the implementation of the tool.
DEFF Research Database (Denmark)
Nielsen, Karen L.; Pedersen, Thomas M.; Udekwu, Klas I.
2012-01-01
Denmark and several other countries experienced the first epidemic of methicillin-resistant Staphylococcus aureus (MRSA) during the period 196575, which was caused by multiresistant isolates of phage complex 83A. In Denmark these MRSA isolates disappeared almost completely, being replaced by other...... phage types, predominantly only penicillin resistant. We investigated whether isolates of this epidemic were associated with a fitness cost, and we employed a mathematical model to ask whether these fitness costs could have led to the observed reduction in frequency. Bacteraemia isolates of S. aureus...... from Denmark have been stored since 1957. We chose 40 S. aureus isolates belonging to phage complex 83A, clonal complex 8 based on spa type, ranging in time of isolation from 1957 to 1980 and with varyous antibiograms, including both methicillin-resistant and -susceptible isolates. The relative fitness...
Wang, Huai-Chun; Susko, Edward; Roger, Andrew J
2014-04-01
Standard protein phylogenetic models use fixed rate matrices of amino acid interchange derived from analyses of large databases. Differences between the stationary amino acid frequencies of these rate matrices from those of a data set of interest are typically adjusted for by matrix multiplication that converts the empirical rate matrix to an exchangeability matrix which is then postmultiplied by the amino acid frequencies in the alignment. The result is a time-reversible rate matrix with stationary amino acid frequencies equal to the data set frequencies. On the basis of population genetics principles, we develop an amino acid substitution-selection model that parameterizes the fitness of an amino acid as the logarithm of the ratio of the frequency of the amino acid to the frequency of the same amino acid under no selection. The model gives rise to a different sequence of matrix multiplications to convert an empirical rate matrix to one that has stationary amino acid frequencies equal to the data set frequencies. We incorporated the substitution-selection model with an improved amino acid class frequency mixture (cF) model to partially take into account site-specific amino acid frequencies in the phylogenetic models. We show that 1) the selection models fit data significantly better than corresponding models without selection for most of the 21 test data sets; 2) both cF and cF selection models favored the phylogenetic trees that were inferred under current sophisticated models and methods for three difficult phylogenetic problems (the positions of microsporidia and breviates in eukaryote phylogeny and the position of the root of the angiosperm tree); and 3) for data simulated under site-specific residue frequencies, the cF selection models estimated trees closer to the generating trees than a standard Г model or cF without selection. We also explored several ways of estimating amino acid frequencies under neutral evolution that are required for these selection
Understanding Systematics in ZZ Ceti Model Fitting to Enable Differential Seismology
Fuchs, J. T.; Dunlap, B. H.; Clemens, J. C.; Meza, J. A.; Dennihy, E.; Koester, D.
2017-03-01
We are conducting a large spectroscopic survey of over 130 Southern ZZ Cetis with the Goodman Spectrograph on the SOAR Telescope. Because it employs a single instrument with high UV throughput, this survey will both improve the signal-to-noise of the sample of SDSS ZZ Cetis and provide a uniform dataset for model comparison. We are paying special attention to systematics in the spectral fitting and quantify three of those systematics here. We show that relative positions in the log g -Teff plane are consistent for these three systematics.
Understanding Systematics in ZZ Ceti Model Fitting to Enable Differential Seismology
Fuchs, J T; Clemens, J C; Meza, J A; Dennihy, E; Koester, D
2016-01-01
We are conducting a large spectroscopic survey of over 130 Southern ZZ Cetis with the Goodman Spectrograph on the SOAR Telescope. Because it employs a single instrument with high UV throughput, this survey will both improve the signal-to-noise of the sample of SDSS ZZ Cetis and provide a uniform dataset for model comparison. We are paying special attention to systematics in the spectral fitting and quantify three of those systematics here. We show that relative positions in the $\\log{g}$-$T_{\\rm eff}$ plane are consistent for these three systematics.
Current status of the Standard Model CKM fit and constraints on $\\Delta F=2$ New Physics
Charles, J; Descotes-Genon, S; Lacker, H; Menzel, A; Monteil, S; Niess, V; Ocariz, J; Orloff, J; Perez, A; Qian, W; Tisserand, V; Trabelsi, K; Urquijo, P; Silva, L Vale
2015-01-01
This letter summarises the status of the global fit of the CKM parameters within the Standard Model performed by the CKMfitter group. Special attention is paid to the inputs for the CKM angles $\\alpha$ and $\\gamma$ and the status of $B_s\\to\\mu\\mu$ and $B_d\\to \\mu\\mu$ decays. We illustrate the current situation for other unitarity triangles. We also discuss the constraints on generic $\\Delta F=2$ New Physics. All results have been obtained with the CKMfitter analysis package, featuring the frequentist statistical approach and using Rfit to handle theoretical uncertainties.
Analytical Light Curve Models of Super-Luminous Supernvae: chi^2-Minimizations of Parameter Fits
Chatzopoulos, E; Vinko, J; Horvath, Z L; Nagy, A
2013-01-01
We present fits of generalized semi-analytic supernova (SN) light curve (LC) models for a variety of power inputs including Ni-56 and Co-56 radioactive decay, magnetar spin-down, and forward and reverse shock heating due to supernova ejecta-circumstellar matter (CSM) interaction. We apply our models to the observed LCs of the H-rich Super Luminous Supernovae (SLSN-II) SN 2006gy, SN 2006tf, SN 2008am, SN 2008es, CSS100217, the H-poor SLSN-I SN 2005ap, SCP06F6, SN 2007bi, SN 2010gx and SN 2010kd as well as to the interacting SN 2008iy and PTF09uj. Our goal is to determine the dominant mechanism that powers the LCs of these extraordinary events and the physical conditions involved in each case. We also present a comparison of our semi-analytical results with recent results from numerical radiation hydrodynamics calculations in the particular case of SN 2006gy in order to explore the strengths and weaknesses of our models. We find that CS shock heating produced by ejecta-CSM interaction provides a better fit to t...
A global fit study on the new agegraphic dark energy model
Zhang, Jing-Fei; Zhang, Xin
2012-01-01
We perform a global fit study on the new agegraphic dark energy (NADE) model in a non-flat universe by using the MCMC method with the full CMB power spectra data from the WMAP 7-yr observations, the SNIa data from Union2.1 sample, BAO data from SDSS DR7 and WiggleZ Dark Energy Survey, and the latest measurements of $H_0$ from HST. We find that the value of $\\Omega_{k0}$ is greater than 0 at least at the 3$\\sigma$ confidence levels (CLs), which implies that the NADE model distinctly favors an open universe. Besides, our results show that the value of the key parameter of NADE model, $n=2.673^{+0.053+0.127+0.199}_{-0.077-0.151-0.222}$, at the 1--3$\\sigma$ CLs, where its best-fit value is significantly smaller than those obtained in previous works. We find that the reason leading to such a change comes from the different SNIa samples used. Our further test indicates that there is a distinct tension between the Union2 sample of SNIa and other observations, and the tension will be relieved once the Union2 sample i...
GRace: a MATLAB-based application for fitting the discrimination-association model.
Stefanutti, Luca; Vianello, Michelangelo; Anselmi, Pasquale; Robusto, Egidio
2014-10-28
The Implicit Association Test (IAT) is a computerized two-choice discrimination task in which stimuli have to be categorized as belonging to target categories or attribute categories by pressing, as quickly and accurately as possible, one of two response keys. The discrimination association model has been recently proposed for the analysis of reaction time and accuracy of an individual respondent to the IAT. The model disentangles the influences of three qualitatively different components on the responses to the IAT: stimuli discrimination, automatic association, and termination criterion. The article presents General Race (GRace), a MATLAB-based application for fitting the discrimination association model to IAT data. GRace has been developed for Windows as a standalone application. It is user-friendly and does not require any programming experience. The use of GRace is illustrated on the data of a Coca Cola-Pepsi Cola IAT, and the results of the analysis are interpreted and discussed.
Wenseleers, Tom; Helanterä, Heikki; Alves, Denise A; Dueñez-Guzmán, Edgar; Pamilo, Pekka
2013-01-01
The conflicts over sex allocation and male production in insect societies have long served as an important test bed for Hamilton's theory of inclusive fitness, but have for the most part been considered separately. Here, we develop new coevolutionary models to examine the interaction between these two conflicts and demonstrate that sex ratio and colony productivity costs of worker reproduction can lead to vastly different outcomes even in species that show no variation in their relatedness structure. Empirical data on worker-produced males in eight species of Melipona bees support the predictions from a model that takes into account the demographic details of colony growth and reproduction. Overall, these models contribute significantly to explaining behavioural variation that previous theories could not account for.
Fitting mathematical models to describe the rheological behaviour of chocolate pastes
Barbosa, Carla; Diogo, Filipa; Alves, M. Rui
2016-06-01
The flow behavior is of utmost importance for the chocolate industry. The objective of this work was to study two mathematical models, Casson and Windhab models that can be used to fit chocolate rheological data and evaluate which better infers or previews the rheological behaviour of different chocolate pastes. Rheological properties (viscosity, shear stress and shear rates) were obtained with a rotational viscometer equipped with a concentric cylinder. The chocolate samples were white chocolate and chocolate with varying percentages in cacao (55%, 70% and 83%). The results showed that the Windhab model was the best to describe the flow behaviour of all the studied samples with higher determination coefficients (r2 > 0.9).
Genetically engineered mouse models of prostate cancer
Nawijn, Martijn C.; Bergman, Andreas M.; van der Poel, Henk G.
2008-01-01
Objectives: Mouse models of prostate cancer are used to test the contribution of individual genes to the transformation process, evaluate the collaboration between multiple genetic lesions observed in a single tumour, and perform preclinical intervention studies in prostate cancer research. Methods:
Bloom, Jesse D
2014-10-01
Phylogenetic analyses of molecular data require a quantitative model for how sequences evolve. Traditionally, the details of the site-specific selection that governs sequence evolution are not known a priori, making it challenging to create evolutionary models that adequately capture the heterogeneity of selection at different sites. However, recent advances in high-throughput experiments have made it possible to quantify the effects of all single mutations on gene function. I have previously shown that such high-throughput experiments can be combined with knowledge of underlying mutation rates to create a parameter-free evolutionary model that describes the phylogeny of influenza nucleoprotein far better than commonly used existing models. Here, I extend this work by showing that published experimental data on TEM-1 beta-lactamase (Firnberg E, Labonte JW, Gray JJ, Ostermeier M. 2014. A comprehensive, high-resolution map of a gene's fitness landscape. Mol Biol Evol. 31:1581-1592) can be combined with a few mutation rate parameters to create an evolutionary model that describes beta-lactamase phylogenies much better than most common existing models. This experimentally informed evolutionary model is superior even for homologs that are substantially diverged (about 35% divergence at the protein level) from the TEM-1 parent that was the subject of the experimental study. These results suggest that experimental measurements can inform phylogenetic evolutionary models that are applicable to homologs that span a substantial range of sequence divergence.
Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data.
Directory of Open Access Journals (Sweden)
Gu Mi
Full Text Available This work is about assessing model adequacy for negative binomial (NB regression, particularly (1 assessing the adequacy of the NB assumption, and (2 assessing the appropriateness of models for NB dispersion parameters. Tools for the first are appropriate for NB regression generally; those for the second are primarily intended for RNA sequencing (RNA-Seq data analysis. The typically small number of biological samples and large number of genes in RNA-Seq analysis motivate us to address the trade-offs between robustness and statistical power using NB regression models. One widely-used power-saving strategy, for example, is to assume some commonalities of NB dispersion parameters across genes via simple models relating them to mean expression rates, and many such models have been proposed. As RNA-Seq analysis is becoming ever more popular, it is appropriate to make more thorough investigations into power and robustness of the resulting methods, and into practical tools for model assessment. In this article, we propose simulation-based statistical tests and diagnostic graphics to address model adequacy. We provide simulated and real data examples to illustrate that our proposed methods are effective for detecting the misspecification of the NB mean-variance relationship as well as judging the adequacy of fit of several NB dispersion models.
Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data.
Mi, Gu; Di, Yanming; Schafer, Daniel W
2015-01-01
This work is about assessing model adequacy for negative binomial (NB) regression, particularly (1) assessing the adequacy of the NB assumption, and (2) assessing the appropriateness of models for NB dispersion parameters. Tools for the first are appropriate for NB regression generally; those for the second are primarily intended for RNA sequencing (RNA-Seq) data analysis. The typically small number of biological samples and large number of genes in RNA-Seq analysis motivate us to address the trade-offs between robustness and statistical power using NB regression models. One widely-used power-saving strategy, for example, is to assume some commonalities of NB dispersion parameters across genes via simple models relating them to mean expression rates, and many such models have been proposed. As RNA-Seq analysis is becoming ever more popular, it is appropriate to make more thorough investigations into power and robustness of the resulting methods, and into practical tools for model assessment. In this article, we propose simulation-based statistical tests and diagnostic graphics to address model adequacy. We provide simulated and real data examples to illustrate that our proposed methods are effective for detecting the misspecification of the NB mean-variance relationship as well as judging the adequacy of fit of several NB dispersion models.
FITTING A THREE DIMENSIONAL PEM FUEL CELL MODEL TO MEASUREMENTS BY TUNING THE POROSITY AND
DEFF Research Database (Denmark)
Bang, Mads; Odgaard, Madeleine; Condra, Thomas Joseph
2004-01-01
A three-dimensional, computational fluid dynamics (CFD) model of a PEM fuel cell is presented. The model consists ofstraight channels, porous gas diffusion layers, porous catalystlayers and a membrane. In this computational domain, most ofthe transport phenomena which govern the performance of th...... on the performance of the fuel cell.The two parameters are shown to be key elements in adjusting thethree-dimensional model to fit measured polarization curves.Results from the proposed model are compared to single cellmeasurements on a test MEA from IRD Fuel Cells.......A three-dimensional, computational fluid dynamics (CFD) model of a PEM fuel cell is presented. The model consists ofstraight channels, porous gas diffusion layers, porous catalystlayers and a membrane. In this computational domain, most ofthe transport phenomena which govern the performance of the......PEM fuel cell are dealt with in detail.The model solves the convective and diffusive transport of thegaseous phase in the fuel cell and allows prediction of theconcentration of the species present. A special feature of themodel is a method that allows detailed modelling and predictionof electrode kinetics...
Cancer Metabolism: A Modeling Perspective
DEFF Research Database (Denmark)
Ghaffari, Pouyan; Mardinoglu, Adil; Nielsen, Jens
2015-01-01
requires both the advancement of experimental technologies for more comprehensive measurement of omics as well as the advancement of robust computational methods for accurate analysis of the generated data. Here, we review cancer-associated reprogramming of metabolism and highlight the capability of genome...... suggest that utilization of amino acids and lipids contributes significantly to cancer cell metabolism. Also recent progresses in our understanding of carcinogenesis have revealed that cancer is a complex disease and cannot be understood through simple investigation of genetic mutations of cancerous cells....... Cancer cells present in complex tumor tissues communicate with the surrounding microenvironment and develop traits which promote their growth, survival, and metastasis. Decoding the full scope and targeting dysregulated metabolic pathways that support neoplastic transformations and their preservation...
Gardner, Heather L; Fenger, Joelle M; London, Cheryl A
2016-01-01
Spontaneous cancers in client-owned dogs closely recapitulate their human counterparts with respect to clinical presentation, histological features, molecular profiles, and response and resistance to therapy, as well as the evolution of drug-resistant metastases. In several instances the incorporation of dogs with cancer into the preclinical development path of cancer therapeutics has influenced outcome by helping to establish pharmacokinetic/pharmacodynamics relationships, dose/regimen, expected clinical toxicities, and ultimately the potential for biologic activity. As our understanding regarding the molecular drivers of canine cancers has improved, unique opportunities have emerged to leverage this spontaneous model to better guide cancer drug development so that therapies likely to fail are eliminated earlier and therapies with true potential are optimized prior to human studies. Both pets and people benefit from this approach, as it provides dogs with access to cutting-edge cancer treatments and helps to insure that people are given treatments more likely to succeed.
Lévy Flights and Self-Similar Exploratory Behaviour of Termite Workers: Beyond Model Fitting
Miramontes, Octavio; DeSouza, Og; Paiva, Leticia Ribeiro; Marins, Alessandra; Orozco, Sirio
2014-01-01
Animal movements have been related to optimal foraging strategies where self-similar trajectories are central. Most of the experimental studies done so far have focused mainly on fitting statistical models to data in order to test for movement patterns described by power-laws. Here we show by analyzing over half a million movement displacements that isolated termite workers actually exhibit a range of very interesting dynamical properties –including Lévy flights– in their exploratory behaviour. Going beyond the current trend of statistical model fitting alone, our study analyses anomalous diffusion and structure functions to estimate values of the scaling exponents describing displacement statistics. We evince the fractal nature of the movement patterns and show how the scaling exponents describing termite space exploration intriguingly comply with mathematical relations found in the physics of transport phenomena. By doing this, we rescue a rich variety of physical and biological phenomenology that can be potentially important and meaningful for the study of complex animal behavior and, in particular, for the study of how patterns of exploratory behaviour of individual social insects may impact not only their feeding demands but also nestmate encounter patterns and, hence, their dynamics at the social scale. PMID:25353958
Levy flights and self-similar exploratory behaviour of termite workers: beyond model fitting.
Directory of Open Access Journals (Sweden)
Octavio Miramontes
Full Text Available Animal movements have been related to optimal foraging strategies where self-similar trajectories are central. Most of the experimental studies done so far have focused mainly on fitting statistical models to data in order to test for movement patterns described by power-laws. Here we show by analyzing over half a million movement displacements that isolated termite workers actually exhibit a range of very interesting dynamical properties--including Lévy flights--in their exploratory behaviour. Going beyond the current trend of statistical model fitting alone, our study analyses anomalous diffusion and structure functions to estimate values of the scaling exponents describing displacement statistics. We evince the fractal nature of the movement patterns and show how the scaling exponents describing termite space exploration intriguingly comply with mathematical relations found in the physics of transport phenomena. By doing this, we rescue a rich variety of physical and biological phenomenology that can be potentially important and meaningful for the study of complex animal behavior and, in particular, for the study of how patterns of exploratory behaviour of individual social insects may impact not only their feeding demands but also nestmate encounter patterns and, hence, their dynamics at the social scale.
A simulation study of person-fit in the Rasch model
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Richard Artner
2016-09-01
Full Text Available The validation of individual test scores in the Rasch model (1-PL model is of primary importance, but the decision which person-fit index to choose is still not sufficiently answered. In this work, a simulation study was conducted in order to compare five well known person-fit indices in terms of specificity and sensitivity, under different testing conditions. Furthermore, this study analyzed the decrease in specificity of Andersen´s Likelihood-Ratio test in case of person-misfit, using the median of the raw score as an internal criterion, as well as the positive effect of removing suspicious respondents with the index C*. The three non-parametric indices Ht, C* and U3 performed slightly better than the parametric indices OUTFIT and INFIT. All indices performed better with a higher number of respondents and a higher number of items. Ht, OUTFIT, and INFIT showed huge deviations between nominal and actual specificity levels. The simulation revealed that person-misfit has a huge negative impact on the specificity of Andersen´s Likelihood-Ratio test. However, the removal of suspicious respondents with C* worked quite well and the nominal specificity can be almost respected if the specificity level of C* is set to 0.95.
Fitting multilevel models in complex survey data with design weights: Recommendations
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Carle Adam C
2009-07-01
Full Text Available Abstract Background Multilevel models (MLM offer complex survey data analysts a unique approach to understanding individual and contextual determinants of public health. However, little summarized guidance exists with regard to fitting MLM in complex survey data with design weights. Simulation work suggests that analysts should scale design weights using two methods and fit the MLM using unweighted and scaled-weighted data. This article examines the performance of scaled-weighted and unweighted analyses across a variety of MLM and software programs. Methods Using data from the 2005–2006 National Survey of Children with Special Health Care Needs (NS-CSHCN: n = 40,723 that collected data from children clustered within states, I examine the performance of scaling methods across outcome type (categorical vs. continuous, model type (level-1, level-2, or combined, and software (Mplus, MLwiN, and GLLAMM. Results Scaled weighted estimates and standard errors differed slightly from unweighted analyses, agreeing more with each other than with unweighted analyses. However, observed differences were minimal and did not lead to different inferential conclusions. Likewise, results demonstrated minimal differences across software programs, increasing confidence in results and inferential conclusions independent of software choice. Conclusion If including design weights in MLM, analysts should scale the weights and use software that properly includes the scaled weights in the estimation.
Spectral observations of Ellerman bombs and fitting with a two-cloud model
Hong, Jie; Li, Ying; Fang, Cheng; Cao, Wenda
2014-01-01
We study the H$\\alpha$ and Ca II 8542 \\r{A} line spectra of four typical Ellerman bombs (EBs) in active region NOAA 11765 on 2013 June 6, observed with the Fast Imaging Solar Spectrograph installed at the 1.6 meter New Solar Telescope at Big Bear Solar Observatory. Considering that EBs may occur in a restricted region in the lower atmosphere, and that their spectral lines show particular features, we propose a two-cloud model to fit the observed line profiles. The lower cloud can account for the wing emission, and the upper cloud is mainly responsible for the absorption at line center. After choosing carefully the free parameters, we get satisfactory fitting results. As expected, the lower cloud shows an increase of the source function, corresponding to a temperature increase of 400--1000 K in EBs relative to the quiet Sun. This is consistent with previous results deduced from semi-empirical models and confirms that a local heating occurs in the lower atmosphere during the appearance of EBs. We also find that...
A PID Positioning Controller with a Curve Fitting Model Based on RFID Technology
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Young-Long Chen
2013-03-01
Full Text Available The global positioning system (GPS is an important research topic to solve outdoor positioning problems, but GPSis unable to locate objects accurately and precisely indoors. Some available systems apply ultrasound or opticaltracking. This paper presents an efficient proportional-integral-derivative (PID controller with curve fitting model formobile robot localization and position estimation which adopts passive radio frequency identification (RFID tags ina space. This scheme is based on a mobile robot carries an RFID reader module which reads the installed low-costpassive tags under the floor in a grid-like pattern. The PID controllers increase the efficiency of captured RFID tagsand the curve fitting model is used to systematically identify the revolutions per minute (RPM of the motor. Wecontrol and monitor the position of the robot from a remote location through a mobile phone via Wi-Fi and Bluetoothnetwork. Experiment results present that the number of captured RFID tags of our proposed scheme outperformsthat of the previous scheme.
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Matthew R Nassar
2013-04-01
Full Text Available Fitting models to behavior is commonly used to infer the latent computational factors responsible for generating behavior. However, the complexity of many behaviors can handicap the interpretation of such models. Here we provide perspectives on problems that can arise when interpreting parameter fits from models that provide incomplete descriptions of behavior. We illustrate these problems by fitting commonly used and neurophysiologically motivated reinforcement-learning models to simulated behavioral data sets from learning tasks. These model fits can pass a host of standard goodness-of-fit tests and other model-selection diagnostics even when the models do not provide a complete description of the behavioral data. We show that such incomplete models can be misleading by yielding biased estimates of the parameters explicitly included in the models. This problem is particularly pernicious when the neglected factors are unknown and therefore not easily identified by model comparisons and similar methods. An obvious conclusion is that a parsimonious description of behavioral data does not necessarily imply an accurate description of the underlying computations. Moreover, general goodness-of-fit measures are not a strong basis to support claims that a particular model can provide a generalized understanding of the computations that govern behavior. To help overcome these challenges, we advocate the design of tasks that provide direct reports of the computational variables of interest. Such direct reports complement model-fitting approaches by providing a more complete, albeit possibly more task-specific, representation of the factors that drive behavior. Computational models then provide a means to connect such task-specific results to a more general algorithmic understanding of the brain.
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Ze-yu MAO
2014-01-01
Full Text Available River ice is a natural phenomenon in cold regions, influenced by meteorology, geomorphology, and hydraulic conditions. River ice processes involve complex interactions between hydrodynamic, mechanical, and thermal processes, and they are also influenced by weather and hydrologic conditions. Because natural rivers are serpentine, with bends, narrows, and straight reaches, the commonly-used one-dimensional river ice models and two-dimensional models based on the rectangular Cartesian coordinates are incapable of simulating the physical phenomena accurately. In order to accurately simulate the complicated river geometry and overcome the difficulties of numerical simulation resulting from both complex boundaries and differences between length and width scales, a two-dimensional river ice numerical model based on a boundary-fitted coordinate transformation method was developed. The presented model considers the influence of the frazil ice accumulation under ice cover and the shape of the leading edge of ice cover during the freezing process. The model is capable of determining the velocity field, the distribution of water temperature, the concentration distribution of frazil ice, the transport of floating ice, the progression, stability, and thawing of ice cover, and the transport, accumulation, and erosion of ice under ice cover. A MacCormack scheme was used to solve the equations numerically. The model was validated with field observations from the Hequ Reach of the Yellow River. Comparison of simulation results with field data indicates that the model is capable of simulating the river ice process with high accuracy.
Observations from using models to fit the gas production of varying volume test cells and landfills.
Lamborn, Julia
2012-12-01
Landfill operators are looking for more accurate models to predict waste degradation and landfill gas production. The simple microbial growth and decay models, whilst being easy to use, have been shown to be inaccurate. Many of the newer and more complex (component) models are highly parameter hungry and many of the required parameters have not been collected or measured at full-scale landfills. This paper compares the results of using different models (LANDGEM, HBM, and two Monod models developed by the author) to fit the gas production of laboratory scale, field test cell and full-scale landfills and discusses some observations that can be made regarding the scalability of gas generation rates. The comparison of these results show that the fast degradation rate that occurs at laboratory scale is not replicated at field-test cell and full-scale landfills. At small scale, all the models predict a slower rate of gas generation than actually occurs. At field test cell and full-scale a number of models predict a faster gas generation than actually occurs. Areas for future work have been identified, which include investigations into the capture efficiency of gas extraction systems and into the parameter sensitivity and identification of the critical parameters for field-test cell and full-scale landfill predication.
The challenges of fitting an item response theory model to the Social Anhedonia Scale.
Reise, Steven P; Horan, William P; Blanchard, Jack J
2011-05-01
This study explored the application of latent variable measurement models to the Social Anhedonia Scale (SAS; Eckblad, Chapman, Chapman, & Mishlove, 1982), a widely used and influential measure in schizophrenia-related research. Specifically, we applied unidimensional and bifactor item response theory (IRT) models to data from a community sample of young adults (n = 2,227). Ordinal factor analyses revealed that identifying a coherent latent structure in the 40-item SAS data was challenging due to (a) the presence of multiple small content clusters (e.g., doublets); (b) modest relations between those clusters, which, in turn, implies a general factor of only modest strength; (c) items that shared little variance with the majority of items; and (d) cross-loadings in bifactor solutions. Consequently, we conclude that SAS responses cannot be modeled accurately by either unidimensional or bifactor IRT models. Although the application of a bifactor model to a reduced 17-item set met with better success, significant psychometric and substantive problems remained. Results highlight the challenges of applying latent variable models to scales that were not originally designed to fit these models.
Ahearn, T S; Staff, R T; Redpath, T W; Semple, S I K
2005-05-07
The use of curve-fitting and compartmental modelling for calculating physiological parameters from measured data has increased in popularity in recent years. Finding the 'best fit' of a model to data involves the minimization of a merit function. An example of a merit function is the sum of the squares of the differences between the data points and the model estimated points. This is facilitated by curve-fitting algorithms. Two curve-fitting methods, Levenberg-Marquardt and MINPACK-1, are investigated with respect to the search start points that they require and the accuracy of the returned fits. We have simulated one million dynamic contrast enhanced MRI curves using a range of parameters and investigated the use of single and multiple search starting points. We found that both algorithms, when used with a single starting point, return unreliable fits. When multiple start points are used, we found that both algorithms returned reliable parameters. However the MINPACK-1 method generally outperformed the Levenberg-Marquardt method. We conclude that the use of a single starting point when fitting compartmental modelling data such as this produces unsafe results and we recommend the use of multiple start points in order to find the global minima.
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Cristina García Magro
2015-06-01
Full Text Available Purpose: The aims of the paper is offers a model of analysis which allows to measure the impact on the performance of fairs, as well as the knowledge or not of the motives of participation of the visitors on the part of the exhibitors. Design/methodology: A review of the literature is established concerning two of the principal interested agents, exhibitors and visitors, focusing. The study is focused on the line of investigation referred to the motives of participation or not in a trade show. According to the information thrown by each perspectives of study, a comparative analysis is carried out in order to determine the degree of existing understanding between both. Findings: The trade shows allow to be studied from an integrated strategic marketing approach. The fit model between the reasons for participation of exhibitors and visitors offer information on the lack of an understanding between exhibitors and visitors, leading to dissatisfaction with the participation, a fact that is reflected in the fair success. The model identified shows that a strategic plan must be designed in which the reason for participation of visitor was incorporated as moderating variable of the reason for participation of exhibitors. The article concludes with the contribution of a series of proposals for the improvement of fairground results. Social implications: The fit model that improve the performance of trade shows, implicitly leads to successful achievement of targets for multiple stakeholders beyond the consideration of visitors and exhibitors. Originality/value: The integrated perspective of stakeholders allows the study of the existing relationships between the principal groups of interest, in such a way that, having knowledge on the condition of the question of the trade shows facilitates the task of the investigator in future academic works and allows that the interested groups obtain a better performance to the participation in fairs, as visitor or as
Improving the Fit of a Land-Surface Model to Data Using its Adjoint
Raoult, Nina; Jupp, Tim; Cox, Peter; Luke, Catherine
2016-04-01
Land-surface models (LSMs) are crucial components of the Earth System Models (ESMs) which are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. In this study, JULES is automatically differentiated using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed, to search for locally optimum parameter sets by calibrating against observations. We present an introduction to the adJULES system and demonstrate its ability to improve the model-data fit using eddy covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the 5 Plant Functional Types (PFTS) in JULES. The optimised PFT-specific parameters improve the performance of JULES over 90% of the FLUXNET sites used in the study. These reductions in error are shown and compared to reductions found due to site-specific optimisations. Finally, we show that calculation of the 2nd derivative of JULES allows us to produce posterior probability density functions of the parameters and how knowledge of parameter values is constrained by observations.
Computational Modelling in Cancer: Methods and Applications
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Konstantina Kourou
2015-01-01
Full Text Available Computational modelling of diseases is an emerging field, proven valuable for the diagnosis, prognosis and treatment of the disease. Cancer is one of the diseases where computational modelling provides enormous advancements, allowing the medical professionals to perform in silico experiments and gain insights prior to any in vivo procedure. In this paper, we review the most recent computational models that have been proposed for cancer. Well known databases used for computational modelling experiments, as well as, the various markup language representations are discussed. In addition, recent state of the art research studies related to tumour growth and angiogenesis modelling are presented.
Explicit finite element modelling of the impaction of metal press-fit acetabular components.
Hothi, H S; Busfield, J J C; Shelton, J C
2011-03-01
Metal press-fit cups and shells are widely used in hip resurfacing and total hip replacement procedures. These acetabular components are inserted into a reamed acetabula cavity by either impacting their inner polar surface (shells) or outer rim (cups). Two-dimensional explicit dynamics axisymmetric finite element models were developed to simulate these impaction methods. Greater impact velocities were needed to insert the components when the interference fit was increased; a minimum velocity of 2 m/s was required to fully seat a component with a 2 mm interference between the bone and outer diameter. Changing the component material from cobalt-chromium to titanium alloy resulted in a reduction in the number of impacts on the pole to seat it from 14 to nine. Of greatest significance, it was found that locking a rigid cap to the cup or shell rim resulted in up to nine fewer impactions being necessary to seat it than impacting directly on the polar surface or using a cap free from the rim of the component, as is the case with many commercial resurfacing cup impaction devices currently used. This is important to impactor design and could make insertion easier and also reduce acetabula bone damage.
Energy Technology Data Exchange (ETDEWEB)
Chatzopoulos, E.; Wheeler, J. Craig; Vinko, J. [Department of Astronomy, University of Texas at Austin, Austin, TX (United States); Horvath, Z. L.; Nagy, A., E-mail: manolis@astro.as.utexas.edu [Department of Optics and Quantum Electronics, University of Szeged (Hungary)
2013-08-10
We present fits of generalized semi-analytic supernova (SN) light curve (LC) models for a variety of power inputs including {sup 56}Ni and {sup 56}Co radioactive decay, magnetar spin-down, and forward and reverse shock heating due to supernova ejecta-circumstellar matter (CSM) interaction. We apply our models to the observed LCs of the H-rich superluminous supernovae (SLSN-II) SN 2006gy, SN 2006tf, SN 2008am, SN 2008es, CSS100217, the H-poor SLSN-I SN 2005ap, SCP06F6, SN 2007bi, SN 2010gx, and SN 2010kd, as well as to the interacting SN 2008iy and PTF 09uj. Our goal is to determine the dominant mechanism that powers the LCs of these extraordinary events and the physical conditions involved in each case. We also present a comparison of our semi-analytical results with recent results from numerical radiation hydrodynamics calculations in the particular case of SN 2006gy in order to explore the strengths and weaknesses of our models. We find that CS shock heating produced by ejecta-CSM interaction provides a better fit to the LCs of most of the events we examine. We discuss the possibility that collision of supernova ejecta with hydrogen-deficient CSM accounts for some of the hydrogen-deficient SLSNe (SLSN-I) and may be a plausible explanation for the explosion mechanism of SN 2007bi, the pair-instability supernova candidate. We characterize and discuss issues of parameter degeneracy.
Fitting Data to Model: Structural Equation Modeling Diagnosis Using Two Scatter Plots
Yuan, Ke-Hai; Hayashi, Kentaro
2010-01-01
This article introduces two simple scatter plots for model diagnosis in structural equation modeling. One plot contrasts a residual-based M-distance of the structural model with the M-distance for the factor score. It contains information on outliers, good leverage observations, bad leverage observations, and normal cases. The other plot contrasts…
The shape of dark matter haloes - II. The GALACTUS H I modelling & fitting tool
Peters, S. P. C.; van der Kruit, P. C.; Allen, R. J.; Freeman, K. C.
2017-01-01
We present a new H I modelling tool called GALACTUS. The program has been designed to perform automated fits of disc-galaxy models to observations. It includes a treatment for the self-absorption of gas. The software has been released into the public domain. We describe the design philosophy and inner workings of the program. After this, we model the face-on galaxy NGC 2403 using both self-absorption and optically thin models, showing that self-absorption occurs even in face-on galaxies. These results are then used to model an edge-on galaxy. It is shown that the maximum surface brightness plateaus seen in Paper I of this series are indeed signs of self-absorption. The apparent H I mass of an edge-on galaxy can be drastically lower compared with that same galaxy seen face-on. The Tully-Fisher relation is found to be relatively free from self-absorption issues.
Kuhlman, J. M.
1979-01-01
The aerodynamic design of a wind-tunnel model of a wing representative of that of a subsonic jet transport aircraft, fitted with winglets, was performed using two recently developed optimal wing-design computer programs. Both potential flow codes use a vortex lattice representation of the near-field of the aerodynamic surfaces for determination of the required mean camber surfaces for minimum induced drag, and both codes use far-field induced drag minimization procedures to obtain the required spanloads. One code uses a discrete vortex wake model for this far-field drag computation, while the second uses a 2-D advanced panel wake model. Wing camber shapes for the two codes are very similar, but the resulting winglet camber shapes differ widely. Design techniques and considerations for these two wind-tunnel models are detailed, including a description of the necessary modifications of the design geometry to format it for use by a numerically controlled machine for the actual model construction.
Electrically detected magnetic resonance modeling and fitting: An equivalent circuit approach
Energy Technology Data Exchange (ETDEWEB)
Leite, D. M. G., E-mail: dmgleite@fc.unesp.br [UNIFEI—Universidade Federal de Itajubá, Av. BPS, 1303, 37500-903 Itajubá, MG (Brazil); Batagin-Neto, A.; Nunes-Neto, O. [UNESP—Univ Estadual Paulista, POSMAT—Programa de Pós-Graduação em Ciência e Tecnologia de Materiais, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01, 17033-360 Bauru, SP (Brazil); Gómez, J. A. [Departamento de Física, FFCLRP-USP, Av. Bandeirantes 3900, 14040-901 Ribeirão Preto, SP (Brazil); Graeff, C. F. O. [UNESP—Univ Estadual Paulista, POSMAT—Programa de Pós-Graduação em Ciência e Tecnologia de Materiais, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01, 17033-360 Bauru, SP (Brazil); DF-FC, UNESP—Univ Estadual Paulista, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01, 17033-360 Bauru, SP (Brazil)
2014-01-21
The physics of electrically detected magnetic resonance (EDMR) quadrature spectra is investigated. An equivalent circuit model is proposed in order to retrieve crucial information in a variety of different situations. This model allows the discrimination and determination of spectroscopic parameters associated to distinct resonant spin lines responsible for the total signal. The model considers not just the electrical response of the sample but also features of the measuring circuit and their influence on the resulting spectral lines. As a consequence, from our model, it is possible to separate different regimes, which depend basically on the modulation frequency and the RC constant of the circuit. In what is called the high frequency regime, it is shown that the sign of the signal can be determined. Recent EDMR spectra from Alq{sub 3} based organic light emitting diodes, as well as from a-Si:H reported in the literature, were successfully fitted by the model. Accurate values of g-factor and linewidth of the resonant lines were obtained.
FIT ANALYSIS OF INDOSAT DOMPETKU BUSINESS MODEL USING A STRATEGIC DIAGNOSIS APPROACH
Directory of Open Access Journals (Sweden)
Fauzi Ridwansyah
2015-09-01
Full Text Available Mobile payment is an industry's response to global and regional technological-driven, as well as national social-economical driven in less cash society development. The purposes of this study were 1 identifying positioning of PT. Indosat in providing a response to Indonesian mobile payment market, 2 analyzing Indosat’s internal capabilities and business model fit with environment turbulence, and 3 formulating the optimum mobile payment business model development design for Indosat. The method used in this study was a combination of qualitative and quantitative analysis through in-depth interviews with purposive judgment sampling. The analysis tools used in this study were Business Model Canvas (MBC and Ansoff’s Strategic Diagnosis. The interviewees were the representatives of PT. Indosat internal management and mobile payment business value chain stakeholders. Based on BMC mapping which is then analyzed by strategic diagnosis model, a considerable gap (>1 between the current market environment and Indosat strategy of aggressiveness with the expected future of environment turbulence level was obtained. Therefore, changes in the competitive strategy that need to be conducted include 1 developing a new customer segment, 2 shifting the value proposition that leads to the extensification of mobile payment, 3 monetizing effective value proposition, and 4 integrating effective collaboration for harmonizing company’s objective with the government's vision. Keywords: business model canvas, Indosat, mobile payment, less cash society, strategic diagnosis
Body-Fitted Detonation Shock Dynamics and the Pseudo-Reaction-Zone Energy Release Model
Meyer, Chad; Quirk, James; Short, Mark; Chqiuete, Carlos
2016-11-01
Programmed-burn methods are a class of models used to propagate a detonation wave, without the high resolution cost associated with a direct numerical simulation. They separate the detonation evolution calculation into two components: timing and energy release. The timing component is usually calculated with a Detonation Shock Dynamics model, a surface evolution representation that relates the normal velocity of the surface (Dn) to its local curvature. The energy release component must appropriately capture the degree of energy change associated with chemical reaction while simultaneously remaining synchronized with the timing component. The Pseudo-Reaction-Zone (PRZ) model is a reactive burn like energy release model, converting reactants into products, but with a conversion rate that is a function of the DSD surface Dn field. As such, it requires the DSD calculation produce smooth Dn fields, a challenge in complex geometries. We describe a new body-fitted approach to the Detonation Shock Dynamics calculation which produces the required smooth Dn fields, and a method for calibrating the PRZ model such that the rate of energy release remains as synced as possible with the timing component. We show results for slab, rate-stick and arc geometries.
A new fit-for-purpose model testing framework: Decision Crash Tests
Tolson, Bryan; Craig, James
2016-04-01
Decision-makers in water resources are often burdened with selecting appropriate multi-million dollar strategies to mitigate the impacts of climate or land use change. Unfortunately, the suitability of existing hydrologic simulation models to accurately inform decision-making is in doubt because the testing procedures used to evaluate model utility (i.e., model validation) are insufficient. For example, many authors have identified that a good standard framework for model testing called the Klemes Crash Tests (KCTs), which are the classic model validation procedures from Klemeš (1986) that Andréassian et al. (2009) rename as KCTs, have yet to become common practice in hydrology. Furthermore, Andréassian et al. (2009) claim that the progression of hydrological science requires widespread use of KCT and the development of new crash tests. Existing simulation (not forecasting) model testing procedures such as KCTs look backwards (checking for consistency between simulations and past observations) rather than forwards (explicitly assessing if the model is likely to support future decisions). We propose a fundamentally different, forward-looking, decision-oriented hydrologic model testing framework based upon the concept of fit-for-purpose model testing that we call Decision Crash Tests or DCTs. Key DCT elements are i) the model purpose (i.e., decision the model is meant to support) must be identified so that model outputs can be mapped to management decisions ii) the framework evaluates not just the selected hydrologic model but the entire suite of model-building decisions associated with model discretization, calibration etc. The framework is constructed to directly and quantitatively evaluate model suitability. The DCT framework is applied to a model building case study on the Grand River in Ontario, Canada. A hypothetical binary decision scenario is analysed (upgrade or not upgrade the existing flood control structure) under two different sets of model building
Goodness-of-fit tests for vector autoregressive models in time series
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The paper proposes and studies some diagnostic tools for checking the goodness-of-fit of general parametric vector autoregressive models in time series. The resulted tests are asymptotically chi-squared under the null hypothesis and can detect the alternatives converging to the null at a parametric rate. The tests involve weight functions,which provides us with the flexibility to choose scores for enhancing power performance,especially under directional alternatives. When the alternatives are not directional,we construct asymptotically distribution-free maximin tests for a large class of alternatives. A possibility to construct score-based omnibus tests is discussed when the alternative is saturated. The power performance is also investigated. In addition,when the sample size is small,a nonparametric Monte Carlo test approach for dependent data is proposed to improve the performance of the tests. The algorithm is easy to implement. Simulation studies and real applications are carried out for illustration.
Corneal modeling using conic section fits of PAR corneal topography system measurements
Zipper, Stanley; Manns, Fabrice; Fernandez, Viviana; Sandadi, Samith; Ho, Arthur; Parel, Jean-Marie A.
2001-06-01
The purpose of this study was to measure the average shape and variability of human corneas and to develop a tool for analyzing, height, curvature, and aberrations based on a conic section model. Fresh Eye Bank Eyes were placed in Dextran until the corneal thickness reached a physiological value. The eyes were placed in a custom made holder and measured using an intraoperative PAR Corneal Topography System (CTS) mounted on an operation microscope. Topography was measured before and after removal of the epithelium. A series of MATLAB functions were written to analyze the raw-z (height) data in polar coordinates. The functions fit conic sections to the PAR CTS data along hemi-meridians at 5 degree(s) intervals. The conic shape factor and apical radius were used to calculate and display the curvature. The dependence of these parameters with meridional position was examined.
Strain estimation in 3D by fitting linear and planar data to the March model
Mulchrone, Kieran F.; Talbot, Christopher J.
2016-08-01
The probability density function associated with the March model is derived and used in a maximum likelihood method to estimate the best fit distribution and 3D strain parameters for a given set of linear or planar data. Typically it is assumed that in the initial state (pre-strain) linear or planar data are uniformly distributed on the sphere which means the number of strain parameters estimated needs to be reduced so that the numerical technique succeeds. Essentially this requires that the data are rotated into a suitable reference frame prior to analysis. The method has been applied to a suitable example from the Dalradian of SW Scotland and results obtained are consistent with those from an independent method of strain analysis. Despite March theory having been incorporated deep into the fabric of geological strain analysis, its full potential as a simple direct 3D strain analytical tool has not been achieved. The method developed here may help remedy this situation.
Fast hybrid fitting energy-based active contour model for target detection
Institute of Scientific and Technical Information of China (English)
Dengwei Wang; Tianxu Zhang; Luxin Yan
2011-01-01
A novel hybrid fitting energy-based active contour model in the level set framework is proposed.The method fuses the region and boundary information of the target to achieve accurate and robust detection performance.A special extra term that penalizes the deviation of the level set function from a signed distance function is also included in our method. This term allows the time-consuming redistancing operation to be removed completely.Moreover,a fast unconditionally stable numerical scheme is introduced to solve the problem.Experimental results on real infrared images show that our method can improve target detection performance efficiently in terms of the number of iterations and the wasted central processing unit(CPU) time.
Modeling the Aneuploidy Control of Cancer
Directory of Open Access Journals (Sweden)
Wang Zhong
2010-07-01
Full Text Available Abstract Background Aneuploidy has long been recognized to be associated with cancer. A growing body of evidence suggests that tumorigenesis, the formation of new tumors, can be attributed to some extent to errors occurring at the mitotic checkpoint, a major cell cycle control mechanism that acts to prevent chromosome missegregation. However, so far no statistical model has been available quantify the role aneuploidy plays in determining cancer. Methods We develop a statistical model for testing the association between aneuploidy loci and cancer risk in a genome-wide association study. The model incorporates quantitative genetic principles into a mixture-model framework in which various genetic effects, including additive, dominant, imprinting, and their interactions, are estimated by implementing the EM algorithm. Results Under the new model, a series of hypotheses tests are formulated to explain the pattern of the genetic control of cancer through aneuploid loci. Simulation studies were performed to investigate the statistical behavior of the model. Conclusions The model will provide a tool for estimating the effects of genetic loci on aneuploidy abnormality in genome-wide studies of cancer cells.
On the Model-Based Bootstrap with Missing Data: Obtaining a "P"-Value for a Test of Exact Fit
Savalei, Victoria; Yuan, Ke-Hai
2009-01-01
Evaluating the fit of a structural equation model via bootstrap requires a transformation of the data so that the null hypothesis holds exactly in the sample. For complete data, such a transformation was proposed by Beran and Srivastava (1985) for general covariance structure models and applied to structural equation modeling by Bollen and Stine…
Enders, Craig K.
2002-01-01
Proposed a method for extending the Bollen-Stine bootstrap model (K. Bollen and R. Stine, 1992) fit to structural equation models with missing data. Developed a Statistical Analysis System macro program to implement this procedure, and assessed its usefulness in a simulation. The new method yielded model rejection rates close to the nominal 5%…
A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)
Cantó, J.; Curiel, S.; Martínez-Gómez, E.
2009-07-01
Context: Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims: We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (asexual genetic algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two examples: the orbits of exoplanets by taking a set of radial velocity data, and the spectral energy distribution (SED) observed towards a YSO (Young Stellar Object). Methods: The algorithm AGA may also be called genetic, although it differs from standard genetic algorithms in two main aspects: a) the initial population is not encoded; and b) the new generations are constructed by asexual reproduction. Results: Applying our algorithm in optimizing some complicated functions, we find the global maxima within a few iterations. For model fitting to the orbits of exoplanets and the SED of a YSO, we estimate the parameters and their associated errors.
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Ortendahl Jesse
2007-10-01
Full Text Available Abstract Background To provide quantitative insight into current U.S. policy choices for cervical cancer prevention, we developed a model of human papillomavirus (HPV and cervical cancer, explicitly incorporating uncertainty about the natural history of disease. Methods We developed a stochastic microsimulation of cervical cancer that distinguishes different HPV types by their incidence, clearance, persistence, and progression. Input parameter sets were sampled randomly from uniform distributions, and simulations undertaken with each set. Through systematic reviews and formal data synthesis, we established multiple epidemiologic targets for model calibration, including age-specific prevalence of HPV by type, age-specific prevalence of cervical intraepithelial neoplasia (CIN, HPV type distribution within CIN and cancer, and age-specific cancer incidence. For each set of sampled input parameters, likelihood-based goodness-of-fit (GOF scores were computed based on comparisons between model-predicted outcomes and calibration targets. Using 50 randomly resampled, good-fitting parameter sets, we assessed the external consistency and face validity of the model, comparing predicted screening outcomes to independent data. To illustrate the advantage of this approach in reflecting parameter uncertainty, we used the 50 sets to project the distribution of health outcomes in U.S. women under different cervical cancer prevention strategies. Results Approximately 200 good-fitting parameter sets were identified from 1,000,000 simulated sets. Modeled screening outcomes were externally consistent with results from multiple independent data sources. Based on 50 good-fitting parameter sets, the expected reductions in lifetime risk of cancer with annual or biennial screening were 76% (range across 50 sets: 69–82% and 69% (60–77%, respectively. The reduction from vaccination alone was 75%, although it ranged from 60% to 88%, reflecting considerable parameter
Hajigeorgiou, Photos G.
2016-12-01
An analytical model for the diatomic potential energy function that was recently tested as a universal function (Hajigeorgiou, 2010) has been further modified and tested as a suitable model for direct-potential-fit analysis. Applications are presented for the ground electronic states of three diatomic molecules: oxygen, carbon monoxide, and hydrogen fluoride. The adjustable parameters of the extended Lennard-Jones potential model are determined through nonlinear regression by fits to calculated rovibrational energy term values or experimental spectroscopic line positions. The model is shown to lead to reliable, compact and simple representations for the potential energy functions of these systems and could therefore be classified as a suitable and attractive model for direct-potential-fit analysis.
Hypoxia in models of lung cancer
DEFF Research Database (Denmark)
Graves, Edward E; Vilalta, Marta; Cecic, Ivana K
2010-01-01
PURPOSE: To efficiently translate experimental methods from bench to bedside, it is imperative that laboratory models of cancer mimic human disease as closely as possible. In this study, we sought to compare patterns of hypoxia in several standard and emerging mouse models of lung cancer...... to establish the appropriateness of each for evaluating the role of oxygen in lung cancer progression and therapeutic response. EXPERIMENTAL DESIGN: Subcutaneous and orthotopic human A549 lung carcinomas growing in nude mice as well as spontaneous K-ras or Myc-induced lung tumors grown in situ......H2AX foci in vitro and in vivo. Finally, our findings were compared with oxygen electrode measurements of human lung cancers. RESULTS: Minimal fluoroazomycin arabinoside and pimonidazole accumulation was seen in tumors growing within the lungs, whereas subcutaneous tumors showed substantial trapping...
Genome scale metabolic modeling of cancer
DEFF Research Database (Denmark)
Nilsson, Avlant; Nielsen, Jens
2016-01-01
been used as scaffolds for analysis of high throughput data to allow mechanistic interpretation of changes in expression. Finally, GEMs allow quantitative flux predictions using flux balance analysis (FBA). Here we critically review the requirements for successful FBA simulations of cancer cells......Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization...... of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome...
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Loreen eHertäg
2012-09-01
Full Text Available 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.
Mathematical Modeling of Allelopathy. III. A Model for Curve-Fitting Allelochemical Dose Responses
Liu, Li; An, Min; Johnson, Ian R.; Lovett, John V.
2003-01-01
Bioassay techniques are often used to study the effects of allelochemicals on plant processes, and it is generally observed that the processes are stimulated at low allelochemical concentrations and inhibited as the concentrations increase. A simple empirical model is presented to analyze this type of response. The stimulation-inhibition properties of allelochemical-dose responses can be described by the parameters in the model. The indices, p% reductions, are calculated to assess the alleloc...
Fitting density models to observational data - The local Schmidt law in molecular clouds
Lombardi, Marco; Alves, João
2013-01-01
We consider the general problem of fitting a parametric density model to discrete observations, taken to follow a non-homogeneous Poisson point process. This class of models is very common, and can be used to describe many astrophysical processes, including the distribution of protostars in molecular clouds. We give the expression for the likelihood of a given spatial density distribution of protostars and apply it to infer the most probable dependence of the protostellar surface density on the gas surface density. Finally, we apply this general technique to model the distribution of protostars in the Orion molecular cloud and robustly derive the local star formation scaling (Schmidt) law for a molecular cloud. We find that in this cloud the protostellar surface density, \\Sigma_YSO, is directly proportional to the square gas column density, here expressed as infrared extinction in the K-band, A_K: more precisely, \\Sigma_YSO = (1.65 +/- 0.19) A_K^(2.03 +/- 0.15) stars pc^-2.
Modeling of physical fitness of young karatyst on the pre basic training
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Galimskyi V.A.
2014-05-01
Full Text Available Purpose : to develop a program of physical fitness for the correction of the pre basic training on the basis of model performance. Material: 57 young karate sportsmen of 9-11 years old took part in the research. Results : the level of general and special physical preparedness of young karate 9-11 years old was determined. Classes in the control group occurred in the existing program for yous sports school Muay Thai (Thailand boxing. For the experimental group has developed a program of selective development of general and special physical qualities of model-based training sessions. Special program contains 6 direction: 1. Development of static and dynamic balance; 2. Development of vestibular stability (precision movements after rotation; 3. Development rate movements; 4. The development of the capacity for rapid restructuring movements; 5. Development capabilities to differentiate power and spatial parameters of movement; 6. Development of the ability to perform jumping movements of rotation. Development of special physical qualities continued to work to improve engineering complex shock motions on the place and with movement. Conclusions : the use of selective development of special physical qualities based models of training sessions has a significant performance advantage over the control group.
Slater, Graham J; Harmon, Luke J; Wegmann, Daniel; Joyce, Paul; Revell, Liam J; Alfaro, Michael E
2012-03-01
In recent years, a suite of methods has been developed to fit multiple rate models to phylogenetic comparative data. However, most methods have limited utility at broad phylogenetic scales because they typically require complete sampling of both the tree and the associated phenotypic data. Here, we develop and implement a new, tree-based method called MECCA (Modeling Evolution of Continuous Characters using ABC) that uses a hybrid likelihood/approximate Bayesian computation (ABC)-Markov-Chain Monte Carlo approach to simultaneously infer rates of diversification and trait evolution from incompletely sampled phylogenies and trait data. We demonstrate via simulation that MECCA has considerable power to choose among single versus multiple evolutionary rate models, and thus can be used to test hypotheses about changes in the rate of trait evolution across an incomplete tree of life. We finally apply MECCA to an empirical example of body size evolution in carnivores, and show that there is no evidence for an elevated rate of body size evolution in the pinnipeds relative to terrestrial carnivores. ABC approaches can provide a useful alternative set of tools for future macroevolutionary studies where likelihood-dependent approaches are lacking.
Fitting a Thurstonian IRT model to forced-choice data using Mplus.
Brown, Anna; Maydeu-Olivares, Alberto
2012-12-01
To counter response distortions associated with the use of rating scales (a.k.a. Likert scales), items can be presented in a comparative fashion, so that respondents are asked to rank the items within blocks (forced-choice format). However, classical scoring procedures for these forced-choice designs lead to ipsative data, which presents psychometric challenges that are well described in the literature. Recently, Brown and Maydeu-Olivares (Educational and Psychological Measurement 71: 460-502, 2011a) introduced a model based on Thurstone's law of comparative judgment, which overcomes the problems of ipsative data. Here, we provide a step-by-step tutorial for coding forced-choice responses, specifying a Thurstonian item response theory model that is appropriate for the design used, assessing the model's fit, and scoring individuals on psychological attributes. Estimation and scoring is performed using Mplus, and a very straightforward Excel macro is provided that writes full Mplus input files for any forced-choice design. Armed with these tools, using a forced-choice design is now as easy as using ratings.
A simple periodic-forced model for dengue fitted to incidence data in Singapore.
Andraud, Mathieu; Hens, Niel; Beutels, Philippe
2013-07-01
Dengue is the world's major arbovirosis and therefore an important public health concern in endemic areas. The availability of weekly reports of dengue cases in Singapore offers the opportunity to analyze the transmission dynamics and the impact of vector control strategies. Based on a previous model studying the impact of vector control strategies in Singapore during the 2005 outbreak, a simple vector-host model accounting for seasonal fluctuation in vector density was developed to estimate the parameters governing the vector population dynamics using dengue fever incidence data from August 2003 to December 2007. The impact of vector control, which consisted principally of a systematic removal of actual and potential breeding sites during a six-week period in 2005, was also investigated. Although our approach does not account for the complex life cycle of the vector, the good fit between data and model outputs showed that the impact of seasonality on the transmission dynamics is highly important. Moreover, the periodic fluctuations of the vector population were found in phase with temperature variations, suggesting a strong climate effect on the vector density and, in turn, on the transmission dynamics.
Minimal see-saw model predicting best fit lepton mixing angles
Energy Technology Data Exchange (ETDEWEB)
King, Stephen F., E-mail: king@soton.ac.uk
2013-07-09
We discuss a minimal predictive see-saw model in which the right-handed neutrino mainly responsible for the atmospheric neutrino mass has couplings to (ν{sub e},ν{sub μ},ν{sub τ}) proportional to (0,1,1) and the right-handed neutrino mainly responsible for the solar neutrino mass has couplings to (ν{sub e},ν{sub μ},ν{sub τ}) proportional to (1,4,2), with a relative phase η=−2π/5. We show how these patterns of couplings could arise from an A{sub 4} family symmetry model of leptons, together with Z{sub 3} and Z{sub 5} symmetries which fix η=−2π/5 up to a discrete phase choice. The PMNS matrix is then completely determined by one remaining parameter which is used to fix the neutrino mass ratio m{sub 2}/m{sub 3}. The model predicts the lepton mixing angles θ{sub 12}≈34{sup ∘},θ{sub 23}≈41{sup ∘},θ{sub 13}≈9.5{sup ∘}, which exactly coincide with the current best fit values for a normal neutrino mass hierarchy, together with the distinctive prediction for the CP violating oscillation phase δ≈106{sup ∘}.
Why Simple Stellar Population models do not fit the colours of Galactic open clusters
Piskunov, A E; Schilbach, E; Röser, S; Scholz, R -D; Zinnecker, H
2009-01-01
(...) We have found a disagreement between the observed integrated colours of 650 local Galactic clusters and theoretical colours of present-day SSP models and seek an explanation for this discrepancy. We check the hypothesis that the systematic offset between observed and theoretical colours, which is $(B$$-$$V)\\approx 0.3$ \\textbf{and $(J$$-$$K_s)\\approx 0.8$}, is due to neglecting the discrete nature of the underlying mass function. Using Monte Carlo simulations we construct artificial clusters of coeval stars drawn from a mass distribution according to the Salpeter IMF and compare them with corresponding "continuous-IMF" SSP models. If the discreteness of the IMF is taken into account, the model fits the observations perfectly and is able to explain naturally a number of red "outliers" observed in the empirical colour-age relation. We find that the \\textit{systematic} offset between the continuous- and discrete-IMF colours reaches its maximum of about 0.5 in $(B$$-$$V)$ for a cluster mass $M_c=10^2 m_\\odo...
Experimental model for non-Newtonian fluid viscosity estimation: Fit to mathematical expressions
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Guillem Masoliver i Marcos
2017-01-01
Full Text Available The construction process of a viscometer, developed in collaboration with a final project student, is here presented. It is intended to be used by first year's students to know the viscosity as a fluid property, for both Newtonian and non-Newtonian flows. Viscosity determination is crucial for the fluids behaviour knowledge related to their reologic and physical properties. These have great implications in engineering aspects such as friction or lubrication. With the present experimental model device three different fluids are analyzed (water, kétchup and a mixture with cornstarch and water. Tangential stress is measured versus velocity in order to characterize all the fluids in different thermal conditions. A mathematical fit process is proposed to be done in order to adjust the results to expected analytical expressions, obtaining good results for these fittings, with R2 greater than 0.88 in any case.
Poisson distribution and process as a well-fitting pattern for counting variables in biologic models
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Lucietta Betti
2012-09-01
Full Text Available One of the major criticisms directed to basic research on high dilution effects is the lack of a steady statistical approach; therefore, it seems crucial to fix some milestones in statistical analysis of this kind of experimentation. Since plant research in homeopathy has been recently developed and one of the mostly used models is based on in vitro seed germination, here we propose a statistical approach focused on the Poisson distribution, that satisfactorily fits the number of non-germinated seeds. Poisson distribution is a discrete-valued model often used in statistics when representing the number X of specific events (telephone calls, industrial machine failures, genetic mutations etc. that occur in a fixed period of time, supposing that instant probability of occurrence of such events is constant. If we denote with ÃŽÂ» the average number of events that occur within the fixed period, the probability of observing exactly k events is: P(k = e-ÃŽÂ» ÃŽÂ»k /k! , k = 0, 1,2,Ã¢â‚¬Â¦ This distribution is commonly used when dealing with rare effects, in the sense that it has to be almost impossible to have two events at the same time. Poisson distribution is the basic model of the socalled Poisson process, which is a counting process N(t, where t is a time parameter, having these properties: -The process starts with zero: N(0 = 0; -The increments are independent; -The number of events that occur in a period of time d(t follows a Poisson distribution with parameter proportional to d(t; -The waiting time, i.e. the time between an event and another one, follows and exponential distribution. In a series of experiments performed by our research group ([1], [2]., [3], [4] we tried to apply this distribution to the number X of non-germinated seeds out of a fixed number N* of seeds in a Petri dish (usually N* = 33 or N* = 36. The goodness-of-fit was checked by different tests (Kolmogorov distance and chi-squared, as well as
Zhou, Liming; Yang, Yuxing; Yuan, Shiying
2006-02-01
A new algorithm, the coordinates transform iterative optimizing method based on the least square curve fitting model, is presented. This arithmetic is used for extracting the bio-impedance model parameters. It is superior to other methods, for example, its speed of the convergence is quicker, and its calculating precision is higher. The objective to extract the model parameters, such as Ri, Re, Cm and alpha, has been realized rapidly and accurately. With the aim at lowering the power consumption, decreasing the price and improving the price-to-performance ratio, a practical bio-impedance measure system with double CPUs has been built. It can be drawn from the preliminary results that the intracellular resistance Ri increased largely with an increase in working load during sitting, which reflects the ischemic change of lower limbs.
Semenov, Yuri S; Novozhilov, Artem S
2016-05-01
A two-valued fitness landscape is introduced for the classical Eigen's quasispecies model. This fitness landscape can be considered as a direct generalization of the so-called single- or sharply peaked landscape. A general, non-permutation invariant quasispecies model is studied, and therefore the dimension of the problem is [Formula: see text], where N is the sequence length. It is shown that if the fitness function is equal to [Formula: see text] on a G-orbit A and is equal to w elsewhere, then the mean population fitness can be found as the largest root of an algebraic equation of degree at most [Formula: see text]. Here G is an arbitrary isometry group acting on the metric space of sequences of zeroes and ones of the length N with the Hamming distance. An explicit form of this exact algebraic equation is given in terms of the spherical growth function of the G-orbit A. Motivated by the analysis of the two-valued fitness landscapes, an abstract generalization of Eigen's model is introduced such that the sequences are identified with the points of a finite metric space X together with a group of isometries acting transitively on X. In particular, a simplicial analog of the original quasispecies model is discussed, which can be considered as a mathematical model of the switching of the antigenic variants for some bacteria.
Evapotranspiration measurement and modeling without fitting parameters in high-altitude grasslands
Ferraris, Stefano; Previati, Maurizio; Canone, Davide; Dematteis, Niccolò; Boetti, Marco; Balocco, Jacopo; Bechis, Stefano
2016-04-01
Mountain grasslands are important, also because one sixth of the world population lives inside watershed dominated by snowmelt. Also, grasslands provide food to both domestic and selvatic animals. The global warming will probably accelerate the hydrological cycle and increase the drought risk. The combination of measurements, modeling and remote sensing can furnish knowledge in such faraway areas (e.g.: Brocca et al., 2013). A better knowledge of water balance can also allow to optimize the irrigation (e.g.: Canone et al., 2015). This work is meant to build a model of water balance in mountain grasslands, ranging between 1500 and 2300 meters asl. The main input is the Digital Terrain Model, which is more reliable in grasslands than both in the woods and in the built environment. It drives the spatial variability of shortwave solar radiation. The other atmospheric forcings are more problematic to estimate, namely air temperature, wind and longwave radiation. Ad hoc routines have been written, in order to interpolate in space the meteorological hourly time variability. The soil hydraulic properties are less variable than in the plains, but the soil depth estimation is still an open issue. The soil vertical variability has been modeled taking into account the main processes: soil evaporation, root uptake, and fractured bedrock percolation. The time variability latent heat flux and soil moisture results have been compared with the data measured in an eddy covariance station. The results are very good, given the fact that the model has no fitting parameters. The space variability results have been compared with the results of a model based on Landsat 7 and 8 data, applied over an area of about 200 square kilometers. The spatial correlation is quite in agreement between the two models. Brocca et al. (2013). "Soil moisture estimation in alpine catchments through modelling and satellite observations". Vadose Zone Journal, 12(3), 10 pp. Canone et al. (2015). "Field
Branching process models of cancer
Durrett, Richard
2015-01-01
This volume develops results on continuous time branching processes and applies them to study rate of tumor growth, extending classic work on the Luria-Delbruck distribution. As a consequence, the authors calculate the probability that mutations that confer resistance to treatment are present at detection and quantify the extent of tumor heterogeneity. As applications, the authors evaluate ovarian cancer screening strategies and give rigorous proofs for results of Heano and Michor concerning tumor metastasis. These notes should be accessible to students who are familiar with Poisson processes and continuous time. Richard Durrett is mathematics professor at Duke University, USA. He is the author of 8 books, over 200 journal articles, and has supervised more than 40 Ph.D. students. Most of his current research concerns the applications of probability to biology: ecology, genetics, and most recently cancer.
Satellite image blind restoration based on surface fitting and multivariate model
Institute of Scientific and Technical Information of China (English)
CHEN Xin-bing; YANG Shi-zhi; WANG Xian-hua; QIAO Yan-li
2009-01-01
Owing to the blurring effect from atmosphere and camera system in the satellite imaging a blind image restoration algo-rithm is proposed which includes the modulation transfer function (MTF) estimation and the image restoration. In the MTF estimation stage, based on every degradation process of satellite imaging-chain, a combined parametric model of MTF is given and used to fit the surface of normalized logarithmic amplitude spectrum of degraded image. In the image restoration stage, a maximum a posteriori (MAP) based edge-preserving image restoration method is presented which introduces multivariate Laplacian model to characterize the prior distribution of wavelet coefficients of original image. During the image restoration, in order to avoid solving high nonlinear equations, optimization transfer algorithm is adopted to decom-pose the image restoration procedure into two simple steps: Landweber iteration and wavelet thresholding denoising. In the numerical experiment, the satellite image restoration results from SPOT-5 and high resolution camera (HR) of China & Brazil earth resource satellite (CBERS-02B) ane compared, and the proposed algorithm is superior in the image edge preservation and noise inhibition.
A Mathematical Images Group Model to Estimate the Sound Level in a Close-Fitting Enclosure
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Michael J. Panza
2014-01-01
Full Text Available This paper describes a special mathematical images model to determine the sound level inside a close-fitting sound enclosure. Such an enclosure is defined as the internal air volume defined by a machine vibration noise source at one wall and a parallel reflecting wall located very close to it and acts as the outside radiating wall of the enclosure. Four smaller surfaces define a parallelepiped for the volume. The main reverberation group is between the two large parallel planes. Viewed as a discrete line-type source, the main group is extended as additional discrete line-type source image groups due to reflections from the four smaller surfaces. The images group approach provides a convergent solution for the case where hard reflective surfaces are modeled with absorption coefficients equal to zero. Numerical examples are used to calculate the sound pressure level incident on the outside wall and the effect of adding high absorption to the front wall. This is compared to the result from the general large room diffuse reverberant field enclosure formula for several hard wall absorption coefficients and distances between machine and front wall. The images group method is shown to have low sensitivity to hard wall absorption coefficient value and presents a method where zero sound absorption for hard surfaces can be used rather than an initial hard surface sound absorption estimate or measurement to predict the internal sound levels the effect of adding absorption.
A simulation-based goodness-of-fit test for random effects in generalized linear mixed models
DEFF Research Database (Denmark)
Waagepetersen, Rasmus
2006-01-01
The goodness-of-fit of the distribution of random effects in a generalized linear mixed model is assessed using a conditional simulation of the random effects conditional on the observations. Provided that the specified joint model for random effects and observations is correct, the marginal...
A simulation-based goodness-of-fit test for random effects in generalized linear mixed models
DEFF Research Database (Denmark)
Waagepetersen, Rasmus Plenge
The goodness-of-fit of the distribution of random effects in a generalized linear mixed model is assessed using a conditional simulation of the random effects conditional on the observations. Provided that the specified joint model for random effects and observations is correct, the marginal...
Assessment of performance of survival prediction models for cancer prognosis
Directory of Open Access Journals (Sweden)
Chen Hung-Chia
2012-07-01
Full Text Available Abstract Background Cancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments. Methods We compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models. Results A public breast cancer dataset was used to compare several performance metrics using five prediction models. 1 For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2 The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3 Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results. Conclusions 1 Different performance metrics for evaluation of a survival prediction model may give different conclusions in
Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting
Energy Technology Data Exchange (ETDEWEB)
Ross, James C., E-mail: jross@bwh.harvard.edu [Channing Laboratory, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Surgical Planning Lab, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Laboratory of Mathematics in Imaging, Brigham and Women' s Hospital, Boston, Massachusetts 02126 (United States); Kindlmann, Gordon L. [Computer Science Department and Computation Institute, University of Chicago, Chicago, Illinois 60637 (United States); Okajima, Yuka; Hatabu, Hiroto [Department of Radiology, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Díaz, Alejandro A. [Pulmonary and Critical Care Division, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02215 and Department of Pulmonary Diseases, Pontificia Universidad Católica de Chile, Santiago (Chile); Silverman, Edwin K. [Channing Laboratory, Brigham and Women' s Hospital, Boston, Massachusetts 02215 and Pulmonary and Critical Care Division, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02215 (United States); Washko, George R. [Pulmonary and Critical Care Division, Brigham and Women' s Hospital and Harvard Medical School, Boston, Massachusetts 02215 (United States); Dy, Jennifer [ECE Department, Northeastern University, Boston, Massachusetts 02115 (United States); Estépar, Raúl San José [Department of Radiology, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Surgical Planning Lab, Brigham and Women' s Hospital, Boston, Massachusetts 02215 (United States); Laboratory of Mathematics in Imaging, Brigham and Women' s Hospital, Boston, Massachusetts 02126 (United States)
2013-12-15
Purpose: Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable. Methods: The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes. Results: The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT. Conclusions: The
Park, Yu Rang; Lee, Hye Won; Cho, Sung Bum; Kim, Ju Han
2007-01-01
The development of functional genomics including transcriptomics, proteomics and metabolomics allow us to monitor a large number of key cellular pathways simultaneously. Several technology-specific data models have been introduced for the representation of functional genomics experimental data, including the MicroArray Gene Expression-Object Model (MAGE-OM), the Proteomics Experiment Data Repository (PEDRo), and the Tissue MicroArray-Object Model (TMA-OM). Despite the increasing number of cancer studies using multiple functional genomics technologies, there is still no integrated data model for multiple functional genomics experimental and clinical data. We propose an object-oriented data model for cancer genomics research, Cancer Genomics Object Model (CaGe-OM). We reference four data models: Functional Genomic-Object Model, MAGE-OM, TMAOM and PEDRo. The clinical and histopathological information models are created by analyzing cancer management workflow and referencing the College of American Pathology Cancer Protocols and National Cancer Institute Common Data Elements. The CaGe-OM provides a comprehensive data model for integrated storage and analysis of clinical and multiple functional genomics data.
Directory of Open Access Journals (Sweden)
Rasouli Mahboobeh
2011-10-01
Full Text Available Abstract Background Gastrointestinal (GI tract cancer is one of the common causes of the mortality due to cancer in most developing countries such as Iran. The digestive tract is the major organ involved in the cancer. The northern part of the country, surrounded the Caspian Sea coast, is well known and the region with highest regional incidence of the GI tract cancer. In this paper our aim is to study the most common risk factors affecting the survival of the patients suffering from GI tract cancer using parametric models with frailty. Methods This research was a prospective study. Information of 484 cases with GI cancer was collected from Babol Cancer Registration Center during 1990-1991. The risk factors we studied are age, sex, family history of cancer, marital status, smoking status, occupation, race, medication status, education, residence (urban, rural, type of cancer, migration status (indigenous, non-native. The studied cases were followed up until 2006 for 15 years. Hazard ratio was used to interpret the death risk. The effect of the factors in the study on the patients survival are studied under a family of parametric models including Weibull, Exponential, Log-normal, and the Log-logistic model. The models are fitted using with and without frailty. The Akaike information criterion (AIC was considered to compare between competing models. Results Out of 484 patients in the study, 321 (66.3% were males and 163 (33.7% were females. The average age of the patient at the time of the diagnosis was 59 yr and 55 yr for the males and females respectively. Furthermore, 359 (74.2% patients suffered from esophageal, 110 (22.7% patients recognized with gastric, and 15 (3.1% patients with colon cancer. Survival rates after 1, 3, and 5 years of the diagnosis were 24%, 16%, and 15%, respectively. We found that the family history of the cancer is a significant factor on the death risk under all statistical models in the study. The comparison of AIC
Directory of Open Access Journals (Sweden)
Eloranta Sandra
2011-06-01
Full Text Available Abstract Background When the mortality among a cancer patient group returns to the same level as in the general population, that is, the patients no longer experience excess mortality, the patients still alive are considered "statistically cured". Cure models can be used to estimate the cure proportion as well as the survival function of the "uncured". One limitation of parametric cure models is that the functional form of the survival of the "uncured" has to be specified. It can sometimes be hard to find a survival function flexible enough to fit the observed data, for example, when there is high excess hazard within a few months from diagnosis, which is common among older age groups. This has led to the exclusion of older age groups in population-based cancer studies using cure models. Methods Here we have extended the flexible parametric survival model to incorporate cure as a special case to estimate the cure proportion and the survival of the "uncured". Flexible parametric survival models use splines to model the underlying hazard function, and therefore no parametric distribution has to be specified. Results We have compared the fit from standard cure models to our flexible cure model, using data on colon cancer patients in Finland. This new method gives similar results to a standard cure model, when it is reliable, and better fit when the standard cure model gives biased estimates. Conclusions Cure models within the framework of flexible parametric models enables cure modelling when standard models give biased estimates. These flexible cure models enable inclusion of older age groups and can give stage-specific estimates, which is not always possible from parametric cure models.
ProFit: Bayesian galaxy fitting tool
Robotham, A. S. G.; Taranu, D.; Tobar, R.
2016-12-01
ProFit is a Bayesian galaxy fitting tool that uses the fast C++ image generation library libprofit (ascl:1612.003) and a flexible R interface to a large number of likelihood samplers. It offers a fully featured Bayesian interface to galaxy model fitting (also called profiling), using mostly the same standard inputs as other popular codes (e.g. GALFIT ascl:1104.010), but it is also able to use complex priors and a number of likelihoods.
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.
Preclinical fluorescent mouse models of pancreatic cancer
Bouvet, Michael; Hoffman, Robert M.
2007-02-01
Here we describe our cumulative experience with the development and preclinical application of several highly fluorescent, clinically-relevant, metastatic orthotopic mouse models of pancreatic cancer. These models utilize the human pancreatic cancer cell lines which have been genetically engineered to selectively express high levels of the bioluminescent green fluorescent (GFP) or red fluorescent protein (RFP). Fluorescent tumors are established subcutaneously in nude mice, and tumor fragments are then surgically transplanted onto the pancreas. Locoregional tumor growth and distant metastasis of these orthotopic implants occurs spontaneously and rapidly throughout the abdomen in a manner consistent with clinical human disease. Highly specific, high-resolution, real-time visualization of tumor growth and metastasis may be achieved in vivo without the need for contrast agents, invasive techniques, or expensive imaging equipment. We have shown a high correlation between florescent optical imaging and magnetic resonance imaging in these models. Alternatively, transplantation of RFP-expressing tumor fragments onto the pancreas of GFP-expressing transgenic mice may be used to facilitate visualization of tumor-host interaction between the pancreatic tumor fragments and host-derived stroma and vasculature. Such in vivo models have enabled us to serially visualize and acquire images of the progression of pancreatic cancer in the live animal, and to demonstrate the real-time antitumor and antimetastatic effects of several novel therapeutic strategies on pancreatic malignancy. These fluorescent models are therefore powerful and reliable tools with which to investigate human pancreatic cancer and therapeutic strategies directed against it.
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Isabelle Naegelen
2015-01-01
Full Text Available Neutrophils participate in the maintenance of host integrity by releasing various cytotoxic proteins during degranulation. Due to recent advances, a major role has been attributed to neutrophil-derived cytokine secretion in the initiation, exacerbation, and resolution of inflammatory responses. Because the release of neutrophil-derived products orchestrates the action of other immune cells at the infection site and, thus, can contribute to the development of chronic inflammatory diseases, we aimed to investigate in more detail the spatiotemporal regulation of neutrophil-mediated release mechanisms of proinflammatory mediators. Purified human neutrophils were stimulated for different time points with lipopolysaccharide. Cells and supernatants were analyzed by flow cytometry techniques and used to establish secretion profiles of granules and cytokines. To analyze the link between cytokine release and degranulation time series, we propose an original strategy based on linear fitting, which may be used as a guideline, to (i define the relationship of granule proteins and cytokines secreted to the inflammatory site and (ii investigate the spatial regulation of neutrophil cytokine release. The model approach presented here aims to predict the correlation between neutrophil-derived cytokine secretion and degranulation and may easily be extrapolated to investigate the relationship between other types of time series of functional processes.
Naegelen, Isabelle; Beaume, Nicolas; Plançon, Sébastien; Schenten, Véronique; Tschirhart, Eric J.; Bréchard, Sabrina
2015-01-01
Neutrophils participate in the maintenance of host integrity by releasing various cytotoxic proteins during degranulation. Due to recent advances, a major role has been attributed to neutrophil-derived cytokine secretion in the initiation, exacerbation, and resolution of inflammatory responses. Because the release of neutrophil-derived products orchestrates the action of other immune cells at the infection site and, thus, can contribute to the development of chronic inflammatory diseases, we aimed to investigate in more detail the spatiotemporal regulation of neutrophil-mediated release mechanisms of proinflammatory mediators. Purified human neutrophils were stimulated for different time points with lipopolysaccharide. Cells and supernatants were analyzed by flow cytometry techniques and used to establish secretion profiles of granules and cytokines. To analyze the link between cytokine release and degranulation time series, we propose an original strategy based on linear fitting, which may be used as a guideline, to (i) define the relationship of granule proteins and cytokines secreted to the inflammatory site and (ii) investigate the spatial regulation of neutrophil cytokine release. The model approach presented here aims to predict the correlation between neutrophil-derived cytokine secretion and degranulation and may easily be extrapolated to investigate the relationship between other types of time series of functional processes. PMID:26579547
Madsen, Jonas S; Lin, Yu-Cheng; Squyres, Georgia R; Price-Whelan, Alexa; de Santiago Torio, Ana; Song, Angela; Cornell, William C; Sørensen, Søren J; Xavier, Joao B; Dietrich, Lars E P
2015-12-01
As biofilms grow, resident cells inevitably face the challenge of resource limitation. In the opportunistic pathogen Pseudomonas aeruginosa PA14, electron acceptor availability affects matrix production and, as a result, biofilm morphogenesis. The secreted matrix polysaccharide Pel is required for pellicle formation and for colony wrinkling, two activities that promote access to O2. We examined the exploitability and evolvability of Pel production at the air-liquid interface (during pellicle formation) and on solid surfaces (during colony formation). Although Pel contributes to the developmental response to electron acceptor limitation in both biofilm formation regimes, we found variation in the exploitability of its production and necessity for competitive fitness between the two systems. The wild type showed a competitive advantage against a non-Pel-producing mutant in pellicles but no advantage in colonies. Adaptation to the pellicle environment selected for mutants with a competitive advantage against the wild type in pellicles but also caused a severe disadvantage in colonies, even in wrinkled colony centers. Evolution in the colony center produced divergent phenotypes, while adaptation to the colony edge produced mutants with clear competitive advantages against the wild type in this O2-replete niche. In general, the structurally heterogeneous colony environment promoted more diversification than the more homogeneous pellicle. These results suggest that the role of Pel in community structure formation in response to electron acceptor limitation is unique to specific biofilm models and that the facultative control of Pel production is required for PA14 to maintain optimum benefit in different types of communities.
A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)
Canto, J; Martinez-Gomez, E; 10.1051/0004-6361/200911740
2009-01-01
Context. Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims. We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (Asexual Genetic Algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two e...
A Multivariate Fit Luminosity Function and World Model for Long GRBs
Shahmoradi, Amir
2012-01-01
It is proposed that the luminosity function, the comoving-frame spectral correlations and distributions of cosmological Long-duration Gamma-Ray Bursts (LGRBs) may be very well described as multivariate log-normal distribution. This result is based on careful selection, analysis and modeling of the spectral parameters of LGRBs in the largest catalog of Gamma-Ray Bursts available to date: 2130 BATSE GRBs, while taking into account the detection threshold and possible selection effects on observational data. Constraints on the joint quadru-variate distribution of the isotropic peak luminosity, the total isotropic emission, the comoving-frame time-integrated spectral peak energy and the comoving-frame duration of LGRBs are derived. Extensive goodness-of-fit tests are performed. The presented analysis provides evidence for a relatively large fraction of LGRBs that have been missed by BATSE detector with total isotropic emissions extending down to 10^49 [erg] and observed spectral peak energies as low as 5 [KeV]. T...
Zheng, Wenjun; Tekpinar, Mustafa
2014-01-01
To circumvent the difficulty of directly solving high-resolution biomolecular structures, low-resolution structural data from Cryo-electron microscopy (EM) and small angle solution X-ray scattering (SAXS) are increasingly used to explore multiple conformational states of biomolecular assemblies. One promising venue to obtain high-resolution structural models from low-resolution data is via data-constrained flexible fitting. To this end, we have developed a new method based on a coarse-grained Cα-only protein representation, and a modified form of the elastic network model (ENM) that allows large-scale conformational changes while maintaining the integrity of local structures including pseudo-bonds and secondary structures. Our method minimizes a pseudo-energy which linearly combines various terms of the modified ENM energy with an EM/SAXS-fitting score and a collision energy that penalizes steric collisions. Unlike some previous flexible fitting efforts using the lowest few normal modes, our method effectively utilizes all normal modes so that both global and local structural changes can be fully modeled with accuracy. This method is also highly efficient in computing time. We have demonstrated our method using adenylate kinase as a test case which undergoes a large open-to-close conformational change. The EM-fitting method is available at a web server (http://enm.lobos.nih.gov), and the SAXS-fitting method is available as a pre-compiled executable upon request.
Directory of Open Access Journals (Sweden)
Terrapon Nicolas
2012-05-01
Full Text Available Abstract Background Hidden Markov Models (HMMs are a powerful tool for protein domain identification. The Pfam database notably provides a large collection of HMMs which are widely used for the annotation of proteins in new sequenced organisms. In Pfam, each domain family is represented by a curated multiple sequence alignment from which a profile HMM is built. In spite of their high specificity, HMMs may lack sensitivity when searching for domains in divergent organisms. This is particularly the case for species with a biased amino-acid composition, such as P. falciparum, the main causal agent of human malaria. In this context, fitting HMMs to the specificities of the target proteome can help identify additional domains. Results Using P. falciparum as an example, we compare approaches that have been proposed for this problem, and present two alternative methods. Because previous attempts strongly rely on known domain occurrences in the target species or its close relatives, they mainly improve the detection of domains which belong to already identified families. Our methods learn global correction rules that adjust amino-acid distributions associated with the match states of HMMs. These rules are applied to all match states of the whole HMM library, thus enabling the detection of domains from previously absent families. Additionally, we propose a procedure to estimate the proportion of false positives among the newly discovered domains. Starting with the Pfam standard library, we build several new libraries with the different HMM-fitting approaches. These libraries are first used to detect new domain occurrences with low E-values. Second, by applying the Co-Occurrence Domain Discovery (CODD procedure we have recently proposed, the libraries are further used to identify likely occurrences among potential domains with higher E-values. Conclusion We show that the new approaches allow identification of several domain families previously absent in
A Parametric Model of Shoulder Articulation for Virtual Assessment of Space Suit Fit
Kim, K. Han; Young, Karen S.; Bernal, Yaritza; Boppana, Abhishektha; Vu, Linh Q.; Benson, Elizabeth A.; Jarvis, Sarah; Rajulu, Sudhakar L.
2016-01-01
Suboptimal suit fit is a known risk factor for crewmember shoulder injury. Suit fit assessment is however prohibitively time consuming and cannot be generalized across wide variations of body shapes and poses. In this work, we have developed a new design tool based on the statistical analysis of body shape scans. This tool is aimed at predicting the skin deformation and shape variations for any body size and shoulder pose for a target population. This new process, when incorporated with CAD software, will enable virtual suit fit assessments, predictively quantifying the contact volume, and clearance between the suit and body surface at reduced time and cost.
Hagell, Peter; Westergren, Albert
Sample size is a major factor in statistical null hypothesis testing, which is the basis for many approaches to testing Rasch model fit. Few sample size recommendations for testing fit to the Rasch model concern the Rasch Unidimensional Measurement Models (RUMM) software, which features chi-square and ANOVA/F-ratio based fit statistics, including Bonferroni and algebraic sample size adjustments. This paper explores the occurrence of Type I errors with RUMM fit statistics, and the effects of algebraic sample size adjustments. Data with simulated Rasch model fitting 25-item dichotomous scales and sample sizes ranging from N = 50 to N = 2500 were analysed with and without algebraically adjusted sample sizes. Results suggest the occurrence of Type I errors with N less then or equal to 500, and that Bonferroni correction as well as downward algebraic sample size adjustment are useful to avoid such errors, whereas upward adjustment of smaller samples falsely signal misfit. Our observations suggest that sample sizes around N = 250 to N = 500 may provide a good balance for the statistical interpretation of the RUMM fit statistics studied here with respect to Type I errors and under the assumption of Rasch model fit within the examined frame of reference (i.e., about 25 item parameters well targeted to the sample).
Quantitative assessment model for gastric cancer screening
Institute of Scientific and Technical Information of China (English)
Kun Chen; Wei-Ping Yu; Liang Song; Yi-Min Zhu
2005-01-01
AIM: To set up a mathematic model for gastric cancer screening and to evaluate its function in mass screening for gastric cancer.METHODS: A case control study was carried on in 66patients and 198 normal people, then the risk and protective factors of gastric cancer were determined, including heavy manual work, foods such as small yellow-fin tuna, dried small shrimps, squills, crabs, mothers suffering from gastric diseases, spouse alive, use of refrigerators and hot food,etc. According to some principles and methods of probability and fuzzy mathematics, a quantitative assessment model was established as follows: first, we selected some factors significant in statistics, and calculated weight coefficient for each one by two different methods; second, population space was divided into gastric cancer fuzzy subset and non gastric cancer fuzzy subset, then a mathematic model for each subset was established, we got a mathematic expression of attribute degree (AD).RESULTS: Based on the data of 63 patients and 693 normal people, AD of each subject was calculated. Considering the sensitivity and specificity, the thresholds of AD values calculated were configured with 0.20 and 0.17, respectively.According to these thresholds, the sensitivity and specificity of the quantitative model were about 69% and 63%.Moreover, statistical test showed that the identification outcomes of these two different calculation methods were identical (P＞0.05).CONCLUSION: The validity of this method is satisfactory.It is convenient, feasible, economic and can be used to determine individual and population risks of gastric cancer.
Quasi – biological model of radiogenic cancer morbidity
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A. T. Gubin
2015-01-01
Full Text Available The methods: Linear differential equations were used to formalize contemporary assumptions of self –sustaining tissue cell kinetics under the impact of adverse factors, on the formation and repairing of cell “pre-cancer” defects, on inheritance and retaining such defects in daughter cells which results in malignant neoplasms, on age-dependent impairment of human body’s function to eliminate such cells.The results: The model reproduces the well-known regularities of radiogenic cancer morbidity increase depending on instantaneous radiation exposure age and on attained age: the relative reduction at increased radiation age which the model attributes to age decrease of stem cells, relative reduction at increased time after radiation induced by “sorting out” of cells with “pre-cancer” defects, absolute increase with age proportional to natural cause mortality rate.The relevance of the developed quasi-biological model is displayed via comparison to the ICRP model for radiogenic increase of solid carcinomas’ morbidity after single radiation exposure. The latter model had been developed after Japanese cohort observations. For both genders high goodness-of-fit was achieved between the models at values of Gompertz’ law factor which had been defined for men and women in this cohort via selecting the value of the only free parameter indicating age-dependent exponential retardation of stem cells’ division.The conclusion: The proposed model suggests that the estimation of radiogenic risk inter-population transfer can be done on the basis of the data on age-dependent mortality intensity increase from all natural causes. The model also creates the premises for inter-species transfer of risk following the well-known parameters of cell populations’ kinetics in animal’s organs and tissues and Gompertz’s law parameters. This model is applicable also for analyses of age-dependent changes of background cancer morbidity.
Cancer immunotherapy : insights from transgenic animal models
McLaughlin, PMJ; Kroesen, BJ; Harmsen, MC; de Leij, LFMH
2001-01-01
A wide range of strategies in cancer immunotherapy has been developed in the last decade, some of which are currently being used in clinical settings. The development of these immunotherapeutical strategies has been facilitated by the generation of relevant transgenic animal models. Since the differ
DEFF Research Database (Denmark)
Vang, Jakob Rabjerg; Zhou, Fan; Andreasen, Søren Juhl;
2015-01-01
A high temperature PEM (HTPEM) fuel cell model capable of simulating both steady state and dynamic operation is presented. The purpose is to enable extraction of unknown parameters from sets of impedance spectra and polarisation curves. The model is fitted to two polarisation curves and four...... impedance spectra measured on a Dapozol 77 MEA. The model is capable of achieving good agreement with the recorded curves. Except at OCV, where the voltage is overpredicted, the simulated polarisation curves deviate maximum 3.0% from the measurements. The impedance spectra deviate maximum 3.7%. The fitted...... parameter values are within the range reported in literature. The only exception is the catalyst layer acid content, which is an order of magnitude lower. This may derive from acid migration. The model is used to illustrate the effect of reactant dynamics on the impedance spectrum. The model can aid...
Towards a multiscale model of colorectal cancer
Institute of Scientific and Technical Information of China (English)
Ingeborg MM van Leeuwen; Carina M Edwards; Mohammad Ilyas; Helen M Byrne
2007-01-01
Colorectal cancer (CRC) is one of the best characterised cancers, with extensive data documenting the sequential gene mutations that underlie its development.Complementary datasets are also being generated describing changes in protein and RNA expression,tumour biology and clinical outcome. Both the quantity and the variety of information are inexorably increasing and there is now an accompanying need to integrate these highly disparate datasets. In this article we aim to explain why we believe that mathematical modelling represents a natural tool or language with which to integrate these data and, in so doing, to provide insight into CRC.
Hansen, Mark; Cai, Li; Monroe, Scott; Li, Zhen
2014-01-01
It is a well-known problem in testing the fit of models to multinomial data that the full underlying contingency table will inevitably be sparse for tests of reasonable length and for realistic sample sizes. Under such conditions, full-information test statistics such as Pearson's X[superscript 2]?? and the likelihood ratio statistic…
Energy Technology Data Exchange (ETDEWEB)
Onaka, T.; Jong, T. de; Willems, F.J. (Amsterdam Univ. (NL))
1989-12-01
We have fitted dust shell models to the IRAS LRS spectra of 109 M Mira variables. The main assumptions in the model calculations are: (i) the dust shell is spherical and optically thin, (ii) the dust grains consist of aluminum oxide and amorphous magnesium silicate, (iii) the mass loss rate is constant, (iv) the stellar photosphere is characterized by R = 3 x 10{sup 13} cm and T = 2500 K. Best fit models are calculated for each star. A model is completely determined by five parameters: the dust temperatures at the inner boundaries of the aluminum oxide and silicate dust shells, the column densities of each dust grain component, and the distance to the star. It turns out that the 1 - 200 {mu}m infrared energy distributions calculated for the best fit parameters also provide quite satisfactory fits to the observed near- and far-infrared broad-band data for most sources. The material presented here forms the basis for a study of dust condensation in the circumstellar shells around Mira variables.
O'Neill, James M.; Clark, Jeffrey K.; Jones, James A.
2016-01-01
Background: In elementary grades, comprehensive health education curricula have demonstrated effectiveness in addressing singular health issues. The Michigan Model for Health (MMH) was implemented and evaluated to determine its impact on nutrition, physical fitness, and safety knowledge and skills. Methods: Schools (N = 52) were randomly assigned…
Meijer, Rob R.; Tendeiro, Jorge N.
2012-01-01
We extend a recent didactic by Magis, Raiche, and Beland on the use of the l[subscript z] and l[subscript z]* person-fit statistics. We discuss a number of possibly confusing details and show that it is important to first investigate item response theory model fit before assessing person fit. Furthermore, it is argued that appropriate…
An examination of disparities in cancer incidence in Texas using Bayesian random coefficient models
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Corey Sparks
2015-09-01
Full Text Available Disparities in cancer risk exist between ethnic groups in the United States. These disparities often result from differential access to healthcare, differences in socioeconomic status and differential exposure to carcinogens. This study uses cancer incidence data from the population based Texas Cancer Registry to investigate the disparities in digestive and respiratory cancers from 2000 to 2008. A Bayesian hierarchical regression approach is used. All models are fit using the INLA method of Bayesian model estimation. Specifically, a spatially varying coefficient model of the disparity between Hispanic and Non-Hispanic incidence is used. Results suggest that a spatio-temporal heterogeneity model best accounts for the observed Hispanic disparity in cancer risk. Overall, there is a significant disadvantage for the Hispanic population of Texas with respect to both of these cancers, and this disparity varies significantly over space. The greatest disparities between Hispanics and Non-Hispanics in digestive and respiratory cancers occur in eastern Texas, with patterns emerging as early as 2000 and continuing until 2008.
Fitness club
2011-01-01
General fitness Classes Enrolments are open for general fitness classes at CERN taking place on Monday, Wednesday, and Friday lunchtimes in the Pump Hall (building 216). There are shower facilities for both men and women. It is possible to pay for 1, 2 or 3 classes per week for a minimum of 1 month and up to 6 months. Check out our rates and enrol at: http://cern.ch/club-fitness Hope to see you among us! CERN Fitness Club fitness.club@cern.ch
Linking the Fits, Fitting the Links: Connecting Different Types of PO Fit to Attitudinal Outcomes
Leung, Aegean; Chaturvedi, Sankalp
2011-01-01
In this paper we explore the linkages among various types of person-organization (PO) fit and their effects on employee attitudinal outcomes. We propose and test a conceptual model which links various types of fits--objective fit, perceived fit and subjective fit--in a hierarchical order of cognitive information processing and relate them to…
On fitting the k-C* first order model to batch loaded sub-surface treatment wetlands.
Stein, O R; Towler, B W; Hook, P B; Biederman, J A
2007-01-01
The k-C* first order model was fit to time-series COD data collected from batch-loaded model wetlands. Four replicates of four plant species treatments; Carex utriculata (sedge), Schoenoplectus acutus (bulrush), Typha latifolia (cattail) and unplanted controls were compared. Temperature was varied by 4 degrees C from 24 degrees C to 4 degrees C to 24 degrees C over a year-long period. One mathematical fit was made for each wetland replicate at each temperature setting (192 fits). Temperature effects on both parameters were subsequently estimated by fitting the Arrhenius relationship to the estimated coefficients. Inherent interactions between k and C* make values dependent on sample timing and statistical technique for either time series (batch load) or distance profile (plug flow) data. Coefficients calibrated using the Levenberg-Marquardt method are compared to values previously reported using a nonlinear mixed effect regression technique. Overall conclusions are similar across approaches: (a) the magnitude of the coefficients varies strongly by species; (b) the rate constant k decreases with increasing temperature; and (c) temperature and species variation in the residual concentration C* is greater than the variation in k, such that variation in k alone is a poor predictor of performance. However, the magnitudes of the coefficients, especially the rate parameter k, vary between the statistical techniques, highlighting the need to better document the statistical routines used to calibrate the k-C* model.
Modeling cancer progression via pathway dependencies.
Directory of Open Access Journals (Sweden)
Elena J Edelman
2008-02-01
Full Text Available Cancer is a heterogeneous disease often requiring a complexity of alterations to drive a normal cell to a malignancy and ultimately to a metastatic state. Certain genetic perturbations have been implicated for initiation and progression. However, to a great extent, underlying mechanisms often remain elusive. These genetic perturbations are most likely reflected by the altered expression of sets of genes or pathways, rather than individual genes, thus creating a need for models of deregulation of pathways to help provide an understanding of the mechanisms of tumorigenesis. We introduce an integrative hierarchical analysis of tumor progression that discovers which a priori defined pathways are relevant either throughout or in particular steps of progression. Pathway interaction networks are inferred for these relevant pathways over the steps in progression. This is followed by the refinement of the relevant pathways to those genes most differentially expressed in particular disease stages. The final analysis infers a gene interaction network for these refined pathways. We apply this approach to model progression in prostate cancer and melanoma, resulting in a deeper understanding of the mechanisms of tumorigenesis. Our analysis supports previous findings for the deregulation of several pathways involved in cell cycle control and proliferation in both cancer types. A novel finding of our analysis is a connection between ErbB4 and primary prostate cancer.
Molitor, John
2012-03-01
Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;175(5):368-375) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields.
DEFF Research Database (Denmark)
Midtgaard, J; Christensen, Jesper Frank; Tolver, Anders
2013-01-01
Sedentary behavior and impaired cardiovascular reserve capacity are common late effects of cancer therapy emphasizing the need for effective strategies to increase physical activity (PA) in cancer survivors. We examined the efficacy of a 12-month exercise-based rehabilitation program on self...
Quasispecies on Fitness Landscapes.
Schuster, Peter
2016-01-01
Selection-mutation dynamics is studied as adaptation and neutral drift on abstract fitness landscapes. Various models of fitness landscapes are introduced and analyzed with respect to the stationary mutant distributions adopted by populations upon them. The concept of quasispecies is introduced, and the error threshold phenomenon is analyzed. Complex fitness landscapes with large scatter of fitness values are shown to sustain error thresholds. The phenomenological theory of the quasispecies introduced in 1971 by Eigen is compared to approximation-free numerical computations. The concept of strong quasispecies understood as mutant distributions, which are especially stable against changes in mutations rates, is presented. The role of fitness neutral genotypes in quasispecies is discussed.
van der Niet, Anneke G.; Hartman, Esther; Smith, Joanne; Visscher, Chris
2014-01-01
Objectives: The relationship between physical fitness and academic achievement in children has received much attention, however, whether executive functioning plays a mediating role in this relationship is unclear. The aim of this study therefore was to investigate the relationships between physical
Testing the Youth Physical Activity Promotion Model: Fatness and Fitness as Enabling Factors
Chen, Senlin; Welk, Gregory J.; Joens-Matre, Roxane R.
2014-01-01
As the prevalence of childhood obesity increases, it is important to examine possible differences in psychosocial correlates of physical activity between normal weight and overweight children. The study examined fatness (weight status) and (aerobic) fitness as Enabling factors related to youth physical activity within the Youth Physical Activity…
Directory of Open Access Journals (Sweden)
Yun Wang
2016-01-01
Full Text Available Gamma Gaussian inverse Wishart cardinalized probability hypothesis density (GGIW-CPHD algorithm was always used to track group targets in the presence of cluttered measurements and missing detections. A multiple models GGIW-CPHD algorithm based on best-fitting Gaussian approximation method (BFG and strong tracking filter (STF is proposed aiming at the defect that the tracking error of GGIW-CPHD algorithm will increase when the group targets are maneuvering. The best-fitting Gaussian approximation method is proposed to implement the fusion of multiple models using the strong tracking filter to correct the predicted covariance matrix of the GGIW component. The corresponding likelihood functions are deduced to update the probability of multiple tracking models. From the simulation results we can see that the proposed tracking algorithm MM-GGIW-CPHD can effectively deal with the combination/spawning of groups and the tracking error of group targets in the maneuvering stage is decreased.
Alcalá-Quintana, Rocío; García-Pérez, Miguel A
2013-12-01
Research on temporal-order perception uses temporal-order judgment (TOJ) tasks or synchrony judgment (SJ) tasks in their binary SJ2 or ternary SJ3 variants. In all cases, two stimuli are presented with some temporal delay, and observers judge the order of presentation. Arbitrary psychometric functions are typically fitted to obtain performance measures such as sensitivity or the point of subjective simultaneity, but the parameters of these functions are uninterpretable. We describe routines in MATLAB and R that fit model-based functions whose parameters are interpretable in terms of the processes underlying temporal-order and simultaneity judgments and responses. These functions arise from an independent-channels model assuming arrival latencies with exponential distributions and a trichotomous decision space. Different routines fit data separately for SJ2, SJ3, and TOJ tasks, jointly for any two tasks, or also jointly for the three tasks (for common cases in which two or even the three tasks were used with the same stimuli and participants). Additional routines provide bootstrap p-values and confidence intervals for estimated parameters. A further routine is included that obtains performance measures from the fitted functions. An R package for Windows and source code of the MATLAB and R routines are available as Supplementary Files.
Ferreira, Abílio G T; Henrique, Douglas S; Vieira, Ricardo A M; Maeda, Emilyn M; Valotto, Altair A
2015-03-01
The objective of this study was to evaluate four mathematical models with regards to their fit to lactation curves of Holstein cows from herds raised in the southwestern region of the state of Parana, Brazil. Initially, 42,281 milk production records from 2005 to 2011 were obtained from "Associação Paranaense de Criadores de Bovinos da Raça Holandesa (APCBRH)". Data lacking dates of drying and total milk production at 305 days of lactation were excluded, resulting in a remaining 15,142 records corresponding to 2,441 Holstein cows. Data were sorted according to the parity order (ranging from one to six), and within each parity order the animals were divided into quartiles (Q25%, Q50%, Q75% and Q100%) corresponding to 305-day lactation yield. Within each parity order, for each quartile, four mathematical models were adjusted, two of which were predominantly empirical (Brody and Wood) whereas the other two presented more mechanistic characteristics (models Dijkstra and Pollott). The quality of fit was evaluated by the corrected Akaike information criterion. The Wood model showed the best fit in almost all evaluated situations and, therefore, may be considered as the most suitable model to describe, at least empirically, the lactation curves of Holstein cows raised in Southwestern Parana.
Directory of Open Access Journals (Sweden)
Abílio G.T. Ferreira
2015-03-01
Full Text Available The objective of this study was to evaluate four mathematical models with regards to their fit to lactation curves of Holstein cows from herds raised in the southwestern region of the state of Parana, Brazil. Initially, 42,281 milk production records from 2005 to 2011 were obtained from "Associação Paranaense de Criadores de Bovinos da Raça Holandesa (APCBRH". Data lacking dates of drying and total milk production at 305 days of lactation were excluded, resulting in a remaining 15,142 records corresponding to 2,441 Holstein cows. Data were sorted according to the parity order (ranging from one to six, and within each parity order the animals were divided into quartiles (Q25%, Q50%, Q75% and Q100% corresponding to 305-day lactation yield. Within each parity order, for each quartile, four mathematical models were adjusted, two of which were predominantly empirical (Brody and Wood whereas the other two presented more mechanistic characteristics (models Dijkstra and Pollott. The quality of fit was evaluated by the corrected Akaike information criterion. The Wood model showed the best fit in almost all evaluated situations and, therefore, may be considered as the most suitable model to describe, at least empirically, the lactation curves of Holstein cows raised in Southwestern Parana.
Seasonality of Influenza A(H7N9) Virus in China—Fitting Simple Epidemic Models to Human Cases
Lin, Qianying; Lin, Zhigui; Chiu, Alice P. Y.; He, Daihai
2016-01-01
Background Three epidemic waves of influenza A(H7N9) (hereafter ‘H7N9’) human cases have occurred between March 2013 and July 2015 in China. However, the underlying transmission mechanism remains unclear. Our main objective is to use mathematical models to study how seasonality, secular changes and environmental transmission play a role in the spread of H7N9 in China. Methods Data on human cases and chicken cases of H7N9 infection were downloaded from the EMPRES-i Global Animal Disease Information System. We modelled on chicken-to-chicken transmission, assuming a constant ratio of 10−6 human case per chicken case, and compared the model fit with the observed human cases. We developed three different modified Susceptible-Exposed-Infectious-Recovered-Susceptible models: (i) a non-periodic transmission rate model with an environmental class, (ii) a non-periodic transmission rate model without an environmental class, and (iii) a periodic transmission rate model with an environmental class. We then estimated the key epidemiological parameters and compared the model fit using Akaike Information Criterion and Bayesian Information Criterion. Results Our results showed that a non-periodic transmission rate model with an environmental class provided the best model fit to the observed human cases in China during the study period. The estimated parameter values were within biologically plausible ranges. Conclusions This study highlighted the importance of considering secular changes and environmental transmission in the modelling of human H7N9 cases. Secular changes were most likely due to control measures such as Live Poultry Markets closures that were implemented during the initial phase of the outbreaks in China. Our results suggested that environmental transmission via viral shedding of infected chickens had contributed to the spread of H7N9 human cases in China. PMID:26963937
Mouse models for BRAF-induced cancers.
Pritchard, C; Carragher, L; Aldridge, V; Giblett, S; Jin, H; Foster, C; Andreadi, C; Kamata, T
2007-11-01
Oncogenic mutations in the BRAF gene are detected in approximately 7% of human cancer samples with a particularly high frequency of mutation in malignant melanomas. Over 40 different missense BRAF mutations have been found, but the vast majority (>90%) represent a single nucleotide change resulting in a valine-->glutamate mutation at residue 600 ((V600E)BRAF). In cells cultured in vitro, (V600E)BRAF is able to stimulate endogenous MEK [MAPK (mitogen-activated protein kinase)/ERK (extracellular-signal-regulated kinase) kinase] and ERK phosphorylation leading to an increase in cell proliferation, cell survival, transformation, tumorigenicity, invasion and vascular development. Many of these hallmarks of cancer can be reversed by treatment of cells with siRNA (small interfering RNA) to BRAF or by inhibiting MEK, indicating that BRAF and MEK are attractive therapeutic targets in cancer samples with BRAF mutations. In order to fully understand the role of oncogenic BRAF in cancer development in vivo as well as to test the in vivo efficacy of anti-BRAF or anti-MEK therapies, GEMMs (genetically engineered mouse models) have been generated in which expression of oncogenic BRaf is conditionally dependent on the Cre recombinase. The delivery/activation of the Cre recombinase can be regulated in both a temporal and spatial manner and therefore these mouse models can be used to recapitulate the somatic mutation of BRAF that occurs in different tissues in the development of human cancer. The data so far obtained following Cre-mediated activation in haemopoietic tissue and the lung indicate that (V600E)BRAF mutation can drive tumour initiation and that its primary effect is to induce high levels of cyclin D1-mediated cell proliferation. However, hallmarks of OIS (oncogene-induced senescence) are evident that restrain further development of the tumour.
Fitness Club
2011-01-01
The CERN Fitness Club is organising Zumba Classes on the first Wednesday of each month, starting 7 September (19.00 – 20.00). What is Zumba®? It’s an exhilarating, effective, easy-to-follow, Latin-inspired, calorie-burning dance fitness-party™ that’s moving millions of people toward joy and health. Above all it’s great fun and an excellent work out. Price: 22 CHF/person Sign-up via the following form: https://espace.cern.ch/club-fitness/Lists/Zumba%20Subscription/NewForm.aspx For more info: fitness.club@cern.ch
Directory of Open Access Journals (Sweden)
Winters-Stone Kerri M
2012-12-01
Full Text Available Abstract Background Women with cancer are significantly more likely to fall than women without cancer placing them at higher risk of fall-related fractures, other injuries and disability. Currently, no evidence-based fall prevention strategies exist that specifically target female cancer survivors. The purpose of the GET FIT (Group Exercise Training for Functional Improvement after Treatment trial is to compare the efficacy of two distinct types of exercise, tai chi versus strength training, to prevent falls in women who have completed treatment for cancer. The specific aims of this study are to: 1 Determine and compare the efficacy of both tai chi training and strength training to reduce falls in older female cancer survivors, 2 Determine the mechanism(s by which tai chi and strength training each reduces falls and, 3 Determine whether or not the benefits of each intervention last after structured training stops. Methods/Design We will conduct a three-group, single-blind, parallel design, randomized controlled trial in women, aged 50–75 years old, who have completed chemotherapy for cancer comparing 1 tai chi 2 strength training and 3 a placebo control group of seated stretching exercise. Women will participate in supervised study programs twice per week for six months and will be followed for an additional six months after formal training stops. The primary outcome in this study is falls, which will be prospectively tracked by monthly self-report. Secondary outcomes are maximal leg strength measured by isokinetic dynamometry, postural stability measured by computerized dynamic posturography and physical function measured by the Physical Performance Battery, all measured at baseline, 3, 6 and 12 months. The sample for this trial (N=429, assuming 25% attrition will provide adequate statistical power to detect at least a 47% reduction in the fall rate over 1 year by being in either of the 2 exercise groups versus the control group. Discussion
Manning, H Charles; Buck, Jason R; Cook, Rebecca S
2016-02-01
Representing an enormous health care and socioeconomic challenge, breast cancer is the second most common cancer in the world and the second most common cause of cancer-related death. Although many of the challenges associated with preventing, treating, and ultimately curing breast cancer are addressable in the laboratory, successful translation of groundbreaking research to clinical populations remains an important barrier. Particularly when compared with research on other types of solid tumors, breast cancer research is hampered by a lack of tractable in vivo model systems that accurately recapitulate the relevant clinical features of the disease. A primary objective of this article was to provide a generalizable overview of the types of in vivo model systems, with an emphasis primarily on murine models, that are widely deployed in preclinical breast cancer research. Major opportunities to advance precision cancer medicine facilitated by molecular imaging of preclinical breast cancer models are discussed.
Kamphuis, P; Oh, S- H; Spekkens, K; Urbancic, N; Serra, P; Koribalski, B S; Dettmar, R -J
2015-01-01
Kinematical parameterisations of disc galaxies, employing emission line observations, are indispensable tools for studying the formation and evolution of galaxies. Future large-scale HI surveys will resolve the discs of many thousands of galaxies, allowing a statistical analysis of their disc and halo kinematics, mass distribution and dark matter content. Here we present an automated procedure which fits tilted-ring models to Hi data cubes of individual, well-resolved galaxies. The method builds on the 3D Tilted Ring Fitting Code (TiRiFiC) and is called FAT (Fully Automated TiRiFiC). To assess the accuracy of the code we apply it to a set of 52 artificial galaxies and 25 real galaxies from the Local Volume HI Survey (LVHIS). Using LVHIS data, we compare our 3D modelling to the 2D modelling methods DiskFit and rotcur. A conservative result is that FAT accurately models the kinematics and the morphologies of galaxies with an extent of eight beams across the major axis in the inclination range 20$^{\\circ}$-90$^{...
Breast Cancer Risk Assessment SAS Macro (Gail Model)
A SAS macro (commonly referred to as the Gail Model) that projects absolute risk of invasive breast cancer according to NCI’s Breast Cancer Risk Assessment Tool (BCRAT) algorithm for specified race/ethnic groups and age intervals.
Ciuchini, Marco; Mishima, Satoshi; Pierini, Maurizio; Reina, Laura; Silvestrini, Luca
2014-01-01
We present updated global fits of the Standard Model and beyond to electroweak precision data, taking into account recent progress in theoretical calculations and experimental measurements. From the fits, we derive model-independent constraints on new physics by introducing oblique and epsilon parameters, and modified $Zb\\bar{b}$ and $HVV$ couplings. Furthermore, we also perform fits of the scale factors of the Higgs-boson couplings to observed signal strengths of the Higgs boson.
Quantum spin model fitting the Yule distribution of oligonucleotides in DNA
Minichini, C
2004-01-01
A quantum spin chain is identified by the labels of a vector state of a Kashiwara crystal basis. The intensity of the one-spin flip is assumed to depend from the variation of the labels. The rank ordered plot of the numerically computed, averaged in time, transition probabilities is nicely fitted by a Yule distribution, which is the observed distribution of the ranked short oligonucleotides frequency in DNA.
Ismail, A.; Hassan, Noor I.
2013-09-01
Cancer is one of the principal causes of death in Malaysia. This study was performed to determine the pattern of rate of cancer deaths at a public hospital in Malaysia over an 11 year period from year 2001 to 2011, to determine the best fitted model of forecasting the rate of cancer deaths using Univariate Modeling and to forecast the rates for the next two years (2012 to 2013). The medical records of the death of patients with cancer admitted at this Hospital over 11 year's period were reviewed, with a total of 663 cases. The cancers were classified according to 10th Revision International Classification of Diseases (ICD-10). Data collected include socio-demographic background of patients such as registration number, age, gender, ethnicity, ward and diagnosis. Data entry and analysis was accomplished using SPSS 19.0 and Minitab 16.0. The five Univariate Models used were Naïve with Trend Model, Average Percent Change Model (ACPM), Single Exponential Smoothing, Double Exponential Smoothing and Holt's Method. The overall 11 years rate of cancer deaths showed that at this hospital, Malay patients have the highest percentage (88.10%) compared to other ethnic groups with males (51.30%) higher than females. Lung and breast cancer have the most number of cancer deaths among gender. About 29.60% of the patients who died due to cancer were aged 61 years old and above. The best Univariate Model used for forecasting the rate of cancer deaths is Single Exponential Smoothing Technique with alpha of 0.10. The forecast for the rate of cancer deaths shows a horizontally or flat value. The forecasted mortality trend remains at 6.84% from January 2012 to December 2013. All the government and private sectors and non-governmental organizations need to highlight issues on cancer especially lung and breast cancers to the public through campaigns using mass media, media electronics, posters and pamphlets in the attempt to decrease the rate of cancer deaths in Malaysia.
Wen, Mingjian; Brommer, Peter; Elliott, Ryan S; Sethna, James P; Tadmor, Ellad B
2016-01-01
Fitted interatomic potentials are widely used in atomistic simulations thanks to their ability to compute the energy and forces on atoms quickly. However, the simulation results crucially depend on the quality of the potential being used. Force matching is a method aimed at constructing reliable and transferable interatomic potentials by matching the forces computed by the potential as closely as possible, with those obtained from first principles calculations. The potfit program is an implementation of the force-matching method that optimizes the potential parameters using a global minimization algorithm followed by a local minimization polish. We extended potfit in two ways. First, we adapted the code to be compliant with the KIM Application Programming Interface (API) standard (part of the Knowledgebase of Interatomic Models Project). This makes it possible to use potfit to fit many KIM potential models, not just those prebuilt into the potfit code. Second, we incorporated the geodesic Levenberg--Marquardt...
Freyth, Katharina; Janowitz, Tim; Nunes, Frank; Voss, Melanie; Heinick, Alexander; Bertaux, Joanne; Scheu, Stefan; Paul, Rüdiger J
2010-10-01
Laboratory breeding conditions of the model organism C. elegans do not correspond with the conditions in its natural soil habitat. To assess the consequences of the differences in environmental conditions, the effects of air composition, medium and bacterial food on reproductive fitness and/or dietary-choice behavior of C. elegans were investigated. The reproductive fitness of C. elegans was maximal under oxygen deficiency and not influenced by a high fractional share of carbon dioxide. In media approximating natural soil structure, reproductive fitness was much lower than in standard laboratory media. In seminatural media, the reproductive fitness of C. elegans was low with the standard laboratory food bacterium E. coli (γ-Proteobacteria), but significantly higher with C. arvensicola (Bacteroidetes) and B. tropica (β-Proteobacteria) as food. Dietary-choice experiments in semi-natural media revealed a low preference of C. elegans for E. coli but significantly higher preferences for C. arvensicola and B. tropica (among other bacteria). Dietary-choice experiments under quasi-natural conditions, which were feasible by fluorescence in situ hybridization (FISH) of bacteria, showed a high preference of C. elegans for Cytophaga-Flexibacter-Bacteroides, Firmicutes, and β-Proteobacteria, but a low preference for γ-Proteobacteria. The results show that data on C. elegans under standard laboratory conditions have to be carefully interpreted with respect to their biological significance.
Quasispecies theory for multiple-peak fitness landscapes
Munoz, Enrique; Saakian, David; Hu, Chin-Kun; Deem, Michael
2007-03-01
We used a path integral representation to solve the Eigen and Crow-Kimura molecular evolution models for the case of multiple fitness peaks with arbitrary fitness and degradation functions. In the general case, we find that the solution to these molecular evolution models can be written as the optimum of a fitness function, with constraints enforced by Lagrange multipliers and with a term accounting for the entropy of the spreading population in sequence space. The results for the Eigen model are applied to consider virus or cancer proliferation under the control of drugs or the immune system.
White, J Wilson; Nickols, Kerry J; Malone, Daniel; Carr, Mark H; Starr, Richard M; Cordoleani, Flora; Baskett, Marissa L; Hastings, Alan; Botsford, Louis W
2016-12-01
Integral projection models (IPMs) have a number of advantages over matrix-model approaches for analyzing size-structured population dynamics, because the latter require parameter estimates for each age or stage transition. However, IPMs still require appropriate data. Typically they are parameterized using individual-scale relationships between body size and demographic rates, but these are not always available. We present an alternative approach for estimating demographic parameters from time series of size-structured survey data using a Bayesian state-space IPM (SSIPM). By fitting an IPM in a state-space framework, we estimate unknown parameters and explicitly account for process and measurement error in a dataset to estimate the underlying process model dynamics. We tested our method by fitting SSIPMs to simulated data; the model fit the simulated size distributions well and estimated unknown demographic parameters accurately. We then illustrated our method using nine years of annual surveys of the density and size distribution of two fish species (blue rockfish, Sebastes mystinus, and gopher rockfish, S. carnatus) at seven kelp forest sites in California. The SSIPM produced reasonable fits to the data, and estimated fishing rates for both species that were higher than our Bayesian prior estimates based on coast-wide stock assessment estimates of harvest. That improvement reinforces the value of being able to estimate demographic parameters from local-scale monitoring data. We highlight a number of key decision points in SSIPM development (e.g., open vs. closed demography, number of particles in the state-space filter) so that users can apply the method to their own datasets.
Evolutionary dynamics of tumor progression with random fitness values
Durrett, Rick; Leder, Kevin; Mayberry, John; Michor, Franziska
2010-01-01
Most human tumors result from the accumulation of multiple genetic and epigenetic alterations in a single cell. Mutations that confer a fitness advantage to the cell are known as driver mutations and are causally related to tumorigenesis. Other mutations, however, do not change the phenotype of the cell or even decrease cellular fitness. While much experimental effort is being devoted to the identification of the different functional effects of individual mutations, mathematical modeling of tumor progression generally considers constant fitness increments as mutations are accumulated. In this paper we study a mathematical model of tumor progression with random fitness increments. We analyze a multi-type branching process in which cells accumulate mutations whose fitness effects are chosen from a distribution. We determine the effect of the fitness distribution on the growth kinetics of the tumor. This work contributes to a quantitative understanding of the accumulation of mutations leading to cancer phenotype...
Using Geometry-Based Metrics as Part of Fitness-for-Purpose Evaluations of 3D City Models
Wong, K.; Ellul, C.
2016-10-01
Three-dimensional geospatial information is being increasingly used in a range of tasks beyond visualisation. 3D datasets, however, are often being produced without exact specifications and at mixed levels of geometric complexity. This leads to variations within the models' geometric and semantic complexity as well as the degree of deviation from the corresponding real world objects. Existing descriptors and measures of 3D data such as CityGML's level of detail are perhaps only partially sufficient in communicating data quality and fitness-for-purpose. This study investigates whether alternative, automated, geometry-based metrics describing the variation of complexity within 3D datasets could provide additional relevant information as part of a process of fitness-for-purpose evaluation. The metrics include: mean vertex/edge/face counts per building; vertex/face ratio; minimum 2D footprint area and; minimum feature length. Each metric was tested on six 3D city models from international locations. The results show that geometry-based metrics can provide additional information on 3D city models as part of fitness-for-purpose evaluations. The metrics, while they cannot be used in isolation, may provide a complement to enhance existing data descriptors if backed up with local knowledge, where possible.
Fitting and Calibrating a Multilevel Mixed-Effects Stem Taper Model for Maritime Pine in NW Spain
Arias-Rodil, Manuel; Castedo-Dorado, Fernando; Cámara-Obregón, Asunción; Diéguez-Aranda, Ulises
2015-01-01
Stem taper data are usually hierarchical (several measurements per tree, and several trees per plot), making application of a multilevel mixed-effects modelling approach essential. However, correlation between trees in the same plot/stand has often been ignored in previous studies. Fitting and calibration of a variable-exponent stem taper function were conducted using data from 420 trees felled in even-aged maritime pine (Pinus pinaster Ait.) stands in NW Spain. In the fitting step, the tree level explained much more variability than the plot level, and therefore calibration at plot level was omitted. Several stem heights were evaluated for measurement of the additional diameter needed for calibration at tree level. Calibration with an additional diameter measured at between 40 and 60% of total tree height showed the greatest improvement in volume and diameter predictions. If additional diameter measurement is not available, the fixed-effects model fitted by the ordinary least squares technique should be used. Finally, we also evaluated how the expansion of parameters with random effects affects the stem taper prediction, as we consider this a key question when applying the mixed-effects modelling approach to taper equations. The results showed that correlation between random effects should be taken into account when assessing the influence of random effects in stem taper prediction. PMID:26630156
Energy Technology Data Exchange (ETDEWEB)
McFee, J.E., E-mail: jemcfee@telus.net; Mosquera, C.M.; Faust, A.A.
2016-08-21
An analysis of digitized pulse waveforms from experiments with LaBr{sub 3}(Ce) and LaCl{sub 3}(Ce) detectors is presented. Pulse waveforms from both scintillator types were captured in the presence of {sup 22}Na and {sup 60}Co sources and also background alone. Two methods to extract pulse shape discrimination (PSD) parameters and estimate energy spectra were compared. The first involved least squares fitting of the pulse waveforms to a physics-based model of one or two exponentially modified Gaussian functions. The second was the conventional gated integration method. The model fitting method produced better PSD than gated integration for LaCl{sub 3}(Ce) and higher resolution energy spectra for both scintillator types. A disadvantage to the model fitting approach is that it is more computationally complex and about 5 times slower. LaBr{sub 3}(Ce) waveforms had a single decay component and showed no ability for alpha/electron PSD. LaCl{sub 3}(Ce) was observed to have short and long decay components and alpha/electron discrimination was observed.
Models for prevention and treatment of cancer: problems vs promises.
Aggarwal, Bharat B; Danda, Divya; Gupta, Shan; Gehlot, Prashasnika
2009-11-01
Current estimates from the American Cancer Society and from the International Union Against Cancer indicate that 12 million cases of cancer were diagnosed last year, with 7 million deaths worldwide; these numbers are expected to double by 2030 (27 million cases with 17 million deaths). Despite tremendous technological developments in all areas, and President Richard Nixon's initiative in the 1974 "War against Cancer", the US cancer incidence is the highest in the world and the cancer death rate has not significantly changed in the last 50 years (193.9 per 100,000 in 1950 vs 193.4 per 100,000 in 2002). Extensive research during the same time, however, has revealed that cancer is a preventable disease that requires major changes in life style; with one third of all cancers assigned to Tobacco, one third to diet, and remaining one third to the environment. Approximately 20 billion dollars are spent annually to find a cure for cancer. We propose that our inability to find a cure to cancer lies in the models used. Whether cell culture or animal studies, no model has yet been found that can reproduce the pathogenesis of the disease in the laboratory. Mono-targeted therapies, till know in most cases, have done a little to make a difference in cancer treatment. Similarly, molecular signatures/predictors of the diagnosis of the disease and response are also lacking. This review discusses the pros and cons of current cancer models based on cancer genetics, cell culture, animal models, cancer biomarkers/signature, cancer stem cells, cancer cell signaling, targeted therapies, therapeutic targets, clinical trials, cancer prevention, personalized medicine, and off-label uses to find a cure for cancer and demonstrates an urgent need for "out of the box" approaches.
Wen, Mingjian; Li, Junhao; Brommer, Peter; Elliott, Ryan S.; Sethna, James P.; Tadmor, Ellad B.
2017-01-01
Fitted interatomic potentials are widely used in atomistic simulations thanks to their ability to compute the energy and forces on atoms quickly. However, the simulation results crucially depend on the quality of the potential being used. Force matching is a method aimed at constructing reliable and transferable interatomic potentials by matching the forces computed by the potential as closely as possible, with those obtained from first principles calculations. The potfit program is an implementation of the force-matching method that optimizes the potential parameters using a global minimization algorithm followed by a local minimization polish. We extended potfit in two ways. First, we adapted the code to be compliant with the KIM Application Programming Interface (API) standard (part of the Knowledgebase of Interatomic Models project). This makes it possible to use potfit to fit many KIM potential models, not just those prebuilt into the potfit code. Second, we incorporated the geodesic Levenberg-Marquardt (LM) minimization algorithm into potfit as a new local minimization algorithm. The extended potfit was tested by generating a training set using the KIM environment-dependent interatomic potential (EDIP) model for silicon and using potfit to recover the potential parameters from different initial guesses. The results show that EDIP is a ‘sloppy model’ in the sense that its predictions are insensitive to some of its parameters, which makes fitting more difficult. We find that the geodesic LM algorithm is particularly efficient for this case. The extended potfit code is the first step in developing a KIM-based fitting framework for interatomic potentials for bulk and two-dimensional materials. The code is available for download via https://www.potfit.net.
Falahati Marvast, Fatemeh; Arabalibeik, Hossein; Alipour, Fatemeh; Sheikhtaheri, Abbas; Nouri, Leila; Soozande, Mehdi; Yarmahmoodi, Masood
2016-01-01
Keratoconus is a progressive non-inflammatory disease of the cornea. Rigid gas permeable contact lenses (RGPs) are prescribed when the disease progresses. Contact lens fitting and assessment is very difficult in these patients and is a concern of ophthalmologists and optometrists. In this study, a hierarchical fuzzy system is used to capture the expertise of experienced ophthalmologists during the lens evaluation phase of prescription. The system is fine-tuned using genetic algorithms. Sensitivity, specificity and accuracy of the final system are 88.9%, 94.4% and 92.6% respectively.
Dai, Junyi; Kerestes, Rebecca; Upton, Daniel J.; Busemeyer, Jerome R.; Stout, Julie C.
2015-01-01
The Iowa Gambling Task (IGT) and the Soochow Gambling Task (SGT) are two experience-based risky decision-making tasks for examining decision-making deficits in clinical populations. Several cognitive models, including the expectancy-valence learning (EVL) model and the prospect valence learning (PVL) model, have been developed to disentangle the motivational, cognitive, and response processes underlying the explicit choices in these tasks. The purpose of the current study was to develop an improved model that can fit empirical data better than the EVL and PVL models and, in addition, produce more consistent parameter estimates across the IGT and SGT. Twenty-six opiate users (mean age 34.23; SD 8.79) and 27 control participants (mean age 35; SD 10.44) completed both tasks. Eighteen cognitive models varying in evaluation, updating, and choice rules were fit to individual data and their performances were compared to that of a statistical baseline model to find a best fitting model. The results showed that the model combining the prospect utility function treating gains and losses separately, the decay-reinforcement updating rule, and the trial-independent choice rule performed the best in both tasks. Furthermore, the winning model produced more consistent individual parameter estimates across the two tasks than any of the other models. PMID:25814963
Directory of Open Access Journals (Sweden)
Junyi eDai
2015-03-01
Full Text Available The Iowa Gambling Task (IGT and the Soochow Gambling Task (SGT are two experience-based risky decision-making tasks for examining decision-making deficits in clinical populations. Several cognitive models, including the expectancy-valence learning model (EVL and the prospect valence learning model (PVL, have been developed to disentangle the motivational, cognitive, and response processes underlying the explicit choices in these tasks. The purpose of the current study was to develop an improved model that can fit empirical data better than the EVL and PVL models and, in addition, produce more consistent parameter estimates across the IGT and SGT. Twenty-six opiate users (mean age 34.23; SD 8.79 and 27 control participants (mean age 35; SD 10.44 completed both tasks. Eighteen cognitive models varying in evaluation, updating, and choice rules were fit to individual data and their performances were compared to that of a statistical baseline model to find a best fitting model. The results showed that the model combining the prospect utility function treating gains and losses separately, the decay-reinforcement updating rule, and the trial-independent choice rule performed the best in both tasks. Furthermore, the winning model produced more consistent individual parameter estimates across the two tasks than any of the other models.
Dynamic modeling and analysis of vortex filament motion using a novel curve-fitting method
Directory of Open Access Journals (Sweden)
Chang-Joo Kim
2016-02-01
Full Text Available Applications of a novel curve-fitting technique are presented to efficiently predict the motion of the vortex filament, which is trailed from a rigid body such as wings and rotors. The governing equations of the motion, when a Lagrangian approach with the present curve-fitting method is applied, can be transformed into an easily solvable form of the system of nonlinear ordinary differential equations. The applicability of Bézier curves, B-spline, and Lagrange interpolating polynomials is investigated. Local Lagrange interpolating polynomials with a shift operator are proposed as the best selection for applications, since it provides superior system characteristics with minimum computing time, compared to other methods. In addition, the Gauss quadrature formula with local refinement strategy has been developed for an accurate prediction of the induced velocity computed with the line integration of the Biot–Savart law. Rotary-wing problems including a vortex ring problem are analyzed to show the efficiency, accuracy, and flexibility in the applications of the proposed method.
Next-to-leading order unitarity fits in Two-Higgs-Doublet models with soft $\\mathbb{Z}_2$ breaking
Cacchio, Vincenzo; Eberhardt, Otto; Murphy, Christopher W
2016-01-01
We fit the next-to-leading order unitarity conditions to the Two-Higgs-Doublet model with a softly broken $\\mathbb{Z}_2$ symmetry. In doing so, we alleviate the existing uncertainty on how to treat higher order corrections to quartic couplings of its Higgs potential. A simplified approach to implementing the next-to-leading order unitarity conditions is presented. These new bounds are then combined with all other relevant constraints, including the complete set of LHC Run I data. The upper $95\\%$ bounds we find are $4.2$ on the absolute values of the quartic couplings, and $235$ GeV ($100$ GeV) for the mass degeneracies between the heavy Higgs particles in the type I (type II) scenario. In type II, we exclude an unbroken $\\mathbb{Z}_2$ symmetry with a probability of $95\\%$. All fits are performed using the open-source code HEPfit.
Dog models of naturally occurring cancer.
Rowell, Jennie L; McCarthy, Donna O; Alvarez, Carlos E
2011-07-01
Studies using dogs provide an ideal solution to the gap in animal models for natural disease and translational medicine. This is evidenced by approximately 400 inherited disorders being characterized in domesticated dogs, most of which are relevant to humans. There are several hundred isolated populations of dogs (breeds) and each has a vastly reduced genetic variation compared with humans; this simplifies disease mapping and pharmacogenomics. Dogs age five- to eight-fold faster than do humans, share environments with their owners, are usually kept until old age and receive a high level of health care. Farseeing investigators recognized this potential and, over the past decade, have developed the necessary tools and infrastructure to utilize this powerful model of human disease, including the sequencing of the dog genome in 2005. Here, we review the nascent convergence of genetic and translational canine models of spontaneous disease, focusing on cancer.
Brodie, E.; King, E.; Molins, S.; Karaoz, U.; Steefel, C. I.; Banfield, J. F.; Beller, H. R.; Anantharaman, K.; Ligocki, T. J.; Trebotich, D.
2015-12-01
Pore-scale processes mediated by microorganisms underlie a range of critical ecosystem services, regulating carbon stability, nutrient flux, and the purification of water. Advances in cultivation-independent approaches now provide us with the ability to reconstruct thousands of genomes from microbial populations from which functional roles may be assigned. With this capability to reveal microbial metabolic potential, the next step is to put these microbes back where they belong to interact with their natural environment, i.e. the pore scale. At this scale, microorganisms communicate, cooperate and compete across their fitness landscapes with communities emerging that feedback on the physical and chemical properties of their environment, ultimately altering the fitness landscape and selecting for new microbial communities with new properties and so on. We have developed a trait-based model of microbial activity that simulates coupled functional guilds that are parameterized with unique combinations of traits that govern fitness under dynamic conditions. Using a reactive transport framework, we simulate the thermodynamics of coupled electron donor-acceptor reactions to predict energy available for cellular maintenance, respiration, biomass development, and enzyme production. From metagenomics, we directly estimate some trait values related to growth and identify the linkage of key traits associated with respiration and fermentation, macromolecule depolymerizing enzymes, and other key functions such as nitrogen fixation. Our simulations were carried out to explore abiotic controls on community emergence such as seasonally fluctuating water table regimes across floodplain organic matter hotspots. Simulations and metagenomic/metatranscriptomic observations highlighted the many dependencies connecting the relative fitness of functional guilds and the importance of chemolithoautotrophic lifestyles. Using an X-Ray microCT-derived soil microaggregate physical model combined
Fitness Club
2012-01-01
Open to All: http://cern.ch/club-fitness fitness.club@cern.ch Boxing Your supervisor makes your life too tough ! You really need to release the pressure you've been building up ! Come and join the fit-boxers. We train three times a week in Bd 216, classes for beginners and advanced available. Visit our website cern.ch/Boxing General Fitness Escape from your desk with our general fitness classes, to strengthen your heart, muscles and bones, improve you stamina, balance and flexibility, achieve new goals, be more productive and experience a sense of well-being, every Monday, Wednesday and Friday lunchtime, Tuesday mornings before work and Thursday evenings after work – join us for one of our monthly fitness workshops. Nordic Walking Enjoy the great outdoors; Nordic Walking is a great way to get your whole body moving and to significantly improve the condition of your muscles, heart and lungs. It will boost your energy levels no end. Pilates A body-conditioning technique de...
Extensive fitness and human cooperation.
van Hateren, J H
2015-12-01
Evolution depends on the fitness of organisms, the expected rate of reproducing. Directly getting offspring is the most basic form of fitness, but fitness can also be increased indirectly by helping genetically related individuals (such as kin) to increase their fitness. The combined effect is known as inclusive fitness. Here it is argued that a further elaboration of fitness has evolved, particularly in humans. It is called extensive fitness and it incorporates producing organisms that are merely similar in phenotype. The evolvability of this mechanism is illustrated by computations on a simple model combining heredity and behaviour. Phenotypes are driven into the direction of high fitness through a mechanism that involves an internal estimate of fitness, implicitly made within the organism itself. This mechanism has recently been conjectured to be responsible for producing agency and goals. In the model, inclusive and extensive fitness are both implemented by letting fitness increase nonlinearly with the size of subpopulations of similar heredity (for the indirect part of inclusive fitness) and of similar phenotype (for the phenotypic part of extensive fitness). Populations implementing extensive fitness outcompete populations implementing mere inclusive fitness. This occurs because groups with similar phenotype tend to be larger than groups with similar heredity, and fitness increases more when groups are larger. Extensive fitness has two components, a direct component where individuals compete in inducing others to become like them and an indirect component where individuals cooperate and help others who are already similar to them.
... cancer Non-Hodgkin lymphoma Ovarian cancer Pancreatic cancer Testicular cancer Thyroid cancer Uterine cancer Symptoms Symptoms of cancer ... tumor Obesity Pancreatic cancer Prostate cancer Stomach cancer Testicular cancer Throat or larynx cancer Thyroid cancer Patient Instructions ...
A superstatistical model of metastasis and cancer survival
Chen, L Leon
2007-01-01
We introduce a superstatistical model for the progression statistics of malignant cancer cells. The metastatic cascade is modeled as a complex nonequilibrium system with several macroscopic pathways and inverse-chi-square distributed parameters of the underlying Poisson processes. The predictions of the model are in excellent agreement with observed survival time probability distributions of breast cancer patients.
A Stochastic Model for Cancer Stem Cell Origin in Metastatic Colon Cancer
Odoux, Christine; Fohrer, Helene; Hoppo, Toshitaka; Guzik, Lynda; Stolz, Donna Beer; Lewis, Dale W.; Gollin, Susanne M.; Gamblin, T. Clark; Geller, David A.; Lagasse, Eric
2008-01-01
Human cancers have been found to include transformed stem cells that may drive cancer progression to metastasis. Here we report that metastatic colon cancer contains clonally derived tumor cells with all of the critical properties expected of stem cells, including self-renewal and to the ability to differentiate into mature colon cells. Additionally, when injected into mice, these cells initiated tumors that closely resemble human cancer. Karyotype analyses of parental and clonally-derived tumor cells expressed many consistent (clonal), along with unique chromosomal aberrations, suggesting the presence of chromosomal instability in the cancer stem cells. Thus, this new model for cancer origin and metastatic progression includes features of both the hierarchical model for cancerous stem cells and the stochastic model, driven by the observation of chromosomal instability. PMID:18757407
Entropy, complexity, and Markov diagrams for random walk cancer models.
Newton, Paul K; Mason, Jeremy; Hurt, Brian; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Kuhn, Peter
2014-12-19
The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential.
Entropy, complexity, and Markov diagrams for random walk cancer models
Newton, Paul K.; Mason, Jeremy; Hurt, Brian; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Kuhn, Peter
2014-12-01
The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential.
Exchange interactions in [2 × 2] Cu(II) grids: on the reliability of the fitting spin models.
Calzado, Carmen J; Evangelisti, Stefano
2014-02-21
This paper reports a theoretical analysis of the electronic structure and magnetic properties of a ferromagnetic Cu(II) [2 × 2] grid. The calculations confirm a quintet (S = 2) ground state and an energy-level distribution of the magnetic states in accordance with Heisenberg behaviour. The whole set of first- and second-neighbour magnetic coupling constants has been evaluated, all in agreement with the structure and arrangement of the Cu 3dx(2) - y(2) magnetic orbitals. The results indicate that the dominant interaction in the system is the ferromagnetic coupling between the nearest Cu sites. The calculated J values suggest a C(2v) spin-spin interaction pattern, instead of the D(4h) model employed in the magnetic data fit. However, both spin models provide similar plots of the thermal dependence of the susceptibility and magnetic moment data. This study highlights the fact that the spin models resulting from the fittings can be just effective models, capable of correctly reproducing the macroscopic properties, although not always in accordance with the microscopic interactions governing these properties.
The electroweak fit of the standard model after the discovery of a new boson at the LHC
Energy Technology Data Exchange (ETDEWEB)
Baak, M.; Hoecker, A.; Schott, M. [European Organization for Nuclear Research (CERN), Geneva (Switzerland); Goebel, M.; Kennedy, D.; Moenig, K. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany); Haller, J.; Kogler, R. [Hamburg Univ. (Germany). Inst. fuer Experimentalphysik; Stelzer, J. [Michigan State Univ., East Lansing, MI (United States). Dept. of Physics and Astronomy; Collaboration: The Gfitter Group
2012-09-15
In view of the discovery of a new boson by the ATLAS and CMS Collaborations at the LHC, we present an update of the global Standard Model (SM) fit to electroweak precision data. Assuming the new particle to be the SM Higgs boson, all fundamental parameters of the SM are known allowing, for the first time, to overconstrain the SM at the electroweak scale and assert its validity. Including the effects of radiative corrections and the experimental and theoretical uncertainties, the global fit exhibits a p-value of 0.07. The mass measurements by ATLAS and CMS agree within 1.3{sigma} with the indirect determination M{sub H}=94{sup +25}{sub -22} GeV. Within the SM the W boson mass and the effective weak mixing angle can be accurately predicted to be M{sub W}=80.359{+-}0.011 GeV and sin{sup 2}{theta}{sup l}{sub eff}=0.23150{+-}0.00010 from the global fit. These results are compatible with, and exceed in precision, the direct measurements. For the indirect determination of the top quark mass we find m{sub t}=175.8{sup +2.7}{sub -2.4} GeV, in agreement with the kinematic and cross-section based measurements.
Yu, Tai-Kuei; Yu, Tai-Yi
2010-01-01
Understanding learners' behaviour, perceptions and influence in terms of learner performance is crucial to predict the use of electronic learning systems. By integrating the task-technology fit (TTF) model and the theory of planned behaviour (TPB), this paper investigates the online learning utilisation of Taiwanese students. This paper provides a…
Percentile Analysis for Goodness-of-Fit Comparisons of Models to Data
2014-07-01
obtaining a high R2. One solution to the problem is to consider a metric that is both sensitive to the number of data points under investigation as well...other facets of the model (e.g., its parsimony, breath, and ability; see Cassimatis, Bello , & Langley, 2008). 4. Model A and Model B have...278. Busemeyer, J. R. & Diederich, A. (2010). Cognitive Modeling. Sage. Cassimatis, N., Bello , P. & Langley, P. (2008). Ability, breadth and
Liu, Xing
2008-01-01
The proportional odds (PO) model, which is also called cumulative odds model (Agresti, 1996, 2002 ; Armstrong & Sloan, 1989; Long, 1997, Long & Freese, 2006; McCullagh, 1980; McCullagh & Nelder, 1989; Powers & Xie, 2000; O'Connell, 2006), is one of the most commonly used models for the analysis of ordinal categorical data and comes from the class…
Zheng, Hao; Rathouz, Paul J
2015-07-01
For quantitative behavior genetic (e.g., twin) studies, Purcell proposed a novel model for testing gene-by-measured environment (GxM) interactions while accounting for gene-by-environment correlation. Rathouz et al. expanded this model into a broader class of non-linear biometric models for quantifying and testing such interactions. In this work, we propose a novel factorization of the likelihood for this class of models, and adopt numerical integration techniques to achieve model estimation, especially for those without close-form likelihood. The validity of our procedures is established through numerical simulation studies. The new procedures are illustrated in a twin study analysis of the moderating effect of birth weight on the genetic influences on childhood anxiety. A second example is given in an online appendix. Both the extant GxM models and the new non-linear models critically assume normality of all structural components, which implies continuous, but not normal, manifest response variables.
Indian Academy of Sciences (India)
O Scholten; A Usov
2010-08-01
To describe photo- and meson-induced reactions on the nucleon, one is faced with a rather extensive coupled-channel problem. Ignoring the effects of channel coupling, as one would do in describing a certain reaction at the tree level, invariably creates a large inconsistency between the different reactions that are described. In addition, the imaginary parts of the amplitude, which are related through the optical theorem, to total cross-sections, are directly reflected in certain polarization observables. Performing a full coupled-channel calculation thus offers the possibility to implement the maximum number of constraints. The drawback one is faced with is to arrive at a simultaneous fit of a large number of reaction channels. While some of the parameters are common to many reactions, one is still faced with the challenge to optimize a large number of parameters in a highly non-linear calculation. Here we show that such an approach is possible and present some results for photoinduced strangeness production.
Vovchenko, Volodymyr
2016-01-01
The hadron-resonance gas (HRG) model with eigenvolume corrections is employed to fit the hadron yield data of the NA49 collaboration for central Pb+Pb collisions at $\\sqrt{s_{NN}}$ = 6.3, 7.6, 8.8, 12.3, and 17.3 GeV, the hadron midrapidity yield data of the STAR collaboration for Au+Au collisions at $\\sqrt{s_{NN}}$ = 200 GeV, and the hadron midrapidity yield data of the ALICE collaboration for Pb+Pb collisions at $\\sqrt{s_{NN}}$ = 2760 GeV. The influence of the EV corrections is studied within two different formulations of the EV HRG model. For the case of the point-particle HRG the extracted values of temperature and chemical potential are consistent with previous findings. The situation is very different when we apply the eigenvolume corrections with mass-proportional eigenvolumes $v_i \\sim m_i$, fixed to different values of the proton hard-core radius of $r_p$. At given bombarding energy the EV HRG model fits do not just yield a single $T-\\mu_B$ pair, but a whole range of $T-\\mu_B$ pairs. These pairs form...
Dijkstra, T.K.; Henseler, J.
2011-01-01
The recent advent of nonlinear structural equation models with indices poses a new challenge to the measurement of scientific constructs. We discuss, exemplify and add to a family of statistical methods aimed at creating linear indices, and compare their suitability in a complex path model with line
Fitting macroevolutionary models to phylogenies: an example using vertebrate body sizes
Mooers, Arne Ø.; Schluter, Dolph
1998-01-01
How do traits change through time and with speciation? We present a simple and generally applicable method for comparing various models of the macroevolution of traits within a maximum likelihood framework. We illustrate four such models: 1) variance among species accumulates in direct proportion to
Fitness effects of beneficial mutations: the mutational landscape model in experimental evolution
DEFF Research Database (Denmark)
Betancourt, Andrea J.; Bollback, Jonathan Paul
2006-01-01
The mutational landscape model is a theoretical model describing sequence evolution in natural populations. However, recent experimental work has begun to test its predictions in laboratory populations of microbes. Several of these studies have focused on testing the prediction that the effects...
de Vries, S O; Fidler, Vaclav; Kuipers, Wietze D; Hunink, Maria G M
1998-01-01
The purpose of this study was to develop a model that predicts the outcome of supervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a six
A mathematical model of cancer cells with phenotypic plasticity
Directory of Open Access Journals (Sweden)
Da Zhou
2015-12-01
Full Text Available Purpose: The phenotypic plasticity of cancer cells is recently becoming a cutting-edge research area in cancer, which challenges the cellular hierarchy proposed by the conventional cancer stem cell theory. In this study, we establish a mathematical model for describing the phenotypic plasticity of cancer cells, based on which we try to find some salient features that can characterize the dynamic behavior of the phenotypic plasticity especially in comparison to the hierarchical model of cancer cells. Methods: We model cancer as population dynamics composed of different phenotypes of cancer cells. In this model, not only can cancer cells divide (symmetrically and asymmetrically and die, but they can also convert into other cellular phenotypes. According to the Law of Mass Action, the cellular processes can be captured by a system of ordinary differential equations (ODEs. On one hand, we can analyze the long-term stability of the model by applying qualitative method of ODEs. On the other hand, we are also concerned about the short-term behavior of the model by studying its transient dynamics. Meanwhile, we validate our model to the cell-state dynamics in published experimental data.Results: Our results show that the phenotypic plasticity plays important roles in both stabilizing the distribution of different phenotypic mixture and maintaining the cancer stem cells proportion. In particular, the phenotypic plasticity model shows decided advantages over the hierarchical model in predicting the phenotypic equilibrium and cancer stem cells’ overshoot reported in previous biological experiments in cancer cell lines.Conclusion: Since the validity of the phenotypic plasticity paradigm and the conventional cancer stem cell theory is still debated in experimental biology, it is worthy of theoretically searching for good indicators to distinguish the two models through quantitative methods. According to our study, the phenotypic equilibrium and overshoot
Chen, Ligong; Durkin, Kathleen A; Casida, John E
2006-03-28
Several major insecticides, including alpha-endosulfan, lindane, and fipronil, and the botanical picrotoxinin are noncompetitive antagonists (NCAs) for the GABA receptor. We showed earlier that human beta(3) homopentameric GABA(A) receptor recognizes all of the important GABAergic insecticides and reproduces the high insecticide sensitivity and structure-activity relationships of the native insect receptor. Despite large structural diversity, the NCAs are proposed to fit a single binding site in the chloride channel lumen lined by five transmembrane 2 segments. This hypothesis is examined with the beta(3) homopentamer by mutagenesis, pore structure studies, NCA binding, and molecular modeling. The 15 amino acids in the cytoplasmic half of the pore were mutated to cysteine, serine, or other residue for 22 mutants overall. Localization of A-1'C, A2'C, T6'C, and L9'C (index numbers for the transmembrane 2 region) in the channel lumen was established by disulfide cross-linking. Binding of two NCA radioligands [(3)H]1-(4-ethynylphenyl)-4-n-propyl-2,6,7-trioxabicyclo[2.2.2]octane and [(3)H] 3,3-bis-trifluoromethyl-bicyclo[2,2,1]heptane-2,2-dicarbonitrile was dramatically reduced with 8 of the 15 mutated positions, focusing attention on A2', T6', and L9' as proposed binding sites, consistent with earlier mutagenesis studies. The cytoplasmic half of the beta3 homopentamer pore was modeled as an alpha-helix. The six NCAs listed above plus t-butylbicyclophosphorothionate fit the 2' to 9' pore region forming hydrogen bonds with the T6' hydroxyl and hydrophobic interactions with A2', T6', and L9' alkyl substituents, thereby blocking the channel. Thus, widely diverse NCA structures fit the same GABA receptor beta subunit site with important implications for insecticide cross-resistance and selective toxicity between insects and mammals.
Dauenhauer, Brian; Keating, Xiaofen; Lambdin, Dolly
2016-08-01
Response to intervention (RtI) models are frequently used in schools to tailor academic instruction to the needs of students. The purpose of this study was to examine the effects of using RtI to promote physical activity (PA) and fitness in one urban elementary school. Ninety-nine students in grades 2-5 participated in up to three tiers of intervention throughout the course of one school year. Tier one included 150 min/week of physical education (increased from 90 min/week the previous year) and coordinated efforts to improve school health. Tier two consisted of 30 min/week of small group instruction based on goal setting and social support. Tier three included an after-school program for parents and children focused on healthy living. PA, cardiovascular fitness, and body composition were assessed before and after the interventions using pedometers, a 20-m shuttle run, and height/weight measurements. From pre- to post-testing, PA remained relatively stable in tier one and increased by 2349 steps/day in tier two. Cardiovascular fitness increased in tiers one and two by 1.17 and 1.35 ml/kg/min, respectively. Although body mass index did not change, 17 of the 99 students improved their weight status over the course of the school year, resulting in an overall decline in the prevalence of overweight/obesity from 59.6 to 53.5 %. Preliminary results suggest that the RtI model can be an effective way to structure PA/health interventions in an elementary school setting.
Prostate cancer detection from model-free T1-weighted time series and diffusion imaging
Haq, Nandinee F.; Kozlowski, Piotr; Jones, Edward C.; Chang, Silvia D.; Goldenberg, S. Larry; Moradi, Mehdi
2015-03-01
The combination of Dynamic Contrast Enhanced (DCE) images with diffusion MRI has shown great potential in prostate cancer detection. The parameterization of DCE images to generate cancer markers is traditionally performed based on pharmacokinetic modeling. However, pharmacokinetic models make simplistic assumptions about the tissue perfusion process, require the knowledge of contrast agent concentration in a major artery, and the modeling process is sensitive to noise and fitting instabilities. We address this issue by extracting features directly from the DCE T1-weighted time course without modeling. In this work, we employed a set of data-driven features generated by mapping the DCE T1 time course to its principal component space, along with diffusion MRI features to detect prostate cancer. The optimal set of DCE features is extracted with sparse regularized regression through a Least Absolute Shrinkage and Selection Operator (LASSO) model. We show that when our proposed features are used within the multiparametric MRI protocol to replace the pharmacokinetic parameters, the area under ROC curve is 0.91 for peripheral zone classification and 0.87 for whole gland classification. We were able to correctly classify 32 out of 35 peripheral tumor areas identified in the data when the proposed features were used with support vector machine classification. The proposed feature set was used to generate cancer likelihood maps for the prostate gland.
Forecasting Age-Specific Brain Cancer Mortality Rates Using Functional Data Analysis Models
Directory of Open Access Journals (Sweden)
Keshav P. Pokhrel
2015-01-01
Full Text Available Incidence and mortality rates are considered as a guideline for planning public health strategies and allocating resources. We apply functional data analysis techniques to model age-specific brain cancer mortality trend and forecast entire age-specific functions using exponential smoothing state-space models. The age-specific mortality curves are decomposed using principal component analysis and fit functional time series model with basis functions. Nonparametric smoothing methods are used to mitigate the existing randomness in the observed data. We use functional time series model on age-specific brain cancer mortality rates and forecast mortality curves with prediction intervals using exponential smoothing state-space model. We also present a disparity of brain cancer mortality rates among the age groups together with the rate of change of mortality rates. The data were obtained from the Surveillance, Epidemiology and End Results (SEER program of the United States. The brain cancer mortality rates, classified under International Classification Disease code ICD-O-3, were extracted from SEER*Stat software.
Fitness Club
2012-01-01
The CERN Fitness Club is pleased to announce its new early morning class which will be taking place on: Tuesdays from 24th April 07:30 to 08:15 216 (Pump Hall, close to entrance C) – Facilities include changing rooms and showers. The Classes: The early morning classes will focus on workouts which will help you build not only strength and stamina, but will also improve your balance, and coordination. Our qualified instructor Germana will accompany you throughout the workout to ensure you stay motivated so you achieve the best results. Sign up and discover the best way to start your working day full of energy! How to subscribe? We invite you along to a FREE trial session, if you enjoy the activity, please sign up via our website: https://espace.cern.ch/club-fitness/Activities/SUBSCRIBE.aspx. * * * * * * * * Saturday 28th April Get in shape for the summer at our fitness workshop and zumba dance party: Fitness workshop with Germana 13:00 to 14:30 - 216 (Pump Hall) Price...
Fitness Club
2012-01-01
Get in Shape for Summer with the CERN Fitness Club Saturday 23 June 2012 from 14:30 to 16.30 (doors open at 14.00) Germana’s Fitness Workshop. Build strength and stamina, sculpt and tone your body and get your heart pumping with Germana’s workout mixture of Cardio Attack, Power Pump, Power Step, Cardio Combat and Cross-Training. Where: 216 (Pump room – equipped with changing rooms and showers). What to wear: comfortable clothes and indoor sports shoes + bring a drink! How much: 15 chf Sign up here: https://espace.cern.ch/club-fitness/Lists/Test_Subscription/NewForm.aspx? Join the Party and dance yourself into shape at Marco + Marials Zumba Masterclass. Saturday 30 June 2012 from 15:00 to 16:30 Marco + Mariel’s Zumba Masterclass Where: 216 (Pump room – equipped with changing rooms and showers). What to wear: comfortable clothes and indoor sports shoes + bring a drink! How much: 25 chf Sign up here: https://espace.cern.ch/club-fitness/Lists/Zumba%20...
Fitness club
2013-01-01
Nordic Walking Classes Come join the Nordic walking classes and outings offered by the CERN Fitness Club starting September 2013. Our licensed instructor Christine offers classes for people who’ve never tried Nordic Walking and who would like to learn the technique, and outings for people who have completed the classes and enjoy going out as a group. Course 1: Tuesdays 12:30 - 13:30 24 September, 1 October, 8 October, 15 October Course 2: Tuesdays 12:30 - 13:30 5 November, 12 November, 19 November, 26 November Outings will take place on Thursdays (12:30 to 13:30) from 12 September 2013. We meet at the CERN Club Barracks car park (close to Entrance A) 10 minutes before departure. Prices: 50 CHF for 4 classes, including the 10 CHF Club membership. Payments made directly to instructor. Renting Poles: Poles can be rented from Christine at 5 CHF / hour. Subscription: Please subscribe at: http://cern.ch/club-fitness Looking forward to seeing you among us! Fitness Club FitnessClub@c...
Vicente-Dueñas, Carolina; Hauer, Julia; Ruiz-Roca, Lucía; Ingenhag, Deborah; Rodríguez-Meira, Alba; Auer, Franziska; Borkhardt, Arndt; Sánchez-García, Isidro
2015-06-01
Cancer is a clonal malignant disease originated in a single cell and characterized by the accumulation of partially differentiated cells that are phenotypically reminiscent of normal stages of differentiation. According to current models, therapeutic strategies that block oncogene activity are likely to selectively target tumor cells. However, recent evidences have revealed that cancer stem cells could arise through a tumor stem cell reprogramming mechanism, suggesting that genetic lesions that initiate the cancer process might be dispensable for tumor progression and maintenance. This review addresses the impact of these results toward a better understanding of cancer development and proposes new approaches to treat cancer in the future.
Bhatnagar, Tarun; Dutta, Tapati; Stover, John; Godbole, Sheela; Sahu, Damodar; Boopathi, Kangusamy; Bembalkar, Shilpa; Singh, Kh. Jitenkumar; Goyal, Rajat; Pandey, Arvind; Mehendale, Sanjay M.
2016-01-01
Models are designed to provide evidence for strategic program planning by examining the impact of different interventions on projected HIV incidence. We employed the Goals Model to fit the HIV epidemic curves in Andhra Pradesh, Maharashtra and Tamil Nadu states of India where HIV epidemic is considered to have matured and in a declining phase. Input data in the Goals Model consisted of demographic, epidemiological, transmission-related and risk group wise behavioral parameters. The HIV prevalence curves generated in the Goals Model for each risk group in the three states were compared with the epidemic curves generated by the Estimation and Projection Package (EPP) that the national program is routinely using. In all the three states, the HIV prevalence trends for high-risk populations simulated by the Goals Model matched well with those derived using state-level HIV surveillance data in the EPP. However, trends for the low- and medium-risk populations differed between the two models. This highlights the need to generate more representative and robust data in these sub-populations and consider some structural changes in the modeling equation and parameters in the Goals Model to effectively use it to assess the impact of future strategies of HIV control in various sub-populations in India at the sub-national level. PMID:27711212
Mouse models of estrogen receptor-positive breast cancer
Directory of Open Access Journals (Sweden)
Shakur Mohibi
2011-01-01
Full Text Available Breast cancer is the most frequent malignancy and second leading cause of cancer-related deaths among women. Despite advances in genetic and biochemical analyses, the incidence of breast cancer and its associated mortality remain very high. About 60 - 70% of breast cancers are Estrogen Receptor alpha (ER-α positive and are dependent on estrogen for growth. Selective estrogen receptor modulators (SERMs have therefore provided an effective targeted therapy to treat ER-α positive breast cancer patients. Unfortunately, development of resistance to endocrine therapy is frequent and leads to cancer recurrence. Our understanding of molecular mechanisms involved in the development of ER-α positive tumors and their resistance to ER antagonists is currently limited due to lack of experimental models of ER-α positive breast cancer. In most mouse models of breast cancer, the tumors that form are typically ER-negative and independent of estrogen for their growth. However, in recent years more attention has been given to develop mouse models that develop different subtypes of breast cancers, including ER-positive tumors. In this review, we discuss the currently available mouse models that develop ER-α positive mammary tumors and their potential use to elucidate the molecular mechanisms of ER-α positive breast cancer development and endocrine resistance.
Review of Animal Models of Prostate Cancer Bone Metastasis
Directory of Open Access Journals (Sweden)
Jessica K. Simmons
2014-06-01
Full Text Available Prostate cancer bone metastases are associated with a poor prognosis and are considered incurable. Insight into the formation and growth of prostate cancer bone metastasis is required for development of new imaging and therapeutic strategies to combat this devastating disease. Animal models are indispensable in investigating cancer pathogenesis and evaluating therapeutics. Multiple animal models of prostate cancer bone metastasis have been developed, but few effectively model prostatic neoplasms and osteoblastic bone metastases as they occur in men. This review discusses the animal models that have been developed to investigate prostate cancer bone metastasis, with a focus on canine models and also includes human xenograft and rodent models. Adult dogs spontaneously develop benign prostatic hyperplasia and prostate cancer with osteoblastic bone metastases. Large animal models, such as dogs, are needed to develop new molecular imaging tools and effective focal intraprostatic therapy. None of the available models fully reflect the metastatic disease seen in men, although the various models have provided important insight into the metastatic process. As additional models are developed and knowledge from the different models is combined, the molecular mechanisms of prostate cancer bone metastasis can be deciphered and targeted for development of novel therapies and molecular diagnostic imaging.
Gilkey, Roderick; Kilts, Clint
2007-11-01
Recent neuroscientific research shows that the health of your brain isn't, as experts once thought, just the product of childhood experiences and genetics; it reflects your adult choices and experiences as well. Professors Gilkey and Kilts of Emory University's medical and business schools explain how you can strengthen your brain's anatomy, neural networks, and cognitive abilities, and prevent functions such as memory from deteriorating as you age. The brain's alertness is the result of what the authors call cognitive fitness -a state of optimized ability to reason, remember, learn, plan, and adapt. Certain attitudes, lifestyle choices, and exercises enhance cognitive fitness. Mental workouts are the key. Brain-imaging studies indicate that acquiring expertise in areas as diverse as playing a cello, juggling, speaking a foreign language, and driving a taxicab expands your neural systems and makes them more communicative. In other words, you can alter the physical makeup of your brain by learning new skills. The more cognitively fit you are, the better equipped you are to make decisions, solve problems, and deal with stress and change. Cognitive fitness will help you be more open to new ideas and alternative perspectives. It will give you the capacity to change your behavior and realize your goals. You can delay senescence for years and even enjoy a second career. Drawing from the rapidly expanding body of neuroscience research as well as from well-established research in psychology and other mental health fields, the authors have identified four steps you can take to become cognitively fit: understand how experience makes the brain grow, work hard at play, search for patterns, and seek novelty and innovation. Together these steps capture some of the key opportunities for maintaining an engaged, creative brain.
PREDICT : model for prediction of survival in localized prostate cancer
Kerkmeijer, Linda G W; Monninkhof, Evelyn M.; van Oort, Inge M.; van der Poel, Henk G.; de Meerleer, Gert; van Vulpen, Marco
2016-01-01
Purpose: Current models for prediction of prostate cancer-specific survival do not incorporate all present-day interventions. In the present study, a pre-treatment prediction model for patients with localized prostate cancer was developed.Methods: From 1989 to 2008, 3383 patients were treated with I
An improved nonlinear model of HEMTs with independent transconductance tail-off fitting
Institute of Scientific and Technical Information of China (English)
Liu Linsheng
2011-01-01
We present an improved large-signal device model of GaAs/GaN HEMTs, amenable for use in commercial nonlinear simulators. The proposed model includes a new exponential function to independently control the transconductance compression/tail-offbehaviors. The main advantage of this model is to provide a simple and coherent description of the bias-dependent drain current (I-V) that is valid in all regions of operation. All aspects of the model are validated for 0.25-μm gate-length GaAs and GaN HEMT processes. The simulation results of DC/pulsed I-V, RF large-signal power and intermodulation distortion products show excellent agreement with the measured data.
Some Properties of A Lack-of-Fit Test for a Linear Errors in Variables Model
Institute of Scientific and Technical Information of China (English)
Li-xing Zhu; Heng-jian Cui; K.W.Ng
2004-01-01
The relationship between the linear errors-in-variables model and the corresponding ordinary linear model in statistical inference is studied.It is shown that normality of the distribution of covariate is a necessary and su cient condition for the equivalence.Therefore,testing for lack-of-t in linear errors-in-variables model can be converted into testing for it in the corresponding ordinary linear model under normality assumption.A test of score type is constructed and the limiting chi-squared distribution is derived under the null hypothesis.Furthermore,we discuss the power of the test and the choice of the weight function involved in the test statistic.
SDSS-II: Determination of shape and color parameter coefficients for SALT-II fit model
Energy Technology Data Exchange (ETDEWEB)
Dojcsak, L.; Marriner, J.; /Fermilab
2010-08-01
In this study we look at the SALT-II model of Type IA supernova analysis, which determines the distance moduli based on the known absolute standard candle magnitude of the Type IA supernovae. We take a look at the determination of the shape and color parameter coefficients, {alpha} and {beta} respectively, in the SALT-II model with the intrinsic error that is determined from the data. Using the SNANA software package provided for the analysis of Type IA supernovae, we use a standard Monte Carlo simulation to generate data with known parameters to use as a tool for analyzing the trends in the model based on certain assumptions about the intrinsic error. In order to find the best standard candle model, we try to minimize the residuals on the Hubble diagram by calculating the correct shape and color parameter coefficients. We can estimate the magnitude of the intrinsic errors required to obtain results with {chi}{sup 2}/degree of freedom = 1. We can use the simulation to estimate the amount of color smearing as indicated by the data for our model. We find that the color smearing model works as a general estimate of the color smearing, and that we are able to use the RMS distribution in the variables as one method of estimating the correct intrinsic errors needed by the data to obtain the correct results for {alpha} and {beta}. We then apply the resultant intrinsic error matrix to the real data and show our results.
Fitting a Turbulent Cloud Model to CO Observations of Starless Bok Globules
Hegmann, M.; Hengel, C.; Röllig, M.; Kegel, W. H.
We present observations of five starless Bok globules in transitions of 12CO (J=2-1 and {J=3-2}), 13CO (J=2-1), and C18O (J=2-1) which have been obtained at the Heinrich-Hertz-Telescope. For an analysis of the data we use the model of Kegel et al. (see e.g. Piehler & Kegel 1995, A&A 297, 841; Hegmann & Kegel 2000, A&A 359, 405) which describes an isothermal sphere stabilized by turbulent and thermal pressure. This approach deals with the full NLTE radiative transfer problem and accounts for a turbulent velocity field with finite correlation length. By a comparison of observed and calculated line profiles we are able not only to determine the kinetic temperature, hydrogen density and CO coloumn density of the globules, but also to study the properties of the turbulent velocity field, i.e. the variance of its one-point-distribution and its correlation length. We consider our model to be an alternative tool for the evaluation of molecular lines emitted by molecular clouds. The model assumptions are certainly closer to reality than the assumptions behind the standard evaluation models, as for example the LVG model. Our current study shows that that the results obtained from our model can differ significantly from those obtained from a LVG analysis.
Directory of Open Access Journals (Sweden)
Vlasis Polychronopoulos
2010-01-01
Full Text Available SUMMARY. Collaboration of a multidisciplinary team of experts on the functional evaluation of patients with lung cancer was facilitated by the European Respiratory Society (ERS and the European Society of Thoracic Surgery (ESTS, in order to draw up recommendations and provide clinicians with clear, up-to-date guidelines on their fitness for surgery and chemo-radiotherapy. The subject was divided into various different topics, each of which was then assigned to at least two experts. The authors searched the literature according to their own strategies, with no central literature Review and compiled draft reports on each topic, which were then reviewed, discussed and voted on by the entire expert panel. The evidence supporting each recommendation was summarized, and graded as described by the Scottish Intercollegiate Guidelines Network Grading Review Group. Clinical practice guidelines were generated and finalized in a functional algorithm for risk stratification of the lung resection candidates, with emphasis on the cardiological evaluation, forced expiratory volume in 1 s (FEV1, systematic carbon monoxide lung diffusion capacity (DLCO and exercise testing. In contrast to lung resection, for which the scientific evidence is more robust, it was not possible to recommend any specific test, cut-off value, or algorithm for chemo-radiotherapy, due to the lack of data. It is highly recommended that patients with lung cancer should be managed in specialized units by experienced multidisciplinary teams. Pneumon 2010, 23(1:80-102.
Bayesian spatio-temporal modelling of tobacco-related cancer mortality in Switzerland
Directory of Open Access Journals (Sweden)
Verena Jürgens
2013-05-01
Full Text Available Tobacco smoking is a main cause of disease in Switzerland; lung cancer being the most common cancer mortality in men and the second most common in women. Although disease-specific mortality is decreasing in men, it is steadily increasing in women. The four language regions in this country might play a role in this context as they are influenced in different ways by the cultural and social behaviour of neighbouring countries. Bayesian hierarchical spatio-temporal, negative binomial models were fitted on subgroup-specific death rates indirectly standardized by national references to explore age- and gender-specific spatio-temporal patterns of mortality due to lung cancer and other tobacco-related cancers in Switzerland for the time period 1969-2002. Differences influenced by linguistic region and life in rural or urban areas were also accounted for. Male lung cancer mortality was found to be rather homogeneous in space, whereas women were confirmed to be more affected in urban regions. Compared to the German-speaking part, female mortality was higher in the French-speaking part of the country, a result contradicting other reports of similar comparisons between France and Germany. The spatio-temporal patterns of mortality were similar for lung cancer and other tobacco-related cancers. The estimated mortality maps can support the planning in health care services and evaluation of a national tobacco control programme. Better understanding of spatial and temporal variation of cancer of the lung and other tobacco-related cancers may help in allocating resources for more effective screening, diagnosis and therapy. The methodology can be applied to similar studies in other settings.
The hamster cheek pouch model for field cancerization studies.
Monti-Hughes, Andrea; Aromando, Romina F; Pérez, Miguel A; Schwint, Amanda E; Itoiz, Maria E
2015-02-01
External carcinogens, such as tobacco and alcohol, induce molecular changes in large areas of oral mucosa, which increase the risk of malignant transformation. This condition, known as 'field cancerization', can be detected in biopsy specimens using histochemical techniques, even before histological alterations are identified. The efficacy of these histochemical techniques as biomarkers of early cancerization must be demonstrated in appropriate models. The hamster cheek pouch oral cancer model, universally employed in biological studies and in studies for the prevention and treatment of oral cancer, is also an excellent model of field cancerization. The carcinogen is applied in solution to the surface of the mucosa and induces alterations that recapitulate the stages of cancerization in human oral mucosa. We have demonstrated that the following can be used for the early detection of cancerized tissue: silver staining of nucleolar organizer regions; the Feulgen reaction to stain DNA followed by ploidy analysis; immunohistochemical analysis of fibroblast growth factor-2, immunohistochemical labeling of proliferating cells to demonstrate an increase of epithelial cell proliferation in the absence of inflammation; and changes in markers of angiogenesis (i.e. those indicating vascular endothelial growth factor activity, endothelial cell proliferation and vascular density). The hamster cheek pouch model of oral cancer was also proposed and validated by our group for boron neutron capture therapy studies for the treatment of oral cancer. Clinical trials of this novel treatment modality have been performed and are underway for certain tumor types and localizations. Having demonstrated the efficacy of boron neutron capture therapy to control tumors in the hamster cheek pouch oral cancer model, we adapted the model for the long-term study of field cancerized tissue. We demonstrated the inhibitory effect of boron neutron capture therapy on tumor development in field
Statistics of Dark Matter Substructure: I. Model and Universal Fitting Functions
Jiang, Fangzhou
2014-01-01
We present a new, semi-analytical model describing the evolution of dark matter subhaloes. The model uses merger trees constructed using the method of Parkinson et al. (2008) to describe the masses and redshifts of subhaloes at accretion, which are subsequently evolved using a simple model for the orbit-averaged mass loss rates. The model is extremely fast, treats subhaloes of all orders, accounts for scatter in orbital properties and halo concentrations, and uses a simple recipe to convert subhalo mass to maximum circular velocity. The model accurately reproduces the average subhalo mass and velocity functions in numerical simulations. The inferred subhalo mass loss rates imply that an average dark matter subhalo loses in excess of 80 percent of its infall mass during its first radial orbit within the host halo. We demonstrate that the total mass fraction in subhaloes is tightly correlated with the `dynamical age' of the host halo, defined as the number of halo dynamical times that have elapsed since its for...
“Psychosocial Interventions for Cancer Survivors, Caregivers and Family Members—One Size Does Not Fit All: My Perspective as a Young Adult Survivor, Advocate and Oncology Social Worker” a personal reflection by Mary Grace Bontempo page
Cramer, K M
2000-10-01
Research shows that using highly self-aware participants yields studies of higher reliability, validity, and statistical power; dispositional self-awareness is commonly measured using the Fenigstein Self-Consciousness Scale (Fenigstein, Scheier, & Buss, 1975). This study used confirmatory factor analysis to compare various factor models that may underlie that scale. Two independent student samples (296 from Bernstein, Teng, & Garbin, 1986, and 350 from a large Canadian university) completed the scale. Using 6 fit criteria, results from both samples supported the Burnkrant and Page (1984) 4-factor model, namely, that self-consciousness consists of 3 principle scales: Social Anxiety, Public Self-Consciousness, and Private Self-Consciousness (divided into Internal State Awareness and Self-Reflectiveness). We discuss the psychometric implications of enhancing scale reliability, validity, and self-awareness.
Crowgey, Theresa; Peters, Katherine B; Hornsby, Whitney E; Lane, Amy; McSherry, Frances; Herndon, James E; West, Miranda J; Williams, Christina L; Jones, Lee W
2014-06-01
The purpose of this study was to examine the relationship between self-reported exercise behavior, cardiorespiratory fitness (CRF), and cognitive function in early breast cancer patients. Thirty-seven breast cancer patients following completion of chemotherapy (median 16 months) and 14 controls were studied. Cognitive function was assessed using the Central Nervous System (CNS) Vital Signs software (CNS Vital Signs, LLC, Morrisville, N.C., USA), a computerized test battery consisting of 9 cognitive subtests. Exercise behavior was evaluated using the Godin Leisure Time Exercise Questionnaire, and CRF was assessed via a cardiopulmonary exercise test to assess peak oxygen consumption. Patients' mean total exercise was 184 ± 141 min·week(-1) compared with 442 ± 315 min·week(-1) in controls (p exercise guidelines (i.e., ≥150 min of moderate-intensity or vigorous exercise per week) compared with 57% of controls (p = 0.014). Patients' peak oxygen consumption averaged 23.5 ± 6.3 mL·kg(-1)·min(-1) compared with 30.6 ± 7.0 mL·kg(-1)·min(-1) in controls (p exercise, peak oxygen consumption, and the majority of cognitive subdomain scores; however, there was a significant positive correlation between exercise and visual memory (r = 0.47, p = 0.004). In conclusion, breast cancer patients following the completion of primary adjuvant chemotherapy exhibit, in general, worse cognitive performance than healthy women from the general population, and such performance may be related to their level of exercise behavior.
Wu, Hulin; Huang, Yangxin; Dykes, Carrie; Liu, Dacheng; Ma, Jingming; Perelson, Alan S; Demeter, Lisa M
2006-03-01
Growth competition assays have been developed to quantify the relative fitnesses of human immunodeficiency virus (HIV-1) mutants. In this article we develop mathematical models to describe viral/cellular dynamic interactions in the assay experiment, from which new competitive fitness indices or parameters are defined. These indices include the log fitness ratio (LFR), the log relative fitness (LRF), and the production rate ratio (PRR). From the population genetics perspective, we clarify the confusion and correct the inconsistency in the definition of relative fitness in the literature of HIV-1 viral fitness. The LFR and LRF are easier to estimate from the experimental data than the PRR, which was misleadingly defined as the relative fitness in recent HIV-1 research literature. Calculation and estimation methods based on two data points and multiple data points were proposed and were carefully studied. In particular, we suggest using both standard linear regression (method of least squares) and a measurement error model approach for more-accurate estimates of competitive fitness parameters from multiple data points. The developed methodologies are generally applicable to any growth competition assays. A user-friendly computational tool also has been developed and is publicly available on the World Wide Web at http://www.urmc.rochester.edu/bstools/vfitness/virusfitness.htm.
Some Fast Methods for Fitting Some One-parameter Spatial Models
Directory of Open Access Journals (Sweden)
R. J. Martin
2005-01-01
Full Text Available It is common in geographic modelling to use a one-parameter spatial model to specify the inverse covariance matrix in terms of I-bW, for some known matrix W. Exact Gaussian maximum likelihood estimation of b requires evaluation of the determinant of the covariance matrix. For large data sets, this evaluation of the determinant can be slow and good approximations can be useful. Seventy regional configurations are used to consider some approximations to the determinant of I-bW that are fast to evaluate, and their usefulness is compared.
Group Practices and Partnerships: A traditional model that Fits Many Situations.
Pickering, Stephen R
2015-01-01
The traditional group practice model can take many forms, including general practitioners, specialists, and combinations, as well as solo practitioners sharing space and staff, partnerships, and other legal entities. These practices may share some or all staff functions, including contracting for some functions. The essential characteristic is that those treating patients also have full control over and often direct management of the business aspects of the practice. The most important requirements for success in this model may be a common philosophy of patient care and mutual trust regarding business matters.
A Hierarchical Probability Model of Colon Cancer
Kelly, Michael
2010-01-01
We consider a model of fixed size $N = 2^l$ in which there are $l$ generations of daughter cells and a stem cell. In each generation $i$ there are $2^{i-1}$ daughter cells. At each integral time unit the cells split so that the stem cell splits into a stem cell and generation 1 daughter cell and the generation $i$ daughter cells become two cells of generation $i+1$. The last generation is removed from the population. The stem cell gets first and second mutations at rates $u_1$ and $u_2$ and the daughter cells get first and second mutations at rates $v_1$ and $v_2$. We find the distribution for the time it takes to get two mutations as $N$ goes to infinity and the mutation rates go to 0. We also find the distribution for the location of the mutations. Several outcomes are possible depending on how fast the rates go to 0. The model considered has been proposed by Komarova (2007) as a model for colon cancer.
Directory of Open Access Journals (Sweden)
James M McCaw
2011-04-01
Full Text Available We present a method to measure the relative transmissibility ("transmission fitness" of one strain of a pathogen compared to another. The model is applied to data from "competitive mixtures" experiments in which animals are co-infected with a mixture of two strains. We observe the mixture in each animal over time and over multiple generations of transmission. We use data from influenza experiments in ferrets to demonstrate the approach. Assessment of the relative transmissibility between two strains of influenza is important in at least three contexts: 1 Within the human population antigenically novel strains of influenza arise and compete for susceptible hosts. 2 During a pandemic event, a novel sub-type of influenza competes with the existing seasonal strain(s. The unfolding epidemiological dynamics are dependent upon both the population's susceptibility profile and the inherent transmissibility of the novel strain compared to the existing strain(s. 3 Neuraminidase inhibitors (NAIs, while providing significant potential to reduce transmission of influenza, exert selective pressure on the virus and so promote the emergence of drug-resistant strains. Any adverse outcome due to selection and subsequent spread of an NAI-resistant strain is exquisitely dependent upon the transmission fitness of that strain. Measurement of the transmission fitness of two competing strains of influenza is thus of critical importance in determining the likely time-course and epidemiology of an influenza outbreak, or the potential impact of an intervention measure such as NAI distribution. The mathematical framework introduced here also provides an estimate for the size of the transmitted inoculum. We demonstrate the framework's behaviour using data from ferret transmission studies, and through simulation suggest how to optimise experimental design for assessment of transmissibility. The method introduced here for assessment of mixed transmission events has
Properties of and algorithms for fitting three-way component models with offset terms
Kiers, Henk A. L.
2006-01-01
Prior to a three-way component analysis of a three-way data set, it is customary to preprocess the data by centering and/or rescaling them. Harshman and Lundy (1984) considered that three-way data actually consist of a three-way model part, which in fact pertains to ratio scale measurements, as welt
Are Earth System model software engineering practices fit for purpose? A case study.
Easterbrook, S. M.; Johns, T. C.
2009-04-01
We present some analysis and conclusions from a case study of the culture and practices of scientists at the Met Office and Hadley Centre working on the development of software for climate and Earth System models using the MetUM infrastructure. The study examined how scientists think about software correctness, prioritize their requirements in making changes, and develop a shared understanding of the resulting models. We conclude that highly customized techniques driven strongly by scientific research goals have evolved for verification and validation of such models. In a formal software engineering context these represents costly, but invaluable, software integration tests with considerable benefits. The software engineering practices seen also exhibit recognisable features of both agile and open source software development projects - self-organisation of teams consistent with a meritocracy rather than top-down organisation, extensive use of informal communication channels, and software developers who are generally also users and science domain experts. We draw some general conclusions on whether these practices work well, and what new software engineering challenges may lie ahead as Earth System models become ever more complex and petascale computing becomes the norm.
Fitting Social Network Models Using Varying Truncation Stochastic Approximation MCMC Algorithm
Jin, Ick Hoon
2013-10-01
The exponential random graph model (ERGM) plays a major role in social network analysis. However, parameter estimation for the ERGM is a hard problem due to the intractability of its normalizing constant and the model degeneracy. The existing algorithms, such as Monte Carlo maximum likelihood estimation (MCMLE) and stochastic approximation, often fail for this problem in the presence of model degeneracy. In this article, we introduce the varying truncation stochastic approximation Markov chain Monte Carlo (SAMCMC) algorithm to tackle this problem. The varying truncation mechanism enables the algorithm to choose an appropriate starting point and an appropriate gain factor sequence, and thus to produce a reasonable parameter estimate for the ERGM even in the presence of model degeneracy. The numerical results indicate that the varying truncation SAMCMC algorithm can significantly outperform the MCMLE and stochastic approximation algorithms: for degenerate ERGMs, MCMLE and stochastic approximation often fail to produce any reasonable parameter estimates, while SAMCMC can do; for nondegenerate ERGMs, SAMCMC can work as well as or better than MCMLE and stochastic approximation. The data and source codes used for this article are available online as supplementary materials. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
Heliospheric Propagation of Coronal Mass Ejections: Drag-Based Model Fitting
Žic, T; Temmer, M
2015-01-01
The so-called drag-based model (DBM) simulates analytically the propagation of coronal mass ejections (CMEs) in interplanetary space and allows the prediction of their arrival times and impact speeds at any point in the heliosphere ("target"). The DBM is based on the assumption that beyond a distance of about 20 solar radii from the Sun, the dominant force acting on CMEs is the "aerodynamic" drag force. In the standard form of DBM, the user provisionally chooses values for the model input parameters, by which the kinematics of the CME over the entire Sun--"target" distance range is defined. The choice of model input parameters is usually based on several previously undertaken statistical studies. In other words, the model is used by ad hoc implementation of statistics-based values of the input parameters, which are not necessarily appropriate for the CME under study. Furthermore, such a procedure lacks quantitative information on how well the simulation reproduces the coronagraphically observed kinematics of ...
2012-09-30
including humans . By using sporadic observations together with an underlying process model, we can infer how individuals are interacting with their... cetaceans (e.g. gray whales – (Bradford et al. 2012)), the right whale analysis provides a framework for analyzing many different mammalian species
Fitting the Mixed Rasch Model to a Reading Comprehension Test: Identifying Reader Types
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…
Predicting VO2peak from Submaximal- and Peak Exercise Models: The HUNT 3 Fitness Study, Norway.
Directory of Open Access Journals (Sweden)
Henrik Loe
Full Text Available Peak oxygen uptake (VO2peak is seldom assessed in health care settings although being inversely linked to cardiovascular risk and all-cause mortality. The aim of this study was to develop VO2peak prediction models for men and women based on directly measured VO2peak from a large healthy population.VO2peak prediction models based on submaximal- and peak performance treadmill work were derived from multiple regression analysis. 4637 healthy men and women aged 20-90 years were included. Data splitting was used to generate validation and cross-validation samples.The accuracy for the peak performance models were 10.5% (SEE = 4.63 mL⋅kg(-1⋅min(-1 and 11.5% (SEE = 4.11 mL⋅kg(-1⋅min(-1 for men and women, respectively, with 75% and 72% of the variance explained. For the submaximal performance models accuracy were 14.1% (SEE = 6.24 mL⋅kg(-1⋅min(-1 and 14.4% (SEE = 5.17 mL⋅kg(-1⋅min(-1 for men and women, respectively, with 55% and 56% of the variance explained. The validation and cross-validation samples displayed SEE and variance explained in agreement with the total sample. Cross-classification between measured and predicted VO2peak accurately classified 91% of the participants within the correct or nearest quintile of measured VO2peak.Judicious use of the exercise prediction models presented in this study offers valuable information in providing a fairly accurate assessment of VO2peak, which may be beneficial for risk stratification in health care settings.
A CONTRASTIVE ANALYSIS OF THE FACTORIAL STRUCTURE OF THE PCL-R: WHICH MODEL FITS BEST THE DATA?
Directory of Open Access Journals (Sweden)
Beatriz Pérez
2015-01-01
Full Text Available The aim of this study was to determine which of the factorial solutions proposed for the Hare Psychopathy Checklist-Revised (PCL-R of two, three, four factors, and unidimensional fitted best the data. Two trained and experienced independent raters scored 197 prisoners from the Villabona Penitentiary (Asturias, Spain, age range 21 to 73 years (M = 36.0, SD = 9.7, of whom 60.12% were reoffenders and 73% had committed violent crimes. The results revealed that the two-factor correlational, three-factor hierarchical without testlets, four-factor correlational and hierarchical, and unidimensional models were a poor fit for the data (CFI ≤ .86, and the three-factor model with testlets was a reasonable fit for the data (CFI = .93. The scale resulting from the three-factor hierarchical model with testlets (13 items classified psychopathy significantly higher than the original 20-item scale. The results are discussed in terms of their implications for theoretical models of psychopathy, decision-making, prison classification and intervention, and prevention. Se diseñó un estudio con el objetivo de conocer cuál de las soluciones factoriales propuestas para la Hare Psychopathy Checklist-Revised (PCL-R de dos, tres y cuatro factores y unidimensional era la que presentaba mejor ajuste a los datos. Para ello, dos evaluadores entrenados y con experiencia evaluaron de forma independiente a 197 internos en la prisión Villabona (Asturias, España, con edades comprendidas entre los 21 y los 73 años (M = 36.0, DT = 9.7, de los cuales el 60.12% eran reincidentes y el 73% había cometido delitos violentos. Los resultados mostraron que los modelos unidimensional, correlacional de 2 factores, jerárquico de 3 factores sin testlest y correlacional y jerárquico de 4 factores, presentaban un pobre ajuste con los datos (CFI ≤ .86 y un ajuste razonable del modelo jerárquico de tres factores con testlets (CFI = .93. La escala resultante del modelo de tres factores
Fitness Club
2012-01-01
Nordic Walking Classes Sessions of four classes of one hour each are held on Tuesdays. RDV barracks parking at Entrance A, 10 minutes before class time. Session 1 = 11.09 / 18.09 / 25.09 / 02.10, 18:15 - 19:15 Session 2 = 25.09 / 02.10 / 09.10 / 16.10, 12:30 - 13:30 Session 3 = 23.10 / 30.10 / 06.11 / 13.11, 12:30 - 13:30 Session 4 = 20.11 / 27.11 / 04.12 / 11.12, 12:30 - 13:30 Prices 40 CHF per session + 10 CHF club membership 5 CHF/hour pole rental Check out our schedule and enroll at http://cern.ch/club-fitness Hope to see you among us! fitness.club@cern.ch In spring 2012 there was a long-awaited progress in CERN Fitness club. We have officially opened a Powerlifting @ CERN, and the number of members of the new section has been increasing since then reaching 70+ people in less than 4 months. Powerlifting is a strength sport, which is simple as 1-2-3 and efficient. The "1-2-3" are the three basic lifts (bench press...
Models of helping and coping in cancer care.
Northouse, L L; Wortman, C B
1990-02-01
This paper provides a theoretical analysis of four models of helping and coping as they relate to cancer care. The four conceptual models focus on the issue of whether or not patients should be viewed as responsible for the cause or the treatment of their cancer. The moral model, characterized by the holistic health movement, holds patients responsible for both causing and resolving health problems. The compensatory model, exemplified by cancer education programs, attributes low responsibility to patients for causing health problems but high responsibility for resolving them. The medical model views patients as neither responsible for causing nor for resolving health problems. The enlightenment model, typified by the healing movement, holds people responsible for causing their health problems, but not for resolving them. An attempt is made to examine existing programs in cancer care in light of these models. The present analysis addresses the following questions. Why is each of these models appealing? Why are they sometimes embraced by patients or health care providers? What are the benefits and disadvantages of using each of these models with cancer patients? What happens when the health care provider and patient hold different models regarding the patient's responsibility or participation in the cause of the disease or its treatment? Further research is needed to determine the conditions under which a particular model results in better health outcomes for patients, and to assess how factors such as extent of disease or type of cancer influence the patient's choice of a model.
Barsdell, B. R.; Barnes, D. G.; Fluke, C. J.
2011-07-01
Structural parameters are normally extracted from observed galaxies by fitting analytic light profiles to the observations. Obtaining accurate fits to high-resolution images is a computationally expensive task, requiring many model evaluations and convolutions with the imaging point spread function. While these algorithms contain high degrees of parallelism, current implementations do not exploit this property. With ever-growing volumes of observational data, an inability to make use of advances in computing power can act as a constraint on scientific outcomes. This is the motivation behind our work, which aims to implement the model-fitting procedure on a graphics processing unit (GPU). We begin by analysing the algorithms involved in model evaluation with respect to their suitability for modern many-core computing architectures like GPUs, finding them to be well-placed to take advantage of the high memory bandwidth offered by this hardware. Following our analysis, we briefly describe a preliminary implementation of the model fitting procedure using freely-available GPU libraries. Early results suggest a speed-up of around 10× over a CPU implementation. We discuss the opportunities such a speed-up could provide, including the ability to use more computationally expensive but better-performing fitting routines to increase the quality and robustness of fits.
Visualization-Directed Interactive Model-Fitting to Spectral Data Cubes
Fluke, Christopher J; Barnes, David G
2010-01-01
Spectral datasets obtained at radio frequencies and optical/IR wavelengths are increasing in complexity as new facilities and instruments come online, resulting in an increased need to visualize and quantitatively analyze the velocity structures. As the visible structure in spectral data cubes is not purely spatial, additional insight is required to relate structures in 2D space plus line-of-sight velocity to their true three-dimensional (3D) structures. This can be achieved through the use of models that are converted to velocity-space representations. We have used the S2PLOT programming library to enable intuitive, interactive comparison between 3D models and spectral data, with potential for improved understanding of the spatial configurations. We also report on the use of 3D Cartesian shapelets to support quantitative analysis.
Establishing of the Transplanted Animal Models for Human Lung Cancer
Institute of Scientific and Technical Information of China (English)
Xingli Zhang; Jinchang Wu
2009-01-01
Lung cancer is the leading cause of cancer mortality worldwide.Even with the applications of excision,radiotherapy,chemotherapy,and gene therapy,the 5 year survival rate is only 15% in the USA.Clinically relevant laboratory animal models of the disease could greatly facilitate understanding of the pathogenesis of lung cancer,its progression,invasion and metastasis.Transplanted lung cancer models are of special interest and are widely used today.Such models are essential tools in accelerating development of new therapies for lung cancer.In this communication we will present a brief overview of the hosts,sites and pathways used to establish transplanted animal lung tumor models.
2010-09-30
also traveled in June to St. Andrews , Scotland to work the other members of the PCAD modeling sub-group: John Harwood, Len Thomas, and Leslie New...In addition to colleagues at St. Andrews , we are working closely with Mark Hindell, Clive McMahon, Dan Costa, Patrick Robinson (elephant seal... Conger , A. R. Knowlton, M. K. Marx, C. K. Slay, S. D. Kraus and B. N. White (2007). "Patterns of male reproductive success in a highly promiscuous
Impact of calibration fitting models on the clinical value of chromogranin A
Ferraro, Simona; Marano, Giuseppe; Ciardi, Laura; Vendramin, Chiara; Bongo, Angelo S.; Bellomo, Giorgio; Boracchi, Patrizia; Biganzoli, Elia M.
2009-01-01
Background: The clinical relevance of chromogranin A (CgA) concentrations depends on the analytical performance of the assay. The goal of the present study was to define the clinical involvements in CgA calibration models by evaluating the confidence intervals (CIs) for values from patients who were undergoing monitoring for disease. Methods: Thirty calibration curves for the CgA assay [immunoradiometric assay (IRMA), (CIS-BIO)] were built using linear regression (LR), and four-parameter log...
Modelled Group Fitted XAFS Debye-Waller factors for Zn metalloproteins
Dimakis, Nicholas; Bunker, Grant
2003-03-01
X-ray Absorption Fine Structure spectroscopy is one of the few direct methods for determining the structure of metalloprotein active sites that are applicable to noncrystalline proteins in solutions and membranes. Considerable progress has been made in the calculation of photoelectron scattering aspects of XAFS,but calculation of the vibrational aspects has lagged because of the difficulty of the accurate calculations. Recently we have presented initial results that enabled practical numerical evaluation of XAFS multiple scattering Debye Waller Factors (MSDWFs) of Zn ions bound to histidines in metalloproteins. Recently we have refined our Zn-histidine model to provide more accurate first shell single scattering Debye-Waller parameters, and we have developed a model for Zn-cysteine model that described the MSDWFs enabling for the first time quantitative full single- and multiple-scattering XAFS data analysis of Zn/His/Cys sites at arbitrary temperatures, without the use of ad hoc assumptions. This opens up a wide class of important Zn proteins for study by these methods. Illustrative examples will be presented.
Schultz, F W; Boer, R; de Koning, H J
2012-07-01
The MISCAN-lung model was designed to simulate population trends in lung cancer (LC) for comprehensive surveillance of the disease, to relate past exposure to risk factors to (observed) LC incidence and mortality, and to estimate the impact of cancer-control interventions. MISCAN-lung employs the technique of stochastic microsimulation of life histories affected by risk factors. It includes the two-stage clonal expansion model for carcinogenesis and a detailed LC progression model; the latter is specifically intended for the evaluation of screenings. This article elucidates further the principles of MISCAN-lung and describes its application to a comparative study within the CISNET Lung Working Group on the impact of tobacco control on U.S. LC mortality. MISCAN-lung yields an estimate of the number of LC deaths avoided during 1975-2000. The potential number of avoidable LC deaths, had everybody quit smoking in 1965, is 2.2 million; 750,000 deaths (30%) were avoided in the United States due to actual tobacco control interventions. The model fits in the actual tobacco-control scenario, providing credibility to the estimates of other scenarios, although considering survey-reported smoking trends alone has limitations.
Calibration Methods Used in Cancer Simulation Models and Suggested Reporting Guidelines
Stout, Natasha K.; Knudsen, Amy B.; Kong, Chung Yin (Joey); McMahon, Pamela M.; Gazelle, G. Scott
2009-01-01
Background Increasingly, computer simulation models are used for economic and policy evaluation in cancer prevention and control. A model’s predictions of key outcomes such as screening effectiveness depends on the values of unobservable natural history parameters. Calibration is the process of determining the values of unobservable parameters by constraining model output to replicate observed data. Because there are many approaches for model calibration and little consensus on best practices, we surveyed the literature to catalogue the use and reporting of these methods in cancer simulation models. Methods We conducted a MEDLINE search (1980 through 2006) for articles on cancer screening models and supplemented search results with articles from our personal reference databases. For each article, two authors independently abstracted pre-determined items using a standard form. Data items included cancer site, model type, methods used for determination of unobservable parameter values, and description of any calibration protocol. All authors reached consensus on items of disagreement. Reviews and non-cancer models were excluded. Articles describing analytical models which estimate parameters with statistical approaches (e.g., maximum likelihood) were catalogued separately. Models that included unobservable parameters were analyzed and classified by whether calibration methods were reported and if so, the methods used. Results The review process yielded 154 articles that met our inclusion criteria and of these, we concluded that 131 may have used calibration methods to determine model parameters. Although the term “calibration” was not always used, descriptions of calibration or “model fitting” were found in 50% (n=66) of the articles with an additional 16% (n=21) providing a reference to methods. Calibration target data were identified in nearly all of these articles. Other methodologic details such as the goodness-of-fit metric were discussed in 54% (n=47
Directory of Open Access Journals (Sweden)
Tirza Z Tamin
2015-04-01
Full Text Available Aim: to design a model and assess the effectiveness of endurance exercise to increase physical fitness in intelectual disability (ID patients with obesity. Methods: a randomized-controlled clinical trial was performed in ID patients with obesity aged 10-30 years old from all Special School in DKI Jakarta, which were randomly allocated into 3 groups and then given 3 different type of exercises: lower extremity muscles endurance exercise for 20 RM followed by cardiorespiratory endurance exercise for 24-25 minutes (type I, lower extremity muscles endurance exercises for 10 RM followed by cardiorespiratory endurance exercises for 26-27 minutes (type II, and threw a tennis ball with 10 m distance for 10 minutes as control (type III. These program was performed 3 times a week for 4 months. Assesment of the exercise effectiveness was done by measuring maximum load that can be lifted and six-minutes walking test on rectangular track which was converted with the VO2 max prediction formula. Analysis was perfomed with Kruskal Wallis test. Results: two hundred and twelve (212 subjects were included in the study, randomly allocated into three types (I, II, and III of exercises groups. The type II of endurance exercise model was proved to be more effective in increasing lower extremity muscles endurance level compared to type I and III for ID patients with obesity (p<0.05. Meanwhile, type I of endurance exercise model was proved to be more effective in increasing cardiorespiratory endurance level compared to type II and III for ID patients with obesity (p<0.05. Conclusion: lower extremity muscles endurance exercise followed by a cardiorespiratory endurance exercise can be used to increase physical fitness in ID patients with obesity. Key words: intelectual disability patient, obesity, lower extremity muscles and cardiorespiratory endurance exercise, lower extremity muscles endurance level, cardiorespiratory endurance level.
Marconi, M.; Molinaro, R.; Ripepi, V.; Cioni, M.-R. L.; Clementini, G.; Moretti, M. I.; Ragosta, F.; de Grijs, R.; Groenewegen, M. A. T.; Ivanov, V. D.
2017-04-01
We present the results of the χ2 minimization model fitting technique applied to optical and near-infrared photometric and radial velocity data for a sample of nine fundamental and three first overtone classical Cepheids in the Small Magellanic Cloud (SMC). The near-infrared photometry (JK filters) was obtained by the European Southern Observatory (ESO) public survey 'VISTA near-infrared Y, J, Ks survey of the Magellanic Clouds system' (VMC). For each pulsator, isoperiodic model sequences have been computed by adopting a non-linear convective hydrodynamical code in order to reproduce the multifilter light and (when available) radial velocity curve amplitudes and morphological details. The inferred individual distances provide an intrinsic mean value for the SMC distance modulus of 19.01 mag and a standard deviation of 0.08 mag, in agreement with the literature. Moreover, the intrinsic masses and luminosities of the best-fitting model show that all these pulsators are brighter than the canonical evolutionary mass-luminosity relation (MLR), suggesting a significant efficiency of core overshooting and/or mass-loss. Assuming that the inferred deviation from the canonical MLR is only due to mass-loss, we derive the expected distribution of percentage mass-loss as a function of both the pulsation period and the canonical stellar mass. Finally, a good agreement is found between the predicted mean radii and current period-radius (PR) relations in the SMC available in the literature. The results of this investigation support the predictive capabilities of the adopted theoretical scenario and pave the way for the application to other extensive data bases at various chemical compositions, including the VMC Large Magellanic Cloud pulsators and Galactic Cepheids with Gaia parallaxes.
A Monte Carlo-adjusted goodness-of-fit test for parametric models describing spatial point patterns
Dao, Ngocanh
2014-04-03
Assessing the goodness-of-fit (GOF) for intricate parametric spatial point process models is important for many application fields. When the probability density of the statistic of the GOF test is intractable, a commonly used procedure is the Monte Carlo GOF test. Additionally, if the data comprise a single dataset, a popular version of the test plugs a parameter estimate in the hypothesized parametric model to generate data for theMonte Carlo GOF test. In this case, the test is invalid because the resulting empirical level does not reach the nominal level. In this article, we propose a method consisting of nested Monte Carlo simulations which has the following advantages: the bias of the resulting empirical level of the test is eliminated, hence the empirical levels can always reach the nominal level, and information about inhomogeneity of the data can be provided.We theoretically justify our testing procedure using Taylor expansions and demonstrate that it is correctly sized through various simulation studies. In our first data application, we discover, in agreement with Illian et al., that Phlebocarya filifolia plants near Perth, Australia, can follow a homogeneous Poisson clustered process that provides insight into the propagation mechanism of these plants. In our second data application, we find, in contrast to Diggle, that a pairwise interaction model provides a good fit to the micro-anatomy data of amacrine cells designed for analyzing the developmental growth of immature retina cells in rabbits. This article has supplementary material online. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
Lepping, R. P.; Wu, C.-C.; Berdichevsky, D. B.; Szabo, A.
2015-01-01
We fitted the parameters of magnetic clouds (MCs) as identified in the Wind spacecraft data from early 2010 to the end of 2012 using the model of Lepping, Jones, and Burlaga (J. Geophys. Res. 95, 1195, 1990). The interval contains 48 MCs and 39 magnetic cloud-like (MCL) events. This work is a continuation of MC model fittings of the earlier Wind sets, including those in a recent publication, which covers 2007 to 2009. This period (2010 - 2012) mainly covers the maximum portion of Solar Cycle 24. Between the previous and current interval, we document 5.7 years of MCs observations. For this interval, the occurrence frequency of MCs markedly increased in the last third of the time. In addition, over approximately the last six years, the MC type (i.e. the profile of the magnetic-field direction within an MC, such as North-to-South, South-to-North, all South) dramatically evolved to mainly North-to-South types when compared to earlier years. Furthermore, this evolution of MC type is consistent with global solar magnetic-field changes predicted by Bothmer and Rust (Coronal Mass Ejections, 139, 1997). Model fit parameters for the MCs are listed for 2010 - 2012. For the 5.7 year interval, the observed MCs are found to be slower, weaker in estimated axial magnetic-field intensity, and shorter in duration than those of the earlier 12.3 years, yielding much lower axial magnetic-field fluxes. For about the first half of this 5.7 year period, i.e. up to the end of 2009, there were very few associated MC-driven shock waves (distinctly fewer than the long-term average of about 50 % of MCs). But since 2010, such driven shocks have increased markedly, reflecting similar statistics as the long-term averages. We estimate that 56 % of the total observed MCs have upstream shocks when the full interval of 1995 - 2012 is considered. However, only 28 % of the total number of MCLs have driven shocks over the same period. Some interplanetary shocks during the 2010 - 2012 interval are seen
A fully Bayesian method for jointly fitting instrumental calibration and X-ray spectral models
Energy Technology Data Exchange (ETDEWEB)
Xu, Jin; Yu, Yaming [Department of Statistics, University of California, Irvine, Irvine, CA 92697-1250 (United States); Van Dyk, David A. [Statistics Section, Imperial College London, Huxley Building, South Kensington Campus, London SW7 2AZ (United Kingdom); Kashyap, Vinay L.; Siemiginowska, Aneta; Drake, Jeremy; Ratzlaff, Pete [Smithsonian Astrophysical Observatory, 60 Garden Street, Cambridge, MA 02138 (United States); Connors, Alanna; Meng, Xiao-Li, E-mail: jinx@uci.edu, E-mail: yamingy@ics.uci.edu, E-mail: dvandyk@imperial.ac.uk, E-mail: vkashyap@cfa.harvard.edu, E-mail: asiemiginowska@cfa.harvard.edu, E-mail: jdrake@cfa.harvard.edu, E-mail: pratzlaff@cfa.harvard.edu, E-mail: meng@stat.harvard.edu [Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, MA 02138 (United States)
2014-10-20
Owing to a lack of robust principled methods, systematic instrumental uncertainties have generally been ignored in astrophysical data analysis despite wide recognition of the importance of including them. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. We previously introduced a pragmatic Bayesian method to address this problem. The method is 'pragmatic' in that it introduced an ad hoc technique that simplified computation by neglecting the potential information in the data for narrowing the uncertainty for the calibration product. Following that work, we use a principal component analysis to efficiently represent the uncertainty of the effective area of an X-ray (or γ-ray) telescope. Here, however, we leverage this representation to enable a principled, fully Bayesian method that coherently accounts for the calibration uncertainty in high-energy spectral analysis. In this setting, the method is compared with standard analysis techniques and the pragmatic Bayesian method. The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product—here the effective area, conditional on the adopted spectral model. In this way, it can yield more accurate and efficient estimates of the source parameters along with valid estimates of their uncertainty. Provided that the source spectrum can be accurately described by a parameterized model, this method allows rigorous inference about the effective area by quantifying which possible curves are most consistent with the data.
Experimentally fitted biodynamic models for pedestrian-structure interaction in walking situations
Toso, Marcelo André; Gomes, Herbert Martins; da Silva, Felipe Tavares; Pimentel, Roberto Leal
2016-05-01
The interaction between moving humans and structures usually occurs in slender structures in which the level of vibration is potentially high. Furthermore, there is the addition of mass to the structural system due to the presence of people and an increase in damping due to the human body´s ability to absorb vibrational energy. In this paper, a test campaign is presented to obtain parameters for a single degree of freedom (SDOF) biodynamic model that represents the action of a walking pedestrian in the vertical direction. The parameters of this model are the mass (m), damping (c) and stiffness (k). The measurements were performed on a force platform, and the inputs were the spectral acceleration amplitudes of the first three harmonics at the waist level of the test subjects and the corresponding amplitudes of the first three harmonics of the vertical ground reaction force. This leads to a system of nonlinear equations that is solved using a gradient-based optimization algorithm. A set of individuals took part in the tests to ensure inter-subject variability, and, regression expressions and an artificial neural network (ANN) were used to relate the biodynamic parameters to the pacing rate and the body mass of the pedestrians. The results showed some scatter in damping and stiffness that could not be precisely correlated with the masses and pacing rates of the subjects. The use of the ANN resulted in significant improvements in the parameter expressions with a low uncertainty. Finally, the measured vertical accelerations on a prototype footbridge show the adequacy of the numerical model for the representation of the effects of walking pedestrians on a structure. The results are consistent for many crowd densities.
A Fully Bayesian Method for Jointly Fitting Instrumental Calibration and X-Ray Spectral Models
Xu, Jin; van Dyk, David A.; Kashyap, Vinay L.; Siemiginowska, Aneta; Connors, Alanna; Drake, Jeremy; Meng, Xiao-Li; Ratzlaff, Pete; Yu, Yaming
2014-10-01
Owing to a lack of robust principled methods, systematic instrumental uncertainties have generally been ignored in astrophysical data analysis despite wide recognition of the importance of including them. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. We previously introduced a pragmatic Bayesian method to address this problem. The method is "pragmatic" in that it introduced an ad hoc technique that simplified computation by neglecting the potential information in the data for narrowing the uncertainty for the calibration product. Following that work, we use a principal component analysis to efficiently represent the uncertainty of the effective area of an X-ray (or γ-ray) telescope. Here, however, we leverage this representation to enable a principled, fully Bayesian method that coherently accounts for the calibration uncertainty in high-energy spectral analysis. In this setting, the method is compared with standard analysis techniques and the pragmatic Bayesian method. The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product—here the effective area, conditional on the adopted spectral model. In this way, it can yield more accurate and efficient estimates of the source parameters along with valid estimates of their uncertainty. Provided that the source spectrum can be accurately described by a parameterized model, this method allows rigorous inference about the effective area by quantifying which possible curves are most consistent with the data.
A murine model for bladder cancer.
Murphy, G P; Sandberg, A A; Pontes, J E; Ochi, H; Yoshida, M; Williams, P D
1984-01-01
Growth characteristics, survival time, and various other parameters such as chromosome studies and DNA synthesis were evaluated in a transplantable transitional cell mouse bladder tumor induced by N-[4-5-nitro-2-furyl)-2-thiazolyl] formamide (FANFT). When the tumor was implanted subcutaneously, the mice were observed to survive mean 43 + 7 days (mean +/- SEM) with an average tumor burden of mean 8.45 +/- 0.60 gm (mean +/- SEM) of solid tumor tissue. In the tumor control animals, lung metastasis was noted in 3 animals at 42-49 days post implantation. The histological appearance of the primary tumor and the lung metastasis presented an undifferentiated anaplastic tumor with many spindle cells. The modal number of chromosome is 65 with several markers identifiable as abnormal in morphology. A significant decrease (p less than 0.001) in DNA synthesis was noted between 13 days and 20 days post implantation. In the evaluation of chemotherapy drugs, Cis-dichloro-trans-dihydroxy-bis-iso propylamine platinum IV (CHIP), Cis-diaminedichloroplatinum II (DDP), Cyclophosphamide (CTX) and Methotrexate (MTX) tumor growth was significantly retarded (p less than 0.005) in the DDP treated groups, however survival was not improved. Survival was significantly improved in the CTX treated group (p less than 0.001), although no significant decrease was noted in tumor growth. Lung metastasis was noted in all groups. This model has certain characteristics which make it a good model to study locally invasive bladder cancer.
STATISTICAL EVALUATION OF FITTING ACCURACY OF GLOBAL AND LOCAL DIGITAL ELEVATION MODELS IN IRAN
Directory of Open Access Journals (Sweden)
F. Alidoost
2013-09-01
Full Text Available Digital Elevation Models (DEMs are one of the most important data for various applications such as hydrological studies, topography mapping and ortho image generation. There are well-known DEMs of the whole world that represent the terrain's surface at variable resolution and they are also freely available for 99% of the globe. However, it is necessary to assess the quality of the global DEMs for the regional scale applications.These models are evaluated by differencing with other reference DEMs or ground control points (GCPs in order to estimate the quality and accuracy parameters over different land cover types. In this paper, a comparison of ASTER GDEM ver2, SRTM DEM with more than 800 reference GCPs and also with a local elevation model over the area of Iran is presented. This study investigates DEM’s characteristics such as systematic error (bias, vertical accuracy and outliers for DEMs using both the usual (Mean error, Root Mean Square Error, Standard Deviation and the robust (Median, Normalized Median Absolute Deviation, Sample Quantiles descriptors. Also, the visual assessment tools are used to illustrate the quality of DEMs, such as normalized histograms and Q-Q plots. The results of the study confirmed that there is a negative elevation bias of approximately 5 meters of GDEM ver2. The measured RMSE and NMAD for elevation differences of GDEM-GCPs are 7.1 m and 3.2 m, respectively, while these values for SRTM and GCPs are 9.0 m and 4.4 m. On the other hand, in comparison with the local DEM, GDEM ver2 exhibits the RMSE of about 6.7 m, a little higher than the RMSE of SRTM (5.1 m.The results of height difference classification and other statistical analysis of GDEM ver2-local DEM and SRTM-local DEM reveal that SRTM is slightly more accurate than GDEM ver2. Accordingly, SRTM has no noticeable bias and shift from Local DEM and they have more consistency to each other, while GDEM ver2 has always a negative bias.
Braam, K.I.; van Dijk, E.M.; Veening, M.A.; Bierings, M.B.; Merks, J.H.M.; Grootenhuis, M.A.; Chinapaw, M.J.M.; Sinnema, G.; Takken, T.; Huisman, J.; Kaspers, G.J.L.; van Dulmen-den Broeder, E.
2010-01-01
ABSTRACT: BACKGROUND: Childhood cancer and its treatment have considerable impact on a child's physical and mental wellbeing. Especially long-term administration of chemotherapy and/or radiotherapy impairs physical fitness both during and after therapy, when children often present with muscle weakne
Directory of Open Access Journals (Sweden)
Youhua Chen
2016-09-01
Full Text Available In this report, a maximum likelihood model is developed to incorporate data uncertainty in response and explanatory variables when fitting power-law bivariate relationships in ecology and evolution. This simple likelihood model is applied to an empirical data set related to the allometric relationship between body mass and length of Sciuridae species worldwide. The results show that the values of parameters estimated by the proposed likelihood model are substantially different from those fitted by the nonlinear least-of-square (NLOS method. Accordingly, the power-law models fitted by both methods have different curvilinear shapes. These discrepancies are caused by the integration of measurement errors in the proposed likelihood model, in which NLOS method fails to do. Because the current likelihood model and the NLOS method can show different results, the inclusion of measurement errors may offer new insights into the interpretation of scaling or power laws in ecology and evolution.
Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.
Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L
2017-02-01
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.
Modeling the fitness consequences of a cyanophage-encoded photosynthesis gene.
Directory of Open Access Journals (Sweden)
Jason G Bragg
Full Text Available BACKGROUND: Phages infecting marine picocyanobacteria often carry a psbA gene, which encodes a homolog to the photosynthetic reaction center protein, D1. Host encoded D1 decays during phage infection in the light. Phage encoded D1 may help to maintain photosynthesis during the lytic cycle, which in turn could bolster the production of deoxynucleoside triphosphates (dNTPs for phage genome replication. METHODOLOGY/PRINCIPAL FINDINGS: To explore the consequences to a phage of encoding and expressing psbA, we derive a simple model of infection for a cyanophage/host pair--cyanophage P-SSP7 and Prochlorococcus MED4--for which pertinent laboratory data are available. We first use the model to describe phage genome replication and the kinetics of psbA expression by host and phage. We then examine the contribution of phage psbA expression to phage genome replication under constant low irradiance (25 microE m(-2 s(-1. We predict that while phage psbA expression could lead to an increase in the number of phage genomes produced during a lytic cycle of between 2.5 and 4.5% (depending on parameter values, this advantage can be nearly negated by the cost of psbA in elongating the phage genome. Under higher irradiance conditions that promote D1 degradation, however, phage psbA confers a greater advantage to phage genome replication. CONCLUSIONS/SIGNIFICANCE: These analyses illustrate how psbA may benefit phage in the dynamic ocean surface mixed layer.
The conceptual basis of mathematics in cardiology IV: statistics and model fitting.
Bates, Jason H T; Sobel, Burton E
2003-06-01
This is the fourth in a series of four articles developed for the readers of Coronary Artery Disease. Without language ideas cannot be articulated. What may not be so immediately obvious is that they cannot be formulated either. One of the essential languages of cardiology is mathematics. Unfortunately, medical education does not emphasize, and in fact, often neglects empowering physicians to think mathematically. Reference to statistics, conditional probability, multicompartmental modeling, algebra, calculus and transforms is common but often without provision of genuine conceptual understanding. At the University of Vermont College of Medicine, Professor Bates developed a course designed to address these deficiencies. The course covered mathematical principles pertinent to clinical cardiovascular and pulmonary medicine and research. It focused on fundamental concepts to facilitate formulation and grasp of ideas. This series of four articles was developed to make the material available for a wider audience. The articles will be published sequentially in Coronary Artery Disease. Beginning with fundamental axioms and basic algebraic manipulations they address algebra, function and graph theory, real and complex numbers, calculus and differential equations, mathematical modeling, linear system theory and integral transforms and statistical theory. The principles and concepts they address provide the foundation needed for in-depth study of any of these topics. Perhaps of even more importance, they should empower cardiologists and cardiovascular researchers to utilize the language of mathematics in assessing the phenomena of immediate pertinence to diagnosis, pathophysiology and therapeutics. The presentations are interposed with queries (by Coronary Artery Disease abbreviated as CAD) simulating the nature of interactions that occurred during the course itself. Each article concludes with one or more examples illustrating application of the concepts covered to
AN ANIMAL MODEL FITS FOR STUDYING DIVERGENCES AMONG DIABETIC MICROVASCULAR COMPLICATIONS
Directory of Open Access Journals (Sweden)
Stella Maris Martínez
2005-08-01
Full Text Available SUMMARYA comparison is made between data reported by Kanauchi et al (1998 in patients with a rare occurring divergence (advanced nephropathy without retinopathy and others, obtained in a similar line of rats (eSS, accepted as a general model for type 2 diabetes. This comparison reveals attracting analogies from different standpoints (methods employed, age, gender, lack of obesity, duration and control of diabetes, biochemical - total urinary protein excretion, serum creatinine and clearance of creatinine - and microscopic analysis. Such analogies allow proposing to eSS as an animal model for the particular study of the referred nephro- retinian divergences as well as others, opportunely reported in diabetic patients.RESUMENSe comunica una comparación hecha entre datos reportados por Kanauchi et al (1998 en pacientes con una rara divergencia (neuropatía avanzada sin retinopatía y otros obtenidos en una línea similar de ratas (eSS, aceptada como modelo general para el estudio de la diabetes tipo 2. Dicha comparación arroja analogía atractivas desde distintos puntos de vista (métodos empleados, edad, género, ausencia de obesidad, duración y control de la diabetes, análisis bioquímicos - excreción proteica urinaria total, creatininemia y clearance de creatinina y microscópicos. Tales analogías permiten proponer a las ratas eSS como modelo para el estudio particular de las referidas divergencias reno-retinianas así como de otras, oportunamente comunicadas en pacientes diabéticos.
Fitness club
2013-01-01
Nordic Walking Classes New session of 4 classes of 1 hour each will be held on Tuesdays in May 2013. Meet at the CERN barracks parking at Entrance A, 10 minutes before class time. Dates and time: 07.05, 14.05, 21.05 and 28.05, fom 12 h 30 to 13 h 30 Prices: 40 CHF per session + 10 CHF club membership – 5 CHF / hour pole rental Check out our schedule and enroll at http://cern.ch/club-fitness Hope to see you among us!
Organoids as Models for Neoplastic Transformation | Office of Cancer Genomics
Cancer models strive to recapitulate the incredible diversity inherent in human tumors. A key challenge in accurate tumor modeling lies in capturing the panoply of homo- and heterotypic cellular interactions within the context of a three-dimensional tissue microenvironment. To address this challenge, researchers have developed organotypic cancer models (organoids) that combine the 3D architecture of in vivo tissues with the experimental facility of 2D cell lines.
Risk Prediction Model for Colorectal Cancer: National Health Insurance Corporation Study, Korea
Aesun Shin; Jungnam Joo; Hye-Ryung Yang; Jeongin Bak; Yunjin Park; Jeongseon Kim; Jae Hwan Oh; Byung-Ho Nam
2014-01-01
PURPOSE: Incidence and mortality rates of colorectal cancer have been rapidly increasing in Korea during last few decades. Development of risk prediction models for colorectal cancer in Korean men and women is urgently needed to enhance its prevention and early detection. METHODS: Gender specific five-year risk prediction models were developed for overall colorectal cancer, proximal colon cancer, distal colon cancer, colon cancer and rectal cancer. The model was developed using data from a po...
Score, pseudo-score and residual diagnostics for goodness-of-fit of spatial point process models
DEFF Research Database (Denmark)
Baddeley, Adrian; Rubak, Ege Holger; Møller, Jesper
We develop newtools for formal inference and informalmodel validation in the analysis of spatial point pattern data. The score test is generalised to a ‘pseudo-score’ test derived from Besag’s pseudolikelihood, and to a class of diagnostics based on point process residuals. The results lend...... theoretical support to the established practice of using functional summary statistics such as Ripley’s K-function, when testing for complete spatial randomness; and they provide new tools such as the compensator of the K-function for testing other fitted models. The results also support localisation methods...... such as the scan statistic and smoothed residual plots. Software for computing the diagnostics is provided....
A MULTIVARIATE FIT LUMINOSITY FUNCTION AND WORLD MODEL FOR LONG GAMMA-RAY BURSTS
Energy Technology Data Exchange (ETDEWEB)
Shahmoradi, Amir, E-mail: amir@physics.utexas.edu [Institute for Fusion Studies, The University of Texas at Austin, TX 78712 (United States)
2013-04-01
It is proposed that the luminosity function, the rest-frame spectral correlations, and distributions of cosmological long-duration (Type-II) gamma-ray bursts (LGRBs) may be very well described as a multivariate log-normal distribution. This result is based on careful selection, analysis, and modeling of LGRBs' temporal and spectral variables in the largest catalog of GRBs available to date: 2130 BATSE GRBs, while taking into account the detection threshold and possible selection effects. Constraints on the joint rest-frame distribution of the isotropic peak luminosity (L{sub iso}), total isotropic emission (E{sub iso}), the time-integrated spectral peak energy (E{sub p,z}), and duration (T{sub 90,z}) of LGRBs are derived. The presented analysis provides evidence for a relatively large fraction of LGRBs that have been missed by the BATSE detector with E{sub iso} extending down to {approx}10{sup 49} erg and observed spectral peak energies (E{sub p} ) as low as {approx}5 keV. LGRBs with rest-frame duration T{sub 90,z} {approx}< 1 s or observer-frame duration T{sub 90} {approx}< 2 s appear to be rare events ({approx}< 0.1% chance of occurrence). The model predicts a fairly strong but highly significant correlation ({rho} = 0.58 {+-} 0.04) between E{sub iso} and E{sub p,z} of LGRBs. Also predicted are strong correlations of L{sub iso} and E{sub iso} with T{sub 90,z} and moderate correlation between L{sub iso} and E{sub p,z}. The strength and significance of the correlations found encourage the search for underlying mechanisms, though undermine their capabilities as probes of dark energy's equation of state at high redshifts. The presented analysis favors-but does not necessitate-a cosmic rate for BATSE LGRBs tracing metallicity evolution consistent with a cutoff Z/Z{sub Sun} {approx} 0.2-0.5, assuming no luminosity-redshift evolution.
Öhrn, Anders; Hermida-Ramon, Jose M; Karlström, Gunnar
2016-05-10
The effects of charge overlap, or charge penetration, are neglected in most force fields and interaction terms in QM/MM methods. The effects are however significant at intermolecular distances near the van der Waals minimum. In the present study, we propose a method to evaluate the intermolecular Coloumb interaction using Slater-type functions, thus explicitly modeling the charge overlap. The computational cost of the method is low, which allows it to be used in large systems with most force fields as well as in QM/MM schemes. The charge distribution is modeled as a distributed multipole expansion up to quadrupole and Slater-type functions of angular momentum up to L = 1. The exponents of the Slater-type functions are obtained using a divide-and-conquer method to avoid the curse of dimensionality that otherwise is present for large nonlinear optimizations. A Levenberg-Marquardt algorithm is applied in the fitting process. A set of parameters is obtained for each molecule, and the process is fully automated. Calculations have been performed in the carbon monoxide and the water dimers to illustrate the model. Results show a very good accuracy of the model with relative errors in the electrostatic potential lower than 3% over all reasonable separations. At very short distances where the charge overlaps is the most significant, errors are lower than 8% and lower than 3.5% at distances near the van der Waals minimum.
Full SED fitting with the KOSMA-\\tau\\ PDR code - I. Dust modelling
Röllig, M; Ossenkopf, V; Glück, C
2012-01-01
We revised the treatment of interstellar dust in the KOSMA-\\tau\\ PDR model code to achieve a consistent description of the dust-related physics in the code. The detailed knowledge of the dust properties is then used to compute the dust continuum emission together with the line emission of chemical species. We coupled the KOSMA-\\tau\\ PDR code with the MCDRT (multi component dust radiative transfer) code to solve the frequency-dependent radiative transfer equations and the thermal balance equation in a dusty clump under the assumption of spherical symmetry, assuming thermal equilibrium in calculating the dust temperatures, neglecting non-equilibrium effects. We updated the calculation of the photoelectric heating and extended the parametrization range for the photoelectric heating toward high densities and UV fields. We revised the computation of the H2 formation on grain surfaces to include the Eley-Rideal effect, thus allowing for high-temperature H2 formation. We demonstrate how the different optical propert...
The T61 human breast cancer xenograft: an experimental model of estrogen therapy of breast cancer
DEFF Research Database (Denmark)
Brunner, N; Spang-Thomsen, M; Cullen, K
1996-01-01
Endocrine therapy is one of the principal treatment modalities of breast cancer, both in an adjuvant setting and in advanced disease. The T61 breast cancer xenograft described here provides an experimental model of the effects of estrogen treatment at a molecular level. T61 is an estrogen recepto...
Improved numerical solutions for chaotic-cancer-model
Yasir, Muhammad; Ahmad, Salman; Ahmed, Faizan; Aqeel, Muhammad; Akbar, Muhammad Zubair
2017-01-01
In biological sciences, dynamical system of cancer model is well known due to its sensitivity and chaoticity. Present work provides detailed computational study of cancer model by counterbalancing its sensitive dependency on initial conditions and parameter values. Cancer chaotic model is discretized into a system of nonlinear equations that are solved using the well-known Successive-Over-Relaxation (SOR) method with a proven convergence. This technique enables to solve large systems and provides more accurate approximation which is illustrated through tables, time history maps and phase portraits with detailed analysis.
Improved numerical solutions for chaotic-cancer-model
Directory of Open Access Journals (Sweden)
Muhammad Yasir
2017-01-01
Full Text Available In biological sciences, dynamical system of cancer model is well known due to its sensitivity and chaoticity. Present work provides detailed computational study of cancer model by counterbalancing its sensitive dependency on initial conditions and parameter values. Cancer chaotic model is discretized into a system of nonlinear equations that are solved using the well-known Successive-Over-Relaxation (SOR method with a proven convergence. This technique enables to solve large systems and provides more accurate approximation which is illustrated through tables, time history maps and phase portraits with detailed analysis.
A multi-phenotypic cancer model with cell plasticity.
Zhou, Da; Wang, Yue; Wu, Bin
2014-09-21
The conventional cancer stem cell (CSC) theory indicates a hierarchy of CSCs and non-stem cancer cells (NSCCs), that is, CSCs can differentiate into NSCCs but not vice versa. However, an alternative paradigm of CSC theory with reversible cell plasticity among cancer cells has received much attention very recently. Here we present a generalized multi-phenotypic cancer model by integrating cell plasticity with the conventional hierarchical structure of cancer cells. We prove that under very weak assumption, the nonlinear dynamics of multi-phenotypic proportions in our model has only one stable steady state and no stable limit cycle. This result theoretically explains the phenotypic equilibrium phenomena reported in various cancer cell lines. Furthermore, according to the transient analysis of our model, it is found that cancer cell plasticity plays an essential role in maintaining the phenotypic diversity in cancer especially during the transient dynamics. Two biological examples with experimental data show that the phenotypic conversions from NCSSs to CSCs greatly contribute to the transient growth of CSCs proportion shortly after the drastic reduction of it. In particular, an interesting overshooting phenomenon of CSCs proportion arises in three-phenotypic example. Our work may pave the way for modeling and analyzing the multi-phenotypic cell population dynamics with cell plasticity.
Stress physiology in marine mammals: how well do they fit the terrestrial model?
Atkinson, Shannon; Crocker, Daniel; Houser, Dorian; Mashburn, Kendall
2015-07-01
Stressors are commonly accepted as the causal factors, either internal or external, that evoke physiological responses to mediate the impact of the stressor. The majority of research on the physiological stress response, and costs incurred to an animal, has focused on terrestrial species. This review presents current knowledge on the physiology of the stress response in a lesser studied group of mammals, the marine mammals. Marine mammals are an artificial or pseudo grouping from a taxonomical perspective, as this group represents several distinct and diverse orders of mammals. However, they all are fully or semi-aquatic animals and have experienced selective pressures that have shaped their physiology in a manner that differs from terrestrial relatives. What these differences are and how they relate to the stress response is an efflorescent topic of study. The identification of the many facets of the stress response is critical to marine mammal management and conservation efforts. Anthropogenic stressors in marine ecosystems, including ocean noise, pollution, and fisheries interactions, are increasing and the dramatic responses of some marine mammals to these stressors have elevated concerns over the impact of human-related activities on a diverse group of animals that are difficult to monitor. This review covers the physiology of the stress response in marine mammals and places it in context of what is known from research on terrestrial mammals, particularly with respect to mediator activity that diverges from generalized terrestrial models. Challenges in conducting research on stress physiology in marine mammals are discussed and ways to overcome these challenges in the future are suggested.
Whiteman-Sandland, Jessica; Hawkins, Jemma; Clayton, Debbie
2016-08-23
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.
Pre-clinical Orthotopic Murine Model of Human Prostate Cancer.
Shahryari, Varahram; Nip, Hannah; Saini, Sharanjot; Dar, Altaf A; Yamamura, Soichiro; Mitsui, Yozo; Colden, Melissa; Bucay, Nathan; Tabatabai, Laura Z; Greene, Kirsten; Deng, Guoren; Tanaka, Yuichiro; Dahiya, Rajvir; Majid, Shahana
2016-08-29
To study the multifaceted biology of prostate cancer, pre-clinical in vivo models offer a range of options to uncover critical biological information about this disease. The human orthotopic prostate cancer xenograft mouse model provides a useful alternative approach for understanding the specific interactions between genetically and molecularly altered tumor cells, their organ microenvironment, and for evaluation of efficacy of therapeutic regimens. This is a well characterized model designed to study the molecular events of primary tumor development and it recapitulates the early events in the metastatic cascade prior to embolism and entry of tumor cells into the circulation. Thus it allows elucidation of molecular mechanisms underlying the initial phase of metastatic disease. In addition, this model can annotate drug targets of clinical relevance and is a valuable tool to study prostate cancer progression. In this manuscript we describe a detailed procedure to establish a human orthotopic prostate cancer xenograft mouse model.
Cuoco, Alessandro; Heisig, Jan; Krämer, Michael
2016-01-01
We analyse the excess in the $\\gamma$-ray emission from the center of our galaxy observed by Fermi-LAT in terms of dark matter annihilation within the scalar Higgs portal model. In particular, we include the astrophysical uncertainties from the dark matter distribution and allow for unspecified additional dark matter components. We demonstrate through a detailed numerical fit that the strength and shape of the $\\gamma$-ray spectrum can indeed be described by the model in various regions of dark matter masses and couplings. Constraints from invisible Higgs decays, direct dark matter searches, indirect searches in dwarf galaxies and for $\\gamma$-ray lines, and constraints from the dark matter relic density reduce the parameter space to dark matter masses near the Higgs resonance. We find two viable regions: one where the Higgs-dark matter coupling is of ${\\cal O}(10^{-2})$, and an additional dark matter component beyond the scalar WIMP of our model is preferred, and one region where the Higgs-dark matter coupli...
Directory of Open Access Journals (Sweden)
Erida Gjini
2016-03-01
Full Text Available The efficacy of vaccines is typically estimated prior to implementation, on the basis of randomized controlled trials. This does not preclude, however, subsequent assessment post-licensure, while mass-immunization and nonlinear transmission feedbacks are in place. In this paper we show how cross-sectional prevalence data post-vaccination can be interpreted in terms of pathogen transmission processes and vaccine parameters, using a dynamic epidemiological model. We advocate the use of such frameworks for model-based vaccine evaluation in the field, fitting trajectories of cross-sectional prevalence of pathogen strains before and after intervention. Using SI and SIS models, we illustrate how prevalence ratios in vaccinated and non-vaccinated hosts depend on true vaccine efficacy, the absolute and relative strength of competition between target and non-target strains, the time post follow-up, and transmission intensity. We argue that a mechanistic approach should be added to vaccine efficacy estimation against multi-type pathogens, because it naturally accounts for inter-strain competition and indirect effects, leading to a robust measure of individual protection per contact. Our study calls for systematic attention to epidemiological feedbacks when interpreting population level impact. At a broader level, our parameter estimation procedure provides a promising proof of principle for a generalizable framework to infer vaccine efficacy post-licensure.
Asymptotics of the goodness-of-fit test for a partial linear model with randomly censored data
Institute of Scientific and Technical Information of China (English)
CHEN; Min(
2003-01-01
(semiparametric) partial and generalized spline models, Ann. Statist., 1988, 16: 113.［16］Eubank, R. L., Spiegeman, C. H., Testing the goodness of fit of a linear model via nonparametric regression techniques, J. Amer. Statist. Assoc., 1990, 85: 387.［17］Hardle, W., Mammen, E., Comparing non-parametric versus parametric regression fits, Ann. Statist., 1993,21: 1926.［18］Hardle, W., Mammen, E., Müller, M., Testing parametric versus semiparametric modeling in generalized linear models, J. Amer. Statist. Assoc., 1998, 93: 1461.［19］Hardle, W., Marron, J. S., Semiparametric comparison of regression curves, Ann. Statist., 1990, 18: 63.［20］King, G., Testing the equality of two regression curves using linear smoothers, Statist. & Probab. Lett., 1991,12: 239.［21］Miiller, H. G., Goodness-of-fit diagnostic for regression models, Sand. J. Statist., 1993, 19: 157.［22］Stute, W., Nonparametric model checks for regression, Ann. Statist., 1997, 25: 613.［23］Stute, W., Mantetga, G., Quindimil, M. P., Bootstrap approximations in model cheeks for regression, J. Amer.Statist. Assoc., 1998, 93: 141.［24］Stute, W., Thies, S., Zhu, L. X., Model checks for regression: An innovation process approach, Ann. Statist.,1998, 26: 1916.［25］Stute, W., Nonlinear censored regression, Statistica Sinica, 1999, 9:1089.［26］Wang, Q. H., Zhu, L. X., Estimation in partial linear error-in-variables models with censored data, Commun.in Statist. The. and Meth., 2001, .［27］Lo, S. H., Singh, K., The product-limit estimator and the bootstrap: some asymptotic representations, Probab.Theory and Related Fields, 1986, 71: 455.［28］Zhou, M., Some properties of the Kaplan-Meier estimator for independent, nonidentically distributed random variables, Ann. Statist., 1991, 19: 2266.［29］Hall, P., Heyde, C. C., Martingale Limit Theory and Its Applications, New York: Academic Press, 1980.［30］Pollard, D., Convergence of Stochastic Processes, New York: Springer-Verlag, 1984.［31
Barsdell, Benjamin R; Fluke, Christopher J
2011-01-01
Structural parameters are normally extracted from observed galaxies by fitting analytic light profiles to the observations. Obtaining accurate fits to high-resolution images is a computationally expensive task, requiring many model evaluations and convolutions with the imaging point spread function. While these algorithms contain high degrees of parallelism, current implementations do not exploit this property. With evergrowing volumes of observational data, an inability to make use of advances in computing power can act as a constraint on scientific outcomes. This is the motivation behind our work, which aims to implement the model-fitting procedure on a graphics processing unit (GPU). We begin by analysing the algorithms involved in model evaluation with respect to their suitability for modern many-core computing architectures like GPUs, finding them to be well-placed to take advantage of the high memory bandwidth offered by this hardware. Following our analysis, we briefly describe a preliminary implementa...
Modelling mutational landscapes of human cancers in vitro
Olivier, Magali; Weninger, Annette; Ardin, Maude; Huskova, Hana; Castells, Xavier; Vallée, Maxime P.; McKay, James; Nedelko, Tatiana; Muehlbauer, Karl-Rudolf; Marusawa, Hiroyuki; Alexander, John; Hazelwood, Lee; Byrnes, Graham; Hollstein, Monica; Zavadil, Jiri
2014-03-01
Experimental models that recapitulate mutational landscapes of human cancers are needed to decipher the rapidly expanding data on human somatic mutations. We demonstrate that mutation patterns in immortalised cell lines derived from primary murine embryonic fibroblasts (MEFs) exposed in vitro to carcinogens recapitulate key features of mutational signatures observed in human cancers. In experiments with several cancer-causing agents we obtained high genome-wide concordance between human tumour mutation data and in vitro data with respect to predominant substitution types, strand bias and sequence context. Moreover, we found signature mutations in well-studied human cancer driver genes. To explore endogenous mutagenesis, we used MEFs ectopically expressing activation-induced cytidine deaminase (AID) and observed an excess of AID signature mutations in immortalised cell lines compared to their non-transgenic counterparts. MEF immortalisation is thus a simple and powerful strategy for modelling cancer mutation landscapes that facilitates the interpretation of human tumour genome-wide sequencing data.
Cancer systems biology and modeling: microscopic scale and multiscale approaches.
Masoudi-Nejad, Ali; Bidkhori, Gholamreza; Hosseini Ashtiani, Saman; Najafi, Ali; Bozorgmehr, Joseph H; Wang, Edwin
2015-02-01
Cancer has become known as a complex and systematic disease on macroscopic, mesoscopic and microscopic scales. Systems biology employs state-of-the-art computational theories and high-throughput experimental data to model and simulate complex biological procedures such as cancer, which involves genetic and epigenetic, in addition to intracellular and extracellular complex interaction networks. In this paper, different systems biology modeling techniques such as systems of differential equations, stochastic methods, Boolean networks, Petri nets, cellular automata methods and agent-based systems are concisely discussed. We have compared the mentioned formalisms and tried to address the span of applicability they can bear on emerging cancer modeling and simulation approaches. Different scales of cancer modeling, namely, microscopic, mesoscopic and macroscopic scales are explained followed by an illustration of angiogenesis in microscopic scale of the cancer modeling. Then, the modeling of cancer cell proliferation and survival are examined on a microscopic scale and the modeling of multiscale tumor growth is explained along with its advantages.
Submission Form for Peer-Reviewed Cancer Risk Prediction Models
If you have information about a peer-reviewd cancer risk prediction model that you would like to be considered for inclusion on this list, submit as much information as possible through the form on this page.
Directory of Open Access Journals (Sweden)
Morton Daniel J
2012-06-01
Full Text Available Abstract Background Haemophilus influenzae requires heme for aerobic growth and possesses multiple mechanisms to obtain this essential nutrient. Methods An insertional mutation in tonB was constructed and the impact of the mutation on virulence and fitness in a chinchilla model of otitis media was determined. The tonB insertion mutant strain was significantly impacted in both virulence and fitness as compared to the wildtype strain in this model. Conclusions The tonB gene of H. influenzae is required for the establishment and maintenance of middle ear infection in this chinchilla model of bacterial disease.
Models of reactive oxygen species in cancer
Lu, Weiqin; Ogasawara, Marcia A.; Huang, Peng
2007-01-01
Increased generation of reactive oxygen species (ROS) has been observed in cancer, degenerative diseases, and other pathological conditions. ROS can stimulate cell proliferation, promote genetic instability, and induce adaptive responses that enable cancer cells to maintain their malignant phenotypes. However, when cellular redox balance is severely disturbed, high levels of ROS may cause various damages leading to cell death. The studies of ROS effects on biological systems, their underlying...
Hancock, Gregory R.; Freeman, Mara J.
2001-01-01
Provides select power and sample size tables and interpolation strategies associated with the root mean square error of approximation test of not close fit under standard assumed conditions. The goal is to inform researchers conducting structural equation modeling about power limitations when testing a model. (SLD)
Directory of Open Access Journals (Sweden)
Malcolm A West
Full Text Available BACKGROUND: In the United Kingdom, patients with locally advanced rectal cancer routinely receive neoadjuvant chemoradiotherapy. However, the effects of this on physical fitness are unclear. This pilot study is aimed to investigate the effect of neoadjuvant chemoradiotherapy on objectively measured in vivo muscle mitochondrial function and whole-body physical fitness. METHODS: We prospectively studied 12 patients with rectal cancer who completed standardized neoadjuvant chemoradiotherapy, recruited from a large tertiary cancer centre, between October 2012 and July 2013. All patients underwent a cardiopulmonary exercise test and a phosphorus magnetic resonance spectroscopy quadriceps muscle exercise-recovery study before and after neoadjuvant chemoradiotherapy. Data were analysed and reported blind to patient identity and clinical course. Primary variables of interest were the two physical fitness measures; oxygen uptake at estimated anaerobic threshold and oxygen uptake at Peak exercise (ml.kg-1.min-1, and the post-exercise phosphocreatine recovery rate constant (min-1, a measure of muscle mitochondrial capacity in vivo. RESULTS: Median age was 67 years (IQR 64-75. Differences (95%CI in all three primary variables were significantly negative post-NACRT: Oxygen uptake at estimated anaerobic threshold -2.4 ml.kg-1.min-1 (-3.8, -0.9, p = 0.004; Oxygen uptake at Peak -4.0 ml.kg-1.min-1 (-6.8, -1.1, p = 0.011; and post-exercise phosphocreatine recovery rate constant -0.34 min-1 (-0.51, -0.17, p<0.001. CONCLUSION: The significant decrease in both whole-body physical fitness and in vivo muscle mitochondrial function raises the possibility that muscle mitochondrial mechanisms, no doubt multifactorial, may be important in deterioration of physical fitness following neoadjuvant chemoradiotherapy. This may have implications for targeted interventions to improve physical fitness pre-surgery. TRIAL REGISTRATION: Clinicaltrials.gov registration NCT01859442.
Dawis, Rene V.; Gati, Itamar; Hesketh, Beryl; Prediger, Dale J.; Rounds, James; McKenna, Molly C.; Hubert, Lawrence; Day, Susan X.; Tracey, Terence J. G.; Darcy, Maria; Kovalski, Theresa M.
2000-01-01
Includes "P-E [Person-Environment] Fit as Paradigm" (Dawis); "Pitfalls of Congruence Research" (Gati); "The Next Millennium of 'Fit' Research" (Hesketh); "Holland's Hexagon Is Alive and Well--Though Somewhat out of Shape" (Prediger); "Tinsley on Holland: A Misshapen Argument" (Rounds, McKenna,…
DEFF Research Database (Denmark)
Deforche, Koen; Cozzi-Lepri, Alessandro; Theys, Kristof
2008-01-01
BACKGROUND: A method has been developed to estimate a fitness landscape experienced by HIV-1 under treatment selective pressure as a function of the genotypic sequence thereby also estimating the genetic barrier to resistance. METHODS: We evaluated the performance of two estimated fitness landsca...
AOM/DSS Model of Colitis-Associated Cancer.
Parang, Bobak; Barrett, Caitlyn W; Williams, Christopher S
2016-01-01
Our understanding of colitis-associated carcinoma (CAC) has benefited substantially from mouse models that faithfully recapitulate human CAC. Chemical models, in particular, have enabled fast and efficient analysis of genetic and environmental modulators of CAC without the added requirement of time-intensive genetic crossings. Here we describe the Azoxymethane (AOM)/Dextran Sodium Sulfate (DSS) mouse model of inflammatory colorectal cancer.
Mathematical models in cell biology and cancer chemotherapy
Eisen, Martin
1979-01-01
The purpose of this book is to show how mathematics can be applied to improve cancer chemotherapy. Unfortunately, most drugs used in treating cancer kill both normal and abnormal cells. However, more cancer cells than normal cells can be destroyed by the drug because tumor cells usually exhibit different growth kinetics than normal cells. To capitalize on this last fact, cell kinetics must be studied by formulating mathematical models of normal and abnormal cell growth. These models allow the therapeutic and harmful effects of cancer drugs to be simulated quantitatively. The combined cell and drug models can be used to study the effects of different methods of administering drugs. The least harmful method of drug administration, according to a given criterion, can be found by applying optimal control theory. The prerequisites for reading this book are an elementary knowledge of ordinary differential equations, probability, statistics, and linear algebra. In order to make this book self-contained, a chapter on...
A computational model for cancer growth by using complex networks
Galvão, Viviane; Miranda, José G. V.
2008-09-01
In this work we propose a computational model to investigate the proliferation of cancerous cell by using complex networks. In our model the network represents the structure of available space in the cancer propagation. The computational scheme considers a cancerous cell randomly included in the complex network. When the system evolves the cells can assume three states: proliferative, non-proliferative, and necrotic. Our results were compared with experimental data obtained from three human lung carcinoma cell lines. The computational simulations show that the cancerous cells have a Gompertzian growth. Also, our model simulates the formation of necrosis, increase of density, and resources diffusion to regions of lower nutrient concentration. We obtain that the cancer growth is very similar in random and small-world networks. On the other hand, the topological structure of the small-world network is more affected. The scale-free network has the largest rates of cancer growth due to hub formation. Finally, our results indicate that for different average degrees the rate of cancer growth is related to the available space in the network.
1981-01-01
REPRESENTING THE 5TRETCHED PONQ DE LEON (S.PI.) 9€ ASS _!"Q SHIP FITTED WITH TWO SETS OF DESIGN CONTRAROTATING PROPELLERS (MODEL 5362; PROPELLERS 4731...TYPE OF REPORT & PERIOD COVERED AN ANALYSIS OF THE PROPULSION EXPERIMENTS PER- Final FORMED ON A MODEL REPRESENTING THE STRETCHED PONCE DE LEON (SPDL...number) A ser ies of propulsion exper ments were performed on Model 5362, representing a Stretched PONCE DE LEON Clas RO/RO ship. The model was fitted
Fast fitting of non-Gaussian state-space models to animal movement data via Template Model Builder
DEFF Research Database (Denmark)
Albertsen, Christoffer Moesgaard; Whoriskey, Kim; Yurkowski, David
2015-01-01
State-space models (SSM) are often used for analyzing complex ecological processes that are not observed directly, such as marine animal movement. When outliers are present in the measurements, special care is needed in the analysis to obtain reliable location and process estimates. Here we...
Hermann, Philipp; Mrkvička, Tomáš; Mattfeldt, Torsten; Minárová, Mária; Helisová, Kateřina; Nicolis, Orietta; Wartner, Fabian; Stehlík, Milan
2015-08-15
Fractals are models of natural processes with many applications in medicine. The recent studies in medicine show that fractals can be applied for cancer detection and the description of pathological architecture of tumors. This fact is not surprising, as due to the irregular structure, cancerous cells can be interpreted as fractals. Inspired by Sierpinski carpet, we introduce a flexible parametric model of random carpets. Randomization is introduced by usage of binomial random variables. We provide an algorithm for estimation of parameters of the model and illustrate theoretical and practical issues in generation of Sierpinski gaskets and Hausdorff measure calculations. Stochastic geometry models can also serve as models for binary cancer images. Recently, a Boolean model was applied on the 200 images of mammary cancer tissue and 200 images of mastopathic tissue. Here, we describe the Quermass-interaction process, which can handle much more variations in the cancer data, and we apply it to the images. It was found out that mastopathic tissue deviates significantly stronger from Quermass-interaction process, which describes interactions among particles, than mammary cancer tissue does. The Quermass-interaction process serves as a model describing the tissue, which structure is broken to a certain level. However, random fractal model fits well for mastopathic tissue. We provide a novel discrimination method between mastopathic and mammary cancer tissue on the basis of complex wavelet-based self-similarity measure with classification rates more than 80%. Such similarity measure relates to Hurst exponent and fractional Brownian motions. The R package FractalParameterEstimation is developed and introduced in the paper.
On The Robustness of z=0-1 Galaxy Size Measurements Through Model and Non-Parametric Fits
Mosleh, Moein; Franx, Marijn
2013-01-01
We present the size-stellar mass relations of nearby (z=0.01-0.02) SDSS galaxies, for samples selected by color, morphology, Sersic index n, and specific star formation rate. Several commonly-employed size measurement techniques are used, including single Sersic fits, two-component Sersic models and a non-parametric method. Through simple simulations we show that the non-parametric and two-component Sersic methods provide the most robust effective radius measurements, while those based on single Sersic profiles are often overestimates, especially for massive red/early-type galaxies. Using our robust sizes, we show that for all sub-samples, the mass-size relations are shallow at low stellar masses and steepen above ~3-4 x 10^{10}\\Msun. The mass-size relations for galaxies classified as late-type, low-n, and star-forming are consistent with each other, while blue galaxies follow a somewhat steeper relation. The mass-size relations of early-type, high-n, red, and quiescent galaxies all agree with each other but ...
Development of A Mouse Model of Menopausal Ovarian Cancer
Directory of Open Access Journals (Sweden)
Elizabeth R. Smith
2014-02-01
Full Text Available Despite significant understanding of the genetic mutations involved in ovarian epithelial cancer and advances in genomic approaches for expression and mutation profiling of tumor tissues, several key questions in ovarian cancer biology remain enigmatic: the mechanism for the well-established impact of reproductive factors on ovarian cancer risk remains obscure; questions of the cell of origin of ovarian cancer continue to be debated; and the precursor lesion, sequence, or events in progression remain to be defined. Suitable mouse models should complement the analysis of human tumor tissues and may provide clues to these questions currently perplexing ovarian cancer biology.A potentially useful model is the germ cell-deficient Wv (white spotting variant mutant mouse line, which may be used to study the impact of menopausal physiology on the increased risk of ovarian cancer. The Wv mice harbor a point mutation in c-Kit that reduces the receptor tyrosine kinase activity to about 1-5% (it is not a null mutation. Homozygous Wv mutant females have a reduced ovarian germ cell reservoir at birth and the follicles are rapidly depleted upon reaching reproductive maturity, but other biological phenotypes are minimal and the mice have a normal life span. The loss of ovarian function precipitates changes in hormonal and metabolic activity that model features of menopause in humans. As a consequence of follicle depletion, the Wv ovaries develop ovarian tubular adenomas, a benign epithelial tumor corresponding to surface epithelial invaginations and papillomatosis that mark human ovarian aging. Ongoing work will test the possibility of converting the benign epithelial tubular adenomas into neoplastic tumors by addition of an oncogenic mutation, such as of Tp53, to model the genotype and biology of serous ovarian cancer.Model based on the Wv mice may have the potential to gain biological and etiological insights into ovarian cancer development and prevention.
Molecular targets in urothelial cancer: detection, treatment, and animal models of bladder cancer
Smolensky, Dmitriy; Rathore, Kusum; Cekanova, Maria
2016-01-01
Bladder cancer remains one of the most expensive cancers to treat in the United States due to the length of required treatment and degree of recurrence. In order to treat bladder cancer more effectively, targeted therapies are being investigated. In order to use targeted therapy in a patient, it is important to provide a genetic background of the patient. Recent advances in genome sequencing, as well as transcriptome analysis, have identified major pathway components altered in bladder cancer. The purpose of this review is to provide a broad background on bladder cancer, including its causes, diagnosis, stages, treatments, animal models, as well as signaling pathways in bladder cancer. The major focus is given to the PI3K/AKT pathway, p53/pRb signaling pathways, and the histone modification machinery. Because several promising immunological therapies are also emerging in the treatment of bladder cancer, focus is also given on general activation of the immune system for the treatment of bladder cancer. PMID:27784990
Molecular genetics of cancer and tumorigenesis: Drosophila models
Institute of Scientific and Technical Information of China (English)
Wu-Min Deng
2011-01-01
Why do some cells not respond to normal control of cell division and become tumorous? Which signals trigger some tumor cells to migrate and colonize other tissues? What genetic factors are responsible for tumorigenesis and cancer development? What environmental factors play a role in cancer formation and progression? In how many ways can our bodies prevent and restrict the growth of cancerous cells?How can we identify and deliver effective drugs to fight cancer? In the fight against cancer,which kills more people than any other disease,these and other questions have long interested researchers from a diverse range of fields.To answer these questions and to fight cancer more effectively,we must increase our understanding of basic cancer biology.Model organisms,including the fruit fly Drosophila melanogaster,have played instrumental roles in our understanding of this devastating disease and the search for effective cures.Drosophila and its highly effective,easy-touse,and ever-expanding genetic tools have contributed toand enriched our knowledge of cancer and tumor formation tremendously.
A mathematical prognosis model for pancreatic cancer patients receiving immunotherapy.
Li, Xuefang; Xu, Jian-Xin
2016-10-07
Pancreatic cancer is one of the most deadly types of cancer since it typically spreads rapidly and can seldom be detected in its early stage. Pancreatic cancer therapy is thus a challenging task, and appropriate prognosis or assessment for pancreatic cancer therapy is of critical importance. In this work, based on available clinical data in Niu et al. (2013) we develop a mathematical prognosis model that can predict the overall survival of pancreatic cancer patients who receive immunotherapy. The mathematical model incorporates pancreatic cancer cells, pancreatic stellate cells, three major classes of immune effector cells CD8+ T cells, natural killer cells, helper T cells, and two major classes of cytokines interleukin-2 (IL-2) and interferon-γ (IFN-γ). The proposed model describes the dynamic interaction between tumor and immune cells. In order for the model to be able to generate appropriate prognostic results for disease progression, the distribution and stability properties of equilibria in the mathematical model are computed and analysed in absence of treatments. In addition, numerical simulations for disease progression with or without treatments are performed. It turns out that the median overall survival associated with CIK immunotherapy is prolonged from 7 to 13months compared with the survival without treatment, this is consistent with the clinical data observed in Niu et al. (2013). The validity of the proposed mathematical prognosis model is thus verified. Our study confirms that immunotherapy offers a better prognosis for pancreatic cancer patients. As a direct extension of this work, various new therapy methods that are under exploration and clinical trials could be assessed or evaluated using the newly developed mathematical prognosis model.