Functional Generalized Additive Models.
McLean, Mathew W; Hooker, Giles; Staicu, Ana-Maria; Scheipl, Fabian; Ruppert, David
2014-01-01
We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·,·) is an unknown regression function and X(t) is a functional covariate. Rather than having an additive model in a finite number of principal components as in Müller and Yao (2008), our model incorporates the functional predictor directly and thus our model can be viewed as the natural functional extension of generalized additive models. We estimate F(·,·) using tensor-product B-splines with roughness penalties. A pointwise quantile transformation of the functional predictor is also considered to ensure each tensor-product B-spline has observed data on its support. The methods are evaluated using simulated data and their predictive performance is compared with other competing scalar-on-function regression alternatives. We illustrate the usefulness of our approach through an application to brain tractography, where X(t) is a signal from diffusion tensor imaging at position, t, along a tract in the brain. In one example, the response is disease-status (case or control) and in a second example, it is the score on a cognitive test. R code for performing the simulations and fitting the FGAM can be found in supplemental materials available online.
Defining Function in the Functional Medicine Model.
Bland, Jeffrey
2017-02-01
In the functional medicine model, the word function is aligned with the evolving understanding that disease is an endpoint and function is a process. Function can move both forward and backward. The vector of change in function through time is, in part, determined by the unique interaction of an individual's genome with their environment, diet, and lifestyle. The functional medicine model for health care is concerned less with what we call the dysfunction or disease, and more about the dynamic processes that resulted in the person's dysfunction. The previous concept of functional somatic syndromes as psychosomatic in origin has now been replaced with a new concept of function that is rooted in the emerging 21st-century understanding of systems network-enabled biology.
Modeling Protein Domain Function
ERIC Educational Resources Information Center
Baker, William P.; Jones, Carleton "Buck"; Hull, Elizabeth
2007-01-01
This simple but effective laboratory exercise helps students understand the concept of protein domain function. They use foam beads, Styrofoam craft balls, and pipe cleaners to explore how domains within protein active sites interact to form a functional protein. The activity allows students to gain content mastery and an understanding of the…
Brain Functioning Models for Learning.
ERIC Educational Resources Information Center
Tipps, Steve; And Others
This paper describes three models of brain function, each of which contributes to an integrated understanding of human learning. The first model, the up-and-down model, emphasizes the interconnection between brain structures and functions, and argues that since physiological, emotional, and cognitive responses are inseparable, the learning context…
Computational Models for Neuromuscular Function
Valero-Cuevas, Francisco J.; Hoffmann, Heiko; Kurse, Manish U.; Kutch, Jason J.; Theodorou, Evangelos A.
2011-01-01
Computational models of the neuromuscular system hold the potential to allow us to reach a deeper understanding of neuromuscular function and clinical rehabilitation by complementing experimentation. By serving as a means to distill and explore specific hypotheses, computational models emerge from prior experimental data and motivate future experimental work. Here we review computational tools used to understand neuromuscular function including musculoskeletal modeling, machine learning, control theory, and statistical model analysis. We conclude that these tools, when used in combination, have the potential to further our understanding of neuromuscular function by serving as a rigorous means to test scientific hypotheses in ways that complement and leverage experimental data. PMID:21687779
Ecosystem structure and function modeling
Humphries, H.C.; Baron, J.S.; Jensen, M.E.; Bourgeron, P.
2001-01-01
An important component of ecological assessments is the ability to predict and display changes in ecosystem structure and function over a variety of spatial and temporal scales. These changes can occur over short (less than 1 year) or long time frames (over 100 years). Models may emphasize structural responses (changes in species composition, growth forms, canopy height, amount of old growth, etc.) or functional responses (cycling of carbon, nutrients, and water). Both are needed to display changes in ecosystem components for use in robust ecological assessments. Structure and function models vary in the ecosystem components included, algorithms employed, level of detail, and spatial and temporal scales incorporated. They range from models that track individual organisms to models of broad-scale landscape changes. This chapter describes models appropriate for ecological assessments. The models selected for inclusion can be implemented in a spatial framework and for the most part have been run in more than one system.
Interaction Models for Functional Regression
USSET, JOSEPH; STAICU, ANA-MARIA; MAITY, ARNAB
2015-01-01
A functional regression model with a scalar response and multiple functional predictors is proposed that accommodates two-way interactions in addition to their main effects. The proposed estimation procedure models the main effects using penalized regression splines, and the interaction effect by a tensor product basis. Extensions to generalized linear models and data observed on sparse grids or with measurement error are presented. A hypothesis testing procedure for the functional interaction effect is described. The proposed method can be easily implemented through existing software. Numerical studies show that fitting an additive model in the presence of interaction leads to both poor estimation performance and lost prediction power, while fitting an interaction model where there is in fact no interaction leads to negligible losses. The methodology is illustrated on the AneuRisk65 study data. PMID:26744549
NASA Astrophysics Data System (ADS)
Hibbard, Bill
2012-05-01
Orseau and Ring, as well as Dewey, have recently described problems, including self-delusion, with the behavior of agents using various definitions of utility functions. An agent's utility function is defined in terms of the agent's history of interactions with its environment. This paper argues, via two examples, that the behavior problems can be avoided by formulating the utility function in two steps: 1) inferring a model of the environment from interactions, and 2) computing utility as a function of the environment model. Basing a utility function on a model that the agent must learn implies that the utility function must initially be expressed in terms of specifications to be matched to structures in the learned model. These specifications constitute prior assumptions about the environment so this approach will not work with arbitrary environments. But the approach should work for agents designed by humans to act in the physical world. The paper also addresses the issue of self-modifying agents and shows that if provided with the possibility to modify their utility functions agents will not choose to do so, under some usual assumptions.
Modeling the Schwarzschild Green's function
NASA Astrophysics Data System (ADS)
Mark, Zachary; Zimmerman, Aaron; Chen, Yanbei
2017-01-01
At sufficiently late times, gravitational waveforms from extreme mass ratio inspirals consist of a sum of quasinormal modes, power law tails, and modes related to the matter source, such as the horizon mode (Zimmerman and Chen 2011). Due to the complexity of the exact curved spacetime Green function, making precise predictions about each component is difficult. We discuss the validity of a simple model for the scalar Schwarzschild Green's function. For observers at future null infinity, we model the Green's function as a simple function describing the direct radiation that matches to a single quasinormal mode at a retarded time related to the light ring location. As applications of the model, we describe the excitation process of the single quasinormal mode and the horizon mode, showing that waveform from the inspiralling object is in precise correspondence to the response of driven, damped harmonic oscillator.
Functional CAR models for large spatially correlated functional datasets.
Zhang, Lin; Baladandayuthapani, Veerabhadran; Zhu, Hongxiao; Baggerly, Keith A; Majewski, Tadeusz; Czerniak, Bogdan A; Morris, Jeffrey S
2016-01-01
We develop a functional conditional autoregressive (CAR) model for spatially correlated data for which functions are collected on areal units of a lattice. Our model performs functional response regression while accounting for spatial correlations with potentially nonseparable and nonstationary covariance structure, in both the space and functional domains. We show theoretically that our construction leads to a CAR model at each functional location, with spatial covariance parameters varying and borrowing strength across the functional domain. Using basis transformation strategies, the nonseparable spatial-functional model is computationally scalable to enormous functional datasets, generalizable to different basis functions, and can be used on functions defined on higher dimensional domains such as images. Through simulation studies, we demonstrate that accounting for the spatial correlation in our modeling leads to improved functional regression performance. Applied to a high-throughput spatially correlated copy number dataset, the model identifies genetic markers not identified by comparable methods that ignore spatial correlations.
Transfer Function Identification Using Orthogonal Fourier Transform Modeling Functions
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.
2013-01-01
A method for transfer function identification, including both model structure determination and parameter estimation, was developed and demonstrated. The approach uses orthogonal modeling functions generated from frequency domain data obtained by Fourier transformation of time series data. The method was applied to simulation data to identify continuous-time transfer function models and unsteady aerodynamic models. Model fit error, estimated model parameters, and the associated uncertainties were used to show the effectiveness of the method for identifying accurate transfer function models from noisy data.
From data to function: functional modeling of poultry genomics data.
McCarthy, F M; Lyons, E
2013-09-01
One of the challenges of functional genomics is to create a better understanding of the biological system being studied so that the data produced are leveraged to provide gains for agriculture, human health, and the environment. Functional modeling enables researchers to make sense of these data as it reframes a long list of genes or gene products (mRNA, ncRNA, and proteins) by grouping based upon function, be it individual molecular functions or interactions between these molecules or broader biological processes, including metabolic and signaling pathways. However, poultry researchers have been hampered by a lack of functional annotation data, tools, and training to use these data and tools. Moreover, this lack is becoming more critical as new sequencing technologies enable us to generate data not only for an increasingly diverse range of species but also individual genomes and populations of individuals. We discuss the impact of these new sequencing technologies on poultry research, with a specific focus on what functional modeling resources are available for poultry researchers. We also describe key strategies for researchers who wish to functionally model their own data, providing background information about functional modeling approaches, the data and tools to support these approaches, and the strengths and limitations of each. Specifically, we describe methods for functional analysis using Gene Ontology (GO) functional summaries, functional enrichment analysis, and pathways and network modeling. As annotation efforts begin to provide the fundamental data that underpin poultry functional modeling (such as improved gene identification, standardized gene nomenclature, temporal and spatial expression data and gene product function), tool developers are incorporating these data into new and existing tools that are used for functional modeling, and cyberinfrastructure is being developed to provide the necessary extendibility and scalability for storing and
Crossing Hazard Functions in Common Survival Models.
Zhang, Jiajia; Peng, Yingwei
2009-10-15
Crossing hazard functions have extensive applications in modeling survival data. However, existing studies in the literature mainly focus on comparing crossed hazard functions and estimating the time at which the hazard functions cross, and there is little theoretical work on conditions under which hazard functions from a model will have a crossing. In this paper, we investigate crossing status of hazard functions from the proportional hazards (PH) model, the accelerated hazard (AH) model, and the accelerated failure time (AFT) model. We provide and prove conditions under which the hazard functions from the AH and the AFT models have no crossings or a single crossing. A few examples are also provided to demonstrate how the conditions can be used to determine crossing status of hazard functions from the three models.
Error latency estimation using functional fault modeling
NASA Technical Reports Server (NTRS)
Manthani, S. R.; Saxena, N. R.; Robinson, J. P.
1983-01-01
A complete modeling of faults at gate level for a fault tolerant computer is both infeasible and uneconomical. Functional fault modeling is an approach where units are characterized at an intermediate level and then combined to determine fault behavior. The applicability of functional fault modeling to the FTMP is studied. Using this model a forecast of error latency is made for some functional blocks. This approach is useful in representing larger sections of the hardware and aids in uncovering system level deficiencies.
Functional Risk Modeling for Lunar Surface Systems
NASA Technical Reports Server (NTRS)
Thomson, Fraser; Mathias, Donovan; Go, Susie; Nejad, Hamed
2010-01-01
We introduce an approach to risk modeling that we call functional modeling , which we have developed to estimate the capabilities of a lunar base. The functional model tracks the availability of functions provided by systems, in addition to the operational state of those systems constituent strings. By tracking functions, we are able to identify cases where identical functions are provided by elements (rovers, habitats, etc.) that are connected together on the lunar surface. We credit functional diversity in those cases, and in doing so compute more realistic estimates of operational mode availabilities. The functional modeling approach yields more realistic estimates of the availability of the various operational modes provided to astronauts by the ensemble of surface elements included in a lunar base architecture. By tracking functional availability the effects of diverse backup, which often exists when two or more independent elements are connected together, is properly accounted for.
Bootstrapped models for intrinsic random functions
Campbell, K.
1988-08-01
Use of intrinsic random function stochastic models as a basis for estimation in geostatistical work requires the identification of the generalized covariance function of the underlying process. The fact that this function has to be estimated from data introduces an additional source of error into predictions based on the model. This paper develops the sample reuse procedure called the bootstrap in the context of intrinsic random functions to obtain realistic estimates of these errors. Simulation results support the conclusion that bootstrap distributions of functionals of the process, as well as their kriging variance, provide a reasonable picture of variability introduced by imperfect estimation of the generalized covariance function.
Bootstrapped models for intrinsic random functions
Campbell, K.
1987-01-01
The use of intrinsic random function stochastic models as a basis for estimation in geostatistical work requires the identification of the generalized covariance function of the underlying process, and the fact that this function has to be estimated from the data introduces an additional source of error into predictions based on the model. This paper develops the sample reuse procedure called the ''bootstrap'' in the context of intrinsic random functions to obtain realistic estimates of these errors. Simulation results support the conclusion that bootstrap distributions of functionals of the process, as well as of their ''kriging variance,'' provide a reasonable picture of the variability introduced by imperfect estimation of the generalized covariance function.
Model dielectric functions and conservation laws
NASA Astrophysics Data System (ADS)
Shirley, Eric L.
2003-03-01
There continues to be a need for calculating dielectric screening of charges in solids. Most work has been done in the random-phase approximation (RPA) with minor variations, which proves to be quite accurate for many applications. However, this is still a time-consuming and computationally intensive approach, and model dielectric functions can be valuable for this reason. This talk discusses several conservation laws related to dielectric screening and a model dielectric function that obeys such laws. Shortcomings of model functions that are difficult to overcome will be touched on, and a possible means of combining results from RPA and model calculations will be addressed.
Wavelet-based functional mixed models
Morris, Jeffrey S.; Carroll, Raymond J.
2009-01-01
Summary Increasingly, scientific studies yield functional data, in which the ideal units of observation are curves and the observed data consist of sets of curves that are sampled on a fine grid. We present new methodology that generalizes the linear mixed model to the functional mixed model framework, with model fitting done by using a Bayesian wavelet-based approach. This method is flexible, allowing functions of arbitrary form and the full range of fixed effects structures and between-curve covariance structures that are available in the mixed model framework. It yields nonparametric estimates of the fixed and random-effects functions as well as the various between-curve and within-curve covariance matrices. The functional fixed effects are adaptively regularized as a result of the non-linear shrinkage prior that is imposed on the fixed effects’ wavelet coefficients, and the random-effect functions experience a form of adaptive regularization because of the separately estimated variance components for each wavelet coefficient. Because we have posterior samples for all model quantities, we can perform pointwise or joint Bayesian inference or prediction on the quantities of the model. The adaptiveness of the method makes it especially appropriate for modelling irregular functional data that are characterized by numerous local features like peaks. PMID:19759841
Response Surface Modeling Using Multivariate Orthogonal Functions
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.; DeLoach, Richard
2001-01-01
A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.
Prediction of Chemical Function: Model Development and ...
The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi
Forward and reverse transfer function model synthesis
NASA Technical Reports Server (NTRS)
Houghton, J. R.
1985-01-01
A process for synthesizing a mathematical model for a linear mechanical system using the forward and reverse Fourier transform functions is described. The differential equation for a system model is given. The Bode conversion of the differential equation, and the frequency and time-domain optimization matching of the model to the forward and reverse transform functions using the geometric simplex method of Nelder and Mead (1965) are examined. The effect of the window function on the linear mechanical system is analyzed. The model is applied to two examples; in one the signal damps down before the end of the time window and in the second the signal has significant energy at the end of the time window.
Modeling NMR lineshapes using logspline density functions.
Raz, J; Fernandez, E J; Gillespie, J
1997-08-01
Distortions in the FID and spin echo due to magnetic field inhomogeneity are proved to have a representation as the characteristic function of some probability distribution. In the special case that the distribution is Cauchy, the model reduces to the conventional Lorentzian model. A more general and flexible representation is presented using the Fourier transform of a logspline density. An algorithm for fitting the model is described, the performance of the model and algorithm is investigated in applications to real and simulated data sets, and the logspline approach is compared to a previous Hermitian spline approach and to the Lorentzian model. The logspline model is more parsimonious than the Hermitian spline model, provides a better fit to real data, and is much less biased than the Lorentzian model.
Incorporating covariates in skewed functional data models.
Li, Meng; Staicu, Ana-Maria; Bondell, Howard D
2015-07-01
We introduce a class of covariate-adjusted skewed functional models (cSFM) designed for functional data exhibiting location-dependent marginal distributions. We propose a semi-parametric copula model for the pointwise marginal distributions, which are allowed to depend on covariates, and the functional dependence, which is assumed covariate invariant. The proposed cSFM framework provides a unifying platform for pointwise quantile estimation and trajectory prediction. We consider a computationally feasible procedure that handles densely as well as sparsely observed functional data. The methods are examined numerically using simulations and is applied to a new tractography study of multiple sclerosis. Furthermore, the methodology is implemented in the R package cSFM, which is publicly available on CRAN.
Generalized exponential function and discrete growth models
NASA Astrophysics Data System (ADS)
Souto Martinez, Alexandre; Silva González, Rodrigo; Lauri Espíndola, Aquino
2009-07-01
Here we show that a particular one-parameter generalization of the exponential function is suitable to unify most of the popular one-species discrete population dynamic models into a simple formula. A physical interpretation is given to this new introduced parameter in the context of the continuous Richards model, which remains valid for the discrete case. From the discretization of the continuous Richards’ model (generalization of the Gompertz and Verhulst models), one obtains a generalized logistic map and we briefly study its properties. Notice, however that the physical interpretation for the introduced parameter persists valid for the discrete case. Next, we generalize the (scramble competition) θ-Ricker discrete model and analytically calculate the fixed points as well as their stabilities. In contrast to previous generalizations, from the generalized θ-Ricker model one is able to retrieve either scramble or contest models.
SMJ's analysis of Ising model correlation functions
NASA Astrophysics Data System (ADS)
Kadanoff, Leo P.; Kohmoto, Mahito
1980-05-01
In a series of recent publications Sato, Miwa, and Jimbo (SMJ) have shown how to derive multispin correlation functions of the two-dimensional Ising model in the continuum, or scaling, limit by analyzing the behavior of the solutions to the two-dimensional version of the Dirac equation. The major purpose of the present work is to describe SMJ's analysis more discursively and in terms closer to that used in previous studies of the Ising model. In addition, new and more compact expressions for their basic equations are derived. A single new answer is obtained: the form of the three-spin correlation function at criticality.
Work Functions for Models of Scandate Surfaces
NASA Technical Reports Server (NTRS)
Mueller, Wolfgang
1997-01-01
The electronic structure, surface dipole properties, and work functions of scandate surfaces have been investigated using the fully relativistic scattered-wave cluster approach. Three different types of model surfaces are considered: (1) a monolayer of Ba-Sc-O on W(100), (2) Ba or BaO adsorbed on Sc2O3 + W, and (3) BaO on SC2O3 + WO3. Changes in the work function due to Ba or BaO adsorption on the different surfaces are calculated by employing the depolarization model of interacting surface dipoles. The largest work function change and the lowest work function of 1.54 eV are obtained for Ba adsorbed on the Sc-O monolayer on W(100). The adsorption of Ba on Sc2O3 + W does not lead to a low work function, but the adsorption of BaO results in a work function of about 1.6-1.9 eV. BaO adsorbed on Sc2O3 + WO3, or scandium tungstates, may also lead to low work functions.
A factor analysis model for functional genomics
Kustra, Rafal; Shioda, Romy; Zhu, Mu
2006-01-01
Background Expression array data are used to predict biological functions of uncharacterized genes by comparing their expression profiles to those of characterized genes. While biologically plausible, this is both statistically and computationally challenging. Typical approaches are computationally expensive and ignore correlations among expression profiles and functional categories. Results We propose a factor analysis model (FAM) for functional genomics and give a two-step algorithm, using genome-wide expression data for yeast and a subset of Gene-Ontology Biological Process functional annotations. We show that the predictive performance of our method is comparable to the current best approach while our total computation time was faster by a factor of 4000. We discuss the unique challenges in performance evaluation of algorithms used for genome-wide functions genomics. Finally, we discuss extensions to our method that can incorporate the inherent correlation structure of the functional categories to further improve predictive performance. Conclusion Our factor analysis model is a computationally efficient technique for functional genomics and provides a clear and unified statistical framework with potential for incorporating important gene ontology information to improve predictions. PMID:16630343
Chromatin fiber functional organization: Some plausible models
NASA Astrophysics Data System (ADS)
Lesne, A.; Victor, J.-M.
2006-03-01
We here present a modeling study of the chromatin fiber functional organization. Multi-scale modeling is required to unravel the complex interplay between the fiber and the DNA levels. It suggests plausible scenarios, including both physical and biological aspects, for fiber condensation, its targeted decompaction, and transcription regulation. We conclude that a major role of the chromatin fiber structure might be to endow DNA with allosteric potentialities and to control DNA transactions by an epigenetic tuning of its mechanical and topological constraints.
The Goodwin model: behind the Hill function.
Gonze, Didier; Abou-Jaoudé, Wassim
2013-01-01
The Goodwin model is a 3-variable model demonstrating the emergence of oscillations in a delayed negative feedback-based system at the molecular level. This prototypical model and its variants have been commonly used to model circadian and other genetic oscillators in biology. The only source of non-linearity in this model is a Hill function, characterizing the repression process. It was mathematically shown that to obtain limit-cycle oscillations, the Hill coefficient must be larger than 8, a value often considered unrealistic. It is indeed difficult to explain such a high coefficient with simple cooperative dynamics. We present here molecular models of the standard Goodwin model, based on single or multisite phosphorylation/dephosphorylation processes of a transcription factor, which have been previously shown to generate switch-like responses. We show that when the phosphorylation/dephosphorylation processes are fast enough, the limit-cycle obtained with a multisite phosphorylation-based mechanism is in very good quantitative agreement with the oscillations observed in the Goodwin model. Conditions in which the detailed mechanism is well approximated by the Goodwin model are given. A variant of the Goodwin model which displays sharp thresholds and relaxation oscillations is also explained by a double phosphorylation/dephosphorylation-based mechanism through a bistable behavior. These results not only provide rational support for the Goodwin model but also highlight the crucial role of the speed of post-translational processes, whose response curve are usually established at a steady state, in biochemical oscillators.
Food Protein Functionality--A New Model.
Foegeding, E Allen
2015-12-01
Proteins in foods serve dual roles as nutrients and structural building blocks. The concept of protein functionality has historically been restricted to nonnutritive functions--such as creating emulsions, foams, and gels--but this places sole emphasis on food quality considerations and potentially overlooks modifications that may also alter nutritional quality or allergenicity. A new model is proposed that addresses the function of proteins in foods based on the length scale(s) responsible for the function. Properties such as flavor binding, color, allergenicity, and digestibility are explained based on the structure of individual molecules; placing this functionality at the nano/molecular scale. At the next higher scale, applications in foods involving gelation, emulsification, and foam formation are based on how proteins form secondary structures that are seen at the nano and microlength scales, collectively called the mesoscale. The macroscale structure represents the arrangements of molecules and mesoscale structures in a food. Macroscale properties determine overall product appearance, stability, and texture. The historical approach of comparing among proteins based on forming and stabilizing specific mesoscale structures remains valid but emphasis should be on a common means for structure formation to allow for comparisons across investigations. For applications in food products, protein functionality should start with identification of functional needs across scales. Those needs are then evaluated relative to how processing and other ingredients could alter desired molecular scale properties, or proper formation of mesoscale structures. This allows for a comprehensive approach to achieving the desired function of proteins in foods.
Mixture models for distance sampling detection functions.
Miller, David L; Thomas, Len
2015-01-01
We present a new class of models for the detection function in distance sampling surveys of wildlife populations, based on finite mixtures of simple parametric key functions such as the half-normal. The models share many of the features of the widely-used "key function plus series adjustment" (K+A) formulation: they are flexible, produce plausible shapes with a small number of parameters, allow incorporation of covariates in addition to distance and can be fitted using maximum likelihood. One important advantage over the K+A approach is that the mixtures are automatically monotonic non-increasing and non-negative, so constrained optimization is not required to ensure distance sampling assumptions are honoured. We compare the mixture formulation to the K+A approach using simulations to evaluate its applicability in a wide set of challenging situations. We also re-analyze four previously problematic real-world case studies. We find mixtures outperform K+A methods in many cases, particularly spiked line transect data (i.e., where detectability drops rapidly at small distances) and larger sample sizes. We recommend that current standard model selection methods for distance sampling detection functions are extended to include mixture models in the candidate set.
Maximum entropy models of ecosystem functioning
NASA Astrophysics Data System (ADS)
Bertram, Jason
2014-12-01
Using organism-level traits to deduce community-level relationships is a fundamental problem in theoretical ecology. This problem parallels the physical one of using particle properties to deduce macroscopic thermodynamic laws, which was successfully achieved with the development of statistical physics. Drawing on this parallel, theoretical ecologists from Lotka onwards have attempted to construct statistical mechanistic theories of ecosystem functioning. Jaynes' broader interpretation of statistical mechanics, which hinges on the entropy maximisation algorithm (MaxEnt), is of central importance here because the classical foundations of statistical physics do not have clear ecological analogues (e.g. phase space, dynamical invariants). However, models based on the information theoretic interpretation of MaxEnt are difficult to interpret ecologically. Here I give a broad discussion of statistical mechanical models of ecosystem functioning and the application of MaxEnt in these models. Emphasising the sample frequency interpretation of MaxEnt, I show that MaxEnt can be used to construct models of ecosystem functioning which are statistical mechanical in the traditional sense using a savanna plant ecology model as an example.
Maximum entropy models of ecosystem functioning
Bertram, Jason
2014-12-05
Using organism-level traits to deduce community-level relationships is a fundamental problem in theoretical ecology. This problem parallels the physical one of using particle properties to deduce macroscopic thermodynamic laws, which was successfully achieved with the development of statistical physics. Drawing on this parallel, theoretical ecologists from Lotka onwards have attempted to construct statistical mechanistic theories of ecosystem functioning. Jaynes’ broader interpretation of statistical mechanics, which hinges on the entropy maximisation algorithm (MaxEnt), is of central importance here because the classical foundations of statistical physics do not have clear ecological analogues (e.g. phase space, dynamical invariants). However, models based on the information theoretic interpretation of MaxEnt are difficult to interpret ecologically. Here I give a broad discussion of statistical mechanical models of ecosystem functioning and the application of MaxEnt in these models. Emphasising the sample frequency interpretation of MaxEnt, I show that MaxEnt can be used to construct models of ecosystem functioning which are statistical mechanical in the traditional sense using a savanna plant ecology model as an example.
Modelling the ecological niche from functional traits
Kearney, Michael; Simpson, Stephen J.; Raubenheimer, David; Helmuth, Brian
2010-01-01
The niche concept is central to ecology but is often depicted descriptively through observing associations between organisms and habitats. Here, we argue for the importance of mechanistically modelling niches based on functional traits of organisms and explore the possibilities for achieving this through the integration of three theoretical frameworks: biophysical ecology (BE), the geometric framework for nutrition (GF) and dynamic energy budget (DEB) models. These three frameworks are fundamentally based on the conservation laws of thermodynamics, describing energy and mass balance at the level of the individual and capturing the prodigious predictive power of the concepts of ‘homeostasis’ and ‘evolutionary fitness’. BE and the GF provide mechanistic multi-dimensional depictions of climatic and nutritional niches, respectively, providing a foundation for linking organismal traits (morphology, physiology, behaviour) with habitat characteristics. In turn, they provide driving inputs and cost functions for mass/energy allocation within the individual as determined by DEB models. We show how integration of the three frameworks permits calculation of activity constraints, vital rates (survival, development, growth, reproduction) and ultimately population growth rates and species distributions. When integrated with contemporary niche theory, functional trait niche models hold great promise for tackling major questions in ecology and evolutionary biology. PMID:20921046
A Generic Modeling Process to Support Functional Fault Model Development
NASA Technical Reports Server (NTRS)
Maul, William A.; Hemminger, Joseph A.; Oostdyk, Rebecca; Bis, Rachael A.
2016-01-01
Functional fault models (FFMs) are qualitative representations of a system's failure space that are used to provide a diagnostic of the modeled system. An FFM simulates the failure effect propagation paths within a system between failure modes and observation points. These models contain a significant amount of information about the system including the design, operation and off nominal behavior. The development and verification of the models can be costly in both time and resources. In addition, models depicting similar components can be distinct, both in appearance and function, when created individually, because there are numerous ways of representing the failure space within each component. Generic application of FFMs has the advantages of software code reuse: reduction of time and resources in both development and verification, and a standard set of component models from which future system models can be generated with common appearance and diagnostic performance. This paper outlines the motivation to develop a generic modeling process for FFMs at the component level and the effort to implement that process through modeling conventions and a software tool. The implementation of this generic modeling process within a fault isolation demonstration for NASA's Advanced Ground System Maintenance (AGSM) Integrated Health Management (IHM) project is presented and the impact discussed.
Modeling of functionally graded piezoelectric ultrasonic transducers.
Rubio, Wilfredo Montealegre; Buiochi, Flávio; Adamowski, Julio Cezar; Silva, Emílio Carlos Nelli
2009-05-01
The application of functionally graded material (FGM) concept to piezoelectric transducers allows the design of composite transducers without interfaces, due to the continuous change of property values. Thus, large improvements can be achieved, as reduction of stress concentration, increasing of bonding strength, and bandwidth. This work proposes to design and to model FGM piezoelectric transducers and to compare their performance with non-FGM ones. Analytical and finite element (FE) modeling of FGM piezoelectric transducers radiating a plane pressure wave in fluid medium are developed and their results are compared. The ANSYS software is used for the FE modeling. The analytical model is based on FGM-equivalent acoustic transmission-line model, which is implemented using MATLAB software. Two cases are considered: (i) the transducer emits a pressure wave in water and it is composed of a graded piezoceramic disk, and backing and matching layers made of homogeneous materials; (ii) the transducer has no backing and matching layer; in this case, no external load is simulated. Time and frequency pressure responses are obtained through a transient analysis. The material properties are graded along thickness direction. Linear and exponential gradation functions are implemented to illustrate the influence of gradation on the transducer pressure response, electrical impedance, and resonance frequencies.
Functional Security Model: Managers Engineers Working Together
NASA Astrophysics Data System (ADS)
Guillen, Edward Paul; Quintero, Rulfo
2008-05-01
Information security has a wide variety of solutions including security policies, network architectures and technological applications, they are usually designed and implemented by security architects, but in its own complexity this solutions are difficult to understand by company managers and they are who finally fund the security project. The main goal of the functional security model is to achieve a solid security platform reliable and understandable in the whole company without leaving of side the rigor of the recommendations and the laws compliance in a single frame. This paper shows a general scheme of the model with the use of important standards and tries to give an integrated solution.
A Green's function quantum average atom model
Starrett, Charles Edward
2015-05-21
A quantum average atom model is reformulated using Green's functions. This allows integrals along the real energy axis to be deformed into the complex plane. The advantage being that sharp features such as resonances and bound states are broadened by a Lorentzian with a half-width chosen for numerical convenience. An implementation of this method therefore avoids numerically challenging resonance tracking and the search for weakly bound states, without changing the physical content or results of the model. A straightforward implementation results in up to a factor of 5 speed-up relative to an optimized orbital based code.
Mathematical Models of Cardiac Pacemaking Function
NASA Astrophysics Data System (ADS)
Li, Pan; Lines, Glenn T.; Maleckar, Mary M.; Tveito, Aslak
2013-10-01
Over the past half century, there has been intense and fruitful interaction between experimental and computational investigations of cardiac function. This interaction has, for example, led to deep understanding of cardiac excitation-contraction coupling; how it works, as well as how it fails. However, many lines of inquiry remain unresolved, among them the initiation of each heartbeat. The sinoatrial node, a cluster of specialized pacemaking cells in the right atrium of the heart, spontaneously generates an electro-chemical wave that spreads through the atria and through the cardiac conduction system to the ventricles, initiating the contraction of cardiac muscle essential for pumping blood to the body. Despite the fundamental importance of this primary pacemaker, this process is still not fully understood, and ionic mechanisms underlying cardiac pacemaking function are currently under heated debate. Several mathematical models of sinoatrial node cell membrane electrophysiology have been constructed as based on different experimental data sets and hypotheses. As could be expected, these differing models offer diverse predictions about cardiac pacemaking activities. This paper aims to present the current state of debate over the origins of the pacemaking function of the sinoatrial node. Here, we will specifically review the state-of-the-art of cardiac pacemaker modeling, with a special emphasis on current discrepancies, limitations, and future challenges.
The Pleiades mass function: Models versus observations
NASA Astrophysics Data System (ADS)
Moraux, E.; Kroupa, P.; Bouvier, J.
2004-10-01
Two stellar-dynamical models of binary-rich embedded proto-Orion-Nebula-type clusters that evolve to Pleiades-like clusters are studied with an emphasis on comparing the stellar mass function with observational constraints. By the age of the Pleiades (about 100 Myr) both models show a similar degree of mass segregation which also agrees with observational constraints. This thus indicates that the Pleiades is well relaxed and that it is suffering from severe amnesia. It is found that the initial mass function (IMF) must have been indistinguishable from the standard or Galactic-field IMF for stars with mass m ≲ 2 M⊙, provided the Pleiades precursor had a central density of about 104.8 stars/pc3. A denser model with 105.8 stars/pc3 also leads to reasonable agreement with observational constraints, but owing to the shorter relaxation time of the embedded cluster it evolves through energy equipartition to a mass-segregated condition just prior to residual-gas expulsion. This model consequently preferentially loses low-mass stars and brown dwarfs (BDs), but the effect is not very pronounced. The empirical data indicate that the Pleiades IMF may have been steeper than the Salpeter for stars with m⪆ 2 M⊙.
Parametric modeling of quantile regression coefficient functions.
Frumento, Paolo; Bottai, Matteo
2016-03-01
Estimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile regression is foremost among the utilized methods. The coefficients of a quantile regression model depend on the order of the quantile being estimated. For example, the coefficients for the median are generally different from those of the 10th centile. In this article, we describe an approach to modeling the regression coefficients as parametric functions of the order of the quantile. This approach may have advantages in terms of parsimony, efficiency, and may expand the potential of statistical modeling. Goodness-of-fit measures and testing procedures are discussed, and the results of a simulation study are presented. We apply the method to analyze the data that motivated this work. The described method is implemented in the qrcm R package.
Transfer function modeling of damping mechanisms in distributed parameter models
NASA Technical Reports Server (NTRS)
Slater, J. C.; Inman, D. J.
1994-01-01
This work formulates a method for the modeling of material damping characteristics in distributed parameter models which may be easily applied to models such as rod, plate, and beam equations. The general linear boundary value vibration equation is modified to incorporate hysteresis effects represented by complex stiffness using the transfer function approach proposed by Golla and Hughes. The governing characteristic equations are decoupled through separation of variables yielding solutions similar to those of undamped classical theory, allowing solution of the steady state as well as transient response. Example problems and solutions are provided demonstrating the similarity of the solutions to those of the classical theories and transient responses of nonviscous systems.
Functionalized Anatomical Models for EM-Neuron Interaction Modeling
Neufeld, Esra; Cassará, Antonino Mario; Montanaro, Hazael; Kuster, Niels; Kainz, Wolfgang
2017-01-01
The understanding of interactions between electromagnetic (EM) fields and nerves are crucial in contexts ranging from therapeutic neurostimulation to low frequency EM exposure safety. To properly consider the impact of in-vivo induced field inhomogeneity on non-linear neuronal dynamics, coupled EM-neuronal dynamics modeling is required. For that purpose, novel functionalized computable human phantoms have been developed. Their implementation and the systematic verification of the integrated anisotropic quasi-static EM solver and neuronal dynamics modeling functionality, based on the method of manufactured solutions and numerical reference data, is described. Electric and magnetic stimulation of the ulnar and sciatic nerve were modeled to help understanding a range of controversial issues related to the magnitude and optimal determination of strength-duration (SD) time constants. The results indicate the importance of considering the stimulation-specific inhomogeneous field distributions (especially at tissue interfaces), realistic models of non-linear neuronal dynamics, very short pulses, and suitable SD extrapolation models. These results and the functionalized computable phantom will influence and support the development of safe and effective neuroprosthetic devices and novel electroceuticals. Furthermore they will assist the evaluation of existing low frequency exposure standards for the entire population under all exposure conditions. PMID:27224508
Functionalized anatomical models for EM-neuron Interaction modeling
NASA Astrophysics Data System (ADS)
Neufeld, Esra; Cassará, Antonino Mario; Montanaro, Hazael; Kuster, Niels; Kainz, Wolfgang
2016-06-01
The understanding of interactions between electromagnetic (EM) fields and nerves are crucial in contexts ranging from therapeutic neurostimulation to low frequency EM exposure safety. To properly consider the impact of in vivo induced field inhomogeneity on non-linear neuronal dynamics, coupled EM-neuronal dynamics modeling is required. For that purpose, novel functionalized computable human phantoms have been developed. Their implementation and the systematic verification of the integrated anisotropic quasi-static EM solver and neuronal dynamics modeling functionality, based on the method of manufactured solutions and numerical reference data, is described. Electric and magnetic stimulation of the ulnar and sciatic nerve were modeled to help understanding a range of controversial issues related to the magnitude and optimal determination of strength-duration (SD) time constants. The results indicate the importance of considering the stimulation-specific inhomogeneous field distributions (especially at tissue interfaces), realistic models of non-linear neuronal dynamics, very short pulses, and suitable SD extrapolation models. These results and the functionalized computable phantom will influence and support the development of safe and effective neuroprosthetic devices and novel electroceuticals. Furthermore they will assist the evaluation of existing low frequency exposure standards for the entire population under all exposure conditions.
Dynamic geometry, brain function modeling, and consciousness.
Roy, Sisir; Llinás, Rodolfo
2008-01-01
Pellionisz and Llinás proposed, years ago, a geometric interpretation towards understanding brain function. This interpretation assumes that the relation between the brain and the external world is determined by the ability of the central nervous system (CNS) to construct an internal model of the external world using an interactive geometrical relationship between sensory and motor expression. This approach opened new vistas not only in brain research but also in understanding the foundations of geometry itself. The approach named tensor network theory is sufficiently rich to allow specific computational modeling and addressed the issue of prediction, based on Taylor series expansion properties of the system, at the neuronal level, as a basic property of brain function. It was actually proposed that the evolutionary realm is the backbone for the development of an internal functional space that, while being purely representational, can interact successfully with the totally different world of the so-called "external reality". Now if the internal space or functional space is endowed with stochastic metric tensor properties, then there will be a dynamic correspondence between events in the external world and their specification in the internal space. We shall call this dynamic geometry since the minimal time resolution of the brain (10-15 ms), associated with 40 Hz oscillations of neurons and their network dynamics, is considered to be responsible for recognizing external events and generating the concept of simultaneity. The stochastic metric tensor in dynamic geometry can be written as five-dimensional space-time where the fifth dimension is a probability space as well as a metric space. This extra dimension is considered an imbedded degree of freedom. It is worth noticing that the above-mentioned 40 Hz oscillation is present both in awake and dream states where the central difference is the inability of phase resetting in the latter. This framework of dynamic
Linear functional minimization for inverse modeling
Barajas-Solano, David A.; Wohlberg, Brendt Egon; Vesselinov, Velimir Valentinov; Tartakovsky, Daniel M.
2015-06-01
In this paper, we present a novel inverse modeling strategy to estimate spatially distributed parameters of nonlinear models. The maximum a posteriori (MAP) estimators of these parameters are based on a likelihood functional, which contains spatially discrete measurements of the system parameters and spatiotemporally discrete measurements of the transient system states. The piecewise continuity prior for the parameters is expressed via Total Variation (TV) regularization. The MAP estimator is computed by minimizing a nonquadratic objective equipped with the TV operator. We apply this inversion algorithm to estimate hydraulic conductivity of a synthetic confined aquifer from measurements of conductivity and hydraulic head. The synthetic conductivity field is composed of a low-conductivity heterogeneous intrusion into a high-conductivity heterogeneous medium. Our algorithm accurately reconstructs the location, orientation, and extent of the intrusion from the steady-state data only. Finally, addition of transient measurements of hydraulic head improves the parameter estimation, accurately reconstructing the conductivity field in the vicinity of observation locations.
Frequency response function-based model updating using Kriging model
NASA Astrophysics Data System (ADS)
Wang, J. T.; Wang, C. J.; Zhao, J. P.
2017-03-01
An acceleration frequency response function (FRF) based model updating method is presented in this paper, which introduces Kriging model as metamodel into the optimization process instead of iterating the finite element analysis directly. The Kriging model is taken as a fast running model that can reduce solving time and facilitate the application of intelligent algorithms in model updating. The training samples for Kriging model are generated by the design of experiment (DOE), whose response corresponds to the difference between experimental acceleration FRFs and its counterpart of finite element model (FEM) at selected frequency points. The boundary condition is taken into account, and a two-step DOE method is proposed for reducing the number of training samples. The first step is to select the design variables from the boundary condition, and the selected variables will be passed to the second step for generating the training samples. The optimization results of the design variables are taken as the updated values of the design variables to calibrate the FEM, and then the analytical FRFs tend to coincide with the experimental FRFs. The proposed method is performed successfully on a composite structure of honeycomb sandwich beam, after model updating, the analytical acceleration FRFs have a significant improvement to match the experimental data especially when the damping ratios are adjusted.
Modeling the three-point correlation function
Marin, Felipe; Wechsler, Risa; Frieman, Joshua A.; Nichol, Robert; /Portsmouth U., ICG
2007-04-01
We present new theoretical predictions for the galaxy three-point correlation function (3PCF) using high-resolution dissipationless cosmological simulations of a flat {Lambda}CDM Universe which resolve galaxy-size halos and subhalos. We create realistic mock galaxy catalogs by assigning luminosities and colors to dark matter halos and subhalos, and we measure the reduced 3PCF as a function of luminosity and color in both real and redshift space. As galaxy luminosity and color are varied, we find small differences in the amplitude and shape dependence of the reduced 3PCF, at a level qualitatively consistent with recent measurements from the SDSS and 2dFGRS. We confirm that discrepancies between previous 3PCF measurements can be explained in part by differences in binning choices. We explore the degree to which a simple local bias model can fit the simulated 3PCF. The agreement between the model predictions and galaxy 3PCF measurements lends further credence to the straightforward association of galaxies with CDM halos and subhalos.
Linearized Functional Minimization for Inverse Modeling
Wohlberg, Brendt; Tartakovsky, Daniel M.; Dentz, Marco
2012-06-21
Heterogeneous aquifers typically consist of multiple lithofacies, whose spatial arrangement significantly affects flow and transport. The estimation of these lithofacies is complicated by the scarcity of data and by the lack of a clear correlation between identifiable geologic indicators and attributes. We introduce a new inverse-modeling approach to estimate both the spatial extent of hydrofacies and their properties from sparse measurements of hydraulic conductivity and hydraulic head. Our approach is to minimize a functional defined on the vectors of values of hydraulic conductivity and hydraulic head fields defined on regular grids at a user-determined resolution. This functional is constructed to (i) enforce the relationship between conductivity and heads provided by the groundwater flow equation, (ii) penalize deviations of the reconstructed fields from measurements where they are available, and (iii) penalize reconstructed fields that are not piece-wise smooth. We develop an iterative solver for this functional that exploits a local linearization of the mapping from conductivity to head. This approach provides a computationally efficient algorithm that rapidly converges to a solution. A series of numerical experiments demonstrates the robustness of our approach.
A Prediction Model of the Capillary Pressure J-Function
Xu, W. S.; Luo, P. Y.; Sun, L.; Lin, N.
2016-01-01
The capillary pressure J-function is a dimensionless measure of the capillary pressure of a fluid in a porous medium. The function was derived based on a capillary bundle model. However, the dependence of the J-function on the saturation Sw is not well understood. A prediction model for it is presented based on capillary pressure model, and the J-function prediction model is a power function instead of an exponential or polynomial function. Relative permeability is calculated with the J-function prediction model, resulting in an easier calculation and results that are more representative. PMID:27603701
Models of Protocellular Structure, Function and Evolution
NASA Technical Reports Server (NTRS)
New, Michael H.; Pohorille, Andrew; Szostak, Jack W.; Keefe, Tony; Lanyi, Janos K.; DeVincenzi, Donald L. (Technical Monitor)
2001-01-01
In the absence of any record of protocells, the most direct way to test our understanding, of the origin of cellular life is to construct laboratory models that capture important features of protocellular systems. Such efforts are currently underway in a collaborative project between NASA-Ames, Harvard Medical School and University of California. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures. The centerpiece of this project is a method for the in vitro evolution of protein enzymes toward arbitrary catalytic targets. A similar approach has already been developed for nucleic acids in which a small number of functional molecules are selected from a large, random population of candidates. The selected molecules are next vastly multiplied using the polymerase chain reaction.
Models of protocellular structures, functions and evolution
NASA Technical Reports Server (NTRS)
Pohorille, Andrew; New, Michael H.; DeVincenzi, Donald L. (Technical Monitor)
2000-01-01
The central step in the origin of life was the emergence of organized structures from organic molecules available on the early earth. These predecessors to modern cells, called 'proto-cells,' were simple, membrane bounded structures able to maintain themselves, grow, divide, and evolve. Since there is no fossil record of these earliest of life forms, it is a scientific challenge to discover plausible mechanisms for how these entities formed and functioned. To meet this challenge, it is essential to create laboratory models of protocells that capture the main attributes associated with living systems, while remaining consistent with known, or inferred, protobiological conditions. This report provides an overview of a project which has focused on protocellular metabolism and the coupling of metabolism to energy transduction. We have assumed that the emergence of systems endowed with genomes and capable of Darwinian evolution was preceded by a pre-genomic phase, in which protocells functioned and evolved using mostly proteins, without self-replicating nucleic acids such as RNA.
Linear functional minimization for inverse modeling
Barajas-Solano, David A.; Wohlberg, Brendt Egon; Vesselinov, Velimir Valentinov; ...
2015-06-01
In this paper, we present a novel inverse modeling strategy to estimate spatially distributed parameters of nonlinear models. The maximum a posteriori (MAP) estimators of these parameters are based on a likelihood functional, which contains spatially discrete measurements of the system parameters and spatiotemporally discrete measurements of the transient system states. The piecewise continuity prior for the parameters is expressed via Total Variation (TV) regularization. The MAP estimator is computed by minimizing a nonquadratic objective equipped with the TV operator. We apply this inversion algorithm to estimate hydraulic conductivity of a synthetic confined aquifer from measurements of conductivity and hydraulicmore » head. The synthetic conductivity field is composed of a low-conductivity heterogeneous intrusion into a high-conductivity heterogeneous medium. Our algorithm accurately reconstructs the location, orientation, and extent of the intrusion from the steady-state data only. Finally, addition of transient measurements of hydraulic head improves the parameter estimation, accurately reconstructing the conductivity field in the vicinity of observation locations.« less
Modeling of GPS tropospheric delay wet Neill mapping function (NMF)
NASA Astrophysics Data System (ADS)
Sakidin, Hamzah; Ahmad, Asmala; Bugis, Ismadi
2014-10-01
The modeling of the GPS tropospheric delay mapping function should be revised by modifying or simplify its mathematical model. Some current mapping functions models are separated into hydrostatic and the wet part. The current tropospheric delay models use mapping functions in the form of continued fractions. This model is quite complex and need to be simplified. By using regression method, the wet mapping function models has been selected to be simplified. There are eleven operations for wet mapping function component of Neill Mapping Function (NMF), to be carried out before getting the mapping function scale factor. So, there is a need to simplify the mapping function models to allow faster calculation and also better understanding of the models.
Modeling Bamboo as a Functionally Graded Material
Silva, Emilio Carlos Nelli; Walters, Matthew C.; Paulino, Glaucio H.
2008-02-15
Natural fibers are promising for engineering applications due to their low cost. They are abundantly available in tropical and subtropical regions of the world, and they can be employed as construction materials. Among natural fibers, bamboo has been widely used for housing construction around the world. Bamboo is an optimized composite material which exploits the concept of Functionally Graded Material (FGM). Biological structures, such as bamboo, are composite materials that have complicated shapes and material distribution inside their domain, and thus the use of numerical methods such as the finite element method and multiscale methods such as homogenization, can help to further understanding of the mechanical behavior of these materials. The objective of this work is to explore techniques such as the finite element method and homogenization to investigate the structural behavior of bamboo. The finite element formulation uses graded finite elements to capture the varying material distribution through the bamboo wall. To observe bamboo behavior under applied loads, simulations are conducted considering a spatially-varying Young's modulus, an averaged Young's modulus, and orthotropic constitutive properties obtained from homogenization theory. The homogenization procedure uses effective, axisymmetric properties estimated from the spatially-varying bamboo composite. Three-dimensional models of bamboo cells were built and simulated under tension, torsion, and bending load cases.
Mirror neurons: functions, mechanisms and models.
Oztop, Erhan; Kawato, Mitsuo; Arbib, Michael A
2013-04-12
Mirror neurons for manipulation fire both when the animal manipulates an object in a specific way and when it sees another animal (or the experimenter) perform an action that is more or less similar. Such neurons were originally found in macaque monkeys, in the ventral premotor cortex, area F5 and later also in the inferior parietal lobule. Recent neuroimaging data indicate that the adult human brain is endowed with a "mirror neuron system," putatively containing mirror neurons and other neurons, for matching the observation and execution of actions. Mirror neurons may serve action recognition in monkeys as well as humans, whereas their putative role in imitation and language may be realized in human but not in monkey. This article shows the important role of computational models in providing sufficient and causal explanations for the observed phenomena involving mirror systems and the learning processes which form them, and underlines the need for additional circuitry to lift up the monkey mirror neuron circuit to sustain the posited cognitive functions attributed to the human mirror neuron system.
Models of Protocellular Structure, Function and Evolution
NASA Technical Reports Server (NTRS)
New, Michael H.; Pohorille, Andrew; Szostak, Jack W.; Keefe, Tony; Lanyi, Janos K.
2001-01-01
In the absence of any record of protocells, the most direct way to test our understanding of the origin of cellular life is to construct laboratory models that capture important features of protocellular systems. Such efforts are currently underway in a collaborative project between NASA-Ames, Harvard Medical School and University of California. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures. The centerpiece of this project is a method for the in vitro evolution of protein enzymes toward arbitrary catalytic targets. A similar approach has already been developed for nucleic acids in which a small number of functional molecules are selected from a large, random population of candidates. The selected molecules are next vastly multiplied using the polymerase chain reaction. A mutagenic approach, in which the sequences of selected molecules are randomly altered, can yield further improvements in performance or alterations of specificities. Unfortunately, the catalytic potential of nucleic acids is rather limited. Proteins are more catalytically capable but cannot be directly amplified. In the new technique, this problem is circumvented by covalently linking each protein of the initial, diverse, pool to the RNA sequence that codes for it. Then, selection is performed on the proteins, but the nucleic acids are replicated. Additional information is contained in the original extended abstract.
Modeling Bamboo as a Functionally Graded Material
NASA Astrophysics Data System (ADS)
Silva, Emílio Carlos Nelli; Walters, Matthew C.; Paulino, Glaucio H.
2008-02-01
Natural fibers are promising for engineering applications due to their low cost. They are abundantly available in tropical and subtropical regions of the world, and they can be employed as construction materials. Among natural fibers, bamboo has been widely used for housing construction around the world. Bamboo is an optimized composite material which exploits the concept of Functionally Graded Material (FGM). Biological structures, such as bamboo, are composite materials that have complicated shapes and material distribution inside their domain, and thus the use of numerical methods such as the finite element method and multiscale methods such as homogenization, can help to further understanding of the mechanical behavior of these materials. The objective of this work is to explore techniques such as the finite element method and homogenization to investigate the structural behavior of bamboo. The finite element formulation uses graded finite elements to capture the varying material distribution through the bamboo wall. To observe bamboo behavior under applied loads, simulations are conducted considering a spatially-varying Young's modulus, an averaged Young's modulus, and orthotropic constitutive properties obtained from homogenization theory. The homogenization procedure uses effective, axisymmetric properties estimated from the spatially-varying bamboo composite. Three-dimensional models of bamboo cells were built and simulated under tension, torsion, and bending load cases.
School Teams up for SSP Functional Models
NASA Astrophysics Data System (ADS)
Pignolet, G.; Lallemand, R.; Celeste, A.; von Muldau, H.
2002-01-01
Space Solar Power systems appear increasingly as one of the major solutions to the upcoming global energy crisis, by collecting solar energy in space where this is most easy, and sending it by microwave beam to the surface of the planet, where the need for controlled energy is located. While fully operational systems are still decades away, the need for major development efforts is with us now. Yet, for many decision-makers and for most of the public, SSP often still sounds like science fiction. Six functional demonstration systems, based on the Japanese SPS-2000 concept, have been built as a result of a cooperation between France and Japan, and they are currently used extensively, in Japan, in Europe and in North America, for executive presentations as well as for public exhibitions. There is demand for more models, both for science museums and for use by energy dedicated groups, and a senior high school in La Reunion, France, has picked up the challenge to make the production of such models an integrated practical school project for pre-college students. In December 2001, the administration and the teachers of the school have evaluated the feasibility of the project and eventually taken the go decision for the school year 2002- 2003, when for education purposes a temporary "school business company" will be incorporated with the goal to study and manufacture a limited series of professional quality SSP demonstration models, and to sell them world- wide to institutions and advocacy groups concerned with energy problems and with the environment. The different sections of the school will act as the different services of an integrated business : based on the current existing models, the electronic section will redesign the energy management system and the microwave projector module, while the mechanical section of the school will adapt and re-conceive the whole packaging of the demonstrator. The French and foreign language sections will write up a technical manual for
Kolker, Eugene
2009-01-01
Background Predicting protein function from primary sequence is an important open problem in modern biology. Not only are there many thousands of proteins of unknown function, current approaches for predicting function must be improved upon. One problem in particular is overly-specific function predictions which we address here with a new statistical model of the relationship between protein sequence similarity and protein function similarity. Methodology Our statistical model is based on sets of proteins with experimentally validated functions and numeric measures of function specificity and function similarity derived from the Gene Ontology. The model predicts the similarity of function between two proteins given their amino acid sequence similarity measured by statistics from the BLAST sequence alignment algorithm. A novel aspect of our model is that it predicts the degree of function similarity shared between two proteins over a continuous range of sequence similarity, facilitating prediction of function with an appropriate level of specificity. Significance Our model shows nearly exact function similarity for proteins with high sequence similarity (bit score >244.7, e-value >1e−62, non-redundant NCBI protein database (NRDB)) and only small likelihood of specific function match for proteins with low sequence similarity (bit score <54.6, e-value <1e−05, NRDB). For sequence similarity ranges in between our annotation model shows an increasing relationship between function similarity and sequence similarity, but with considerable variability. We applied the model to a large set of proteins of unknown function, and predicted functions for thousands of these proteins ranging from general to very specific. We also applied the model to a data set of proteins with previously assigned, specific functions that were electronically based. We show that, on average, these prior function predictions are more specific (quite possibly overly-specific) compared to
Predicting Transfer Performance: A Comparison of Competing Function Learning Models
ERIC Educational Resources Information Center
McDaniel, Mark A.; Dimperio, Eric; Griego, Jacqueline A.; Busemeyer, Jerome R.
2009-01-01
The population of linear experts (POLE) model suggests that function learning and transfer are mediated by activation of a set of prestored linear functions that together approximate the given function (Kalish, Lewandowsky, & Kruschke, 2004). In the extrapolation-association (EXAM) model, an exemplar-based architecture associates trained input…
Functional state modelling approach validation for yeast and bacteria cultivations
Roeva, Olympia; Pencheva, Tania
2014-01-01
In this paper, the functional state modelling approach is validated for modelling of the cultivation of two different microorganisms: yeast (Saccharomyces cerevisiae) and bacteria (Escherichia coli). Based on the available experimental data for these fed-batch cultivation processes, three different functional states are distinguished, namely primary product synthesis state, mixed oxidative state and secondary product synthesis state. Parameter identification procedures for different local models are performed using genetic algorithms. The simulation results show high degree of adequacy of the models describing these functional states for both S. cerevisiae and E. coli cultivations. Thus, the local models are validated for the cultivation of both microorganisms. This fact is a strong structure model verification of the functional state modelling theory not only for a set of yeast cultivations, but also for bacteria cultivation. As such, the obtained results demonstrate the efficiency and efficacy of the functional state modelling approach. PMID:26740778
Functional state modelling approach validation for yeast and bacteria cultivations.
Roeva, Olympia; Pencheva, Tania
2014-09-03
In this paper, the functional state modelling approach is validated for modelling of the cultivation of two different microorganisms: yeast (Saccharomyces cerevisiae) and bacteria (Escherichia coli). Based on the available experimental data for these fed-batch cultivation processes, three different functional states are distinguished, namely primary product synthesis state, mixed oxidative state and secondary product synthesis state. Parameter identification procedures for different local models are performed using genetic algorithms. The simulation results show high degree of adequacy of the models describing these functional states for both S. cerevisiae and E. coli cultivations. Thus, the local models are validated for the cultivation of both microorganisms. This fact is a strong structure model verification of the functional state modelling theory not only for a set of yeast cultivations, but also for bacteria cultivation. As such, the obtained results demonstrate the efficiency and efficacy of the functional state modelling approach.
A Functional Test Platform for the Community Land Model
Xu, Yang; Thornton, Peter E; King, Anthony Wayne; Steed, Chad A; Gu, Lianhong; Schuchart, Joseph
2014-01-01
A functional test platform is presented to create direct linkages between site measurements and the process-based ecosystem model within the Community Earth System Models (CESM). The platform consists of three major parts: 1) interactive user interfaces, 2) functional test model and 3) observational datasets. It provides much needed integration interfaces for both field experimentalists and ecosystem modelers to improve the model s representation of ecosystem processes within the CESM framework without large software overhead.
Study on Dielectric Function Models for Surface Plasmon Resonance Structure
Adikan, Faisal Rafiq Mahamd
2014-01-01
The most common permittivity function models are compared and identifying the best model for further studies is desired. For this study, simulations using several different models and an analytical analysis on a practical surface Plasmon structure were done with an accuracy of ∼94.4% with respect to experimental data. Finite element method, combined with dielectric properties extracted from the Brendel-Bormann function model, was utilized, the latter being chosen from a comparative study on four available models. PMID:24616635
Schwinger model Green functions with topological effects
NASA Astrophysics Data System (ADS)
Radożycki, Tomasz
1999-11-01
The fermion propagator and the four-fermion Green function in massless QED2 are explicitly found with topological effects taken into account. The corrections due to instanton sectors k=+/-1, contributing to the propagator, are shown to be just the homogenous terms admitted by the Dyson-Schwinger equation for S. In the case of the four-fermion function also sectors k=+/-2 are included in the consideration. The quark condensates are then calculated and are shown to satisfy the cluster property. The θ dependence exhibited by the Green functions corresponds to and may be removed by performing certain chiral gauge transformation.
Fourier functional analysis for unsteady aerodynamic modeling
NASA Technical Reports Server (NTRS)
Lan, C. Edward; Chin, Suei
1991-01-01
A method based on Fourier analysis is developed to analyze the force and moment data obtained in large amplitude forced oscillation tests at high angles of attack. The aerodynamic models for normal force, lift, drag, and pitching moment coefficients are built up from a set of aerodynamic responses to harmonic motions at different frequencies. Based on the aerodynamic models of harmonic data, the indicial responses are formed. The final expressions for the models involve time integrals of the indicial type advocated by Tobak and Schiff. Results from linear two- and three-dimensional unsteady aerodynamic theories as well as test data for a 70-degree delta wing are used to verify the models. It is shown that the present modeling method is accurate in producing the aerodynamic responses to harmonic motions and the ramp type motions. The model also produces correct trend for a 70-degree delta wing in harmonic motion with different mean angles-of-attack. However, the current model cannot be used to extrapolate data to higher angles-of-attack than that of the harmonic motions which form the aerodynamic model. For linear ramp motions, a special method is used to calculate the corresponding frequency and phase angle at a given time. The calculated results from modeling show a higher lift peak for linear ramp motion than for harmonic ramp motion. The current model also shows reasonably good results for the lift responses at different angles of attack.
Modelling protein functional domains in signal transduction using Maude
NASA Technical Reports Server (NTRS)
Sriram, M. G.
2003-01-01
Modelling of protein-protein interactions in signal transduction is receiving increased attention in computational biology. This paper describes recent research in the application of Maude, a symbolic language founded on rewriting logic, to the modelling of functional domains within signalling proteins. Protein functional domains (PFDs) are a critical focus of modern signal transduction research. In general, Maude models can simulate biological signalling networks and produce specific testable hypotheses at various levels of abstraction. Developing symbolic models of signalling proteins containing functional domains is important because of the potential to generate analyses of complex signalling networks based on structure-function relationships.
Latent Growth Modeling for Logistic Response Functions
ERIC Educational Resources Information Center
Choi, Jaehwa; Harring, Jeffrey R.; Hancock, Gregory R.
2009-01-01
Throughout much of the social and behavioral sciences, latent growth modeling (latent curve analysis) has become an important tool for understanding individuals' longitudinal change. Although nonlinear variations of latent growth models appear in the methodological and applied literature, a notable exclusion is the treatment of growth following…
Dispersion analysis with inverse dielectric function modelling.
Mayerhöfer, Thomas G; Ivanovski, Vladimir; Popp, Jürgen
2016-11-05
We investigate how dispersion analysis can profit from the use of a Lorentz-type description of the inverse dielectric function. In particular at higher angles of incidence, reflectance spectra using p-polarized light are dominated by bands from modes that have their transition moments perpendicular to the surface. Accordingly, the spectra increasingly resemble inverse dielectric functions. A corresponding description can therefore eliminate the complex dependencies of the dispersion parameters, allow their determination and facilitate a more accurate description of the optical properties of single crystals.
Hydroacoustic forcing function modeling using DNS database
NASA Technical Reports Server (NTRS)
Zawadzki, I.; Gershfield, J. L.; Na, Y.; Wang, M.
1996-01-01
A wall pressure frequency spectrum model (Blake 1971 ) has been evaluated using databases from Direct Numerical Simulations (DNS) of a turbulent boundary layer (Na & Moin 1996). Good agreement is found for moderate to strong adverse pressure gradient flows in the absence of separation. In the separated flow region, the model underpredicts the directly calculated spectra by an order of magnitude. The discrepancy is attributed to the violation of the model assumptions in that part of the flow domain. DNS computed coherence length scales and the normalized wall pressure cross-spectra are compared with experimental data. The DNS results are consistent with experimental observations.
Stochastic Functional Data Analysis: A Diffusion Model-based Approach
Zhu, Bin; Song, Peter X.-K.; Taylor, Jeremy M.G.
2011-01-01
Summary This paper presents a new modeling strategy in functional data analysis. We consider the problem of estimating an unknown smooth function given functional data with noise. The unknown function is treated as the realization of a stochastic process, which is incorporated into a diffusion model. The method of smoothing spline estimation is connected to a special case of this approach. The resulting models offer great flexibility to capture the dynamic features of functional data, and allow straightforward and meaningful interpretation. The likelihood of the models is derived with Euler approximation and data augmentation. A unified Bayesian inference method is carried out via a Markov Chain Monte Carlo algorithm including a simulation smoother. The proposed models and methods are illustrated on some prostate specific antigen data, where we also show how the models can be used for forecasting. PMID:21418053
Using a Functional Model to Develop a Mathematical Formula
ERIC Educational Resources Information Center
Otto, Charlotte A.; Everett, Susan A.; Luera, Gail R.
2008-01-01
The unifying theme of models was incorporated into a required Science Capstone course for pre-service elementary teachers based on national standards in science and mathematics. A model of a teeter-totter was selected for use as an example of a functional model for gathering data as well as a visual model of a mathematical equation for developing…
Variable-Domain Functional Regression for Modeling ICU Data.
Gellar, Jonathan E; Colantuoni, Elizabeth; Needham, Dale M; Crainiceanu, Ciprian M
2014-12-01
We introduce a class of scalar-on-function regression models with subject-specific functional predictor domains. The fundamental idea is to consider a bivariate functional parameter that depends both on the functional argument and on the width of the functional predictor domain. Both parametric and nonparametric models are introduced to fit the functional coefficient. The nonparametric model is theoretically and practically invariant to functional support transformation, or support registration. Methods were motivated by and applied to a study of association between daily measures of the Intensive Care Unit (ICU) Sequential Organ Failure Assessment (SOFA) score and two outcomes: in-hospital mortality, and physical impairment at hospital discharge among survivors. Methods are generally applicable to a large number of new studies that record a continuous variables over unequal domains.
Kajiwara, Tsuyoshi; Sasaki, Toru; Takeuchi, Yasuhiro
2015-02-01
We present a constructive method for Lyapunov functions for ordinary differential equation models of infectious diseases in vivo. We consider models derived from the Nowak-Bangham models. We construct Lyapunov functions for complex models using those of simpler models. Especially, we construct Lyapunov functions for models with an immune variable from those for models without an immune variable, a Lyapunov functions of a model with absorption effect from that for a model without absorption effect. We make the construction clear for Lyapunov functions proposed previously, and present new results with our method.
A Local and Global Function Model of the Liver
Wang, Hesheng; Feng, Mary; Jackson, Andrew; Ten Haken, Randall K.; Lawrence, Theodore S.; Cao, Yue
2015-01-01
Purposes To develop a local and global function model in the liver based upon regional and organ function measurements to support individualized adaptive radiation therapy (RT). Methods and Materials A local and global model for liver function was developed to include both functional volume and the effect of functional variation of subunits. Adopting the assumption of parallel architecture in the liver, the global function was composed of a sum of local function probabilities of subunits, varying between 0 and 1. The model was fit to 59 datasets of liver regional and organ function measures from 23 patients obtained prior to, during and 1 month after RT. The local function probabilities of subunits were modeled by a sigmoid function in relating to MRI-derived portal venous perfusion values. The global function was fitted to a logarithm of an indocyanine green retention rate at 15 min (an overall liver function measure). Cross-validation was performed by leave-m-out tests. The model was further evaluated by fitting to the data divided based upon whether the patients had hepatocellular carcinoma (HCC) or not. Results The liver function model showed that 1) a perfusion value of 68.6 ml/(100g·min) yielded a local function probability of 0.5; 2) the probability reached 0.9 at a perfusion value of 98 ml/(100g·min), and 3) at a probability of 0.03 (corresponding perfusion of 38 ml/(100g·min)) or lower, the contribution to global function was lost. Cross-validations showed that the model parameters were stable. The model fitted to the data from the patients with HCC indicated that the same amount of portal venous perfusion was translated into less local function probability than the patients with non-HCC tumors. Conclusions The developed liver function model could provide a means to better assess individual and regional dose responses of hepatic functions, and provide guidance for individualized treatment planning of RT. PMID:26700712
Functional Difference Equations and an Epidemic Model.
1980-06-09
ADDRESS 12. REPORT DATE AIR FORCE OFFICE OF SCIENTIFIC RESEARC 913 June 9, 1980 BOLLING AIR FORCE BASE , WASHINGTON, D.tI,3. NUMBEROFAGS 14. MONITORING...allowed spatial effects in an S - I model to arrive at the equation t S(t,x) = S(t,x).J B(;x, )S(t+6,0) dAdO in some region f cR. If X is the ordered
Evaluation of a differentiation model of preschoolers' executive functions.
Howard, Steven J; Okely, Anthony D; Ellis, Yvonne G
2015-01-01
Despite the prominent role of executive functions in children's emerging competencies, there remains debate regarding the structure and development of executive functions. In an attempt to reconcile these discrepancies, a differentiation model of executive function development was evaluated in the early years using 6-month age groupings. Specifically, 281 preschoolers completed measures of working memory, inhibition, and shifting. Results contradicted suggestions that executive functions follow a single trajectory of progressive separation in childhood, instead suggesting that these functions may undergo a period of integration in the preschool years. These results highlight potential problems with current practices and theorizing in executive function research.
Gluon Fragmentation Functions in the Nambu-Jona-Lasinio Model
NASA Astrophysics Data System (ADS)
Yang, Dong-Jing; Li, Hsiang-nan
We derive gluon fragmentation functions in the Nambu-Jona-Lasinio (NJL) model by approximating a gluon as a fictitious color-octet quark-anti-quark (qbar{q}) pair. Gluon elementary fragmentation functions are derived from the quark and anti-quark elementary fragmentation functions for emitting specific mesons in the NJL model under the requirement that the qbar{q} pair maintains in the flavor-singlet state after meson emission. An iteration method and an inverse matrix method based on the gluon elementary fragmentation functions then yield the gluon fragmentation functions at the model scale. It is found that the resultant gluon fragmentation functions are stable with respect to variation of relevant model parameters, especially after QCD evolution to a higher scale is implemented. We show that the inclusion of the gluon fragmentation functions into the theoretical predictions from only the quark fragmentation functions greatly improve the agreement with the SLD data for the pion and kaon productions in e+e- annihilation. Our proposal provides a plausible construct for the gluon fragmentation functions, which are supposed to be null in the NJL model.
Thermoplasmonics modeling: A Green's function approach
NASA Astrophysics Data System (ADS)
Baffou, Guillaume; Quidant, Romain; Girard, Christian
2010-10-01
We extend the discrete dipole approximation (DDA) and the Green’s dyadic tensor (GDT) methods—previously dedicated to all-optical simulations—to investigate the thermodynamics of illuminated plasmonic nanostructures. This extension is based on the use of the thermal Green’s function and a original algorithm that we named Laplace matrix inversion. It allows for the computation of the steady-state temperature distribution throughout plasmonic systems. This hybrid photothermal numerical method is suited to investigate arbitrarily complex structures. It can take into account the presence of a dielectric planar substrate and is simple to implement in any DDA or GDT code. Using this numerical framework, different applications are discussed such as thermal collective effects in nanoparticles assembly, the influence of a substrate on the temperature distribution and the heat generation in a plasmonic nanoantenna. This numerical approach appears particularly suited for new applications in physics, chemistry, and biology such as plasmon-induced nanochemistry and catalysis, nanofluidics, photothermal cancer therapy, or phase-transition control at the nanoscale.
Calibrating the ECCO ocean general circulation model using Green's functions
NASA Technical Reports Server (NTRS)
Menemenlis, D.; Fu, L. L.; Lee, T.; Fukumori, I.
2002-01-01
Green's functions provide a simple, yet effective, method to test and calibrate General-Circulation-Model(GCM) parameterizations, to study and quantify model and data errors, to correct model biases and trends, and to blend estimates from different solutions and data products.
Tactile Teaching: Exploring Protein Structure/Function Using Physical Models
ERIC Educational Resources Information Center
Herman, Tim; Morris, Jennifer; Colton, Shannon; Batiza, Ann; Patrick, Michael; Franzen, Margaret; Goodsell, David S.
2006-01-01
The technology now exists to construct physical models of proteins based on atomic coordinates of solved structures. We review here our recent experiences in using physical models to teach concepts of protein structure and function at both the high school and the undergraduate levels. At the high school level, physical models are used in a…
Psychobiological models of hippocampal function in learning and memory.
Gluck, M A; Myers, C E
1997-01-01
We review current computational models of hippocampal function in learning and memory, concentrating on those that make strongest contact with psychological issues and behavioral data. Some models build upon Marr's early theories for modeling hippocampal field CA3's putative role in the fast, temporary storage of episodic memories. Other models focus on hippocampal involvement in incrementally learned associations, such as classical conditioning. More recent efforts have attempted to bring functional interpretations of the hippocampal region in closer contact with underlying anatomy and physiology. In reviewing these psychobiological models, three major themes emerge. First, computational models provide the conceptual glue to bind together data from multiple levels of analysis. Second, models serve as important tools to integrate data from both animal and human studies. Third, previous psychological models that capture important behavioral principles of memory provide an important top-down constraint for developing computational models of the neural bases of these behaviors.
Models of neural networks with fuzzy activation functions
NASA Astrophysics Data System (ADS)
Nguyen, A. T.; Korikov, A. M.
2017-02-01
This paper investigates the application of a new form of neuron activation functions that are based on the fuzzy membership functions derived from the theory of fuzzy systems. On the basis of the results regarding neuron models with fuzzy activation functions, we created the models of fuzzy-neural networks. These fuzzy-neural network models differ from conventional networks that employ the fuzzy inference systems using the methods of neural networks. While conventional fuzzy-neural networks belong to the first type, fuzzy-neural networks proposed here are defined as the second-type models. The simulation results show that the proposed second-type model can successfully solve the problem of the property prediction for time – dependent signals. Neural networks with fuzzy impulse activation functions can be widely applied in many fields of science, technology and mechanical engineering to solve the problems of classification, prediction, approximation, etc.
Modeling Dynamic Functional Neuroimaging Data Using Structural Equation Modeling
ERIC Educational Resources Information Center
Price, Larry R.; Laird, Angela R.; Fox, Peter T.; Ingham, Roger J.
2009-01-01
The aims of this study were to present a method for developing a path analytic network model using data acquired from positron emission tomography. Regions of interest within the human brain were identified through quantitative activation likelihood estimation meta-analysis. Using this information, a "true" or population path model was then…
ERIC Educational Resources Information Center
Beretvas, S. Natasha; Walker, Cindy M.
2012-01-01
This study extends the multilevel measurement model to handle testlet-based dependencies. A flexible two-level testlet response model (the MMMT-2 model) for dichotomous items is introduced that permits assessment of differential testlet functioning (DTLF). A distinction is made between this study's conceptualization of DTLF and that of…
Gluon fragmentation functions in the Nambu-Jona-Lasinio model
NASA Astrophysics Data System (ADS)
Yang, Dong-Jing; Li, Hsiang-nan
2016-09-01
We derive gluon fragmentation functions in the Nambu-Jona-Lasinio (NJL) model by treating a gluon as a pair of color lines formed by a fictitious quark and antiquark (q q ¯). Gluon elementary fragmentation functions are obtained from the quark and antiquark elementary fragmentation functions for emitting specific mesons in the NJL model under the requirement that the q q ¯ pair maintains in the flavor-singlet state after meson emissions. An integral equation, which iterates the gluon elementary fragmentation functions to all orders, is then solved to yield the gluon fragmentation functions at a model scale. It is observed that these solutions are stable with respect to variation of relevant model parameters, especially after QCD evolution to a higher scale is implemented. We show that the inclusion of the gluon fragmentation functions into the theoretical predictions from only the quark fragmentation functions greatly improves the agreement with the SLD data for the pion and kaon productions in e+e- annihilation. Our proposal provides a plausible construct for the gluon fragmentation functions, which are supposed to be null in the NJL model.
Evaluation of the storage function model parameter characteristics
NASA Astrophysics Data System (ADS)
Sugiyama, Hironobu; Kadoya, Mutsumi; Nagai, Akihiro; Lansey, Kevin
1997-04-01
The storage function hydrograph model is one of the most commonly used models for flood runoff analysis in Japan. This paper studies the generality of the approach and its application to Japanese basins. Through a comparison of the basic equations for the models, the storage function model parameters, K, P, and T1, are shown to be related to the terms, k and p, in the kinematic wave model. This analysis showed that P and p are identical and K and T1 can be related to k, the basin area and its land use. To apply the storage function model throughout Japan, regional parameter relationships for K and T1 were developed for different land-use conditions using data from 22 watersheds and 91 flood events. These relationships combine the kinematic wave parameters with general topographic information using Hack's Law. The sensitivity of the parameters and their physical significance are also described.
A pairwise interaction model for multivariate functional and longitudinal data.
Chiou, Jeng-Min; Müller, Hans-Georg
2016-06-01
Functional data vectors consisting of samples of multivariate data where each component is a random function are encountered increasingly often but have not yet been comprehensively investigated. We introduce a simple pairwise interaction model that leads to an interpretable and straightforward decomposition of multivariate functional data and of their variation into component-specific processes and pairwise interaction processes. The latter quantify the degree of pairwise interactions between the components of the functional data vectors, while the component-specific processes reflect the functional variation of a particular functional vector component that cannot be explained by the other components. Thus the proposed model provides an extension of the usual notion of a covariance or correlation matrix for multivariate vector data to functional data vectors and generates an interpretable functional interaction map. The decomposition provided by the model can also serve as a basis for subsequent analysis, such as study of the network structure of functional data vectors. The decomposition of the total variance into componentwise and interaction contributions can be quantified by an [Formula: see text]-like decomposition. We provide consistency results for the proposed methods and illustrate the model by applying it to sparsely sampled longitudinal data from the Baltimore Longitudinal Study of Aging, examining the relationships between body mass index and blood fats.
Functional models of power electronic components for system studies
NASA Technical Reports Server (NTRS)
Tam, Kwa-Sur; Yang, Lifeng; Dravid, Narayan
1991-01-01
A novel approach to model power electronic circuits has been developed to facilitate simulation studies of system-level issues. The underlying concept for this approach is to develop an equivalent circuit, the functional model, that performs the same functions as the actual circuit but whose operation can be simulated by using larger time step size and the reduction in model complexity, the computation time required by a functional model is significantly shorter than that required by alternative approaches. The authors present this novel modeling approach and discuss the functional models of two major power electronic components, the DC/DC converter unit and the load converter, that are being considered by NASA for use in the Space Station Freedom electric power system. The validity of these models is established by comparing the simulation results with available experimental data and other simulation results obtained by using a more established modeling approach. The usefulness of this approach is demonstrated by incorporating these models into a power system model and simulating the system responses and interactions between components under various conditions.
Distances in spaces of physical models: partition functions versus spectra
NASA Astrophysics Data System (ADS)
Cornelissen, Gunther; Kontogeorgis, Aristides
2017-01-01
We study the relation between convergence of partition functions (seen as general Dirichlet series) and convergence of spectra and their multiplicities. We describe applications to convergence in physical models, e.g., related to topology change and averaging in cosmology.
Joint Modeling of Anatomical and Functional Connectivity for Population Studies
Rathi, Yogesh; Kubicki, Marek; Westin, Carl-Fredrik; Golland, Polina
2015-01-01
We propose a novel probabilistic framework to merge information from diffusion weighted imaging tractography and resting-state functional magnetic resonance imaging correlations to identify connectivity patterns in the brain. In particular, we model the interaction between latent anatomical and functional connectivity and present an intuitive extension to population studies. We employ the EM algorithm to estimate the model parameters by maximizing the data likelihood. The method simultaneously infers the templates of latent connectivity for each population and the differences in connectivity between the groups. We demonstrate our method on a schizophrenia study. Our model identifies significant increases in functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior cingulate region and the temporal lobe in schizophrenia. We further establish that our model learns predictive differences between the control and clinical populations, and that combining the two modalities yields better results than considering each one in isolation. PMID:21878411
Accuracy of functional surfaces on comparatively modeled protein structures
Zhao, Jieling; Dundas, Joe; Kachalo, Sema; Ouyang, Zheng; Liang, Jie
2012-01-01
Identification and characterization of protein functional surfaces are important for predicting protein function, understanding enzyme mechanism, and docking small compounds to proteins. As the rapid speed of accumulation of protein sequence information far exceeds that of structures, constructing accurate models of protein functional surfaces and identify their key elements become increasingly important. A promising approach is to build comparative models from sequences using known structural templates such as those obtained from structural genome projects. Here we assess how well this approach works in modeling binding surfaces. By systematically building three-dimensional comparative models of proteins using Modeller, we determine how well functional surfaces can be accurately reproduced. We use an alpha shape based pocket algorithm to compute all pockets on the modeled structures, and conduct a large-scale computation of similarity measurements (pocket RMSD and fraction of functional atoms captured) for 26,590 modeled enzyme protein structures. Overall, we find that when the sequence fragment of the binding surfaces has more than 45% identity to that of the tempalte protein, the modeled surfaces have on average an RMSD of 0.5 Å, and contain 48% or more of the binding surface atoms, with nearly all of the important atoms in the signatures of binding pockets captured. PMID:21541664
Enhancements to the SSME transfer function modeling code
NASA Technical Reports Server (NTRS)
Irwin, R. Dennis; Mitchell, Jerrel R.; Bartholomew, David L.; Glenn, Russell D.
1995-01-01
This report details the results of a one year effort by Ohio University to apply the transfer function modeling and analysis tools developed under NASA Grant NAG8-167 (Irwin, 1992), (Bartholomew, 1992) to attempt the generation of Space Shuttle Main Engine High Pressure Turbopump transfer functions from time domain data. In addition, new enhancements to the transfer function modeling codes which enhance the code functionality are presented, along with some ideas for improved modeling methods and future work. Section 2 contains a review of the analytical background used to generate transfer functions with the SSME transfer function modeling software. Section 2.1 presents the 'ratio method' developed for obtaining models of systems that are subject to single unmeasured excitation sources and have two or more measured output signals. Since most of the models developed during the investigation use the Eigensystem Realization Algorithm (ERA) for model generation, Section 2.2 presents an introduction of ERA, and Section 2.3 describes how it can be used to model spectral quantities. Section 2.4 details the Residue Identification Algorithm (RID) including the use of Constrained Least Squares (CLS) and Total Least Squares (TLS). Most of this information can be found in the report (and is repeated for convenience). Section 3 chronicles the effort of applying the SSME transfer function modeling codes to the a51p394.dat and a51p1294.dat time data files to generate transfer functions from the unmeasured input to the 129.4 degree sensor output. Included are transfer function modeling attempts using five methods. The first method is a direct application of the SSME codes to the data files and the second method uses the underlying trends in the spectral density estimates to form transfer function models with less clustering of poles and zeros than the models obtained by the direct method. In the third approach, the time data is low pass filtered prior to the modeling process in an
Functional Linear Models for Association Analysis of Quantitative Traits
Fan, Ruzong; Wang, Yifan; Mills, James L.; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Xiong, Momiao
2014-01-01
Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study. PMID:24130119
A no extensive statistical model for the nucleon structure function
Trevisan, Luis A.; Mirez, Carlos
2013-03-25
We studied an application of nonextensive thermodynamics to describe the structure function of nucleon, in a model where the usual Fermi-Dirac and Bose-Einstein energy distribution were replaced by the equivalent functions of the q-statistical. The parameters of the model are given by an effective temperature T, the q parameter (from Tsallis statistics), and two chemical potentials given by the corresponding up (u) and down (d) quark normalization in the nucleon.
Conventional modeling of the multilayer perceptron using polynomial basis functions
NASA Technical Reports Server (NTRS)
Chen, Mu-Song; Manry, Michael T.
1993-01-01
A technique for modeling the multilayer perceptron (MLP) neural network, in which input and hidden units are represented by polynomial basis functions (PBFs), is presented. The MLP output is expressed as a linear combination of the PBFs and can therefore be expressed as a polynomial function of its inputs. Thus, the MLP is isomorphic to conventional polynomial discriminant classifiers or Volterra filters. The modeling technique was successfully applied to several trained MLP networks.
Functional linear models for association analysis of quantitative traits.
Fan, Ruzong; Wang, Yifan; Mills, James L; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao
2013-11-01
Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study.
ILNCSIM: improved lncRNA functional similarity calculation model.
Huang, Yu-An; Chen, Xing; You, Zhu-Hong; Huang, De-Shuang; Chan, Keith C C
2016-05-03
Increasing observations have indicated that lncRNAs play a significant role in various critical biological processes and the development and progression of various human diseases. Constructing lncRNA functional similarity networks could benefit the development of computational models for inferring lncRNA functions and identifying lncRNA-disease associations. However, little effort has been devoted to quantifying lncRNA functional similarity. In this study, we developed an Improved LNCRNA functional SIMilarity calculation model (ILNCSIM) based on the assumption that lncRNAs with similar biological functions tend to be involved in similar diseases. The main improvement comes from the combination of the concept of information content and the hierarchical structure of disease directed acyclic graphs for disease similarity calculation. ILNCSIM was combined with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association to further evaluate its performance. As a result, new model obtained reliable performance in the leave-one-out cross validation (AUCs of 0.9316 and 0.9074 based on MNDR and Lnc2cancer databases, respectively), and 5-fold cross validation (AUCs of 0.9221 and 0.9033 for MNDR and Lnc2cancer databases), which significantly improved the prediction performance of previous models. It is anticipated that ILNCSIM could serve as an effective lncRNA function prediction model for future biomedical researches.
ILNCSIM: improved lncRNA functional similarity calculation model
You, Zhu-Hong; Huang, De-Shuang; Chan, Keith C.C.
2016-01-01
Increasing observations have indicated that lncRNAs play a significant role in various critical biological processes and the development and progression of various human diseases. Constructing lncRNA functional similarity networks could benefit the development of computational models for inferring lncRNA functions and identifying lncRNA-disease associations. However, little effort has been devoted to quantifying lncRNA functional similarity. In this study, we developed an Improved LNCRNA functional SIMilarity calculation model (ILNCSIM) based on the assumption that lncRNAs with similar biological functions tend to be involved in similar diseases. The main improvement comes from the combination of the concept of information content and the hierarchical structure of disease directed acyclic graphs for disease similarity calculation. ILNCSIM was combined with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association to further evaluate its performance. As a result, new model obtained reliable performance in the leave-one-out cross validation (AUCs of 0.9316 and 0.9074 based on MNDR and Lnc2cancer databases, respectively), and 5-fold cross validation (AUCs of 0.9221 and 0.9033 for MNDR and Lnc2cancer databases), which significantly improved the prediction performance of previous models. It is anticipated that ILNCSIM could serve as an effective lncRNA function prediction model for future biomedical researches. PMID:27028993
Computational models of basal-ganglia pathway functions: focus on functional neuroanatomy.
Schroll, Henning; Hamker, Fred H
2013-12-30
Over the past 15 years, computational models have had a considerable impact on basal-ganglia research. Most of these models implement multiple distinct basal-ganglia pathways and assume them to fulfill different functions. As there is now a multitude of different models, it has become complex to keep track of their various, sometimes just marginally different assumptions on pathway functions. Moreover, it has become a challenge to oversee to what extent individual assumptions are corroborated or challenged by empirical data. Focusing on computational, but also considering non-computational models, we review influential concepts of pathway functions and show to what extent they are compatible with or contradict each other. Moreover, we outline how empirical evidence favors or challenges specific model assumptions and propose experiments that allow testing assumptions against each other.
Computational models of basal-ganglia pathway functions: focus on functional neuroanatomy
Schroll, Henning; Hamker, Fred H.
2013-01-01
Over the past 15 years, computational models have had a considerable impact on basal-ganglia research. Most of these models implement multiple distinct basal-ganglia pathways and assume them to fulfill different functions. As there is now a multitude of different models, it has become complex to keep track of their various, sometimes just marginally different assumptions on pathway functions. Moreover, it has become a challenge to oversee to what extent individual assumptions are corroborated or challenged by empirical data. Focusing on computational, but also considering non-computational models, we review influential concepts of pathway functions and show to what extent they are compatible with or contradict each other. Moreover, we outline how empirical evidence favors or challenges specific model assumptions and propose experiments that allow testing assumptions against each other. PMID:24416002
Ensemble modeling with pedotransfer functions in the hydropedological context
Technology Transfer Automated Retrieval System (TEKTRAN)
Uncertainty of soil water content and/or soil water flux estimates with soil water models has recently become of a particular interest in various applications. This work provides examples of using pedotransfer functions (PTFs) to build ensembles of models to characterize the uncertainty of simulatio...
Single-index varying coefficient model for functional responses.
Luo, Xinchao; Zhu, Lixing; Zhu, Hongtu
2016-12-01
Recently, massive functional data have been widely collected over space across a set of grid points in various imaging studies. It is interesting to correlate functional data with various clinical variables, such as age and gender, in order to address scientific questions of interest. The aim of this article is to develop a single-index varying coefficient (SIVC) model for establishing a varying association between functional responses (e.g., image) and a set of covariates. It enjoys several unique features of both varying-coefficient and single-index models. An estimation procedure is developed to estimate varying coefficient functions, the index function, and the covariance function of individual functions. The optimal integration of information across different grid points is systematically delineated and the asymptotic properties (e.g., consistency and convergence rate) of all estimators are examined. Simulation studies are conducted to assess the finite-sample performance of the proposed estimation procedure. Furthermore, our real data analysis of a white matter tract dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study confirms the advantage and accuracy of SIVC model over the popular varying coefficient model.
Consistent two-lifetime model for spectral functions of superconductors
NASA Astrophysics Data System (ADS)
Herman, František; Hlubina, Richard
2017-03-01
Recently it has been found that models with at least two lifetimes have to be considered when analyzing the angle-resolved photoemission data in the nodal region of the cuprates [Kondo et al., Nat. Commun. 6, 7699 (2015), 10.1038/ncomms8699]. In this paper we compare two such models. First we show that the phenomenological model used by Kondo et al. violates the sum rule for the occupation number. Next we consider the recently proposed model of the so-called Dynes superconductors, wherein the two lifetimes measure the strengths of pair-conserving and pair-breaking processes. We demonstrate that the model of the Dynes superconductors is fully consistent with known exact results, and we study in detail the resulting spectral functions. Finally, we show that the spectral functions in the nodal region of the cuprates can be fitted well by the model of the Dynes superconductors.
Additive Functions in Boolean Models of Gene Regulatory Network Modules
Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H.; Provero, Paolo; Giacobini, Mario
2011-01-01
Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity
Additive functions in boolean models of gene regulatory network modules.
Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H; Provero, Paolo; Giacobini, Mario
2011-01-01
Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity
The SWISS-MODEL Repository—new features and functionality
Bienert, Stefan; Waterhouse, Andrew; de Beer, Tjaart A. P.; Tauriello, Gerardo; Studer, Gabriel; Bordoli, Lorenza; Schwede, Torsten
2017-01-01
SWISS-MODEL Repository (SMR) is a database of annotated 3D protein structure models generated by the automated SWISS-MODEL homology modeling pipeline. It currently holds >400 000 high quality models covering almost 20% of Swiss-Prot/UniProtKB entries. In this manuscript, we provide an update of features and functionalities which have been implemented recently. We address improvements in target coverage, model quality estimates, functional annotations and improved in-page visualization. We also introduce a new update concept which includes regular updates of an expanded set of core organism models and UniProtKB-based targets, complemented by user-driven on-demand update of individual models. With the new release of the modeling pipeline, SMR has implemented a REST-API and adopted an open licencing model for accessing model coordinates, thus enabling bulk download for groups of targets fostering re-use of models in other contexts. SMR can be accessed at https://swissmodel.expasy.org/repository. PMID:27899672
Principles for modeling and functional simulation of biological microstructures
NASA Astrophysics Data System (ADS)
Kriete, Andres
1997-04-01
This paper discusses some aspects of computer based modeling of biological microstructures. The workflow tom model and simulate a biological structure is described as a feedback- loop. Beside the system definition by structural and dynamical properties, the simulation is discussed as a mathematical representation coupled with a computer visualization. As an example, the investigation of the functional behavior of lung structures is described with special emphasis to the modeling of respiratory units.
Phillips' Lambda function: Data summary and physical model
NASA Astrophysics Data System (ADS)
Irisov, V.; Plant, W.
2016-03-01
Measurements of Phillips' Lambda function describing the average length of breakers on the ocean per unit area at speed cb are summarized. An expression is developed that fits these data within reasonable bounds. A physical model for the Lambda function is derived based on the assumption that breaking occurs when the surface steepness exceeds a threshold value. The energy contained in the breaking region is related to the fifth power of the breaker speed, as Phillips showed, and from this the probability of finding a breaker with a speed cb may be determined from a simulation of the long-wave surface based on a linear superposition of Fourier components. This probability is directly related to the Lambda function so that a form for this function can be determined. The Lambda function so determined agrees in both shape and intensity with the fit to the measured Lambda functions.
Analyzing the Boer-Mulders function within different quark models
Courtoy, A.; Vento, V.; Scopetta, S.
2009-10-01
A general formalism for the evaluation of time-reversal odd parton distributions is applied here to calculate the Boer-Mulders function. The same formalism when applied to evaluate the Sivers function led to results which fulfill the Burkardt sum rule quite well. The calculation here has been performed for two different models of proton structure: a constituent quark model and the MIT bag model. In the latter case, important differences are found with respect to a previous evaluation in the same framework, a feature already encountered in the calculation of the Sivers function. The results obtained are consistent with the present wisdom, i.e., the contributions for the u and d flavors turn out to have the same sign, following the pattern suggested analyzing the model-independent features of the impact parameter dependent generalized parton distributions. It is therefore confirmed that the present approach is suitable for the analysis of time-reversal odd distribution functions. A critical comparison between the outcomes of the two models, as well as between the results of the calculations for the Sivers and Boer-Mulders functions, is also carried out.
A potential field model using generalized sigmoid functions.
Ren, Jing; McIsaac, Kenneth A; Patel, Rajni V; Peters, Terry M
2007-04-01
The lack of a potential field model capable of providing accurate representations of objects of arbitrary shapes is considered one major limitation in applying the artificial potential field method in many practical applications. In this correspondence, we propose a potential function based on generalized sigmoid functions. The generalized sigmoid model can be constructed from combinations of implicit primitives or from sampled surface data. The constructed potential field model can achieve an accurate analytic description of objects in two or three dimensions and requires very modest computation at run time. In this correspondence, applications of the generalized sigmoid model in path-planning tasks for mobile robots and in haptic feedback tasks are presented. The validation results in this correspondence show that the model can effectively allow the user or mobile robot to avoid penetrations of obstacles while successfully accomplishing the task.
APP physiological and pathophysiological functions: insights from animal models.
Guo, Qinxi; Wang, Zilai; Li, Hongmei; Wiese, Mary; Zheng, Hui
2012-01-01
The amyloid precursor protein (APP) has been under intensive study in recent years, mainly due to its critical role in the pathogenesis of Alzheimer's disease (AD). β-Amyloid (Aβ) peptides generated from APP proteolytic cleavage can aggregate, leading to plaque formation in human AD brains. Point mutations of APP affecting Aβ production are found to be causal for hereditary early onset familial AD. It is very likely that elucidating the physiological properties of APP will greatly facilitate the understanding of its role in AD pathogenesis. A number of APP loss- and gain-of-function models have been established in model organisms including Caenorhabditis elegans, Drosophila, zebrafish and mouse. These in vivo models provide us valuable insights into APP physiological functions. In addition, several knock-in mouse models expressing mutant APP at a physiological level are available to allow us to study AD pathogenesis without APP overexpression. This article will review the current physiological and pathophysiological animal models of APP.
Modeling scattering in turbid media using the Gegenbauer phase function
NASA Astrophysics Data System (ADS)
Calabro, Katherine W.; Cassarly, William
2015-03-01
The choice of scattering phase function is critically important in the modeling of photon propagation in turbid media, particularly when the scattering path within the material is on the order of several mean free path lengths. For tissue applications, the single parameter Henyey-Greenstein (HG) phase function is known to underestimate the contribution of backscattering, while phase functions based on Mie theory can be more complex than necessary due to the multitude of parameter inputs. In this work, the two term Gegenbauer phase function is highlighted as an effective compromise between HG and Mie, as demonstrated when fitting the various phase function to measured data from phantom materials. Further comparison against the Modified Henyey-Greenstein (MHG) phase function, another two term function, demonstrates that the Gegenbauer function provides better control of the higher order phase function moments, and hence allows for a wider range of values for the similarity parameter, γ. Wavelength dependence of the Gegenbauer parameters is also investigated using a range of theoretical particle distributions. Finally, extraction of the scattering properties of solid turbid samples from angularly resolved transmission measurements is performed using an iterative Monte Carlo optimization technique. Fitting results using Gegenbauer, HG, MHG, and Mie phase functions are compared.
Quark fragmentation functions in NJL-jet model
NASA Astrophysics Data System (ADS)
Bentz, Wolfgang; Matevosyan, Hrayr; Thomas, Anthony
2014-09-01
We report on our studies of quark fragmentation functions in the Nambu-Jona-Lasinio (NJL) - jet model. The results of Monte-Carlo simulations for the fragmentation functions to mesons and nucleons, as well as to pion and kaon pairs (dihadron fragmentation functions) are presented. The important role of intermediate vector meson resonances for those semi-inclusive deep inelastic production processes is emphasized. Our studies are very relevant for the extraction of transverse momentum dependent quark distribution functions from measured scattering cross sections. We report on our studies of quark fragmentation functions in the Nambu-Jona-Lasinio (NJL) - jet model. The results of Monte-Carlo simulations for the fragmentation functions to mesons and nucleons, as well as to pion and kaon pairs (dihadron fragmentation functions) are presented. The important role of intermediate vector meson resonances for those semi-inclusive deep inelastic production processes is emphasized. Our studies are very relevant for the extraction of transverse momentum dependent quark distribution functions from measured scattering cross sections. Supported by Grant in Aid for Scientific Research, Japanese Ministry of Education, Culture, Sports, Science and Technology, Project No. 20168769.
Connectotyping: model based fingerprinting of the functional connectome.
Miranda-Dominguez, Oscar; Mills, Brian D; Carpenter, Samuel D; Grant, Kathleen A; Kroenke, Christopher D; Nigg, Joel T; Fair, Damien A
2014-01-01
A better characterization of how an individual's brain is functionally organized will likely bring dramatic advances to many fields of study. Here we show a model-based approach toward characterizing resting state functional connectivity MRI (rs-fcMRI) that is capable of identifying a so-called "connectotype", or functional fingerprint in individual participants. The approach rests on a simple linear model that proposes the activity of a given brain region can be described by the weighted sum of its functional neighboring regions. The resulting coefficients correspond to a personalized model-based connectivity matrix that is capable of predicting the timeseries of each subject. Importantly, the model itself is subject specific and has the ability to predict an individual at a later date using a limited number of non-sequential frames. While we show that there is a significant amount of shared variance between models across subjects, the model's ability to discriminate an individual is driven by unique connections in higher order control regions in frontal and parietal cortices. Furthermore, we show that the connectotype is present in non-human primates as well, highlighting the translational potential of the approach.
Cluster density functional theory for lattice models based on the theory of Möbius functions
NASA Astrophysics Data System (ADS)
Lafuente, Luis; Cuesta, José A.
2005-08-01
Rosenfeld's fundamental-measure theory for lattice models is given a rigorous formulation in terms of the theory of Möbius functions of partially ordered sets. The free-energy density functional is expressed as an expansion in a finite set of lattice clusters. This set is endowed with a partial order, so that the coefficients of the cluster expansion are connected to its Möbius function. Because of this, it is rigorously proven that a unique such expansion exists for any lattice model. The low-density analysis of the free-energy functional motivates a redefinition of the basic clusters (zero-dimensional cavities) which guarantees a correct zero-density limit of the pair and triplet direct correlation functions. This new definition extends Rosenfeld's theory to lattice models with any kind of short-range interaction (repulsive or attractive, hard or soft, one or multicomponent ...). Finally, a proof is given that these functionals have a consistent dimensional reduction, i.e. the functional for dimension d' can be obtained from that for dimension d (d' < d) if the latter is evaluated at a density profile confined to a d'-dimensional subset.
Gravity modeling: the Jacobian function and its approximation
NASA Astrophysics Data System (ADS)
Strykowski, G.; Lauritsen, N. L. B.
2012-04-01
In mathematics, the elements of a Jacobian matrix are the first-order partial derivatives of a scalar function or a vector function with respect to another vector. In inversion theory of geophysics the elements of a Jacobian matrix are a measure of the change of the output signal caused by a local perturbation of a parameter of a given (Earth) model. The elements of a Jacobian matrix can be determined from the general Jacobian function. In gravity modeling this function consists of the "geometrical part" (related to the relative location in 3D of a field point with respect to the source element) and the "source-strength part" (related to the change of mass density of the source element). The explicit (functional) expressions for the Jacobian function can be quite complicated and depend both on the coordinates used (Cartesian, spherical, ellipsoidal) and on the mathematical parametrization of the source (e.g. the homogenous rectangular prism). In practice, and irrespective of the exact expression for the Jacobian function, its value on a computer will always be rounded to a finite number of digits. In fact, in using the exact formulas such finite representation may cause numerical instabilities. If the Jacobian function is smooth enough, it is an advantage to approximate it by a simpler function, e.g. a piecewise-polynomial, which numerically is more robust than the exact formulas and which is more suitable for the subsequent integration. In our contribution we include a whole family of the Jacobian functions which are associated with all the partial derivatives of the gravitational potential of order 0 to 2, i.e. including all the elements of the gravity gradient tensor. The quality of the support points for the subsequent polynomial approximation of the Jacobian function is ensured by using the exact prism formulas in quadruple precision. We will show some first results. Also, we will discuss how such approximated Jacobian functions can be used for large scale
REVIEW: Zebrafish: A Renewed Model System For Functional Genomics
NASA Astrophysics Data System (ADS)
Wen, Xiao-Yan
2008-01-01
In the post genome era, a major goal in molecular biology is to determine the function of the many thousands of genes present in the vertebrate genome. The zebrafish (Danio rerio) provides an almost ideal genetic model to identify the biological roles of these novel genes, in part because their embryos are transparent and develop rapidly. The zebrafish has many advantages over mouse for genome-wide mutagenesis studies, allowing for easier, cheaper and faster functional characterization of novel genes in the vertebrate genome. Many molecular research tools such as chemical mutagenesis, transgenesis, gene trapping, gene knockdown, TILLING, gene targeting, RNAi and chemical genetic screen are now available in zebrafish. Combining all the forward, reverse, and chemical genetic tools, it is expected that zebrafish will make invaluable contribution to vertebrate functional genomics in functional annotation of the genes, modeling human diseases and drug discoveries.
Mining functional modules in genetic networks with decomposable graphical models.
Dejori, Mathäus; Schwaighofer, Anton; Tresp, Volker; Stetter, Martin
2004-01-01
In recent years, graphical models have become an increasingly important tool for the structural analysis of genome-wide expression profiles at the systems level. Here we present a new graphical modelling technique, which is based on decomposable graphical models, and apply it to a set of gene expression profiles from acute lymphoblastic leukemia (ALL). The new method explains probabilistic dependencies of expression levels in terms of the concerted action of underlying genetic functional modules, which are represented as so-called "cliques" in the graph. In addition, the method uses continuous-valued (instead of discretized) expression levels, and makes no particular assumption about their probability distribution. We show that the method successfully groups members of known functional modules to cliques. Our method allows the evaluation of the importance of genes for global cellular functions based on both link count and the clique membership count.
Bread dough rheology: Computing with a damage function model
NASA Astrophysics Data System (ADS)
Tanner, Roger I.; Qi, Fuzhong; Dai, Shaocong
2015-01-01
We describe an improved damage function model for bread dough rheology. The model has relatively few parameters, all of which can easily be found from simple experiments. Small deformations in the linear region are described by a gel-like power-law memory function. A set of large non-reversing deformations - stress relaxation after a step of shear, steady shearing and elongation beginning from rest, and biaxial stretching, is used to test the model. With the introduction of a revised strain measure which includes a Mooney-Rivlin term, all of these motions can be well described by the damage function described in previous papers. For reversing step strains, larger amplitude oscillatory shearing and recoil reasonable predictions have been found. The numerical methods used are discussed and we give some examples.
Model dielectric function for 2D semiconductors including substrate screening
Trolle, Mads L.; Pedersen, Thomas G.; Véniard, Valerie
2017-01-01
Dielectric screening of excitons in 2D semiconductors is known to be a highly non-local effect, which in reciprocal space translates to a strong dependence on momentum transfer q. We present an analytical model dielectric function, including the full non-linear q-dependency, which may be used as an alternative to more numerically taxing ab initio screening functions. By verifying the good agreement between excitonic optical properties calculated using our model dielectric function, and those derived from ab initio methods, we demonstrate the versatility of this approach. Our test systems include: Monolayer hBN, monolayer MoS2, and the surface exciton of a 2 × 1 reconstructed Si(111) surface. Additionally, using our model, we easily take substrate screening effects into account. Hence, we include also a systematic study of the effects of substrate media on the excitonic optical properties of MoS2 and hBN. PMID:28117326
Interference Function of Crystalline Embryo Model of Amorphous Metals. I
NASA Astrophysics Data System (ADS)
Hamada, Tadashi; Fujita, Francisco Eiichi
1982-07-01
A simple and possible structural model of amorphous metals based on the concept of crystalline embryos is proposed. The quasi-crystalline clusters are supposed to exist in the liquid state, be enhanced during supercooling, and be frozen as the crystalline embryos in the amorphous state by rapid quenching. A model assembly of atoms containing the crystalline embryos and the boundary regions is constructed, and the pair correlation function and the interference function are calculated. The interference function of the b.c.c. embryo model is in good agreement with experimental ones. It is concluded that the structure of the boundary connecting the embryos plays an essential role as well as the ordered part in the embryos in the diffraction phenomena of the amorphous structures. The importance of chemical clusters and metalloid atoms is also suggested and discussed.
Transverse momentum dependent distribution functions in the bag model
Harut A. Avakian; Efremov, A. V.; Schweitzer, P.; Yuan, F.
2010-04-01
Leading and subleading twist transverse momentum dependent parton distribution functions (TMDs) are studied in a quark model framework provided by the bag model. A complete set of relations among different TMDs is derived, and the question is discussed how model-(in)dependent such relations are. A connection of the pretzelosity distribution and quark orbital angular momentum is derived. Numerical results are presented, and applications for phenomenology discussed. In particular, it is shown that in the valence-x region the bag model supports a Gaussian Ansatz for the transverse momentum dependence of TMDs.
Conserved Functional Motifs and Homology Modeling to Predict Hidden Moonlighting Functional Sites
Wong, Aloysius; Gehring, Chris; Irving, Helen R.
2015-01-01
Moonlighting functional centers within proteins can provide them with hitherto unrecognized functions. Here, we review how hidden moonlighting functional centers, which we define as binding sites that have catalytic activity or regulate protein function in a novel manner, can be identified using targeted bioinformatic searches. Functional motifs used in such searches include amino acid residues that are conserved across species and many of which have been assigned functional roles based on experimental evidence. Molecules that were identified in this manner seeking cyclic mononucleotide cyclases in plants are used as examples. The strength of this computational approach is enhanced when good homology models can be developed to test the functionality of the predicted centers in silico, which, in turn, increases confidence in the ability of the identified candidates to perform the predicted functions. Computational characterization of moonlighting functional centers is not diagnostic for catalysis but serves as a rapid screening method, and highlights testable targets from a potentially large pool of candidates for subsequent in vitro and in vivo experiments required to confirm the functionality of the predicted moonlighting centers. PMID:26106597
An APL function for modeling p-wave induced liquefaction
NASA Astrophysics Data System (ADS)
Doehring, Donald O.; Charlie, Wayne A.; Veyera, George E.
This paper presents an APL function that models particle acceleration, velocity, displacement, and porewater pressure responses induced by the passage of compressional waves through water-saturated soil. Inputs to the function include: mass of soil elements, boundary conditions, spring constants, damping ratio, forces applied to the first element, threshold strain and a time increment. Output closely approximates the results of laboratory and field measurements of this phenomenon.
Dissecting the two models of TCR structure-function relationships.
Cohn, Melvin
2016-08-01
There are only two comprehensive models attempting to account for the TCR structure-function relationships, referred to as the Standard or Centric model (Model I) and the Tritope model (Model II). This essay is written to analyze comparatively the two formulations of restrictive reactivity, stressing in particular the logic of each. Model I is essentially built on an analogy between the TCR and the BCR. Given a TCR with only one combining site (paratope), restrictive recognition requires that its ligand be viewed as a composite structure between the peptide and restricting element. It is this relationship that entrains a set of correlates that makes Model I untenable. Model II is predicated on the postulate that the recognition of the allele-specific determinants expressed by MHC-encoded restricting elements (R) is germline encoded and selected, whereas the recognition of peptide (P) is somatically encoded and selected. These selective pressures must operate on definable structures and this, in turn, necessitates a multiply recognitive T cell antigen receptor (TCR) with independent anti-R and anti-P paratopes that function coherently to signal restrictive reactivity. The consequences of this "two repertoire" postulate give us a concept of TCR structure quite distinct from that at present generally accepted, as well as a surprising relationship between numbers of functional TCR V gene segments and allele-specific determinants in the species. In the end, both models must deal with the relationship between the epitope-paratope interaction(s) and the signals to the T cell necessary for its differentiation and function.
Testing galaxy formation models with galaxy stellar mass functions
NASA Astrophysics Data System (ADS)
Lim, S. H.; Mo, H. J.; Lan, T.-W.; Ménard, B.
2017-01-01
We compare predictions of a number of empirical models and numerical simulations of galaxy formation to the conditional stellar mass functions of galaxies in groups of different masses obtained recently by Lan et al. to test how well different models accommodate the data. The observational data clearly prefer a model in which star formation in low-mass haloes changes behaviour at a characteristic redshift zc ˜ 2. There is also tentative evidence that this characteristic redshift depends on environment, becoming zc ˜ 4 in regions that eventually evolve into rich clusters of galaxies. The constrained model is used to understand how galaxies form and evolve in dark matter haloes, and to make predictions for other statistical properties of the galaxy population, such as the stellar mass functions of galaxies at high z, the star formation, and stellar mass assembly histories in dark matter haloes. A comparison of our model predictions with those of other empirical models shows that different models can make vastly different predictions, even though all of them are tuned to match the observed stellar mass functions of galaxies.
Using special functions to model the propagation of airborne diseases
NASA Astrophysics Data System (ADS)
Bolaños, Daniela
2014-06-01
Some special functions of the mathematical physics are using to obtain a mathematical model of the propagation of airborne diseases. In particular we study the propagation of tuberculosis in closed rooms and we model the propagation using the error function and the Bessel function. In the model, infected individual emit pathogens to the environment and this infect others individuals who absorb it. The evolution in time of the concentration of pathogens in the environment is computed in terms of error functions. The evolution in time of the number of susceptible individuals is expressed by a differential equation that contains the error function and it is solved numerically for different parametric simulations. The evolution in time of the number of infected individuals is plotted for each numerical simulation. On the other hand, the spatial distribution of the pathogen around the source of infection is represented by the Bessel function K0. The spatial and temporal distribution of the number of infected individuals is computed and plotted for some numerical simulations. All computations were made using software Computer algebra, specifically Maple. It is expected that the analytical results that we obtained allow the design of treatment rooms and ventilation systems that reduce the risk of spread of tuberculosis.
Using Lambert W function and error function to model phase change on microfluidics
NASA Astrophysics Data System (ADS)
Bermudez Garcia, Anderson
2014-05-01
Solidification and melting modeling on microfluidics are solved using Lambert W's function and error's functions. Models are formulated using the heat's diffusion equation. The generic posed case is the melting of a slab with time dependent surface temperature, having a micro or nano-fluid liquid phase. At the beginning the solid slab is at melting temperature. A slab's face is put and maintained at temperature greater than the melting limit and varying in time. Lambert W function and error function are applied via Maple to obtain the analytic solution evolution of the front of microfluidic-solid interface, it is analytically computed and slab's corresponding melting time is determined. It is expected to have analytical results to be useful for food engineering, cooking engineering, pharmaceutical engineering, nano-engineering and bio-medical engineering.
The Thirring-Wess model revisited: a functional integral approach
Belvedere, L.V. . E-mail: armflavio@if.uff.br
2005-06-01
We consider the Wess-Zumino-Witten theory to obtain the functional integral bosonization of the Thirring-Wess model with an arbitrary regularization parameter. Proceeding a systematic of decomposing the Bose field algebra into gauge-invariant- and gauge-non-invariant field subalgebras, we obtain the local decoupled quantum action. The generalized operator solutions for the equations of motion are reconstructed from the functional integral formalism. The isomorphism between the QED {sub 2} (QCD {sub 2}) with broken gauge symmetry by a regularization prescription and the Abelian (non-Abelian) Thirring-Wess model with a fixed bare mass for the meson field is established.
Controller design for TS models using delayed nonquadratic Lyapunov functions.
Lendek, Zsofia; Guerra, Thierry-Marie; Lauber, Jimmy
2015-03-01
In the last few years, nonquadratic Lyapunov functions have been more and more frequently used in the analysis and controller design for Takagi-Sugeno fuzzy models. In this paper, we developed relaxed conditions for controller design using nonquadratic Lyapunov functions and delayed controllers and give a general framework for the use of such Lyapunov functions. The two controller design methods developed in this framework outperform and generalize current state-of-the-art methods. The proposed methods are extended to robust and H∞ control and α -sample variation.
Bessel functions in mass action modeling of memories and remembrances
NASA Astrophysics Data System (ADS)
Freeman, Walter J.; Capolupo, Antonio; Kozma, Robert; Olivares del Campo, Andrés; Vitiello, Giuseppe
2015-10-01
Data from experimental observations of a class of neurological processes (Freeman K-sets) present functional distribution reproducing Bessel function behavior. We model such processes with couples of damped/amplified oscillators which provide time dependent representation of Bessel equation. The root loci of poles and zeros conform to solutions of K-sets. Some light is shed on the problem of filling the gap between the cellular level dynamics and the brain functional activity. Breakdown of time-reversal symmetry is related with the cortex thermodynamic features. This provides a possible mechanism to deduce lifetime of recorded memory.
Semiparametric Stochastic Modeling of the Rate Function in Longitudinal Studies
Zhu, Bin; Taylor, Jeremy M.G.; Song, Peter X.-K.
2011-01-01
In longitudinal biomedical studies, there is often interest in the rate functions, which describe the functional rates of change of biomarker profiles. This paper proposes a semiparametric approach to model these functions as the realizations of stochastic processes defined by stochastic differential equations. These processes are dependent on the covariates of interest and vary around a specified parametric function. An efficient Markov chain Monte Carlo algorithm is developed for inference. The proposed method is compared with several existing methods in terms of goodness-of-fit and more importantly the ability to forecast future functional data in a simulation study. The proposed methodology is applied to prostate-specific antigen profiles for illustration. Supplementary materials for this paper are available online. PMID:22423170
Penalized spline estimation for functional coefficient regression models.
Cao, Yanrong; Lin, Haiqun; Wu, Tracy Z; Yu, Yan
2010-04-01
The functional coefficient regression models assume that the regression coefficients vary with some "threshold" variable, providing appreciable flexibility in capturing the underlying dynamics in data and avoiding the so-called "curse of dimensionality" in multivariate nonparametric estimation. We first investigate the estimation, inference, and forecasting for the functional coefficient regression models with dependent observations via penalized splines. The P-spline approach, as a direct ridge regression shrinkage type global smoothing method, is computationally efficient and stable. With established fixed-knot asymptotics, inference is readily available. Exact inference can be obtained for fixed smoothing parameter λ, which is most appealing for finite samples. Our penalized spline approach gives an explicit model expression, which also enables multi-step-ahead forecasting via simulations. Furthermore, we examine different methods of choosing the important smoothing parameter λ: modified multi-fold cross-validation (MCV), generalized cross-validation (GCV), and an extension of empirical bias bandwidth selection (EBBS) to P-splines. In addition, we implement smoothing parameter selection using mixed model framework through restricted maximum likelihood (REML) for P-spline functional coefficient regression models with independent observations. The P-spline approach also easily allows different smoothness for different functional coefficients, which is enabled by assigning different penalty λ accordingly. We demonstrate the proposed approach by both simulation examples and a real data application.
Hypnosis as a model of functional neurologic disorders.
Deeley, Q
2017-01-01
In the 19th century it was recognized that neurologic symptoms could be caused by "morbid ideation" as well as organic lesions. The subsequent observation that hysteric (now called "functional") symptoms could be produced and removed by hypnotic suggestion led Charcot to hypothesize that suggestion mediated the effects of ideas on hysteric symptoms through as yet unknown effects on brain activity. The advent of neuroimaging 100 years later revealed strikingly similar neural correlates in experiments matching functional symptoms with clinical analogs created by suggestion. Integrative models of suggested and functional symptoms regard these alterations in brain function as the endpoint of a broader set of changes in information processing due to suggestion. These accounts consider that suggestions alter experience by mobilizing representations from memory systems, and altering causal attributions, during preconscious processing which alters the content of what is provided to our highly edited subjective version of the world. Hypnosis as a model for functional symptoms draws attention to how radical alterations in experience and behavior can conform to the content of mental representations through effects on cognition and brain function. Experimental study of functional symptoms and their suggested counterparts in hypnosis reveals the distinct and shared processes through which this can occur.
"Sloppy" nuclear energy density functionals: Effective model reduction
NASA Astrophysics Data System (ADS)
Nikšić, Tamara; Vretenar, Dario
2016-08-01
Concepts from information geometry are used to analyze parameter sensitivity for a nuclear energy density functional, representative of a class of semiempirical functionals that start from a microscopically motivated ansatz for the density dependence of the energy of a system of protons and neutrons. It is shown that such functionals are "sloppy," namely, characterized by an exponential range of sensitivity to parameter variations. Responsive to only a few stiff parameter combinations, sloppy functionals exhibit an exponential decrease of sensitivity to variations of the remaining soft parameters. By interpreting the space of model predictions as a manifold embedded in the data space, with the parameters of the functional as coordinates on the manifold, it is also shown that the exponential distribution of model manifold widths corresponds to the range of parameter sensitivity. Using the manifold boundary approximation method, we illustrate how to systematically construct effective nuclear density functionals of successively lower dimension in parameter space until sloppiness is eventually eliminated and the resulting functional contains only stiff combinations of parameters.
A two phase harmonic model for left ventricular function.
Dubi, Shay; Dubi, Chen; Dubi, Yonatan
2007-11-01
A minimal model for mechanical motion of the left ventricle is proposed. The model assumes the left ventricle to be a harmonic oscillator with two distinct phases, simulating the systolic and diastolic phases, at which both the amplitude and the elastic constant of the oscillator are different. Taking into account the pressure within the left ventricle, the model shows qualitative agreement with functional parameters of the left ventricle. The model allows for a natural explanation of heart failure with preserved systolic left ventricular function, also termed diastolic heart failure. Specifically, the rise in left ventricular filling pressures following increased left-ventricular wall stiffness is attributed to a mechanism aimed at preserving heart rate and cardiac output.
Cohesive fracture model for functionally graded fiber reinforced concrete
Park, Kyoungsoo; Paulino, Glaucio H.; Roesler, Jeffery
2010-06-15
A simple, effective, and practical constitutive model for cohesive fracture of fiber reinforced concrete is proposed by differentiating the aggregate bridging zone and the fiber bridging zone. The aggregate bridging zone is related to the total fracture energy of plain concrete, while the fiber bridging zone is associated with the difference between the total fracture energy of fiber reinforced concrete and the total fracture energy of plain concrete. The cohesive fracture model is defined by experimental fracture parameters, which are obtained through three-point bending and split tensile tests. As expected, the model describes fracture behavior of plain concrete beams. In addition, it predicts the fracture behavior of either fiber reinforced concrete beams or a combination of plain and fiber reinforced concrete functionally layered in a single beam specimen. The validated model is also applied to investigate continuously, functionally graded fiber reinforced concrete composites.
Predicting plants -modeling traits as a function of environment
NASA Astrophysics Data System (ADS)
Franklin, Oskar
2016-04-01
A central problem in understanding and modeling vegetation dynamics is how to represent the variation in plant properties and function across different environments. Addressing this problem there is a strong trend towards trait-based approaches, where vegetation properties are functions of the distributions of functional traits rather than of species. Recently there has been enormous progress in in quantifying trait variability and its drivers and effects (Van Bodegom et al. 2012; Adier et al. 2014; Kunstler et al. 2015) based on wide ranging datasets on a small number of easily measured traits, such as specific leaf area (SLA), wood density and maximum plant height. However, plant function depends on many other traits and while the commonly measured trait data are valuable, they are not sufficient for driving predictive and mechanistic models of vegetation dynamics -especially under novel climate or management conditions. For this purpose we need a model to predict functional traits, also those not easily measured, and how they depend on the plants' environment. Here I present such a mechanistic model based on fitness concepts and focused on traits related to water and light limitation of trees, including: wood density, drought response, allocation to defense, and leaf traits. The model is able to predict observed patterns of variability in these traits in relation to growth and mortality, and their responses to a gradient of water limitation. The results demonstrate that it is possible to mechanistically predict plant traits as a function of the environment based on an eco-physiological model of plant fitness. References Adier, P.B., Salguero-Gómez, R., Compagnoni, A., Hsu, J.S., Ray-Mukherjee, J., Mbeau-Ache, C. et al. (2014). Functional traits explain variation in plant lifehistory strategies. Proc. Natl. Acad. Sci. U. S. A., 111, 740-745. Kunstler, G., Falster, D., Coomes, D.A., Hui, F., Kooyman, R.M., Laughlin, D.C. et al. (2015). Plant functional traits
Predicting transfer performance: a comparison of competing function learning models.
McDaniel, Mark A; Dimperio, Eric; Griego, Jacqueline A; Busemeyer, Jerome R
2009-01-01
The population of linear experts (POLE) model suggests that function learning and transfer are mediated by activation of a set of prestored linear functions that together approximate the given function (Kalish, Lewandowsky, & Kruschke, 2004). In the extrapolation-association (EXAM) model, an exemplar-based architecture associates trained input values with their paired output values. Transfer incorporates a linear rule-based response mechanism (McDaniel & Busemeyer, 2005). Learners were trained on a functional relationship defined by 2 linear-function segments with mirror slopes. In Experiment 1, 1 segment was densely trained and 1 was sparsely trained; in Experiment 2, both segments were trained equally, but the 2 segments were widely separated. Transfer to new input values was tested. For each model, training performance for each individual participant was fit, and transfer predictions were generated. POLE generally better fit the training data than did EXAM, but EXAM was more accurate at predicting (and fitting) transfer behaviors. It was especially telling that in Experiment 2 the transfer pattern was more consistent with EXAM's but not POLE's predictions, even though the presentation of salient linear segments during training dovetailed with POLE's approach.
Laguerre-Gauss basis functions in observer models
NASA Astrophysics Data System (ADS)
Burgess, Arthur E.
2003-05-01
Observer models based on linear classifiers with basis functions (channels) are useful for evaluation of detection performance with medical images. They allow spatial domain calculations with a covariance matrix of tractable size. The term "channelized Fisher-Hotelling observer" will be used here. It is also called the "channelized Hotelling observer" model. There are an infinite number of basis function (channel ) sets that could be employed. Examples of channel sets that have been used include: difference of Gaussian (DOG) filters, difference of Mesa (DOM) filters and Laguerre-Gauss (LG) basis functions. Another option, sums of LG functions (LGS), will also be presented here. This set has the advantage of having no DC response. The effect of the number of images used to estimate model observer performance will be described, for both filtered 1/f3 noise and GE digital mammogram backgrounds. Finite sample image sets introduce both bias and variance to the estimate. The results presented here agree with previous work on linear classifiers. The LGS basis set gives a small but statistically significant reduction in bias. However, this may not be of much practical benefit. Finally, the effect of varying the number of basis functions included in the set will be addressed. It was found that four LG bases or three LGS bases are adequate.
AGSM Functional Fault Models for Fault Isolation Project
NASA Technical Reports Server (NTRS)
Harp, Janicce Leshay
2014-01-01
This project implements functional fault models to automate the isolation of failures during ground systems operations. FFMs will also be used to recommend sensor placement to improve fault isolation capabilities. The project enables the delivery of system health advisories to ground system operators.
Colombian ocean waves and coasts modeled by special functions
NASA Astrophysics Data System (ADS)
Duque Tisnés, Simón
2013-06-01
Modeling the ocean bottom and surface of both Atlantic and Pacific Oceans near the Colombian coast is a subject of increasing attention due to the possibility of finding oil deposits that haven't been discovered, and as a way of monitoring the ocean limits of Colombia with other countries not only covering the possibility of naval intrusion but as a chance to detect submarine devices that are used by illegal groups for different unwished purposes. In the development of this topic it would be necessary to use Standard Hydrodynamic Equations to model the mathematical shape of ocean waves that will take differential equations forms. Those differential equations will be solved using computer algebra software and methods. The mentioned solutions will involve the use of Special Functions such as Bessel Functions, Whittaker, Heun, and so on. Using the Special Functions mentioned above, the obtained results will be simulated by numerical methods obtaining the typical patterns around the Colombian coasts (both surface and bottom). Using this simulation as a non-perturbed state, any change in the patter could be taken as an external perturbation caused by a strange body or device in an specific area or region modeled, building this simulation as an ocean radar or an unusual object finder. It's worth mentioning that the use of stronger or more rigorous methods and more advanced Special Functions would generate better theoretical results, building a more accurate simulation model that would lead to a finest detection.
Integrated Counseling Services for Exceptional Children: A Functional, Noncategorical Model.
ERIC Educational Resources Information Center
Frith, Greg H.; And Others
1983-01-01
Describes the system-wide counseling program of the Vestavia Hills Public Schools, which facilitates interaction between regular and special education. Special emphasis is placed on three major support systems (staffing considerations, school and community resources, and peer participation) incorporated into a noncategorical functional model. (JAC)
An Adaptive Complex Network Model for Brain Functional Networks
Gomez Portillo, Ignacio J.; Gleiser, Pablo M.
2009-01-01
Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution. PMID:19738902
An Analytic Function of Lunar Surface Temperature for Exospheric Modeling
NASA Technical Reports Server (NTRS)
Hurley, Dana M.; Sarantos, Menelaos; Grava, Cesare; Williams, Jean-Pierre; Retherford, Kurt D.; Siegler, Matthew; Greenhagen, Benjamin; Paige, David
2014-01-01
We present an analytic expression to represent the lunar surface temperature as a function of Sun-state latitude and local time. The approximation represents neither topographical features nor compositional effects and therefore does not change as a function of selenographic latitude and longitude. The function reproduces the surface temperature measured by Diviner to within +/-10 K at 72% of grid points for dayside solar zenith angles of less than 80, and at 98% of grid points for nightside solar zenith angles greater than 100. The analytic function is least accurate at the terminator, where there is a strong gradient in the temperature, and the polar regions. Topographic features have a larger effect on the actual temperature near the terminator than at other solar zenith angles. For exospheric modeling the effects of topography on the thermal model can be approximated by using an effective longitude for determining the temperature. This effective longitude is randomly redistributed with 1 sigma of 4.5deg. The resulting ''roughened'' analytical model well represents the statistical dispersion in the Diviner data and is expected to be generally useful for future models of lunar surface temperature, especially those implemented within exospheric simulations that address questions of volatile transport.
The fundamental structure function of oscillator noise models
NASA Technical Reports Server (NTRS)
Greenhall, C. A.
1983-01-01
Continuous-time models of oscillator phase noise x(t) usually have stationary nth differences, for some n. The covariance structure of such a model can be characterized in the time domain by the structure function: D sub n (t;gamma sub 1, gamma sub 2) = E delta (n) sub gamma sub 1 x(s+t) delta(n) sub gamma sub 2 x (s). Although formulas for the special case D sub 2 (0;gamma,gamma) (the Allan variance times 2 gamma(2)) exist for power-law spectral models, certain estimation problems require a more complete knowledge of (0). Exhibited is a much simpler function of one time variable, D(t), from which (0) can easily be obtained from the spectral density by uncomplicated integrations. Believing that D(t) is the simplest function of time that holds the same information as (0), D(t) is called the fundamental structure function. D(t) is computed for several power-law spectral models. Two examples are D(t) = K/t/(3) for random walk FM, D(t) = Kt(2) 1n/t/ for flicker FM. Then, to demonstrate its use, a BASIC program is given that computes means and variances of two Allan variance estimators, one of which incorporates a method of frequency drift estimation and removal.
Verbal Neuropsychological Functions in Aphasia: An Integrative Model
ERIC Educational Resources Information Center
Vigliecca, Nora Silvana; Báez, Sandra
2015-01-01
A theoretical framework which considers the verbal functions of the brain under a multivariate and comprehensive cognitive model was statistically analyzed. A confirmatory factor analysis was performed to verify whether some recognized aphasia constructs can be hierarchically integrated as latent factors from a homogenously verbal test. The Brief…
Park, Yu Rang; Lee, Hye Won; Cho, Sung Bum; Kim, Ju Han
2007-01-01
The development of functional genomics including transcriptomics, proteomics and metabolomics allow us to monitor a large number of key cellular pathways simultaneously. Several technology-specific data models have been introduced for the representation of functional genomics experimental data, including the MicroArray Gene Expression-Object Model (MAGE-OM), the Proteomics Experiment Data Repository (PEDRo), and the Tissue MicroArray-Object Model (TMA-OM). Despite the increasing number of cancer studies using multiple functional genomics technologies, there is still no integrated data model for multiple functional genomics experimental and clinical data. We propose an object-oriented data model for cancer genomics research, Cancer Genomics Object Model (CaGe-OM). We reference four data models: Functional Genomic-Object Model, MAGE-OM, TMAOM and PEDRo. The clinical and histopathological information models are created by analyzing cancer management workflow and referencing the College of American Pathology Cancer Protocols and National Cancer Institute Common Data Elements. The CaGe-OM provides a comprehensive data model for integrated storage and analysis of clinical and multiple functional genomics data.
Comments on a model influence functional for quantum systems
NASA Astrophysics Data System (ADS)
Wu, David; Carmeli, Benny; Chandler, David
1988-02-01
We continue the study of a model non-Gaussian influence functional proposed by Allinger, Carmeli, and Chandler [J. Chem. Phys. 84, 1724 (1986)] to approximate the exact influence functional resulting from integrating out all quantum states but those of primary interest. The premise of this work is that the influence of many secondary states on a single primary state can be closely approximated by the influence of a degenerate level of states with equal coupling to the primary state. The degeneracy reflects the fluctuations possible among the secondary states and in fact can be associated with the partition function of the exact secondary states. The new calculations presented herein emphasize the importance of entropic-like effects properly described by this degeneracy, and for the models we examine, our basic premise is shown to be correct.
Systemic Modeling of Biological Functions in Consideration of Physiome Project
NASA Astrophysics Data System (ADS)
Minamitani, Haruyuki
Emerging of the physiome project provides various influences on the medical, biological and pharmaceutical development. In this paper, as an example of physiome research, neural network model analysis providing the conduction mechanisms of pain and tactile sensations was presented, and the functional relations between neural activities of the network cells and stimulus intensity applied on the peripheral receptive fields were described. The modeling presented here is based on the various assumptions made by the results of physiological and anatomical studies reported in the literature. The functional activities of spinothalamic and thalamocortical cells show a good agreement with the physiological and psychophysical functions of somatosensory system that are very instructive for covering the gap between physiologically and psychophysically aspects of pain and tactile sensation.
Differential Expression and Network Inferences through Functional Data Modeling
Telesca, Donatello; Inoue, Lurdes Y.T.; Neira, Mauricio; Etzioni, Ruth; Gleave, Martin; Nelson, Colleen
2010-01-01
Time–course microarray data consist of mRNA expression from a common set of genes collected at different time points. Such data are thought to reflect underlying biological processes developing over time. In this article we propose a model that allows us to examine differential expression and gene network relationships using time course microarray data. We model each gene expression profile as a random functional transformation of the scale, amplitude and phase of a common curve. Inferences about the gene–specific amplitude parameters allow us to examine differential gene expression. Inferences about measures of functional similarity based on estimated time transformation functions allow us to examine gene networks while accounting for features of the gene expression profiles. We discuss applications to simulated data as well as to microarray data on prostate cancer progression. PMID:19053995
OFMTutor: An operator function model intelligent tutoring system
NASA Technical Reports Server (NTRS)
Jones, Patricia M.
1989-01-01
The design, implementation, and evaluation of an Operator Function Model intelligent tutoring system (OFMTutor) is presented. OFMTutor is intended to provide intelligent tutoring in the context of complex dynamic systems for which an operator function model (OFM) can be constructed. The human operator's role in such complex, dynamic, and highly automated systems is that of a supervisory controller whose primary responsibilities are routine monitoring and fine-tuning of system parameters and occasional compensation for system abnormalities. The automated systems must support the human operator. One potentially useful form of support is the use of intelligent tutoring systems to teach the operator about the system and how to function within that system. Previous research on intelligent tutoring systems (ITS) is considered. The proposed design for OFMTutor is presented, and an experimental evaluation is described.
Development on electromagnetic impedance function modeling and its estimation
Sutarno, D.
2015-09-30
Today the Electromagnetic methods such as magnetotellurics (MT) and controlled sources audio MT (CSAMT) is used in a broad variety of applications. Its usefulness in poor seismic areas and its negligible environmental impact are integral parts of effective exploration at minimum cost. As exploration was forced into more difficult areas, the importance of MT and CSAMT, in conjunction with other techniques, has tended to grow continuously. However, there are obviously important and difficult problems remaining to be solved concerning our ability to collect process and interpret MT as well as CSAMT in complex 3D structural environments. This talk aim at reviewing and discussing the recent development on MT as well as CSAMT impedance functions modeling, and also some improvements on estimation procedures for the corresponding impedance functions. In MT impedance modeling, research efforts focus on developing numerical method for computing the impedance functions of three dimensionally (3-D) earth resistivity models. On that reason, 3-D finite elements numerical modeling for the impedances is developed based on edge element method. Whereas, in the CSAMT case, the efforts were focused to accomplish the non-plane wave problem in the corresponding impedance functions. Concerning estimation of MT and CSAMT impedance functions, researches were focused on improving quality of the estimates. On that objective, non-linear regression approach based on the robust M-estimators and the Hilbert transform operating on the causal transfer functions, were used to dealing with outliers (abnormal data) which are frequently superimposed on a normal ambient MT as well as CSAMT noise fields. As validated, the proposed MT impedance modeling method gives acceptable results for standard three dimensional resistivity models. Whilst, the full solution based modeling that accommodate the non-plane wave effect for CSAMT impedances is applied for all measurement zones, including near-, transition
Optimal hemodynamic response model for functional near-infrared spectroscopy
Kamran, Muhammad A.; Jeong, Myung Yung; Mannan, Malik M. N.
2015-01-01
Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650–950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > tcritical and p-value < 0.05). PMID:26136668
Comparison of γZ-structure function models
Rislow, Benjamin C.
2013-11-07
The γZ-box is an important contribution to the proton's weak charge. The γZ-box is calculated dispersively and depends on γZ-structure functions, F{sub 1,2,3}{sup γZ}(x,Q{sup 2}). At present there is no data for these structure functions and they must be modeled by modifying existing fits to electromagnetic data. Each group that has studied the γZ-box used different modifications. The results of the PVDIS experiment at Jefferson Lab may provide a first test of the validity of each group's models. I present details of the different models and their predictions for the PVDIS result.
A Method of EC Model Implementation Using Web Service Functions
NASA Astrophysics Data System (ADS)
Kurihara, Jun; Koizumi, Hisao; Ishikawa, Toshiyuki; Dasai, Takashi
In recent years, advances in computer and communication technology and the associated rapid increase in the number of Internet users are encouraging advances in Electronic Commerce (EC). Business models of EC are being actively developed by many different enterprises and engineers, and implemented in many kinds of fields. Meanwhile Web services that reuse remote components over the Internet are drawing attention. Web services are based on SOAP/WSDL/UDDI and are given an important position as the infrastructure of the EC systems. The article analyzes the functions and structures of various business models, establishing the patterns of their distinctive and common features, and proposes a method of determining the implementation specifications of business models utilizing these patterns and Web service functions. This method has been applied to a parts purchasing system, which is a typical pattern of the B to B (Business to Business) EC applications. The article also discusses the results of evaluating this prototype system.
Identifying Model-Based Reconfiguration Goals through Functional Deficiencies
NASA Technical Reports Server (NTRS)
Benazera, Emmanuel; Trave-Massuyes, Louise
2004-01-01
Model-based diagnosis is now advanced to the point autonomous systems face some uncertain and faulty situations with success. The next step toward more autonomy is to have the system recovering itself after faults occur, a process known as model-based reconfiguration. After faults occur, given a prediction of the nominal behavior of the system and the result of the diagnosis operation, this paper details how to automatically determine the functional deficiencies of the system. These deficiencies are characterized in the case of uncertain state estimates. A methodology is then presented to determine the reconfiguration goals based on the deficiencies. Finally, a recovery process interleaves planning and model predictive control to restore the functionalities in prioritized order.
Calculations of multiquark functions in effective models of strong interaction
Jafarov, R. G.; Rochev, V. E.
2013-09-15
In this paper we present our results of the investigation of multiquark equations in the Nambu-Jona-Lasinio model with chiral symmetry of SU(2) group in the mean-field expansion. To formulate the mean-field expansion we have used an iteration scheme of solution of the Schwinger-Dyson equations with the fermion bilocal source. We have considered the equations for Green functions of the Nambu-Jona-Lasinio model up to third step for this iteration scheme. To calculate the high-order corrections to the mean-field approximation, we propose the method of the Legendre transformation with respect to the bilocal source, which allows effectively to take into account the symmetry constraints related with the chiral Ward identity. We discuss also the problem of calculating the multiquark functions in the mean-field expansion for Nambu-Jona-Lasinio-type models with other types of the multifermion sources.
Future of Plant Functional Types in Terrestrial Biosphere Models
NASA Astrophysics Data System (ADS)
Wullschleger, S. D.; Euskirchen, E. S.; Iversen, C. M.; Rogers, A.; Serbin, S.
2015-12-01
Earth system models describe the physical, chemical, and biological processes that govern our global climate. While it is difficult to single out one component as being more important than another in these sophisticated models, terrestrial vegetation is a critical player in the biogeochemical and biophysical dynamics of the Earth system. There is much debate, however, as to how plant diversity and function should be represented in these models. Plant functional types (PFTs) have been adopted by modelers to represent broad groupings of plant species that share similar characteristics (e.g. growth form) and roles (e.g. photosynthetic pathway) in ecosystem function. In this review the PFT concept is traced from its origin in the early 1800s to its current use in regional and global dynamic vegetation models (DVMs). Special attention is given to the representation and parameterization of PFTs and to validation and benchmarking of predicted patterns of vegetation distribution in high-latitude ecosystems. These ecosystems are sensitive to changing climate and thus provide a useful test case for model-based simulations of past, current, and future distribution of vegetation. Models that incorporate the PFT concept predict many of the emerging patterns of vegetation change in tundra and boreal forests, given known processes of tree mortality, treeline migration, and shrub expansion. However, representation of above- and especially belowground traits for specific PFTs continues to be problematic. Potential solutions include developing trait databases and replacing fixed parameters for PFTs with formulations based on trait co-variance and empirical trait-environment relationships. Surprisingly, despite being important to land-atmosphere interactions of carbon, water, and energy, PFTs such as moss and lichen are largely absent from DVMs. Close collaboration among those involved in modelling with the disciplines of taxonomy, biogeography, ecology, and remote sensing will be
Reducing equifinality of hydrological models by integrating Functional Streamflow Disaggregation
NASA Astrophysics Data System (ADS)
Lüdtke, Stefan; Apel, Heiko; Nied, Manuela; Carl, Peter; Merz, Bruno
2014-05-01
A universal problem of the calibration of hydrological models is the equifinality of different parameter sets derived from the calibration of models against total runoff values. This is an intrinsic problem stemming from the quality of the calibration data and the simplified process representation by the model. However, discharge data contains additional information which can be extracted by signal processing methods. An analysis specifically developed for the disaggregation of runoff time series into flow components is the Functional Streamflow Disaggregation (FSD; Carl & Behrendt, 2008). This method is used in the calibration of an implementation of the hydrological model SWIM in a medium sized watershed in Thailand. FSD is applied to disaggregate the discharge time series into three flow components which are interpreted as base flow, inter-flow and surface runoff. In addition to total runoff, the model is calibrated against these three components in a modified GLUE analysis, with the aim to identify structural model deficiencies, assess the internal process representation and to tackle equifinality. We developed a model dependent (MDA) approach calibrating the model runoff components against the FSD components, and a model independent (MIA) approach comparing the FSD of the model results and the FSD of calibration data. The results indicate, that the decomposition provides valuable information for the calibration. Particularly MDA highlights and discards a number of standard GLUE behavioural models underestimating the contribution of soil water to river discharge. Both, MDA and MIA yield to a reduction of the parameter ranges by a factor up to 3 in comparison to standard GLUE. Based on these results, we conclude that the developed calibration approach is able to reduce the equifinality of hydrological model parameterizations. The effect on the uncertainty of the model predictions is strongest by applying MDA and shows only minor reductions for MIA. Besides
Pathway logic modeling of protein functional domains in signal transduction.
Talcott, C; Eker, S; Knapp, M; Lincoln, P; Laderoute, K
2004-01-01
Protein functional domains (PFDs) are consensus sequences within signaling molecules that recognize and assemble other signaling components into complexes. Here we describe the application of an approach called Pathway Logic to the symbolic modeling signal transduction networks at the level of PFDs. These models are developed using Maude, a symbolic language founded on rewriting logic. Models can be queried (analyzed) using the execution, search and model-checking tools of Maude. We show how signal transduction processes can be modeled using Maude at very different levels of abstraction involving either an overall state of a protein or its PFDs and their interactions. The key insight for the latter is our algebraic representation of binding interactions as a graph.
Improving nonlinear modeling capabilities of functional link adaptive filters.
Comminiello, Danilo; Scarpiniti, Michele; Scardapane, Simone; Parisi, Raffaele; Uncini, Aurelio
2015-09-01
The functional link adaptive filter (FLAF) represents an effective solution for online nonlinear modeling problems. In this paper, we take into account a FLAF-based architecture, which separates the adaptation of linear and nonlinear elements, and we focus on the nonlinear branch to improve the modeling performance. In particular, we propose a new model that involves an adaptive combination of filters downstream of the nonlinear expansion. Such combination leads to a cooperative behavior of the whole architecture, thus yielding a performance improvement, particularly in the presence of strong nonlinearities. An advanced architecture is also proposed involving the adaptive combination of multiple filters on the nonlinear branch. The proposed models are assessed in different nonlinear modeling problems, in which their effectiveness and capabilities are shown.
Linking geophysics and soil function modelling - two examples
NASA Astrophysics Data System (ADS)
Krüger, J.; Franko, U.; Werban, U.; Dietrich, P.; Behrens, T.; Schmidt, K.; Fank, J.; Kroulik, M.
2011-12-01
iSOIL - "Interactions between soil related sciences - Linking geophysics, soil science and digital soil mapping" is a Collaborative Project (Grant Agreement number 211386) co-funded by the Research DG of the European Commission within the RTD activities of the FP7 Thematic Priority Environment. The iSOIL project aims at reliable mapping of soil properties and soil functions with various methods including geophysical, spectroscopic and monitoring techniques. The general procedure contains three steps (i) geophysical monitoring, (ii) generation of soil property maps and (iii) process modelling. The objective of this work is to demonstrate the methodological procedure on two different examples. Example A focuses on the turnover conditions for soil organic matter (SOM) since many soil functions in a direct or indirect way depend on SOM and SOM depletion is amongst the worst soil threats. Example B deals with the dynamics of soil water and the direct influence on crop biomass production. The applied CANDY model (Franko et al. 1995) was developed to describe dynamics of soil organic matter and mineral nitrogen as well as soil water and temperature. The new module PLUS extends CANDY to simulate crop biomass production based on environmental influences (Krüger et al. 2011). The methodological procedure of example A illustrates a model application for a field site in the Czech Republic using generated soil maps from combined geophysical data. Modelling requires a complete set of soil parameters. Combining measured soil properties and data of geophysical measurements (electrical conductivity and gamma spectrometry) is the basis for digital soil mapping which provided data about clay, silt and sand as well as SOC content. With these data pedotransfer functions produce detailed soil input data (e.g. bulk and particle density, field capacity, wilting point, saturated conductivity) for the rooted soil profile. CANDY calculated different indicators for SOM and gave hints about
A propositional representation model of anatomical and functional brain data.
Maturana, Pablo; Batrancourt, Bénédicte
2011-01-01
Networks can represent a large number of systems. Recent advances in the domain of networks have been transferred to the field of neuroscience. For example, the graph model has been used in neuroscience research as a methodological tool to examine brain networks organization, topology and complex dynamics, as well as a framework to test the structure-function hypothesis using neuroimaging data. In the current work we propose a graph-theoretical framework to represent anatomical, functional and neuropsychological assessment instruments information. On the one hand, interrelationships between anatomic elements constitute an anatomical graph. On the other hand, a functional graph contains several cognitive functions and their more elementary cognitive processes. Finally, the neuropsychological assessment instruments graph includes several neuropsychological tests and scales linked with their different sub-tests and variables. The two last graphs are connected by relations of type "explore" linking a particular instrument with the cognitive function it explores. We applied this framework to a sample of patients with focal brain damage. Each patient was related to: (i) the cerebral entities injured (assessed with structural neuroimaging data) and (ii) the neusopsychological assessment tests carried out (weight by performance). Our model offers a suitable platform to visualize patients' relevant information, facilitating the representation, standardization and sharing of clinical data. At the same time, the integration of a large number of patients in this framework will make possible to explore relations between anatomy (injured entities) and function (performance in different tests assessing different cognitive functions) and the use of neurocomputational tools for graph analysis may help diagnostic and contribute to the comprehension of neural bases of cognitive functions.
A Comparison of Functional Models for Use in the Function-Failure Design Method
NASA Technical Reports Server (NTRS)
Stock, Michael E.; Stone, Robert B.; Tumer, Irem Y.
2006-01-01
When failure analysis and prevention, guided by historical design knowledge, are coupled with product design at its conception, shorter design cycles are possible. By decreasing the design time of a product in this manner, design costs are reduced and the product will better suit the customer s needs. Prior work indicates that similar failure modes occur with products (or components) with similar functionality. To capitalize on this finding, a knowledge base of historical failure information linked to functionality is assembled for use by designers. One possible use for this knowledge base is within the Elemental Function-Failure Design Method (EFDM). This design methodology and failure analysis tool begins at conceptual design and keeps the designer cognizant of failures that are likely to occur based on the product s functionality. The EFDM offers potential improvement over current failure analysis methods, such as FMEA, FMECA, and Fault Tree Analysis, because it can be implemented hand in hand with other conceptual design steps and carried throughout a product s design cycle. These other failure analysis methods can only truly be effective after a physical design has been completed. The EFDM however is only as good as the knowledge base that it draws from, and therefore it is of utmost importance to develop a knowledge base that will be suitable for use across a wide spectrum of products. One fundamental question that arises in using the EFDM is: At what level of detail should functional descriptions of components be encoded? This paper explores two approaches to populating a knowledge base with actual failure occurrence information from Bell 206 helicopters. Functional models expressed at various levels of detail are investigated to determine the necessary detail for an applicable knowledge base that can be used by designers in both new designs as well as redesigns. High level and more detailed functional descriptions are derived for each failed component based
Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong
2009-01-01
Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.
Functional response models to estimate feeding rates of wading birds
Collazo, J.A.; Gilliam, J.F.; Miranda-Castro, L.
2010-01-01
Forager (predator) abundance may mediate feeding rates in wading birds. Yet, when modeled, feeding rates are typically derived from the purely prey-dependent Holling Type II (HoII) functional response model. Estimates of feeding rates are necessary to evaluate wading bird foraging strategies and their role in food webs; thus, models that incorporate predator dependence warrant consideration. Here, data collected in a mangrove swamp in Puerto Rico in 1994 were reanalyzed, reporting feeding rates for mixed-species flocks after comparing fits of the HoII model, as used in the original work, to the Beddington-DeAngelis (BD) and Crowley-Martin (CM) predator-dependent models. Model CM received most support (AIC c wi = 0.44), but models BD and HoII were plausible alternatives (AIC c ??? 2). Results suggested that feeding rates were constrained by predator abundance. Reductions in rates were attributed to interference, which was consistent with the independently observed increase in aggression as flock size increased (P < 0.05). Substantial discrepancies between the CM and HoII models were possible depending on flock sizes used to model feeding rates. However, inferences derived from the HoII model, as used in the original work, were sound. While Holling's Type II and other purely prey-dependent models have fostered advances in wading bird foraging ecology, evaluating models that incorporate predator dependence could lead to a more adequate description of data and processes of interest. The mechanistic bases used to derive models used here lead to biologically interpretable results and advance understanding of wading bird foraging ecology.
ERIC Educational Resources Information Center
Campbell, Todd; Oh, Phil Seok; Maughn, Milo; Kiriazis, Nick; Zuwallack, Rebecca
2015-01-01
The current review examined modeling literature in top science education journals to better understand the pedagogical functions of modeling instruction reported over the last decade. Additionally, the review sought to understand the extent to which different modeling pedagogies were employed, the discursive acts that were identified as important,…
Prostaglandin signaling suppresses beneficial microglial function in Alzheimer's disease models.
Johansson, Jenny U; Woodling, Nathaniel S; Wang, Qian; Panchal, Maharshi; Liang, Xibin; Trueba-Saiz, Angel; Brown, Holden D; Mhatre, Siddhita D; Loui, Taylor; Andreasson, Katrin I
2015-01-01
Microglia, the innate immune cells of the CNS, perform critical inflammatory and noninflammatory functions that maintain normal neural function. For example, microglia clear misfolded proteins, elaborate trophic factors, and regulate and terminate toxic inflammation. In Alzheimer's disease (AD), however, beneficial microglial functions become impaired, accelerating synaptic and neuronal loss. Better understanding of the molecular mechanisms that contribute to microglial dysfunction is an important objective for identifying potential strategies to delay progression to AD. The inflammatory cyclooxygenase/prostaglandin E2 (COX/PGE2) pathway has been implicated in preclinical AD development, both in human epidemiology studies and in transgenic rodent models of AD. Here, we evaluated murine models that recapitulate microglial responses to Aβ peptides and determined that microglia-specific deletion of the gene encoding the PGE2 receptor EP2 restores microglial chemotaxis and Aβ clearance, suppresses toxic inflammation, increases cytoprotective insulin-like growth factor 1 (IGF1) signaling, and prevents synaptic injury and memory deficits. Our findings indicate that EP2 signaling suppresses beneficial microglia functions that falter during AD development and suggest that inhibition of the COX/PGE2/EP2 immune pathway has potential as a strategy to restore healthy microglial function and prevent progression to AD.
A new algebraic transition model based on stress length function
NASA Astrophysics Data System (ADS)
Xiao, Meng-Juan; She, Zhen-Su
2016-11-01
Transition, as one of the two biggest challenges in turbulence research, is of critical importance for engineering application. For decades, the fundamental research seems to be unable to capture the quantitative details in real transition process. On the other hand, numerous empirical parameters in engineering transition models provide no unified description of the transition under varying physical conditions. Recently, we proposed a symmetry-based approach to canonical wall turbulence based on stress length function, which is here extended to describe the transition via a new algebraic transition model. With a multi-layer analytic form of the stress length function in both the streamwise and wall normal directions, the new model gives rise to accurate description of the mean field and friction coefficient, comparing with both the experimental and DNS results at different inlet conditions. Different types of transition process, such as the transition with varying incoming turbulence intensities or that with blow and suck disturbance, are described by only two or three model parameters, each of which has their own specific physical interpretation. Thus, the model enables one to extract physical information from both experimental and DNS data to reproduce the transition process, which may prelude to a new class of generalized transition model for engineering applications.
System identification and model reduction using modulating function techniques
NASA Technical Reports Server (NTRS)
Shen, Yan
1993-01-01
Weighted least squares (WLS) and adaptive weighted least squares (AWLS) algorithms are initiated for continuous-time system identification using Fourier type modulating function techniques. Two stochastic signal models are examined using the mean square properties of the stochastic calculus: an equation error signal model with white noise residuals, and a more realistic white measurement noise signal model. The covariance matrices in each model are shown to be banded and sparse, and a joint likelihood cost function is developed which links the real and imaginary parts of the modulated quantities. The superior performance of above algorithms is demonstrated by comparing them with the LS/MFT and popular predicting error method (PEM) through 200 Monte Carlo simulations. A model reduction problem is formulated with the AWLS/MFT algorithm, and comparisons are made via six examples with a variety of model reduction techniques, including the well-known balanced realization method. Here the AWLS/MFT algorithm manifests higher accuracy in almost all cases, and exhibits its unique flexibility and versatility. Armed with this model reduction, the AWLS/MFT algorithm is extended into MIMO transfer function system identification problems. The impact due to the discrepancy in bandwidths and gains among subsystem is explored through five examples. Finally, as a comprehensive application, the stability derivatives of the longitudinal and lateral dynamics of an F-18 aircraft are identified using physical flight data provided by NASA. A pole-constrained SIMO and MIMO AWLS/MFT algorithm is devised and analyzed. Monte Carlo simulations illustrate its high-noise rejecting properties. Utilizing the flight data, comparisons among different MFT algorithms are tabulated and the AWLS is found to be strongly favored in almost all facets.
Functional Linear Model with Zero-value Coefficient Function at Sub-regions.
Zhou, Jianhui; Wang, Nae-Yuh; Wang, Naisyin
2013-01-01
We propose a shrinkage method to estimate the coefficient function in a functional linear regression model when the value of the coefficient function is zero within certain sub-regions. Besides identifying the null region in which the coefficient function is zero, we also aim to perform estimation and inferences for the nonparametrically estimated coefficient function without over-shrinking the values. Our proposal consists of two stages. In stage one, the Dantzig selector is employed to provide initial location of the null region. In stage two, we propose a group SCAD approach to refine the estimated location of the null region and to provide the estimation and inference procedures for the coefficient function. Our considerations have certain advantages in this functional setup. One goal is to reduce the number of parameters employed in the model. With a one-stage procedure, it is needed to use a large number of knots in order to precisely identify the zero-coefficient region; however, the variation and estimation difficulties increase with the number of parameters. Owing to the additional refinement stage, we avoid this necessity and our estimator achieves superior numerical performance in practice. We show that our estimator enjoys the Oracle property; it identifies the null region with probability tending to 1, and it achieves the same asymptotic normality for the estimated coefficient function on the non-null region as the functional linear model estimator when the non-null region is known. Numerically, our refined estimator overcomes the shortcomings of the initial Dantzig estimator which tends to under-estimate the absolute scale of non-zero coefficients. The performance of the proposed method is illustrated in simulation studies. We apply the method in an analysis of data collected by the Johns Hopkins Precursors Study, where the primary interests are in estimating the strength of association between body mass index in midlife and the quality of life in
Task-specific functional brain geometry from model maps.
Langs, Georg; Samaras, Dimitris; Paragios, Nikos; Honorio, Jean; Alia-Klein, Nelly; Tomasi, Dardo; Volkow, Nora D; Goldstein, Rita Z
2008-01-01
In this paper we propose model maps to derive and represent the intrinsic functional geometry of a brain from functional magnetic resonance imaging (fMRI) data for a specific task. Model maps represent the coherence of behavior of individual fMRI-measurements for a set of observations, or a time sequence. The maps establish a relation between individual positions in the brain by encoding the blood oxygen level dependent (BOLD) signal over a time period in a Markov chain. They represent this relation by mapping spatial positions to a new metric space, the model map. In this map the Euclidean distance between two points relates to the joint modeling behavior of their signals and thus the co-dependencies of the corresponding signals. The map reflects the functional as opposed to the anatomical geometry of the brain. It provides a quantitative tool to explore and study global and local patterns of resource allocation in the brain. To demonstrate the merit of this representation, we report quantitative experimental results on 29 fMRI time sequences, each with sub-sequences corresponding to 4 different conditions for two groups of individuals. We demonstrate that drug abusers exhibit lower differentiation in brain interactivity between baseline and reward related tasks, which could not be quantified until now.
Functional modelling of planar cell polarity: an approach for identifying molecular function
2013-01-01
Background Cells in some tissues acquire a polarisation in the plane of the tissue in addition to apical-basal polarity. This polarisation is commonly known as planar cell polarity and has been found to be important in developmental processes, as planar polarity is required to define the in-plane tissue coordinate system at the cellular level. Results We have built an in-silico functional model of cellular polarisation that includes cellular asymmetry, cell-cell signalling and a response to a global cue. The model has been validated and parameterised against domineering non-autonomous wing hair phenotypes in Drosophila. Conclusions We have carried out a systematic comparison of in-silico polarity phenotypes with patterns observed in vivo under different genetic manipulations in the wing. This has allowed us to classify the specific functional roles of proteins involved in generating cell polarity, providing new hypotheses about their specific functions, in particular for Pk and Dsh. The predictions from the model allow direct assignment of functional roles of genes from genetic mosaic analysis of Drosophila wings. PMID:23672397
Photonic encryption : modeling and functional analysis of all optical logic.
Tang, Jason D.; Schroeppel, Richard Crabtree; Robertson, Perry J.
2004-10-01
With the build-out of large transport networks utilizing optical technologies, more and more capacity is being made available. Innovations in Dense Wave Division Multiplexing (DWDM) and the elimination of optical-electrical-optical conversions have brought on advances in communication speeds as we move into 10 Gigabit Ethernet and above. Of course, there is a need to encrypt data on these optical links as the data traverses public and private network backbones. Unfortunately, as the communications infrastructure becomes increasingly optical, advances in encryption (done electronically) have failed to keep up. This project examines the use of optical logic for implementing encryption in the photonic domain to achieve the requisite encryption rates. This paper documents the innovations and advances of work first detailed in 'Photonic Encryption using All Optical Logic,' [1]. A discussion of underlying concepts can be found in SAND2003-4474. In order to realize photonic encryption designs, technology developed for electrical logic circuits must be translated to the photonic regime. This paper examines S-SEED devices and how discrete logic elements can be interconnected and cascaded to form an optical circuit. Because there is no known software that can model these devices at a circuit level, the functionality of S-SEED devices in an optical circuit was modeled in PSpice. PSpice allows modeling of the macro characteristics of the devices in context of a logic element as opposed to device level computational modeling. By representing light intensity as voltage, 'black box' models are generated that accurately represent the intensity response and logic levels in both technologies. By modeling the behavior at the systems level, one can incorporate systems design tools and a simulation environment to aid in the overall functional design. Each black box model takes certain parameters (reflectance, intensity, input response), and models the optical ripple and time delay
Coupled vibro-acoustic model updating using frequency response functions
NASA Astrophysics Data System (ADS)
Nehete, D. V.; Modak, S. V.; Gupta, K.
2016-03-01
Interior noise in cavities of motorized vehicles is of increasing significance due to the lightweight design of these structures. Accurate coupled vibro-acoustic FE models of such cavities are required so as to allow a reliable design and analysis. It is, however, experienced that the vibro-acoustic predictions using these models do not often correlate acceptably well with the experimental measurements and hence require model updating. Both the structural and the acoustic parameters addressing the stiffness as well as the damping modeling inaccuracies need to be considered simultaneously in the model updating framework in order to obtain an accurate estimate of these parameters. It is also noted that the acoustic absorption properties are generally frequency dependent. This makes use of modal data based methods for updating vibro-acoustic FE models difficult. In view of this, the present paper proposes a method based on vibro-acoustic frequency response functions that allow updating of a coupled FE model by considering simultaneously the parameters associated with both the structural as well as the acoustic model of the cavity. The effectiveness of the proposed method is demonstrated through numerical studies on a 3D rectangular box cavity with a flexible plate. Updating parameters related to the material property, stiffness of joints between the plate and the rectangular cavity and the properties of absorbing surfaces of the acoustic cavity are considered. The robustness of the method under presence of noise is also studied.
Adding ecosystem function to agent-based land use models
Yadav, V.; Del Grosso, S.J.; Parton, W.J.; Malanson, G.P.
2015-01-01
The objective of this paper is to examine issues in the inclusion of simulations of ecosystem functions in agent-based models of land use decision-making. The reasons for incorporating these simulations include local interests in land fertility and global interests in carbon sequestration. Biogeochemical models are needed in order to calculate such fluxes. The Century model is described with particular attention to the land use choices that it can encompass. When Century is applied to a land use problem the combinatorial choices lead to a potentially unmanageable number of simulation runs. Century is also parameter-intensive. Three ways of including Century output in agent-based models, ranging from separately calculated look-up tables to agents running Century within the simulation, are presented. The latter may be most efficient, but it moves the computing costs to where they are most problematic. Concern for computing costs should not be a roadblock. PMID:26191077
Computation of Schenberg response function by using finite element modelling
NASA Astrophysics Data System (ADS)
Frajuca, C.; Bortoli, F. S.; Magalhaes, N. S.
2016-05-01
Schenberg is a detector of gravitational waves resonant mass type, with a central frequency of operation of 3200 Hz. Transducers located on the surface of the resonating sphere, according to a distribution half-dodecahedron, are used to monitor a strain amplitude. The development of mechanical impedance matchers that act by increasing the coupling of the transducers with the sphere is a major challenge because of the high frequency and small in size. The objective of this work is to study the Schenberg response function obtained by finite element modeling (FEM). Finnaly, the result is compared with the result of the simplified model for mass spring type system modeling verifying if that is suitable for the determination of sensitivity detector, as the conclusion the both modeling give the same results.
Model of bidirectional reflectance distribution function for metallic materials
NASA Astrophysics Data System (ADS)
Wang, Kai; Zhu, Jing-Ping; Liu, Hong; Hou, Xun
2016-09-01
Based on the three-component assumption that the reflection is divided into specular reflection, directional diffuse reflection, and ideal diffuse reflection, a bidirectional reflectance distribution function (BRDF) model of metallic materials is presented. Compared with the two-component assumption that the reflection is composed of specular reflection and diffuse reflection, the three-component assumption divides the diffuse reflection into directional diffuse and ideal diffuse reflection. This model effectively resolves the problem that constant diffuse reflection leads to considerable error for metallic materials. Simulation and measurement results validate that this three-component BRDF model can improve the modeling accuracy significantly and describe the reflection properties in the hemisphere space precisely for the metallic materials.
A representation of Jacchia's thermospheric models in spherical harmonic functions
NASA Technical Reports Server (NTRS)
Blum, P.; Harris, I.
1974-01-01
The Jacchia models are represented in terms of spherical harmonic functions. This representation has the advantage of ease of comparison with other global theoretical and empirical models that use this mathematical form. Furthermore, it is analytic, continuous, and has continuous derivatives all over the globe. The representation of the exospheric temperatures shows clearly the amplitudes of the various periodic terms and uses relatively few constants. An example of a similar representation for the total mass density at a particular height and level of solar activity is given as well.
FuGE: Functional Genomics Experiment Object Model.
Jones, Andrew R; Pizarro, Angel; Spellman, Paul; Miller, Michael
2006-01-01
This is an interim report on the Functional Genomics Experiment (FuGE) Object Model. FuGE is a framework for creating data standards for high-throughput biological experiments, developed by a consortium of researchers from academia and industry. FuGE supports rich annotation of samples, protocols, instruments, and software, as well as providing extension points for technology specific details. It has been adopted by microarray and proteomics standards bodies as a basis for forthcoming standards. It is hoped that standards developers for other omics techniques will join this collaborative effort; widespread adoption will allow uniform annotation of common parts of functional genomics workflows, reduce standard development and learning times through the sharing of consistent practice, and ease the construction of software for accessing and integrating functional genomics data.
Consistent Evolution of Software Artifacts and Non-Functional Models
2014-11-14
be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE...on the problem of filling the gap between non-functional analysis of software models and software development artifacts. Its main motivation stems...value that they can lead to each other is often lost. Instead it is commonly recognized that a tighter interconnection of practices, instruments and
A Role-Functional Model of Design Problem Solving
1991-12-01
material that was in the design, (3) removing some material from the design, ( 4 ) including new information in the design space, and (5) commenting on...ITechnical Feb 1, 1988 - July 31, 1991 4 . TITLE AND SUBTITLE 5 6NIV8 MRERSos A Role-Functional Model of Design Problem Solving- PE 6115N PR RR04206...94305-3096 GK- 4 9. SPONSORING/ MONITORING AGENCY NAME(S) AND ADDRESSES) 10. SPONSORING/ MONITORING Cognitive Science Program AGENCY REPORT NUMBIR
Risk prediction for myocardial infarction via generalized functional regression models.
Ieva, Francesca; Paganoni, Anna M
2016-08-01
In this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of electrocardiographic traces of patients whose pre-hospital electrocardiogram (ECG) has been sent to 118 Dispatch Center of Milan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed from noisy observations. The biological variability is then removed by a nonlinear registration procedure based on landmarks. Thus, in order to perform a data-driven dimensional reduction, a multivariate functional principal component analysis is carried out on the variance-covariance matrix of the reconstructed and registered ECGs and their first derivatives. We use the scores of the Principal Components decomposition as covariates in a generalized linear model to predict the presence of the disease in a new patient. Hence, a new semi-automatic diagnostic procedure is proposed to estimate the risk of infarction (in the case of interest, the probability of being affected by Left Bundle Brunch Block). The performance of this classification method is evaluated and compared with other methods proposed in literature. Finally, the robustness of the procedure is checked via leave-j-out techniques.
Longitudinal Functional Magnetic Resonance Imaging in Animal Models
Silva, Afonso C.; Liu, Junjie V.; Hirano, Yoshiyuki; Leoni, Renata F.; Merkle, Hellmut; Mackel, Julie B.; Zhang, Xian Feng; Nascimento, George C.; Stefanovic, Bojana
2016-01-01
Functional magnetic resonance imaging (fMRI) has had an essential role in furthering our understanding of brain physiology and function. fMRI techniques are nowadays widely applied in neuroscience research, as well as in translational and clinical studies. The use of animal models in fMRI studies has been fundamental in helping elucidate the mechanisms of cerebral blood flow regulation, and in the exploration of basic neuroscience questions, such as the mechanisms of perception, behavior, and cognition. Because animals are inherently noncompliant, most fMRI performed to date have required the use of anesthesia, which interferes with brain function and compromises interpretability and applicability of results to our understanding of human brain function. An alternative approach that eliminates the need for anesthesia involves training the animal to tolerate physical restraint during the data acquisition. In the present work we review these two different approaches to obtaining fMRI data from animal models, with a specific focus on the acquisition of longitudinal data from the same subjects. PMID:21279608
Di Maggio, Jimena; Fernández, Carolina; Parodi, Elisa R; Diaz, M Soledad; Estrada, Vanina
2016-01-01
In this paper we address the formulation of two mechanistic water quality models that differ in the way the phytoplankton community is described. We carry out parameter estimation subject to differential-algebraic constraints and validation for each model and comparison between models performance. The first approach aggregates phytoplankton species based on their phylogenetic characteristics (Taxonomic group model) and the second one, on their morpho-functional properties following Reynolds' classification (Functional group model). The latter approach takes into account tolerance and sensitivity to environmental conditions. The constrained parameter estimation problems are formulated within an equation oriented framework, with a maximum likelihood objective function. The study site is Paso de las Piedras Reservoir (Argentina), which supplies water for consumption for 450,000 population. Numerical results show that phytoplankton morpho-functional groups more closely represent each species growth requirements within the group. Each model performance is quantitatively assessed by three diagnostic measures. Parameter estimation results for seasonal dynamics of the phytoplankton community and main biogeochemical variables for a one-year time horizon are presented and compared for both models, showing the functional group model enhanced performance. Finally, we explore increasing nutrient loading scenarios and predict their effect on phytoplankton dynamics throughout a one-year time horizon.
Prion Function and Pathophysiology in Non-Mammalian Models.
Guerrero, N; Meynard, M M; Borgonovo, J; Palma, K; Concha, M L; Hetz, C
2017-02-19
More than thirty years have passed since the discovery of the prion protein (PrP) and its causative role in transmissible spongiform encephalopathy. Since a combination of both gain- and loss-of-function mechanisms may underlay prion pathogenesis, understanding its physiological role may give important clues about disease mechanisms. Historically, the primary strategy for prion research has involved the use of human tissue, cell cultures and mammalian animal models. Nevertheless, experimental difficulties of in vivo studies and some controversial observations obtained in these systems have triggered the search for alternative animal models. PrPC is highly conserved in mammals, and PrPC-related orthologs are expressed in zebrafish, a vertebrate model organism suitable to study the mechanisms associated with human diseases. Invertebrate models, as they do not express PrPC have served to investigate the neurotoxic mechanisms of mammalian PrP. Here we review most recent advances in the study of PrP function in normal and pathogenic conditions based on non-mammalian studies, highlighting the contribution of zebrafish, fly and worms to our current understanding of PrP biology.
Functional Tricuspid Regurgitation Model in a Beating Heart Platform.
Jaworek, Michal; Piola, Marco; Lucherini, Federico; Gelpi, Guido; Castagna, Marco; Lentini, Giuliana; Antona, Carlo; Fiore, Gianfranco B; Vismara, Riccardo
2017-01-03
Currently, clinicians are seeking new, minimally invasive treatment options for functional tricuspid regurgitation (FTR). Challenging tricuspid complexity requires the evaluation of the treatment techniques in adequate and realistic preclinical scenario. The purpose of this paper is to describe the design and functional assessment of a novel passive beating heart model of the pulmonary circulation with the possibility to tightly control FTR.The model housed porcine hearts actuated by a volumetric pump that cyclically pressurized the right ventricle. The in-vitro FTR model exploited the tendency of the ventricle to dilate under pressure. The dilation entailed papillary muscles displacement and valve annulus enlargement, thus inducing tricuspid valve insufficiency. Employment of constraint bands allowed to restore valve competency.The system provided consistent replication of the main determinants of the pulmonary hemodynamics in a wide range of working conditions. The experimental model of FTR was reliable, easily controllable and showed good stability over time. Echocardiography and fiberscope imaging provided a unique opportunity to investigate valve dynamics. These features make the platform suitable for realistic training purposes and testing of the upcoming FTR therapies.
Medical image denoising using one-dimensional singularity function model.
Luo, Jianhua; Zhu, Yuemin; Hiba, Bassem
2010-03-01
A novel denoising approach is proposed that is based on a spectral data substitution mechanism through using a mathematical model of one-dimensional singularity function analysis (1-D SFA). The method consists in dividing the complete spectral domain of the noisy signal into two subsets: the preserved set where the spectral data are kept unchanged, and the substitution set where the original spectral data having lower signal-to-noise ratio (SNR) are replaced by those reconstructed using the 1-D SFA model. The preserved set containing original spectral data is determined according to the SNR of the spectrum. The singular points and singularity degrees in the 1-D SFA model are obtained through calculating finite difference of the noisy signal. The theoretical formulation and experimental results demonstrated that the proposed method allows more efficient denoising while introducing less distortion, and presents significant improvement over conventional denoising methods.
Modeling the relation between cardiac pump function and myofiber mechanics.
Arts, T; Bovendeerd, P; Delhaas, T; Prinzen, F
2003-05-01
Complexity of the geometry and structure of the heart hampers easy modeling of cardiac mechanics. The modeling can however be simplified considerably when using the hypothesis that in the normal heart myofiber structure and geometry adapt, until load is evenly distributed. A simple and realistic relationship is found between the hemodynamic variables cavity pressure and volume, and myofiber load parameters stress and strain. The most important geometric parameter in the latter relation is the ratio of cavity volume to wall volume, while actual geometry appears practically irrelevant. Applying the found relationship, a realistic maximum is set to left ventricular pressure after chronic pressure load. Pressures exceeding this level are likely to cause decompensation and heart failure. Furthermore, model is presented to simulate left and right ventricular pump function with left-right interaction.
Eigen model with general fitness functions and degradation rates
NASA Astrophysics Data System (ADS)
Hu, Chin-Kun; Saakian, David B.
2006-03-01
We present an exact solution of Eigen's quasispecies model with a general degradation rate and fitness functions, including a square root decrease of fitness with increasing Hamming distance from the wild type. The found behavior of the model with a degradation rate is analogous to a viral quasi-species under attack by the immune system of the host. Our exact solutions also revise the known results of neutral networks in quasispecies theory. To explain the existence of mutants with large Hamming distances from the wild type, we propose three different modifications of the Eigen model: mutation landscape, multiple adjacent mutations, and frequency-dependent fitness in which the steady state solution shows a multi-center behavior.
Functional GI disorders: from animal models to drug development
Mayer, E A; Bradesi, S; Chang, L; Spiegel, B M R; Bueller, J A; Naliboff, B D
2014-01-01
Despite considerable efforts by academic researchers and by the pharmaceutical industry, the development of novel pharmacological treatments for irritable bowel syndrome (IBS) and other functional gastrointestinal (GI) disorders has been slow and disappointing. The traditional approach to identifying and evaluating novel drugs for these symptom-based syndromes has relied on a fairly standard algorithm using animal models, experimental medicine models and clinical trials. In the current article, the empirical basis for this process is reviewed, focusing on the utility of the assessment of visceral hypersensitivity and GI transit, in both animals and humans, as well as the predictive validity of preclinical and clinical models of IBS for identifying successful treatments for IBS symptoms and IBS-related quality of life impairment. A review of published evidence suggests that abdominal pain, defecation-related symptoms (urgency, straining) and psychological factors all contribute to overall symptom severity and to health-related quality of life. Correlations between readouts obtained in preclinical and clinical models and respective symptoms are small, and the ability to predict drug effectiveness for specific as well as for global IBS symptoms is limited. One possible drug development algorithm is proposed which focuses on pharmacological imaging approaches in both preclinical and clinical models, with decreased emphasis on evaluating compounds in symptom-related animal models, and more rapid screening of promising candidate compounds in man. PMID:17965064
Modeling transport in the kidney: investigating function and dysfunction
2010-01-01
Mathematical models of water and solute transport in the kidney have significantly expanded our understanding of renal function in both health and disease. This review describes recent theoretical developments and emphasizes the relevance of model findings to major unresolved questions and controversies. These include the fundamental processes by which urine is concentrated in the inner medulla, the ultrastructural basis of proteinuria, irregular flow oscillation patterns in spontaneously hypertensive rats, and the mechanisms underlying the hypotensive effects of thiazides. Macroscopic models of water, NaCl, and urea transport in populations of nephrons have served to test, confirm, or refute a number of hypotheses related to the urine concentrating mechanism. Other macroscopic models focus on the mechanisms, role, and irregularities of renal hemodynamic control and on the regulation of renal oxygenation. At the mesoscale, models of glomerular filtration have yielded significant insight into the ultrastructural basis underlying a number of disorders. At the cellular scale, models of epithelial solute transport and pericyte Ca2+ signaling are being used to elucidate transport pathways and the effects of hormones and drugs. Areas where further theoretical progress is conditional on experimental advances are also identified. PMID:19889951
Functionalization of carbon nanotubes: Characterization, modeling and composite applications
NASA Astrophysics Data System (ADS)
Wang, Shiren
Carbon nanotubes have demonstrated exceptional mechanical, thermal and electrical properties, and are regarded as one of the most promising reinforcement materials for the next generation of high performance structural and multifunctional composites. However, to date, most application attempts have been hindered by several technical roadblocks, such as poor dispersion and weak interfacial bonding. In this dissertation, several innovative functionalization methods were proposed, studied to overcome these technical issues in order to realize the full potential of nanotubes as reinforcement. These functionalization methods included precision sectioning of nanotubes using an ultra-microtome, electron-beam irradiation, amino and epoxide group grafting. The characterization results of atomic force microscope, transmission electronic microscope and Raman suggested that aligned carbon nanotubes can be precisely sectioned with controlled length and minimum sidewall damage. This study also designed and demonstrated new covalent functionalization approaches through unique epoxy-grafting and one-step amino-grafting, which have potential of scale-up for composite applications. In addition, the dissertation also successfully tailored the structure and properties of the thin nanotube film through electron beam irradiation. Significant improvement of both mechanical and electrical conducting properties of the irradiated nanotube films or buckypapers was achieved. All these methods demonstrated effectiveness in improving dispersion and interfacial bonding in the epoxy resin, resulting in considerable improvements in composite mechanical properties. Modeling of functionalization methods also provided further understanding and offered the reasonable explanations of SWNTs length distribution as well as carbon nanostructure transformation upon electron-beam irradiation. Both experimental and modeling results provide important foundations for the further comprehensively investigation of
Mouse models for the evaluation of osteocyte functions.
Komori, Toshihisa
2014-02-01
Osteocytes establish an extensive intracellular and extracellular communication system via gap junction-coupled cell processes and canaliculi, through which cell processes pass throughout bone, and the communication system is extended to osteoblasts on the bone surface. To examine the osteocyte function, several mouse models were established. To ablate osteocytes, osteocytes death was induced by diphtheria toxin. However, any types of osteocyte death result in necrosis, because dying osteocytes are not phagocytosed by scavengers. After the rupture of cytoplasmic membrane, immunostimulatory molecules are released from lacunae to bone surface through canaliculi, and stimulate macrophages. The stimulated macrophages produce interleukin (IL)-1, IL-6, and tumor necrosis factor-alpha (TNF-α), which are the most important proinflammatory cytokines triggering inflammatory bone loss. Therefore, the osteocyte ablation results in necrosis-induced severe osteoporosis. In conditional knockout mice of gap junction protein alpha-1 (GJA1), which encodes connexin 43 in Gap junction, using dentin matrix protein 1 (DMP1) Cre transgenic mice, osteocyte apoptosis and enhanced bone resorption occur, because extracellular communication is intact. Overexpression of Bcl-2 in osteoblasts using 2.3 kb collagen type I alpha1 (COL1A1) promoter causes osteocyte apoptosis due to the severe reduction in the number of osteocyte processes, resulting in the disruption of both intracellular and extracellular communication systems. This mouse model unraveled osteocyte functions. Osteocytes negatively regulate bone mass by stimulating osteoclastogenesis and inhibiting osteoblast function in physiological condition. Osteocytes are responsible for bone loss in unloaded condition, and osteocytes augment their functions by further stimulating osteoclastogenesis and further inhibiting osteoblast function, at least partly, through the upregulation of receptor activator of nuclear factor-kappa B ligand
Mouse Models for the Evaluation of Osteocyte Functions
2014-01-01
Osteocytes establish an extensive intracellular and extracellular communication system via gap junction-coupled cell processes and canaliculi, through which cell processes pass throughout bone, and the communication system is extended to osteoblasts on the bone surface. To examine the osteocyte function, several mouse models were established. To ablate osteocytes, osteocytes death was induced by diphtheria toxin. However, any types of osteocyte death result in necrosis, because dying osteocytes are not phagocytosed by scavengers. After the rupture of cytoplasmic membrane, immunostimulatory molecules are released from lacunae to bone surface through canaliculi, and stimulate macrophages. The stimulated macrophages produce interleukin (IL)-1, IL-6, and tumor necrosis factor-alpha (TNF-α), which are the most important proinflammatory cytokines triggering inflammatory bone loss. Therefore, the osteocyte ablation results in necrosis-induced severe osteoporosis. In conditional knockout mice of gap junction protein alpha-1 (GJA1), which encodes connexin 43 in Gap junction, using dentin matrix protein 1 (DMP1) Cre transgenic mice, osteocyte apoptosis and enhanced bone resorption occur, because extracellular communication is intact. Overexpression of Bcl-2 in osteoblasts using 2.3 kb collagen type I alpha1 (COL1A1) promoter causes osteocyte apoptosis due to the severe reduction in the number of osteocyte processes, resulting in the disruption of both intracellular and extracellular communication systems. This mouse model unraveled osteocyte functions. Osteocytes negatively regulate bone mass by stimulating osteoclastogenesis and inhibiting osteoblast function in physiological condition. Osteocytes are responsible for bone loss in unloaded condition, and osteocytes augment their functions by further stimulating osteoclastogenesis and further inhibiting osteoblast function, at least partly, through the upregulation of receptor activator of nuclear factor-kappa B ligand
Structure-function analysis of vitamin D and VDR model.
Yamada, S; Yamamoto, K; Masuno, H
2000-05-01
In the first section, the general three-dimensional structure of the ligand-binding domain (LBD) of nuclear receptors (NR) was briefly described on the basis of their x-ray crystal structures. Emphasis was placed on the three major conformations of NR-LBD and their role in the transactivation function. In the second part, the structure-function relationship of vitamin D was analyzed based on the ligand structure, in particular by using systematic conformational analysis as a tool. On the basis of the conformational analysis of the vitamin D side chain and studies using conformationally restricted synthetic vitamin D analogs, we suggested the active space region concept of vitamin D: The vitamin D side-chain region was grouped into five regions (A, G, EA, EG and F). Activity orders, in terms of the spatial region, found by these studies are as follows: Affinity for vitamin D receptor (VDR), EA>A>F>G>EG; Affinity for vitamin D binding protein (DBP), A>G,EA, EG; Target gene transactivation, EA>F>A>EG G; Cell differentiation, EA>F>A>EG G; Bone calcium mobilization, EA>G A>F EG; Intestinal calcium absorption, EA=A G>EG. In the third section, homology modeling of VDR-LBD and docking of the natural ligand, 1,25-(OH)2D3, into the ligand binding cavity of the model are described. Amino acid residues forming hydrogen bonds with the biologically important 1alpha- and 25-OH groups were identified: 1alpha-OH forms a pincer-type hydrogen bond with R274 and S237 and 25-OH with H397. This VDR-LBD/1,25-(OH)2D3 docking model was firmly substantiated by mutation analysis. Using this VDR model, the structure-function relationship of highly potent vitamin D analogs was discussed.
A Model of School Counseling Supervision: The Goals, Functions, Roles, and Systems Model
ERIC Educational Resources Information Center
Wood, Chris; Rayle, Andrea Dixon
2006-01-01
The authors outline the Goals, Functions, Roles, and Systems Model (GFRS), a school counseling-specific model for supervising school counselors-in-training (SCITs). The GFRS was created as a guide for assisting in supervising and preparing SCITs for the multifaceted tasks they will undertake in their internships and careers. The components of this…
Two-point functions in a holographic Kondo model
NASA Astrophysics Data System (ADS)
Erdmenger, Johanna; Hoyos, Carlos; O'Bannon, Andy; Papadimitriou, Ioannis; Probst, Jonas; Wu, Jackson M. S.
2017-03-01
We develop the formalism of holographic renormalization to compute two-point functions in a holographic Kondo model. The model describes a (0 + 1)-dimensional impurity spin of a gauged SU( N ) interacting with a (1 + 1)-dimensional, large- N , strongly-coupled Conformal Field Theory (CFT). We describe the impurity using Abrikosov pseudo-fermions, and define an SU( N )-invariant scalar operator O built from a pseudo-fermion and a CFT fermion. At large N the Kondo interaction is of the form O^{\\dagger}O, which is marginally relevant, and generates a Renormalization Group (RG) flow at the impurity. A second-order mean-field phase transition occurs in which O condenses below a critical temperature, leading to the Kondo effect, including screening of the impurity. Via holography, the phase transition is dual to holographic superconductivity in (1 + 1)-dimensional Anti-de Sitter space. At all temperatures, spectral functions of O exhibit a Fano resonance, characteristic of a continuum of states interacting with an isolated resonance. In contrast to Fano resonances observed for example in quantum dots, our continuum and resonance arise from a (0 + 1)-dimensional UV fixed point and RG flow, respectively. In the low-temperature phase, the resonance comes from a pole in the Green's function of the form - i< O >2, which is characteristic of a Kondo resonance.
Holonomy spin foam models: asymptotic geometry of the partition function
NASA Astrophysics Data System (ADS)
Hellmann, Frank; Kaminski, Wojciech
2013-10-01
We study the asymptotic geometry of the spin foam partition function for a large class of models, including the models of Barrett and Crane, Engle, Pereira, Rovelli and Livine, and, Freidel and Krasnov. The asymptotics is taken with respect to the boundary spins only, no assumption of large spins is made in the interior. We give a sufficient criterion for the existence of the partition function. We find that geometric boundary data is suppressed unless its interior continuation satisfies certain accidental curvature constraints. This means in particular that most Regge manifolds are suppressed in the asymptotic regime. We discuss this explicitly for the case of the configurations arising in the 3-3 Pachner move. We identify the origin of these accidental curvature constraints as an incorrect twisting of the face amplitude upon introduction of the Immirzi parameter and propose a way to resolve this problem, albeit at the price of losing the connection to the SU(2) boundary Hilbert space. The key methodological innovation that enables these results is the introduction of the notion of wave front sets, and the adaptation of tools for their study from micro local analysis to the case of spin foam partition functions.
Bayesian network models in brain functional connectivity analysis
Zhang, Sheng; Li, Chiang-shan R.
2013-01-01
Much effort has been made to better understand the complex integration of distinct parts of the human brain using functional magnetic resonance imaging (fMRI). Altered functional connectivity between brain regions is associated with many neurological and mental illnesses, such as Alzheimer and Parkinson diseases, addiction, and depression. In computational science, Bayesian networks (BN) have been used in a broad range of studies to model complex data set in the presence of uncertainty and when expert prior knowledge is needed. However, little is done to explore the use of BN in connectivity analysis of fMRI data. In this paper, we present an up-to-date literature review and methodological details of connectivity analyses using BN, while highlighting caveats in a real-world application. We present a BN model of fMRI dataset obtained from sixty healthy subjects performing the stop-signal task (SST), a paradigm widely used to investigate response inhibition. Connectivity results are validated with the extant literature including our previous studies. By exploring the link strength of the learned BN’s and correlating them to behavioral performance measures, this novel use of BN in connectivity analysis provides new insights to the functional neural pathways underlying response inhibition. PMID:24319317
Material-specific transfer function model and SNR in CT
NASA Astrophysics Data System (ADS)
Brunner, Claudia C.; Kyprianou, Iacovos S.
2013-10-01
This study presents an analytical model for the edge spread function (ESF) of a clinical CT system that allows reliable fits of noisy ESF data. The model was used for the calculation of the material-specific transfer function TF and an estimation of the signal transfer and the signal-to-noise ratio (SNR) in 2D. Images of the Catphan phantom were acquired with a clinical Siemens Somatom Sensation Cardiac 64 CT scanner combining four different x-ray tube outputs (40, 150, 250 and 350 mAs) with four different reconstruction filters, which covered the range from very smooth (B10s) to very sharp (B70s). The images of the high- and mid-contrast cylinders of the phantom’s ‘Geometry and Sensitometry’ module (air, Teflon, Delrin and PMP) were used to sample material-specific ESF curves. The ESF curves were fitted with the analytical model we developed based on a linear combination of Boltzmann and Gaussian functions. The analytical model of the ESF was used to obtain the Fourier-based material-specific transfer function TF, as well as the spatial-domain point spread function (PSF). TF was subsequently used to estimate the signal transfer, which was compared to the actual reconstructed image of a 3.0 mm diameter Teflon pin. The noise power spectrum (NPS) was calculated from images of a uniform water phantom under the same technique parameters. The task-specific SNR was calculated for all technique parameters from the model-based TF, the measured NPS and simulated 3 mm diameter disc signals modeling the aforementioned materials. Bootstrapping was performed to estimate the standard deviation of the TF and the SNR. The analytical model we developed accurately captured the features of the CT ESF data. The coefficient of determination R2, a metric that describes the goodness of the fit, had a median value of 0.9995, and decreased for low tube output, low contrast and the sharp reconstruction filter. Our analysis showed that ESF, PSF and TF depended not only on the
[The Function of REM Sleep: Implications from Transgenic Mouse Models].
Kashiwagi, Mitsuaki; Hayashi, Yu
2016-10-01
Our sleep is composed of rapid eye movement (REM) sleep and non-REM (NREM) sleep. REM sleep is the major source of dreams, whereas synchronous cortical oscillations, called slow waves, are observed during NREM sleep. Both stages are unique to certain vertebrate species, and therefore, REM and NREM sleep are thought to be involved in higher-order brain functions. While several studies have revealed the importance of NREM sleep in growth hormone secretion, memory consolidation and brain metabolite clearance, the functions of REM sleep are currently almost totally unknown. REM sleep functions cannot be easily indicated from classical REM sleep deprivation experiments, where animals are forced to wake up whenever they enter REM sleep, because such experiments produce extreme stress due to the stimuli and because REM sleep is under strong homeostatic regulation. To overcome these issues, we developed a novel transgenic mouse model in which REM sleep can be manipulated. Using these mice, we found that REM sleep enhances slow wave activity during the subsequent NREM sleep. Slow wave activity is known to contribute to memory consolidation and synaptic plasticity. Thus, REM sleep might be involved in higher-order brain functions through its role in enhancing slow wave activity.
Assessing Functional Performance in the Mdx Mouse Model
Aartsma-Rus, Annemieke; van Putten, Maaike
2014-01-01
Duchenne muscular dystrophy (DMD) is a severe and progressive muscle wasting disorder for which no cure is available. Nevertheless, several potential pharmaceutical compounds and gene therapy approaches have progressed into clinical trials. With improvement in muscle function being the most important end point in these trials, a lot of emphasis has been placed on setting up reliable, reproducible, and easy to perform functional tests to pre clinically assess muscle function, strength, condition, and coordination in the mdx mouse model for DMD. Both invasive and noninvasive tests are available. Tests that do not exacerbate the disease can be used to determine the natural history of the disease and the effects of therapeutic interventions (e.g. forelimb grip strength test, two different hanging tests using either a wire or a grid and rotarod running). Alternatively, forced treadmill running can be used to enhance disease progression and/or assess protective effects of therapeutic interventions on disease pathology. We here describe how to perform these most commonly used functional tests in a reliable and reproducible manner. Using these protocols based on standard operating procedures enables comparison of data between different laboratories. PMID:24747372
Gene3D: modelling protein structure, function and evolution.
Yeats, Corin; Maibaum, Michael; Marsden, Russell; Dibley, Mark; Lee, David; Addou, Sarah; Orengo, Christine A
2006-01-01
The Gene3D release 4 database and web portal (http://cathwww.biochem.ucl.ac.uk:8080/Gene3D) provide a combined structural, functional and evolutionary view of the protein world. It is focussed on providing structural annotation for protein sequences without structural representatives--including the complete proteome sets of over 240 different species. The protein sequences have also been clustered into whole-chain families so as to aid functional prediction. The structural annotation is generated using HMM models based on the CATH domain families; CATH is a repository for manually deduced protein domains. Amongst the changes from the last publication are: the addition of over 100 genomes and the UniProt sequence database, domain data from Pfam, metabolic pathway and functional data from COGs, KEGG and GO, and protein-protein interaction data from MINT and BIND. The website has been rebuilt to allow more sophisticated querying and the data returned is presented in a clearer format with greater functionality. Furthermore, all data can be downloaded in a simple XML format, allowing users to carry out complex investigations at their own computers.
Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.
Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao
2016-04-01
To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.
NASA Technical Reports Server (NTRS)
Oneal, Melvin R.; Task, H. Lee; Genco, Louis V.
1992-01-01
Viewgraphs on the effect of microgravity on several visual functions during STS shuttle missions are presented. The purpose, methods, results, and discussion are discussed. The visual function tester model 1 is used.
Teleseismic receiver functions modeling of the eastern Indian craton
NASA Astrophysics Data System (ADS)
Mandal, Prantik; Biswas, Koushik
2016-09-01
We estimate receiver functions (RFs) through the time-domain deconvolution using three-component broadband data of 100 teleseismic events (30° ⩽ ∧ ⩽ 90°) from 15 seismographs in the eastern Indian craton. Estimated radial RFs show a positive phase at 4.6-5.8 s delay time corresponding to the crustal thicknesses of 37-46 km. Through the differential evolution (DE) waveform inversion modeling of radial receiver functions, we delineate the crustal structure at 15 broadband stations. On an average, the Archean Singhbhum Odisha Craton (SOC) is characterized by a thick crust of 43 ± 3 km in comparison to a relatively thin crust of 41 ± 1 km underlying the Proterozoic Chotanagpur Granite Gneissic terrain (CGGT). While, a thin crust of 38 ± 1 km characterizes the younger Eastern Ghats Mobile Belt (EGMB). The main results of our modeling reveal a 46 km thick Archean crust underlying the Singhbhum granite (SG) of 3.6 Ga, which is characterized by a 3 km crustal thickening probably resulted from the Archean subduction process. Our modeling also detects a 2-3 km crustal thinning with the thinnest crust of 37 km below the region near South Singhbhum Shear Zone, which could be attributed to the 1.6 Ga plume activity associated with Dalma volcanic. Our modeling also led to the delineation of a crustal thinning of 2-3 km underlying the region in EGMB, which was influenced by a much younger (∼117 Ma) Rajmahal magmatism associated with the Gondwana break-up episode. However, our study could not detect any age-dependent variation of crustal thicknesses in the eastern Indian craton. The main result of our modeling suggests a two-phase crustal evolution process for the SOC viz. older E-W crustal thickening due to E-W plate compression and later crustal thinning episodes associated with the Dalma volcanism in the north and the Rajmahal volcanism in the South.
Dynamic causal modelling for functional near-infrared spectroscopy
Tak, S.; Kempny, A.M.; Friston, K.J.; Leff, A.P.; Penny, W.D.
2015-01-01
Functional near-infrared spectroscopy (fNIRS) is an emerging technique for measuring changes in cerebral hemoglobin concentration via optical absorption changes. Although there is great interest in using fNIRS to study brain connectivity, current methods are unable to infer the directionality of neuronal connections. In this paper, we apply Dynamic Causal Modelling (DCM) to fNIRS data. Specifically, we present a generative model of how observed fNIRS data are caused by interactions among hidden neuronal states. Inversion of this generative model, using an established Bayesian framework (variational Laplace), then enables inference about changes in directed connectivity at the neuronal level. Using experimental data acquired during motor imagery and motor execution tasks, we show that directed (i.e., effective) connectivity from the supplementary motor area to the primary motor cortex is negatively modulated by motor imagery, and this suppressive influence causes reduced activity in the primary motor cortex during motor imagery. These results are consistent with findings of previous functional magnetic resonance imaging (fMRI) studies, suggesting that the proposed method enables one to infer directed interactions in the brain mediated by neuronal dynamics from measurements of optical density changes. PMID:25724757
Droplet model for autocorrelation functions in an Ising ferromagnet
NASA Technical Reports Server (NTRS)
Tang, Chao; Nakanishi, Hiizu; Langer, J. S.
1989-01-01
The autocorrelation function of Ising spins in an ordered phase is studied via a droplet model. Only noninteracting spherical droplets are considered. The Langevin equation which describes fluctuations in the radius of a single droplet is studied in detail. A general description of the transformation to a Fokker-Planck equations and the ways in which a spectral analysis of that equation can be used to compute the autocorrelation function is given. It is shown that the eigenvalues of the Fokker-Planck operator form (1) a continuous spectrum of relaxation rates starting from zero for d = 2, (2) a continuous spectrum with a finite gap for d = 3, and (3) a discrete spectrum for d greater than 4, where d is the spatial dimensionality. Detailed solutions for various cases are presented.
Defining Predictive Probability Functions for Species Sampling Models.
Lee, Jaeyong; Quintana, Fernando A; Müller, Peter; Trippa, Lorenzo
2013-01-01
We review the class of species sampling models (SSM). In particular, we investigate the relation between the exchangeable partition probability function (EPPF) and the predictive probability function (PPF). It is straightforward to define a PPF from an EPPF, but the converse is not necessarily true. In this paper we introduce the notion of putative PPFs and show novel conditions for a putative PPF to define an EPPF. We show that all possible PPFs in a certain class have to define (unnormalized) probabilities for cluster membership that are linear in cluster size. We give a new necessary and sufficient condition for arbitrary putative PPFs to define an EPPF. Finally, we show posterior inference for a large class of SSMs with a PPF that is not linear in cluster size and discuss a numerical method to derive its PPF.
Nonequilibrium Anderson model made simple with density functional theory
NASA Astrophysics Data System (ADS)
Kurth, S.; Stefanucci, G.
2016-12-01
The single-impurity Anderson model is studied within the i-DFT framework, a recently proposed extension of density functional theory (DFT) for the description of electron transport in the steady state. i-DFT is designed to give both the steady current and density at the impurity, and it requires the knowledge of the exchange-correlation (xc) bias and on-site potential (gate). In this work we construct an approximation for both quantities which is accurate in a wide range of temperatures, gates, and biases, thus providing a simple and unifying framework to calculate the differential conductance at negligible computational cost in different regimes. Our results mark a substantial advance for DFT and may inform the construction of functionals applicable to other correlated systems.
AMFESYS: Modelling and diagnosis functions for operations support
NASA Technical Reports Server (NTRS)
Wheadon, J.
1993-01-01
Packetized telemetry, combined with low station coverage for close-earth satellites, may introduce new problems in presenting to the operator a clear picture of what the spacecraft is doing. A recent ESOC study has gone some way to show, by means of a practical demonstration, how the use of subsystem models combined with artificial intelligence techniques, within a real-time spacecraft control system (SCS), can help to overcome these problems. A spin-off from using these techniques can be an improvement in the reliability of the telemetry (TM) limit-checking function, as well as the telecommand verification function, of the Spacecraft Control systems (SCS). The problem and how it was addressed, including an overview of the 'AMF Expert System' prototype are described, and proposes further work which needs to be done to prove the concept. The Automatic Mirror Furnace is part of the payload of the European Retrievable Carrier (EURECA) spacecraft, which was launched in July 1992.
a Global Model for Long-Range Interaction `DAMPING Functions'
NASA Astrophysics Data System (ADS)
Myatt, Philip Thomas; McCourt, Frederick R. W.; Le Roy, Robert J.
2016-06-01
In recent years, `damping functions', which characterize the weakening of inverse-power-sum long-range interatomic interaction energies with increasing electron overlap, have become an increasing important component of models for diatomic molecule interaction potentials. However, a key feature of models for damping functions, their portability, has received little scrutiny. The present work set out to examine all available ab initio induction and dispersion damping function data and to attempt to devise a `global' scheme for diatomic molecule damping functions. It appears that while neutral (H, He, Li, and Ne, homonuclear and mixed) and anion (H^- with H, He and Li) species obey (approximately) one common rule, proton plus neutral (H^+ with H, He and Li) and non-proton-cation plus neutral systems (He^+ and Li^+ with H, He and Li), must each be treated separately. However, for all three cases, a version of the Douketis-Scoles-Thakkar (ionization potential)power factor is a key scaling parameter. R.J. Le Roy, C. C. Haugen, J. Tao and Hui Li, Mol. Phys. 109,435 (2011). P.J. Knowles and W.J. Meath,J. Mol. Phys. 60, 1143 (1987); R.J. Wheatley and W.J. Meath,J. Mol. Phys. 80, 25 (1993); R.J. Wheatley and W.J. Meath J. Chem. Phys. 179, 341 (1994); R.J. Wheatley and W.J. Meath,J. Chem. Phys. 203, 209 (1996). C. Douketis,G. Scoles, S. Marchetti, M. Zen and A. J. Thakkar, J. Chem. Phys. 76, 3057 (1982).
Effects of exercise on brain functions in diabetic animal models.
Yi, Sun Shin
2015-05-15
Human life span has dramatically increased over several decades, and the quality of life has been considered to be equally important. However, diabetes mellitus (DM) characterized by problems related to insulin secretion and recognition has become a serious health problem in recent years that threatens human health by causing decline in brain functions and finally leading to neurodegenerative diseases. Exercise is recognized as an effective therapy for DM without medication administration. Exercise studies using experimental animals are a suitable option to overcome this drawback, and animal studies have improved continuously according to the needs of the experimenters. Since brain health is the most significant factor in human life, it is very important to assess brain functions according to the different exercise conditions using experimental animal models. Generally, there are two types of DM; insulin-dependent type 1 DM and an insulin-independent type 2 DM (T2DM); however, the author will mostly discuss brain functions in T2DM animal models in this review. Additionally, many physiopathologic alterations are caused in the brain by DM such as increased adiposity, inflammation, hormonal dysregulation, uncontrolled hyperphagia, insulin and leptin resistance, and dysregulation of neurotransmitters and declined neurogenesis in the hippocampus and we describe how exercise corrects these alterations in animal models. The results of changes in the brain environment differ according to voluntary, involuntary running exercises and resistance exercise, and gender in the animal studies. These factors have been mentioned in this review, and this review will be a good reference for studying how exercise can be used with therapy for treating DM.
Modeling the biomechanics of articular eminence function in anthropoid primates.
Terhune, Claire E
2011-11-01
One of the most prominent features of the cranial component of the temporomandibular joint (TMJ) is the articular eminence (AE). This bar of bone is the primary surface upon which the condyle translates and rotates during movements of the mandible, and is therefore the primary point at which forces are transmitted from the mandible to the cranium during loading of the masticatory apparatus. The shape of the AE is highly variable across primates, and the raised eminence of humans has often been considered a defining feature of the human TMJ, yet few data exist to address whether this variation is functionally significant. This study used a broad interspecific sample of anthropoid primates to elaborate upon and test the predictions of a previously proposed model of AE function. This model suggests that AE inclination acts to resist non-normal forces at the TMJ, thereby maximizing bite forces (BFs). AE inclination was predicted to covary with two specific features of the masticatory apparatus: height of the TMJ above the occlusal plane; and inclination of the masticatory muscles. A correlate of this model is that taxa utilizing more resistant food objects should also exhibit relatively more inclined AEs. Results of the correlation analyses found that AE inclination is strongly correlated with height of the TMJ above the occlusal plane, but less so with inclination of the masticatory muscles. Furthermore, pairwise comparisons of closely related taxa with documented dietary differences found that the AE is consistently more inclined in taxa that utilize more resistant food items. These data preliminarily suggest that variation in AE morphology across anthropoid primates is functionally related to maximizing BFs, and add to the growing dataset of masticatory morphologies linked to feeding behavior.
Oncogenic PTEN functions and models in T-cell malignancies.
Tesio, M; Trinquand, A; Macintyre, E; Asnafi, V
2016-07-28
PTEN is a protein phosphatase that is crucial to prevent the malignant transformation of T-cells. Although a numerous mechanisms regulate its expression and function, they are often altered in T-cell acute lymphoblastic leukaemias and T-cell lymphomas. As such, PTEN inactivation frequently occurs in these malignancies, where it can be associated with chemotherapy resistance and poor prognosis. Different Pten knockout models recapitulated the development of T-cell leukaemia/lymphoma, demonstrating that PTEN loss is at the center of a complex oncogenic network that sustains and drives tumorigenesis via the activation of multiple signalling pathways. These aspects and their therapeutic implications are discussed in this review.
Transfer function modeling of damping mechanisms in viscoelastic plates
NASA Technical Reports Server (NTRS)
Slater, J. C.; Inman, D. J.
1991-01-01
This work formulates a method for the modeling of material damping characteristics in plates. The Sophie German equation of classical plate theory is modified to incorporate hysteresis effects represented by complex stiffness using the transfer function approach proposed by Golla and Hughes, (1985). However, this procedure is not limited to this representation. The governing characteristic equation is decoupled through separation of variables, yielding a solution similar to that of undamped classical plate theory, allowing solution of the steady state as well as the transient response problem.
Modeling structure-function interdependence of pulmonary gas exchange.
Weibel, Ewald R
2008-01-01
Modeling functional processes, such as gas exchange, that occur deep in the lung far from where one can directly observe, depends on knowledge about the precise and quantitative design of the structure of the gas exchanger. This is the case as well for the actual arrangement of alveoli and blood capillaries at the gas exchange surface as for the disposition of gas exchange units with respect to the airway and vascular trees. The serial arrangement of alveoli and their perfusion as parallel units have important consequences for gas exchange.
Modeling integrated sensor/actuator functions in realistic environments
NASA Astrophysics Data System (ADS)
Kim, Jae-Wan; Varadan, Vasundara V.; Varadan, Vijay K.
1993-07-01
Smart materials are expected to adapt to their environment and provide a useful response to changes in the environment. Both the sensor and actuator functions with the appropriate feedback mechanism must be integrated and comprise the `brains' of the material. Piezoelectric ceramics have proved to be effective as both sensors and actuators for a wide variety of applications. Thus, realistic simulation models are needed that can predict the performance of smart materials that incorporate piezoceramics. The environment may include the structure on which the transducers are mounted, fluid medium and material damping. In all cases, the smart material should sense the change and make a useful response. A hybrid numerical method involving finite element modeling in the plate structure and transducer region and a plane wave representation in the fluid region is used. The simulation of the performance of smart materials are performed.
New models for analyzing mast cell functions in vivo.
Reber, Laurent L; Marichal, Thomas; Galli, Stephen J
2012-12-01
In addition to their well-accepted role as critical effector cells in anaphylaxis and other acute IgE-mediated allergic reactions, mast cells (MCs) have been implicated in a wide variety of processes that contribute to disease or help to maintain health. Although some of these roles were first suggested by analyses of MC products or functions in vitro, it is critical to determine whether, and under which circumstances, such potential roles actually can be performed by MCs in vivo. This review discusses recent advances in the development and analysis of mouse models to investigate the roles of MCs and MC-associated products during biological responses in vivo, and comments on some of the similarities and differences in the results obtained with these newer versus older models of MC deficiency.
Engelmann spruce site index models: a comparison of model functions and parameterizations.
Nigh, Gordon
2015-01-01
Engelmann spruce (Picea engelmannii Parry ex Engelm.) is a high-elevation species found in western Canada and western USA. As this species becomes increasingly targeted for harvesting, better height growth information is required for good management of this species. This project was initiated to fill this need. The objective of the project was threefold: develop a site index model for Engelmann spruce; compare the fits and modelling and application issues between three model formulations and four parameterizations; and more closely examine the grounded-Generalized Algebraic Difference Approach (g-GADA) model parameterization. The model fitting data consisted of 84 stem analyzed Engelmann spruce site trees sampled across the Engelmann Spruce - Subalpine Fir biogeoclimatic zone. The fitted models were based on the Chapman-Richards function, a modified Hossfeld IV function, and the Schumacher function. The model parameterizations that were tested are indicator variables, mixed-effects, GADA, and g-GADA. Model evaluation was based on the finite-sample corrected version of Akaike's Information Criteria and the estimated variance. Model parameterization had more of an influence on the fit than did model formulation, with the indicator variable method providing the best fit, followed by the mixed-effects modelling (9% increase in the variance for the Chapman-Richards and Schumacher formulations over the indicator variable parameterization), g-GADA (optimal approach) (335% increase in the variance), and the GADA/g-GADA (with the GADA parameterization) (346% increase in the variance). Factors related to the application of the model must be considered when selecting the model for use as the best fitting methods have the most barriers in their application in terms of data and software requirements.
Engelmann Spruce Site Index Models: A Comparison of Model Functions and Parameterizations
Nigh, Gordon
2015-01-01
Engelmann spruce (Picea engelmannii Parry ex Engelm.) is a high-elevation species found in western Canada and western USA. As this species becomes increasingly targeted for harvesting, better height growth information is required for good management of this species. This project was initiated to fill this need. The objective of the project was threefold: develop a site index model for Engelmann spruce; compare the fits and modelling and application issues between three model formulations and four parameterizations; and more closely examine the grounded-Generalized Algebraic Difference Approach (g-GADA) model parameterization. The model fitting data consisted of 84 stem analyzed Engelmann spruce site trees sampled across the Engelmann Spruce – Subalpine Fir biogeoclimatic zone. The fitted models were based on the Chapman-Richards function, a modified Hossfeld IV function, and the Schumacher function. The model parameterizations that were tested are indicator variables, mixed-effects, GADA, and g-GADA. Model evaluation was based on the finite-sample corrected version of Akaike’s Information Criteria and the estimated variance. Model parameterization had more of an influence on the fit than did model formulation, with the indicator variable method providing the best fit, followed by the mixed-effects modelling (9% increase in the variance for the Chapman-Richards and Schumacher formulations over the indicator variable parameterization), g-GADA (optimal approach) (335% increase in the variance), and the GADA/g-GADA (with the GADA parameterization) (346% increase in the variance). Factors related to the application of the model must be considered when selecting the model for use as the best fitting methods have the most barriers in their application in terms of data and software requirements. PMID:25853472
Models for predicting objective function weights in prostate cancer IMRT
Boutilier, Justin J. Lee, Taewoo; Craig, Tim; Sharpe, Michael B.; Chan, Timothy C. Y.
2015-04-15
Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR
Passive ventricular mechanics modelling using MRI of structure and function.
Wang, V Y; Lam, H I; Ennis, D B; Young, A A; Nash, M P
2008-01-01
Patients suffering from dilated cardiomyopathy or myocardial infarction can develop left ventricular (LV) diastolic impairment. The LV remodels its structure and function to adapt to pathophysiological changes in geometry and loading conditions and this remodeling process can alter the passive ventricular mechanics. In order to better understand passive ventricular mechanics, a LV finite element model was developed to incorporate physiological and mechanical information derived from in vivo magnetic resonance imaging (MRI) tissue tagging, in vivo LV cavity pressure recording and ex vivo diffusion tensor MRI (DTMRI) of a canine heart. MRI tissue tagging enables quantitative evaluation of cardiac mechanical function with high spatial and temporal resolution, whilst the direction of maximum water diffusion (the primary eigenvector) in each voxel of a DTMRI directly correlates with the myocardial fibre orientation. This model was customized to the geometry of the canine LV during diastasis by fitting the segmented epicardial and endocardial surface data from tagged MRI using nonlinear finite element fitting techniques. Myofibre orientations, extracted from DTMRI of the same heart, were incorporated into this geometric model using a free form deformation methodology. Pressure recordings, temporally synchronized to the tissue tagging MRI data, were used to simulate the LV deformation during diastole. Simulation of the diastolic LV mechanics allowed us to estimate the stiffness of the passive LV myocardium based on kinematic data obtained from tagged MRI. This integrated physiological model will allow more insight into the regional passive diastolic mechanics of the LV on an individualized basis, thereby improving our understanding of the underlying structural basis of mechanical dysfunction in pathological conditions.
A marked correlation function for constraining modified gravity models
NASA Astrophysics Data System (ADS)
White, Martin
2016-11-01
Future large scale structure surveys will provide increasingly tight constraints on our cosmological model. These surveys will report results on the distance scale and growth rate of perturbations through measurements of Baryon Acoustic Oscillations and Redshift-Space Distortions. It is interesting to ask: what further analyses should become routine, so as to test as-yet-unknown models of cosmic acceleration? Models which aim to explain the accelerated expansion rate of the Universe by modifications to General Relativity often invoke screening mechanisms which can imprint a non-standard density dependence on their predictions. This suggests density-dependent clustering as a `generic' constraint. This paper argues that a density-marked correlation function provides a density-dependent statistic which is easy to compute and report and requires minimal additional infrastructure beyond what is routinely available to such survey analyses. We give one realization of this idea and study it using low order perturbation theory. We encourage groups developing modified gravity theories to see whether such statistics provide discriminatory power for their models.
Model updating using antiresonant frequencies identified from transmissibility functions
NASA Astrophysics Data System (ADS)
Meruane, V.
2013-02-01
Traditional model updating methods make use of modal information as natural frequencies and mode shapes. Natural frequencies can be accurately identified, but this is not the case for mode shapes. Mode shapes are usually accurate to within 10% at best, which can reduce the accuracy of the updated model. To solve this problem, some researchers have proposed antiresonant frequencies as an alternative to mode shapes. Antiresonances are identified easier and more accurately than mode shapes. In addition, antiresonances provide the same information as mode shapes and natural frequencies together. This article presents a new methodology to identify antiresonant frequencies from transmissibility measurements. A transmissibility function represents the relation in the frequency domain of the measured response of two points in the structure. Hence, it does not involve the measurement of excitation forces. These antiresonances are used to update the numerical models of two experimental structures: An 8-dof mass-spring system, and an exhaust system of a car. In both cases, the algorithm is tested first to update the numerical model of the structure, and second, to assess experimental damage.
Constructing biological pathway models with hybrid functional Petri nets.
Doi, Atsushi; Fujita, Sachie; Matsuno, Hiroshi; Nagasaki, Masao; Miyano, Satoru
2004-01-01
In many research projects on modeling and analyzing biological pathways, the Petri net has been recognized as a promising method for representing biological pathways. From the pioneering works by Reddy et al., 1993, and Hofestädt, 1994, that model metabolic pathways by traditional Petri net, several enhanced Petri nets such as colored Petri net, stochastic Petri net, and hybrid Petri net have been used for modeling biological phenomena. Recently, Matsuno et al., 2003b, introduced the hybrid functional Petri net (HFPN) in order to give a more intuitive and natural modeling method for biological pathways than these existing Petri nets. Although the paper demonstrates the effectiveness of HFPN with two examples of gene regulation mechanism for circadian rhythms and apoptosis signaling pathway, there has been no detailed explanation about the method of HFPN construction for these examples. The purpose of this paper is to describe method to construct biological pathways with the HFPN step-by-step. The method is demonstrated by the well-known glycolytic pathway controlled by the lac operon gene regulatory mechanism.
Constructing biological pathway models with hybrid functional petri nets.
Doi, Atsushi; Fujita, Sachie; Matsuno, Hiroshi; Nagasaki, Masao; Miyano, Satoru
2011-01-01
In many research projects on modeling and analyzing biological pathways, the Petri net has been recognized as a promising method for representing biological pathways. From the pioneering works by Reddy et al., 1993, and Hofestädt, 1994, that model metabolic pathways by traditional Petri net, several enhanced Petri nets such as colored Petri net, stochastic Petri net, and hybrid Petri net have been used for modeling biological phenomena. Recently, Matsuno et al., 2003b, introduced the hybrid functional Petri net (HFPN) in order to give a more intuitive and natural modeling method for biological pathways than these existing Petri nets. Although the paper demonstrates the effectiveness of HFPN with two examples of gene regulation mechanism for circadian rhythms and apoptosis signaling pathway, there has been no detailed explanation about the method of HFPN construction for these examples. The purpose of this paper is to describe method to construct biological pathways with the HFPN step-by-step. The method is demonstrated by the well-known glycolytic pathway controlled by the lac operon gene regulatory mechanism.
Modeling functional piezoelectricity in perovskite superlattices with competing instabilities
NASA Astrophysics Data System (ADS)
Swartz, Charles; Wu, Xifan
2012-02-01
Multi-component Perovskite Superlattices (SLs) of the form ABO3, provide a very promising avenue for the design of materials with multifunctional properties. Furthermore the interfaces of such multi-component SLs are home to competing anti-ferrodistortive and ferroelectric instabilities which can produce unexpected functionalities. However, at present first principles calculations exceeding more than 10 units cells, are particularly costly as they scale with the valence electrons as N^3. We present a first-principles modeling technique that allows us to accurately model the piezoelectric strains of paraelectric/ferroelectric SLs, BaTiO3/CaTiO3 and PbTiO3/SrTiO3, under a fixed displacement field. The model is based on a maximally localized wannier center layer polarization technique, as well as a truncated cluster expansion, that makes use of the fact that such PE/FE SLs have been shown to have highly localized ionic and electronic interface effects. The prediction of the piezoelectricity for a SL of an arbitrary stacking sequence will be demonstrated. We also use our model to conduct a systemic study of the interface effects on piezoelectric response in the above SLs paying special attention to a strong non-linear effect observed in Bulk SrTiO3.
The integrated Earth System Model Version 1: formulation and functionality
Collins, William D.; Craig, Anthony P.; Truesdale, John E.; Di Vittorio, Alan; Jones, Andrew D.; Bond-Lamberty, Benjamin; Calvin, Katherine V.; Edmonds, James A.; Kim, Son H.; Thomson, Allison M.; Patel, Pralit L.; Zhou, Yuyu; Mao, Jiafu; Shi, Xiaoying; Thornton, Peter E.; Chini, Louise M.; Hurtt, George C.
2015-07-23
The integrated Earth System Model (iESM) has been developed as a new tool for pro- jecting the joint human/climate system. The iESM is based upon coupling an Integrated Assessment Model (IAM) and an Earth System Model (ESM) into a common modeling in- frastructure. IAMs are the primary tool for describing the human–Earth system, including the sources of global greenhouse gases (GHGs) and short-lived species, land use and land cover change, and other resource-related drivers of anthropogenic climate change. ESMs are the primary scientific tools for examining the physical, chemical, and biogeochemical impacts of human-induced changes to the climate system. The iESM project integrates the economic and human dimension modeling of an IAM and a fully coupled ESM within a sin- gle simulation system while maintaining the separability of each model if needed. Both IAM and ESM codes are developed and used by large communities and have been extensively applied in recent national and international climate assessments. By introducing heretofore- omitted feedbacks between natural and societal drivers, we can improve scientific under- standing of the human–Earth system dynamics. Potential applications include studies of the interactions and feedbacks leading to the timing, scale, and geographic distribution of emissions trajectories and other human influences, corresponding climate effects, and the subsequent impacts of a changing climate on human and natural systems. This paper de- scribes the formulation, requirements, implementation, testing, and resulting functionality of the first version of the iESM released to the global climate community.
The integrated Earth system model version 1: formulation and functionality
NASA Astrophysics Data System (ADS)
Collins, W. D.; Craig, A. P.; Truesdale, J. E.; Di Vittorio, A. V.; Jones, A. D.; Bond-Lamberty, B.; Calvin, K. V.; Edmonds, J. A.; Kim, S. H.; Thomson, A. M.; Patel, P.; Zhou, Y.; Mao, J.; Shi, X.; Thornton, P. E.; Chini, L. P.; Hurtt, G. C.
2015-07-01
The integrated Earth system model (iESM) has been developed as a new tool for projecting the joint human/climate system. The iESM is based upon coupling an integrated assessment model (IAM) and an Earth system model (ESM) into a common modeling infrastructure. IAMs are the primary tool for describing the human-Earth system, including the sources of global greenhouse gases (GHGs) and short-lived species (SLS), land use and land cover change (LULCC), and other resource-related drivers of anthropogenic climate change. ESMs are the primary scientific tools for examining the physical, chemical, and biogeochemical impacts of human-induced changes to the climate system. The iESM project integrates the economic and human-dimension modeling of an IAM and a fully coupled ESM within a single simulation system while maintaining the separability of each model if needed. Both IAM and ESM codes are developed and used by large communities and have been extensively applied in recent national and international climate assessments. By introducing heretofore-omitted feedbacks between natural and societal drivers, we can improve scientific understanding of the human-Earth system dynamics. Potential applications include studies of the interactions and feedbacks leading to the timing, scale, and geographic distribution of emissions trajectories and other human influences, corresponding climate effects, and the subsequent impacts of a changing climate on human and natural systems. This paper describes the formulation, requirements, implementation, testing, and resulting functionality of the first version of the iESM released to the global climate community.
The integrated Earth system model version 1: formulation and functionality
Collins, W. D.; Craig, A. P.; Truesdale, J. E.; ...
2015-07-23
The integrated Earth system model (iESM) has been developed as a new tool for projecting the joint human/climate system. The iESM is based upon coupling an integrated assessment model (IAM) and an Earth system model (ESM) into a common modeling infrastructure. IAMs are the primary tool for describing the human–Earth system, including the sources of global greenhouse gases (GHGs) and short-lived species (SLS), land use and land cover change (LULCC), and other resource-related drivers of anthropogenic climate change. ESMs are the primary scientific tools for examining the physical, chemical, and biogeochemical impacts of human-induced changes to the climate system. Themore » iESM project integrates the economic and human-dimension modeling of an IAM and a fully coupled ESM within a single simulation system while maintaining the separability of each model if needed. Both IAM and ESM codes are developed and used by large communities and have been extensively applied in recent national and international climate assessments. By introducing heretofore-omitted feedbacks between natural and societal drivers, we can improve scientific understanding of the human–Earth system dynamics. Potential applications include studies of the interactions and feedbacks leading to the timing, scale, and geographic distribution of emissions trajectories and other human influences, corresponding climate effects, and the subsequent impacts of a changing climate on human and natural systems. This paper describes the formulation, requirements, implementation, testing, and resulting functionality of the first version of the iESM released to the global climate community.« less
Informing soil models using pedotransfer functions: challenges and perspectives
NASA Astrophysics Data System (ADS)
Pachepsky, Yakov; Romano, Nunzio
2015-04-01
Pedotransfer functions (PTFs) are empirical relationships between parameters of soil models and more easily obtainable data on soil properties. PTFs have become an indispensable tool in modeling soil processes. As alternative methods to direct measurements, they bridge the data we have and data we need by using soil survey and monitoring data to enable modeling for real-world applications. Pedotransfer is extensively used in soil models addressing the most pressing environmental issues. The following is an attempt to provoke a discussion by listing current issues that are faced by PTF development. 1. As more intricate biogeochemical processes are being modeled, development of PTFs for parameters of those processes becomes essential. 2. Since the equations to express PTF relationships are essentially unknown, there has been a trend to employ highly nonlinear equations, e.g. neural networks, which in theory are flexible enough to simulate any dependence. This, however, comes with the penalty of large number of coefficients that are difficult to estimate reliably. A preliminary classification applied to PTF inputs and PTF development for each of the resulting groups may provide simple, transparent, and more reliable pedotransfer equations. 3. The multiplicity of models, i.e. presence of several models producing the same output variables, is commonly found in soil modeling, and is a typical feature in the PTF research field. However, PTF intercomparisons are lagging behind PTF development. This is aggravated by the fact that coefficients of PTF based on machine-learning methods are usually not reported. 4. The existence of PTFs is the result of some soil processes. Using models of those processes to generate PTFs, and more general, developing physics-based PTFs remains to be explored. 5. Estimating the variability of soil model parameters becomes increasingly important, as the newer modeling technologies such as data assimilation, ensemble modeling, and model
Modeling and Simulations of Particulate Flows through Functionalized Porous Media
NASA Astrophysics Data System (ADS)
Li, Chunhui; Dutta, Prashanta; Liu, Jin
2016-11-01
Transport of particulate fluid through a functionalized porous material is of significant interest in many industrial applications, such as earth sciences, battery designs and water/air purifications. The entire process is complex, which involves the convection of fluid, diffusion of reactants as well as reversible chemical reactions at the fluid-solid interface In this work we present a convection-diffusion-reaction model and simulate the transport of particulate fluid through a functionalized porous media. The porous structures are generated and manipulated through the quartet structure generation set method. The Navier-Stokes with convection-diffusion equations are solved using the lattice Boltzmann method. The chemical reactions at the interface are modeled by an absorption-desorption process and treated as the boundary conditions for above governing equations. Through our simulations we study the effects of porous structures, including porosity, pore orientation, and pore size as well as the kinetic rates of surface reactions on the overall performance of removal efficiency of the species from the solution. Our results show that whole process is highly affected by both the porous structures and absorption rate. The optimal parameters can be achieved by proper design. This work is supported by NSF Grants: CBET-1250107 and CBET -1604211.
Modeling the Galaxy Three-Point Correlation Function
Marin, Felipe; Wechsler, Risa; Frieman, Joshua A.; Nichol, Robert; /Portsmouth U., ICG
2007-06-05
We present new theoretical predictions for the galaxy three-point correlation function (3PCF) using high-resolution dissipationless cosmological simulations of a flat Lambda CDM Universe which resolve galaxy-size halos and subhalos. We create realistic mock galaxy catalogs by assigning luminosities and colors to dark matter halos and subhalos, and we measure the reduced 3PCF as a function of luminosity and color in both real and redshift space. As galaxy luminosity and color are varied, we find small differences in the amplitude and shape dependence of the reduced 3PCF, at a level qualitatively consistent with recent measurements from the SDSS and 2dFGRS. We confirm that discrepancies between previous 3PCF measurements can be explained in part by differences in binning choices. We explore the degree to which a simple local bias model can fit the simulated 3PCF. The agreement between the model predictions and galaxy 3PCF measurements lends further credence to the straightforward association of galaxies with CDM halos and subhalos.
A dynamic model of quadriceps and hamstrings function.
Frigo, C; Pavan, E E; Brunner, R
2010-01-01
The mechanical effect of hamstrings and quadriceps contractions on hip and knee joint motion was investigated using a dynamic model of the musculoskeletal system. The model consisted of 13 anatomically linked segments. The geometry of bones, joints, and muscle attachments was derived from magnetic resonance imaging of a healthy adult. The knee joint was represented by a crossing bars linkage to simulate cruciate ligament function, and muscles were represented by spring actuators. The effects of hamstring and quadriceps contractions, in various combinations, were tested on different configurations of hip and knee joint position in the absence of gravity. In the standing posture, with the foot free to move and the pelvis fixed in space, the effect of semimembranosus (SM) contraction was hip and knee flexion. If the foot was fixed to the ground, SM contraction produced hip extension and knee flexion. The addition of quadriceps contraction reduced or abolished the knee flexion and enhanced hip extension. In all other simulations, SM alone produced knee flexion and hip extension and the combination of SM with vastus (VA) and rectus femoris (RF) contractions resulted in knee extension and enhanced hip extension. Our findings suggest that co-contraction of quadriceps and hamstrings may be a strategy to increase the hip extension function of the hamstrings.
Computer Modeling of the Earliest Cellular Structures and Functions
NASA Technical Reports Server (NTRS)
Pohorille, Andrew; Chipot, Christophe; Schweighofer, Karl
2000-01-01
In the absence of extinct or extant record of protocells (the earliest ancestors of contemporary cells). the most direct way to test our understanding of the origin of cellular life is to construct laboratory models of protocells. Such efforts are currently underway in the NASA Astrobiology Program. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures and developing designs for molecules that perform proto-cellular functions. Many of these functions, such as import of nutrients, capture and storage of energy. and response to changes in the environment are carried out by proteins bound to membrane< We will discuss a series of large-scale, molecular-level computer simulations which demonstrate (a) how small proteins (peptides) organize themselves into ordered structures at water-membrane interfaces and insert into membranes, (b) how these peptides aggregate to form membrane-spanning structures (eg. channels), and (c) by what mechanisms such aggregates perform essential proto-cellular functions, such as proton transport of protons across cell walls, a key step in cellular bioenergetics. The simulations were performed using the molecular dynamics method, in which Newton's equations of motion for each item in the system are solved iteratively. The problems of interest required simulations on multi-nanosecond time scales, which corresponded to 10(exp 6)-10(exp 8) time steps.
Predicting individual brain functional connectivity using a Bayesian hierarchical model.
Dai, Tian; Guo, Ying
2017-02-15
Network-oriented analysis of functional magnetic resonance imaging (fMRI), especially resting-state fMRI, has revealed important association between abnormal connectivity and brain disorders such as schizophrenia, major depression and Alzheimer's disease. Imaging-based brain connectivity measures have become a useful tool for investigating the pathophysiology, progression and treatment response of psychiatric disorders and neurodegenerative diseases. Recent studies have started to explore the possibility of using functional neuroimaging to help predict disease progression and guide treatment selection for individual patients. These studies provide the impetus to develop statistical methodology that would help provide predictive information on disease progression-related or treatment-related changes in neural connectivity. To this end, we propose a prediction method based on Bayesian hierarchical model that uses individual's baseline fMRI scans, coupled with relevant subject characteristics, to predict the individual's future functional connectivity. A key advantage of the proposed method is that it can improve the accuracy of individualized prediction of connectivity by combining information from both group-level connectivity patterns that are common to subjects with similar characteristics as well as individual-level connectivity features that are particular to the specific subject. Furthermore, our method also offers statistical inference tools such as predictive intervals that help quantify the uncertainty or variability of the predicted outcomes. The proposed prediction method could be a useful approach to predict the changes in individual patient's brain connectivity with the progression of a disease. It can also be used to predict a patient's post-treatment brain connectivity after a specified treatment regimen. Another utility of the proposed method is that it can be applied to test-retest imaging data to develop a more reliable estimator for individual
Plant functional type mapping for earth system models
NASA Astrophysics Data System (ADS)
Poulter, B.; Ciais, P.; Hodson, E.; Lischke, H.; Maignan, F.; Plummer, S.; Zimmermann, N. E.
2011-08-01
The sensitivity of global carbon and water cycling to climate variability is coupled directly to land cover and the distribution of vegetation. To investigate biogeochemistry-climate interactions, earth system models require a representation of vegetation distributions that are either prescribed from remote sensing data or simulated via biogeography models. However, the abstraction of earth system state variables in models means that data products derived from remote sensing need to be post-processed for model-data assimilation. Dynamic global vegetation models (DGVM) rely on the concept of plant functional types (PFT) to group shared traits of thousands of plant species into just several classes. Available databases of observed PFT distributions must be relevant to existing satellite sensors and their derived products, and to the present day distribution of managed lands. Here, we develop four PFT datasets based on land-cover information from three satellite sensors (EOS-MODIS 1 km and 0.5 km, SPOT4-VEGETATION 1 km, and ENVISAT-MERIS 0.3 km spatial resolution) that are merged with spatially-consistent Köppen-Geiger climate zones. Using a beta (β) diversity metric to assess reclassification similarity, we find that the greatest uncertainty in PFT classifications occur most frequently between cropland and grassland categories, and in dryland systems between shrubland, grassland and forest categories because of differences in the minimum threshold required for forest cover. The biogeography-biogeochemistry DGVM, LPJmL, is used in diagnostic mode with the four PFT datasets prescribed to quantify the effect of land-cover uncertainty on climatic sensitivity of gross primary productivity (GPP) and transpiration fluxes. Our results show that land-cover uncertainty has large effects in arid regions, contributing up to 30 % (20 %) uncertainty in the sensitivity of GPP (transpiration) to precipitation. The availability of plant functional type datasets that are consistent
Nelson, Celeste M.; Bissell, Mina J.
2010-01-01
In order to understand why cancer develops as well as predict the outcome of pharmacological treatments, we need to model the structure and function of organs in culture so that our experimental manipulations occur under physiological contexts. This review traces the history of the development of a prototypic example, the three-dimensional (3D) model of the mammary gland acinus. We briefly describe the considerable information available on both normal mammary gland function and breast cancer generated by the current model and present future challenges that will require an increase in its complexity. We propose the need for engineered tissues that faithfully recapitulate their native structures to allow a greater understanding of tissue function, dysfunction, and potential therapeutic intervention. PMID:15963732
Recent applications of a synthetic model of cytochrome c oxidase: beyond functional modeling.
Collman, James P; Ghosh, Somdatta
2010-07-05
This account reports recent developments of a functional model for the active site of cytochrome c oxidase (CcO). This CcO mimic not only performs the selective four-electron reduction of oxygen to water but also catalytically reduces oxygen using the biological one-electron reductant, cytochrome c. This functional model has been used to understand other biological reactions of CcO, for example, the interaction between the gaseous hormone, NO, and CcO. A mechanism for inactivating NO-CcO complexes is found to involve a reaction between oxygen and Cu(B). Moreover, NO is shown to be capable of protecting CcO from toxic inhibitors such as CN(-) and CO. Finally, this functional CcO model has been used to show how H(2)S could induce hibernation by reversibly inhibiting the oxygen binding step involved in respiration.
Zebrafish Model for Functional Screening of Flow-Responsive Genes
Serbanovic-Canic, Jovana; de Luca, Amalia; Warboys, Christina; Ferreira, Pedro F.; Luong, Le A.; Hsiao, Sarah; Gauci, Ismael; Mahmoud, Marwa; Feng, Shuang; Souilhol, Celine; Bowden, Neil; Ashton, John-Paul; Walczak, Henning; Firmin, David; Krams, Rob; Mason, Justin C.; Haskard, Dorian O.; Sherwin, Spencer; Ridger, Victoria; Chico, Timothy J.A.
2017-01-01
Objective— Atherosclerosis is initiated at branches and bends of arteries exposed to disturbed blood flow that generates low shear stress. This mechanical environment promotes lesions by inducing endothelial cell (EC) apoptosis and dysfunction via mechanisms that are incompletely understood. Although transcriptome-based studies have identified multiple shear-responsive genes, most of them have an unknown function. To address this, we investigated whether zebrafish embryos can be used for functional screening of mechanosensitive genes that regulate EC apoptosis in mammalian arteries. Approach and Results— First, we demonstrated that flow regulates EC apoptosis in developing zebrafish vasculature. Specifically, suppression of blood flow in zebrafish embryos (by targeting cardiac troponin) enhanced that rate of EC apoptosis (≈10%) compared with controls exposed to flow (≈1%). A panel of candidate regulators of apoptosis were identified by transcriptome profiling of ECs from high and low shear stress regions of the porcine aorta. Genes that displayed the greatest differential expression and possessed 1 to 2 zebrafish orthologues were screened for the regulation of apoptosis in zebrafish vasculature exposed to flow or no-flow conditions using a knockdown approach. A phenotypic change was observed in 4 genes; p53-related protein (PERP) and programmed cell death 2–like protein functioned as positive regulators of apoptosis, whereas angiopoietin-like 4 and cadherin 13 were negative regulators. The regulation of perp, cdh13, angptl4, and pdcd2l by shear stress and the effects of perp and cdh13 on EC apoptosis were confirmed by studies of cultured EC exposed to flow. Conclusions— We conclude that a zebrafish model of flow manipulation coupled to gene knockdown can be used for functional screening of mechanosensitive genes in vascular ECs, thus providing potential therapeutic targets to prevent or treat endothelial injury at atheroprone sites. PMID:27834691
Incorporating Functional Gene Quantification into Traditional Decomposition Models
NASA Astrophysics Data System (ADS)
Todd-Brown, K. E.; Zhou, J.; Yin, H.; Wu, L.; Tiedje, J. M.; Schuur, E. A. G.; Konstantinidis, K.; Luo, Y.
2014-12-01
Incorporating new genetic quantification measurements into traditional substrate pool models represents a substantial challenge. These decomposition models are built around the idea that substrate availablity, with environmental drivers, limit carbon dioxide respiration rates. In this paradigm, microbial communities optimally adapt to a given substrate and environment on much shorter time scales then the carbon flux of interest. By characterizing the relative shift in biomass of these microbial communities, we informed previously poorly constrained parameters in traditional decomposition models. In this study we coupled a 9 month laboratory incubation study with quantitative gene measurements with traditional CO2 flux measurements plus initial soil organic carbon quantification. GeoChip 5.0 was used to quantify the functional genes associated with carbon cycling at 2 weeks, 3 months and 9 months. We then combined the genes which 'collapsed' over the experiment and assumed that this tracked the relative change in the biomass associated with the 'fast' pool. We further assumed that this biomass was proportional to the 'fast' SOC pool and thus were able to constrain the relative change in the fast SOC pool in our 3-pool decomposition model. We found that biomass quantification described above, combined with traditional CO2 flux and SOC measurements, improve the transfer coefficient estimation in traditional decomposition models. Transfer coefficients are very difficult to characterized using traditional CO2 flux measurements, thus DNA quantification provides new and significant information about the system. Over a 100 year simulation, these new biologically informed parameters resulted in an additional 10% of SOC loss over the traditionally informed parameters.
Geometric and electrostatic modeling using molecular rigidity functions
Mu, Lin; Xia, Kelin; Wei, Guowei
2017-03-01
Geometric and electrostatic modeling is an essential component in computational biophysics and molecular biology. Commonly used geometric representations admit geometric singularities such as cusps, tips and self-intersecting facets that lead to computational instabilities in the molecular modeling. Our present work explores the use of flexibility and rigidity index (FRI), which has a proved superiority in protein B-factor prediction, for biomolecular geometric representation and associated electrostatic analysis. FRI rigidity surfaces are free of geometric singularities. We propose a rigidity based Poisson–Boltzmann equation for biomolecular electrostatic analysis. These approaches to surface and electrostatic modeling are validated by a set of 21 proteins.more » Our results are compared with those of established methods. Finally, being smooth and analytically differentiable, FRI rigidity functions offer excellent curvature analysis, which characterizes concave and convex regions on protein surfaces. Polarized curvatures constructed by using the product of minimum curvature and electrostatic potential is shown to predict potential protein–ligand binding sites.« less
An analysis of extensible modelling for functional genomics data
Jones, Andrew R; Paton, Norman W
2005-01-01
Background Several data formats have been developed for large scale biological experiments, using a variety of methodologies. Most data formats contain a mechanism for allowing extensions to encode unanticipated data types. Extensions to data formats are important because the experimental methodologies tend to be fairly diverse and rapidly evolving, which hinders the creation of formats that will be stable over time. Results In this paper we review the data formats that exist in functional genomics, some of which have become de facto or de jure standards, with a particular focus on how each domain has been modelled, and how each format allows extensions. We describe the tasks that are frequently performed over data formats and analyse how well each task is supported by a particular modelling structure. Conclusion From our analysis, we make recommendations as to the types of modelling structure that are most suitable for particular types of experimental annotation. There are several standards currently under development that we believe could benefit from systematically following a set of guidelines. PMID:16188029
Modeling fire occurrence as a function of landscape
NASA Astrophysics Data System (ADS)
Loboda, T. V.; Carroll, M.; DiMiceli, C.
2011-12-01
Wildland fire is a prominent component of ecosystem functioning worldwide. Nearly all ecosystems experience the impact of naturally occurring or anthropogenically driven fire. Here, we present a spatially explicit and regionally parameterized Fire Occurrence Model (FOM) aimed at developing fire occurrence estimates at landscape and regional scales. The model provides spatially explicit scenarios of fire occurrence based on the available records from fire management agencies, satellite observations, and auxiliary geospatial data sets. Fire occurrence is modeled as a function of the risk of ignition, potential fire behavior, and fire weather using internal regression tree-driven algorithms and empirically established, regionally derived relationships between fire occurrence, fire behavior, and fire weather. The FOM presents a flexible modeling structure with a set of internal globally available default geospatial independent and dependent variables. However, the flexible modeling environment adapts to ingest a variable number, resolution, and content of inputs provided by the user to supplement or replace the default parameters to improve the model's predictive capability. A Southern California FOM instance (SC FOM) was developed using satellite assessments of fire activity from a suite of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, Monitoring Trends in Burn Severity fire perimeters, and auxiliary geospatial information including land use and ownership, utilities, transportation routes, and the Remote Automated Weather Station data records. The model was parameterized based on satellite data acquired between 2001 and 2009 and fire management fire perimeters available prior to 2009. SC FOM predictive capabilities were assessed using observed fire occurrence available from the MODIS active fire product during 2010. The results show that SC FOM provides a realistic estimate of fire occurrence at the landscape level: the fraction of
Functional test of FOOTPRINT pedotransfer functions for the dual-permeability model MACRO
NASA Astrophysics Data System (ADS)
Moeys, J.; Jarvis, N. J.; Stenemo, F.; Hollis, J. M.; Dubus, I. G.; Larsbo, M.; Brown, C. D.; Bromilow, R.; Coquet, Y.; Vachier, P.
2009-04-01
Our ability to assess and predict pollution risks for surface waters and groundwater across larger areas (e.g. catchment and regional scales) relies on our capacity to estimate soil physical and hydrological properties and crop characteristics that are generally required as model parameters. ‘Pedotransfer' functions (PTF) can be used to estimate model parameters from more easily available soil survey data. The EU-FP6 European project FOOTPRINT (www.eu-footprint.org) has supported the development of a full set of PTF's to completely parameterise the pesticide fate model MACRO from only easily available site and soil data for a range of European agronomic, climatic and pedological scenarios The work presented here aimed at assessing the performance of the parameterisation procedures developed in the FOOTPRINT project for MACRO, from a functional point of view. We present a comparison of measured and simulated tracer leaching in medium- to long-term (2 months to 2 years) experiments driven by natural-transient rainfall conditions on 41 lysimeters, representing 15 soil types, located in Sweden, UK and France. For each experiment, the only information used to parameterize the model was a soil profile description, in which each horizon is characterized by its thickness, FAO master horizon type, texture class, organic carbon content and bulk density and knowledge of the tillage (till, no-till, harrowed) and cropping practices (crop type, and sowing dates). The average depth of the lysimeters was 1 meter, each profile containing an average of 4.6 horizons. The soil properties covered a large range of textures (1 to 78% clay), organic matter contents (0 to 29%) and bulk densities (550 to 1870 kg.m-3). Simulations were first conducted without any calibration of parameters. In a second step, we conducted simulations where two crop parameters were optimized (root depth and root water uptake efficiency), in order to estimate the impact of errors in the simulated water balance
Correlation functions of the Aharony-Bergman-Jafferis-Maldacena model
NASA Astrophysics Data System (ADS)
Lee, Bum-Hoon; Gwak, Bogeun; Park, Chanyong
2013-04-01
In the Aharony-Bergman-Jafferis-Maldacena model, we study the three-point function of two heavy operators and an (ir)relevant one. Following the AdS/CFT correspondence, the structure constant in the large ’t Hooft coupling limit can be factorized into two parts. One is the structure constant with a marginal operator, which is fully determined by the physical quantities of heavy operators and gives rise to a result that is consistent with the renormalization-group analysis. The other can be expressed as the universal form depending only on the conformal dimension of an (ir)relevant operator. We also investigate the new size effect of a circular string dual to a certain closed spin chain.
Assessment of Differential Item Functioning under Cognitive Diagnosis Models: The DINA Model Example
ERIC Educational Resources Information Center
Li, Xiaomin; Wang, Wen-Chung
2015-01-01
The assessment of differential item functioning (DIF) is routinely conducted to ensure test fairness and validity. Although many DIF assessment methods have been developed in the context of classical test theory and item response theory, they are not applicable for cognitive diagnosis models (CDMs), as the underlying latent attributes of CDMs are…
Empirical evaluation of scoring functions for Bayesian network model selection.
Liu, Zhifa; Malone, Brandon; Yuan, Changhe
2012-01-01
In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning algorithms to learn the network structures. The suboptimal structures found by the approximation methods have unknown quality and may affect the reliability of their conclusions. Our study uses an optimal algorithm to learn Bayesian network structures from datasets generated from a set of gold standard Bayesian networks. Because all optimal algorithms always learn equivalent networks, this ensures that only the choice of scoring function affects the learned networks. Another shortcoming of the previous studies stems from their use of random synthetic networks as test cases. There is no guarantee that these networks reflect real-world data. We use real-world data to generate our gold-standard structures, so our experimental design more closely approximates real-world situations. A major finding of our study suggests that, in contrast to results reported by several prior works, the Minimum Description Length (MDL) (or equivalently, Bayesian information criterion (BIC)) consistently outperforms other scoring functions such as Akaike's information criterion (AIC), Bayesian Dirichlet equivalence score (BDeu), and factorized normalized maximum likelihood (fNML) in recovering the underlying Bayesian network structures. We believe this finding is a result of using both datasets generated from real-world applications rather than from random processes used in previous studies and learning algorithms to select high-scoring structures rather than selecting random models. Other findings of our study support existing work, e.g., large sample sizes result in learning structures closer to the true underlying structure; the BDeu score is sensitive to the parameter settings; and the fNML performs pretty well on small datasets. We also
Empirical evaluation of scoring functions for Bayesian network model selection
2012-01-01
In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning algorithms to learn the network structures. The suboptimal structures found by the approximation methods have unknown quality and may affect the reliability of their conclusions. Our study uses an optimal algorithm to learn Bayesian network structures from datasets generated from a set of gold standard Bayesian networks. Because all optimal algorithms always learn equivalent networks, this ensures that only the choice of scoring function affects the learned networks. Another shortcoming of the previous studies stems from their use of random synthetic networks as test cases. There is no guarantee that these networks reflect real-world data. We use real-world data to generate our gold-standard structures, so our experimental design more closely approximates real-world situations. A major finding of our study suggests that, in contrast to results reported by several prior works, the Minimum Description Length (MDL) (or equivalently, Bayesian information criterion (BIC)) consistently outperforms other scoring functions such as Akaike's information criterion (AIC), Bayesian Dirichlet equivalence score (BDeu), and factorized normalized maximum likelihood (fNML) in recovering the underlying Bayesian network structures. We believe this finding is a result of using both datasets generated from real-world applications rather than from random processes used in previous studies and learning algorithms to select high-scoring structures rather than selecting random models. Other findings of our study support existing work, e.g., large sample sizes result in learning structures closer to the true underlying structure; the BDeu score is sensitive to the parameter settings; and the fNML performs pretty well on small datasets. We also
Plant functional type mapping for earth system models
NASA Astrophysics Data System (ADS)
Poulter, B.; Ciais, P.; Hodson, E.; Lischke, H.; Maignan, F.; Plummer, S.; Zimmermann, N. E.
2011-11-01
The sensitivity of global carbon and water cycling to climate variability is coupled directly to land cover and the distribution of vegetation. To investigate biogeochemistry-climate interactions, earth system models require a representation of vegetation distributions that are either prescribed from remote sensing data or simulated via biogeography models. However, the abstraction of earth system state variables in models means that data products derived from remote sensing need to be post-processed for model-data assimilation. Dynamic global vegetation models (DGVM) rely on the concept of plant functional types (PFT) to group shared traits of thousands of plant species into usually only 10-20 classes. Available databases of observed PFT distributions must be relevant to existing satellite sensors and their derived products, and to the present day distribution of managed lands. Here, we develop four PFT datasets based on land-cover information from three satellite sensors (EOS-MODIS 1 km and 0.5 km, SPOT4-VEGETATION 1 km, and ENVISAT-MERIS 0.3 km spatial resolution) that are merged with spatially-consistent Köppen-Geiger climate zones. Using a beta (ß) diversity metric to assess reclassification similarity, we find that the greatest uncertainty in PFT classifications occur most frequently between cropland and grassland categories, and in dryland systems between shrubland, grassland and forest categories because of differences in the minimum threshold required for forest cover. The biogeography-biogeochemistry DGVM, LPJmL, is used in diagnostic mode with the four PFT datasets prescribed to quantify the effect of land-cover uncertainty on climatic sensitivity of gross primary productivity (GPP) and transpiration fluxes. Our results show that land-cover uncertainty has large effects in arid regions, contributing up to 30% (20%) uncertainty in the sensitivity of GPP (transpiration) to precipitation. The availability of PFT datasets that are consistent with current
Semiparametric Bayesian local functional models for diffusion tensor tract statistics☆
Hua, Zhaowei; Dunson, David B.; Gilmore, John H.; Styner, Martin A.; Zhu, Hongtu
2012-01-01
We propose a semiparametric Bayesian local functional model (BFM) for the analysis of multiple diffusion properties (e.g., fractional anisotropy) along white matter fiber bundles with a set of covariates of interest, such as age and gender. BFM accounts for heterogeneity in the shape of the fiber bundle diffusion properties among subjects, while allowing the impact of the covariates to vary across subjects. A nonparametric Bayesian LPP2 prior facilitates global and local borrowings of information among subjects, while an infinite factor model flexibly represents low-dimensional structure. Local hypothesis testing and credible bands are developed to identify fiber segments, along which multiple diffusion properties are significantly associated with covariates of interest, while controlling for multiple comparisons. Moreover, BFM naturally group subjects into more homogeneous clusters. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFM. We apply BFM to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment in new born infants. PMID:22732565
Neocaridina denticulata: A Decapod Crustacean Model for Functional Genomics.
Mykles, Donald L; Hui, Jerome H L
2015-11-01
A decapod crustacean model is needed for understanding the molecular mechanisms underlying physiological processes, such as reproduction, sex determination, molting and growth, immunity, regeneration, and response to stress. Criteria for selection are: life-history traits, adult size, availability and ease of culture, and genomics and genetic manipulation. Three freshwater species are considered: cherry shrimp, Neocaridina denticulata; red swamp crayfish, Procambarus clarkii; and redclaw crayfish, Cherax quadricarinatus. All three are readily available, reproduce year round, and grow rapidly. The crayfish species require more space for culture than does N. denticulata. The transparent cuticle of cherry shrimp provides for direct assessment of reproductive status, stage of molt, and tissue-specific expression of reporter genes, and facilitates screening of mutations affecting phenotype. Moreover, a preliminary genome of N. denticulata is available and efforts toward complete genome sequencing and transcriptome sequencing have been initiated. Neocaridina denticulata possesses the best combination of traits that make it most suitable as a model for functional genomics. The next step is to obtain the complete genome sequence and to develop molecular technologies for the screening of mutants and for manipulating tissue-specific gene expression.
Model-based HSF using by target point control function
NASA Astrophysics Data System (ADS)
Kim, Seongjin; Do, Munhoe; An, Yongbae; Choi, Jaeseung; Yang, Hyunjo; Yim, Donggyu
2015-03-01
As the technology node shrinks, ArF Immersion reaches the limitation of wafer patterning, furthermore weak point during the mask processing is generated easily. In order to make strong patterning result, the design house conducts lithography rule checking (LRC). Despite LRC processing, we found the weak point at the verification stage of optical proximity correction (OPC). It is called the hot spot point (HSP). In order to fix the HSP, many studies have been performed. One of the most general hot spot fixing (HSF) methods is that the modification bias which consists of "Line-Resizing" and "Space-Resizing". In addition to the general rule biasing method, resolution enhancement techniques (RET) which includes the inverse lithography technology (ILT) and model based assist feature (MBAF) have been adapted to remove the hot spot and to maximize the process window. If HSP is found during OPC verification stage, various HSF methods can be applied. However, HSF process added on regular OPC procedure makes OPC turn-around time (TAT) increased. In this paper, we introduce a new HSF method that is able to make OPC TAT shorter than the common HSF method. The new HSF method consists of two concepts. The first one is that OPC target point is controlled to fix HSP. Here, the target point should be moved to optimum position at where the edge placement error (EPE) can be 0 at critical points. Many parameters such as a model accuracy or an OPC recipe become the cause of larger EPE. The second one includes controlling of model offset error through target point adjustment. Figure 1 shows the case EPE is not 0. It means that the simulation contour was not targeted well after OPC process. On the other hand, Figure 2 shows the target point is moved -2.5nm by using target point control function. As a result, simulation contour is matched to the original layout. This function can be powerfully adapted to OPC procedure of memory and logic devices.
ERIC Educational Resources Information Center
Slaughter, Susan; Bankes, Jane
2007-01-01
The Functional Transitions Model (FTM) integrates the theoretical notions of progressive functional decline associated with Alzheimer's disease (AD), excess disability, and transitions occurring intermittently along the trajectory of functional decline. Application of the Functional Transitions Model to clinical practice encompasses the paradox of…
Wang, Pengwei; Wang, Zhishun; He, Lianghua
2012-03-30
Functional Magnetic Resonance Imaging (fMRI), measuring Blood Oxygen Level-Dependent (BOLD), is a widely used tool to reveal spatiotemporal pattern of neural activity in human brain. Standard analysis of fMRI data relies on a general linear model and the model is constructed by convolving the task stimuli with a hypothesized hemodynamic response function (HRF). To capture possible phase shifts in the observed BOLD response, the informed basis functions including canonical HRF and its temporal derivative, have been proposed to extend the hypothesized hemodynamic response in order to obtain a good fitting model. Different t contrasts are constructed from the estimated model parameters for detecting the neural activity between different task conditions. However, the estimated model parameters corresponding to the orthogonal basis functions have different physical meanings. It remains unclear how to combine the neural features detected by the two basis functions and construct t contrasts for further analyses. In this paper, we have proposed a novel method for representing multiple basis functions in complex domain to model the task-driven fMRI data. Using this method, we can treat each pair of model parameters, corresponding respectively to canonical HRF and its temporal derivative, as one complex number for each task condition. Using the specific rule we have defined, we can conveniently perform arithmetical operations on the estimated model parameters and generate different t contrasts. We validate this method using the fMRI data acquired from twenty-two healthy participants who underwent an auditory stimulation task.
Park, Eun-Young; Kim, Won-Ho
2013-05-01
Physical therapy intervention for children with cerebral palsy (CP) is focused on reducing neurological impairments, improving strength, and preventing the development of secondary impairments in order to improve functional outcomes. However, relationship between motor impairments and functional outcome has not been proved definitely. This study confirmed the construct of motor impairment and performed structural equation modeling (SEM) between motor impairment, gross motor function, and functional outcomes of regarding activities of daily living in children with CP. 98 children (59 boys, 39 girls) with CP participated in this cross-sectional study. Mean age was 11 y 5 mo (SD 1 y 9 mo). The Manual Muscle Test (MMT), the Modified Ashworth Scale (MAS), range of motion (ROM) measurement, and the selective motor control (SMC) scale were used to assess motor impairments. Gross motor function and functional outcomes were measured using the Gross Motor Function Measure (GMFM) and the Functional Skills domain of the Pediatric Evaluation of Disability Inventory (PEDI) respectively. Measurement of motor impairment was consisted of strength, spasticity, ROM, and SMC. The construct of motor impairment was confirmed though an examination of a measurement model. The proposed SEM model showed good fit indices. Motor impairment effected gross motor function (β=-.0869). Gross motor function and motor impairment affected functional outcomes directly (β=0.890) and indirectly (β=-0.773) respectively. We confirmed that the construct of motor impairment consist of strength, spasticity, ROM, and SMC and it was identified through measurement model analysis. Functional outcomes are best predicted by gross motor function and motor impairments have indirect effects on functional outcomes.
Wightman function and vacuum fluctuations in higher dimensional brane models
Saharian, Aram A.
2006-02-15
The Wightman function and the vacuum expectation value of the field square are evaluated for a massive scalar field with a general curvature coupling parameter subject to Robin boundary conditions on two codimension-one parallel branes located on a (D+1)-dimensional background spacetime AdS{sub D{sub 1}}{sub +1}x{sigma} with a warped internal space {sigma}. The general case of different Robin coefficients on separate branes is considered. The application of the generalized Abel-Plana formula for the series over zeros of combinations of cylinder functions allows us to manifestly extract the part due to the bulk without boundaries. Unlike the purely anti-de Sitter (AdS) bulk, the vacuum expectation value of the field square induced by a single brane, in addition to the distance from the brane, depends also on the position of the brane in the bulk. The brane induced part in this expectation value vanishes when the brane position tends to the AdS horizon or the AdS boundary. The asymptotic behavior of the vacuum densities near the branes and at large distances is investigated. The contribution of Kaluza-Klein modes along {sigma} is discussed in various limiting cases. In the limit when the curvature radius for the AdS spacetime tends to infinity, we derive the results for two parallel Robin plates on the background spacetime R{sup (D{sub 1},1)}x{sigma}. For strong gravitational fields corresponding to large values of the AdS energy scale, both the single brane and interference parts of the expectation values integrated over the internal space are exponentially suppressed. As an example the case {sigma}=S{sup 1} is considered, corresponding to the AdS{sub D+1} bulk with one compactified dimension. An application to the higher dimensional generalization of the Randall-Sundrum brane model with arbitrary mass terms on the branes is discussed.
GSK-3: Functional Insights from Cell Biology and Animal Models
Kaidanovich-Beilin, Oksana; Woodgett, James Robert
2011-01-01
Glycogen synthase kinase-3 (GSK-3) is a widely expressed and highly conserved serine/threonine protein kinase encoded in mammals by two genes that generate two related proteins: GSK-3α and GSK-3β. GSK-3 is active in cells under resting conditions and is primarily regulated through inhibition or diversion of its activity. While GSK-3 is one of the few protein kinases that can be inactivated by phosphorylation, the mechanisms of GSK-3 regulation are more varied and not fully understood. Precise control appears to be achieved by a combination of phosphorylation, localization, and sequestration by a number of GSK-3-binding proteins. GSK-3 lies downstream of several major signaling pathways including the phosphatidylinositol 3′ kinase pathway, the Wnt pathway, Hedgehog signaling and Notch. Specific pools of GSK-3, which differ in intracellular localization, binding partner affinity, and relative amount are differentially sensitized to several distinct signaling pathways and these sequestration mechanisms contribute to pathway insulation and signal specificity. Dysregulation of signaling pathways involving GSK-3 is associated with the pathogenesis of numerous neurological and psychiatric disorders and there are data suggesting GSK-3 isoform-selective roles in several of these. Here, we review the current knowledge of GSK-3 regulation and targets and discuss the various animal models that have been employed to dissect the functions of GSK-3 in brain development and function through the use of conventional or conditional knockout mice as well as transgenic mice. These studies have revealed fundamental roles for these protein kinases in memory, behavior, and neuronal fate determination and provide insights into possible therapeutic interventions. PMID:22110425
Application of Gaussian Process Modeling to Analysis of Functional Unreliability
R. Youngblood
2014-06-01
This paper applies Gaussian Process (GP) modeling to analysis of the functional unreliability of a “passive system.” GPs have been used widely in many ways [1]. The present application uses a GP for emulation of a system simulation code. Such an emulator can be applied in several distinct ways, discussed below. All applications illustrated in this paper have precedents in the literature; the present paper is an application of GP technology to a problem that was originally analyzed [2] using neural networks (NN), and later [3, 4] by a method called “Alternating Conditional Expectations” (ACE). This exercise enables a multifaceted comparison of both the processes and the results. Given knowledge of the range of possible values of key system variables, one could, in principle, quantify functional unreliability by sampling from their joint probability distribution, and performing a system simulation for each sample to determine whether the function succeeded for that particular setting of the variables. Using previously available system simulation codes, such an approach is generally impractical for a plant-scale problem. It has long been recognized, however, that a well-trained code emulator or surrogate could be used in a sampling process to quantify certain performance metrics, even for plant-scale problems. “Response surfaces” were used for this many years ago. But response surfaces are at their best for smoothly varying functions; in regions of parameter space where key system performance metrics may behave in complex ways, or even exhibit discontinuities, response surfaces are not the best available tool. This consideration was one of several that drove the work in [2]. In the present paper, (1) the original quantification of functional unreliability using NN [2], and later ACE [3], is reprised using GP; (2) additional information provided by the GP about uncertainty in the limit surface, generally unavailable in other representations, is discussed
From Boolean Network Model to Continuous Model Helps in Design of Functional Circuits
Zhang, Dongliang; Wu, Jiayi; Ouyang, Qi
2015-01-01
Computational circuit design with desired functions in a living cell is a challenging task in synthetic biology. To achieve this task, numerous methods that either focus on small scale networks or use evolutionary algorithms have been developed. Here, we propose a two-step approach to facilitate the design of functional circuits. In the first step, the search space of possible topologies for target functions is reduced by reverse engineering using a Boolean network model. In the second step, continuous simulation is applied to evaluate the performance of these topologies. We demonstrate the usefulness of this method by designing an example biological function: the SOS response of E. coli. Our numerical results show that the desired function can be faithfully reproduced by candidate networks with different parameters and initial conditions. Possible circuits are ranked according to their robustness against perturbations in parameter and gene expressions. The biological network is among the candidate networks, yet novel designs can be generated. Our method provides a scalable way to design robust circuits that can achieve complex functions, and makes it possible to uncover design principles of biological networks. PMID:26061094
ERIC Educational Resources Information Center
Okawa, Yayoi; Nakamura, Shigemi; Kudo, Minako; Ueda, Satoshi
2009-01-01
The purpose of this study is to confirm the working hypothesis on two major models of functioning decline and two corresponding models of rehabilitation program in an older population through detailed interviews with the persons who have functioning declines and on-the-spot observations of key activities on home visits. A total of 542…
A quantitative confidence signal detection model: 1. Fitting psychometric functions
Yi, Yongwoo
2016-01-01
Perceptual thresholds are commonly assayed in the laboratory and clinic. When precision and accuracy are required, thresholds are quantified by fitting a psychometric function to forced-choice data. The primary shortcoming of this approach is that it typically requires 100 trials or more to yield accurate (i.e., small bias) and precise (i.e., small variance) psychometric parameter estimates. We show that confidence probability judgments combined with a model of confidence can yield psychometric parameter estimates that are markedly more precise and/or markedly more efficient than conventional methods. Specifically, both human data and simulations show that including confidence probability judgments for just 20 trials can yield psychometric parameter estimates that match the precision of those obtained from 100 trials using conventional analyses. Such an efficiency advantage would be especially beneficial for tasks (e.g., taste, smell, and vestibular assays) that require more than a few seconds for each trial, but this potential benefit could accrue for many other tasks. PMID:26763777
Applying Quality Function Deployment Model in Burn Unit Service Improvement.
Keshtkaran, Ali; Hashemi, Neda; Kharazmi, Erfan; Abbasi, Mehdi
2016-01-01
Quality function deployment (QFD) is one of the most effective quality design tools. This study applies QFD technique to improve the quality of the burn unit services in Ghotbedin Hospital in Shiraz, Iran. First, the patients' expectations of burn unit services and their priorities were determined through Delphi method. Thereafter, burn unit service specifications were determined through Delphi method. Further, the relationships between the patients' expectations and service specifications and also the relationships between service specifications were determined through an expert group's opinion. Last, the final importance scores of service specifications were calculated through simple additive weighting method. The findings show that burn unit patients have 40 expectations in six different areas. These expectations are in 16 priority levels. Burn units also have 45 service specifications in six different areas. There are four-level relationships between the patients' expectations and service specifications and four-level relationships between service specifications. The most important burn unit service specifications have been identified in this study. The QFD model developed in the study can be a general guideline for QFD planners and executives.
Dicentric chromosomes: unique models to study centromere function and inactivation.
Stimpson, Kaitlin M; Matheny, Justyne E; Sullivan, Beth A
2012-07-01
Dicentric chromosomes are products of genome rearrangement that place two centromeres on the same chromosome. Depending on the organism, dicentric stability varies after formation. In humans, dicentrics occur naturally in a substantial portion of the population and usually segregate successfully in mitosis and meiosis. Their stability has been attributed to inactivation of one of the two centromeres, creating a functionally monocentric chromosome that can segregate normally during cell division. The molecular basis for centromere inactivation is not well understood, although studies in model organisms and in humans suggest that genomic and epigenetic mechanisms can be involved. Furthermore, constitutional dicentric chromosomes ascertained in patients presumably represent the most stable chromosomes, so the spectrum of dicentric fates, if it exists, is not entirely clear. Studies of engineered or induced dicentrics in budding yeast and plants have provided significant insight into the fate of dicentric chromosomes. And, more recently, studies have shown that dicentrics in humans can also undergo multiple fates after formation. Here, we discuss current experimental evidence from various organisms that has deepened our understanding of dicentric behavior and the intriguingly complex process of centromere inactivation.
Modeling of Red Blood Cells and Related Spleen Function
NASA Astrophysics Data System (ADS)
Peng, Zhangli; Pivkin, Igor; Dao, Ming
2011-11-01
A key function of the spleen is to clear red blood cells (RBCs) with abnormal mechanical properties from the circulation. These abnormal mechanical properties may be due to RBC aging or RBC diseases, e.g., malaria and sickle cell anemia. Specifically, 10% of RBCs passing through the spleen are forced to squeeze into the narrow slits between the endothelial cells, and stiffer cells which get stuck are killed and digested by macrophages. To investigate this important physiological process, we employ three different approaches to study RBCs passage through these small slits, including analytical theory, Dissipative Particle Dynamics (DPD) simulation and Multiscale Finite Element Method (MS-FEM). By applying the analytical theory, we estimate the critical limiting geometries RBCs can pass. By using the DPD method, we study the full fluid-structure interaction problem, and compute RBC deformation under different pressure gradients. By employing the MS-FEM approach, we model the lipid bilayer and the cytoskeleton as two distinct layers, and focus on the cytoskeleton deformation and the bilayer-skeleton interaction force at the molecular level. Finally the results of these three approaches are compared to each other and correlated to the experimental observations.
Density functional studies of model cerium oxide nanoparticles.
Loschen, Christoph; Migani, Annapaola; Bromley, Stefan T; Illas, Francesc; Neyman, Konstantin M
2008-10-01
Density functional plane-wave calculations have been performed to investigate a series of ceria nanoparticles (CeO2-x)(n), n
Vesicle Formation and Endocytosis: Function, Machinery, Mechanisms, and Modeling
Parkar, Nihal S.; Akpa, Belinda S.; Nitsche, Ludwig C.; Wedgewood, Lewis E.; Place, Aaron T.; Sverdlov, Maria S.; Chaga, Oleg
2009-01-01
Abstract Vesicle formation provides a means of cellular entry for extracellular substances and for recycling of membrane constituents. Mechanisms governing the two primary endocytic pathways (i.e., caveolae- and clathrin-mediated endocytosis, as well as newly emerging vesicular pathways) have become the focus of intense investigation to improve our understanding of nutrient, hormone, and drug delivery, as well as opportunistic invasion of pathogens. In this review of endocytosis, we broadly discuss the structural and signaling proteins that compose the molecular machinery governing endocytic vesicle formation (budding, invagination, and fission from the membrane), with some regard for the specificity observed in certain cell types and species. Important biochemical functions of endocytosis and diseases caused by their disruption also are discussed, along with the structures of key components of endocytic pathways and their known mechanistic contributions. The mechanisms by which principal components of the endocytic machinery are recruited to the plasma membrane, where they interact to induce vesicle formation, are discussed, together with computational approaches used to simulate simplified versions of endocytosis with the hope of clarifying aspects of vesicle formation that may be difficult to determine experimentally. Finally, we pose several unanswered questions intended to stimulate further research interest in the cell biology and modeling of endocytosis. Antioxid. Redox Signal. 11, 1301–1312. PMID:19113823
ERIC Educational Resources Information Center
Tumthong, Suwut; Piriyasurawong, Pullop; Jeerangsuwan, Namon
2016-01-01
This research proposes a functional competency development model for academic personnel based on international professional qualification standards in computing field and examines the appropriateness of the model. Specifically, the model consists of three key components which are: 1) functional competency development model, 2) blended training…
Sentürk, Damla; Dalrymple, Lorien S; Nguyen, Danh V
2014-11-30
We propose functional linear models for zero-inflated count data with a focus on the functional hurdle and functional zero-inflated Poisson (ZIP) models. Although the hurdle model assumes the counts come from a mixture of a degenerate distribution at zero and a zero-truncated Poisson distribution, the ZIP model considers a mixture of a degenerate distribution at zero and a standard Poisson distribution. We extend the generalized functional linear model framework with a functional predictor and multiple cross-sectional predictors to model counts generated by a mixture distribution. We propose an estimation procedure for functional hurdle and ZIP models, called penalized reconstruction, geared towards error-prone and sparsely observed longitudinal functional predictors. The approach relies on dimension reduction and pooling of information across subjects involving basis expansions and penalized maximum likelihood techniques. The developed functional hurdle model is applied to modeling hospitalizations within the first 2 years from initiation of dialysis, with a high percentage of zeros, in the Comprehensive Dialysis Study participants. Hospitalization counts are modeled as a function of sparse longitudinal measurements of serum albumin concentrations, patient demographics, and comorbidities. Simulation studies are used to study finite sample properties of the proposed method and include comparisons with an adaptation of standard principal components regression.
NASA Technical Reports Server (NTRS)
Mitchell, Christine M.
1990-01-01
The design, implementation, and empirical evaluation of task-analytic models and intelligent aids for operators in the control of complex dynamic systems, specifically aerospace systems, are studied. Three related activities are included: (1) the models of operator decision making in complex and predominantly automated space systems were used and developed; (2) the Operator Function Model (OFM) was used to represent operator activities; and (3) Operator Function Model Expert System (OFMspert), a stand-alone knowledge-based system was developed, that interacts with a human operator in a manner similar to a human assistant in the control of aerospace systems. OFMspert is an architecture for an operator's assistant that uses the OFM as its system and operator knowledge base and a blackboard paradigm of problem solving to dynamically generate expectations about upcoming operator activities and interpreting actual operator actions. An experiment validated the OFMspert's intent inferencing capability and showed that it inferred the intentions of operators in ways comparable to both a human expert and operators themselves. OFMspert was also augmented with control capabilities. An interface allowed the operator to interact with OFMspert, delegating as much or as little control responsibility as the operator chose. With its design based on the OFM, OFMspert's control capabilities were available at multiple levels of abstraction and allowed the operator a great deal of discretion over the amount and level of delegated control. An experiment showed that overall system performance was comparable for teams consisting of two human operators versus a human operator and OFMspert team.
Random regression models using different functions to model milk flow in dairy cows.
Laureano, M M M; Bignardi, A B; El Faro, L; Cardoso, V L; Tonhati, H; Albuquerque, L G
2014-09-12
We analyzed 75,555 test-day milk flow records from 2175 primiparous Holstein cows that calved between 1997 and 2005. Milk flow was obtained by dividing the mean milk yield (kg) of the 3 daily milking by the total milking time (min) and was expressed as kg/min. Milk flow was grouped into 43 weekly classes. The analyses were performed using a single-trait Random Regression Models that included direct additive genetic, permanent environmental, and residual random effects. In addition, the contemporary group and linear and quadratic effects of cow age at calving were included as fixed effects. Fourth-order orthogonal Legendre polynomial of days in milk was used to model the mean trend in milk flow. The additive genetic and permanent environmental covariance functions were estimated using random regression Legendre polynomials and B-spline functions of days in milk. The model using a third-order Legendre polynomial for additive genetic effects and a sixth-order polynomial for permanent environmental effects, which contained 7 residual classes, proved to be the most adequate to describe variations in milk flow, and was also the most parsimonious. The heritability in milk flow estimated by the most parsimonious model was of moderate to high magnitude.
Crustal structure beneath northeast India inferred from receiver function modeling
NASA Astrophysics Data System (ADS)
Borah, Kajaljyoti; Bora, Dipok K.; Goyal, Ayush; Kumar, Raju
2016-09-01
We estimated crustal shear velocity structure beneath ten broadband seismic stations of northeast India, by using H-Vp/Vs stacking method and a non-linear direct search approach, Neighbourhood Algorithm (NA) technique followed by joint inversion of Rayleigh wave group velocity and receiver function, calculated from teleseismic earthquakes data. Results show significant variations of thickness, shear velocities (Vs) and Vp/Vs ratio in the crust of the study region. The inverted shear wave velocity models show crustal thickness variations of 32-36 km in Shillong Plateau (North), 36-40 in Assam Valley and ∼44 km in Lesser Himalaya (South). Average Vp/Vs ratio in Shillong Plateau is less (1.73-1.77) compared to Assam Valley and Lesser Himalaya (∼1.80). Average crustal shear velocity beneath the study region varies from 3.4 to 3.5 km/s. Sediment structure beneath Shillong Plateau and Assam Valley shows 1-2 km thick sediment layer with low Vs (2.5-2.9 km/s) and high Vp/Vs ratio (1.8-2.1), while it is observed to be of greater thickness (4 km) with similar Vs and high Vp/Vs (∼2.5) in RUP (Lesser Himalaya). Both Shillong Plateau and Assam Valley show thick upper and middle crust (10-20 km), and thin (4-9 km) lower crust. Average Vp/Vs ratio in Assam Valley and Shillong Plateau suggest that the crust is felsic-to-intermediate and intermediate-to-mafic beneath Shillong Plateau and Assam Valley, respectively. Results show that lower crust rocks beneath the Shillong Plateau and Assam Valley lies between mafic granulite and mafic garnet granulite.
NASA Astrophysics Data System (ADS)
Menke, William
2017-02-01
We prove that the problem of inverting Rayleigh wave phase velocity functions c( k ) , where k is wavenumber, for density ρ ( z ) , rigidity μ ( z ) and Lamé parameter λ ( z ) , where z is depth, is fully non-unique, at least in the highly-idealized case where the base Earth model is an isotropic half space. The model functions completely trade off. This is one special case of a common inversion scenario in which one seeks to determine several model functions from a single data function. We explore the circumstances under which this broad class of problems is unique, starting with very simple scenarios, building up to the somewhat more complicated (and common) case where data and model functions are related by convolutions, and then finally, to scale-independent problems (which include the Rayleigh wave problem). The idealized cases that we examine analytically provide insight into the kinds of nonuniqueness that are inherent in the much more complicated problems encountered in modern geophysical imaging (though they do not necessarily provide methods for solving those problems). We also define what is meant by a Backus and Gilbert resolution kernel in this kind of inversion and show under what circumstances a unique localized average of a single model function can be constructed.
Fabrication, Characterization and Modeling of Functionally Graded Materials
NASA Astrophysics Data System (ADS)
Lee, Po-Hua
In the past few decades, a number of theoretical and experimental studies for design, fabrication and performance analysis of solar panel systems (photovoltaic/thermal systems) have been documented. The existing literature shows that the use of solar energy provides a promising solution to alleviate the shortage of natural resources and the environmental pollution associated with electricity generation. A hybrid solar panel has been invented to integrate photovoltaic (PV) cells onto a substrate through a functionally graded material (FGM) with water tubes cast inside, through which water flow serves as both a heat sink and a solar heat collector. Due to the unique and graded material properties of FGMs, this novel design not only supplies efficient thermal harvest and electrical production, but also provides benefits such as structural integrity and material efficiency. In this work, a sedimentation method has been used to fabricate aluminum (Al) and high-density polyethylene (HDPE) FGMs. The size effect of aluminum powder on the material gradation along the depth direction is investigated. Aluminum powder or the mixture of Al and HDPE powder is thoroughly mixed and uniformly dispersed in ethanol and then subjected to sedimentation. During the sedimentation process, the concentration of Al and HDPE particles temporally and spatially changes in the depth direction due to the non-uniform motion of particles; this change further affects the effective viscosity of the suspension and thus changes the drag force of particles. A Stokes' law based model is developed to simulate the sedimentation process, demonstrate the effect of manufacturing parameters on sedimentation, and predict the graded microstructure of deposition in the depth direction. In order to improve the modeling for sedimentation behavior of particles, the Eshelby's equivalent inclusion method (EIM) is presented to determine the interaction between particles, which is not considered in a Stokes' law based
Thienpont, Benedicte; Barata, Carlos; Raldúa, Demetrio
2013-06-01
Maternal thyroxine (T4) plays an essential role in fetal brain development, and even mild and transitory deficits in free-T4 in pregnant women can produce irreversible neurological effects in their offspring. Women of childbearing age are daily exposed to mixtures of chemicals disrupting the thyroid gland function (TGFDs) through the diet, drinking water, air and pharmaceuticals, which has raised the highest concern for the potential additive or synergic effects on the development of mild hypothyroxinemia during early pregnancy. Recently we demonstrated that zebrafish eleutheroembryos provide a suitable alternative model for screening chemicals impairing the thyroid hormone synthesis. The present study used the intrafollicular T4-content (IT4C) of zebrafish eleutheroembryos as integrative endpoint for testing the hypotheses that the effect of mixtures of TGFDs with a similar mode of action [inhibition of thyroid peroxidase (TPO)] was well predicted by a concentration addition concept (CA) model, whereas the response addition concept (RA) model predicted better the effect of dissimilarly acting binary mixtures of TGFDs [TPO-inhibitors and sodium-iodide symporter (NIS)-inhibitors]. However, CA model provided better prediction of joint effects than RA in five out of the six tested mixtures. The exception being the mixture MMI (TPO-inhibitor)-KClO{sub 4} (NIS-inhibitor) dosed at a fixed ratio of EC{sub 10} that provided similar CA and RA predictions and hence it was difficult to get any conclusive result. There results support the phenomenological similarity criterion stating that the concept of concentration addition could be extended to mixture constituents having common apical endpoints or common adverse outcomes. - Highlights: • Potential synergic or additive effect of mixtures of chemicals on thyroid function. • Zebrafish as alternative model for testing the effect of mixtures of goitrogens. • Concentration addition seems to predict better the effect of
Multiple Model Methods for Cost Function Based Multiple Hypothesis Trackers
2006-03-01
MHT’s Gaussian mixture with Multiple Model Adaptive Estimators (MMAEs) or Interacting Multiple Model (IMM) estimators, and replacing the elemental...Kalman Filtering . . . . . . . . . . . . . . . . . . . . . . . . . 2-2 2.3.1 Dynamics Design Models . . . . . . . . . . . . . . . 2-3 2.3.2 Propagation ...Track Life of Various Merging and Pruning Algorithms . . 2-30 3.1. Constant Velocity Truth Model Driven by White Gaussian Noise . . 3-3 3.2. Constant
MODELS-3/CMAQ APPLICATIONS WHICH ILLUSTRATE CAPABILITY AND FUNCTIONALITY
The Models-3/CMAQ developed by the U.S. Environmental Protections Agency (USEPA) is a third generation multiscale, multi-pollutant air quality modeling system within a high-level, object-oriented computer framework (Models-3). It has been available to the scientific community ...
Modeling the fundamental characteristics and processes of the spacecraft functioning
NASA Technical Reports Server (NTRS)
Bazhenov, V. I.; Osin, M. I.; Zakharov, Y. V.
1986-01-01
The fundamental aspects of modeling of spacecraft characteristics by using computing means are considered. Particular attention is devoted to the design studies, the description of physical appearance of the spacecraft, and simulated modeling of spacecraft systems. The fundamental questions of organizing the on-the-ground spacecraft testing and the methods of mathematical modeling were presented.
Cognitive Functioning: A Model for Learning and Problem Solving.
ERIC Educational Resources Information Center
Atkin, Julia A.
This paper outlines a model of learning and problem solving based on ideas derived from information processing models of memory and Ausubel's theory of meaningful learning. The model explicitly deals with the cognitive processes that are required for learning, and defines the conditions necessary for learning as the existence of relevant…
Web services and model-data comparison for the Functional Test Platform
NASA Astrophysics Data System (ADS)
Krassovski, Misha; Wang, Dali
2015-04-01
Web services and model-data comparison for the Functional Test Platform. The realistic representation of key biogeophysical and biogeochemical function is the fundamental on process based ecosystem models. A Functional Test Platform is designed to create direct linkages between site measurements and process-based ecosystem model within the Community Earth System Models (CESM). The platform consists of three major parts: 1) interactive user interfaces, 2) functional test models and 3) observational datasets. The purpose of the observational datasets is to provide an interactive search and visualization capability for direct model-data comparison. The proposed presentation is going to show how web services can be used to feed model-data comparison using AmeriFlux data collection provided by Carbon Dioxide Information Analysis Center (CDIAC) and the way it is coupled with Functional Test Platform for the Community Land Model.
Models of the Protocellular Structures, Functions and Evolution
NASA Technical Reports Server (NTRS)
Pohorille, Andrew; New, Michael; Keefe, Anthony; Szostak, Jack W.; Lanyi, Janos F.; DeVincenzi, Donald L. (Technical Monitor)
2000-01-01
In the absence of extinct or extant record of protocells, the most direct way to test our understanding of the origin of cellular life is to construct laboratory models that capture important features of protocellular systems. Such efforts are currently underway in a collaborative project between NASA-Ames, Harvard medical School and University of California. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures. The centerpiece of this project is a method for the in vitro evolution of protein enzymes toward arbitrary catalytic targets. A similar approach has already been developed for nucleic acids: First, a very large population of candidate molecules is generated using a random synthetic approach. Next, the small numbers of molecules that can accomplish the desired task are selected. These molecules are next vastly multiplied using the polymerase chain reaction. A mutagenic approach, in which the sequences of selected molecules are randomly altered, can yield further improvements in performance or alterations of specificities. Unfortunately, the catalytic potential of nucleic acids is rather limited. Proteins are more catalytically capable but cannot be directly amplified. In the new technique, this problem is circumvented by covalently linking each protein of the initial, diverse, pool to the RNA sequence that codes for it. Then, selection is performed on the proteins, but the nucleic acids are replicated. To date, we have obtained "a proof of concept" by evolving simple, novel proteins capable of selectively binding adenosine tri-phosphate (ATP). Our next goal is to create an enzyme that can phosphorylate amino acids and another to catalyze the formation of peptide bonds in the absence of nucleic acid templates. This latter reaction does not take place in contemporary cells. once developed, these enzymes will be encapsulated in liposomes so that they will function in a simulated cellular
Bright, L A; Mujahid, N; Nanduri, B; McCarthy, F M; Costa, L R R; Burgess, S C; Swiderski, C E
2011-08-01
The equine genome sequence enables the use of high-throughput genomic technologies in equine research, but accurate identification of expressed gene products and interpreting their biological relevance require additional structural and functional genome annotation. Here, we employ the equine genome sequence to identify predicted and known proteins using proteomics and model these proteins into biological pathways, identifying 582 proteins in normal cell-free equine bronchoalveolar lavage fluid (BALF). We improved structural and functional annotation by directly confirming the in vivo expression of 558 (96%) proteins, which were computationally predicted previously, and adding Gene Ontology (GO) annotations for 174 proteins, 108 of which lacked functional annotation. Bronchoalveolar lavage is commonly used to investigate equine respiratory disease, leading us to model the associated proteome and its biological functions. Modelling of protein functions using Ingenuity Pathway Analysis identified carbohydrate metabolism, cell-to-cell signalling, cellular function, inflammatory response, organ morphology, lipid metabolism and cellular movement as key biological processes in normal equine BALF. Comparative modelling of protein functions in normal cell-free bronchoalveolar lavage proteomes from horse, human, and mouse, performed by grouping GO terms sharing common ancestor terms, confirms conservation of functions across species. Ninety-one of 92 human GO categories and 105 of 109 mouse GO categories were conserved in the horse. Our approach confirms the utility of the equine genome sequence to characterize protein networks without antibodies or mRNA quantification, highlights the need for continued structural and functional annotation of the equine genome and provides a framework for equine researchers to aid in the annotation effort.
MODELING FUNCTIONALLY GRADED INTERPHASE REGIONS IN CARBON NANOTUBE REINFORCED COMPOSITES
NASA Technical Reports Server (NTRS)
Seidel, G. D.; Lagoudas, D. C.; Frankland, S. J. V.; Gates, T. S.
2006-01-01
A combination of micromechanics methods and molecular dynamics simulations are used to obtain the effective properties of the carbon nanotube reinforced composites with functionally graded interphase regions. The multilayer composite cylinders method accounts for the effects of non-perfect load transfer in carbon nanotube reinforced polymer matrix composites using a piecewise functionally graded interphase. The functional form of the properties in the interphase region, as well as the interphase thickness, is derived from molecular dynamics simulations of carbon nanotubes in a polymer matrix. Results indicate that the functional form of the interphase can have a significant effect on all the effective elastic constants except for the effective axial modulus for which no noticeable effects are evident.
Exploring Neurofibromin Function in a Yeast Model of NF1
2011-11-01
IRA2, and have gone on to functionally categorize these genes as being either ras-dependent or ras- independent. Strikingly, we identified 17 Pex genes ...that interacted genetically with IRA1, IRA2, or both (Table 1). Pex genes are involved in the biogenesis of peroxisomes, evolutionarily conserved...peroxisome function rather than biogenesis per se. Table 1. Yeast Pex genes that interact genetically with IRA1 or IRA2 or both. PEX Gene
NASA Technical Reports Server (NTRS)
Gutmann, Ethan D.; Small, Eric E.
2007-01-01
Soil hydraulic properties (SHPs) regulate the movement of water in the soil. This in turn plays an important role in the water and energy cycles at the land surface. At present, SHPS are commonly defined by a simple pedotransfer function from soil texture class, but SHPs vary more within a texture class than between classes. To examine the impact of using soil texture class to predict SHPS, we run the Noah land surface model for a wide variety of measured SHPs. We find that across a range of vegetation cover (5 - 80% cover) and climates (250 - 900 mm mean annual precipitation), soil texture class only explains 5% of the variance expected from the real distribution of SHPs. We then show that modifying SHPs can drastically improve model performance. We compare two methods of estimating SHPs: (1) inverse method, and (2) soil texture class. Compared to texture class, inverse modeling reduces errors between measured and modeled latent heat flux from 88 to 28 w/m(exp 2). Additionally we find that with increasing vegetation cover the importance of SHPs decreases and that the van Genuchten m parameter becomes less important, while the saturated conductivity becomes more important.
Takemura, Kazuhisa; Murakami, Hajime
2016-01-01
A probability weighting function (w(p)) is considered to be a nonlinear function of probability (p) in behavioral decision theory. This study proposes a psychophysical model of probability weighting functions derived from a hyperbolic time discounting model and a geometric distribution. The aim of the study is to show probability weighting functions from the point of view of waiting time for a decision maker. Since the expected value of a geometrically distributed random variable X is 1/p, we formulized the probability weighting function of the expected value model for hyperbolic time discounting as w(p) = (1 - k log p)(-1). Moreover, the probability weighting function is derived from Loewenstein and Prelec's (1992) generalized hyperbolic time discounting model. The latter model is proved to be equivalent to the hyperbolic-logarithmic weighting function considered by Prelec (1998) and Luce (2001). In this study, we derive a model from the generalized hyperbolic time discounting model assuming Fechner's (1860) psychophysical law of time and a geometric distribution of trials. In addition, we develop median models of hyperbolic time discounting and generalized hyperbolic time discounting. To illustrate the fitness of each model, a psychological experiment was conducted to assess the probability weighting and value functions at the level of the individual participant. The participants were 50 university students. The results of individual analysis indicated that the expected value model of generalized hyperbolic discounting fitted better than previous probability weighting decision-making models. The theoretical implications of this finding are discussed.
Discrete two-sex models of population dynamics: On modelling the mating function
NASA Astrophysics Data System (ADS)
Bessa-Gomes, Carmen; Legendre, Stéphane; Clobert, Jean
2010-09-01
Although sexual reproduction has long been a central subject of theoretical ecology, until recently its consequences for population dynamics were largely overlooked. This is now changing, and many studies have addressed this issue, showing that when the mating system is taken into account, the population dynamics depends on the relative abundance of males and females, and is non-linear. Moreover, sexual reproduction increases the extinction risk, namely due to the Allee effect. Nevertheless, different studies have identified diverse potential consequences, depending on the choice of mating function. In this study, we investigate the consequences of three alternative mating functions that are frequently used in discrete population models: the minimum; the harmonic mean; and the modified harmonic mean. We consider their consequences at three levels: on the probability that females will breed; on the presence and intensity of the Allee effect; and on the extinction risk. When we consider the harmonic mean, the number of times the individuals of the least abundant sex mate exceeds their mating potential, which implies that with variable sex-ratios the potential reproductive rate is no longer under the modeller's control. Consequently, the female breeding probability exceeds 1 whenever the sex-ratio is male-biased, which constitutes an obvious problem. The use of the harmonic mean is thus only justified if we think that this parameter should be re-defined in order to represent the females' breeding rate and the fact that females may reproduce more than once per breeding season. This phenomenon buffers the Allee effect, and reduces the extinction risk. However, when we consider birth-pulse populations, such a phenomenon is implausible because the number of times females can reproduce per birth season is limited. In general, the minimum or modified harmonic mean mating functions seem to be more suitable for assessing the impact of mating systems on population dynamics.
A Functional Model for the Treatment of Primary Enuresis.
ERIC Educational Resources Information Center
Goldstein, Sam; Book, Robert
1983-01-01
The present model, based upon Alexander and Barton's two-phase model for family therapy, was developed to provide the practicing school psychologist with an efficient, manageable program maximizing successful outcome. The program enables psychologists to adapt primary enuresis intervention strategies to suit their styles and the individual needs…
Modelling Transformations of Quadratic Functions: A Proposal of Inductive Inquiry
ERIC Educational Resources Information Center
Sokolowski, Andrzej
2013-01-01
This paper presents a study about using scientific simulations to enhance the process of mathematical modelling. The main component of the study is a lesson whose major objective is to have students mathematise a trajectory of a projected object and then apply the model to formulate other trajectories by using the properties of function…
Basic Life Functions Instructional Program Model. Field Copy.
ERIC Educational Resources Information Center
Wisconsin State Dept. of Public Instruction, Madison. Div. for Handicapped Children.
Presented is a model, designed by the Wisconsin Department of Public Instruction, for development of an instructional program in basic living skills for trainable mentally retarded children (2- to 20-years-old). The model identifies the following instructional goals: to communicate ideas, to understand one's self and interact with others, to…
Putting the brakes on inhibitory models of frontal lobe function.
Hampshire, Adam
2015-06-01
There has been much recent debate regarding the neural basis of motor response inhibition. An influential hypothesis from the last decade proposes that a module within the right inferior frontal cortex (RIFC) of the human brain is dedicated to supporting response inhibition. However, there is growing evidence to support the alternative view that response inhibition is just one prominent example of the many cognitive control processes that are supported by the same set of 'domain general' functional networks. Here, I test directly between the modular and network accounts of motor response inhibition by applying a combination of data-driven, event-related and functional connectivity analyses to fMRI data from a variety of attention and inhibition tasks. The results demonstrate that there is no inhibitory module within the RIFC. Instead, response inhibition recruits a functionally heterogeneous ensemble of RIFC networks, which can be dissociated from each other in the context of other task demands.
Modeling Functional Motions of Biological Systems by Customized Natural Moves.
Demharter, Samuel; Knapp, Bernhard; Deane, Charlotte M; Minary, Peter
2016-08-23
Simulating the functional motions of biomolecular systems requires large computational resources. We introduce a computationally inexpensive protocol for the systematic testing of hypotheses regarding the dynamic behavior of proteins and nucleic acids. The protocol is based on natural move Monte Carlo, a highly efficient conformational sampling method with built-in customization capabilities that allows researchers to design and perform a large number of simulations to investigate functional motions in biological systems. We demonstrate the use of this protocol on both a protein and a DNA case study. Firstly, we investigate the plasticity of a class II major histocompatibility complex in the absence of a bound peptide. Secondly, we study the effects of the epigenetic mark 5-hydroxymethyl on cytosine on the structure of the Dickerson-Drew dodecamer. We show how our customized natural moves protocol can be used to investigate causal relationships of functional motions in biological systems.
Jewsbury, Paul A; Bowden, Stephen C; Strauss, Milton E
2016-02-01
Executive function is an important concept in neuropsychological and cognitive research, and is often viewed as central to effective clinical assessment of cognition. However, the construct validity of executive function tests is controversial. The switching, inhibition, and updating model is the most empirically supported and replicated factor model of executive function (Miyake et al., 2000). To evaluate the relation between executive function constructs and nonexplicitly executive cognitive constructs, we used confirmatory factor reanalysis guided by the comprehensive Cattell-Horn-Carroll (CHC) model of cognitive abilities. Data from 7 of the best studies supporting the executive function model were reanalyzed, contrasting executive function models and CHC models. Where possible, we examined the effect of specifying executive function factors in addition to the CHC factors. The results suggested that little evidence is available to support updating as a separate factor from general memory factors; that inhibition does not separate from general speed; and that switching is supported as a narrow factor under general speed, but with a more restricted definition than some clinicians and researchers have conceptualized. The replicated executive function factor structure was integrated with the larger body of research on individual difference in cognition, as represented by the CHC model.
Multipole model for the electron group functions method.
Tchougréeff, A L; Tokmachev, A M; Dronskowski, R
2009-10-22
Electron groups provide a natural way to introduce local concepts into quantum chemistry, and the wave functions based on the group products can be considered as a framework for constructing efficient computational methods in terms of "observable" parts of molecular systems. The elements of the group wave functions (electronic structure variables) can be optimized by requiring the number of operations proportional to the size of the molecule. This directly leads to computational methods linearly scaling for large molecular systems. In the present work we consider a particular case of such a wave function implemented for the semiempirical NDDO Hamiltonian. The electron groups are expressed in terms of optimized atomic (hybrid) orbitals with chemical bonds described by geminals and the delocalized groups described by Slater determinants (with or without spin restriction). This scheme is very fast by itself but its speed is considerably limited by the computations of the interatomic Coulomb interactions. Here we develop a consistent method based on group functions which uses the multipole scheme for interatomic interactions. The explicit usage of the atomic multipoles makes the method extremely fast, although the numerical efficiency is largely achieved due to the local character of the electron groups involved. We discuss numerical characteristics of the new method as well as its possible parametrization. We apply this method to study dodecahedral water clusters with hydrogen fluoride substitution and base the analysis on the exhaustive calculation of all symmetry-independent hydrogen-bond networks.
Peak functions for modeling high resolution soil profile data
Technology Transfer Automated Retrieval System (TEKTRAN)
Parametric and non-parametric depth functions have been used to estimate continuous soil profile properties. However, some soil properties, such as those seen in weathered loess, have complex peaked and anisotropic depth distributions. These distributions are poorly handled by common parametric func...
Pump function curve shape for a model lymphatic vessel.
Bertram, C D; Macaskill, C; Moore, J E
2016-07-01
The transport capacity of a contractile segment of lymphatic vessel is defined by its pump function curve relating mean flow-rate and adverse pressure difference. Numerous system characteristics affect curve shape and the magnitude of the generated flow-rates and pressures. Some cannot be varied experimentally, but their separate and interacting effects can be systematically revealed numerically. This paper explores variations in the rate of change of active tension and the form of the relation between active tension and muscle length, factors not known from experiment to functional precision. Whether the pump function curve bends toward or away from the origin depends partly on the curvature of the passive pressure-diameter relation near zero transmural pressure, but rather more on the form of the relation between active tension and muscle length. A pump function curve bending away from the origin defines a well-performing pump by maximum steady output power. This behaviour is favoured by a length/active-tension relationship which sustains tension at smaller lengths. Such a relationship also favours high peak mechanical efficiency, defined as output power divided by the input power obtained from the lymphangion diameter changes and active-tension time-course. The results highlight the need to pin down experimentally the form of the length/active-tension relationship.
Functional Testing Protocols for Commercial Building Efficiency Baseline Modeling Software
Jump, David; Price, Phillip N.; Granderson, Jessica; Sohn, Michael
2013-09-06
This document describes procedures for testing and validating proprietary baseline energy modeling software accuracy in predicting energy use over the period of interest, such as a month or a year. The procedures are designed according to the methodology used for public domain baselining software in another LBNL report that was (like the present report) prepared for Pacific Gas and Electric Company: ?Commercial Building Energy Baseline Modeling Software: Performance Metrics and Method Testing with Open Source Models and Implications for Proprietary Software Testing Protocols? (referred to here as the ?Model Analysis Report?). The test procedure focuses on the quality of the software?s predictions rather than on the specific algorithms used to predict energy use. In this way the software vendor is not required to divulge or share proprietary information about how their software works, while enabling stakeholders to assess its performance.
Prediction of Chemical Function: Model Development and Application
The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...
Isabelle, Boulangeat; Pauline, Philippe; Sylvain, Abdulhak; Roland, Douzet; Luc, Garraud; Sébastien, Lavergne; Sandra, Lavorel; Jérémie, Van Es; Pascal, Vittoz; Wilfried, Thuiller
2013-01-01
The pace of on-going climate change calls for reliable plant biodiversity scenarios. Traditional dynamic vegetation models use plant functional types that are summarized to such an extent that they become meaningless for biodiversity scenarios. Hybrid dynamic vegetation models of intermediate complexity (hybrid-DVMs) have recently been developed to address this issue. These models, at the crossroads between phenomenological and process-based models, are able to involve an intermediate number of well-chosen plant functional groups (PFGs). The challenge is to build meaningful PFGs that are representative of plant biodiversity, and consistent with the parameters and processes of hybrid-DVMs. Here, we propose and test a framework based on few selected traits to define a limited number of PFGs, which are both representative of the diversity (functional and taxonomic) of the flora in the Ecrins National Park, and adapted to hybrid-DVMs. This new classification scheme, together with recent advances in vegetation modeling, constitutes a step forward for mechanistic biodiversity modeling. PMID:24403847
ERIC Educational Resources Information Center
LaRue, Robert H.; Sloman, Kimberly N.; Weiss, Mary Jane; Delmolino, Lara; Hansford, Amy; Szalony, Jill; Madigan, Ryan; Lambright, Nathan M.
2011-01-01
Functional analysis procedures have been effectively used to determine the maintaining variables for challenging behavior and subsequently develop effective interventions. However, fear of evoking dangerous topographies of maladaptive behavior and concerns for reinforcing infrequent maladaptive behavior present challenges for people working in…
NASA Astrophysics Data System (ADS)
Reich, P. B.; Butler, E. E.
2015-12-01
This project will advance global land models by shifting from the current plant functional type approach to one that better utilizes what is known about the importance and variability of plant traits, within a framework of simultaneously improving fundamental physiological relations that are at the core of model carbon cycling algorithms. Existing models represent the global distribution of vegetation types using the Plant Functional Typeconcept. Plant Functional Types are classes of plant species with similar evolutionary and life history withpresumably similar responses to environmental conditions like CO2, water and nutrient availability. Fixedproperties for each Plant Functional Type are specified through a collection of physiological parameters, or traits.These traits, mostly physiological in nature (e.g., leaf nitrogen and longevity) are used in model algorithms to estimate ecosystem properties and/or drive calculated process rates. In most models, 5 to 15 functional types represent terrestrial vegetation; in essence, they assume there are a total of only 5 to 15 different kinds of plants on the entire globe. This assumption of constant plant traits captured within the functional type concept has serious limitations, as a single set of traits does not reflect trait variation observed within and between species and communities. While this simplification was necessary decades past, substantial improvement is now possible. Rather than assigning a small number of constant parameter values to all grid cells in a model, procedures will be developed that predict a frequency distribution of values for any given grid cell. Thus, the mean and variance, and how these change with time, will inform and improve model performance. The trait-based approach will improve land modeling by (1) incorporating patterns and heterogeneity of traits into model parameterization, thus evolving away from a framework that considers large areas of vegetation to have near identical trait
Modeling of pharmaceuticals mixtures toxicity with deviation ratio and best-fit functions models.
Wieczerzak, Monika; Kudłak, Błażej; Yotova, Galina; Nedyalkova, Miroslava; Tsakovski, Stefan; Simeonov, Vasil; Namieśnik, Jacek
2016-11-15
The present study deals with assessment of ecotoxicological parameters of 9 drugs (diclofenac (sodium salt), oxytetracycline hydrochloride, fluoxetine hydrochloride, chloramphenicol, ketoprofen, progesterone, estrone, androstenedione and gemfibrozil), present in the environmental compartments at specific concentration levels, and their mutual combinations by couples against Microtox® and XenoScreen YES/YAS® bioassays. As the quantitative assessment of ecotoxicity of drug mixtures is an complex and sophisticated topic in the present study we have used two major approaches to gain specific information on the mutual impact of two separate drugs present in a mixture. The first approach is well documented in many toxicological studies and follows the procedure for assessing three types of models, namely concentration addition (CA), independent action (IA) and simple interaction (SI) by calculation of a model deviation ratio (MDR) for each one of the experiments carried out. The second approach used was based on the assumption that the mutual impact in each mixture of two drugs could be described by a best-fit model function with calculation of weight (regression coefficient or other model parameter) for each of the participants in the mixture or by correlation analysis. It was shown that the sign and the absolute value of the weight or the correlation coefficient could be a reliable measure for the impact of either drug A on drug B or, vice versa, of B on A. Results of studies justify the statement, that both of the approaches show similar assessment of the mode of mutual interaction of the drugs studied. It was found that most of the drug mixtures exhibit independent action and quite few of the mixtures show synergic or dependent action.
A Model-Based Approach to Constructing Music Similarity Functions
NASA Astrophysics Data System (ADS)
West, Kris; Lamere, Paul
2006-12-01
Several authors have presented systems that estimate the audio similarity of two pieces of music through the calculation of a distance metric, such as the Euclidean distance, between spectral features calculated from the audio, related to the timbre or pitch of the signal. These features can be augmented with other, temporally or rhythmically based features such as zero-crossing rates, beat histograms, or fluctuation patterns to form a more well-rounded music similarity function. It is our contention that perceptual or cultural labels, such as the genre, style, or emotion of the music, are also very important features in the perception of music. These labels help to define complex regions of similarity within the available feature spaces. We demonstrate a machine-learning-based approach to the construction of a similarity metric, which uses this contextual information to project the calculated features into an intermediate space where a music similarity function that incorporates some of the cultural information may be calculated.
Critical Function Models for Operation of the International Space Station
Nelson, William Roy; Bagian, T. M.
2000-11-01
Long duration and exploration class space missions will place new requirements on human performance when compared to current space shuttle missions. Specifically, assembly and operation of the International Space Station (ISS) will place significant new demands on the crew. For example, maintenance of systems that provide habitability will become an ongoing activity for the international flight crews. Tasks for maintaining space station habitability will need to be integrated with tasks associated with scientific research. In addition, tasks and resources will need to be prioritized and allocated dynamically in response to changing operational conditions and unplanned system breakdowns. This paper describes an ongoing program to develop a habitability index (HI) for space operations based on the critical function approach. This pilot project focuses on adaptation of the critical function approach to develop a habitability index specifically tailored for space operations. Further work will then be needed to expand and validate the habitability index for application in the ISS operational environment.
Modeling intracellular signaling underlying striatal function in health and disease
Nair, Anu G; Gutierrez-Arenas, Omar; Eriksson, Olivia; Jauhiainen, Alexandra; Blackwell, Kim T; Kotaleski, Jeanette Hellgren
2014-01-01
Striatum, which is the input nucleus of the basal ganglia, integrates cortical and thalamic glutamatergic inputs with dopaminergic afferents from the substantia nigra pars compacta. The combination of dopamine and glutamate strongly modulates molecular and cellular properties of striatal neurons and the strength of corticostriatal synapses. These actions are performed via intracellular signaling networks, containing several intertwined feedback loops. Understanding the role of dopamine and other neuromodulators requires the development of quantitative dynamical models for describing the intracellular signaling, in order to provide precise unambiguous descriptions and quantitative predictions. Building such models requires integration of data from multiple data sources containing information regarding the molecular interactions, the strength of these interactions, and the subcellular localization of the molecules. Due to the uncertainty, variability, and sparseness of these data, parameter estimation techniques are critical for inferring or constraining the unknown parameters, and sensitivity analysis evaluates which parameters are most critical for a given observed macroscopic behavior. Here, we briefly review the modeling approaches and tools that have been used to investigate biochemical signaling in the striatum, along with some of the models built around striatum. We also suggest a future direction for the development of such models from the, now becoming abundant, high-throughput data. PMID:24560149
Modeling the Transfer Function for the Dark Energy Survey
Chang, C.
2015-03-04
We present a forward-modeling simulation framework designed to model the data products from the Dark Energy Survey (DES). This forward-model process can be thought of as a transfer function—a mapping from cosmological/astronomical signals to the final data products used by the scientists. Using output from the cosmological simulations (the Blind Cosmology Challenge), we generate simulated images (the Ultra Fast Image Simulator) and catalogs representative of the DES data. In this work we demonstrate the framework by simulating the 244 deg2 coadd images and catalogs in five bands for the DES Science Verification data. The simulation output is compared with themore » corresponding data to show that major characteristics of the images and catalogs can be captured. We also point out several directions of future improvements. Two practical examples—star-galaxy classification and proximity effects on object detection—are then used to illustrate how one can use the simulations to address systematics issues in data analysis. With clear understanding of the simplifications in our model, we show that one can use the simulations side-by-side with data products to interpret the measurements. This forward modeling approach is generally applicable for other upcoming and future surveys. It provides a powerful tool for systematics studies that is sufficiently realistic and highly controllable.« less
Modeling the Transfer Function for the Dark Energy Survey
Chang, C.
2015-03-04
We present a forward-modeling simulation framework designed to model the data products from the Dark Energy Survey (DES). This forward-model process can be thought of as a transfer function—a mapping from cosmological/astronomical signals to the final data products used by the scientists. Using output from the cosmological simulations (the Blind Cosmology Challenge), we generate simulated images (the Ultra Fast Image Simulator) and catalogs representative of the DES data. In this work we demonstrate the framework by simulating the 244 deg^{2} coadd images and catalogs in five bands for the DES Science Verification data. The simulation output is compared with the corresponding data to show that major characteristics of the images and catalogs can be captured. We also point out several directions of future improvements. Two practical examples—star-galaxy classification and proximity effects on object detection—are then used to illustrate how one can use the simulations to address systematics issues in data analysis. With clear understanding of the simplifications in our model, we show that one can use the simulations side-by-side with data products to interpret the measurements. This forward modeling approach is generally applicable for other upcoming and future surveys. It provides a powerful tool for systematics studies that is sufficiently realistic and highly controllable.
Predicting and correcting ataxia using a model of cerebellar function
Bhanpuri, Nasir H.; Okamura, Allison M.
2014-01-01
Cerebellar damage results in uncoordinated, variable and dysmetric movements known as ataxia. Here we show that we can reliably model single-joint reaching trajectories of patients (n = 10), reproduce patient-like deficits in the behaviour of controls (n = 11), and apply patient-specific compensations that improve reaching accuracy (P < 0.02). Our approach was motivated by the theory that the cerebellum is essential for updating and/or storing an internal dynamic model that relates motor commands to changes in body state (e.g. arm position and velocity). We hypothesized that cerebellar damage causes a mismatch between the brain’s modelled dynamics and the actual body dynamics, resulting in ataxia. We used both behavioural and computational approaches to demonstrate that specific cerebellar patient deficits result from biased internal models. Our results strongly support the idea that an intact cerebellum is critical for maintaining accurate internal models of dynamics. Importantly, we demonstrate how subject-specific compensation can improve movement in cerebellar patients, who are notoriously unresponsive to treatment. PMID:24812203
Commonsense Psychology and the Functional Requirements of Cognitive Models
2005-07-01
of the work of Sigmund Freud . Functionally, memory repression can be viewed as a procedure that is called of the memory mechanism and that takes...407-428. Nichols, S. and Stich, S. 2002. How to Read Your Own Mind: A Cognitive Theory of Self- Consciousness . In Q. Smith and A. Jokic. (eds... Consciousness : New Philosophical Essays. Oxford University Press. Pryor, L. & Colllins, G. 1992. Reference Features as guides to reasoning about
Empirical Network Model of Human Higher Cognitive Brain Functions
1990-03-31
forms as val. and to 8 lags ( -,-/- 62 msec) for the 4-7 potentials, in keeping with common usage. Hz-filtered intervals. The ERC was defined as the...Functional topography of the graded. Further refinement of the techniques used human brain. In: G. Pfurtscheller and F.H. Lopes da Silva (Eds...sufficiently dense spatial sampling and techniques of spatial enhancement such as Laplacian Transform. Classical neurological and neurolinguistic thinking
The quantum Ising model: finite sums and hyperbolic functions
NASA Astrophysics Data System (ADS)
Damski, Bogdan
2015-10-01
We derive exact closed-form expressions for several sums leading to hyperbolic functions and discuss their applicability for studies of finite-size Ising spin chains. We show how they immediately lead to closed-form expressions for both fidelity susceptibility characterizing the quantum critical point and the coefficients of the counterdiabatic Hamiltonian enabling arbitrarily quick adiabatic driving of the system. Our results generalize and extend the sums presented in the popular Gradshteyn and Ryzhik Table of Integrals, Series, and Products.
The quantum Ising model: finite sums and hyperbolic functions.
Damski, Bogdan
2015-10-30
We derive exact closed-form expressions for several sums leading to hyperbolic functions and discuss their applicability for studies of finite-size Ising spin chains. We show how they immediately lead to closed-form expressions for both fidelity susceptibility characterizing the quantum critical point and the coefficients of the counterdiabatic Hamiltonian enabling arbitrarily quick adiabatic driving of the system. Our results generalize and extend the sums presented in the popular Gradshteyn and Ryzhik Table of Integrals, Series, and Products.
Design, Fabrication, Characterization and Modeling of Integrated Functional Materials
2015-12-01
of the samples was recorded , and is shown in Figure 44 (c), both before and after ten heating trials to confirm that the PNIPAM was not damaged after...Physics Department at the University of South Florida (USF) geared towards precisely addressing this grand challenge of dual integration. A series of...develop the novel science base both in the areas of multi-scale dimensional integration as well as multiple functional integration leading to previously
Evaluation of Analytical Modeling Functions for the Phonation Onset Process.
Petermann, Simon; Kniesburges, Stefan; Ziethe, Anke; Schützenberger, Anne; Döllinger, Michael
2016-01-01
The human voice originates from oscillations of the vocal folds in the larynx. The duration of the voice onset (VO), called the voice onset time (VOT), is currently under investigation as a clinical indicator for correct laryngeal functionality. Different analytical approaches for computing the VOT based on endoscopic imaging were compared to determine the most reliable method to quantify automatically the transient vocal fold oscillations during VO. Transnasal endoscopic imaging in combination with a high-speed camera (8000 fps) was applied to visualize the phonation onset process. Two different definitions of VO interval were investigated. Six analytical functions were tested that approximate the envelope of the filtered or unfiltered glottal area waveform (GAW) during phonation onset. A total of 126 recordings from nine healthy males and 210 recordings from 15 healthy females were evaluated. Three criteria were analyzed to determine the most appropriate computation approach: (1) reliability of the fit function for a correct approximation of VO; (2) consistency represented by the standard deviation of VOT; and (3) accuracy of the approximation of VO. The results suggest the computation of VOT by a fourth-order polynomial approximation in the interval between 32.2 and 67.8% of the saturation amplitude of the filtered GAW.
Computer Modeling of Protocellular Functions: Peptide Insertion in Membranes
NASA Technical Reports Server (NTRS)
Rodriquez-Gomez, D.; Darve, E.; Pohorille, A.
2006-01-01
Lipid vesicles became the precursors to protocells by acquiring the capabilities needed to survive and reproduce. These include transport of ions, nutrients and waste products across cell walls and capture of energy and its conversion into a chemically usable form. In modem organisms these functions are carried out by membrane-bound proteins (about 30% of the genome codes for this kind of proteins). A number of properties of alpha-helical peptides suggest that their associations are excellent candidates for protobiological precursors of proteins. In particular, some simple a-helical peptides can aggregate spontaneously and form functional channels. This process can be described conceptually by a three-step thermodynamic cycle: 1 - folding of helices at the water-membrane interface, 2 - helix insertion into the lipid bilayer and 3 - specific interactions of these helices that result in functional tertiary structures. Although a crucial step, helix insertion has not been adequately studied because of the insolubility and aggregation of hydrophobic peptides. In this work, we use computer simulation methods (Molecular Dynamics) to characterize the energetics of helix insertion and we discuss its importance in an evolutionary context. Specifically, helices could self-assemble only if their interactions were sufficiently strong to compensate the unfavorable Free Energy of insertion of individual helices into membranes, providing a selection mechanism for protobiological evolution.
Evaluation of Analytical Modeling Functions for the Phonation Onset Process
Petermann, Simon; Kniesburges, Stefan; Ziethe, Anke; Schützenberger, Anne; Döllinger, Michael
2016-01-01
The human voice originates from oscillations of the vocal folds in the larynx. The duration of the voice onset (VO), called the voice onset time (VOT), is currently under investigation as a clinical indicator for correct laryngeal functionality. Different analytical approaches for computing the VOT based on endoscopic imaging were compared to determine the most reliable method to quantify automatically the transient vocal fold oscillations during VO. Transnasal endoscopic imaging in combination with a high-speed camera (8000 fps) was applied to visualize the phonation onset process. Two different definitions of VO interval were investigated. Six analytical functions were tested that approximate the envelope of the filtered or unfiltered glottal area waveform (GAW) during phonation onset. A total of 126 recordings from nine healthy males and 210 recordings from 15 healthy females were evaluated. Three criteria were analyzed to determine the most appropriate computation approach: (1) reliability of the fit function for a correct approximation of VO; (2) consistency represented by the standard deviation of VOT; and (3) accuracy of the approximation of VO. The results suggest the computation of VOT by a fourth-order polynomial approximation in the interval between 32.2 and 67.8% of the saturation amplitude of the filtered GAW. PMID:27066108
Spherical Harmonics Functions Modelling of Meteorological Parameters in PWV Estimation
NASA Astrophysics Data System (ADS)
Deniz, Ilke; Mekik, Cetin; Gurbuz, Gokhan
2016-08-01
Aim of this study is to derive temperature, pressure and humidity observations using spherical harmonics modelling and to interpolate for the derivation of precipitable water vapor (PWV) of TUSAGA-Active stations in the test area encompassing 38.0°-42.0° northern latitudes and 28.0°-34.0° eastern longitudes of Turkey. In conclusion, the meteorological parameters computed by using GNSS observations for the study area have been modelled with a precision of ±1.74 K in temperature, ±0.95 hPa in pressure and ±14.88 % in humidity. Considering studies on the interpolation of meteorological parameters, the precision of temperature and pressure models provide adequate solutions. This study funded by the Scientific and Technological Research Council of Turkey (TUBITAK) (The Estimation of Atmospheric Water Vapour with GPS Project, Project No: 112Y350).
ERIC Educational Resources Information Center
Magis, David
2015-01-01
The purpose of this note is to study the equivalence of observed and expected (Fisher) information functions with polytomous item response theory (IRT) models. It is established that observed and expected information functions are equivalent for the class of divide-by-total models (including partial credit, generalized partial credit, rating…
A Classroom Note on: Modeling Functions with the TI-83/84 Calculator
ERIC Educational Resources Information Center
Lubowsky, Jack
2011-01-01
In Pre-Calculus courses, students are taught the composition and combination of functions to model physical applications. However, when combining two or more functions into a single more complicated one, students may lose sight of the physical picture which they are attempting to model. A block diagram, or flow chart, in which each block…
Psychometric Properties on Lecturers' Beliefs on Teaching Function: Rasch Model Analysis
ERIC Educational Resources Information Center
Mofreh, Samah Ali Mohsen; Ghafar, Mohammed Najib Abdul; Omar, Abdul Hafiz Hj; Mosaku, Monsurat; Ma'ruf, Amar
2014-01-01
This paper focuses on the psychometric analysis of lecturers' beliefs on teaching function (LBTF) survey using Rasch Model analysis. The sample comprised 34 Community Colleges' lecturers. The Rasch Model is applied to produce specific measurements on the lecturers' beliefs on teaching function in order to generalize results and inferential…
Defining a Model for Mitochondrial Function in mESC Differentiation
Defining a Model for Mitochondrial Function in mESC DifferentiationDefining a Model for Mitochondrial Function in mESC Differentiation Differentiating embryonic stem cells (ESCs) undergo mitochondrial maturation leading to a switch from a system dependent upon glycolysis to a re...
RAPID ASSESSMENT OF URBAN WETLANDS: FUNCTIONAL ASSESSMENT MODEL DEVELOPMENT AND EVALUATION
The objective of this study was to test the ability of existing hydrogeomorphic (HGM) functional assessment models and our own proposed models to predict rates of nitrate production and removal, functions critical to water quality protection, in forested riparian wetlands in nort...
Functional scale-free networks in the two-dimensional Abelian sandpile model
NASA Astrophysics Data System (ADS)
Zarepour, M.; Niry, M. D.; Valizadeh, A.
2015-07-01
Recently, the similarity of the functional network of the brain and the Ising model was investigated by Chialvo [Nat. Phys. 6, 744 (2010), 10.1038/nphys1803]. This similarity supports the idea that the brain is a self-organized critical system. In this study we derive a functional network of the two-dimensional Bak-Tang-Wiesenfeld sandpile model as a self-organized critical model, and compare its characteristics with those of the functional network of the brain, obtained from functional magnetic resonance imaging.
Improvement in Detection of Differential Item Functioning Using a Mixture Item Response Theory Model
ERIC Educational Resources Information Center
Maij-de Meij, Annette M.; Kelderman, Henk; van der Flier, Henk
2010-01-01
Usually, methods for detection of differential item functioning (DIF) compare the functioning of items across manifest groups. However, the manifest groups with respect to which the items function differentially may not necessarily coincide with the true source of the bias. It is expected that DIF detection under a model that includes a latent DIF…
PROGRAPH Diagrams--A New Old System for Teaching Functional Modelling
ERIC Educational Resources Information Center
Siller, Hans-Stefan
2009-01-01
This paper shows the basic concept of Functional Modelling in mathematics education which has become more and more important in recent years. Hence it is necessary to think about suitable graphical methods to explain the fundamental idea of a function and its influence on values and other functions. PROGRAPH diagrams are a potentially good way to…
NASA Astrophysics Data System (ADS)
Ito, Mitsuyo; Koya, Yoshiharu; Mizoshiri, Isao
Presently, many of the already proposed blood circulation models are mainly partial models although they are precise models. A complete model that is a combination of these partial models are difficult to analyze because it is complicated to consider both the viscosity of blood and circulatory details at the same time. So, it is difficult to control the model parameters in order to adapt to various cases of circulatory diseases. This paper proposes a complete circulation model as a lumped electrical circuit, which is comparatively simple. In the circuit model, total blood is modeled as seven lumped capacitors, representing the functions of atriums, ventricles, arteries, veins and lungs. We regard the variation of the ventricle capacitance as the driving force of the complete circulation model. In our model, we considered only the variation of pressure between each part and the blood capacity of each part. In particular, the contraction function of the left ventricle is examined under the consideration of whole blood circulation.
A Simple Model System to Demonstrate Antibody Structure and Functions.
ERIC Educational Resources Information Center
O'Kennedy, Richard
1991-01-01
A model that can be used to show the arrangement of light and heavy chains, disulfide linkages, domains, and subclass variations in antibodies is given. It can be constructed and modified to illustrate Fab, F(ab')2, and Fc fragments, single domain and bifunctional antibodies, and labeling of antibodies. (Author)
A Model for Minimizing Numeric Function Generator Complexity and Delay
2007-12-01
model_Linear_NonUniform_Basic with the size of the number system (n) and the number of segments ( mins ). The author’s MATLAB m-file segments.m returns the...Navigator ............................................................20 2. MATLAB ...103 APPENDIX A. MATLAB SOURCE CODE............................................................105 A.1
Insights into mast cell functions in asthma using mouse models.
Lei, Ying; Gregory, Joshua A; Nilsson, Gunnar P; Adner, Mikael
2013-10-01
Therapeutics targeting specific mechanisms of asthma have shown promising results in mouse models of asthma. However, these successes have not transferred well to the clinic or to the treatment of asthma sufferers. We suggest a reason for this incongruity is that mast cell-dependent responses, which may play an important role in the pathogenesis of both atopic and non-atopic asthma, are not a key component in most of the current asthma mouse models. Two reasons for this are that wild type mice have, in contrast to humans, a negligible number of mast cells localized in the smaller airways and in the parenchyma, and that only specific protocols show mast cell-dependent reactions. The development of mast cell-deficient mice and the reconstitution of mast cells within these mice have opened up the possibility to generate mouse models of asthma with a marked role of mast cells. In addition, mast cell-deficient mice engrafted with mast cells have a distribution of mast cells more similar to humans. In this article we review and highlight the mast cell-dependent and -independent responses with respect to airway hyperresponsiveness and inflammation in asthma models using mast cell-deficient and mast cell-engrafted mice.
A Functional Model of Sensemaking in a Neurocognitive Architecture
Lebiere, Christian; Paik, Jaehyon; Rutledge-Taylor, Matthew; Staszewski, James; Anderson, John R.
2013-01-01
Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core information-foraging and hypothesis-updating sensemaking processes applied to complex spatial probability estimation and decision-making tasks. While the model was developed in a hybrid symbolic-statistical cognitive architecture, its correspondence to neural frameworks in terms of both structure and mechanisms provided a direct bridge between rational and neural levels of description. Compared against data from two participant groups, the model correctly predicted both the presence and degree of four biases: confirmation, anchoring and adjustment, representativeness, and probability matching. It also favorably predicted human performance in generating probability distributions across categories, assigning resources based on these distributions, and selecting relevant features given a prior probability distribution. This model provides a constrained theoretical framework describing cognitive biases as arising from three interacting factors: the structure of the task environment, the mechanisms and limitations of the cognitive architecture, and the use of strategies to adapt to the dual constraints of cognition and the environment. PMID:24302930
The Ecosystem Functions Model: A Tool for Restoration Planning
2004-09-01
for 1) investigating cumulative impacts that tend to plague watershed studies – the EFM’s ability to act in a complementary role for reservoir ... simulation and river hydraulics models makes technical support for projects more holistic, 2) improving knowledge transfer between restoration projects
NASA Astrophysics Data System (ADS)
Mukhin, Evgeny; Tarasov, Vitaly; Varchenko, Alexander
2011-10-01
Consider a tensor product of finite-dimensional irreducible ??;N+1-modules and its decomposition into irreducible modules. The ??;N+1 Gaudin model assigns to each multiplicity space of that decomposition a commutative (Bethe) algebra of linear operators acting on the multiplicity space. The Bethe ansatz method is a method to find eigenvectors and eigenvalues of the Bethe algebra. One starts with a critical point of a suitable (master) function and constructs an eigenvector of the Bethe algebra. In this paper we consider the algebra of functions on the critical set of the associated master function and show that the action of this algebra on itself is isomorphic to the action of the Bethe algebra on a suitable subspace of the multiplicity space. As a byproduct we prove that the Bethe vectors corresponding to different critical points of the master function are linearly independent and, in particular, nonzero.
NASA Astrophysics Data System (ADS)
Yang, Wu; Liu, Li; Zhou, Si-Da; Ma, Zhi-Sai
2015-10-01
This work proposes a Moving Kriging (MK) shape function modeling method for modal identification of linear time-varying (LTV) structural systems based on vector time-dependent autoregressive moving average (VTARMA) models. It aims to avoid the functional subspaces selection of the conventional functional series VTARMA (FS-VTARMA) models. Instead of the common basis functions, it constructs the time-varying coefficients on the time nodes with the MK shape functions in a compact support domain. The merit of the MK shape function is to determine its shape parameters upon vector random vibration signals adaptively. Model identification is effectively dealt with through an optimization scheme that decomposes the identification problem into two subproblems: estimating model parameters via two-stage least squares (2SLS) method and estimating shape function parameters via a discrete-continuous-variable hybrid optimization. In addition, the model order selection is achieved by the optimization scheme. This method has been validated by a Monte Carlo study of simulation case and further by an experimental test case, and the performance and potential advantages are illustrated.
Modeling the two-point correlation of the vector stream function
NASA Technical Reports Server (NTRS)
Oberlack, M.; Rogers, M. M.; Reynolds, W. C.
1994-01-01
A new model for the two-point vector stream function correlation has been developed using tensor invariant arguments and evaluated by the comparison of model predictions with DNS data for incompressible homogeneous turbulent shear flow. This two-point vector stream function model correlation can then be used to calculate the two-point velocity correlation function and other quantities useful in turbulence modeling. The model assumes that the two-point vector stream function correlation can be written in terms of the separation vector and a new tensor function that depends only on the magnitude of the separation vector. The model has a single free model coefficient, which has been chosen by comparison with the DNS data. The relative error of the model predictions of the two-point vector stream function correlation is only a few percent for a broad range of the model coefficient. Predictions of the derivatives of this correlation, which are of interest in turbulence modeling, may not be this accurate.
Knock-Out Models Reveal New Aquaporin Functions
Verkman, Alan S.
2013-01-01
Knockout mice have been informative in the discovery of unexpected biological functions of aquaporins. Knockout mice have confirmed the predicted roles of aquaporins in transepithelial fluid transport, as in the urinary concentrating mechanism and glandular fluid secretion. A less obvious, though predictable role of aquaporins is in tissue swelling under stress, as in the brain in stroke, tumor and infection. Phenotype analysis of aquaporin knockout mice has revealed several unexpected cellular roles of aquaporins whose mechanisms are being elucidated. Aquaporins facilitate cell migration, as seen in aquaporin-dependent tumor angiogenesis and tumor metastasis, by a mechanism that may involve facilitated water transport in lamellipodia of migrating cells. The ‘aquaglyceroporins’, aquaporins that transport both glycerol and water, regulate glycerol content in epidermis, fat and other tissues, and lead to a multiplicity of interesting consequences of gene disruption including dry skin, resistance to skin carcinogenesis, impaired cell proliferation and altered fat metabolism. An even more surprising role of a mammalian aquaporin is in neural signal transduction in the central nervous system. The many roles of aquaporins might be exploited for clinical benefit by modulation of aquaporin expression/function – as diuretics, and in the treatment of brain swelling, glaucoma, epilepsy, obesity and cancer. PMID:19096787
Behavioral Analyses of Taste Function and Ingestion in Rodent Models
Spector, Alan C.
2015-01-01
In 1975, at the start of my junior year in college, I took a course on experimental methods in psychology from Dr. James C. Smith, when he was a Visiting Professor at Penn State University. That experience set me on the professional path of studying the neural bases of taste function and ingestion on which I remain to this day. Along the way, I did my graduate work at Florida State University under the tutelage of Jim, I did my postdoctoral training at the University of Pennsylvania under the supervision of Harvey Grill, and I also worked closely with Ralph Norgren, who was at the Penn State Medical College. This article briefly summarizes some of the lessons I learned from my mentors and highlights a few key research findings arising from my privilege of working with gifted students and postdocs. After close to 40 years of being a student of the gustatory system and ingestive behavior, it is still with the greatest conviction that I believe rigorous analysis of behavior is indispensable to any effort seeking to understand brain function. PMID:25892670
Natural Interaction Metaphors for Functional Validations of Virtual Car Models.
Moehring, Mathias; Froehlich, Bernd
2011-09-01
Natural Interaction in virtual environments is a key requirement for the virtual validation of functional aspects in automotive product development processes. Natural Interaction is the metaphor people encounter in reality: the direct manipulation of objects by their hands. To enable this kind of Natural Interaction, we propose a pseudophysical metaphor that is both plausible enough to provide realistic interaction and robust enough to meet the needs of industrial applications. Our analysis of the most common types of objects in typical automotive scenarios guided the development of a set of refined grasping heuristics to support robust finger-based interaction of multiple hands and users. The objects' behavior in reaction to the users' finger motions is based on pseudophysical simulations, which also take various types of constrained objects into account. In dealing with real-world scenarios, we had to introduce the concept of Normal Proxies, which extend objects with appropriate normals for improved grasp detection and grasp stability. An expert review revealed that our interaction metaphors allow for an intuitive and reliable assessment of several functionalities of objects found in a car interior. Follow-up user studies showed that overall task performance and usability are similar for CAVE and HMD environments. For larger objects and more gross manipulation, using the CAVE without employing a virtual hand representation is preferred, but for more fine-grained manipulation and smaller objects, the HMD turns out to be beneficial.
Functional mathematical model of dual pathway AV nodal conduction.
Climent, A M; Guillem, M S; Zhang, Y; Millet, J; Mazgalev, T N
2011-04-01
Dual atrioventricular (AV) nodal pathway physiology is described as two different wave fronts that propagate from the atria to the His bundle: one with a longer effective refractory period [fast pathway (FP)] and a second with a shorter effective refractory period [slow pathway (SP)]. By using His electrogram alternance, we have developed a mathematical model of AV conduction that incorporates dual AV nodal pathway physiology. Experiments were performed on five rabbit atrial-AV nodal preparations to develop and test the presented model. His electrogram alternances from the inferior margin of the His bundle were used to identify fast and slow wave front propagations. The ability to predict AV conduction time and the interaction between FP and SP wave fronts have been analyzed during regular and irregular atrial rhythms (e.g., atrial fibrillation). In addition, the role of dual AV nodal pathway wave fronts in the generation of Wenckebach periodicities has been illustrated. Finally, AV node ablative modifications have been evaluated. The model accurately reproduced interactions between FP and SP during regular and irregular atrial pacing protocols. In all experiments, specificity and sensitivity higher than 85% were obtained in the prediction of the pathway responsible for conduction. It has been shown that, during atrial fibrillation, the SP ablation significantly increased the mean HH interval (204 ± 39 vs. 274 ± 50 ms, P < 0.05), whereas FP ablation did not produce significant slowing of ventricular rate. The presented mathematical model can help in understanding some of the intriguing AV node mechanisms and should be considered as a step forward in the studies of AV nodal conduction.
Functional Decomposition of Modeling and Simulation Terrain Database Generation Process
2008-09-19
Department of the Army position unless so designated by other authorized documents. DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE...with ArcGIS by Environmental Systems Research Institute ( ESRI ) and TerraTools by TerraSim, respec- tively. ER D C /TEC SR -08-1 5...Common Data Model Framework (CDMF) contains a set of tools for creating and analyzing EDMs. CDMF is a government-off-the-shelf technology designed and
Emissivity as a Function of Surface Roughness: A Computer Model.
1986-08-29
dependance on surface roughness sheds some light on ship wake measurements (8] , and corrects some of the analysis of spatial sea surface temperature...variation recently reported in (6) . The wind wave spectral dependance of surface emissivity also indicates that shorter wavelengths, such as...definition, a power spectrum contains no phase dependance . Therefore, in order to create a reasonable model of the surface elevation, we assume that the
A discriminant function model for admission at undergraduate university level
NASA Astrophysics Data System (ADS)
Ali, Hamdi F.; Charbaji, Abdulrazzak; Hajj, Nada Kassim
1992-09-01
The study is aimed at predicting objective criteria based on a statistically tested model for admitting undergraduate students to Beirut University College. The University is faced with a dual problem of having to select only a fraction of an increasing number of applicants, and of trying to minimize the number of students placed on academic probation (currently 36 percent of new admissions). Out of 659 new students, a sample of 272 students (45 percent) were selected; these were all the students on the Dean's list and on academic probation. With academic performance as the dependent variable, the model included ten independent variables and their interactions. These variables included the type of high school, the language of instruction in high school, recommendations, sex, academic average in high school, score on the English Entrance Examination, the major in high school, and whether the major was originally applied for by the student. Discriminant analysis was used to evaluate the relative weight of the independent variables, and from the analysis three equations were developed, one for each academic division in the College. The predictive power of these equations was tested by using them to classify students not in the selected sample into successful and unsuccessful ones. Applicability of the model to other institutions of higher learning is discussed.
Modeled Microgravity Affects Fibroblast Functions Related to Wound Healing
NASA Astrophysics Data System (ADS)
Cialdai, Francesca; Vignali, Leonardo; Morbidelli, Lucia; Colciago, Alessandra; Celotti, Fabio; Santi, Alice; Caselli, Anna; Cirri, Paolo; Monici, Monica
2017-02-01
Wound healing is crucial for the survival of an organism. Therefore, in the perspective of space exploration missions, it is important to understand if and how microgravity conditions affect the behavior of the cell populations involved in wound healing and the evolution of the process. Since fibroblasts are the major players in tissue repair, this study was focused on the behavior of fibroblasts in microgravity conditions, modeled by a RCCS. Cell cytoskeleton was studied by immunofluorescence microscopy, the ability to migrate was assessed by microchemotaxis and scratch assay, and the expression of markers of fibroblast activation, angiogenesis, and inflammation was assessed by western blot. Results revealed that after cell exposure to modeled microgravity conditions, a thorough rearrangement of microtubules occurred and α-SMA bundles were replaced by a tight network of faulty and disorganized filaments. Exposure to modeled microgravity induced a decrease in α-SMA and E-CAD expressions. Also, the expression of the pro-angiogenic protein VEGF decreased, while that of the inflammatory signal COX-2 increased. Fibroblast ability to adhere, migrate, and respond to chemoattractants (PRP), closely related to cytoskeleton integrity and membrane junctions, was significantly impaired. Nevertheless, PRP was able to partially restore fibroblast migration.
Modeled Microgravity Affects Fibroblast Functions Related to Wound Healing
NASA Astrophysics Data System (ADS)
Cialdai, Francesca; Vignali, Leonardo; Morbidelli, Lucia; Colciago, Alessandra; Celotti, Fabio; Santi, Alice; Caselli, Anna; Cirri, Paolo; Monici, Monica
2017-01-01
Wound healing is crucial for the survival of an organism. Therefore, in the perspective of space exploration missions, it is important to understand if and how microgravity conditions affect the behavior of the cell populations involved in wound healing and the evolution of the process. Since fibroblasts are the major players in tissue repair, this study was focused on the behavior of fibroblasts in microgravity conditions, modeled by a RCCS. Cell cytoskeleton was studied by immunofluorescence microscopy, the ability to migrate was assessed by microchemotaxis and scratch assay, and the expression of markers of fibroblast activation, angiogenesis, and inflammation was assessed by western blot. Results revealed that after cell exposure to modeled microgravity conditions, a thorough rearrangement of microtubules occurred and α-SMA bundles were replaced by a tight network of faulty and disorganized filaments. Exposure to modeled microgravity induced a decrease in α-SMA and E-CAD expressions. Also, the expression of the pro-angiogenic protein VEGF decreased, while that of the inflammatory signal COX-2 increased. Fibroblast ability to adhere, migrate, and respond to chemoattractants (PRP), closely related to cytoskeleton integrity and membrane junctions, was significantly impaired. Nevertheless, PRP was able to partially restore fibroblast migration.
Assessment of models for pedestrian dynamics with functional principal component analysis
NASA Astrophysics Data System (ADS)
Chraibi, Mohcine; Ensslen, Tim; Gottschalk, Hanno; Saadi, Mohamed; Seyfried, Armin
2016-06-01
Many agent based simulation approaches have been proposed for pedestrian flow. As such models are applied e.g. in evacuation studies, the quality and reliability of such models is of vital interest. Pedestrian trajectories are functional data and thus functional principal component analysis is a natural tool to assess the quality of pedestrian flow models beyond average properties. In this article we conduct functional Principal Component Analysis (PCA) for the trajectories of pedestrians passing through a bottleneck. In this way it is possible to assess the quality of the models not only on basis of average values but also by considering its fluctuations. We benchmark two agent based models of pedestrian flow against the experimental data using PCA average and stochastic features. Functional PCA proves to be an efficient tool to detect deviation between simulation and experiment and to assess quality of pedestrian models.
Vanoirbeek, Jeroen A J; Rinaldi, Manuela; De Vooght, Vanessa; Haenen, Steven; Bobic, Sonja; Gayan-Ramirez, Ghislaine; Hoet, Peter H M; Verbeken, Erik; Decramer, Marc; Nemery, Benoit; Janssens, Wim
2010-01-01
Pulmonary function analysis is an important tool in the evaluation of mouse respiratory disease models, but much controversy still exists on the validity of some tests. Most commonly used pulmonary function variables of humans are not routinely applied in mice, and the question of which pulmonary function is optimal for the monitoring of a particular disease model remains largely unanswered. Our study aimed to delineate the potential and restrictions of existing pulmonary function techniques in different respiratory disease models, and to determine some common variables between humans and mice. A noninvasive (unrestrained plethysmography) and two invasive pulmonary function devices (forced maneuvers system from Buxco Research Systems [Wilmington, NC] and forced oscillation technique from SCIREQ [Montreal, PQ, Canada]) were evaluated in well-established models of asthma (protein and chemical induced): a model of elastase-induced pulmonary emphysema, and a model of bleomycin-induced pulmonary fibrosis. In contrast to noninvasive tests, both invasive techniques were efficacious for the quantification of parenchymal disease via changes in functional residual capacity, total lung capacity, vital capacity, and compliance of the respiratory system. Airflow obstruction and airflow limitation at baseline were only present in emphysema, but could be significantly induced after methacholine challenge in mice with asthma, which correlated best with an increase of respiratory resistance. Invasive pulmonary functions allow distinction between respiratory diseases in mice by clinically relevant variables, and should become standard in the functional evaluation of pathological disease models.
Performance Evaluation of Color Models in the Fusion of Functional and Anatomical Images.
Ganasala, Padma; Kumar, Vinod; Prasad, A D
2016-05-01
Fusion of the functional image with an anatomical image provides additional diagnostic information. It is widely used in diagnosis, treatment planning, and follow-up of oncology. Functional image is a low-resolution pseudo color image representing the uptake of radioactive tracer that gives the important metabolic information. Whereas, anatomical image is a high-resolution gray scale image that gives structural details. Fused image should consist of all the anatomical details without any changes in the functional content. This is achieved through fusion in de-correlated color model and the choice of color model has greater impact on the fusion outcome. In the present work, suitability of different color models for functional and anatomical image fusion is studied. After converting the functional image into de-correlated color model, the achromatic component of functional image is fused with an anatomical image by using proposed nonsubsampled shearlet transform (NSST) based image fusion algorithm to get new achromatic component with all the anatomical details. This new achromatic and original chromatic channels of functional image are converted to RGB format to get fused functional and anatomical image. Fusion is performed in different color models. Different cases of SPECT-MRI images are used for this color model study. Based on visual and quantitative analysis of fused images, the best color model for the stated purpose is determined.
Walraven, Jeremy Allen; Blecke, Jill; Baker, Michael Sean; Clemens, Rebecca C.; Mitchell, John Anthony; Brake, Matthew Robert; Epp, David S.; Wittwer, Jonathan W.
2008-10-01
This report summarizes the functional, model validation, and technology readiness testing of the Sandia MEMS Passive Shock Sensor in FY08. Functional testing of a large number of revision 4 parts showed robust and consistent performance. Model validation testing helped tune the models to match data well and identified several areas for future investigation related to high frequency sensitivity and thermal effects. Finally, technology readiness testing demonstrated the integrated elements of the sensor under realistic environments.
1983-06-01
REPORT HL-83-10 0 US-Army Corps .FUNCTIONAL DESIGN OF CONTROL STRUCTURES FOR OREGON INLET, NORTH CAROLINA Hydraulic Model Investigation TI. by Noel W...purpose of the functional model was to investigate flow control characteristics of the proposed jetty system. Important design parameters and other...above design considerations were investigated with a combina- tion fixed-bed and movable-bed physical hydraulic model molded to the bathymetry of the
Model calculations of the Sivers function satisfying the Burkardt sum rule
Courtoy, A.; Vento, V.; Scopetta, S.
2009-04-01
It is shown that, at variance with previous analyses, the MIT bag model can explain the available data of the Sivers function and satisfies the Burkardt sum rule to a few percent accuracy. The agreement is similar to the one recently found in the constituent quark model. Therefore, these two model calculations of the Sivers function are in agreement with the present experimental and theoretical wisdom.
Functional imaging of tumor vascular network in small animal models
NASA Astrophysics Data System (ADS)
Kalchenko, Vyacheslav; Madar-Balakirski, Noa; Kuznetsov, Yuri; Meglinski, Igor; Harmelin, Alon
2011-07-01
In current report we present synchronized in vivo imaging of tumor vascular network and tumor microenvironment obtained by combined use of Dynamic Light Scattering Imaging, Spectrally Enhanced Microscopy, and Fluorescence Intravital Microscopy. Dynamic Light Scattering Imaging is used for functional imaging of the vascular network and blood microcirculation. Spectrally Enhanced Microscopy provides information regarding blood vessel topography. Fluorescence Intravital Microscopy is used for imaging of tumor microvasculature and tumor microenvironment. These well known modalities have been comprehensively validated in the past and are widely used in various bio-medical applications. As shown here, their combined application has great potential for studies of vascular biology. This multi-modal non-invasive diagnostic technique expands our current capacity to investigate blood microcirculation and tumor angiogenesis in vivo, thereby contributing to the development of cancer research and treatment.
Coupled cluster Green function: Model involving single and double excitations
Bhaskaran-Nair, Kiran; Kowalski, Karol; Shelton, William A.
2016-04-14
In this paper we report on the parallel implementation of the coupled-cluster (CC) Green function formulation (GF-CC) employing single and double excitations in the cluster operator (GF-CCSD). The detailed description of the underlying algorithm is provided, including the structure of ionization-potential- and electron-affinity-type intermediate tensors which enable to formulate GF-CC approach in a computationally feasible form. Several examples including calculations of ionization-potentials and electron a*ffinities for benchmark systems, which are juxtaposed against the experimental values, provide an illustration of the accuracies attainable in the GFCCSD simulations. We also discuss the structure of the CCSD self energies and discuss approximation that are geared to reduce the computational cost while maintaining the pole structure of the full GF-CCSD approach.
Configurational study of amino-functionalized silica surfaces: A density functional theory modeling.
Hozhabr Araghi, Samira; Entezari, Mohammad H; Sadeghi Googheri, Mohammad Sadegh
2015-06-01
Despite extensive studies of the amino-functionalized silica surfaces, a comprehensive investigation of the effects of configuration and hydrolysis of 3-aminopropyltriethoxysilan (APTES) molecules attached on silica has not been studied yet. Therefore, the methods of quantum mechanics were used for the study of configuration and hydrolysis forms of APTES molecules attached on the surface. For this purpose, five different categories based on the number of hydrolyzed ethoxy groups including 16 configurations were designed and analyzed by the density functional theory (DFT) method. The steric hindrance as an effective factor on the stability order was extracted from structural analysis. Other impressive parameters such as the effects of hydrogen bond and electron delocalization energy were obtained by using the atoms in molecules (AIM) and natural bond orbitals (NBO) theories. Consequently, it was found that the stability of configurations was attributed to steric effects, hydrogen bond numbers and electron delocalization energy. The maximum stability was achieved when at least two of these parameters cooperate with each other.
Integrated Medical Model (IMM) 4.0 Enhanced Functionalities
NASA Technical Reports Server (NTRS)
Young, M.; Keenan, A. B.; Saile, L.; Boley, L. A.; Walton, M. E.; Shah, R. V.; Kerstman, E. L.; Myers, J. G.
2015-01-01
The Integrated Medical Model is a probabilistic simulation model that uses input data on 100 medical conditions to simulate expected medical events, the resources required to treat, and the resulting impact to the mission for specific crew and mission characteristics. The newest development version of IMM, IMM v4.0, adds capabilities that remove some of the conservative assumptions that underlie the current operational version, IMM v3. While IMM v3 provides the framework to simulate whether a medical event occurred, IMMv4 also simulates when the event occurred during a mission timeline. This allows for more accurate estimation of mission time lost and resource utilization. In addition to the mission timeline, IMMv4.0 features two enhancements that address IMM v3 assumptions regarding medical event treatment. Medical events in IMMv3 are assigned the untreated outcome if any resource required to treat the event was unavailable. IMMv4 allows for partially treated outcomes that are proportional to the amount of required resources available, thus removing the dichotomous treatment assumption. An additional capability IMMv4 is to use an alternative medical resource when the primary resource assigned to the condition is depleted, more accurately reflecting the real-world system. The additional capabilities defining IMM v4.0the mission timeline, partial treatment, and alternate drug result in more realistic predicted mission outcomes. The primary model outcomes of IMM v4.0 for the ISS6 mission, including mission time lost, probability of evacuation, and probability of loss of crew life, are be compared to those produced by the current operational version of IMM to showcase enhanced prediction capabilities.
SysML model of exoplanet archive functionality and activities
NASA Astrophysics Data System (ADS)
Ramirez, Solange
2016-08-01
The NASA Exoplanet Archive is an online service that serves data and information on exoplanets and their host stars to help astronomical research related to search for and characterization of extra-solar planetary systems. In order to provide the most up to date data sets to the users, the exoplanet archive performs weekly updates that include additions into the database and updates to the services as needed. These weekly updates are complex due to interfaces within the archive. I will be presenting a SysML model that helps us perform these update activities in a weekly basis.
Nano-Transistor Modeling: Two Dimensional Green's Function Method
NASA Technical Reports Server (NTRS)
Svizhenko, Alexei; Anantram, M. P.; Govindan, T. R.; Biegel, Bryan
2001-01-01
Two quantum mechanical effects that impact the operation of nanoscale transistors are inversion layer energy quantization and ballistic transport. While the qualitative effects of these features are reasonably understood, a comprehensive study of device physics in two dimensions is lacking. Our work addresses this shortcoming and provides: (a) a framework to quantitatively explore device physics issues such as the source-drain and gate leakage currents, DIBL (Drain Induced Barrier Lowering), and threshold voltage shift due to quantization, and b) a means of benchmarking quantum corrections to semiclassical models (such as density-gradient and quantum-corrected MEDICI).
Aircraft/Air Traffic Management Functional Analysis Model. Version 2.0; User's Guide
NASA Technical Reports Server (NTRS)
Etheridge, Melvin; Plugge, Joana; Retina, Nusrat
1998-01-01
The Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 (FAM 2.0), is a discrete event simulation model designed to support analysis of alternative concepts in air traffic management and control. FAM 2.0 was developed by the Logistics Management Institute (LMI) a National Aeronautics and Space Administration (NASA) contract. This document provides a guide for using the model in analysis. Those interested in making enhancements or modification to the model should consult the companion document, Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 Technical Description.
Quantifying functional connectivity in multi-subject fMRI data using component models.
Madsen, Kristoffer H; Churchill, Nathan W; Mørup, Morten
2017-02-01
Functional magnetic resonance imaging (fMRI) is increasingly used to characterize functional connectivity between brain regions. Given the vast number of between-voxel interactions in high-dimensional fMRI data, it is an ongoing challenge to detect stable and generalizable functional connectivity in the brain among groups of subjects. Component models can be used to define subspace representations of functional connectivity that are more interpretable. It is, however, unclear which component model provides the optimal representation of functional networks for multi-subject fMRI datasets. A flexible cross-validation approach that assesses the ability of the models to predict voxel-wise covariance in new data, using three different measures of generalization was proposed. This framework is used to compare a range of component models with varying degrees of flexibility in their representation of functional connectivity, evaluated on both simulated and experimental resting-state fMRI data. It was demonstrated that highly flexible subject-specific component subspaces, as well as very constrained average models, are poor predictors of whole-brain functional connectivity, whereas the best-generalizing models account for subject variability within a common spatial subspace. Within this set of models, spatial Independent Component Analysis (sICA) on concatenated data provides more interpretable brain patterns, whereas a consistent-covariance model that accounts for subject-specific network scaling (PARAFAC2) provides greater stability in functional connectivity relationships between components and their spatial representations. The proposed evaluation framework is a promising quantitative approach to evaluating component models, and reveals important differences between subspace models in terms of predictability, robustness, characterization of subject variability, and interpretability of the model parameters. Hum Brain Mapp 38:882-899, 2017. © 2016 Wiley Periodicals, Inc.
Side-branch resonators modelling with Green's function methods
NASA Astrophysics Data System (ADS)
Perrey-Debain, E.; Maréchal, R.; Ville, J. M.
2014-09-01
This paper deals with strategies for computing efficiently the propagation of sound waves in ducts containing passive components. In many cases of practical interest, these components are acoustic cavities which are connected to the duct. Though standard Finite Element software could be used for the numerical prediction of sound transmission through such a system, the method is known to be extremely demanding, both in terms of data preparation and computation, especially in the mid-frequency range. To alleviate this, a numerical technique that exploits the benefit of the FEM and the BEM approach has been devised. First, a set of eigenmodes is computed in the cavity to produce a numerical impedance matrix connecting the pressure and the acoustic velocity on the duct wall interface. Then an integral representation for the acoustic pressure in the main duct is used. By choosing an appropriate Green's function for the duct, the integration procedure is limited to the duct-cavity interface only. This allows an accurate computation of the scattering matrix of such an acoustic system with a numerical complexity that grows very mildly with the frequency. Typical applications involving Helmholtz and Herschel-Quincke resonators are presented.
A Note on the Item Information Function of the Four-Parameter Logistic Model
ERIC Educational Resources Information Center
Magis, David
2013-01-01
This article focuses on four-parameter logistic (4PL) model as an extension of the usual three-parameter logistic (3PL) model with an upper asymptote possibly different from 1. For a given item with fixed item parameters, Lord derived the value of the latent ability level that maximizes the item information function under the 3PL model. The…
ERIC Educational Resources Information Center
Matzke, Orville R.
The purpose of this study was to formulate a linear programming model to simulate a foundation type support program and to apply this model to a state support program for the public elementary and secondary school districts in the State of Iowa. The model was successful in producing optimal solutions to five objective functions proposed for…
ERIC Educational Resources Information Center
Patarapichayatham, Chalie; Kamata, Akihito; Kanjanawasee, Sirichai
2012-01-01
Model specification issues on the cross-level two-way differential item functioning model were previously investigated by Patarapichayatham et al. (2009). Their study clarified that an incorrect model specification can easily lead to biased estimates of key parameters. The objective of this article is to provide further insights on the issue by…
Two Models for Exploring the Anti-predator Function of Eyespots.
ERIC Educational Resources Information Center
Cundy, J. M.; Allen, J. A.
1988-01-01
Describes two working models of moths that can be used to test the eyespot function as an aversive property. Uses simple mechanical and electrical models as the "moths" and wild birds as the predators. Includes diagrams, methods, models, validation statements, and a discussion. (RT)
Reduced Rank Mixed Effects Models for Spatially Correlated Hierarchical Functional Data
Zhou, Lan; Huang, Jianhua Z.; Martinez, Josue G.; Maity, Arnab; Baladandayuthapani, Veerabhadran; Carroll, Raymond J.
2010-01-01
SUMMARY Hierarchical functional data are widely seen in complex studies where sub-units are nested within units, which in turn are nested within treatment groups. We propose a general framework of functional mixed effects model for such data: within unit and within sub-unit variations are modeled through two separate sets of principal components; the sub-unit level functions are allowed to be correlated. Penalized splines are used to model both the mean functions and the principal components functions, where roughness penalties are used to regularize the spline fit. An EM algorithm is developed to fit the model, while the specific covariance structure of the model is utilized for computational efficiency to avoid storage and inversion of large matrices. Our dimension reduction with principal components provides an effective solution to the difficult tasks of modeling the covariance kernel of a random function and modeling the correlation between functions. The proposed methodology is illustrated using simulations and an empirical data set from a colon carcinogenesis study. Supplemental materials are available online. PMID:20396628
Model approach to starch functionality in bread making.
Goesaert, Hans; Leman, Pedro; Delcour, Jan A
2008-08-13
We used modified wheat starches in gluten-starch flour models to study the role of starch in bread making. Incorporation of hydroxypropylated starch in the recipe reduced loaf volume and initial crumb firmness and increased crumb gas cell size. Firming rate and firmness after storage increased for loaves containing the least hydroxypropylated starch. Inclusion of cross-linked starch had little effect on loaf volume or crumb structure but increased crumb firmness. The firming rate was mostly similar to that of control samples. Presumably, the moment and extent of starch gelatinization and the concomitant water migration influence the structure formation during baking. Initial bread firmness seems determined by the rigidity of the gelatinized granules and leached amylose. Amylopectin retrogradation and strengthening of a long-range network by intensifying the inter- and intramolecular starch-starch and possibly also starch-gluten interactions (presumably because of water incorporation in retrograded amylopectin crystallites) play an important role in firming.
Molecular modelling of miraculin: Structural analyses and functional hypotheses.
Paladino, Antonella; Costantini, Susan; Colonna, Giovanni; Facchiano, Angelo M
2008-02-29
Miraculin is a plant protein that displays the peculiar property of modifying taste by swiching sour into a sweet taste. Its monomer is flavourless at all pH as well as at high concentration; the dimer form elicits its taste-modifying activity at acidic pH; a tetrameric form is also reported as active. Two histidine residues, located in exposed regions, are the main responsible of miraculin activity, as demonstrated by mutagenesis studies. Since structural data of miraculin are not available, we have predicted its three-dimensional structure and simulated both its dimer and tetramer forms by comparative modelling and molecular docking techniques. Finally, molecular dynamics simulations at different pH conditions have indicated that at acidic pH the dimer assumes a widely open conformation, in agreement with the hypotheses coming from other studies.
Dong, Leihua; Xiong, Lihua; Zheng, Yanfeng
2013-01-01
Three different hydrological models are chosen to simulate rainfall-runoff relationships under each of three objective functions including mean squared errors of squared transformed flows, squared root transformed flows and logarithmic transformed flows; thus nine individual models are constructed. By weighted averaging over these nine models, the method of Bayesian model averaging (BMA) was used to provide both the mean value and the uncertainty intervals of flow prediction. Three kinds of uncertainty information can be generated: the uncertainty of individual member model's predictions; the total uncertainty of BMA mean prediction; the between-model and within-model uncertainties in the BMA scheme. Based on the estimated results in this study, the coupling of multiple models with multiple objective functions in general offers better results for both the mean prediction and the uncertainty intervals for the runoffs in a selected basin in Han River, China, than the individual models.
NASA Astrophysics Data System (ADS)
Wirth, Erin A.; Long, Maureen D.; Moriarty, John C.
2016-10-01
Teleseismic receiver functions contain information regarding Earth structure beneath a seismic station. P-to-SV converted phases are often used to characterize crustal and upper mantle discontinuities and isotropic velocity structures. More recently, P-to-SH converted energy has been used to interrogate the orientation of anisotropy at depth, as well as the geometry of dipping interfaces. Many studies use a trial-and-error forward modeling approach to the interpretation of receiver functions, generating synthetic receiver functions from a user-defined input model of Earth structure and amending this model until it matches major features in the actual data. While often successful, such an approach makes it impossible to explore model space in a systematic and robust manner, which is especially important given that solutions are likely non-unique. Here, we present a Markov chain Monte Carlo algorithm with Gibbs sampling for the interpretation of anisotropic receiver functions. Synthetic examples are used to test the viability of the algorithm, suggesting that it works well for models with a reasonable number of free parameters (< ˜20). Additionally, the synthetic tests illustrate that certain parameters are well constrained by receiver function data, while others are subject to severe tradeoffs - an important implication for studies that attempt to interpret Earth structure based on receiver function data. Finally, we apply our algorithm to receiver function data from station WCI in the central United States. We find evidence for a change in anisotropic structure at mid-lithospheric depths, consistent with previous work that used a grid search approach to model receiver function data at this station. Forward modeling of receiver functions using model space search algorithms, such as the one presented here, provide a meaningful framework for interrogating Earth structure from receiver function data.
NASA Astrophysics Data System (ADS)
Wirth, Erin A.; Long, Maureen D.; Moriarty, John C.
2017-01-01
Teleseismic receiver functions contain information regarding Earth structure beneath a seismic station. P-to-SV converted phases are often used to characterize crustal and upper-mantle discontinuities and isotropic velocity structures. More recently, P-to-SH converted energy has been used to interrogate the orientation of anisotropy at depth, as well as the geometry of dipping interfaces. Many studies use a trial-and-error forward modeling approach for the interpretation of receiver functions, generating synthetic receiver functions from a user-defined input model of Earth structure and amending this model until it matches major features in the actual data. While often successful, such an approach makes it impossible to explore model space in a systematic and robust manner, which is especially important given that solutions are likely non-unique. Here, we present a Markov chain Monte Carlo algorithm with Gibbs sampling for the interpretation of anisotropic receiver functions. Synthetic examples are used to test the viability of the algorithm, suggesting that it works well for models with a reasonable number of free parameters (<˜20). Additionally, the synthetic tests illustrate that certain parameters are well constrained by receiver function data, while others are subject to severe trade-offs-an important implication for studies that attempt to interpret Earth structure based on receiver function data. Finally, we apply our algorithm to receiver function data from station WCI in the central United States. We find evidence for a change in anisotropic structure at mid-lithospheric depths, consistent with previous work that used a grid search approach to model receiver function data at this station. Forward modeling of receiver functions using model space search algorithms, such as the one presented here, provide a meaningful framework for interrogating Earth structure from receiver function data.
Berhane, Kiros; Molitor, Nuoo-Ting
2008-10-01
Flexible multilevel models are proposed to allow for cluster-specific smooth estimation of growth curves in a mixed-effects modeling format that includes subject-specific random effects on the growth parameters. Attention is then focused on models that examine between-cluster comparisons of the effects of an ecologic covariate of interest (e.g. air pollution) on nonlinear functionals of growth curves (e.g. maximum rate of growth). A Gibbs sampling approach is used to get posterior mean estimates of nonlinear functionals along with their uncertainty estimates. A second-stage ecologic random-effects model is used to examine the association between a covariate of interest (e.g. air pollution) and the nonlinear functionals. A unified estimation procedure is presented along with its computational and theoretical details. The models are motivated by, and illustrated with, lung function and air pollution data from the Southern California Children's Health Study.
Prestat, Emmanuel; David, Maude M.; Hultman, Jenni; Ta , Neslihan; Lamendella, Regina; Dvornik, Jill; Mackelprang, Rachel; Myrold, David D.; Jumpponen, Ari; Tringe, Susannah G.; Holman, Elizabeth; Mavromatis, Konstantinos; Jansson, Janet K.
2014-09-26
A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. ‘profiles’) were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associated functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/.
Employee subjective well-being and physiological functioning: An integrative model.
Kuykendall, Lauren; Tay, Louis
2015-01-01
Research shows that worker subjective well-being influences physiological functioning-an early signal of poor health outcomes. While several theoretical perspectives provide insights on this relationship, the literature lacks an integrative framework explaining the relationship. We develop a conceptual model explaining the link between subjective well-being and physiological functioning in the context of work. Integrating positive psychology and occupational stress perspectives, our model explains the relationship between subjective well-being and physiological functioning as a result of the direct influence of subjective well-being on physiological functioning and of their common relationships with work stress and personal resources, both of which are influenced by job conditions.
The polarized structure function of the nucleons with a non-extensive statistical quark model
Trevisan, Luis A.; Mirez, Carlos
2013-05-06
We studied an application of nonextensive thermodynamics to describe the polarized structure function of nucleon, in a model where the usual Fermi-Dirac and Bose-Einstein energy distribution, often used in the statistical models, were replaced by the equivalent functions of the q-statistical. The parameters of the model are given by an effective temperature T, the q parameter (from Tsallis statistics), and the chemical potentials given by the corresponding up (u) and down (d) quark normalization in the nucleon and by {Delta}u and {Delta}d of the polarized functions.
New data model with better functionality for VLab
NASA Astrophysics Data System (ADS)
da Silveira, P. R.; Wentzcovitch, R. M.; Karki, B. B.
2009-12-01
The VLab infrastructure and architecture was further developed to allow for several new features. First, workflows for first principles calculations of thermodynamics properties and static elasticity programmed in Java as Web Services can now be executed by multiple users. Second, jobs generated by these workflows can now be executed in batch in multiple servers. A simple internal schedule was implemented to handle hundreds of execution packages generated by multiple users and avoid the overload on servers. Third, a new data model was implemented to guarantee integrity of a project (workflow execution) in case of failure. The latter can happen in an execution package or in a workflow phase. By recording all executed steps of a project, its execution can be resumed after dynamic alteration of parameters through the VLab Portal. Fourth, batch jobs can also be monitored through the portal. Now, better and faster interaction with servers is achieved using Ajax technology. Finally, plots are now created on the Vlab server using Gnuplot 4.2.2. Research supported by NSF grants ATM 0428774 (VLab). Vlab is hosted by the Minnesota Supercomputing Institute.
A More General Model for Testing Measurement Invariance and Differential Item Functioning.
Bauer, Daniel J
2016-06-06
The evaluation of measurement invariance is an important step in establishing the validity and comparability of measurements across individuals. Most commonly, measurement invariance has been examined using 1 of 2 primary latent variable modeling approaches: the multiple groups model or the multiple-indicator multiple-cause (MIMIC) model. Both approaches offer opportunities to detect differential item functioning within multi-item scales, and thereby to test measurement invariance, but both approaches also have significant limitations. The multiple groups model allows 1 to examine the invariance of all model parameters but only across levels of a single categorical individual difference variable (e.g., ethnicity). In contrast, the MIMIC model permits both categorical and continuous individual difference variables (e.g., sex and age) but permits only a subset of the model parameters to vary as a function of these characteristics. The current article argues that moderated nonlinear factor analysis (MNLFA) constitutes an alternative, more flexible model for evaluating measurement invariance and differential item functioning. We show that the MNLFA subsumes and combines the strengths of the multiple group and MIMIC models, allowing for a full and simultaneous assessment of measurement invariance and differential item functioning across multiple categorical and/or continuous individual difference variables. The relationships between the MNLFA model and the multiple groups and MIMIC models are shown mathematically and via an empirical demonstration. (PsycINFO Database Record
Evidence supporting the importance of microbial functional groups in decomposition models
NASA Astrophysics Data System (ADS)
Todd-Brown, K. E.; Lu, L.; Allison, S. D.
2010-12-01
Microbial communities mediate organic carbon decomposition in both soil and marine environments. Decomposition depends on microbes that produce extracellular enzymes to degrade complex organic matter, as well as microbes that mineralize simple organic matter to CO2. Therefore microbes could be represented in Earth system models as functional groups based on the extracellular enzymes they produce. However, the importance of including the functional diversity of microbes in decomposition models has been unclear. In this study we simulated microbial functional diversity with two strains of Pseudomonas fluorescens bacteria, one of which secretes extracellular protease and one that does not. These two strains were competed on several carbon resources including casein-glucose, casamino acids-glucose and glucose over several days. We then fit a series of models to the resulting data: 1) an explicit model representing both biomass and substrate pools, 2) a simplified substrate pool model with two biomass pools and one substrate pool, 3) a simplified biomass pool model with one biomass and two substrate pools, 4) a simplified biomass/substrate pool model with one biomass and one substrate pool, and 5) a single carbon pool model. We found that the explicit model (#1) fit the laboratory data significantly better than the other models, suggesting that functional groups and substrate pools should be represented in global decomposition models with time steps on the order of hours.
NASA Technical Reports Server (NTRS)
Wahba, Grace
1987-01-01
A partial spline model is a model for a response as a function of several variables, which is the sum of a smooth function of several variables and a parametric function of the same plus possibly some other variables. Partial spline models in one and several variables, with direct and indirect data, with Gaussian errors and as an extension of GLIM to partially penalized GLIM models are described. Application to the modeling of change of regime in several variables is described. Interaction splines are introduced and described and their potential use for modeling non-linear interactions between variables by semiparametric methods is noted. Reference is made to recent work in efficient computational methods.
Chen, Yongsheng; Persaud, Bhagwant
2014-09-01
Crash modification factors (CMFs) for road safety treatments are developed as multiplicative factors that are used to reflect the expected changes in safety performance associated with changes in highway design and/or the traffic control features. However, current CMFs have methodological drawbacks. For example, variability with application circumstance is not well understood, and, as important, correlation is not addressed when several CMFs are applied multiplicatively. These issues can be addressed by developing safety performance functions (SPFs) with components of crash modification functions (CM-Functions), an approach that includes all CMF related variables, along with others, while capturing quantitative and other effects of factors and accounting for cross-factor correlations. CM-Functions can capture the safety impact of factors through a continuous and quantitative approach, avoiding the problematic categorical analysis that is often used to capture CMF variability. There are two formulations to develop such SPFs with CM-Function components - fully specified models and hierarchical models. Based on sample datasets from two Canadian cities, both approaches are investigated in this paper. While both model formulations yielded promising results and reasonable CM-Functions, the hierarchical model was found to be more suitable in retaining homogeneity of first-level SPFs, while addressing CM-Functions in sub-level modeling. In addition, hierarchical models better capture the correlations between different impact factors.
NASA Astrophysics Data System (ADS)
Troitskaya, Yuliya; Abramov, Victor; Ermoshkin, Alexey; Zuikova, Emma; Kazakov, Vassily; Sergeev, Daniil; Kandaurov, Alexandr
2014-05-01
Satellite remote sensing is one of the main techniques of monitoring severe weather conditions over the ocean. The principal difficulty of the existing algorithms of retrieving wind based on dependence of microwave backscattering cross-section on wind speed (Geophysical Model Function, GMF) is due to its saturation at winds exceeding 25 - 30 m/s. Recently analysis of dual- and quad-polarization C-band radar return measured from satellite Radarsat-2 suggested that the cross-polarized radar return has much higher sensitivity to the wind speed than co-polarized back scattering [1] and conserved sensitivity to wind speed at hurricane conditions [2]. Since complete collocation of these data was not possible and time difference in flight legs and SAR images acquisition was up to 3 hours, these two sets of data were compared in [2] only statistically. The main purpose of this paper is investigation of the functional dependence of cross-polarized radar cross-section on the wind speed in laboratory experiment. Since cross-polarized radar return is formed due to scattering at small-scale structures of the air-sea interface (short-crested waves, foam, sprays, etc), which are well reproduced in laboratory conditions, then the approach based on laboratory experiment on radar scattering of microwaves at the water surface under hurricane wind looks feasible. The experiments were performed in the Wind-wave flume located on top of the Large Thermostratified Tank of the Institute of Applied Physics, where the airflow was produced in the flume with the straight working part of 10 m and operating cross section 0.40?0.40 sq. m, the axis velocity can be varied from 5 to 25 m/s. Microwave measurements were carried out by a coherent Doppler X-band (3.2 cm) scatterometer with the consequent receive of linear polarizations. Experiments confirmed higher sensitivity to the wind speed of the cross-polarized radar return. Simultaneously parameters of the air flow in the turbulent boundary layer
Theoretical model for mesoscopic-level scale-free self-organization of functional brain networks.
Piersa, Jaroslaw; Piekniewski, Filip; Schreiber, Tomasz
2010-11-01
In this paper, we provide theoretical and numerical analysis of a geometric activity flow network model which is aimed at explaining mathematically the scale-free functional graph self-organization phenomena emerging in complex nervous systems at a mesoscale level. In our model, each unit corresponds to a large number of neurons and may be roughly seen as abstracting the functional behavior exhibited by a single voxel under functional magnetic resonance imaging (fMRI). In the course of the dynamics, the units exchange portions of formal charge, which correspond to waves of activity in the underlying microscale neuronal circuit. The geometric model abstracts away the neuronal complexity and is mathematically tractable, which allows us to establish explicit results on its ground states and the resulting charge transfer graph modeling functional graph of the network. We show that, for a wide choice of parameters and geometrical setups, our model yields a scale-free functional connectivity with the exponent approaching 2, which is in agreement with previous empirical studies based on fMRI. The level of universality of the presented theory allows us to claim that the model does shed light on mesoscale functional self-organization phenomena of the nervous system, even without resorting to closer details of brain connectivity geometry which often remain unknown. The material presented here significantly extends our previous work where a simplified mean-field model in a similar spirit was constructed, ignoring the underlying network geometry.
Flexible parametric modelling of the cause-specific cumulative incidence function.
Lambert, Paul C; Wilkes, Sally R; Crowther, Michael J
2017-04-30
Competing risks arise with time-to-event data when individuals are at risk of more than one type of event and the occurrence of one event precludes the occurrence of all other events. A useful measure with competing risks is the cause-specific cumulative incidence function (CIF), which gives the probability of experiencing a particular event as a function of follow-up time, accounting for the fact that some individuals may have a competing event. When modelling the cause-specific CIF, the most common model is a semi-parametric proportional subhazards model. In this paper, we propose the use of flexible parametric survival models to directly model the cause-specific CIF where the effect of follow-up time is modelled using restricted cubic splines. The models provide smooth estimates of the cause-specific CIF with the important advantage that the approach is easily extended to model time-dependent effects. The models can be fitted using standard survival analysis tools by a combination of data expansion and introducing time-dependent weights. Various link functions are available that allow modelling on different scales and have proportional subhazards, proportional odds and relative absolute risks as particular cases. We conduct a simulation study to evaluate how well the spline functions approximate subhazard functions with complex shapes. The methods are illustrated using data from the European Blood and Marrow Transplantation Registry showing excellent agreement between parametric estimates of the cause-specific CIF and those obtained from a semi-parametric model. We also fit models relaxing the proportional subhazards assumption using alternative link functions and/or including time-dependent effects. Copyright © 2016 John Wiley & Sons, Ltd.
Operator functional state estimation based on EEG-data-driven fuzzy model.
Zhang, Jianhua; Yin, Zhong; Yang, Shaozeng; Wang, Rubin
2016-10-01
This paper proposed a max-min-entropy-based fuzzy partition method for fuzzy model based estimation of human operator functional state (OFS). The optimal number of fuzzy partitions for each I/O variable of fuzzy model is determined by using the entropy criterion. The fuzzy models were constructed by using Wang-Mendel method. The OFS estimation results showed the practical usefulness of the proposed fuzzy modeling approach.
Genetic algorithm with an improved fitness function for (N)ARX modelling
NASA Astrophysics Data System (ADS)
Chen, Q.; Worden, K.; Peng, P.; Leung, A. Y. T.
2007-02-01
In this article a new fitness function is introduced in an attempt to improve the quality of the auto-regressive with exogenous inputs (ARX) model using a genetic algorithm (GA). The GA is employed to identify the coefficients and the number of time lags of the models of dynamic systems with the new fitness function which is based on the prediction error and the correlation functions between the prediction error and the input and output signals. The new fitness function provides the GA with a better performance in the evolution process. Two examples of the ARX modelling of a linear and a non-linear (NARX) simulated dynamic system are examined using the proposed fitness function.
Capturing the essence of folding and functions of biomolecules using coarse-grained models.
Hyeon, Changbong; Thirumalai, D
2011-09-27
The distances over which biological molecules and their complexes can function range from a few nanometres, in the case of folded structures, to millimetres, for example, during chromosome organization. Describing phenomena that cover such diverse length, and also time, scales requires models that capture the underlying physics for the particular length scale of interest. Theoretical ideas, in particular, concepts from polymer physics, have guided the development of coarse-grained models to study folding of DNA, RNA and proteins. More recently, such models and their variants have been applied to the functions of biological nanomachines. Simulations using coarse-grained models are now poised to address a wide range of problems in biology.
ERIC Educational Resources Information Center
Herndon, Mary Anne
1978-01-01
In a model of the functioning of short term memory, the encoding of information for subsequent storage in long term memory is simulated. In the encoding process, semantically equivalent paragraphs are detected for recombination into a macro information unit. (HOD)
Wan, Songlin; Zhang, Xiangchao; He, Xiaoying; Xu, Min
2016-12-20
Computer controlled optical surfacing requires an accurate tool influence function (TIF) for reliable path planning and deterministic fabrication. Near the edge of the workpieces, the TIF has a nonlinear removal behavior, which will cause a severe edge-roll phenomenon. In the present paper, a new edge pressure model is developed based on the finite element analysis results. The model is represented as the product of a basic pressure function and a correcting function. The basic pressure distribution is calculated according to the surface shape of the polishing pad, and the correcting function is used to compensate the errors caused by the edge effect. Practical experimental results demonstrate that the new model can accurately predict the edge TIFs with different overhang ratios. The relative error of the new edge model can be reduced to 15%.
The Use of Haemoglobin as a Model for Teaching the Relationship Between Structure and Function
ERIC Educational Resources Information Center
Diggins, F. W. E.
1974-01-01
Presents information about an atomic model of haemoglobin and describes the oxygenation mechanism as a teaching principle to illustrate the relationship between structure and function at the molecular level. (Author/PEB)
NASA Technical Reports Server (NTRS)
Freilich, Michael H.; Dunbar, R. S.
1993-01-01
One year of global surface wind products from multiple operational numerical weather prediction (NWP) forecast/analysis systems are used as comparison data to derive an empirical wind speed model function for the Geosat altimeter. The resulting model function is nearly identical to the modified Chelton-Wentz model for wind speeds from 4.5 to 15 m/s. Highly skewed distributions at low wind speeds are consistent with specular reflections and antenna mispointing errors hypothesized by others on the basis of extremely limited data. Mesoscale variability in the wind field and synoptic scale errors in the NWP products are shown to account for about 30 percent of the observed scatter of sigma(0) at each wind speed. The remaining scatter is largest at low winds, and decreases to a nearly constant value of about 12 percent at speeds greater than 7 m/s. Model function uncertainty expressed more traditionally in units of wind speed is examined for historical model functions as well as the present NWP-based model. The historical models have significant biases at high wind speeds owing to the lack of comparison in situ data used in their construction. The present study demonstrates that modern operational NWP surface wind products are sufficiently accurate to allow development of fully empirical model functions and associated error analyses.
Ruggieri, Alexander P; Pakhomov, Serguei V; Chute, Christopher G
2004-01-01
In an effort to unearth semantic models that could prove fruitful to functional-status terminology development we applied the "frame semantic" method, derived from the linguistic theory of thematic roles currently exemplified in the Berkeley "FrameNet" Project. Full descriptive sentences with functional-status conceptual meaning were derived from structured content within a corpus of questionnaire assessment instruments commonly used in clinical practice for functional-status assessment. Syntactic components in those sentences were delineated through manual annotation and mark-up. The annotated syntactic constituents were tagged as frame elements according to their semantic role within the context of the derived functional-status expression. Through this process generalizable "semantic frames" were elaborated with recurring "frame elements". The "frame semantic" method as an approach to rendering semantic models for functional-status terminology development and its use as a basis for machine recognition of functional status data in clinical narratives are discussed.
Binder model system to be used for determination of prepolymer functionality
NASA Technical Reports Server (NTRS)
Martinelli, F. J.; Hodgkin, J. H.
1971-01-01
Development of a method for determining the functionality distribution of prepolymers used for rocket binders is discussed. Research has been concerned with accurately determining the gel point of a model polyester system containing a single trifunctional crosslinker, and the application of these methods to more complicated model systems containing a second trifunctional crosslinker, monofunctional ingredients, or a higher functionality crosslinker. Correlations of observed with theoretical gel points for these systems would allow the methods to be applied directly to prepolymers.
The Data Collection Matrix Model: A Tool for Functional Area and Program Evaluation.
ERIC Educational Resources Information Center
Coker, Dana Rosenberg; Friedel, Janice Nahra
1991-01-01
The data collection matrix makes possible the integration of functional area data from numerous assessment sources and presentation of the information in a unified composite report. This model is discussed in relation to the various assessment instruments and the evaluation of functional areas and programs in colleges and universities. (Author/MSE)
Understanding Complex Natural Systems by Articulating Structure-Behavior-Function Models
ERIC Educational Resources Information Center
Vattam, Swaroop S.; Goel, Ashok K.; Rugaber, Spencer; Hmelo-Silver, Cindy E.; Jordan, Rebecca; Gray, Steven; Sinha, Suparna
2011-01-01
Artificial intelligence research on creative design has led to Structure-Behavior-Function (SBF) models that emphasize functions as abstractions for organizing understanding of physical systems. Empirical studies on understanding complex systems suggest that novice understanding is shallow, typically focusing on their visible structures and…
Nonperturbative spectral-density function for the Anderson model at arbitrary temperatures
NASA Technical Reports Server (NTRS)
Neal, Henry L.
1991-01-01
Using a nonperturbative self-energy solution for the nondegenerate Anderson model, the temperature-dependent spectral-density function is calculated in the symmetric limit. The function is found to give reliable results for all values of the parameter u and inverse temperature beta.
A model distribution function for relativistic bi-Maxwellian with drift
Naito, O.
2013-04-15
A model distribution function for relativistic bi-Maxwellian with drift is proposed, based on the maximum entropy principle and the relativistic canonical transformation. Since the obtained expression is compatible with the existing distribution functions and has a relatively simple form as well as smoothness, it might serve as a useful tool in the research fields of space or high temperature fusion plasmas.
Training Public School Special Educators to Implement Two Functional Analysis Models
ERIC Educational Resources Information Center
Rispoli, Mandy; Neely, Leslie; Healy, Olive; Gregori, Emily
2016-01-01
The purpose of this study was to investigate the efficacy and efficiency of a training package to teach public school special educators to conduct functional analyses of challenging behavior. Six public school educators were divided into two cohorts of three and were taught two models of functional analysis of challenging behavior: traditional and…
Efficient algorithm for computing exact partition functions of lattice polymer models
NASA Astrophysics Data System (ADS)
Hsieh, Yu-Hsin; Chen, Chi-Ning; Hu, Chin-Kun
2016-12-01
Polymers are important macromolecules in many physical, chemical, biological and industrial problems. Studies on simple lattice polymer models are very helpful for understanding behaviors of polymers. We develop an efficient algorithm for computing exact partition functions of lattice polymer models, and we use this algorithm and personal computers to obtain exact partition functions of the interacting self-avoiding walks with N monomers on the simple cubic lattice up to N = 28 and on the square lattice up to N = 40. Our algorithm can be extended to study other lattice polymer models, such as the HP model for protein folding and the charged HP model for protein aggregation. It also provides references for checking accuracy of numerical partition functions obtained by simulations.
Uga, Minako; Dan, Ippeita; Sano, Toshifumi; Dan, Haruka; Watanabe, Eiju
2014-01-01
Abstract. An increasing number of functional near-infrared spectroscopy (fNIRS) studies utilize a general linear model (GLM) approach, which serves as a standard statistical method for functional magnetic resonance imaging (fMRI) data analysis. While fMRI solely measures the blood oxygen level dependent (BOLD) signal, fNIRS measures the changes of oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb) signals at a temporal resolution severalfold higher. This suggests the necessity of adjusting the temporal parameters of a GLM for fNIRS signals. Thus, we devised a GLM-based method utilizing an adaptive hemodynamic response function (HRF). We sought the optimum temporal parameters to best explain the observed time series data during verbal fluency and naming tasks. The peak delay of the HRF was systematically changed to achieve the best-fit model for the observed oxy- and deoxy-Hb time series data. The optimized peak delay showed different values for each Hb signal and task. When the optimized peak delays were adopted, the deoxy-Hb data yielded comparable activations with similar statistical power and spatial patterns to oxy-Hb data. The adaptive HRF method could suitably explain the behaviors of both Hb parameters during tasks with the different cognitive loads during a time course, and thus would serve as an objective method to fully utilize the temporal structures of all fNIRS data. PMID:26157973
NASA Astrophysics Data System (ADS)
Stradi, Daniele; Martinez, Umberto; Blom, Anders; Brandbyge, Mads; Stokbro, Kurt
2016-04-01
Metal-semiconductor contacts are a pillar of modern semiconductor technology. Historically, their microscopic understanding has been hampered by the inability of traditional analytical and numerical methods to fully capture the complex physics governing their operating principles. Here we introduce an atomistic approach based on density functional theory and nonequilibrium Green's function, which includes all the relevant ingredients required to model realistic metal-semiconductor interfaces and allows for a direct comparison between theory and experiments via I -Vbias curve simulations. We apply this method to characterize an Ag/Si interface relevant for photovoltaic applications and study the rectifying-to-Ohmic transition as a function of the semiconductor doping. We also demonstrate that the standard "activation energy" method for the analysis of I -Vbias data might be inaccurate for nonideal interfaces as it neglects electron tunneling, and that finite-size atomistic models have problems in describing these interfaces in the presence of doping due to a poor representation of space-charge effects. Conversely, the present method deals effectively with both issues, thus representing a valid alternative to conventional procedures for the accurate characterization of metal-semiconductor interfaces.
ERIC Educational Resources Information Center
Engelhard, George, Jr.; Wang, Jue
2014-01-01
The authors of the Focus article pose important questions regarding whether or not performance-based tasks related to executive functioning are best viewed as reflective or formative indicators. Miyake and Friedman (2012) define executive functioning (EF) as "a set of general-purpose control mechanisms, often linked to the prefrontal cortex…
Integrative approaches for modeling regulation and function of the respiratory system.
Ben-Tal, Alona; Tawhai, Merryn H
2013-01-01
Mathematical models have been central to understanding the interaction between neural control and breathing. Models of the entire respiratory system-which comprises the lungs and the neural circuitry that controls their ventilation-have been derived using simplifying assumptions to compartmentalize each component of the system and to define the interactions between components. These full system models often rely-through necessity-on empirically derived relationships or parameters, in addition to physiological values. In parallel with the development of whole respiratory system models are mathematical models that focus on furthering a detailed understanding of the neural control network, or of the several functions that contribute to gas exchange within the lung. These models are biophysically based, and rely on physiological parameters. They include single-unit models for a breathing lung or neural circuit, through to spatially distributed models of ventilation and perfusion, or multicircuit models for neural control. The challenge is to bring together these more recent advances in models of neural control with models of lung function, into a full simulation for the respiratory system that builds upon the more detailed models but remains computationally tractable. This requires first understanding the mathematical models that have been developed for the respiratory system at different levels, and which could be used to study how physiological levels of O2 and CO2 in the blood are maintained.
Integrative approaches for modeling regulation and function of the respiratory system
Ben-Tal, Alona
2013-01-01
Mathematical models have been central to understanding the interaction between neural control and breathing. Models of the entire respiratory system – which comprises the lungs and the neural circuitry that controls their ventilation - have been derived using simplifying assumptions to compartmentalise each component of the system and to define the interactions between components. These full system models often rely – through necessity - on empirically derived relationships or parameters, in addition to physiological values. In parallel with the development of whole respiratory system models are mathematical models that focus on furthering a detailed understanding of the neural control network, or of the several functions that contribute to gas exchange within the lung. These models are biophysically based, and rely on physiological parameters. They include single-unit models for a breathing lung or neural circuit, through to spatially-distributed models of ventilation and perfusion, or multi-circuit models for neural control. The challenge is to bring together these more recent advances in models of neural control with models of lung function, into a full simulation for the respiratory system that builds upon the more detailed models but remains computationally tractable. This requires first understanding the mathematical models that have been developed for the respiratory system at different levels, and which could be used to study how physiological levels of O2 and CO2 in the blood are maintained. PMID:24591490
Functional Fault Modeling of a Cryogenic System for Real-Time Fault Detection and Isolation
NASA Technical Reports Server (NTRS)
Ferrell, Bob; Lewis, Mark; Oostdyk, Rebecca; Perotti, Jose
2009-01-01
When setting out to model and/or simulate a complex mechanical or electrical system, a modeler is faced with a vast array of tools, software, equations, algorithms and techniques that may individually or in concert aid in the development of the model. Mature requirements and a well understood purpose for the model may considerably shrink the field of possible tools and algorithms that will suit the modeling solution. Is the model intended to be used in an offline fashion or in real-time? On what platform does it need to execute? How long will the model be allowed to run before it outputs the desired parameters? What resolution is desired? Do the parameters need to be qualitative or quantitative? Is it more important to capture the physics or the function of the system in the model? Does the model need to produce simulated data? All these questions and more will drive the selection of the appropriate tools and algorithms, but the modeler must be diligent to bear in mind the final application throughout the modeling process to ensure the model meets its requirements without needless iterations of the design. The purpose of this paper is to describe the considerations and techniques used in the process of creating a functional fault model of a liquid hydrogen (LH2) system that will be used in a real-time environment to automatically detect and isolate failures.
NASA Astrophysics Data System (ADS)
Miwa, Tetsuji
2013-03-01
Studies on integrable models in statistical mechanics and quantum field theory originated in the works of Bethe on the one-dimensional quantum spin chain and the work of Onsager on the two-dimensional Ising model. I will talk on the discovery in 1977 of the link between quantum field theory in the scaling limit of the two-dimensional Ising model and the theory of monodromy preserving linear ordinary differential equations. This work was the staring point of our journey with Michio Jimbo in integrable models, the journey which finally led us to the exact results on the correlation functions of quantum spin chains in 1992.
A Functional Model of the Digital Extensor Mechanism: Demonstrating Biomechanics with Hair Bands
ERIC Educational Resources Information Center
Cloud, Beth A.; Youdas, James W.; Hellyer, Nathan J.; Krause, David A.
2010-01-01
The action of muscles about joints can be explained through analysis of their spatial relationship. A functional model of these relationships can be valuable in learning and understanding the muscular action about a joint. A model can be particularly helpful when examining complex actions across multiple joints such as in the digital extensor…
ERIC Educational Resources Information Center
Kamis-Gould, Edna; And Others
1991-01-01
A model for quality assurance (QA) in psychiatric hospitals is described. Its functions (general QA, utilization review, clinical records, evaluation, management information systems, risk management, and infection control), subfunctions, and corresponding staffing requirements are reviewed. This model was designed to foster standardization in QA…
Item Purification in Differential Item Functioning Using Generalized Linear Mixed Models
ERIC Educational Resources Information Center
Liu, Qian
2011-01-01
For this dissertation, four item purification procedures were implemented onto the generalized linear mixed model for differential item functioning (DIF) analysis, and the performance of these item purification procedures was investigated through a series of simulations. Among the four procedures, forward and generalized linear mixed model (GLMM)…
ERIC Educational Resources Information Center
Davis, James; Leslie, Ray; Billington, Susan; Slater, Peter R.
2010-01-01
The use of "Origami" is presented as an accessible and transferable modeling system through which to convey the intricacies of molecular shape and highlight structure-function relationships. The implementation of origami has been found to be a versatile alternative to conventional ball-and-stick models, possessing the key advantages of being both…
An Examination of the Domain of Multivariable Functions Using the Pirie-Kieren Model
ERIC Educational Resources Information Center
Sengul, Sare; Yildiz, Sevda Goktepe
2016-01-01
The aim of this study is to employ the Pirie-Kieren model so as to examine the understandings relating to the domain of multivariable functions held by primary school mathematics preservice teachers. The data obtained was categorized according to Pirie-Kieren model and demonstrated visually in tables and bar charts. The study group consisted of…
Evaluation of a Digital Library by Means of Quality Function Deployment (QFD) and the Kano Model
ERIC Educational Resources Information Center
Garibay, Cecilia; Gutierrez, Humberto; Figueroa, Arturo
2010-01-01
This paper proposes utilizing a combination of the Quality Function Deployment (QFD)-Kano model as a useful tool to evaluate service quality. The digital library of the University of Guadalajara (Mexico) is presented as a case study. Data to feed the QFD-Kano model was gathered by an online questionnaire that was made available to users on the…
Poor Vision, Functioning, and Depressive Symptoms: A Test of the Activity Restriction Model
ERIC Educational Resources Information Center
Bookwala, Jamila; Lawson, Brendan
2011-01-01
Purpose: This study tested the applicability of the activity restriction model of depressed affect to the context of poor vision in late life. This model hypothesizes that late-life stressors contribute to poorer mental health not only directly but also indirectly by restricting routine everyday functioning. Method: We used data from a national…
3D finite element model of the chinchilla ear for characterizing middle ear functions.
Wang, Xuelin; Gan, Rong Z
2016-10-01
Chinchilla is a commonly used animal model for research of sound transmission through the ear. Experimental measurements of the middle ear transfer function in chinchillas have shown that the middle ear cavity greatly affects the tympanic membrane (TM) and stapes footplate (FP) displacements. However, there is no finite element (FE) model of the chinchilla ear available in the literature to characterize the middle ear functions with the anatomical features of the chinchilla ear. This paper reports a recently completed 3D FE model of the chinchilla ear based on X-ray micro-computed tomography images of a chinchilla bulla. The model consisted of the ear canal, TM, middle ear ossicles and suspensory ligaments, and the middle ear cavity. Two boundary conditions of the middle ear cavity wall were simulated in the model as the rigid structure and the partially flexible surface, and the acoustic-mechanical coupled analysis was conducted with these two conditions to characterize the middle ear function. The model results were compared with experimental measurements reported in the literature including the TM and FP displacements and the middle ear input admittance in chinchilla ear. An application of this model was presented to identify the acoustic role of the middle ear septa-a unique feature of chinchilla middle ear cavity. This study provides the first 3D FE model of the chinchilla ear for characterizing the middle ear functions through the acoustic-mechanical coupled FE analysis.
Sauerbrei, Willi; Royston, Patrick; Binder, Harald
2007-12-30
In developing regression models, data analysts are often faced with many predictor variables that may influence an outcome variable. After more than half a century of research, the 'best' way of selecting a multivariable model is still unresolved. It is generally agreed that subject matter knowledge, when available, should guide model building. However, such knowledge is often limited, and data-dependent model building is required. We limit the scope of the modelling exercise to selecting important predictors and choosing interpretable and transportable functions for continuous predictors. Assuming linear functions, stepwise selection and all-subset strategies are discussed; the key tuning parameters are the nominal P-value for testing a variable for inclusion and the penalty for model complexity, respectively. We argue that stepwise procedures perform better than a literature-based assessment would suggest. Concerning selection of functional form for continuous predictors, the principal competitors are fractional polynomial functions and various types of spline techniques. We note that a rigorous selection strategy known as multivariable fractional polynomials (MFP) has been developed. No spline-based procedure for simultaneously selecting variables and functional forms has found wide acceptance. Results of FP and spline modelling are compared in two data sets. It is shown that spline modelling, while extremely flexible, can generate fitted curves with uninterpretable 'wiggles', particularly when automatic methods for choosing the smoothness are employed. We give general recommendations to practitioners for carrying out variable and function selection. While acknowledging that further research is needed, we argue why MFP is our preferred approach for multivariable model building with continuous covariates.
Dynamical model for longitudinal wave functions in light-front holographic QCD
Chabysheva, Sophia S.; Hiller, John R.
2013-10-15
We construct a Schrödinger-like equation for the longitudinal wave function of a meson in the valence qq{sup -bar} sector, based on the ’t Hooft model for large-N two-dimensional QCD, and combine this with the usual transverse equation from light-front holographic QCD, to obtain a model for mesons with massive quarks. The computed wave functions are compared with the wave function ansatz of Brodsky and de Téramond and used to compute decay constants and parton distribution functions. The basis functions used to solve the longitudinal equation may be useful for more general calculations of meson states in QCD. -- Highlights: •Provide relativistic quark model based on light-front holographic QCD. •Incorporate dependence on quark mass. •Consistent with the Brodsky–de Téramond quark-wave-function ansatz. •Compute meson decay constants and parton distribution functions. •Illustrate use of basis functions that could be convenient for more general numerical calculations in light-front QCD.
Classification Models for Pulmonary Function using Motion Analysis from Phone Sensors
Cheng, Qian; Juen, Joshua; Bellam, Shashi; Fulara, Nicholas; Close, Deanna; Silverstein, Jonathan C.; Schatz, Bruce
2016-01-01
Smartphones are ubiquitous, but it is unknown what physiological functions can be monitored at clinical quality. Pulmonary function is a standard measure of health status for cardiopulmonary patients. We have shown phone sensors can accurately measure walking patterns. Here we show that improved classification models can accurately measure pulmonary function, with sole inputs being sensor data from carried phones. Twenty-four cardiopulmonary patients performed six minute walk tests in pulmonary rehabilitation at a regional hospital. They carried smartphones running custom software recording phone motion. For every patient, every ten-second interval was correctly computed. The trained model perfectly computed the GOLD level 1/2/3, which is a standard categorization of pulmonary function as measured by spirometry. These results are encouraging towards field trials with passive monitors always running in the background. We expect patients can simply carry their phones during daily living, while supporting automatic computation ofpulmonary function for health monitoring. PMID:28269835
A note on population analysis of dissolution-absorption models using the inverse Gaussian function.
Wang, Jian; Weiss, Michael; D'Argenio, David Z
2008-06-01
Because conventional absorption models often fail to describe plasma concentration-time profiles following oral administration, empirical input functions such as the inverse Gaussian function have been successfully used. The purpose of this note is to extend this model by adding a first-order absorption process and to demonstrate the application of population analysis using maximum likelihood estimation via the EM algorithm (implemented in ADAPT 5). In one example, the analysis of bioavailability data of an extended-release formulation, as well as the mean dissolution times estimated in vivo and in vitro with the use of the inverse Gaussian function, is well in accordance, suggesting that the inverse Gaussian function indeed accounts for the in vivo dissolution process. In the other example, the kinetics of trapidil in patients with liver disease, the absorption/dissolution parameters are characterized by a high interindividual variability. Adding a first-order absorption process to the inverse Gaussian function improved the fit in both cases.
A numerical study of the string function using a primitive equation ocean model
NASA Astrophysics Data System (ADS)
Tyler, R. H.; Käse, R.
We use results from a primitive-equation ocean numerical model (SCRUM) to test a theoretical 'string function' formulation put forward by Tyler and Käse in another article in this issue. The string function acts as a stream function for the large-scale potential energy flow under the combined beta and topographic effects. The model results verify that large-scale anomalies propagate along the string function contours with a speed correctly given by the cross-string gradient. For anomalies having a scale similar to the Rossby radius, material rates of change in the layer mass following the string velocity are balanced by material rates of change in relative vorticity following the flow velocity. It is shown that large-amplitude anomalies can be generated when wind stress is resonant with the string function configuration.
Robust estimation of mean and dispersion functions in extended generalized additive models.
Croux, Christophe; Gijbels, Irène; Prosdocimi, Ilaria
2012-03-01
Generalized linear models are a widely used method to obtain parametric estimates for the mean function. They have been further extended to allow the relationship between the mean function and the covariates to be more flexible via generalized additive models. However, the fixed variance structure can in many cases be too restrictive. The extended quasilikelihood (EQL) framework allows for estimation of both the mean and the dispersion/variance as functions of covariates. As for other maximum likelihood methods though, EQL estimates are not resistant to outliers: we need methods to obtain robust estimates for both the mean and the dispersion function. In this article, we obtain functional estimates for the mean and the dispersion that are both robust and smooth. The performance of the proposed method is illustrated via a simulation study and some real data examples.
Aircraft/Air Traffic Management Functional Analysis Model: Technical Description. 2.0
NASA Technical Reports Server (NTRS)
Etheridge, Melvin; Plugge, Joana; Retina, Nusrat
1998-01-01
The Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 (FAM 2.0), is a discrete event simulation model designed to support analysis of alternative concepts in air traffic management and control. FAM 2.0 was developed by the Logistics Management Institute (LMI) under a National Aeronautics and Space Administration (NASA) contract. This document provides a technical description of FAM 2.0 and its computer files to enable the modeler and programmer to make enhancements or modifications to the model. Those interested in a guide for using the model in analysis should consult the companion document, Aircraft/Air Traffic Management Functional Analysis Model, Version 2.0 Users Manual.
Shi, J Q; Wang, B; Will, E J; West, R M
2012-11-20
We propose a new semiparametric model for functional regression analysis, combining a parametric mixed-effects model with a nonparametric Gaussian process regression model, namely a mixed-effects Gaussian process functional regression model. The parametric component can provide explanatory information between the response and the covariates, whereas the nonparametric component can add nonlinearity. We can model the mean and covariance structures simultaneously, combining the information borrowed from other subjects with the information collected from each individual subject. We apply the model to dose-response curves that describe changes in the responses of subjects for differing levels of the dose of a drug or agent and have a wide application in many areas. We illustrate the method for the management of renal anaemia. An individual dose-response curve is improved when more information is included by this mechanism from the subject/patient over time, enabling a patient-specific treatment regime.
Chen, Minxin; Li, Xiantao; Liu, Chun
2014-08-14
We present a numerical method to approximate the memory functions in the generalized Langevin models for the collective dynamics of macromolecules. We first derive the exact expressions of the memory functions, obtained from projection to subspaces that correspond to the selection of coarse-grain variables. In particular, the memory functions are expressed in the forms of matrix functions, which will then be approximated by Krylov-subspace methods. It will also be demonstrated that the random noise can be approximated under the same framework, and the second fluctuation-dissipation theorem is automatically satisfied. The accuracy of the method is examined through several numerical examples.
Beta Functions in Chirally Deformed Supersymmetric Sigma Models in Two Dimensions
NASA Astrophysics Data System (ADS)
Vainshtein, Arkady
We study two-dimensional sigma models where the chiral deformation diminished the original 𝒩 =(2, 2) supersymmetry to the chiral one, 𝒩 =(0, 2). Such heterotic models were discovered previously on the world sheet of non-Abelian stringy solitons supported by certain four-dimensional 𝒩 = 1 theories. We study geometric aspects and holomorphic properties of these models, and derive a number of exact expressions for the β functions in terms of the anomalous dimensions analogous to the NSVZ β function in four-dimensional Yang-Mills. Instanton calculus provides a straightforward method for the derivation.
Beta functions in Chirally deformed supersymmetric sigma models in two dimensions
NASA Astrophysics Data System (ADS)
Vainshtein, Arkady
2016-10-01
We study two-dimensional sigma models where the chiral deformation diminished the original 𝒩 = (2, 2) supersymmetry to the chiral one, 𝒩 = (0, 2). Such heterotic models were discovered previously on the world sheet of non-Abelian stringy solitons supported by certain four-dimensional 𝒩 = 1 theories. We study geometric aspects and holomorphic properties of these models, and derive a number of exact expressions for the β functions in terms of the anomalous dimensions analogous to the NSVZ β function in four-dimensional Yang-Mills. Instanton calculus provides a straightforward method for the derivation.
Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform
Wang, Chun-Li; Yang, Yueh-Lung; Wu, Wen-Hsiang; Tsai, Tung-Hu; Chang, Hen-Hong
2016-01-01
We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to study the pulse wave signal. By extracting features called the spectral pulse signature, and based on functional regression, we characterize the hemodynamics from the radial pulse wave signals recorded by the sphygmomanometer. Analysis results suggest the potential of the proposed signal processing approach to extract health-related hemodynamics features. PMID:27304979
Particle-hole optical model and strength functions for high-energy giant resonances
Urin, M. H.
2010-08-15
A formulation of the particle-hole optical model is proposed for describing the contribution of the fragmentation effect to the formation of strength functions for high-energy giant resonances. The model is based on the Bethe-Goldstone equation for the energy-averaged particle-hole Green's function. In this equation, the particle-hole interaction that is induced by a virtual excitation of multiquasiparticle configurations and in which, upon averaging over energy, an imaginary part is contained is taken into account. An analogy with the single-quasiparticle optical model is discussed.
A preliminary C-band scatterometer model function for the ERS-1 AMI instrument
NASA Technical Reports Server (NTRS)
Freilich, M. H.; Dunbar, R. S.
1993-01-01
Backscatter cross section (sigma(sub 0)) measurements from the ERS-1 scatterometer are collocated with surface wind velocity data from operational Numerical Weather Prediction (NWP) analyses to yield a fully empirical C band model function. The empirical model does not obey a power law at speeds less than 5 m/s, and upwind/crosswind ratios are small for all incidence angles at low wind speeds and for small incidence angles at all wind speeds. Downwind sigma(sub 0) exceeds upwind values for incidence angles below approximately 28 degrees. The full ERS-1 scatterometer data set has been reprocessed using the NWP model function.
Isgur-Wise function in a QCD-inspired potential model with WKB approximation
NASA Astrophysics Data System (ADS)
Hazarika, Bhaskar Jyoti; Choudhury, D. K.
2017-03-01
We use Wentzel-Kramers-Brillouin (WKB) approximation for calculating the slope and curvature of Isgur-Wise function in a QCD-inspired potential model. This work is an extension of the approximation methods to the QCD-inspired potential model. The approach hints at an effective range of distance for calculating the slope and curvature of Isgur-Wise function. Comparison is also made with those of Dalgarno method and variationally improved perturbation theory (VIPT) as well as other models to show the advantages of using WKB approximation.
A prototype symbolic model of canonical functional neuroanatomy of the motor system.
Talos, Ion-Florin; Rubin, Daniel L; Halle, Michael; Musen, Mark; Kikinis, Ron
2008-04-01
Recent advances in bioinformatics have opened entire new avenues for organizing, integrating and retrieving neuroscientific data, in a digital, machine-processable format, which can be at the same time understood by humans, using ontological, symbolic data representations. Declarative information stored in ontological format can be perused and maintained by domain experts, interpreted by machines, and serve as basis for a multitude of decision support, computerized simulation, data mining, and teaching applications. We have developed a prototype symbolic model of canonical neuroanatomy of the motor system. Our symbolic model is intended to support symbolic look up, logical inference and mathematical modeling by integrating descriptive, qualitative and quantitative functional neuroanatomical knowledge. Furthermore, we show how our approach can be extended to modeling impaired brain connectivity in disease states, such as common movement disorders. In developing our ontology, we adopted a disciplined modeling approach, relying on a set of declared principles, a high-level schema, Aristotelian definitions, and a frame-based authoring system. These features, along with the use of the Unified Medical Language System (UMLS) vocabulary, enable the alignment of our functional ontology with an existing comprehensive ontology of human anatomy, and thus allow for combining the structural and functional views of neuroanatomy for clinical decision support and neuroanatomy teaching applications. Although the scope of our current prototype ontology is limited to a particular functional system in the brain, it may be possible to adapt this approach for modeling other brain functional systems as well.
a Radiative Transfer Equation/phase Function Approach to Vegetation Canopy Reflectance Modeling
NASA Astrophysics Data System (ADS)
Randolph, Marion Herbert
Vegetation canopy reflectance models currently in use differ considerably in their treatment of the radiation scattering problem, and it is this fundamental difference which stimulated this investigation of the radiative transfer equation/phase function approach. The primary objective of this thesis is the development of vegetation canopy phase functions which describe the probability of radiation scattering within a canopy in terms of its biological and physical characteristics. In this thesis a technique based upon quadrature formulae is used to numerically generate a variety of vegetation canopy phase functions. Based upon leaf inclination distribution functions, phase functions are generated for plagiophile, extremophile, erectophile, spherical, planophile, blue grama (Bouteloua gracilis), and soybean canopies. The vegetation canopy phase functions generated are symmetric with respect to the incident and exitant angles, and hence satisfy the principle of reciprocity. The remaining terms in the radiative transfer equation are also derived in terms of canopy geometry and optical properties to complete the development of the radiative transfer equation/phase function description for vegetation canopy reflectance modeling. In order to test the radiative transfer equation/phase function approach the iterative discrete ordinates method for solving the radiative transfer equation is implemented. In comparison with field data, the approach tends to underestimate the visible reflectance and overestimate infrared reflectance. The approach does compare well, however, with other extant canopy reflectance models; for example, it agrees to within ten to fifteen percent of the Suits model (Suits, 1972). Sensitivity analysis indicates that canopy geometry may influence reflectance as much as 100 percent for a given wavelength. Optical thickness produces little change in reflectance after a depth of 2.5 (Leaf area index of 4.0) is reached, and reflectance generally increases
Liao, Wenlin; Dai, Yifan; Xie, Xuhui; Zhou, Lin
2014-07-01
Ion beam figuring (IBF) is established for the final precision figuring of high-performance optical components, where the figuring accuracy is guaranteed by the stability of the removal function and the solution accuracy of the dwell time. In this deterministic method, the figuring process can be represented by a two-dimensional (2D) convolution operation of a constant removal function and the dwell time. However, we have found that the current 2D convolution operation cannot factually describe the IBF process of curved surfaces, which neglects the influences of the projection distortion and the workpiece geometry on the removal function. Consequently, the current 2D convolution algorithm would influence the solution accuracy for the dwell time and reduce the convergence of the figuring process. In this part, based on the material removal characteristics of IBF, a mathematical model of the removal function is developed theoretically and verified experimentally. Research results show that the removal function during IBF of a curved surface is actually a dynamic function in the 2D convolution algorithm. The mathematical modeling of the dynamic removal function provides theoretical foundations for our proposed new algorithm in the next part, and final verification experiments indicate that this algorithm can effectively improve the accuracy of the dwell time solution for the IBF of curved surfaces.
Constitutive Modeling of Skeletal Muscle Tissue with an Explicit Strain-Energy Function
Odegard, G.M.; Donahue, T.L. Haut; Morrow, D.A.; Kaufman, K.R.
2010-01-01
While much work has previously been done in the modeling of skeletal muscle, no model has, to date, been developed that describes the mechanical behavior with an explicit strain-energy function associated with the active response of skeletal muscle tissue. A model is presented herein that has been developed to accommodate this design consideration using a robust dynamical approach. The model shows excellent agreement with a previously published model of both the active and passive length-tension properties of skeletal muscle. PMID:19045546
Altered Gastrointestinal Function in the Neuroligin-3 Mouse Model of Autism
2013-10-01
and aggression in a mouse model of autism . Invited manuscript: Swaminathan, M, Balasuriya, G, Hill-Yardin EL., Bornstein, JC. Video imaging of...the Neuroligin-3 Mouse Model of Autism PRINCIPAL INVESTIGATOR: Professor Joel Bornstein CONTRACTING ORGANIZATION: The University of...Gastrointestinal function in the Neuroligin-3 Mouse Model of Autism 5b. GRANT NUMBER W81XWH-12-1-0494 GRANT110132 47 GRANT110132 47 5c
NASA Astrophysics Data System (ADS)
Pavlick, R.; Schimel, D.
2014-12-01
Dynamic Global Vegetation Models (DGVMs) typically employ only a small set of Plant Functional Types (PFTs) to represent the vast diversity of observed vegetation forms and functioning. There is growing evidence, however, that this abstraction may not adequately represent the observed variation in plant functional traits, which is thought to play an important role for many ecosystem functions and for ecosystem resilience to environmental change. The geographic distribution of PFTs in these models is also often based on empirical relationships between present-day climate and vegetation patterns. Projections of future climate change, however, point toward the possibility of novel regional climates, which could lead to no-analog vegetation compositions incompatible with the PFT paradigm. Here, we present results from the Jena Diversity-DGVM (JeDi-DGVM), a novel traits-based vegetation model, which simulates a large number of hypothetical plant growth strategies constrained by functional tradeoffs, thereby allowing for a more flexible temporal and spatial representation of the terrestrial biosphere. First, we compare simulated present-day geographical patterns of functional traits with empirical trait observations (in-situ and from airborne imaging spectroscopy). The observed trait patterns are then used to improve the tradeoff parameterizations of JeDi-DGVM. Finally, focusing primarily on the simulated leaf traits, we run the model with various amounts of trait diversity. We quantify the effects of these modeled biodiversity manipulations on simulated ecosystem fluxes and stocks for both present-day conditions and transient climate change scenarios. The simulation results reveal that the coarse treatment of plant functional traits by current PFT-based vegetation models may contribute substantial uncertainty regarding carbon-climate feedbacks. Further development of trait-based models and further investment in global in-situ and spectroscopic plant trait observations
A phenotypic in vitro model for the main determinants of human whole heart function
Stancescu, Maria; Molnar, Peter; McAleer, Christopher W.; McLamb, William; Long, Christopher J.; Oleaga, Carlota; Prot, Jean-Matthieu; Hickman, James J.
2015-01-01
This article details the construction and testing of a phenotypic assay system that models in vivo cardiac function in a parallel in vitro environment with human stem cell derived cardiomyocytes. The major determinants of human whole-heart function were experimentally modeled by integrating separate 2D cellular systems with BioMicroelectromechanical Systems (BioMEMS) constructs. The model featured a serum-free defined medium to enable both acute and chronic evaluation of drugs and toxins. The integration of data from both systems produced biologically relevant predictions of cardiac function in response to varying concentrations of selected drugs. Sotalol, norepinephrine and verapamil were shown to affect the measured parameters according to their specific mechanism of action, in agreement with clinical data. This system is applicable for cardiac side effect assessment, general toxicology, efficacy studies, and evaluation of in vitro cellular disease models in body-on-a-chip systems. PMID:25978005
Zaĭtseva, N V; Trusov, P V; Kir'ianov, D A
2012-01-01
The mathematic concept model presented describes accumulation of functional disorders associated with environmental factors, plays predictive role and is designed for assessments of possible effects caused by heterogenous factors with variable exposures. Considering exposure changes with self-restoration process opens prospects of using the model to evaluate, analyse and manage occupational risks. To develop current theoretic approaches, the authors suggested a model considering age-related body peculiarities, systemic interactions of organs, including neuro-humoral regulation, accumulation of functional disorders due to external factors, rehabilitation of functions during treatment. General objective setting covers defining over a hundred unknow coefficients that characterize speed of various processes within the body. To solve this problem, the authors used iteration approach, successive identification, that starts from the certain primary approximation of the model parameters and processes subsequent updating on the basis of new theoretic and empirical knowledge.
Coull, Brent A
2011-06-01
In many biomedical investigations, a primary goal is the identification of subjects who are susceptible to a given exposure or treatment of interest. We focus on methods for addressing this question in longitudinal studies when interest focuses on relating susceptibility to a subject's baseline or mean outcome level. In this context, we propose a random intercepts-functional slopes model that relaxes the assumption of linear association between random coefficients in existing mixed models and yields an estimate of the functional form of this relationship. We propose a penalized spline formulation for the nonparametric function that represents this relationship, and implement a fully Bayesian approach to model fitting. We investigate the frequentist performance of our method via simulation, and apply the model to data on the effects of particulate matter on coronary blood flow from an animal toxicology study. The general principles introduced here apply more broadly to settings in which interest focuses on the relationship between baseline and change over time.
A phenotypic in vitro model for the main determinants of human whole heart function.
Stancescu, Maria; Molnar, Peter; McAleer, Christopher W; McLamb, William; Long, Christopher J; Oleaga, Carlota; Prot, Jean-Matthieu; Hickman, James J
2015-08-01
This article details the construction and testing of a phenotypic assay system that models in vivo cardiac function in a parallel in vitro environment with human stem cell derived cardiomyocytes. The major determinants of human whole-heart function were experimentally modeled by integrating separate 2D cellular systems with BioMicroelectromechanical Systems (BioMEMS) constructs. The model features a serum-free defined medium to enable both acute and chronic evaluation of drugs and toxins. The integration of data from both systems produced biologically relevant predictions of cardiac function in response to varying concentrations of selected drugs. Sotalol, norepinephrine and verapamil were shown to affect the measured parameters according to their specific mechanism of action, in agreement with clinical data. This system is applicable for cardiac side effect assessment, general toxicology, efficacy studies, and evaluation of in vitro cellular disease models in body-on-a-chip systems.
Vanheel, Hanne; Masaoka, Tatsuhiro; Salim Rasoel, Shadea; Tóth, Joran; Houben, Els; Verbeke, Kristin; De Hertogh, Gert; Berghe, Pieter Vanden; Tack, Jan; Farré, Ricard
2014-01-01
Background Impaired intestinal barrier function, low-grade inflammation and altered neuronal control are reported in functional gastrointestinal disorders. However, the sequence of and causal relation between these events is unclear, necessitating a spontaneous animal model. The aim of this study was to describe the natural history of intestinal permeability, mucosal and neuromuscular inflammation and nitrergic motor neuron function during the lifetime of the BioBreeding (BB) rat. Methods Normoglycemic BB-diabetes prone (DP) and control rats were sacrificed at different ages and jejunum was harvested to characterize intestinal permeability, inflammation and neuromuscular function. Results Both structural and functional evidence of increased intestinal permeability was found in young BB-DP rats from the age of 50 days. In older animals, starting in the mucosa from 70 days and in half of the animals also in the muscularis propria from 110 days, an inflammatory reaction, characterized by an influx of polymorphonuclear cells and higher myeloperoxidase activity, was observed. Finally, in animals older than 110 days, coinciding with a myenteric ganglionitis, a loss of nitrergic neurons and motor function was demonstrated. Conclusion In the BB-rat, mucosal inflammatory cell infiltration is preceded by intestinal barrier dysfunction and followed by myenteric ganglionitis and loss of nitrergic function. This sequence supports a primary role for impaired barrier function and provides an insightful model for the pathogenesis of functional gastrointestinal disorders. PMID:25354336
A functional-dynamic reflection on participatory processes in modeling projects.
Seidl, Roman
2015-12-01
The participation of nonscientists in modeling projects/studies is increasingly employed to fulfill different functions. However, it is not well investigated if and how explicitly these functions and the dynamics of a participatory process are reflected by modeling projects in particular. In this review study, I explore participatory modeling projects from a functional-dynamic process perspective. The main differences among projects relate to the functions of participation-most often, more than one per project can be identified, along with the degree of explicit reflection (i.e., awareness and anticipation) on the dynamic process perspective. Moreover, two main approaches are revealed: participatory modeling covering diverse approaches and companion modeling. It becomes apparent that the degree of reflection on the participatory process itself is not always explicit and perfectly visible in the descriptions of the modeling projects. Thus, the use of common protocols or templates is discussed to facilitate project planning, as well as the publication of project results. A generic template may help, not in providing details of a project or model development, but in explicitly reflecting on the participatory process. It can serve to systematize the particular project's approach to stakeholder collaboration, and thus quality management.
Takagi-Sugeno fuzzy models in the framework of orthonormal basis functions.
Machado, Jeremias B; Campello, Ricardo J G B; Amaral, Wagner Caradori
2013-06-01
An approach to obtain Takagi-Sugeno (TS) fuzzy models of nonlinear dynamic systems using the framework of orthonormal basis functions (OBFs) is presented in this paper. This approach is based on an architecture in which local linear models with ladder-structured generalized OBFs (GOBFs) constitute the fuzzy rule consequents and the outputs of the corresponding GOBF filters are input variables for the rule antecedents. The resulting GOBF-TS model is characterized by having only real-valued parameters that do not depend on any user specification about particular types of functions to be used in the orthonormal basis. The fuzzy rules of the model are initially obtained by means of a well-known technique based on fuzzy clustering and least squares. Those rules are then simplified, and the model parameters (GOBF poles, GOBF expansion coefficients, and fuzzy membership functions) are subsequently adjusted by using a nonlinear optimization algorithm. The exact gradients of an error functional with respect to the parameters to be optimized are computed analytically. Those gradients provide exact search directions for the optimization process, which relies solely on input-output data measured from the system to be modeled. An example is presented to illustrate the performance of this approach in the modeling of a complex nonlinear dynamic system.
Malloy, Elizabeth J.; Morris, Jeffrey S.; Adar, Sara D.; Suh, Helen; Gold, Diane R.; Coull, Brent A.
2010-01-01
Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient is necessary for estimation. Additional issues in this setting are the possibility of exposure measurement error and the need to incorporate additional potential confounders, such as meteorological or co-pollutant measures, that themselves may have effects that vary over time. To accommodate all these features, we develop wavelet-based linear mixed distributed lag models that incorporate repeated measures of functional data as covariates into a linear mixed model. A Bayesian approach to model fitting uses wavelet shrinkage to regularize functional coefficients. We show that, as long as the exposure error induces fine-scale variability in the functional exposure profile and the distributed lag function representing the exposure effect varies smoothly in time, the model corrects for the exposure measurement error without further adjustment. Both these conditions are likely to hold in the environmental applications we consider. We examine properties of the method using simulations and apply the method to data from a study examining the association between PM, measured as hourly averages for 1–7 days, and markers of acute systemic inflammation. We use the method to fully control for the effects of confounding by other time-varying predictors, such as temperature and co-pollutants. PMID:20156988
Torfs, Elena; Balemans, Sophie; Locatelli, Florent; Diehl, Stefan; Bürger, Raimund; Laurent, Julien; François, Pierre; Nopens, Ingmar
2017-03-01
Advanced 1-D models for Secondary Settling Tanks (SSTs) explicitly account for several phenomena that influence the settling process (such as hindered settling and compression settling). For each of these phenomena a valid mathematical expression needs to be selected and its parameters calibrated to obtain a model that can be used for operation and control. This is, however, a challenging task as these phenomena may occur simultaneously. Therefore, the presented work evaluates several available expressions for hindered settling based on long-term batch settling data. Specific attention is paid to the behaviour of these hindered settling functions in the compression region in order to evaluate how the modelling of sludge compression is influenced by the choice of a certain hindered settling function. The analysis shows that the exponential hindered settling forms, which are most commonly used in traditional SST models, not only account for hindered settling but partly lump other phenomena (compression) as well. This makes them unsuitable for advanced 1-D models that explicitly include each phenomenon in a modular way. A power-law function is shown to be more appropriate to describe the hindered settling velocity in advanced 1-D SST models.
Study of production functions for modeling forest biomass: An area for research
Nautiyal, J.C. ); Belli, K.L. )
1989-09-01
The usefulness of production functions, mathematical descriptions of production processes, has long been recognized by economists in manufacturing industries, and more recently by agricultural scientists in the field of biological production. As increasing emphasis in forestry is placed on short-rotation, intensive crop management it would seem that foresters would also require production functions for rational timber management planning. These functions could be useful in a number of areas such as: crop tree growth prediction, control of stand development, economic analysis for decision-making purposes, and for determining the so-called elasticities of inputs and outputs. A very general functional form that may be appropriate for the development of forestry models is the transcendental logarithmic, or translog, function. Unfortunately, at this time, sufficiently detailed data do not seem to be available for any tree species to estimate a production function that could make sophisticated intensive forest management possible.
Gaiduk, Alex P.; Staroverov, Viktor N.
2011-01-15
A directly approximated exchange-correlation potential should, by construction, be a functional derivative of some density functional in order to avoid unphysical results. Using generalized gradient approximations (GGAs) as an example, we show that functional derivatives of explicit density functionals have a very rigid inner structure, the knowledge of which allows one to build the entire functional derivative from a small part. Based on this analysis, we develop a method for direct construction of integrable Kohn-Sham potentials. As an illustration, we transform the model potential of van Leeuwen and Baerends (which is not a functional derivative) into a semilocal exchange potential that has a parent GGA, yields accurate energies, and is free from the artifacts inherent in existing semilocal potential approximations.
Harris, Michelle A.; Chang, Wesley S.; Dent, Erik W.; Nordheim, Erik V.; Franzen, Margaret A.
2016-01-01
Abstract Understanding how basic structural units influence function is identified as a foundational/core concept for undergraduate biological and biochemical literacy. It is essential for students to understand this concept at all size scales, but it is often more difficult for students to understand structure–function relationships at the molecular level, which they cannot as effectively visualize. Students need to develop accurate, 3‐dimensional mental models of biomolecules to understand how biomolecular structure affects cellular functions at the molecular level, yet most traditional curricular tools such as textbooks include only 2‐dimensional representations. We used a controlled, backward design approach to investigate how hand‐held physical molecular model use affected students' ability to logically predict structure–function relationships. Brief (one class period) physical model use increased quiz score for females, whereas there was no significant increase in score for males using physical models. Females also self‐reported higher learning gains in their understanding of context‐specific protein function. Gender differences in spatial visualization may explain the gender‐specific benefits of physical model use observed. © 2016 The Authors Biochemistry and Molecular Biology Education published by Wiley Periodicals, Inc. on behalf of International Union of Biochemistry and Molecular Biology, 44(4):326–335, 2016. PMID:26923186
A new approach for determining fully empirical altimeter wind speed model functions
NASA Technical Reports Server (NTRS)
Freilich, M. H.; Challenor, Peter G.
1994-01-01
A statistical technique is developed for determining fully empirical model functions relating altimeter backscatter (sigma(sub 0)) measurements to near-surface neutral stability wind speed. By assuming that sigma(sub 0) varies monotonically and uniquely with wind speed, the method requires knowledge only of the separate, rather than joint distribution functions of sigma(sub 0) and wind speed. Analytic simplifications result from using a Weibull distribution to approximate the global ocean wind speed distribution; several different wind data sets are used to demonstrate the validity of the Weibull approximation. The technique has been applied to 1 year of Geosat data. Validation of the new and historical model functions using an independent buoy data set demonstrates that the present model function not only has small overall bias and root mean square (RMS) errors, but yields smaller systematic error trends with wind speed and pseudowave age than previously published models. The present analysis suggests that generally accuracte altimeter model functions can be derived without the use of colocated measurements, nor is additional significant wave height information measured by the altimeter necessary.
Modeling Differential Item Functioning Using a Generalization of the Multiple-Group Bifactor Model
ERIC Educational Resources Information Center
Jeon, Minjeong; Rijmen, Frank; Rabe-Hesketh, Sophia
2013-01-01
The authors present a generalization of the multiple-group bifactor model that extends the classical bifactor model for categorical outcomes by relaxing the typical assumption of independence of the specific dimensions. In addition to the means and variances of all dimensions, the correlations among the specific dimensions are allowed to differ…
Functional Models for the Oxygen-Evolving Complex of Photosystem II
Cady, Clyde W.; Crabtree, Robert H.; Brudvig, Gary W.
2010-01-01
In the last ten years, a number of advances have been made in the study of the oxygen-evolving complex (OEC) of photosystem II (PSII). Along with this new understanding of the natural system has come rapid advance in chemical models of this system. The advance of PSII model chemistry is seen most strikingly in the area of functional models where the few known systems available when this topic was last reviewed has grown into two families of model systems. In concert with this work, numerous mechanistic proposals for photosynthetic water oxidation have been proposed. Here, we review the recent efforts in functional model chemistry of the oxygen-evolving complex of photosystem II. PMID:21037800
Asymptotic behaviour of two-point functions in multi-species models
NASA Astrophysics Data System (ADS)
Kozlowski, Karol K.; Ragoucy, Eric
2016-05-01
We extract the long-distance asymptotic behaviour of two-point correlation functions in massless quantum integrable models containing multi-species excitations. For such a purpose, we extend to these models the method of a large-distance regime re-summation of the form factor expansion of correlation functions. The key feature of our analysis is a technical hypothesis on the large-volume behaviour of the form factors of local operators in such models. We check the validity of this hypothesis on the example of the SU (3)-invariant XXX magnet by means of the determinant representations for the form factors of local operators in this model. Our approach confirms the structure of the critical exponents obtained previously for numerous models solvable by the nested Bethe Ansatz.
Mouse Models for Studies of In Vivo Functions of HIV-1 Nef.
Zou, Wei; Zhang, Lunli
2016-01-01
In vitro studies have demonstrated that HIV-1 Nef has several important activities, promoting viral replication and pathogenesis. These activities include downregulation of cell surface molecules CD4 and major histocompatibility complex class I, enhancement of viral infectivity, activation of p21-activated kinase 2, and inhibition of immunoglobulin class switching. But how important each in vitro activity is to in vivo Nef function remains elusive. To address this question, several small animal models have been developed in the past two decades, such as Nef transgenic mice, SCID-hu mice, and humanized mice. Each of those models has its own pros and cons. Easy access and relative inexpensiveness have made small animal models the favorite models for HIV research. This review will be focused on the recent progress in the understanding of the in vivo functions of HIV-1 Nef obtained from studies using these small animal models.
Hamilton, Joshua J.; Reed, Jennifer L.
2012-01-01
Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different
White, Jim; Zardava, Kiriaki; Nayagum, Dharumarajen; Powrie, William
2015-04-01
Numerical models of landfill processes need to be able to estimate the capillary pressure and relative permeability of waste as a function of moisture content using analytical equations such as the van Genuchten equations. The paper identifies the range of van Genuchten parameter values for use in models and proposes a formulaic relationship between these parameter values and saturated moisture content. The concept of porous material, its behaviour under unsaturated conditions and Mualem's integral transform equation that estimates relative permeability from capillary pressure are reviewed. The application of the algebraic form of the capillary pressure function proposed by van Genuchten and its application using Mualem's transform to obtain the van Genuchten algebraic functions for relative permeability are discussed. Functional relationships are identified between saturated moisture content and the van Genuchten parameters using a database of results from other sources. These relationships may be used in numerical modelling of unsaturated flow in landfilled waste where the saturated moisture content varies significantly as the result of compression, settlement and degradation. A 2D numerical model simulation of leachate recirculation is used to investigate the sensitivity of the simulation to the introduction of these functional relationships. It is found that the transient liquid and gas flows across the model boundaries appear to be insensitive to whether or not the functions are incorporated into the model algorithm. However it is observed that using the relationships does have some impact on the distribution of the degree of saturation throughout the model and on the transient behaviour of the way in which the recirculation recharges the waste. However it is not thought that this impact would be sufficient to influence the design of a leachate recirculation system.
ERIC Educational Resources Information Center
DeMars, Christine E.; Jurich, Daniel P.
2015-01-01
In educational testing, differential item functioning (DIF) statistics must be accurately estimated to ensure the appropriate items are flagged for inspection or removal. This study showed how using the Rasch model to estimate DIF may introduce considerable bias in the results when there are large group differences in ability (impact) and the data…
Hill, Steven C; Miller, G Edward
2010-05-01
Health-care expenditure regressions are used in a wide variety of economic analyses including risk adjustment and program and treatment evaluations. Recent articles demonstrated that generalized gamma models (GGMs) and extended estimating equations (EEE) models provide flexible approaches to deal with a variety of data problems encountered in expenditure estimation. To date there have been few empirical applications of these models to expenditures. We use data from the US Medical Expenditure Panel Survey to compare the bias, predictive accuracy, and marginal effects of GGM and EEE models with other commonly used regression models in a cross-validation study design. Health-care expenditure distributions vary in the degree of heteroskedasticity, skewness, and kurtosis by type of service and population. To examine the ability of estimators to address a range of data problems, we estimate models of total health expenditures and prescription drug expenditures for two populations, the elderly and privately insured adults. Our findings illustrate the need for researchers to examine their assumptions about link functions: the appropriate link function varies across our four distributions. The EEE model, which has a flexible link function, is a robust estimator that performs as well, or better, than the other models in each distribution.
Sojoudi, Alireza; Goodyear, Bradley G
2016-12-01
Spontaneous fluctuations of blood-oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) signals are highly synchronous between brain regions that serve similar functions. This provides a means to investigate functional networks; however, most analysis techniques assume functional connections are constant over time. This may be problematic in the case of neurological disease, where functional connections may be highly variable. Recently, several methods have been proposed to determine moment-to-moment changes in the strength of functional connections over an imaging session (so called dynamic connectivity). Here a novel analysis framework based on a hierarchical observation modeling approach was proposed, to permit statistical inference of the presence of dynamic connectivity. A two-level linear model composed of overlapping sliding windows of fMRI signals, incorporating the fact that overlapping windows are not independent was described. To test this approach, datasets were synthesized whereby functional connectivity was either constant (significant or insignificant) or modulated by an external input. The method successfully determines the statistical significance of a functional connection in phase with the modulation, and it exhibits greater sensitivity and specificity in detecting regions with variable connectivity, when compared with sliding-window correlation analysis. For real data, this technique possesses greater reproducibility and provides a more discriminative estimate of dynamic connectivity than sliding-window correlation analysis. Hum Brain Mapp 37:4566-4580, 2016. © 2016 Wiley Periodicals, Inc.
Functional MRI and neural responses in a rat model of Alzheimer’s disease
Sanganahalli, Basavaraju G.; Herman, Peter; Behar, Kevin L.; Blumenfeld, Hal; Rothman, Douglas L.; Hyder, Fahmeed
2013-01-01
Based on the hypothesis that brain plaques and tangles can affect cortical functions in Alzheimer's disease (AD) and thus modify functional activity, we investigated functional responses in an AD rat model (called the Samaritan Alzheimer’s rat achieved by ventricular infusion of amyloid peptide) and age-matched healthy control. High-field functional magnetic resonance imaging (fMRI) and extracellular neural activity measurements were applied to characterize sensory-evoked responses. Electrical stimulation of the forepaw led to BOLD and neural responses in the contralateral somatosensory cortex and thalamus. In AD brain we noted much smaller BOLD activation patterns in the somatosensory cortex (i.e., about 50% less activated voxels compared to normal brain). While magnitudes of BOLD and neural responses in the cerebral cortex were markedly attenuated in AD rats compared to normal rats (by about 50%), the dynamic coupling between the BOLD and neural responses in the cerebral cortex, as assessed by transfer function analysis, remained unaltered between the groups. However thalamic BOLD and neural responses were unaltered in AD brain compared to controls. Thus cortical responses in the AD model were indeed diminished compared to controls, but the thalamic responses in the AD and control rats were quite similar. Therefore these results suggest that Alzheimer’s disease may affect cortical function more than subcortical function, which may have implications for interpreting altered human brain functional responses in fMRI studies of Alzheimer’s disease. PMID:23648961
NASA Astrophysics Data System (ADS)
Moeys, J.; Larsbo, M.; Bergström, L.; Brown, C. D.; Coquet, Y.; Jarvis, N. J.
2012-02-01
Estimating pesticide leaching risks at the regional scale requires the ability to completely parameterise a pesticide fate model using only survey data, such as soil and land-use maps. Such parameterisation usually rely on a set of lookup tables and (pedo)transfer functions, relating elementary soil and site properties to model parameters. The aim of this paper is to describe and test a complete set of parameter estimation algorithms developed for the pesticide fate model MACRO, which accounts for preferential flow in soil macropores. We used tracer monitoring data from 16 lysimeter studies, carried out in three European countries, to evaluate the ability of MACRO and this "blind parameterisation" scheme to reproduce measured solute leaching at the base of each lysimeter. We focused on the prediction of early tracer breakthrough due to preferential flow, because this is critical for pesticide leaching. We then calibrated a selected number of parameters in order to assess to what extent the prediction of water and solute leaching could be improved. Our results show that water flow was generally reasonably well predicted (median model efficiency, ME, of 0.42). Although the general pattern of solute leaching was reproduced well by the model, the overall model efficiency was low (median ME = -0.26) due to errors in the timing and magnitude of some peaks. Preferential solute leaching at early pore volumes was also systematically underestimated. Nonetheless, the ranking of soils according to solute loads at early pore volumes was reasonably well estimated (concordance correlation coefficient, CCC, between 0.54 and 0.72). Moreover, we also found that ignoring macropore flow leads to a significant deterioration in the ability of the model to reproduce the observed leaching pattern, and especially the early breakthrough in some soils. Finally, the calibration procedure showed that improving the estimation of solute transport parameters is probably more important than the
NASA Astrophysics Data System (ADS)
Moeys, J.; Larsbo, M.; Bergström, L.; Brown, C. D.; Coquet, Y.; Jarvis, N. J.
2012-07-01
Estimating pesticide leaching risks at the regional scale requires the ability to completely parameterise a pesticide fate model using only survey data, such as soil and land-use maps. Such parameterisations usually rely on a set of lookup tables and (pedo)transfer functions, relating elementary soil and site properties to model parameters. The aim of this paper is to describe and test a complete set of parameter estimation algorithms developed for the pesticide fate model MACRO, which accounts for preferential flow in soil macropores. We used tracer monitoring data from 16 lysimeter studies, carried out in three European countries, to evaluate the ability of MACRO and this "blind parameterisation" scheme to reproduce measured solute leaching at the base of each lysimeter. We focused on the prediction of early tracer breakthrough due to preferential flow, because this is critical for pesticide leaching. We then calibrated a selected number of parameters in order to assess to what extent the prediction of water and solute leaching could be improved. Our results show that water flow was generally reasonably well predicted (median model efficiency, ME, of 0.42). Although the general pattern of solute leaching was reproduced well by the model, the overall model efficiency was low (median ME = -0.26) due to errors in the timing and magnitude of some peaks. Preferential solute leaching at early pore volumes was also systematically underestimated. Nonetheless, the ranking of soils according to solute loads at early pore volumes was reasonably well estimated (concordance correlation coefficient, CCC, between 0.54 and 0.72). Moreover, we also found that ignoring macropore flow leads to a significant deterioration in the ability of the model to reproduce the observed leaching pattern, and especially the early breakthrough in some soils. Finally, the calibration procedure showed that improving the estimation of solute transport parameters is probably more important than the
Parameterizing dose-response models to estimate relative potency functions directly.
Dinse, Gregg E; Umbach, David M
2012-10-01
Many comparative analyses of toxicity assume that the potency of a test chemical relative to a reference chemical is constant, but employing such a restrictive assumption uncritically may generate misleading conclusions. Recent efforts to characterize non-constant relative potency rely on relative potency functions and estimate them secondarily after fitting dose-response models for the test and reference chemicals. We study an alternative approach of specifying a relative potency model a priori and estimating it directly using the dose-response data from both chemicals. We consider a power function in dose as a relative potency model and find that it keeps the two chemicals' dose-response functions within the same family of models for families typically used in toxicology. When differences in the response limits for the test and reference chemicals are attributable to the chemicals themselves, the older two-stage approach is the more convenient. When differences in response limits are attributable to other features of the experimental protocol or when response limits do not differ, the direct approach is straightforward to apply with nonlinear regression methods and simplifies calculation of simultaneous confidence bands. We illustrate the proposed approach using Hill models with dose-response data from U.S. National Toxicology Program bioassays. Though not universally applicable, this method of estimating relative potency functions directly can be profitably applied to a broad family of dose-response models commonly used in toxicology.
NASA Astrophysics Data System (ADS)
Atencia Yepez, A.; Autrán Cerqueira, J.; Urueña, S.; Jurado, R.
2012-01-01
This paper, developed under contract with European Aviation Safety Agency (EASA), analyses in detail which may be the certification implications in the aeronautic industry associated to the application of model-level verification and validation techniques. Particularly, this paper focuses on the problematic of detecting unintended functions by applying Model Coverage Criteria at model level. This point is significantly important for the future extensive use of Model Based approaches in safety critical software, since the uncertainty in the system performance introduced by the unintended functions, which may also lead to unacceptable hazardous or catastrophic events, prevents the system to be compliance with certification requirements. The paper provides a definition and a categorization of unintended functions and gives some relevant examples to assess the efficiency of model- coverage techniques in the detection of UF. The paper explains how this analysis is supported by a methodology based on the study of sources for introducing unintended functions. Finally it is analysed the feasibility of using Model-level verification techniques to support the software certification process.
Bignardi, A B; El Faro, L; Torres Júnior, R A A; Cardoso, V L; Machado, P F; Albuquerque, L G
2011-10-31
We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.
Klika, Václav; Gaffney, Eamonn A; Chen, Ying-Chun; Brown, Cameron P
2016-09-01
There is a long history of mathematical and computational modelling with the objective of understanding the mechanisms governing cartilage׳s remarkable mechanical performance. Nonetheless, despite sophisticated modelling development, simulations of cartilage have consistently lagged behind structural knowledge and thus the relationship between structure and function in cartilage is not fully understood. However, in the most recent generation of studies, there is an emerging confluence between our structural knowledge and the structure represented in cartilage modelling. This raises the prospect of further refinement in our understanding of cartilage function and also the initiation of an engineering-level understanding for how structural degradation and ageing relates to cartilage dysfunction and pathology, as well as informing the potential design of prospective interventions. Aimed at researchers entering the field of cartilage modelling, we thus review the basic principles of cartilage models, discussing the underlying physics and assumptions in relatively simple settings, whilst presenting the derivation of relatively parsimonious multiphase cartilage models consistent with our discussions. We proceed to consider modern developments that start aligning the structure captured in the models with observed complexities. This emphasises the challenges associated with constitutive relations, boundary conditions, parameter estimation and validation in cartilage modelling programmes. Consequently, we further detail how both experimental interrogations and modelling developments can be utilised to investigate and reduce such difficulties before summarising how cartilage modelling initiatives may improve our understanding of cartilage ageing, pathology and intervention.
Miller, J; Fuller, M; Vinod, S; Suchowerska, N; Holloway, L
2009-06-01
A Clinician's discrimination between radiation therapy treatment plans is traditionally a subjective process, based on experience and existing protocols. A more objective and quantitative approach to distinguish between treatment plans is to use radiobiological or dosimetric objective functions, based on radiobiological or dosimetric models. The efficacy of models is not well understood, nor is the correlation of the rank of plans resulting from the use of models compared to the traditional subjective approach. One such radiobiological model is the Normal Tissue Complication Probability (NTCP). Dosimetric models or indicators are more accepted in clinical practice. In this study, three radiobiological models, Lyman NTCP, critical volume NTCP and relative seriality NTCP, and three dosimetric models, Mean Lung Dose (MLD) and the Lung volumes irradiated at 10Gy (V10) and 20Gy (V20), were used to rank a series of treatment plans using, harm to normal (Lung) tissue as the objective criterion. None of the models considered in this study showed consistent correlation with the Radiation Oncologists plan ranking. If radiobiological or dosimetric models are to be used in objective functions for lung treatments, based on this study it is recommended that the Lyman NTCP model be used because it will provide most consistency with traditional clinician ranking.
The GRB luminosity function: prediction of the internal shock model and comparison to observations
NASA Astrophysics Data System (ADS)
Zitouni, H.; Daigne, F.; Mochkovitch, R.
2008-05-01
We compute the expected GRB luminosity function in the internal shock model. We find that if the population of GRB central engines produces all kind of relativistic outflows, from very smooth to highly variable, the luminosity function has to branchs: at low luminosity, the distribution is dominated by low efficiency GRBs and is close to a power law of slope -0.5, whereas at high luminosity, the luminosity function follows the distribution of injected kinetic power. Using Monte Carlo simulations and several observational constrains (BATSE logN-logP diagram, peak energy distribution of bright BATSE bursts, fraction of XRFs in the HETE2 sample), we show that it is currently impossible to distinguish between a single power law or a broken power law luminosity function. However, when the second case is considered, the low-luminosity slope is found to be -0.6+/-0.2, which is compatible with the prediction of the internal shock model.
Alternate forms of the associated Legendre functions for use in geomagnetic modeling.
Alldredge, L.R.; Benton, E.R.
1986-01-01
An inconvenience attending traditional use of associated Legendre functions in global modeling is that the functions are not separable with respect to the 2 indices (order and degree). In 1973 Merilees suggested a way to avoid the problem by showing that associated Legendre functions of order m and degree m+k can be expressed in terms of elementary functions. This note calls attention to some possible gains in time savings and accuracy in geomagnetic modeling based upon this form. For this purpose, expansions of associated Legendre polynomials in terms of sines and cosines of multiple angles are displayed up to degree and order 10. Examples are also given explaining how some surface spherical harmonics can be transformed into true Fourier series for selected polar great circle paths. -from Authors
Signal-tuned Gabor functions as models for stimulus-dependent cortical receptive fields.
Torreão, José R A; Victer, Silvia M C; Amaral, Marcos S
2014-05-01
We propose and analyze a model, based on signal-tuned Gabor functions, for the receptive fields and responses of V1 cells. Signal-tuned Gabor functions are gaussian-modulated sinusoids whose parameters are obtained from a given, spatial, or spectral "tuning" signal. These functions can be proven to yield exact representations of their tuning signals and have recently been proposed as the kernels of a variant Gabor transform-the signal-tuned Gabor transform (STGT)-which allows the accurate detection of spatial and spectral events. Here we show that by modeling the receptive fields of simple and complex cells as signal-tuned Gabor functions and expressing their responses as STGTs, we are able to replicate the properties of these cells when tested with standard grating and slit inputs, at the same time emulating their stimulus-dependent character as revealed by recent neurophysiological studies.
Wang, Kun; Schoonover, Robert W.; Su, Richard; Oraevsky, Alexander; Anastasio, Mark A.
2015-01-01
Optoacoustic tomography (OAT), also known as photoacoustic tomography, is an emerging computed biomedical imaging modality that exploits optical contrast and ultrasonic detection principles. Iterative image reconstruction algorithms that are based on discrete imaging models are actively being developed for OAT due to their ability to improve image quality by incorporating accurate models of the imaging physics, instrument response, and measurement noise. In this work, we investigate the use of discrete imaging models based on Kaiser-Bessel window functions for iterative image reconstruction in OAT. A closed-form expression for the pressure produced by a Kaiser-Bessel function is calculated, which facilitates accurate computation of the system matrix. Computer-simulation and experimental studies are employed to demonstrate the potential advantages of Kaiser-Bessel function-based iterative image reconstruction in OAT. PMID:24770921
Tse, Wai S; Rochelle, Tina L; Cheung, Jacky C K
2011-06-01
The relationship between personality, social functioning, and depression remains unclear. The present study employs structural equation modeling to examine the mediating role of social functioning between harm avoidance (HA), self-directedness (SD), and depression. A sample of 902 individuals completed a self-report questionnaire consisting of the following scales: HA and SD subscales of the Temperament and Character Inventory (TCI), Beck Depression Inventory (BDI), and Social Adaptation Self-Evaluation Scale (SASS). Structural equation modeling via analysis of moment structure was used to estimate the fit of nine related models. Results indicated that social functioning is a mediator between harm avoidance or self-directness and depression. Self-directedness was also shown to have direct effects on depression. The results support the social reinforcement theory of depression and provide a theoretical account of how the variables are related based on correlation methods. Suggestions are offered for future experimental and longitudinal research.
STRONG GRAVITATIONAL LENS MODELING WITH SPATIALLY VARIANT POINT-SPREAD FUNCTIONS
Rogers, Adam; Fiege, Jason D.
2011-12-10
Astronomical instruments generally possess spatially variant point-spread functions, which determine the amount by which an image pixel is blurred as a function of position. Several techniques have been devised to handle this variability in the context of the standard image deconvolution problem. We have developed an iterative gravitational lens modeling code called Mirage that determines the parameters of pixelated source intensity distributions for a given lens model. We are able to include the effects of spatially variant point-spread functions using the iterative procedures in this lensing code. In this paper, we discuss the methods to include spatially variant blurring effects and test the results of the algorithm in the context of gravitational lens modeling problems.
Wang, Kun; Schoonover, Robert W; Su, Richard; Oraevsky, Alexander; Anastasio, Mark A
2014-05-01
Optoacoustic tomography (OAT), also known as photoacoustic tomography, is an emerging computed biomedical imaging modality that exploits optical contrast and ultrasonic detection principles. Iterative image reconstruction algorithms that are based on discrete imaging models are actively being developed for OAT due to their ability to improve image quality by incorporating accurate models of the imaging physics, instrument response, and measurement noise. In this work, we investigate the use of discrete imaging models based on Kaiser-Bessel window functions for iterative image reconstruction in OAT. A closed-form expression for the pressure produced by a Kaiser-Bessel function is calculated, which facilitates accurate computation of the system matrix. Computer-simulation and experimental studies are employed to demonstrate the potential advantages of Kaiser-Bessel function-based iterative image reconstruction in OAT.
One loop beta functions and fixed points in higher derivative sigma models
Percacci, Roberto; Zanusso, Omar
2010-03-15
We calculate the one loop beta functions of nonlinear sigma models in four dimensions containing general two- and four-derivative terms. In the O(N) model there are four such terms and nontrivial fixed points exist for all N{>=}4. In the chiral SU(N) models there are in general six couplings, but only five for N=3 and four for N=2; we find fixed points only for N=2, 3. In the approximation considered, the four-derivative couplings are asymptotically free but the coupling in the two-derivative term has a nonzero limit. These results support the hypothesis that certain sigma models may be asymptotically safe.
The effects of videotape modeling on staff acquisition of functional analysis methodology.
Moore, James W; Fisher, Wayne W
2007-01-01
Lectures and two types of video modeling were compared to determine their relative effectiveness in training 3 staff members to conduct functional analysis sessions. Video modeling that contained a larger number of therapist exemplars resulted in mastery-level performance eight of the nine times it was introduced, whereas neither lectures nor partial video modeling produced significant improvements in performance. Results demonstrated that video modeling provided an effective training strategy but only when a wide range of exemplars of potential therapist behaviors were depicted in the videotape.
The use of structural modelling to infer structure and function in biocontrol agents.
Berry, Colin; Board, Jason
2017-01-01
Homology modelling can provide important insights into the structures of proteins when a related protein structure has already been solved. However, for many proteins, including a number of invertebrate-active toxins and accessory proteins, no such templates exist. In these cases, techniques of ab initio, template-independent modelling can be employed to generate models that may give insight into structure and function. In this overview, examples of both the problems and the potential benefits of ab initio techniques are illustrated. Consistent modelling results may indicate useful approximations to actual protein structures and can thus allow the generation of hypotheses regarding activity that can be tested experimentally.
Optimization of global model composed of radial basis functions using the term-ranking approach
Cai, Peng; Tao, Chao Liu, Xiao-Jun
2014-03-15
A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.
A new anisotropic compact star model having Matese & Whitman mass function
NASA Astrophysics Data System (ADS)
Bhar, Piyali; Ratanpal, B. S.
2016-07-01
Present paper proposed a new singularity free model of anisotropic compact star. The Einstein field equations are solved in closed form by utilizing Matese & Whitman mass function. The model parameters ρ, pr and pt all are well behaved inside the stellar interior and our model satisfies all the required conditions to be physically acceptable. The model given in the present work is compatible with observational data of compact objects like SAX J 1808.4-3658 (SS1), SAX J 1808.4-3658 (SS2) and 4U 1820-30. A particular model of 4U 1820-30 is studied in detail and found that it satisfies all the condition needed for physically acceptable model. The present work is the generalization of Sharma and Ratanpal (Int. J. Mod. Phys. D 22:1350074, 2013) model for compact stars admitting quadratic equation of state.
Turner, Ryan C.; VanGilder, Reyna L.; Naser, Zachary J.; Lucke-Wold, Brandon P.; Bailes, Julian E.; Matsumoto, Rae R.; Huber, Jason D.; Rosen, Charles L.
2016-01-01
Background Concussion remains a symptom-based diagnosis clinically, yet preclinical studies investigating traumatic brain injury, of which concussion is believed to represent a ‘mild’ form, emphasize histological endpoints with functional assessments often minimized or ignored all together. Recently, clinical studies have identified the importance of cognitive and neuropsychiatric symptoms, in addition to somatic complaints, following concussion. How these findings may translate to preclinical studies is unclear at present. Objective To address the contrasting endpoints utilized clinically compared to those in preclinical studies and the potential role of functional assessments in a commonly used model of diffuse axonal injury (DAI).. Methods Animals were subjected to DAI using the impact-acceleration model. Functional and behavioral assessments were conducted over 1 week following DAI prior to completion of histological assessment at 1-week post-DAI. Results We show, despite the suggestion that this model represents concussive injury, no functional impairments as determined using common measures of motor, sensorimotor, cognitive, and neuropsychiatric function following injury over the course of 1 week. The lack of functional deficits is in sharp contrast to neuropathologic findings indicating neural degeneration, astrocyte reactivity, and microglial activation. Conclusion Future studies are needed to identify functional assessments, neurophysiologic techniques, and imaging assessments more apt to distinguish differences following so-called ‘mild’ traumatic brain injury (TBI) in preclinical models and determine whether these models are truly studying concussive or subconcussive injury. These studies are needed to not only understand mechanism of injury and production of subsequent deficits, but also for rigorous evaluation of potential therapeutic agents. PMID:24448183
Exact Gell-Mann-Low function of supersymmetric Kähler sigma models
NASA Astrophysics Data System (ADS)
Morozov, Alexei Y.; Perelomov, Askold M.; Shifman, Michael A.
We consider a broad class of Kähler supersymmetric sigma models in two-dimensional space-time. The exact Gell-Mann-Low function is found within the framework of the method proposed earlier [1, 2] and based on analysis of classical solutions. It is shown that the exact beta function accounting for all orders in the coupling constant actually coincides with the one-loop result.
Measurement and modeling of transfer functions for lightning coupling into the Sago mine.
Morris, Marvin E.; Higgins, Matthew B.
2007-04-01
This report documents measurements and analytical modeling of electromagnetic transfer functions to quantify the ability of cloud-to-ground lightning strokes (including horizontal arc-channel components) to couple electromagnetic energy into the Sago mine located near Buckhannon, WV. Two coupling mechanisms were measured: direct and indirect drive. These transfer functions are then used to predict electric fields within the mine and induced voltages on conductors that were left abandoned in the sealed area of the Sago mine.
Oro-facial functions in experimental models of cerebral palsy: a systematic review.
Lacerda, D C; Ferraz-Pereira, K N; Bezerra de Morais, A T; Costa-de-Santana, B J R; Quevedo, O G; Manhães-de-Castro, R; Toscano, A E
2017-04-01
Children who suffer from cerebral palsy (CP) often present comorbidities in the form of oro-facial dysfunctions. Studies in animals have contributed to elaborate potential therapies aimed at minimising the chronic disability of the syndrome. To systematically review the scientific literature regarding the possible effects that experimental models of CP can have on oro-facial functions. Two independent authors conducted a systematic review in the electronic databases Medline, Scopus, CINAHL, Web of Science and Lilacs, using Mesh and Decs terms in animal models. The motor and sensory parameters of sucking, chewing and swallowing were considered as primary outcomes; reactivity odour, controlled salivation, postural control, head mobility during feeding and the animal's ability to acquire food were secondary outcomes. Ten studies were included in the present review. Most studies used rabbits as experimental models of CP, which was induced by either hypoxia-ischemia, inflammation or intraventricular haemorrhage. Oro-facial functions were altered in all experimental models of CP. However, we found more modifications in hypoxia-ischemia models overall. On the other hand, the model of inflammation was more effective to reproduce higher damage for coordinating sucking and swallowing. All of the CP experimental models that were assessed modified the oral functions in different animal species. However, further studies should be conducted in order to clarify the mechanisms underlying oro-facial damage in order to optimise treatment strategies for children who suffer from CP.
Constrained parametric model for simultaneous inference of two cumulative incidence functions.
Shi, Haiwen; Cheng, Yu; Jeong, Jong-Hyeon
2013-01-01
We propose a parametric regression model for the cumulative incidence functions (CIFs) commonly used for competing risks data. The model adopts a modified logistic model as the baseline CIF and a generalized odds-rate model for covariate effects, and it explicitly takes into account the constraint that a subject with any given prognostic factors should eventually fail from one of the causes such that the asymptotes of the CIFs should add up to one. This constraint intrinsically holds in a nonparametric analysis without covariates, but is easily overlooked in a semiparametric or parametric regression setting. We hence model the CIF from the primary cause assuming the generalized odds-rate transformation and the modified logistic function as the baseline CIF. Under the additivity constraint, the covariate effects on the competing cause are modeled by a function of the asymptote of the baseline distribution and the covariate effects on the primary cause. The inference procedure is straightforward by using the standard maximum likelihood theory. We demonstrate desirable finite-sample performance of our model by simulation studies in comparison with existing methods. Its practical utility is illustrated in an analysis of a breast cancer dataset to assess the treatment effect of tamoxifen, adjusting for age and initial pathological tumor size, on breast cancer recurrence that is subject to dependent censoring by second primary cancers and deaths.
NASA Astrophysics Data System (ADS)
Jian, Wang; Xiaohong, Meng; Hong, Liu; Wanqiu, Zheng; Yaning, Liu; Sheng, Gui; Zhiyang, Wang
2017-03-01
Full waveform inversion and reverse time migration are active research areas for seismic exploration. Forward modeling in the time domain determines the precision of the results, and numerical solutions of finite difference have been widely adopted as an important mathematical tool for forward modeling. In this article, the optimum combined of window functions was designed based on the finite difference operator using a truncated approximation of the spatial convolution series in pseudo-spectrum space, to normalize the outcomes of existing window functions for different orders. The proposed combined window functions not only inherit the characteristics of the various window functions, to provide better truncation results, but also control the truncation error of the finite difference operator manually and visually by adjusting the combinations and analyzing the characteristics of the main and side lobes of the amplitude response. Error level and elastic forward modeling under the proposed combined system were compared with outcomes from conventional window functions and modified binomial windows. Numerical dispersion is significantly suppressed, which is compared with modified binomial window function finite-difference and conventional finite-difference. Numerical simulation verifies the reliability of the proposed method.
Larsen, Flemming H; Engelsen, Søren B
2015-01-01
Microbial polysaccharides represent an important class of microbial polymers with diverse functions such as biofilm formation, thickening, and gelling properties as well as health-promoting properties. The broad range of exopolysaccharide (EPS) functionalities has sparked a renewed interest in this class of molecules. Chemical, enzymatic as well as genetic modifications by metabolic engineering can be used to create large numbers of analogous EPS variants with respect to EPS functionality. While this top-down approach is effective in finding new candidates for desired functionality, there seems to be a lack of the corresponding bottom-up approach. The molecular mechanisms of the desired functionalities can be established from Nuclear Magnetic Resonance (NMR) and molecular models and it is proposed that these models can be fed back into the biotechnology by using a quantitative structure-property approach. In this way it will be possible to tailor specific functionality within a given design space. This perspective will include two well-known commercial microbial EPS examples namely gellan and diutan and show how even a limited use of multiphase NMR and molecular modeling can increase the insight into their different properties, which are based on only minor structural differences.
NASA Astrophysics Data System (ADS)
Asahi, Ryoji; Freeman, A. J.
1998-03-01
Recently proposed nonlocal exchange potential methods such as screened exchange (sX-LDA)(Bylander, Kleinman, Phys. Rev. B 41, 7868 (1990)) and model GW(Gygi, Baldereschi, Phys. Rev. Lett. 62, 2160 (1989)) demonstrated successful extensions of LDA energy bands to treat excited states in semiconductors and insulators. While using different static dielectric functions - a Thomas-Fermi or a Hubbard screening function for the sX-LDA and a step function or an RPA for the model GW - those methods gave surprising agreement of the energy gaps with each other and with experiments. We have investigated semiconductor systems such as Si, Ge, and InSb using the full-potential linearized augmented plane wave (FLAPW) method(Wimmer, Krakauer, Weinert, Freeman, Phys. Rev. B 24, 864 (1981)) within the model GW method including the above dielectric functions. Our focus is on understanding the different results obtained for the structural properties (lattice constants and bulk moduli) and optical properties (band gaps and optical spectra). We find that the results can be interpreted by different long-range screening behavior corresponding to the different static dielectric functions employed in the model GW calculations.
Goebel, Nicole L; Edwards, Christopher A; Follows, Michael J; Zehr, Jonathan P
2014-01-01
Ecosystem-wide primary productivity generally increases with primary producer diversity, emphasizing the importance of diversity for ecosystem function. However, most studies that demonstrate this positive relationship have focused on terrestrial and aquatic benthic systems, with little attention to the diverse marine pelagic primary producers that play an important role in regulating global climate. Here we show how phytoplankton biodiversity enhances overall marine ecosystem primary productivity and other ecosystem functions using a self-organizing ecosystem model. Diversity manipulation numerical experiments reveal positive, asymptotically saturating relationships between ecosystem-wide phytoplankton diversity and functions of productivity, nutrient uptake, remineralization, and diversity metrics used to identify mechanisms shaping these relationships. Increase in productivity with increasing diversity improves modeled ecosystem stability and model robustness and leads to productivity rates that exceed expected yields primarily through niche complementarity and facilitative interactions between coexisting phytoplankton types; the composition of traits in assemblages determines the magnitude of complementarity and selection effects. While findings based on these aggregate measures of diversity effects parallel those from the majority of experimental outcomes of terrestrial and benthic biodiversity-ecosystem function studies, we combine analyses of community diversity effects and investigations of the underlying interactions among phytoplankton types to demonstrate how an increase in recycled production of non-diatoms through an increase in new production of diatoms drives this diversity-cosystem function response. We demonstrate the important role that facilitation plays in the modeled marine plankton and how this facilitative interaction could amplify future climate-driven changes in ocean ecosystem productivity.
The demise of constant price impact functions and single-time step models of speculation
NASA Astrophysics Data System (ADS)
Challet, Damien
2007-08-01
Constant and symmetric price impact functions, most commonly used in agent-based market modelling, are shown to give rise to paradoxical and inconsistent outcomes in the simplest case of arbitrage exploitation when open-hold-close actions are considered. The solution of the paradox lies in the non-constant nature of real-life price impact functions. A simple model that includes explicit position opening, holding, and closing is briefly introduced and its information ecology discussed, shedding new light on the relevance of the Minority Game to the study of financial markets.
A model function of the global bomb tritium distribution in precipitation, 1960-1986
NASA Astrophysics Data System (ADS)
Doney, Scott C.; Glover, David M.; Jenkins, William J.
1992-04-01
The paper presents a model function for predicting the annual mean concentration of the decay-corrected bomb tritium in precipitation over the time period 1960-1986. The model was developed using the World Meteorological Organization/International Atomic Energy Agency data for tritium precipitation. The resulting tritium function is global in scope and includes both marine and continental data. Estimates were obtained of the seasonal cycle of tritium in precipitation, which may be useful for studying atmospheric transport and oceanic processes, such as convection and subduction that occur on seasonal timescales.
Lyapunov function and the basin of attraction for a single-joint muscle-skeletal model.
Giesl, Peter; Wagner, Heiko
2007-04-01
This paper provides an explicit Lyapunov function for a general single-joint muscle-skeletal model. Using this Lyapunov function one can determine analytically large subsets of the basin of attraction of an asymptotically stable equilibrium. Besides providing an analytical tool for the analysis of such a system we consider an elbow model and show that the theoretical predictions are in agreement with experimental results. Moreover, we can thus distinguish between regions where the self-stabilizing properties of the muscle-skeletal system guarantee stability and regions where nerval control and reflexes are necessary.
Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang
2010-07-01
We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.
Parametric modeling of quantile regression coefficient functions with censored and truncated data.
Frumento, Paolo; Bottai, Matteo
2017-02-09
Quantile regression coefficient functions describe how the coefficients of a quantile regression model depend on the order of the quantile. A method for parametric modeling of quantile regression coefficient functions was discussed in a recent article. The aim of the present work is to extend the existing framework to censored and truncated data. We propose an estimator and derive its asymptotic properties. We discuss goodness-of-fit measures, present simulation results, and analyze the data that motivated this article. The described estimator has been implemented in the R package qrcm.
Cong, W; Shen, H; Cong, A; Wang, Y; Wang, G
2007-11-01
Photon propagation in biological tissue is commonly described by the radiative transfer equation, while the phase function in the equation represents the scattering characteristics of the medium and has significant influence on the precision of solution and the efficiency of computation. In this work, we present a generalized Delta-Eddington phase function to simplify the radiative transfer equation to an integral equation with respect to photon fluence rate. Comparing to the popular diffusion approximation model, the solution of the integral equation is highly accurate to model photon propagation in the biological tissue over a broad range of optical parameters. This methodology is validated by Monte Carlo simulation.
Greenwood, Michael; Provatas, Nikolas; Rottler, Jörg
2010-07-23
The phase field crystal (PFC) method is a promising technique for modeling materials with atomic resolution on mesoscopic time scales. While numerically more efficient than classical density functional theory (CDFT), its single mode free energy limits the complexity of structural transformations that can be simulated. We introduce a new PFC model inspired by CDFT, which uses a systematic construction of two-particle correlation functions that allows for a broad class of structural transformations. Our approach considers planar spacings, lattice symmetries, planar atomic densities, and atomic vibrational amplitudes in the unit cell, and parameterizes temperature and anisotropic surface energies. The power of our approach is demonstrated by two examples of structural phase transformations.
Suzuki, Makoto; Sugimura, Yuko; Yamada, Sumio; Omori, Yoshitsugu; Miyamoto, Masaaki; Yamamoto, Jun-ichi
2013-01-01
Cognitive disorders in the acute stage of stroke are common and are important independent predictors of adverse outcome in the long term. Despite the impact of cognitive disorders on both patients and their families, it is still difficult to predict the extent or duration of cognitive impairments. The objective of the present study was, therefore, to provide data on predicting the recovery of cognitive function soon after stroke by differential modeling with logarithmic and linear regression. This study included two rounds of data collection comprising 57 stroke patients enrolled in the first round for the purpose of identifying the time course of cognitive recovery in the early-phase group data, and 43 stroke patients in the second round for the purpose of ensuring that the correlation of the early-phase group data applied to the prediction of each individual's degree of cognitive recovery. In the first round, Mini-Mental State Examination (MMSE) scores were assessed 3 times during hospitalization, and the scores were regressed on the logarithm and linear of time. In the second round, calculations of MMSE scores were made for the first two scoring times after admission to tailor the structures of logarithmic and linear regression formulae to fit an individual's degree of functional recovery. The time course of early-phase recovery for cognitive functions resembled both logarithmic and linear functions. However, MMSE scores sampled at two baseline points based on logarithmic regression modeling could estimate prediction of cognitive recovery more accurately than could linear regression modeling (logarithmic modeling, R(2) = 0.676, P<0.0001; linear regression modeling, R(2) = 0.598, P<0.0001). Logarithmic modeling based on MMSE scores could accurately predict the recovery of cognitive function soon after the occurrence of stroke. This logarithmic modeling with mathematical procedures is simple enough to be adopted in daily clinical practice.
An investigation of the stochastic Hodgkin-Huxley models under noisy rate functions.
Güler, Marifi
2013-09-01
The effects of ion channel fluctuations on the transmembrane voltage activity are potentially profound in small-size excitable membrane patches. Different groups have extended Hodgkin-Huxley equations into stochastic differential equations to capture the effects of ion channel noise analytically (Fox & Lu, 1994; Linaro, Storace, & Giugliano, 2011; Güler, 2013). Studies have shown that the accuracy of spiking statistics by Fox and Lu's model does not match well with the corresponding statistics from the exact microscopic simulations. The models of both Linaro et al. and Güler, however, were found to produce highly accurate statistics. Here we extend the examination of these models to the case in which the rate functions for the opening and closing of gates are under the influence of noise. For that purpose, the usual rate functions are accompanied additively by Ornstein-Uhlenbeck-type stochastic angular variables. Moreover, we argue that the existence of such noise in the rate functions is a plausible physiological phenomenon for finite-size membranes. It is observed that the presence of noise in the rates is not effective on the degree of inaccuracies within the Fox and Lu model. Güler model's accuracy is found to remain high as in the case of noise free rates. But the performance of Linaro et al.'s model is seen to degrade seriously with the increasing strength of the introduced rate function noise. We attribute this failure of Linaro et al.'s model to the use of the covariance function of open channels at the steady state, in its derivation.
Regulatory network reconstruction using an integral additive model with flexible kernel functions
Novikov, Eugene; Barillot, Emmanuel
2008-01-01
Background Reconstruction of regulatory networks is one of the most challenging tasks of systems biology. A limited amount of experimental data and little prior knowledge make the problem difficult to solve. Although models that are currently used for inferring regulatory networks are sometimes able to make useful predictions about the structures and mechanisms of molecular interactions, there is still a strong demand to develop increasingly universal and accurate approaches for network reconstruction. Results The additive regulation model is represented by a set of differential equations and is frequently used for network inference from time series data. Here we generalize this model by converting differential equations into integral equations with adjustable kernel functions. These kernel functions can be selected based on prior knowledge or defined through iterative improvement in data analysis. This makes the integral model very flexible and thus capable of covering a broad range of biological systems more adequately and specifically than previous models. Conclusion We reconstructed network structures from artificial and real experimental data using differential and integral inference models. The artificial data were simulated using mathematical models implemented in JDesigner. The real data were publicly available yeast cell cycle microarray time series. The integral model outperformed the differential one for all cases. In the integral model, we tested the zero-degree polynomial and single exponential kernels. Further improvements could be expected if the kernel were selected more specifically depending on the system. PMID:18218091
A clustering algorithm based on two distance functions for MEC model.
Wang, Ying; Feng, Enmin; Wang, Ruisheng
2007-04-01
Haplotype reconstruction, based on aligned single nucleotide polymorphism (SNP) fragments, is to infer a pair of haplotypes from localized polymorphism data gathered through short genome fragment assembly. This paper first presents two distance functions, which are used to measure the difference degree and similarity degree between SNP fragments. Based on the two distance functions, a clustering algorithm is proposed in order to solve MEC model. The algorithm involves two sections. One is to determine the initial haplotype pair, the other concerns with inferring true haplotype pair by re-clustering. The comparison results prove that our algorithm utilizing two distance functions is effective and feasible.
Carpenter, D.C. ); Hill, R.J. . School of Electronic and Electrical Engineering)
1993-09-01
A method is described for the determination of ground conductivity as a continuous function of depth and frequency for applications along spatially linear structures such as railway tracks. The technique involves measurements of mutual resistance using a modified dipole array excited with AC currents up to audio frequency. After representation of the experimental data by analytic functions, the ground conductivity-depth variation is obtained as a degenerate hypergeometric function. The determined ground conductivity is utilized to model the self and mutual conductance of and between the running rails in a single-track railway. The result is verified by experimental measurement.