Kocherlakota, Narayana R.
2007-01-01
This paper uses an example to show that a model that fits the available data perfectly may provide worse answers to policy questions than an alternative, imperfectly fitting model. The author argues that, in the context of Bayesian estimation, this result can be interpreted as being due to the use of an inappropriate prior over the parameters of shock processes. He urges the use of priors that are obtained from explicit auxiliary information, not from the desire to obtain identification.
Krueger, Andre
2004-01-01
The status of the electroweak precision measurements as of winter 2004 and the global test of the Standard Model are discussed. Important input data are the precision variables measured on the Z resonance at LEP and SLC and the measurements of the W mass at LEP~2 and Tevatron. A new combination of Tevatron experiments CDF and D0 on the top mass allows to set constraints on the radiative corrections and therefore to put improved limits on the mass of the Higgs boson. Additionally the impact of...
Fitting and Interpreting Occupancy Models
Welsh, Alan H.; Lindenmayer, David B; Donnelly, Christine F.
2013-01-01
We show that occupancy models are more difficult to fit than is generally appreciated because the estimating equations often have multiple solutions, including boundary estimates which produce fitted probabilities of zero or one. The estimates are unstable when the data are sparse, making them difficult to interpret, and, even in ideal situations, highly variable. As a consequence, making accurate inference is difficult. When abundance varies over sites (which is the general rule in ecology b...
Fitting and interpreting occupancy models.
Welsh, Alan H; Lindenmayer, David B; Donnelly, Christine F
2013-01-01
We show that occupancy models are more difficult to fit than is generally appreciated because the estimating equations often have multiple solutions, including boundary estimates which produce fitted probabilities of zero or one. The estimates are unstable when the data are sparse, making them difficult to interpret, and, even in ideal situations, highly variable. As a consequence, making accurate inference is difficult. When abundance varies over sites (which is the general rule in ecology because we expect spatial variance in abundance) and detection depends on abundance, the standard analysis suffers bias (attenuation in detection, biased estimates of occupancy and potentially finding misleading relationships between occupancy and other covariates), asymmetric sampling distributions, and slow convergence of the sampling distributions to normality. The key result of this paper is that the biases are of similar magnitude to those obtained when we ignore non-detection entirely. The fact that abundance is subject to detection error and hence is not directly observable, means that we cannot tell when bias is present (or, equivalently, how large it is) and we cannot adjust for it. This implies that we cannot tell which fit is better: the fit from the occupancy model or the fit ignoring the possibility of detection error. Therefore trying to adjust occupancy models for non-detection can be as misleading as ignoring non-detection completely. Ignoring non-detection can actually be better than trying to adjust for it. PMID:23326323
Fitting and interpreting occupancy models.
Alan H Welsh
Full Text Available We show that occupancy models are more difficult to fit than is generally appreciated because the estimating equations often have multiple solutions, including boundary estimates which produce fitted probabilities of zero or one. The estimates are unstable when the data are sparse, making them difficult to interpret, and, even in ideal situations, highly variable. As a consequence, making accurate inference is difficult. When abundance varies over sites (which is the general rule in ecology because we expect spatial variance in abundance and detection depends on abundance, the standard analysis suffers bias (attenuation in detection, biased estimates of occupancy and potentially finding misleading relationships between occupancy and other covariates, asymmetric sampling distributions, and slow convergence of the sampling distributions to normality. The key result of this paper is that the biases are of similar magnitude to those obtained when we ignore non-detection entirely. The fact that abundance is subject to detection error and hence is not directly observable, means that we cannot tell when bias is present (or, equivalently, how large it is and we cannot adjust for it. This implies that we cannot tell which fit is better: the fit from the occupancy model or the fit ignoring the possibility of detection error. Therefore trying to adjust occupancy models for non-detection can be as misleading as ignoring non-detection completely. Ignoring non-detection can actually be better than trying to adjust for it.
Valind, Anders; Jin, Yuesheng; Gisselsson, David
2013-01-01
An unbalanced chromosome number (aneuploidy) is present in most malignant tumours and has been attributed to mitotic mis-segregation of chromosomes. However, recent studies have shown a relatively high rate of chromosomal mis-segregation also in non-neoplastic human cells, while the frequency of aneuploid cells remains low throughout life in most normal tissues. This implies that newly formed aneuploid cells are subject to negative selection in healthy tissues and that attenuation of this selection could contribute to aneuploidy in cancer. To test this, we modelled cellular growth as discrete time branching processes, during which chromosome gains and losses were generated and their host cells subjected to selection pressures of various magnitudes. We then assessed experimentally the frequency of chromosomal mis-segregation as well as the prevalence of aneuploid cells in human non-neoplastic cells and in cancer cells. Integrating these data into our models allowed estimation of the fitness reduction resulting from a single chromosome copy number change to an average of ≈30% in normal cells. In comparison, cancer cells showed an average fitness reduction of only 6% (p = 0.0008), indicative of aneuploidy tolerance. Simulations based on the combined presence of chromosomal mis-segregation and aneuploidy tolerance reproduced distributions of chromosome aberrations in >400 cancer cases with higher fidelity than models based on chromosomal mis-segregation alone. Reverse engineering of aneuploid cancer cell development in silico predicted that aneuploidy intolerance is a stronger limiting factor for clonal expansion of aneuploid cells than chromosomal mis-segregation rate. In conclusion, our findings indicate that not only an elevated chromosomal mis-segregation rate, but also a generalised tolerance to novel chromosomal imbalances contribute to the genomic landscape of human tumours. PMID:23894657
Measured, modeled, and causal conceptions of fitness
Abrams, Marshall
2012-01-01
This paper proposes partial answers to the following questions: in what senses can fitness differences plausibly be considered causes of evolution?What relationships are there between fitness concepts used in empirical research, modeling, and abstract theoretical proposals? How does the relevance of different fitness concepts depend on research questions and methodological constraints? The paper develops a novel taxonomy of fitness concepts, beginning with type fitness (a property of a genoty...
Measured, Modeled, and Causal Conceptions of Fitness
Marshall eAbrams
2012-01-01
This paper proposes partial answers to the following questions: In what senses can fitness differences plausibly be considered causes of evolution? What relationships are there between fitness concepts used in empirical research, modeling, and abstract theoretical proposals? How does the relevance of different fitness concepts depend on research questions and methodological constraints? The paper develops a novel taxonomy of fitness concepts, beginning with type fitness (a property of a ge...
The Model Characteristics of Physical Fitness in CrossFit
Vasilii V. Volkov; Viktor N. Seluyanov
2014-01-01
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 e...
The Model Characteristics of Physical Fitness in CrossFit
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),…
Evaluating Model Fit for Growth Curve Models: Integration of Fit Indices from SEM and MLM Frameworks
Wu, Wei; West, Stephen G.; Taylor, Aaron B.
2009-01-01
Evaluating overall model fit for growth curve models involves 3 challenging issues. (a) Three types of longitudinal data with different implications for model fit may be distinguished: balanced on time with complete data, balanced on time with data missing at random, and unbalanced on time. (b) Traditional work on fit from the structural equation…
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
Screening for colorectal cancer: what fits best?
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.
... gov home http://www.girlshealth.gov/ Home Fitness Fitness Want to look and feel your best? Physical ... are? Check out this info: What is physical fitness? top Physical fitness means you can do everyday ...
A predictive fitness model for influenza
Łuksza, Marta; Lässig, Michael
2014-03-01
The seasonal human influenza A/H3N2 virus undergoes rapid evolution, which produces significant year-to-year sequence turnover in the population of circulating strains. Adaptive mutations respond to human immune challenge and occur primarily in antigenic epitopes, the antibody-binding domains of the viral surface protein haemagglutinin. Here we develop a fitness model for haemagglutinin that predicts the evolution of the viral population from one year to the next. Two factors are shown to determine the fitness of a strain: adaptive epitope changes and deleterious mutations outside the epitopes. We infer both fitness components for the strains circulating in a given year, using population-genetic data of all previous strains. From fitness and frequency of each strain, we predict the frequency of its descendent strains in the following year. This fitness model maps the adaptive history of influenza A and suggests a principled method for vaccine selection. Our results call for a more comprehensive epidemiology of influenza and other fast-evolving pathogens that integrates antigenic phenotypes with other viral functions coupled by genetic linkage.
Model Fit after Pairwise Maximum Likelihood.
Barendse, M T; Ligtvoet, R; Timmerman, M E; Oort, F J
2016-01-01
Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response patterns is computationally very intensive, the sum of the log-likelihoods of the bivariate response patterns is maximized instead. Little is yet known about how to assess model fit when the analysis is based on such a pairwise maximum likelihood (PML) of two-way contingency tables. We propose new fit criteria for the PML method and conduct a simulation study to evaluate their performance in model selection. With large sample sizes (500 or more), PML performs as well the robust weighted least squares analysis of polychoric correlations. PMID:27148136
Fitting and Modeling of AXAF Data with the ASC Fitting Application
Doe, S.; Ljungberg, M.; Siemiginowska, A.; Joye, W.
The AXAF mission will provide X-ray data with unprecedented spatial and spectral resolution. Because of the high quality of these data, the AXAF Science Center will provide a new data analysis system--including a new fitting application. Our intent is to enable users to do fitting that is too awkward with, or beyond, the scope of existing astronomical fitting software. Our main goals are: 1) to take advantage of the full capabilities of the AXAF, we intend to provide a more sophisticated modeling capability (i.e., models that are $f(x,y,E,t)$, models to simulate the response of AXAF instruments, and models that enable ``joint-mode'' fitting, i.e., combined spatial-spectral or spectral-temporal fitting); and 2) to provide users with a wide variety of models, optimization methods, and fit statistics. In this paper, we discuss the use of an object-oriented approach in our implementation, the current features of the fitting application, and the features scheduled to be added in the coming year of development. Current features include: an interactive, command-line interface; a modeling language, which allows users to build models from arithmetic combinations of base functions; a suite of optimization and fit statistics; the ability to perform fits to multiple data sets simultaneously; and, an interface with SM and SAOtng to plot or image data, models, and/or residuals from a fit. We currently provide a modeling capability in one or two dimensions, and have recently made an effort to perform spectral fitting in a manner similar to XSPEC. We also allow users to dynamically link the fitting application to their own algorithms. Our goals for the coming year include incorporating the XSPEC model library as a subset of models available in the application, enabling ``joint-mode'' analysis and adding support for new algorithms.
Evaluating Latent Growth Curve Models Using Individual Fit Statistics
Coffman, Donna L.; Millsap, Roger E.
2006-01-01
The usefulness of assessing individual fit in latent growth curve models was examined. The study used simulated data based on an unconditional and a conditional latent growth curve model with a linear component and a small quadratic component and a linear model was fit to the data. Then the overall fit of linear and quadratic models to these data…
Cardiorespiratory Fitness in Women with and without Lymphedema following Breast Cancer Treatment
2012-01-01
Following breast cancer (BC) treatment, many women develop impairments that may impact cardiorespiratory (CR) fitness. The aims of this study were to 1) evaluate CR fitness in women following BC treatment, 2) evaluate differences in CR fitness in those with and without breast cancer-related lymphedema (BCRL) and compare these to age-matched norms, and 3) evaluate the contribution of predictor variables to CR fitness. 136 women post-BC treatment completed testing: 67 with BCRL, and 69 without....
Curve fitting methods for solar radiation data modeling
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.
Curve fitting methods for solar radiation data modeling
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
On the Fitting of Non-Linear, Empirical Functions for the Fitting of Model Crater Ages
Weaver, B. P.; Hilbe, J. M.; Robbins, S. J.; Plesko, C. S.; Riggs, J. D.
2015-05-01
Fitting model crater production functions to observed crater data is considered an "art" by many, and there is no standard in the field for how best to do it. We will discuss mathematical techniques' pros and cons and make recommendations.
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 ...
Sensitivity of Fit Indices to Misspecification in Growth Curve Models
Wu, Wei; West, Stephen G.
2010-01-01
This study investigated the sensitivity of fit indices to model misspecification in within-individual covariance structure, between-individual covariance structure, and marginal mean structure in growth curve models. Five commonly used fit indices were examined, including the likelihood ratio test statistic, root mean square error of…
topicmodels: An R Package for Fitting Topic Models
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.
Laura Chaddock-Heyman
2015-01-01
As breast cancer treatment is associated with declines in brain and cognitive health, it is important to identify strategies to enhance the cognitive vitality of cancer survivors. In particular, the hippocampus is known to play an important role in brain and memory declines following cancer treatment. The hippocampus is also known for its plasticity and positive association with cardiorespiratory fitness. The present study explores whether cardiorespiratory fitness may hold promise for lesse...
An R package for fitting age, period and cohort models
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.
Taylor, Deborah L.; Nichols, Jeanne F.; Pakiz, Bilgé; Bardwell, Wayne A.; Flatt, Shirley W.; Rock, Cheryl L.
2010-01-01
Background Breast cancer survivors not only experience distressing physical symptoms associated with treatments, but also are faced with psychosocial challenges. Despite growing scientific evidence that physical activity (PA) may mitigate psychosocial distress experienced by women treated for breast cancer, the literature is equivocal. Purpose This study investigated the relationships between cardiorespiratory fitness (CRF), PA, and psychosocial factors in breast cancer survivors. Method Data...
Person-fit to the Five Factor Model of personality
Allik, J.; Realo, A; Mõttus, R.; Borkenau, P.; Kuppens, P.; Hřebíčková, M.
2012-01-01
The Five Factor Model (FFM), a valid model of interindividual differences in the personality of a group of people, reportedly does not always provide a good fit for the individuals of that group. In addition to intraindividual variation across a considerable period of time, meaningful intraindividual variation can be observed within a single test administration. Two person-fit indices showed that the FFM is an adequate model for 95% of the 1,765 target-judge pairs in four different countries ...
Tainted Evidence: Cosmological Model Selection vs. Fitting
Linder, E V; Linder, Eric V.; Miquel, Ramon
2007-01-01
Interpretation of cosmological data to determine the number and values of parameters describing the universe must not rely solely on statistics but involve physical insight. Statistical techniques such as "model selection" or "integrated survey optimization" blindly apply Occam's Razor - this can lead to painful results. We emphasize that the sensitivity to prior probabilities and to the number of models compared can lead to "prior selection" rather than robust model selection. A concrete example demonstrates that Information Criteria can in fact misinform over a large region of parameter space.
FITTING PHOTOIONIZATION MODELS TO PLANETARY NEBULAE
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
2016-09-01
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. PMID:27189564
MAPCLUS: A Mathematical Programming Approach to Fitting the ADCLUS Model.
Arabie, Phipps
1980-01-01
A new computing algorithm, MAPCLUS (Mathematical Programming Clustering), for fitting the Shephard-Arabie ADCLUS (Additive Clustering) model is presented. Details and benefits of the algorithm are discussed. (Author/JKS)
Fitting polytomous Rasch models in SAS
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, an...
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…
Goodness-of-fit methods for nonparametric IRT models
K. Sijtsma; J.H. Straat; L.A. van der Ark
2014-01-01
This chapter has three sections. The first section introduces the unidimensionalmonotone latent variable model for data collected by means of a test or a questionnaire. The second section discusses the use of goodness-of-fit methods for statistical models, in particular, item response models such as
Akaike information criterion to select well-fit resist models
Burbine, Andrew; Fryer, David; Sturtevant, John
2015-03-01
In the field of model design and selection, there is always a risk that a model is over-fit to the data used to train the model. A model is well suited when it describes the physical system and not the stochastic behavior of the particular data collected. K-fold cross validation is a method to check this potential over-fitting to the data by calibrating with k-number of folds in the data, typically between 4 and 10. Model training is a computationally expensive operation, however, and given a wide choice of candidate models, calibrating each one repeatedly becomes prohibitively time consuming. Akaike information criterion (AIC) is an information-theoretic approach to model selection based on the maximized log-likelihood for a given model that only needs a single calibration per model. It is used in this study to demonstrate model ranking and selection among compact resist modelforms that have various numbers and types of terms to describe photoresist behavior. It is shown that there is a good correspondence of AIC to K-fold cross validation in selecting the best modelform, and it is further shown that over-fitting is, in most cases, not indicated. In modelforms with more than 40 fitting parameters, the size of the calibration data set benefits from additional parameters, statistically validating the model complexity.
Automatic fitting of spiking neuron models to electrophysiological recordings
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...
Mouse models of pancreatic cancer
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.
Detailed Atmosphere Model Fits to Disk-Dominated ULX Spectra
Hui, Y; Krolik, Julian H.
2008-01-01
We have chosen 6 Ultra-Luminous X-ray sources from the {\\it XMM-Newton} archive whose spectra have high signal-to-noise and can be fitted solely with a disk model without requiring any power-law component. To estimate systematic errors in the inferred parameters, we fit every spectrum to two different disk models, one based on local blackbody emission (KERRBB) and one based on detailed atmosphere modelling (BHSPEC). Both incorporate full general relativistic treatment of the disk surface brig...
Curve Fitting And Interpolation Model Applied In Nonel Dosage Detection
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.
Flexible competing risks regression modeling and goodness-of-fit
Scheike, Thomas; Zhang, Mei-Jie
2008-01-01
In this paper we consider different approaches for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause......-specific hazards. Another recent approach is to directly model the cumulative incidence by a proportional model (Fine and Gray, J Am Stat Assoc 94:496-509, 1999), and then obtain direct estimates of how covariates influences the cumulative incidence curve. We consider a simple and flexible class of regression...... models that is easy to fit and contains the Fine-Gray model as a special case. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy...
Mumtaz Ali Memon
2014-12-01
Full Text Available Past studies revealed that the existence of congruence between employees and their job as well as organisation produces more favourable attitudes and behaviours. Although considerable research has been conducted on the person-job (P-J fit and person-organization (P-O fit, an in depth review of the literature identifies several research gaps. First, studies have largely focused on examining P-J fit and P-O fit separately. In addition, the relationship of P-J fit and P-O fit, and employee engagement has been less discussed. Lastly, most often studies investigated how antecedents predict outcomes but minimal effort has been made to explore the consequences of these outcomes. This paper makes a twofold contribution. First, it conceptually integrates both P-O fit and P-J fit into a single model. Second, the paper proposes a three-step model that theoretically links P-J fit and P-O fit (antecedents to employee engagement (outcome and turnover intention (consequence. The addition of a third-step would support the evaluation of the outcomes (in terms of the consequences of the overall model and extend the overall scope of the framework. Social exchange theory, Lewin’s field theory, multidimensional model of employee engagement and self-concept-job fit theory are adopted in developing the theoretical linkages among the constructs. Recommendations for future studies are proposed.
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%.
Evolution in random fitness landscapes: the infinite sites model
We consider the evolution of an asexually reproducing population in an uncorrelated random fitness landscape in the limit of infinite genome size, which implies that each mutation generates a new fitness value drawn from a probability distribution g(w). This is the finite population version of Kingman's house of cards model (Kingman 1978 J. Appl. Probab. 15 1). In contrast to Kingman's work, the focus here is on unbounded distributions g(w) which lead to an indefinite growth of the population fitness. The model is solved analytically in the limit of infinite population size N→∞ and simulated numerically for finite N. When the genome-wide mutation probability U is small, the long-time behavior of the model reduces to a point process of fixation events, which is referred to as a diluted record process (DRP). The DRP is similar to the standard record process except that a new record candidate (a number that exceeds all previous entries in the sequence) is accepted only with a certain probability that depends on the values of the current record and the candidate. We develop a systematic analytic approximation scheme for the DRP. At finite U the fitness frequency distribution of the population decomposes into a stationary part due to mutations and a traveling wave component due to selection, which is shown to imply a reduction of the mean fitness by a factor of 1−U compared to the U→0 limit
A Unified, Hardware-Fitted, Cross-GPU Performance Model
Stevens, James; Klöckner, Andreas
2016-01-01
We present a mechanism to symbolically gather performance-relevant operation counts from numerically-oriented subprograms (`kernels') expressed in the Loopy programming system, and apply these counts in a simple, linear model of kernel run time. We use a series of `performance-instructive' kernels to fit the parameters of a unified model to the performance characteristics of GPU hardware from multiple hardware generations and vendors. We evaluate the predictive power of the model on a broad a...
Ongoing Processes in a Fitness Network Model under Restricted Resources.
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.
Person-fit to the Five Factor Model of personality
Allik, J.; Realo, A.; Mõttus, R.; Borkenau, P.; Kuppens, P.; Hřebíčková, Martina
2012-01-01
Roč. 71, č. 1 (2012), s. 35-45. ISSN 1421-0185 R&D Projects: GA ČR GAP407/10/2394 Institutional research plan: CEZ:AV0Z70250504 Keywords : Five Factor Model * cross-cultural comparison * person-fit Subject RIV: AN - Psychology Impact factor: 0.638, year: 2012
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 global electroweak Standard Model fit after the Higgs discovery
Baak, Max
2013-01-01
We present an update of the global Standard Model (SM) fit to electroweak precision data under the assumption that the new particle discovered at the LHC is the SM Higgs boson. In this scenario all parameters entering the calculations of electroweak precision observalbes are known, allowing, for the first time, to over-constrain the SM at the electroweak scale and assert its validity. Within the SM the W boson mass and the effective weak mixing angle can be accurately predicted from the global fit. The results are compatible with, and exceed in precision, the direct measurements. An updated determination of the S, T and U parameters, which parametrize the oblique vacuum corrections, is given. The obtained values show good consistency with the SM expectation and no direct signs of new physics are seen. We conclude with an outlook to the global electroweak fit for a future e+e- collider.
Fit & Strong! Promotes Physical Activity and Well-Being in Older Cancer Survivors
Reynolds, Jana; Thibodeaux, Lorie; Jiang, Luohua; Francis, Kevin; Hochhalter, Angie
2015-01-01
Introduction Physical activity reduces fatigue and depression while improving quality of life in cancer survivors. Exercise is generally considered safe and is recommended to survivors of all ages. Despite the high prevalence of cancer in the elderly, few studies address physical activity interventions targeting this older population. Fit & Strong! is an evidence-based physical activity program shown to improve level of physical activity, exercise-self-efficacy, and mood in older adults wi...
Peel, J. Brent; Sui, Xuemei; Matthews, Charles E.; Adams, Swann A; Hébert, James R; Hardin, James W.; Timothy S Church; Blair, Steven N.
2009-01-01
Although higher levels of physical activity are inversely associated with risk of colon cancer, few prospective studies have evaluated overall digestive system cancer mortality in relation to cardiorespiratory fitness (CRF). The authors examined this association among 38,801 men aged 20−88 years and who performed a maximal treadmill exercise test at baseline in the Aerobics Center Longitudinal Study (Dallas, Texas) during 1974−2003. Mortality was assessed over 29 years of follow-up (1974−2003...
Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS
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 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. Our model-fitting suggestions range from general cultural advice (where possible, use the tools and models that are most common in your subfield) to...
Accumulation and modeling of particles in drinking water pipe fittings
K. Neilands
2012-09-01
Full Text Available The effect of pipe fittings (mainly T-pieces on particle accumulation in drinking water distribution networks were shown in this work. The online measurements of flow and turbidity for cast iron, polyethylene and polyvinyl chloride pipe sections were linked with analysis of pipe geometry. Up to 0.29 kg of the total amount mobilized in T-pieces ranging from DN 100/100–DN 250/250. The accumulated amount of particles in fittings was defined as J and introduced into the existing turbidity model PODDS (prediction of discoloration in distribution systems proposed by Boxall et al. (2001 which describes the erosion of particles leading to discoloration events in drinking water network viz sections of straight pipes. However, this work does not interpret mobilization of particles in pipe fittings which have been considered in this article. T-pieces were the object of this study and depending of the diameter or daily flow velocity, the coefficient J varied from 1.16 to 8.02. The study showed that pipe fittings act as catchment areas for particle accumulation in drinking water networks.
Error propagation with R-matrix model fitting
CHEN; Zhenpeng(陈振鹏); ZHANG; Rui(张瑞); SUN; Yeying(孙业英); LIU; Tingjin(刘廷进)
2003-01-01
The error propagation features with R-matrix model fitting 7Li, 11B and 17O systems have been researched systematically. Some laws of error propagation have been revealed, an experience formula for describing standard error propagation has been established, and the most possible error range for evaluated standard cross section of 6Li (n, α), 10B (n, α) and 10B (n, α1) has been determined.
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. PMID:25680684
Accumulation and modeling of particles in drinking water pipe fittings
K. Neilands
2012-04-01
Full Text Available The effect of pipe fittings – mainly T-pieces – on particle accumulation in drinking water distribution networks is shown in this work. The online measurements of flow and turbidity for cast iron, polyethylene and polyvinylchloride pipe sections have been linked with the analysis of pipe geometry. Up to 0.29 kg of the total mass of particles was found to be accumulated in T-pieces ranging from DN 100/100–DN 250/250. The accumulated amount of particles in the fittings was defined as J and introduced into the existing turbidity model PODDS (Prediction of Discolouration in Distribution Systems proposed by Boxall et al. (2001, which describes the erosion of particles leading to discoloration events in drinking water networks, viz. sections, of straight pipes. It does not interpret the mobilization of particles in pipe fittings, however, which have been considered in this article. T-pieces were the object of this study and depending on the diameter or daily flow velocity, the coefficient J varied from 1.16 to 8.02.
Rapid world modeling: Fitting range data to geometric primitives
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
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
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.
Peel, J Brent; Sui, Xuemei; Matthews, Charles E; Adams, Swann A; Hébert, James R; Hardin, James W; Church, Timothy S; Blair, Steven N
2009-04-01
Although higher levels of physical activity are inversely associated with risk of colon cancer, few prospective studies have evaluated overall digestive system cancer mortality in relation to cardiorespiratory fitness (CRF). The authors examined this association among 38,801 men ages 20 to 88 years who performed a maximal treadmill exercise test at baseline in the Aerobics Center Longitudinal Study (Dallas, TX) during 1974 to 2003. Mortality was assessed over 29 years of follow-up (1974-2003). Two hundred eighty-three digestive system cancer deaths occurred during a mean 17 years of observation. Age-adjusted mortality rates per 10,000 person-years according to low, moderate, and high CRF groups were 6.8, 4.0, and 3.3 for digestive system cancer (P(trend) < 0.001). After adjustment for age, examination year, body mass index, smoking, drinking, family history of cancer, personal history of diabetes, hazard ratios (95% confidence intervals) for overall digestive cancer deaths for those in the middle and upper 40% of the distribution of CRF relative to those in the lowest 20% were 0.66 (0.49-0.88) and 0.56 (0.40-0.80), respectively. Being fit (the upper 80% of CRF) was associated with a lower risk of mortality from colon [0.61 (0.37-1.00)], colorectal [0.58 (0.37-0.92)], and liver cancer [0.28 (0.11-0.72)] compared with being unfit (the lowest 20% of CRF). These findings support a protective role of CRF against total digestive tract, colorectal, and liver cancer deaths in men. PMID:19293313
Peel, J. Brent; Sui, Xuemei; Matthews, Charles E.; Adams, Swann A.; Hébert, James R.; Hardin, James W.; Church, Timothy S.; Blair, Steven N.
2009-01-01
Although higher levels of physical activity are inversely associated with risk of colon cancer, few prospective studies have evaluated overall digestive system cancer mortality in relation to cardiorespiratory fitness (CRF). The authors examined this association among 38,801 men aged 20−88 years and who performed a maximal treadmill exercise test at baseline in the Aerobics Center Longitudinal Study (Dallas, Texas) during 1974−2003. Mortality was assessed over 29 years of follow-up (1974−2003). 283 digestive system cancer deaths occurred during a mean 17-year of observation. Age-adjusted mortality rates per 10,000 person-yrs according to low, moderate, and high CRF groups were 6.8, 4.0, and 3.3 for digestive system cancer (trend p < 0.001). After adjustment for age, examination year, body mass index, smoking, drinking, family history of cancer, personal history of diabetes, hazard ratios for overall digestive cancer deaths (95% confidence interval) for those in the middle and upper 40% of the distribution of CRF relative to those in the lowest 20% were 0.66 (0.49, 0.88) and 0.56 (0.40, 0.80), respectively. Being fit (the upper 80% of CRF) was associated with a lower risk of mortality from colon (0.61 [0.37, 1.00]), colorectal (0.58 [0.37, 0.92]), and liver cancer (0.28 [0.11, 0.72]), compared with being unfit (the lowest 20% of CRF). These findings support a protective role of CRF against total digestive tract, colorectal, and liver cancer deaths in men. PMID:19293313
High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities.
Hart, Traver; Chandrashekhar, Megha; Aregger, Michael; Steinhart, Zachary; Brown, Kevin R; MacLeod, Graham; Mis, Monika; Zimmermann, Michal; Fradet-Turcotte, Amelie; Sun, Song; Mero, Patricia; Dirks, Peter; Sidhu, Sachdev; Roth, Frederick P; Rissland, Olivia S; Durocher, Daniel; Angers, Stephane; Moffat, Jason
2015-12-01
The ability to perturb genes in human cells is crucial for elucidating gene function and holds great potential for finding therapeutic targets for diseases such as cancer. To extend the catalog of human core and context-dependent fitness genes, we have developed a high-complexity second-generation genome-scale CRISPR-Cas9 gRNA library and applied it to fitness screens in five human cell lines. Using an improved Bayesian analytical approach, we consistently discover 5-fold more fitness genes than were previously observed. We present a list of 1,580 human core fitness genes and describe their general properties. Moreover, we demonstrate that context-dependent fitness genes accurately recapitulate pathway-specific genetic vulnerabilities induced by known oncogenes and reveal cell-type-specific dependencies for specific receptor tyrosine kinases, even in oncogenic KRAS backgrounds. Thus, rigorous identification of human cell line fitness genes using a high-complexity CRISPR-Cas9 library affords a high-resolution view of the genetic vulnerabilities of a cell. PMID:26627737
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...
The FIT Model - Fuel-cycle Integration and Tradeoffs
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.
Research on Recruitment Model Based on Person-Organization Fit
Zhen Cheng
2014-01-01
Person-organization fit is a hot problem in the study on human resource management and organizational behavior. To recruit and keep talents who fit company's development will be key to maintain sustainable development and competitiveness and origin.Traditional human resource management focus on person-position fit.People carry out a large number of person-position fit study and apply it to practice. In recent years, researchers begin to realize, in order to achieve the ideal effect, not only ...
Fitting of Parametric Building Models to Oblique Aerial Images
Panday, U. S.; Gerke, M.
2011-09-01
In literature and in photogrammetric workstations many approaches and systems to automatically reconstruct buildings from remote sensing data are described and available. Those building models are being used for instance in city modeling or in cadastre context. If a roof overhang is present, the building walls cannot be estimated correctly from nadir-view aerial images or airborne laser scanning (ALS) data. This leads to inconsistent building outlines, which has a negative influence on visual impression, but more seriously also represents a wrong legal boundary in the cadaster. Oblique aerial images as opposed to nadir-view images reveal greater detail, enabling to see different views of an object taken from different directions. Building walls are visible from oblique images directly and those images are used for automated roof overhang estimation in this research. A fitting algorithm is employed to find roof parameters of simple buildings. It uses a least squares algorithm to fit projected wire frames to their corresponding edge lines extracted from the images. Self-occlusion is detected based on intersection result of viewing ray and the planes formed by the building whereas occlusion from other objects is detected using an ALS point cloud. Overhang and ground height are obtained by sweeping vertical and horizontal planes respectively. Experimental results are verified with high resolution ortho-images, field survey, and ALS data. Planimetric accuracy of 1cm mean and 5cm standard deviation was obtained, while buildings' orientation were accurate to mean of 0.23° and standard deviation of 0.96° with ortho-image. Overhang parameters were aligned to approximately 10cm with field survey. The ground and roof heights were accurate to mean of - 9cm and 8cm with standard deviations of 16cm and 8cm with ALS respectively. The developed approach reconstructs 3D building models well in cases of sufficient texture. More images should be acquired for completeness of
Mouse models of colorectal cancer
Yunguang Tong; Wancai Yang; H. Phillip Koeffler
2011-01-01
Colorectal cancer is one of the most common malignancies in the world. Many mouse models have been developed to evaluate features of colorectal cancer in humans. These can be grouped into genetically-engineered, chemically-induced, and inoculated models. However, none recapitulates all of the characteristics of human colorectal cancer. It is critical to use a specific mouse model to address a particular research question. Here, we review commonly used mouse models for human colorectal cancer.
Nguyen, Alexander; Yoshida, Mitsukuni; Goodarzi, Hani; Tavazoie, Sohail F
2016-01-01
Individual cells within a tumour can exhibit distinct genetic and molecular features. The impact of such diversification on metastatic potential is unknown. Here we identify clonal human breast cancer subpopulations that display different levels of morphological and molecular diversity. Highly variable subpopulations are more proficient at metastatic colonization and chemotherapeutic survival. Through single-cell RNA-sequencing, inter-cell transcript expression variability is identified as a defining feature of the highly variable subpopulations that leads to protein-level variation. Furthermore, we identify high variability in the spliceosomal machinery gene set. Engineered variable expression of the spliceosomal gene SNRNP40 promotes metastasis, attributable to cells with low expression. Clinically, low SNRNP40 expression is associated with metastatic relapse. Our findings reveal transcriptomic variability generation as a mechanism by which cancer subpopulations can diversify gene expression states, which may allow for enhanced fitness under changing environmental pressures encountered during cancer progression. PMID:27138336
Rogers, Laura Q.; Courneya, Kerry S.; Anton, Philip M.; Hopkins-Price, Patricia; Verhulst, Steven; Vicari, Sandra K.; Robbs, Randall S.; Mocharnuk, Robert; McAuley, Edward
2014-01-01
Most breast cancer survivors (BCS) are not meeting recommended physical activity guidelines. Here, we report the effects of the Better Exercise Adherence after Treatment for Cancer (BEAT Cancer) behavior change intervention on physical activity, aerobic fitness, and quality of life (QoL). We randomized 222 post-primary treatment BCS to the 3-month intervention (BEAT Cancer) or usual care (UC). BEAT Cancer combined supervised exercise, face-to-face counseling, and group discussions with taperi...
Melbourne, Andrew; Toussaint, Nicolas; Owen, David; Simpson, Ivor; Anthopoulos, Thanasis; De Vita, Enrico; Atkinson, David; Ourselin, Sebastien
2016-07-01
Multi-modal, multi-parametric Magnetic Resonance (MR) Imaging is becoming an increasingly sophisticated tool for neuroimaging. The relationships between parameters estimated from different individual MR modalities have the potential to transform our understanding of brain function, structure, development and disease. This article describes a new software package for such multi-contrast Magnetic Resonance Imaging that provides a unified model-fitting framework. We describe model-fitting functionality for Arterial Spin Labeled MRI, T1 Relaxometry, T2 relaxometry and Diffusion Weighted imaging, providing command line documentation to generate the figures in the manuscript. Software and data (using the nifti file format) used in this article are simultaneously provided for download. We also present some extended applications of the joint model fitting framework applied to diffusion weighted imaging and T2 relaxometry, in order to both improve parameter estimation in these models and generate new parameters that link different MR modalities. NiftyFit is intended as a clear and open-source educational release so that the user may adapt and develop their own functionality as they require. PMID:26972806
Animal Models of Colorectal Cancer
Johnson, Robert L.; Fleet, James C.
2012-01-01
Colorectal cancer is a heterogeneous disease that afflicts a large number of people in the United States. The use of animal models has the potential to increase our understanding of carcinogenesis, tumor biology, and the impact of specific molecular events on colon biology. In addition, animal models with features of specific human colorectal cancers can be used to test strategies for cancer prevention and treatment. In this review we provide an overview of the mechanisms driving human cancer, we discuss the approaches one can take to model colon cancer in animals, and we describe a number of specific animal models that have been developed for the study of colon cancer. We believe that there are many valuable animal models to study various aspects of human colorectal cancer. However, opportunities for improving upon these models exist. PMID:23076650
Wang, Qiaosong; Jagadeesh, Vignesh; Ressler, Bryan; Piramuthu, Robinson
2014-01-01
Recent advances in consumer depth sensors have created many opportunities for human body measurement and modeling. Estimation of 3D body shape is particularly useful for fashion e-commerce applications such as virtual try-on or fit personalization. In this paper, we propose a method for capturing accurate human body shape and anthropometrics from a single consumer grade depth sensor. We first generate a large dataset of synthetic 3D human body models using real-world body size distributions. ...
A New Finite Interval Lifetime Distribution Model for Fitting Bathtub-Shaped Failure Rate Curve
2015-01-01
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 m...
Lazarević Ljiljana
2008-01-01
This paper deals with the fit indices used in Structural Equation Modelling (SEM) for testing theoretical models and the difficulties that can occur during the testing of theoretical models in different fields of psychology. The paper discusses the basic assumptions of SEM and presents the indices used for assessing the fit of theoretical models. This paper also presents the procedures for calculating the basic statistic for assessing the fit of models (χ2), as well as for calculating the mos...
A versatile curve-fit model for linear to deeply concave rank abundance curves
Neuteboom, J.H.; Struik, P.C.
2005-01-01
A new, flexible curve-fit model for linear to concave rank abundance curves was conceptualized and validated using observational data. The model links the geometric-series model and log-series model and can also fit deeply concave rank abundance curves. The model is based ¿ in an unconventional way
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
Engineered Swine Models of Cancer.
Watson, Adrienne L; Carlson, Daniel F; Largaespada, David A; Hackett, Perry B; Fahrenkrug, Scott C
2016-01-01
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. PMID:27242889
Engineered Swine Models of Cancer
Watson, Adrienne L.; Carlson, Daniel F.; Largaespada, David A.; Hackett, Perry B.; Fahrenkrug, Scott C.
2016-01-01
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.
Engineered Swine Models of Cancer
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.
Tøndel, Kristin; Niederer, Steven A.; Land, Sander; Smith, Nicolas P
2014-01-01
Background Striking a balance between the degree of model complexity and parameter identifiability, while still producing biologically feasible simulations using modelling is a major challenge in computational biology. While these two elements of model development are closely coupled, parameter fitting from measured data and analysis of model mechanisms have traditionally been performed separately and sequentially. This process produces potential mismatches between model and data complexities...
NUTRITION AND FITNESS (PART 1: OBESITY, THE METABOLIC SYNDROME, CARDIOVASCULAR DISEASE, AND CANCER
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
MASSONNET, Goele; Janssen, Paul; Burzykowski, Tomasz
2008-01-01
Frailty models are widely used to model clustered survival data. Classical ways to fit frailty models are likelihood-based. We propose an alternative approach in which the original problem of "fitting a frailty model" is reformulated into the problem of "fitting a linear mixed model" using model transformation. We show that the transformation idea also works for multivariate proportional odds models and for multivariate additive risks models. It therefore bridges segregated methodologies as i...
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…
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…
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…
Mouse models for cancer research
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.
Accumulation and modeling of particles in drinking water pipe fittings
K. Neilands; M. Bernats; J. Rubulis
2012-01-01
The effect of pipe fittings (mainly T-pieces) on particle accumulation in drinking water distribution networks were shown in this work. The online measurements of flow and turbidity for cast iron, polyethylene and polyvinyl chloride pipe sections were linked with analysis of pipe geometry. Up to 0.29 kg of the total amount mobilized in T-pieces ranging from DN 100/100–DN 250/250. The accumulated amount of particles in fittings was defined as J and introduced into ...
Among the many models developed for monitoring the infiltration process those of Philip and Kostiakov have been studied in detail because of their simplicity and the ease of estimating their fitting parameters. The important soil physical factors influencing the fitting parameters in these infiltration models are reported in this study. The results of the study show that the single most important soil property affecting the fitting parameters in these models is the effective porosity. 36 refs, 2 figs, 5 tabs
Multiplex networks with intrinsic fitness: Modeling the merit-fame interplay via latent layers
Fotouhi, Babak; Momeni, Naghmeh
2015-11-01
We consider the problem of growing multiplex networks with intrinsic fitness and inter-layer coupling. The model comprises two layers; one that incorporates fitness and another in which attachments are preferential. In the first layer, attachment probabilities are proportional to fitness values, and in the second layer, proportional to the sum of degrees in both layers. We provide analytical closed-form solutions for the joint distributions of fitness and degrees. We also derive closed-form expressions for the expected value of the degree as a function of fitness. The model alleviates two shortcomings that are present in the current models of growing multiplex networks: homogeneity of connections, and homogeneity of fitness. In this paper, we posit and analyze a growth model that is heterogeneous in both senses.
Revisiting the Global Electroweak Fit of the Standard Model and Beyond with Gfitter
Flächer, Henning; Haller, J; Höcker, A; Mönig, K; Stelzer, J
2009-01-01
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 projec...
Numerical model of induction shrink fits in monolithic formulation
Karban, P.; Kotlan, V.; Doležel, Ivo
Sydney: International COMPUMAG Society, 2011, s. 1-2. ISBN -. [International Conference on the Computation of Electromagnetic s Fields /18./. Sydney (AU), 12.07.2011-15.07.2011] R&D Projects: GA ČR(CZ) GAP102/11/0498; GA AV ČR IAA100760702 Institutional research plan: CEZ:AV0Z20570509 Keywords : coupled problem * shrink fit * magnetic field Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering http://www.compumag2011.com/
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...
Model-fitting approach to kinetic analysis of non-isothermal oxidation of molybdenite
The kinetics of molybdenite oxidation was studied by non-isothermal TGA-DTA with heating rate 5degC.min-1. The model-fitting kinetic approach applied to TGA data. The Coats-Redfern method used of model fitting. The popular model-fitting gives excellent fit non-isothermal data in chemically controlled regime. The apparent activation energy was determined to be about 34.2 kcalmol-1 With pre-exponential factor about 108 sec-1 for extent of reaction less than 0.5
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.
Model Fitting for Predicted Precipitation in Darwin: Some Issues with Model Choice
Farmer, Jim
2010-01-01
In Volume 23(2) of the "Australian Senior Mathematics Journal," Boncek and Harden present an exercise in fitting a Markov chain model to rainfall data for Darwin Airport (Boncek & Harden, 2009). Days are subdivided into those with precipitation and precipitation-free days. The author abbreviates these labels to wet days and dry days. It is…
A New Finite Interval Lifetime Distribution Model for Fitting Bathtub-Shaped Failure Rate Curve
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.
The Model 80 face mask fit-tester
Pasternack, A.
1978-06-01
Leakage from a face mask can be measured quantitatively using ethylene or sulphur hexafluoride as tracer gas. Either ethylene detector tubes or a leak detector are used with a hood which fits over the head of the person tested. The tester can be used for training, to give the wearer a feeling for the correct placement of the face mask and for tightening the straps. It can also be used to check tightness when the hair-style or shape of beard is changed.
TRANSIT MODEL FITTING IN THE KEPLER SCIENCE OPERATIONS CENTER PIPELINE: NEW FEATURES AND PERFORMANCE
Li, Jie; Burke, C. J.; Jenkins, J. M.; Quintana, E. V.; Rowe, J. F.; Seader, S. E.; Tenenbaum, P.; Twicken, J. D.
2013-10-01
We describe new transit model fitting features and performance of the latest release (9.1, July 2013) of the Kepler Science Operations Center (SOC) Pipeline. The targets for which a Threshold Crossing Event (TCE) is generated in the Transiting Planet Search (TPS) component of the pipeline are subsequently processed in the Data Validation (DV) component. Transit model parameters are fitted in DV to transit-like signatures in the light curves of the targets with TCEs. The transit model fitting results are used in diagnostic tests in DV, which help to validate planet candidates and identify false positive detections. The standard transit model includes five fit parameters: transit epoch time (i.e. central time of first transit), orbital period, impact parameter, ratio of planet radius to star radius and ratio of semi-major axis to star radius. Light curves for many targets do not contain enough information to uniquely determine the impact parameter, which results in poor convergence performance of the fitter. In the latest release of the Kepler SOC pipeline, a reduced parameter fit is included in DV: the impact parameter is set to a fixed value and the four remaining parameters are fitted. The standard transit model fit is implemented after a series of reduced parameter fits in which the impact parameter is varied between 0 and 1. Initial values for the standard transit model fit parameters are determined by the reduced parameter fit with the minimum chi-square metric. With reduced parameter fits, the robustness of the transit model fit is improved significantly. Diagnostic plots of the chi-square metrics and reduced parameter fit results illustrate how the fitted parameters vary as a function of impact parameter. Essentially, a family of transiting planet characteristics is determined in DV for each Pipeline TCE. Transit model fitting performance of release 9.1 of the Kepler SOC pipeline is demonstrated with the results of the processing of 16 quarters of flight data
Mumtaz Ali Memon; Rohani Salleh; Mohamed Noor Rosli Baharom
2014-01-01
Past studies revealed that the existence of congruence between employees and their job as well as organisation produces more favourable attitudes and behaviours. Although considerable research has been conducted on the person-job (P-J) fit and person-organization (P-O) fit, an in depth review of the literature identifies several research gaps. First, studies have largely focused on examining P-J fit and P-O fit separately. In addition, the relationship of P-J fit and P-O fit, and employee eng...
Why the Bass Model Fits without Decision Variables
Frank M. Bass; Trichy V. Krishnan; Dipak C. Jain
1994-01-01
Over a large number of new products and technological innovations, the Bass diffusion model (Bass 1969) describes the empirical adoption curve quite well. In this study, we generalize the Bass model to include decision variables such as price and advertising. The generalized model reduces to the Bass model as a special case and explains why the Bass model works so well without including decision variables. We compare our generalized Bass model to other approaches from the literature for inclu...
Mead, A. J.; Peacock, J. A.; Heymans, C.; Joudaki, S.; Heavens, A. F.
2015-12-01
We present an optimized variant of the halo model, designed to produce accurate matter power spectra well into the non-linear regime for a wide range of cosmological models. To do this, we introduce physically motivated free parameters into the halo-model formalism and fit these to data from high-resolution N-body simulations. For a variety of Λ cold dark matter (ΛCDM) and wCDM models, the halo-model power is accurate to ≃ 5 per cent for k ≤ 10h Mpc-1 and z ≤ 2. An advantage of our new halo model is that it can be adapted to account for the effects of baryonic feedback on the power spectrum. We demonstrate this by fitting the halo model to power spectra from the OWLS (OverWhelmingly Large Simulations) hydrodynamical simulation suite via parameters that govern halo internal structure. We are able to fit all feedback models investigated at the 5 per cent level using only two free parameters, and we place limits on the range of these halo parameters for feedback models investigated by the OWLS simulations. Accurate predictions to high k are vital for weak-lensing surveys, and these halo parameters could be considered nuisance parameters to marginalize over in future analyses to mitigate uncertainty regarding the details of feedback. Finally, we investigate how lensing observables predicted by our model compare to those from simulations and from HALOFIT for a range of k-cuts and feedback models and quantify the angular scales at which these effects become important. Code to calculate power spectra from the model presented in this paper can be found at https://github.com/alexander-mead/hmcode.
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.
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.
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.
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.
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.
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.
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.
Envelope: interactive software for modeling and fitting complex isotope distributions
Sykes Michael T
2008-10-01
Full Text Available Abstract Background An important aspect of proteomic mass spectrometry involves quantifying and interpreting the isotope distributions arising from mixtures of macromolecules with different isotope labeling patterns. These patterns can be quite complex, in particular with in vivo metabolic labeling experiments producing fractional atomic labeling or fractional residue labeling of peptides or other macromolecules. In general, it can be difficult to distinguish the contributions of species with different labeling patterns to an experimental spectrum and difficult to calculate a theoretical isotope distribution to fit such data. There is a need for interactive and user-friendly software that can calculate and fit the entire isotope distribution of a complex mixture while comparing these calculations with experimental data and extracting the contributions from the differently labeled species. Results Envelope has been developed to be user-friendly while still being as flexible and powerful as possible. Envelope can simultaneously calculate the isotope distributions for any number of different labeling patterns for a given peptide or oligonucleotide, while automatically summing these into a single overall isotope distribution. Envelope can handle fractional or complete atom or residue-based labeling, and the contribution from each different user-defined labeling pattern is clearly illustrated in the interactive display and is individually adjustable. At present, Envelope supports labeling with 2H, 13C, and 15N, and supports adjustments for baseline correction, an instrument accuracy offset in the m/z domain, and peak width. Furthermore, Envelope can display experimental data superimposed on calculated isotope distributions, and calculate a least-squares goodness of fit between the two. All of this information is displayed on the screen in a single graphical user interface. Envelope supports high-quality output of experimental and calculated
Gfitter - Revisiting the global electroweak fit of the Standard Model and beyond
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 MH=116.4+18.3-1.3 GeV, and the 2σ and 3σ allowed regions [114,145] GeV and [[113,168] and [180,225
Target Fitting and Robustness Analysis in CGE Models
Gabriel Garber; Haddad, Eduardo A.
2012-01-01
This paper proposes a methodology to integrate econometric models with Johansen-type computable general equilibrium (CGE) models in instances when it is necessary to generate results consistent with a subset of variables that are endogenous to both models. Results for a subset of the CGE endogenous variables are generated from econometric models, and set as targets to be replicated by the CGE model. The methodology is further extended for robustness testing of the outcomes in cases which the ...
The issue of statistical power for overall model fit in evaluating structural equation models
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.
Checking the Adequacy of Fit of Models from Split-Plot Designs
Almini, A. A.; Kulahci, Murat; Montgomery, D. C.
2009-01-01
-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...
Lazarević Ljiljana
2008-01-01
Full Text Available This paper deals with the fit indices used in Structural Equation Modelling (SEM for testing theoretical models and the difficulties that can occur during the testing of theoretical models in different fields of psychology. The paper discusses the basic assumptions of SEM and presents the indices used for assessing the fit of theoretical models. This paper also presents the procedures for calculating the basic statistic for assessing the fit of models (χ2, as well as for calculating the most commonly used fit indices, in order to gain a better insight into the advantages and potential difficulties that can occur during their usage. We mention the difficulties regarding the assessment of fit of the model based on χ2 and the discussed fit indices stemming from the sample size, data distribution and assessment methods, wrong specification of model and disturbance of normality and independence of latent variables, as well as the ways in which these difficulties can be overcome. This paper provides a proposal for the approach to presenting the fit indices in reports on studies where theoretical models were tested via SEM.
Gompertzian stochastic model with delay effect to cervical cancer growth
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 [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.
A Gompertzian model with random effects to cervical cancer growth
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.
Flexible competing risks regression modeling and goodness-of-fit
Thomas H. Scheike; Zhang, Mei-Jie
2008-01-01
In this paper we consider different approaches for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause-specific hazards. Another recent approach is to directly model the cumulative incidence by a proportional model (Fine and Gray, J Am Stat Assoc 94:496-509, 1999), and then obtain direct estimates of how c...
Engineered Swine Models of Cancer
Watson, Adrienne L; Carlson, Daniel F.; Largaespada, David A; Hackett, Perry B; Fahrenkrug, Scott C.
2016-01-01
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...
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
Lee, Young-Sun; Wollack, James A.; Douglas, Jeffrey
2009-01-01
The purpose of this study was to assess the model fit of a 2PL through comparison with the nonparametric item characteristic curve (ICC) estimation procedures. Results indicate that three nonparametric procedures implemented produced ICCs that are similar to that of the 2PL for items simulated to fit the 2PL. However for misfitting items,…
Diploid biological evolution models with general smooth fitness landscapes and recombination.
Saakian, David B; Kirakosyan, Zara; Hu, Chin-Kun
2008-06-01
Using a Hamilton-Jacobi equation approach, we obtain analytic equations for steady-state population distributions and mean fitness functions for Crow-Kimura and Eigen-type diploid biological evolution models with general smooth hypergeometric fitness landscapes. Our numerical solutions of diploid biological evolution models confirm the analytic equations obtained. We also study the parallel diploid model for the simple case of recombination and calculate the variance of distribution, which is consistent with numerical results. PMID:18643300
Ideland, Malin; Andrée, Maria; Arvola-Orlander, Auli; Hillbur, Per; Jobér, Anna; Lundegård, Iann; Loken, Marianne; Malmberg, Claes; Serder, Margareta
2012-01-01
This mini-symposium aims to stress issues about how pedagogical models like e.g. SSI, science for girls and ESD construct who fits in or not in the science classroom. These models are developed from a good intention of including "all" students, opening up possibilities for them who often are seen as outsiders in science culture. But we claim that these seemingly democratic pedagogical models fabricate desirable and undesirable subjects. Often, the norms for fitting in can be understood in ter...
Tengiz Mdzinarishvili
Full Text Available At present, carcinogenic models imply that all individuals in a population are susceptible to cancer. These models either ignore a fall of the cancer incidence rate at old ages, or use some poorly identifiable parameters for its accounting. In this work, a new heuristic model is proposed. The model assumes that, in a population, only a small fraction (pool of individuals is susceptible to cancer and decomposes the problem of the carcinogenic modeling on two sequentially solvable problems: (i determination of the age-specific hazard rate in individuals susceptible to cancer (individual hazard rate from the observed hazard rate in the population (population hazard rate; and (ii modelling of the individual hazard rate by a chosen "up" of the theoretical hazard function describing cancer occurrence in individuals in time (age. The model considers carcinogenesis as a failure of individuals susceptible to cancer to resist cancer occurrence in aging and uses, as the theoretical hazard function, the three-parameter Weibull hazard function, often utilized in a failure analysis. The parameters of this function, providing the best fit of the modeled and observed individual hazard rates (determined from the population hazard rates, are the outcomes of the modeling. The model was applied to the pancreatic cancer data. It was shown that, in the populations stratified by gender, race and the geographic area of living, the modeled and observed population hazard rates of pancreatic cancer occurrence have similar turnovers at old ages. The sizes of the pools of individuals susceptible to this cancer: (i depend on gender, race and the geographic area of living; (ii proportionally influence the corresponding population hazard rates; and (iii do not influence the individual hazard rates. The model should be further tested using data on other types of cancer and for the populations stratified by different categorical variables.
A No-Scale Inflationary Model to Fit Them All
Ellis, John; Nanopoulos, Dimitri; Olive, Keith
2014-01-01
The magnitude of B-mode polarization in the cosmic microwave background as measured by BICEP2 favours models of chaotic inflation with a quadratic $m^2 \\phi^2/2$ 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 $R + R^2$ 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 $n_s \\simeq 0.96$.
Fitting Equilibrium Search Models to Labour Market Data
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...
Fitting vast dimensional time-varying covariance models
2008-01-01
Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel and fast way of estimating models of time-varying covariances that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied to hundreds or even thousands of assets. Indeed we can handle the case where the cross-sectional dimension is larger than the time series one. The theory of this new strategy is developed in some detail...
Use of genetic algorithm for fitting Sovova's mass transfer model:
Hrnčič, Dejan; Mernik, Marjan; Knez Hrnčič, Maša
2010-01-01
A genetic algorithm with resizable population has been applied to the estimation of parameters for Sovovaćs mass transfer model. The comparison of results between a genetic algorithm and a global optimizer from the literatureshows that a genetic algorithm performs as good as or better than a global optimizer on a given set of problems. Other benefits of the genetic algorithm, for mass transfer modeling, are simplicity, robustness and efficiency.
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.
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.
A versatile curve-fit model for linear to deeply concave rank abundance curves
Neuteboom, J.H.; Struik, P. C.
2005-01-01
A new, flexible curve-fit model for linear to concave rank abundance curves was conceptualized and validated using observational data. The model links the geometric-series model and log-series model and can also fit deeply concave rank abundance curves. The model is based ¿ in an unconventional way ¿ on the negative- binomial distribution and calculates (like the log-series model) a species-diversity index. The index is defined as the expected number of singleton species (species present with...
Fit of different linear models to the lactation curve of Italian water buffalo
N. P.P. Macciotta; N. Bacciu; Catillo, G; C. Dimauro
2005-01-01
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. Average lactation curves of Italian Buffalo cows have been fitted with good results (Catillo et al., 2002) whereas there is a lack of information on ...
The empirical likelihood goodness-of-fit test for regression model
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.
Mouse models for colorectal cancer
KARIM, BAKTIAR O.; Huso, David L.
2013-01-01
Colorectal cancer (CRC) is the third leading cause of cancer-related death in the United States, with the number of affected people increasing. There are many risk factors that increase CRC risk, including family or personal history of CRC, smoking, consumption of red meat, obesity, and alcohol consumption. Conversely, increased screening, maintaining healthy body weight, not smoking, and limiting intake of red meat are all associated with reduced CRC morbidity and mortality. Mouse models of ...
Fitting the Balding-Nichols model to forensic databases.
Rohlfs, Rori V; Aguiar, Vitor R C; Lohmueller, Kirk E; Castro, Amanda M; Ferreira, Alessandro C S; Almeida, Vanessa C O; Louro, Iuri D; Nielsen, Rasmus
2015-11-01
Large forensic databases provide an opportunity to compare observed empirical rates of genotype matching with those expected under forensic genetic models. A number of researchers have taken advantage of this opportunity to validate some forensic genetic approaches, particularly to ensure that estimated rates of genotype matching between unrelated individuals are indeed slight overestimates of those observed. However, these studies have also revealed systematic error trends in genotype probability estimates. In this analysis, we investigate these error trends and show how they result from inappropriate implementation of the Balding-Nichols model in the context of database-wide matching. Specifically, we show that in addition to accounting for increased allelic matching between individuals with recent shared ancestry, studies must account for relatively decreased allelic matching between individuals with more ancient shared ancestry. PMID:26186694
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.
Fitting dynamic fator models to nonstationary time series
Eichler, M.; Motta, Giovanni; Von Sachs, Rainer
2008-01-01
Factor modelling of a large time series panel has widely proven useful to reduce its cross-sectional dimensionality. This is done by explaining common co-movements in the panel through the existence of a small number of common components, up to some idiosyncratic behaviour of each individual series. To capture serial correlation in the common components, a dynamic structure is used as in traditional (uni- or multivariate) time series analysis of second order structure,i.e. allowing f...
Fitting dynamic factor models to non-stationary time series
Eichler Michael; Motta Giovanni; Sachs Rainer von
2009-01-01
Factor modelling of a large time series panel has widely proven useful to reduce its cross-sectional dimensionality. This is done by explaining common co-movements in the panel through the existence of a small number of common components, up to some idiosyncratic behaviour of each individual series. To capture serial correlation in the common components, a dynamic structure is used as in traditional (uni- or multivariate) time series analysis of second order structure, i.e. allowing for infin...
CPOPT : optimization for fitting CANDECOMP/PARAFAC models.
Dunlavy, Daniel M.; Kolda, Tamara Gibson; Acar, Evrim
2008-10-01
Tensor decompositions (e.g., higher-order analogues of matrix decompositions) are powerful tools for data analysis. In particular, the CANDECOMP/PARAFAC (CP) model has proved useful in many applications such chemometrics, signal processing, and web analysis; see for details. The problem of computing the CP decomposition is typically solved using an alternating least squares (ALS) approach. We discuss the use of optimization-based algorithms for CP, including how to efficiently compute the derivatives necessary for the optimization methods. Numerical studies highlight the positive features of our CPOPT algorithms, as compared with ALS and Gauss-Newton approaches.
Model-independent analysis of dark energy: supernova fitting result
This paper uses supernova data to explore the property of dark energy by some model-independent methods. We first Taylor expand the scale factor a(t) and the luminosity distance dL to the fifth order to find that the deceleration parameter q0 DE0 is less than -1 almost at 1σ level from all the parametrizations used in this paper. We also find that the transition redshift from deceleration phase to acceleration phase is zT ∼ 0.3
Haslinger, Robert; Pipa, Gordon; Brown, Emery
2010-10-01
One approach for understanding the encoding of information by spike trains is to fit statistical models and then test their goodness of fit. The time-rescaling theorem provides a goodness-of-fit test consistent with the point process nature of spike trains. The interspike intervals (ISIs) are rescaled (as a function of the model's spike probability) to be independent and exponentially distributed if the model is accurate. A Kolmogorov-Smirnov (KS) test between the rescaled ISIs and the exponential distribution is then used to check goodness of fit. This rescaling relies on assumptions of continuously defined time and instantaneous events. However, spikes have finite width, and statistical models of spike trains almost always discretize time into bins. Here we demonstrate that finite temporal resolution of discrete time models prevents their rescaled ISIs from being exponentially distributed. Poor goodness of fit may be erroneously indicated even if the model is exactly correct. We present two adaptations of the time-rescaling theorem to discrete time models. In the first we propose that instead of assuming the rescaled times to be exponential, the reference distribution be estimated through direct simulation by the fitted model. In the second, we prove a discrete time version of the time-rescaling theorem that analytically corrects for the effects of finite resolution. This allows us to define a rescaled time that is exponentially distributed, even at arbitrary temporal discretizations. We demonstrate the efficacy of both techniques by fitting generalized linear models to both simulated spike trains and spike trains recorded experimentally in monkey V1 cortex. Both techniques give nearly identical results, reducing the false-positive rate of the KS test and greatly increasing the reliability of model evaluation based on the time-rescaling theorem. PMID:20608868
Structural model of in-group dynamic of 6-10 years old boys’ motor fitness
Ivashchenko O.V.
2015-10-01
Full Text Available Purpose: to determine structural model of in-group dynamic of 6-10 years old boys’ motor fitness. Material: in the research 6 years old boys (n=48, 7 years old (n=45, 8 years old (n=60, 9 years’ age (n=47 and10 years’ age (n=40 participated. We carried out analysis of factorial model of schoolchildren’s motor fitness. Results: we received information for taking decisions in monitoring of physical education. This information is also necessary for working out of effective programs of children’s and adolescents’ physical training. We determined model of motor fitness and specified informative tests for pedagogic control in every age group. In factorial model of boys’ motor fitness the following factor is the most significant: for 6 years - complex development of motor skills; for 7 years - also complex development of motor skills; for 8 years - strength and coordination; for 9 years - complex development of motor skills; for 10 years - complex development of motor skills. Conclusions: In factorial model of 6-10 years old boys’ motor fitness the most significant are backbone and shoulder joints’ mobility, complex manifestation of motor skills, motor coordination. The most informative tests for assessment of different age boys’ motor fitness have been determined.
Marsh, Rebeccah E; Riauka, Terence A; McQuarrie, Steve A
2007-01-01
Increasingly, fractals are being incorporated into pharmacokinetic models to describe transport and chemical kinetic processes occurring in confined and heterogeneous spaces. However, fractal compartmental models lead to differential equations with power-law time-dependent kinetic rate coefficients that currently are not accommodated by common commercial software programs. This paper describes a parameter optimization method for fitting individual pharmacokinetic curves based on a simulated annealing (SA) algorithm, which always converged towards the global minimum and was independent of the initial parameter values and parameter bounds. In a comparison using a classical compartmental model, similar fits by the Gauss-Newton and Nelder-Mead simplex algorithms required stringent initial estimates and ranges for the model parameters. The SA algorithm is ideal for fitting a wide variety of pharmacokinetic models to clinical data, especially those for which there is weak prior knowledge of the parameter values, such as the fractal models. PMID:17706176
Nonlinear models for fitting growth curves of Nellore cows reared in the Amazon Biome
Kedma Nayra da Silva Marinho
2013-09-01
Full Text Available Growth curves of Nellore cows were estimated by comparing six nonlinear models: Brody, Logistic, two alternatives by Gompertz, Richards and Von Bertalanffy. The models were fitted to weight-age data, from birth to 750 days of age of 29,221 cows, born between 1976 and 2006 in the Brazilian states of Acre, Amapá, Amazonas, Pará, Rondônia, Roraima and Tocantins. The models were fitted by the Gauss-Newton method. The goodness of fit of the models was evaluated by using mean square error, adjusted coefficient of determination, prediction error and mean absolute error. Biological interpretation of parameters was accomplished by plotting estimated weights versus the observed weight means, instantaneous growth rate, absolute maturity rate, relative instantaneous growth rate, inflection point and magnitude of the parameters A (asymptotic weight and K (maturing rate. The Brody and Von Bertalanffy models fitted the weight-age data but the other models did not. The average weight (A and growth rate (K were: 384.6±1.63 kg and 0.0022±0.00002 (Brody and 313.40±0.70 kg and 0.0045±0.00002 (Von Bertalanffy. The Brody model provides better goodness of fit than the Von Bertalanffy model.
Generalisations of the two-mutation carcinogenesis model of Moolgavkar, Venzon and Knudson (to allow for an arbitrary number of mutational stages), and of the model of Armitage and Doll, are fitted to the Japanese atomic bomb survivor mortality data. Models with two or three mutations give adequate descriptions of the excess mortality of solid cancers. For leukaemia the fit of the three-mutation model is preferable to that of the two-mutation model. The optimal three-mutation leukaemia model provides a satisfactory fit only when both first and second mutation rates are radiation-affected. Examination of other epidemiological data leads to the conclusion that without some extra stochastic 'stage' appended (such as might be provided by consideration of the process of development of a malignant clone from a single malignant cell) the two-mutation model is perhaps not well able to describe the pattern of excess risk for solid cancers that is often seen after exposure to radiation. The optimal three-mutation models predict low-dose population risks for a current UK population of 5.5-8.0 x 10-2 excess cancer deaths Sv-1, 6.8-9.8 x 10-2 radiation-induced cancer deaths Sv-1 or 1.0-1.4 years of life lost Sv-1. Risks for a current Japanese population are 6.8 x 10-2 excess cancer deaths Sv-1, 8.0 x 10-2 radiation-induced cancer deaths Sv-1, or 1.2 years of life lost Sv-1. (author)
Nielsen, Karen L.; Pedersen, Thomas M.; Udekwu, Klas I.;
2012-01-01
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...... of each isolate was determined in a growth competition assay with a reference isolate. Significant fitness costs of 215 were determined for the MRSA isolates studied. There was a significant negative correlation between number of antibiotic resistances and relative fitness. Multiple regression analysis...... found significantly independent negative correlations between fitness and the presence of mecA or streptomycin resistance. Mathematical modelling confirmed that fitness costs of the magnitude carried by these isolates could result in the disappearance of MRSA prevalence during a time span similar...
Asymptotic Fitness Distribution in the Bak-Sneppen Model of Biological Evolution with Four Species
Schlemm, Eckhard
2012-08-01
We suggest a new method to compute the asymptotic fitness distribution in the Bak-Sneppen model of biological evolution. As applications we derive the full asymptotic distribution in the four-species model, and give an explicit linear recurrence relation for a set of coefficients determining the asymptotic distribution in the five-species model.
Asymptotic fitness distribution in the Bak-Sneppen model of biological evolution with four species
Schlemm, Eckhard
2012-01-01
We suggest a new method to compute the asymptotic fitness distribution in the Bak-Sneppen model of biological evolution. As applications we derive the full asymptotic distribution in the four-species model, and give an explicit linear recurrence relation for a set of coefficients determining the asymptotic distribution in the five-species model.
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.
In inspecting the detailed performance results of surface precision modeling in different external parameter conditions, the integrated chip surfaces should be evaluated and assessed during topographic spatial modeling processes. The application of surface fitting algorithms exerts a considerable influence on topographic mathematical features. The influence mechanisms caused by different surface fitting algorithms on the integrated chip surface facilitate the quantitative analysis of different external parameter conditions. By extracting the coordinate information from the selected physical control points and using a set of precise spatial coordinate measuring apparatus, several typical surface fitting algorithms are used for constructing micro topographic models with the obtained point cloud. In computing for the newly proposed mathematical features on surface models, we construct the fuzzy evaluating data sequence and present a new three dimensional fuzzy quantitative evaluating method. Through this method, the value variation tendencies of topographic features can be clearly quantified. The fuzzy influence discipline among different surface fitting algorithms, topography spatial features, and the external science parameter conditions can be analyzed quantitatively and in detail. In addition, quantitative analysis can provide final conclusions on the inherent influence mechanism and internal mathematical relation in the performance results of different surface fitting algorithms, topographic spatial features, and their scientific parameter conditions in the case of surface micro modeling. The performance inspection of surface precision modeling will be facilitated and optimized as a new research idea for micro-surface reconstruction that will be monitored in a modeling process
An examination of disparities in cancer incidence in Texas using Bayesian random coefficient models
Sparks, Corey
2015-01-01
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 B...
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...
Efficient Constrained Local Model Fitting for Non-Rigid Face Alignment
Lucey, Simon; Wang, Yang; Cox, Mark; Sridharan, Sridha; Cohn, Jeffery F.
2009-01-01
Active appearance models (AAMs) have demonstrated great utility when being employed for non-rigid face alignment/tracking. The “simultaneous” algorithm for fitting an AAM achieves good non-rigid face registration performance, but has poor real time performance (2-3 fps). The “project-out” algorithm for fitting an AAM achieves faster than real time performance (> 200 fps) but suffers from poor generic alignment performance. In this paper we introduce an extension to a discriminative method for...
Training evaluation models: The experience of the European project ADAPT-FIT
V. Carbone; MORVILLO,A
2003-01-01
This article illustrates the experience gained in relation to training evaluation models at the institute for services industry research (IRAT) of the national research council of Italy and the university of Naples 'Parthenope', through an intensive training and research activity implemented as part of the transnational integrated training project (FIT - formazione integrata transnazionale). The fit project, funded by the European programme adapt, is a joint action implemented by the academic...
Optimisation of Ionic Models to Fit Tissue Action Potentials: Application to 3D Atrial Modelling
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.
Use of posterior predictive assessments to evaluate model fit in multilevel logistic regression
Green, Martin J.; Medley, Graham F; Browne, William J.
2009-01-01
Assessing the fit of a model is an important final step in any statistical analysis, but this is not straightforward when complex discrete response models are used. Cross validation and posterior predictions have been suggested as methods to aid model criticism. In this paper a comparison is made between four methods of model predictive assessment in the context of a three level logistic regression model for clinical mastitis in dairy cattle; cross validation, a prediction using the full post...
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.
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. PMID:26086934
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
Improved fitting of PIXE spectra: the Voigt profile and Si(Li) detector modeling
Hildner, M.L. [Sandia National Labs., Livermore, CA (United States); Antolak, A.J. [Sandia National Labs., Livermore, CA (United States); Bench, G.S. [Lawrence Livermore National Lab., CA (United States)
1996-04-11
The true emitted X-ray lineshape as measured by a Si(Li) detector is the convolution of the intrinsic Lorentzian X-ray lineshape and the detector response function. We demonstrate the necessity of using the Voigt profile -the convolution of a Lorentzian and a Gaussian - to fit the full-energy peak portion of directly measured X-ray lines. By incorporating the Voigtian in our PIXE spectrum fitting code, PIXEF, we have found consistent improvement in the quality of fit and calculated elemental yields. We have also found that the Voigtian fit is required to give an accurate ratio of tail to peak intensity. We attribute the tail to a surface layer of incomplete charge collection (ICC) at the front of the detector. Although this model is improved by appropriately accounting for the loss of photoelectrons that travel back to the ICC layer after being emitted from the intrinsic region, it appears to fail when the full-energy peak is fit to a Gaussian. On the other hand, excellent agreement between the improved model and experiment is found when the full-energy peak is fit to a Voigtian. (orig.).
Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses
Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu
2011-01-01
Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…
ANIMAL MODELS OF CANCER: A REVIEW
Archana M Navale
2013-01-01
Cancer is the second leading cause of death worldwide. In USA three persons out of five will develop some type of cancer. Beyond these statistics of mortality, the morbidity due to cancer presents a real scary picture. Last 50 years of research has rendered some types of cancer curable, but still the major fear factor associated with this disease is unchanged. Animal models are classified according to the method of induction of cancer in the animal. Spontaneous tumor models are the most primi...
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.
Optimization of Active Muscle Force-Length Models Using Least Squares Curve Fitting.
Mohammed, Goran Abdulrahman; Hou, Ming
2016-03-01
The objective of this paper is to propose an asymmetric Gaussian function as an alternative to the existing active force-length models, and to optimize this model along with several other existing models by using the least squares curve fitting method. The minimal set of coefficients is identified for each of these models to facilitate the least squares curve fitting. Sarcomere simulated data and one set of rabbits extensor digitorum II experimental data are used to illustrate optimal curve fitting of the selected force-length functions. The results shows that all the curves fit reasonably well with the simulated and experimental data, while the Gordon-Huxley-Julian model and asymmetric Gaussian function are better than other functions in terms of statistical test scores root mean squared error and R-squared. However, the differences in RMSE scores are insignificant (0.3-6%) for simulated data and (0.2-5%) for experimental data. The proposed asymmetric Gaussian model and the method of parametrization of this and the other force-length models mentioned above can be used in the studies on active force-length relationships of skeletal muscles that generate forces to cause movements of human and animal bodies. PMID:26276984
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
13A. Integrative Cancer Care: The Life Over Cancer Model
Block, Keith; Block, Penny; Gyllenhaal, Charlotte; Shoham, Jacob
2013-01-01
Focus Areas: Integrative Algorithms of Care Integrative cancer treatment fully blends conventional cancer treatment with integrative therapies such as diet, supplements, exercise and biobehavioral approaches. The Life Over Cancer model comprises three spheres of intervention: improving lifestyle, improving biochemical environment (terrain), and improving tolerance of conventional treatment. These levels are applied within the context of a life-affirming approach to cancer patients and treatme...
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.
Polytropic model fits to the globular cluster NGC 2419 in Modified Newtonian Dynamics
Ibata, Rodrigo; Sollima, Antonio; Nipoti, Carlo; Bellazzini, Michele; Chapman, Scott; Dalessandro, Emanuele
2011-01-01
We present an analysis of the globular cluster NGC 2419, using a polytropic model in Modified Newtonian Dynamics (MOND) to reproduce recently published high quality data of the structure and kinematics of the system. We show that a specific MOND polytropic model of NGC 2419 suggested by a previous study can be completely ruled out by the data. Furthermore, the highest likelihood fit polytrope in MOND is a substantially worse model (by a factor of approximately 5000) than a Newtonian Michie mo...
Goodness-of- fit tests for multivariate copula-based time series models
Berghaus, Betina; Bücher, Axel
2014-01-01
In recent years, stationary time series models based on copula functions became increasingly popular in econometrics to model nonlinear temporal and cross-sectional dependencies. Within these models, we consider the problem of testing the goodness-of-fit of the parametric form of the underlying copula. Our approach is based on a dependent multiplier bootstrap and it can be applied to any stationary, strongly mixing time series. The method extends recent i.i.d. results by Koj...
Simbolon, Ratna Wati
2016-01-01
This study aimed to evaluate the academic information system implementation using the development of the model HOT (Human Organization Technology) Fit. Development model applied is to use the model TUTO (Top-User Management-Technology-Organization). This study has four independent variables are the Top Management (Top Management) as X1, User (User) as X2, Technology (Technology) as X3 and Organization (Organization) as X4, and one dependent variable is the Academic Information System Services...
Background: In radioactive nuclides atmospheric diffusion models, the empirical dispersion coefficients were deduced under certain experiment conditions, whose difference with nuclear accident conditions is a source of deviation. A better estimation of the radioactive nuclide's actual dispersion process could be done by correcting dispersion coefficients with observation data, and Genetic Algorithm (GA) is an appropriate method for this correction procedure. Purpose: This study is to analyze the fitness functions' influence on the correction procedure and the forecast ability of diffusion model. Methods: GA, coupled with Lagrange dispersion model, was used in a numerical simulation to compare 4 fitness functions' impact on the correction result. Results: In the numerical simulation, the fitness function with observation deviation taken into consideration stands out when significant deviation exists in the observed data. After performing the correction procedure on the Kincaid experiment data, a significant boost was observed in the diffusion model's forecast ability. Conclusion: As the result shows, in order to improve dispersion models' forecast ability using GA, observation data should be given different weight in the fitness function corresponding to their error. (authors)
The FIT 2.0 Model - Fuel-cycle Integration and Tradeoffs
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.
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.
IRT Model Fit Evaluation from Theory to Practice: Progress and Some Unanswered Questions
Cai, Li; Monroe, Scott
2013-01-01
In this commentary, the authors congratulate Professor Alberto Maydeu-Olivares on his article [EJ1023617: "Goodness-of-Fit Assessment of Item Response Theory Models, Measurement: Interdisciplinary Research and Perspectives," this issue] as it provides a much needed overview on the mathematical underpinnings of the theory behind the…
Fit Gap Analysis – The Role of Business Process Reference Models
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.
Universal Screening for Emotional and Behavioral Problems: Fitting a Population-Based Model
Schanding, G. Thomas, Jr.; Nowell, Kerri P.
2013-01-01
Schools have begun to adopt a population-based method to conceptualizing assessment and intervention of students; however, little empirical evidence has been gathered to support this shift in service delivery. The present study examined the fit of a population-based model in identifying students' behavioral and emotional functioning using a…
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. PMID:25106393
McCluskey, Ken W.
2010-01-01
This article presents the author's comments on Hisham B. Ghassib's "Where Does Creativity Fit into a Productivist Industrial Model of Knowledge Production?" Ghassib's article focuses on the transformation of science from pre-modern times to the present. Ghassib (2010) notes that, unlike in an earlier era when the economy depended on static…
Harris, Carole Ruth
2010-01-01
This article presents the author's comments on Hisham Ghassib's article entitled "Where Does Creativity Fit into a Productivist Industrial Model of Knowledge Production?" In his article, Ghassib (2010) provides an overview of the philosophical foundations that led to exact science, its role in what was later to become a driving force in the modern…
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…
Flipo, N.; Monteil, C.; Poulin, M.; de Fouquet, C.; Krimissa, M.
2012-05-01
This study aims at analyzing the water budget of the unconfined Beauce aquifer (8000 km2) over a 35 year period, by modeling the hydrological functioning and quantifying exchanged water fluxes inside the system. A distributed process-based model (DPBM) is implemented to model the surface, the unsaturated zone and the aquifer subsystems. Based on an extensive literature review on multiparameter optimization and inverse problem, a pragmatic hybrid fitting method that couples manual and automatic calibration is developed. Three data subsets are used for calibration (10 year), validation (10 year) and test (35 year). The global piezometric head root-mean-square error is around 2.5 m for the three subsets and is rather uniformly spatially distributed over 78 piezometers. The sensitivity of the simulation to the different steps of the calibration process is investigated. The transmissivity field permits the fitting of the low-frequency signal for long-term filtering of the recharge signal, whereas the storage coefficient filters the signal with a higher frequency. For long-term insight into aquifer system functioning, the priority is thus to first fit the transmissivity field and to assess the distributed aquifer recharge accurately. The fitted DPBM, coupled with a linear model of coregionalization, is then used to quantify the hydrosystem water mass balance between 1974 and 2009, indicating that there is yet no trend of water resources decrease neither due to climate nor to human activities.
Bootstrapping topology and systemic risk of complex network using the fitness model
Musmeci N.; Battiston S.; Caldarelli G.; Puliga M.; Gabrielli A.
2012-01-01
We present a novel method to reconstruct complex network from partial information. We assume to know the links only for a subset of the nodes and to know some non-topological quantity (fitness) characterising every node. The missing links are generated on the basis of the latter quan- tity according to a fitness model calibrated on the subset of nodes for which links are known. We measure the quality of the reconstruction of several topological properties, such as the network density and the ...
Wadehn, Federico; Carnal, David; Loeliger, Hans-Andrea
2015-08-01
Heart rate variability is one of the key parameters for assessing the health status of a subject's cardiovascular system. This paper presents a local model fitting algorithm used for finding single heart beats in photoplethysmogram recordings. The local fit of exponentially decaying cosines of frequencies within the physiological range is used to detect the presence of a heart beat. Using 42 subjects from the CapnoBase database, the average heart rate error was 0.16 BPM and the standard deviation of the absolute estimation error was 0.24 BPM. PMID:26737125
Model-based fit procedure for power-law-like spectra
Milotti, E
2005-01-01
$1/f^\\alpha$ noises are ubiquitous and affect many measurements. These noises are both a nuisance and a peculiarity of several physical systems; in dielectrics, glasses and networked liquids it is very common to study this noise to gather useful information. Sometimes it happens that the noise has a power-law shape only in a certain frequency range, and contains other important features, that are however difficult to study because simple fits often fail. Here I propose a model-based fit procedure that performs well on spectra obtained in a molecular dynamics simulation.
Model-based fit procedure for power-law-like spectra
Milotti, Edoardo
2005-01-01
$1/f^\\alpha$ noises are ubiquitous and affect many measurements. These noises are both a nuisance and a peculiarity of several physical systems; in dielectrics, glasses and networked liquids it is very common to study this noise to gather useful information. Sometimes it happens that the noise has a power-law shape only in a certain frequency range, and contains other important features, that are however difficult to study because simple fits often fail. Here I propose a model-based fit proce...
A Mouse Model for Human Anal Cancer
Stelzer, Marie K.; Pitot, Henry C.; Liem, Amy; Schweizer, Johannes; Mahoney, Charles; Lambert, Paul F.
2010-01-01
Human anal cancers are associated with high-risk human papillomaviruses (HPVs) that cause other anogenital cancers and head and neck cancers. As with other cancers, HPV16 is the most common high-risk HPV in anal cancers. We describe the generation and characterization of a mouse model for human anal cancer. This model makes use of K14E6 and K14E7 transgenic mice in which the HPV16 E6 and E7 genes are directed in their expression to stratified squamous epithelia. HPV16 E6 and E7 possess oncoge...
Comparison and fitting of several Global-to_beam irradiance models in Spain
Pagola, Íñigo; Gastón, Martín; Fernández-Peruchena, Carlos M.; Torres, Jose Luis; Silva, Manuel; Ramírez, Lourdes
2009-01-01
In this paper, a comparison of different global to beam irradiance models has been performed. In a first step, five global-to-beam irradiance models are compared against ground measurements from three sites in Spain. Four of these existing models have also been fitted in the selected sites, and the results are presented as well. Finally, one new model (fully described in paper 11693) is also compared with the same ground measurements. The model comparison has been made by means of first and s...
Estimation of high-resolution dust column density maps: Empirical model fits
Juvela, M
2013-01-01
Sub-millimetre dust emission is an important tracer of density N of dense interstellar clouds. One has to combine surface brightness information at different spatial resolutions, and specific methods are needed to derive N at a resolution higher than the lowest resolution of the observations. Some methods have been discussed in the literature, including a method (in the following, method B) that constructs the N estimate in stages, where the smallest spatial scales being derived only use the shortest wavelength maps. We propose simple model fitting as a flexible way to estimate high-resolution column density maps. Our goal is to evaluate the accuracy of this procedure and to determine whether it is a viable alternative for making these maps. The new method consists of model maps of column density (or intensity at a reference wavelength) and colour temperature. The model is fitted using Markov chain Monte Carlo (MCMC) methods, comparing model predictions with observations at their native resolution. We analyse...
Rather, Manzoor A; Bhat, Bilal A; Qurishi, Mushtaq A
2013-12-15
Natural product based drugs constitute a substantial proportion of the pharmaceutical market particularly in the therapeutic areas of infectious diseases and oncology. The primary focus of any drug development program so far has been to design selective ligands (drugs) that act on single selective disease targets to obtain highly efficacious and safe drugs with minimal side effects. Although this approach has been successful for many diseases, yet there is a significant decline in the number of new drug candidates being introduced into clinical practice over the past few decades. This serious innovation deficit that the pharmaceutical industries are facing is due primarily to the post-marketing failures of blockbuster drugs. Many analysts believe that the current capital-intensive model-"the one drug to fit all" approach will be unsustainable in future and that a new "less investment, more drugs" model is necessary for further scientific growth. It is now well established that many diseases are multi-factorial in nature and that cellular pathways operate more like webs than highways. There are often multiple ways or alternate routes that may be switched on in response to the inhibition of a specific target. This gives rise to the resistant cells or resistant organisms under the specific pressure of a targeted agent, resulting in drug resistance and clinical failure of the drug. Drugs designed to act against individual molecular targets cannot usually combat multifactorial diseases like cancer, or diseases that affect multiple tissues or cell types such as diabetes and immunoinflammatory diseases. Combination drugs that affect multiple targets simultaneously are better at controlling complex disease systems and are less prone to drug resistance. This multicomponent therapy forms the basis of phytotherapy or phytomedicine where the holistic therapeutic effect arises as a result of complex positive (synergistic) or negative (antagonistic) interactions between
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 the distribution of dry and wet spells with alternative probability models
Deni, Sayang Mohd; Jemain, Abdul Aziz
2009-06-01
The development of the rainfall occurrence model is greatly important not only for data-generation purposes, but also in providing informative resources for future advancements in water-related sectors, such as water resource management and the hydrological and agricultural sectors. Various kinds of probability models had been introduced to a sequence of dry (wet) days by previous researchers in the field. Based on the probability models developed previously, the present study is aimed to propose three types of mixture distributions, namely, the mixture of two log series distributions (LSD), the mixture of the log series Poisson distribution (MLPD), and the mixture of the log series and geometric distributions (MLGD), as the alternative probability models to describe the distribution of dry (wet) spells in daily rainfall events. In order to test the performance of the proposed new models with the other nine existing probability models, 54 data sets which had been published by several authors were reanalyzed in this study. Also, the new data sets of daily observations from the six selected rainfall stations in Peninsular Malaysia for the period 1975-2004 were used. In determining the best fitting distribution to describe the observed distribution of dry (wet) spells, a Chi-square goodness-of-fit test was considered. The results revealed that the new method proposed that MLGD and MLPD showed a better fit as more than half of the data sets successfully fitted the distribution of dry and wet spells. However, the existing models, such as the truncated negative binomial and the modified LSD, were also among the successful probability models to represent the sequence of dry (wet) days in daily rainfall occurrence.
Mustonen, Ville; Kinney, Justin; Callan, Curtis G.; Lässig, Michael
2008-01-01
We present a genomewide cross-species analysis of regulation for broad-acting transcription factors in yeast. Our model for binding site evolution is founded on biophysics: the binding energy between transcription factor and site is a quantitative phenotype of regulatory function, and selection is given by a fitness landscape that depends on this phenotype. The model quantifies conservation, as well as loss and gain, of functional binding sites in a coherent way. Its predictions are supported...
Fitting General Relative Risk Models for Survival Time and Matched Case-Control Analysis
Langholz, Bryan; Richardson, David B.
2009-01-01
Cox proportional hazards regression analysis of survival data and conditional logistic regression analysis of matched case-control data are methods that are widely used by epidemiologists. Standard statistical software packages accommodate only log-linear model forms, which imply exponential exposure-response functions and multiplicative interactions. In this paper, the authors describe methods for fitting non-log-linear Cox and conditional logistic regression models. The authors use data fro...
Modeling epidemics of multidrug-resistant M. tuberculosis of heterogeneous fitness
Cohen, Ted; Murray, Megan
2004-01-01
Mathematical models have recently been used to predict the future burden of multidrug-resistant tuberculosis (MDRTB)1-3. These models suggest the threat of multidrug resistance to TB control will depend on the relative ‘fitness’ of MDR strains and imply that if the average fitness of MDR strains is considerably less than that of drug-sensitive strains, the emergence of resistance will not jeopardize the success of tuberculosis control efforts. Multidrug resistance in M. tuberculosis is confer...
Fitting complex population models by combining particle filters with Markov chain Monte Carlo.
Knape, Jonas; de Valpine, Perry
2012-02-01
We show how a recent framework combining Markov chain Monte Carlo (MCMC) with particle filters (PFMCMC) may be used to estimate population state-space models. With the purpose of utilizing the strengths of each method, PFMCMC explores hidden states by particle filters, while process and observation parameters are estimated using an MCMC algorithm. PFMCMC is exemplified by analyzing time series data on a red kangaroo (Macropus rufus) population in New South Wales, Australia, using MCMC over model parameters based on an adaptive Metropolis-Hastings algorithm. We fit three population models to these data; a density-dependent logistic diffusion model with environmental variance, an unregulated stochastic exponential growth model, and a random-walk model. Bayes factors and posterior model probabilities show that there is little support for density dependence and that the random-walk model is the most parsimonious model. The particle filter Metropolis-Hastings algorithm is a brute-force method that may be used to fit a range of complex population models. Implementation is straightforward and less involved than standard MCMC for many models, and marginal densities for model selection can be obtained with little additional effort. The cost is mainly computational, resulting in long running times that may be improved by parallelizing the algorithm. PMID:22624307
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
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
Application of random walk model to fit temperature in 46 gamma world cities from 1901 to 1998
Shaomin Yan; Guang Wu
2010-01-01
Very recently, we have applied the random walk model to fit the global temperature anomaly, CRUTEM3. With encouraging results, we apply the random walk model to fit the temperature walk that is the conversion of recorded tem-perature and real recorded temperature in 46 gamma world cities from 1901 to 1998 in this study. The results show that the random walk model can fit both temperature walk and real recorded temperature although the fitted results from other climate models are unavailable f...
The Beta Problem: The Incompatibility of X-ray and Sunyaev-Zeldovich Model Fitting
Burns, Jack O.; Hallman, E.; Motl, P.; Norman, M.
2006-12-01
We describe an analysis of a large sample of numerically simulated clusters which demonstrates the effects of using X-ray fitted beta-model parameters with Sunyaev-Zeldovich effect (SZE) data. There is a fundamental incompatibility between beta-model fits to X-ray surface brightness profiles and those done with SZE profiles. Since observational SZE radial profiles are in short supply, the X-ray parameters are often used in SZE analysis. We show that this leads to biased estimates of the integrated Compton y-parameter inside r500 and the value of the Hubble constant calculated from clusters. We suggest a simple scaling of the X-ray beta-model parameters which brings these calculated quantities into close agreement with the true values.
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.
POLYTROPIC MODEL FITS TO THE GLOBULAR CLUSTER NGC 2419 IN MODIFIED NEWTONIAN DYNAMICS
We present an analysis of the globular cluster NGC 2419, using a polytropic model in modified Newtonian dynamics (MOND) to reproduce recently published high-quality data on the structure and kinematics of the system. We show that a specific MOND polytropic model of NGC 2419 suggested by a previous study can be completely ruled out by the data. Furthermore, the highest likelihood fit polytrope in MOND is a substantially worse model (by a factor of ∼5000) than a Newtonian Michie model we studied previously. We conclude that the structure and dynamics of NGC 2419 favor Newtonian dynamics and do indeed challenge the MOND theory.
unmarked: An R Package for Fitting Hierarchical Models of Wildlife Occurrence and Abundance
Ian J. Fiske
2011-08-01
Full Text Available Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientific questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mechanisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unified modeling interface. The R package unmarked provides such a unified modeling framework, including tools for data exploration, model fitting, model criticism, post-hoc analysis, and model comparison.
Fitness, inclusive fitness, and optimization
Lehmann L.; Rousset F
2014-01-01
Individual-as-maximizing agent analogies result in a simple understanding of the functioning of the biological world. Identifying the conditions under which individuals can be regarded as fitness maximizing agents is thus of considerable interest to biologists. Here, we compare different concepts of fitness maximization, and discuss within a single framework the relationship between Hamilton's (J Theor Biol 7: 1-16, 1964) model of social interactions, Grafen's (J Evol Biol 20: 1243-1254, 2007...
Correlated Parameter Fit of Arrhenius Model for Thermal Denaturation of Proteins and Cells
Qin, Zhenpeng; Balasubramanian, Saravana Kumar; Wolkers, Willem F.; Pearce, John A.; Bischof, John C.
2014-01-01
Thermal denaturation of proteins is critical to cell injury, food science and other biomaterial processing. For example protein denaturation correlates strongly with cell death by heating, and is increasingly of interest in focal thermal therapies of cancer and other diseases at temperatures which often exceed 50 °C. The Arrhenius model is a simple yet widely used model for both protein denaturation and cell injury. To establish the utility of the Arrhenius model for protein denaturation at 5...
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
Optimizing mouse models for precision cancer prevention.
Le Magnen, Clémentine; Dutta, Aditya; Abate-Shen, Cory
2016-03-01
As cancer has become increasingly prevalent, cancer prevention research has evolved towards placing a greater emphasis on reducing cancer deaths and minimizing the adverse consequences of having cancer. 'Precision cancer prevention' takes into account the collaboration of intrinsic and extrinsic factors in influencing cancer incidence and aggressiveness in the context of the individual, as well as recognizing that such knowledge can improve early detection and enable more accurate discrimination of cancerous lesions. However, mouse models, and particularly genetically engineered mouse (GEM) models, have yet to be fully integrated into prevention research. In this Opinion article, we discuss opportunities and challenges for precision mouse modelling, including the essential criteria of mouse models for prevention research, representative success stories and opportunities for more refined analyses in future studies. PMID:26893066
Furlan, E.; Fischer, W. J.; 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.
2016-05-01
We present key results from the Herschel Orion Protostar Survey: 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 submillimeter photometry from APEX, our SEDs cover 1.2-870 μm and sample the peak of the protostellar envelope emission at ˜100 μm. 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 30,400 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 constraints on protostellar envelope properties. We find that most protostars, and in particular the flat-spectrum sources, are well fit. The median envelope density and median inclination angle decrease from Class 0 to Class I to flat-spectrum protostars, despite the broad range in best-fit parameters in each of the three categories. We also discuss degeneracies in our model parameters. Our results confirm that the different protostellar classes generally correspond to an evolutionary sequence with a decreasing envelope infall rate, but the inclination angle also plays a role in the appearance, and thus interpretation, of the SEDs.
A fit to the simultaneous broadband spectrum of Cygnus X-1 using the transition disk model
Misra, R; Melia, F
1997-01-01
We have used the transition disk model to fit the simultaneous broad band ($2-500$ keV) spectrum of Cygnus X-1 from OSSE and Ginga observations. In this model, the spectrum is produced by saturated Comptonization within the inner region of the accretion disk, where the temperature varies rapidly with radius. In an earlier attempt, we demonstrated the viability of this model by fitting the data from EXOSAT, XMPC balloon and OSSE observations, though these were not made simultaneously. Since the source is known to be variable, however, the results of this fit were not conclusive. In addition, since only once set of observations was used, the good agreement with the data could have been a chance occurrence. Here, we improve considerably upon our earlier analysis by considering four sets of simultaneous observations of Cygnus X-1, using an empirical model to obtain the disk temperature profile. The vertical structure is then obtained using this profile and we show that the analysis is self- consistent. We demonst...
Ritter, Axel; Muñoz-Carpena, Rafael
2013-02-01
SummarySuccess in the use of computer models for simulating environmental variables and processes requires objective model calibration and verification procedures. Several methods for quantifying the goodness-of-fit of observations against model-calculated values have been proposed but none of them is free of limitations and are often ambiguous. When a single indicator is used it may lead to incorrect verification of the model. Instead, a combination of graphical results, absolute value error statistics (i.e. root mean square error), and normalized goodness-of-fit statistics (i.e. Nash-Sutcliffe Efficiency coefficient, NSE) is currently recommended. Interpretation of NSE values is often subjective, and may be biased by the magnitude and number of data points, data outliers and repeated data. The statistical significance of the performance statistics is an aspect generally ignored that helps in reducing subjectivity in the proper interpretation of the model performance. In this work, approximated probability distributions for two common indicators (NSE and root mean square error) are derived with bootstrapping (block bootstrapping when dealing with time series), followed by bias corrected and accelerated calculation of confidence intervals. Hypothesis testing of the indicators exceeding threshold values is proposed in a unified framework for statistically accepting or rejecting the model performance. It is illustrated how model performance is not linearly related with NSE, which is critical for its proper interpretation. Additionally, the sensitivity of the indicators to model bias, outliers and repeated data is evaluated. The potential of the difference between root mean square error and mean absolute error for detecting outliers is explored, showing that this may be considered a necessary but not a sufficient condition of outlier presence. The usefulness of the approach for the evaluation of model performance is illustrated with case studies including those with
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. PMID:26613830
Efficient Constrained Local Model Fitting for Non-Rigid Face Alignment
Wang, Yang; Cox, Mark; Sridharan, Sridha; Cohn, Jeffery F.
2009-01-01
Active appearance models (AAMs) have demonstrated great utility when being employed for non-rigid face alignment/tracking. The “simultaneous” algorithm for fitting an AAM achieves good non-rigid face registration performance, but has poor real time performance (2-3 fps). The “project-out” algorithm for fitting an AAM achieves faster than real time performance (> 200 fps) but suffers from poor generic alignment performance. In this paper we introduce an extension to a discriminative method for non-rigid face registration/tracking referred to as a constrained local model (CLM). Our proposed method is able to achieve superior performance to the “simultaneous” AAM algorithm along with real time fitting speeds (35 fps). We improve upon the canonical CLM formulation, to gain this performance, in a number of ways by employing: (i) linear SVMs as patch-experts, (ii) a simplified optimization criteria, and (iii) a composite rather than additive warp update step. Most notably, our simplified optimization criteria for fitting the CLM divides the problem of finding a single complex registration/warp displacement into that of finding N simple warp displacements. From these N simple warp displacements, a single complex warp displacement is estimated using a weighted least-squares constraint. Another major advantage of this simplified optimization lends from its ability to be parallelized, a step which we also theoretically explore in this paper. We refer to our approach for fitting the CLM as the “exhaustive local search” (ELS) algorithm. Experiments were conducted on the CMU Multi-PIE database. PMID:20046797
Mandal, S.; Choudhury, B. U.
2015-07-01
Sagar Island, setting on the continental shelf of Bay of Bengal, is one of the most vulnerable deltas to the occurrence of extreme rainfall-driven climatic hazards. Information on probability of occurrence of maximum daily rainfall will be useful in devising risk management for sustaining rainfed agrarian economy vis-a-vis food and livelihood security. Using six probability distribution models and long-term (1982-2010) daily rainfall data, we studied the probability of occurrence of annual, seasonal and monthly maximum daily rainfall (MDR) in the island. To select the best fit distribution models for annual, seasonal and monthly time series based on maximum rank with minimum value of test statistics, three statistical goodness of fit tests, viz. Kolmogorove-Smirnov test (K-S), Anderson Darling test ( A 2 ) and Chi-Square test ( X 2) were employed. The fourth probability distribution was identified from the highest overall score obtained from the three goodness of fit tests. Results revealed that normal probability distribution was best fitted for annual, post-monsoon and summer seasons MDR, while Lognormal, Weibull and Pearson 5 were best fitted for pre-monsoon, monsoon and winter seasons, respectively. The estimated annual MDR were 50, 69, 86, 106 and 114 mm for return periods of 2, 5, 10, 20 and 25 years, respectively. The probability of getting an annual MDR of >50, >100, >150, >200 and >250 mm were estimated as 99, 85, 40, 12 and 03 % level of exceedance, respectively. The monsoon, summer and winter seasons exhibited comparatively higher probabilities (78 to 85 %) for MDR of >100 mm and moderate probabilities (37 to 46 %) for >150 mm. For different recurrence intervals, the percent probability of MDR varied widely across intra- and inter-annual periods. In the island, rainfall anomaly can pose a climatic threat to the sustainability of agricultural production and thus needs adequate adaptation and mitigation measures.
Six infiltration models, some obtained by reformulating the fitting parameters of the classical Kostiakov (1932) and Philip (1957) equations, were investigated for their ability to describe water infiltration into highly permeable sandy soils from the Nsukka plains of SE Nigeria. The models were Kostiakov, Modified Kostiakov (A), Modified Kostiakov (B), Philip, Modified Philip (A) and Modified Philip (B). Infiltration data were obtained from double ring infiltrometers on field plots established on a Knadic Paleustult (Nkpologu series) to investigate the effects of land use on soil properties and maize yield. The treatments were; (i) tilled-mulched (TM), (ii) tilled-unmulched (TU), (iii) untilled-mulched (UM), (iv) untilled-unmulched (UU) and (v) continuous pasture (CP). Cumulative infiltration was highest on the TM and lowest on the CP plots. All estimated model parameters obtained by the best fit of measured data differed significantly among the treatments. Based on the magnitude of R2 values, the Kostiakov, Modified Kostiakov (A), Philip and Modified Philip (A) models provided best predictions of cumulative infiltration as a function of time. Comparing experimental with model-predicted cumulative infiltration showed, however, that on all treatments the values predicted by the classical Kostiakov, Philip and Modified Philip (A) models deviated most from experimental data. The other models produced values that agreed very well with measured data. Considering the eases of determining the fitting parameters it is proposed that on soils with high infiltration rates, either Modified Kostiakov model (I = Kta + Ict) or Modified Philip model (I St1/2 + Ict), (where I is cumulative infiltration, K, the time coefficient, t, time elapsed, 'a' the time exponent, Ic the equilibrium infiltration rate and S, the soil water sorptivity), be used for routine characterization of the infiltration process. (author). 33 refs, 3 figs 6 tabs
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.
Tectonic plate under a localized boundary stress: fitting of a zero-range solvable model
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 h, 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
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.
Lee, Chaohong; Zhu, Xiwen; Gao, Kelin
2001-01-01
We introduce the standard distribution width of fitness to characterize the global and individual features of a ecosystem in the Bak-Sneppen evolution model. Through tracking this quantity in evolution, a different hierarchy of avalanche dynamics, $w_{0}$ avalanche is observed. The corresponding gap equation and the self-organized threshold $w_{c}$ are obtained. The critical exponents $\\tau ,$ $\\gamma $and $\\rho $, which describe the behavior of the avalanche size distribution, the average av...
Goodness-of-fit test in a multivariate errors-in-variables model $AX = B$
Kukush, Alexander; Tsaregorodtsev, Yaroslav
2016-01-01
A multivariable functional errors-in-variables model $AX \\approx B$ is considered, where the data matrices $A$ and $B$ are observed with errors and a matrix parameter $X$ is to be estimated. A goodness-of-fit test is constructed based on the total least squares estimator. The proposed test is asymptotically chi-squared under null hypothesis. The power of the test under local alternatives is discussed.
Yunyun Yang; Boying Wu
2012-01-01
We propose a convex image segmentation model in a variational level set formulation. Both the local information and the global information are taken into consideration to get better segmentation results. We first propose a globally convex energy functional to combine the local and global intensity fitting terms. The proposed energy functional is then modified by adding an edge detector to force the active contour to the boundary more easily. We then apply the split Bregman method to minimize ...
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...
Building a better model of cancer
DeGregori James
2006-10-01
Full Text Available Abstract The 2006 Cold Spring Harbor Laboratory meeting on the Mechanisms and Models of Cancer was held August 16–20. The meeting featured several hundred presentations of many short talks (mostly selected from the abstracts and posters, with the airing of a number of exciting new discoveries. We will focus this meeting review on models of cancer (primarily mouse models, highlighting recent advances in new mouse models that better recapitulate sporadic tumorigenesis, demonstrations of tumor addiction to tumor suppressor inactivation, new insight into senescence as a tumor barrier, improved understanding of the evolutionary paths of cancer development, and environmental/immunological influences on cancer.
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.
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.
Model fitting of the kinematics of ten superluminal components in blazar 3C 279
Shan-Jie Qian
2013-01-01
The kinematics of ten superluminal components (C11-C16,C18,C20,C21 and C24) of blazar 3C 279 are studied from VLBI observations.It is shown that their initial trajectory,distance from the core and apparent speed can be well fitted by the precession model proposed by Qian.Combined with the results of the model fit for the six superluminal components (C3,C4,C7a,C8,C9 and C10) already published,the kinematics of sixteen superluminal components can now be consistently interpreted in the precession scenario with their ejection times spanning more than 25 yr (or more than one precession period).The results from model fitting show the possible existence of a common precessing trajectory for these knots within a projected core distance of ～0.2-0.4 mas.In the framework of the jet-precession scenario,we can,for the first time,identify three classes of trajectories which are characterized by their collimation parameters.These different trajectories could be related to the helical structure of magnetic fields in the jet.Through fitting the model,the bulk Lorentz factor,Doppler factor and viewing angle of these knots are derived.It is found that there is no evidence for any correlation between the bulk Lorentz factor of the components and their precession phase (or ejection time).In a companion paper,the kinematics of another seven components (C5a,C6,C7,C17,C19,C22 and C23) have been derived from model fitting,and a binary black-hole/jet scenario was envisaged.The precession model proposed by Qian would be useful for understanding the kinematics of superluminal components in blazar 3C 279 derived from VLBI observations,by disentangling different mechanisms and ingredients.More generally,it might also be helpful for studying the mechanism of jet swing (wobbling) in other blazars.
On the use of the ratio of small to large separations in asteroseismic model fitting
Roxburgh, Ian W
2013-01-01
Context. The use of ratios of small to large separations as a diagnostic of stellar interiors. Aims. To demonstrate that model fitting by comparing observed and model separation ratios at the same n values is in error, and to present a correct procedure. Methods. Theoretical analysis using phase shifts and numerical models. Results. We show that the separation ratios of stellar models with the same interior structure, but different outer layers, are not the same when compared at the same n values, but are the same when evaluated at the same frequencies by interpolation. The separation ratios trace the phase shift differences as a function of frequency not of n. We give examples from model fitting where the ratios at the same n values agree within the error estimates, but do not agree when evaluated at the same frequencies and the models do not have the same interior structure. The correct procedure is to compare observed ratios with those of models interpolated to the observed frequencies.
UROX 2.0: an interactive tool for fitting atomic models into electron-microscopy reconstructions
UROX is software designed for the interactive fitting of atomic models into electron-microscopy reconstructions. The main features of the software are presented, along with a few examples. Electron microscopy of a macromolecular structure can lead to three-dimensional reconstructions with resolutions that are typically in the 30–10 Å range and sometimes even beyond 10 Å. Fitting atomic models of the individual components of the macromolecular structure (e.g. those obtained by X-ray crystallography or nuclear magnetic resonance) into an electron-microscopy map allows the interpretation of the latter at near-atomic resolution, providing insight into the interactions between the components. Graphical software is presented that was designed for the interactive fitting and refinement of atomic models into electron-microscopy reconstructions. Several characteristics enable it to be applied over a wide range of cases and resolutions. Firstly, calculations are performed in reciprocal space, which results in fast algorithms. This allows the entire reconstruction (or at least a sizeable portion of it) to be used by taking into account the symmetry of the reconstruction both in the calculations and in the graphical display. Secondly, atomic models can be placed graphically in the map while the correlation between the model-based electron density and the electron-microscopy reconstruction is computed and displayed in real time. The positions and orientations of the models are refined by a least-squares minimization. Thirdly, normal-mode calculations can be used to simulate conformational changes between the atomic model of an individual component and its corresponding density within a macromolecular complex determined by electron microscopy. These features are illustrated using three practical cases with different symmetries and resolutions. The software, together with examples and user instructions, is available free of charge at http://mem.ibs.fr/UROX/
Fitting a Two-Component Scattering Model to Polarimetric SAR Data from Forests
Freeman, Anthony
2007-01-01
Two simple scattering mechanisms are fitted to polarimetric synthetic aperture radar (SAR) observations of forests. The mechanisms are canopy scatter from a reciprocal medium with azimuthal symmetry and a ground scatter term that can represent double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants or Bragg scatter from a moderately rough surface, which is seen through a layer of vertically oriented scatterers. The model is shown to represent the behavior of polarimetric backscatter from a tropical forest and two temperate forest sites by applying it to data from the National Aeronautic and Space Agency/Jet Propulsion Laboratory's Airborne SAR (AIRSAR) system. Scattering contributions from the two basic scattering mechanisms are estimated for clusters of pixels in polarimetric SAR images. The solution involves the estimation of four parameters from four separate equations. This model fit approach is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem. The model is used to develop an understanding of the ground-trunk double-bounce scattering that is present in the data, which is seen to vary considerably as a function of incidence angle. Two parameters in the model fit appear to exhibit sensitivity to vegetation canopy structure, which is worth further exploration. Results from the model fit for the ground scattering term are compared with estimates from a forward model and shown to be in good agreement. The behavior of the scattering from the ground-trunk interaction is consistent with the presence of a pseudo-Brewster angle effect for the air-trunk scattering interface. If the Brewster angle is known, it is possible to directly estimate the real part of the dielectric constant of the trunks, a key variable in forward modeling of backscatter from forests. It is also shown how, with a priori knowledge of the forest height, an estimate for the
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.
A NON-UNIFORM SEDIMENT TRANSPORT MODEL WITH THE BOUNDARY-FITTING ORTHOGONAL COORDINATE SYSTEM
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.
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.
GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs
Blake MacDonald
2015-04-01
Full Text Available Gaussian process (GP models are commonly used statistical metamodels for emulating expensive computer simulators. Fitting a GP model can be numerically unstable if any pair of design points in the input space are close together. Ranjan, Haynes, and Karsten (2011 proposed a computationally stable approach for fitting GP models to deterministic computer simulators. They used a genetic algorithm based approach that is robust but computationally intensive for maximizing the likelihood. This paper implements a slightly modified version ofthe model proposed by Ranjan et al. (2011 in the R package GPfit. A novel parameterization of the spatial correlation function and a clustering based multi-start gradient based optimization algorithm yield robust optimization that is typically faster than the genetic algorithm based approach. We present two examples with R codes to illustrate the usage of the main functions in GPfit . Several test functions are used for performance comparison with the popular R package mlegp . We also use GPfit for a real application, i.e., for emulating the tidal kinetic energy model for the Bay of Fundy, Nova Scotia, Canada. GPfit is free software and distributed under the General Public License and available from the Comprehensive R Archive Network.
Animal models of ovarian cancer
Shaw Tanya J; Vanderhyden Barbara C; Ethier Jean-François
2003-01-01
Abstract Ovarian cancer is the most lethal of all of the gynecological cancers and can arise from any cell type of the ovary, including germ cells, granulosa or stromal cells. However, the majority of ovarian cancers arise from the surface epithelium, a single layer of cells that covers the surface of the ovary. The lack of a reliable and specific method for the early detection of epithelial ovarian cancer results in diagnosis occurring most commonly at late clinical stages, when treatment is...
Schlemm, Eckhard
2015-01-01
The Bak--Sneppen model is an abstract representation of a biological system that evolves according to the Darwinian principles of random mutation and selection. The species in the system are characterized by a numerical fitness value between zero and one. We show that in the case of five species the steady-state fitness distribution can be obtained as a solution to a linear differential equation of order five with hypergeometric coefficients. Similar representations for the asymptotic fitness...
Model-fitting of the kinematics of superluminal components in blazar 3C 279
Shan-Jie Qian
2012-01-01
A precessing jet-nozzle model with a precession period of about 25 yr has been proposed by Qian to interpret the change with time of the ejection position angle of the superluminal components observed using very long baseline interferometry (VLBI) in the blazar 3C 279.We discuss the kinematic properties of six superluminal knots (C3,C4,C7a,C8,C9 and C10) and show that their trajectory,core-distance and apparent speed,derived from VLBI observations,can be consistently well fitted by the model.Their intrinsic Lorentz factors of bulk superluminal motion are thus derived,and the evidence shows no relation between Lorentz factor and the precession phase.Interestingly,for the C7a and C8 knots,the fitted core-distance ranges from ～0.1 mas to ～0.4mas and for knots C9 and C10 from ～0.2mas to ～1.0-1.5mas.For knot C4,its trajectory and apparent velocity are well fitted in the core-distance range from ～1 mas to ～5 mas,taking into account a curvature of the trajectory at core-distance larger than ～3 mas.The consistent fitting of the kinematics of these components clearly demonstrates that the amplitude function and collimation parameter adopted in the precession model are appropriate and applicable for both the inner and outer parts of the jet in 3C 279,but in some cases the jet curvature in the outer parts (or deviation from the model trajectory) needs to be seriously taken into consideration.With the exception of C4,the ejection position angles derived from the precession model are consistent with the values measured by VLBI observations (within about 3° - 6°).Undoubtedly,the consistent interpretation of the kinematics in terms of the precession model for these superluminal components,with their ejection time spanning ～24 yr,significantly expands its applicability and implies that regular pattems of trajectories (or rotating channels) could exist in some periods.
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
Atmospheric Properties of T Dwarfs Inferred from Model Fits at Low Spectral Resolution
Godfrey, Paige A.; Rice, Emily L.; Filippazzo, Joe; Douglas, Stephanie; BDNYC
2016-01-01
Brown dwarfs are substellar objects that cool over time because they are not massive enough to sustain hydrogen fusion at their cores. While spectral types (M, L, T, Y) generally correlate with decreasing temperature, spectral subclasses (T0, T1, T2, etc.) do not, suggesting that secondary parameters (gravity, metallicity, dust) play a role in the spectral type-temperature relationship. We investigate this relationship for T dwarfs, which make up the coolest fully-populated spectral class of substellar objects. Our sample consists of 154 T dwarfs with low resolution (R~75-100) near-infrared (~0.8-2.5 micron) spectra from the SpeX Prism Library and the literature. We compare each observed spectrum to synthetic spectra from four model grids using a Markov-Chain Monte Carlo analysis to determine robust best-fit parameters and uncertainties. We evaluate the best fit parameters from each model grid per object to constrain how spectral type relates to decreasing temperature and increasing surface gravity and to compare the consistency of each model grid. To test for discrepant results when fitting to relatively narrow wavelength ranges, this analysis is performed on the full spectrum of the Y, J, H, and K bands and on each band separately. New detections of cooler objects extending into the Y dwarf and exoplanet regimes motivate our model comparisons and search for trends with spectral type and other observational properties across the decreasing temperatures in order to better understand the atmospheres of substellar objects, including cool gas giant exoplanets.
Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model
We have developed a model for FROC curve fitting that relates the observer's FROC performance not to the ROC performance that would be obtained if the observer's responses were scored on a per image basis, but rather to a hypothesized ROC performance that the observer would obtain in the task of classifying a set of 'candidate detections' as positive or negative. We adopt the assumptions of the Bunch FROC model, namely that the observer's detections are all mutually independent, as well as assumptions qualitatively similar to, but different in nature from, those made by Chakraborty in his AFROC scoring methodology. Under the assumptions of our model, we show that the observer's FROC performance is a linearly scaled version of the candidate analysis ROC curve, where the scaling factors are just given by the FROC operating point coordinates for detecting initial candidates. Further, we show that the likelihood function of the model parameters given observational data takes on a simple form, and we develop a maximum likelihood method for fitting a FROC curve to this data. FROC and AFROC curves are produced for computer vision observer datasets and compared with the results of the AFROC scoring method. Although developed primarily with computer vision schemes in mind, we hope that the methodology presented here will prove worthy of further study in other applications as well
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...
Jochens, Arne; Caliebe, Amke; Rösler, Uwe; Krawczak, Michael
2011-12-01
The rate of microsatellite mutation is dependent upon both the allele length and the repeat motif, but the exact nature of this relationship is still unknown. We analyzed data on the inheritance of human Y-chromosomal microsatellites in father-son duos, taken from 24 published reports and comprising 15,285 directly observable meioses. At the six microsatellites analyzed (DYS19, DYS389I, DYS390, DYS391, DYS392, and DYS393), a total of 162 mutations were observed. For each locus, we employed a maximum-likelihood approach to evaluate one of several single-step mutation models on the basis of the data. For five of the six loci considered, a novel logistic mutation model was found to provide the best fit according to Akaike's information criterion. This implies that the mutation probability at the loci increases (nonlinearly) with allele length at a rate that differs between upward and downward mutations. For DYS392, the best fit was provided by a linear model in which upward and downward mutation probabilities increase equally with allele length. This is the first study to empirically compare different microsatellite mutation models in a locus-specific fashion. PMID:21968190
Adapted strategic plannig model applied to small business: a case study in the fitness area
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.
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.