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.
Modeling mitochondrial function.
Balaban, Robert S
2006-12-01
The mitochondrion represents a unique opportunity to apply mathematical modeling to a complex biological system. Understanding mitochondrial function and control is important since this organelle is critical in energy metabolism as well as playing key roles in biochemical synthesis, redox control/signaling, and apoptosis. A mathematical model, or hypothesis, provides several useful insights including a rigorous test of the consensus view of the operation of a biological process as well as providing methods of testing and creating new hypotheses. The advantages of the mitochondrial system for applying a mathematical model include the relative simplicity and understanding of the matrix reactions, the ability to study the mitochondria as a independent contained organelle, and, most importantly, one can dynamically measure many of the internal reaction intermediates, on line. The developing ability to internally monitor events within the metabolic network, rather than just the inflow and outflow, is extremely useful in creating critical bounds on complex mathematical models using the individual reaction mechanisms available. However, many serious problems remain in creating a working model of mitochondrial function including the incomplete definition of metabolic pathways, the uncertainty of using in vitro enzyme kinetics, as well as regulatory data in the intact system and the unknown chemical activities of relevant molecules in the matrix. Despite these formidable limitations, the advantages of the mitochondrial system make it one of the best defined mammalian metabolic networks that can be used as a model system for understanding the application and use of mathematical models to study biological systems.
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…
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.
Models in palaeontological functional analysis
Anderson, Philip S. L.; Bright, Jen A.; Gill, Pamela G.; Palmer, Colin; Rayfield, Emily J.
2012-01-01
Models are a principal tool of modern science. By definition, and in practice, models are not literal representations of reality but provide simplifications or substitutes of the events, scenarios or behaviours that are being studied or predicted. All models make assumptions, and palaeontological models in particular require additional assumptions to study unobservable events in deep time. In the case of functional analysis, the degree of missing data associated with reconstructing musculoskeletal anatomy and neuronal control in extinct organisms has, in the eyes of some scientists, rendered detailed functional analysis of fossils intractable. Such a prognosis may indeed be realized if palaeontologists attempt to recreate elaborate biomechanical models based on missing data and loosely justified assumptions. Yet multiple enabling methodologies and techniques now exist: tools for bracketing boundaries of reality; more rigorous consideration of soft tissues and missing data and methods drawing on physical principles that all organisms must adhere to. As with many aspects of science, the utility of such biomechanical models depends on the questions they seek to address, and the accuracy and validity of the models themselves. PMID:21865242
Models in palaeontological functional analysis.
Anderson, Philip S L; Bright, Jen A; Gill, Pamela G; Palmer, Colin; Rayfield, Emily J
2012-02-23
Models are a principal tool of modern science. By definition, and in practice, models are not literal representations of reality but provide simplifications or substitutes of the events, scenarios or behaviours that are being studied or predicted. All models make assumptions, and palaeontological models in particular require additional assumptions to study unobservable events in deep time. In the case of functional analysis, the degree of missing data associated with reconstructing musculoskeletal anatomy and neuronal control in extinct organisms has, in the eyes of some scientists, rendered detailed functional analysis of fossils intractable. Such a prognosis may indeed be realized if palaeontologists attempt to recreate elaborate biomechanical models based on missing data and loosely justified assumptions. Yet multiple enabling methodologies and techniques now exist: tools for bracketing boundaries of reality; more rigorous consideration of soft tissues and missing data and methods drawing on physical principles that all organisms must adhere to. As with many aspects of science, the utility of such biomechanical models depends on the questions they seek to address, and the accuracy and validity of the models themselves.
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.
Modeling Functions with the Calculator Based Ranger.
ERIC Educational Resources Information Center
Sherrill, Donna; Tibbs, Peggy
This paper presents two mathematics activities that model functions studied using the Calculator Based Ranger (CBR) software for TI-82 and TI-83 graphing calculators. The activities concern a bouncing ball experiment and modeling a decaying exponential function. (ASK)
Function Model for Community Health Service Information
NASA Astrophysics Data System (ADS)
Yang, Peng; Pan, Feng; Liu, Danhong; Xu, Yongyong
In order to construct a function model of community health service (CHS) information for development of CHS information management system, Integration Definition for Function Modeling (IDEF0), an IEEE standard which is extended from Structured Analysis and Design(SADT) and now is a widely used function modeling method, was used to classifying its information from top to bottom. The contents of every level of the model were described and coded. Then function model for CHS information, which includes 4 super-classes, 15 classes and 28 sub-classed of business function, 43 business processes and 168 business activities, was established. This model can facilitate information management system development and workflow refinement.
Belief Function Model for Information Retrieval.
ERIC Educational Resources Information Center
Silva, Wagner Teixeira da; Milidiu, Ruy Luiz
1993-01-01
Describes the Belief Function Model for automatic indexing and ranking of documents which is based on a controlled vocabulary and on term frequencies in each document. Belief Function Theory is explained, and the Belief Function Model is compared to the Standard Vector Space Model. (17 references) (LRW)
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.
Computational Modeling of Mitochondrial Function
Cortassa, Sonia; Aon, Miguel A.
2012-01-01
The advent of techniques with the ability to scan massive changes in cellular makeup (genomics, proteomics, etc.) has revealed the compelling need for analytical methods to interpret and make sense of those changes. Computational models built on sound physico-chemical mechanistic basis are unavoidable at the time of integrating, interpreting, and simulating high-throughput experimental data. Another powerful role of computational models is predicting new behavior provided they are adequately validated. Mitochondrial energy transduction has been traditionally studied with thermodynamic models. More recently, kinetic or thermo-kinetic models have been proposed, leading the path toward an understanding of the control and regulation of mitochondrial energy metabolism and its interaction with cytoplasmic and other compartments. In this work, we outline the methods, step-by-step, that should be followed to build a computational model of mitochondrial energetics in isolation or integrated to a network of cellular processes. Depending on the question addressed by the modeler, the methodology explained herein can be applied with different levels of detail, from the mitochondrial energy producing machinery in a network of cellular processes to the dynamics of a single enzyme during its catalytic cycle. PMID:22057575
Classical Testing in Functional Linear Models
Kong, Dehan; Staicu, Ana-Maria; Maity, Arnab
2016-01-01
We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications. PMID:28955155
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.
The universal function in color dipole model
NASA Astrophysics Data System (ADS)
Jalilian, Z.; Boroun, G. R.
2017-10-01
In this work we review color dipole model and recall properties of the saturation and geometrical scaling in this model. Our primary aim is determining the exact universal function in terms of the introduced scaling variable in different distance than the saturation radius. With inserting the mass in calculation we compute numerically the contribution of heavy productions in small x from the total structure function by the fraction of universal functions and show the geometrical scaling is established due to our scaling variable in this study.
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
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
The NJL Model for Quark Fragmentation Functions
T. Ito, W. Bentz, I. Cloet, A W Thomas, K. Yazaki
2009-10-01
A description of fragmentation functions which satisfy the momentum and isospin sum rules is presented in an effective quark theory. Concentrating on the pion fragmentation function, we first explain the reason why the elementary (lowest order) fragmentation process q → qπ is completely inadequate to describe the empirical data, although the “crossed” process π → qq describes the quark distribution functions in the pion reasonably well. Then, taking into account cascade-like processes in a modified jet-model approach, we show that the momentum and isospin sum rules can be satisfied naturally without introducing any ad-hoc parameters. We present numerical results for the Nambu-Jona-Lasinio model in the invariant mass regularization scheme, and compare the results with the empirical parametrizations. We argue that this NJL-jet model provides a very useful framework to calculate the fragmentation functions in an effective chiral quark theory.
Functional model of biological neural networks
2010-01-01
A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks. PMID:22132040
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.
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.
Simple model dielectric functions for insulators
NASA Astrophysics Data System (ADS)
Vos, Maarten; Grande, Pedro L.
2017-05-01
The Drude dielectric function is a simple way of describing the dielectric function of free electron materials, which have an uniform electron density, in a classical way. The Mermin dielectric function describes a free electron gas, but is based on quantum physics. More complex metals have varying electron densities and are often described by a sum of Drude dielectric functions, the weight of each function being taken proportional to the volume with the corresponding density. Here we describe a slight variation on the Drude dielectric functions that describes insulators in a semi-classical way and a form of the Levine-Louie dielectric function including a relaxation time that does the same within the framework of quantum physics. In the optical limit the semi-classical description of an insulator and the quantum physics description coincide, in the same way as the Drude and Mermin dielectric function coincide in the optical limit for metals. There is a simple relation between the coefficients used in the classical and quantum approaches, a relation that ensures that the obtained dielectric function corresponds to the right static refractive index. For water we give a comparison of the model dielectric function at non-zero momentum with inelastic X-ray measurements, both at relative small momenta and in the Compton limit. The Levine-Louie dielectric function including a relaxation time describes the spectra at small momentum quite well, but in the Compton limit there are significant deviations.
Elucidation of SESANS correlation functions through model
Shew, Chwen-Yang; Chen, Wei-Ren
2012-01-01
Several single-modal Debye correlation functions are closely examined to elucidate the behavior of their corresponding SESANS (Spin Echo Small Angle Neutron Scattering) correlation functions. We nd that the upper bound of a Debye correlation function and of its SESANS correlation func- tion is identical. For discrete Debye correlation functions, the peak of SESANS correlation function emerges at their rst discrete point, whereas for continuous Debye correlation functions with greater width, the peak position shifts to a greater value. In both cases, the intensity and shape of the peak of the SESANS correlation function are determined by the width of the normalized Debye correlation functions. In the application, we mimic the intramolecular and intermolecular Debye correlation functions of liquids composed of interacting particles by using the simple models to elucidate their competition in the SESANS correlation function. Our calculations show that the position of the rst minimum of SESANS correlation function shifts to a smaller value as inter- molecular attraction or correlation is enhanced. The minimum value can be positive or negative, and the positive values are observed for the cases equivalent to stronger intermolecular attraction, consistent with literature results based on more sophisticated liquid state theory and simulations.
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.
Understanding human functioning using graphical models
2010-01-01
Background Functioning and disability are universal human experiences. However, our current understanding of functioning from a comprehensive perspective is limited. The development of the International Classification of Functioning, Disability and Health (ICF) on the one hand and recent developments in graphical modeling on the other hand might be combined and open the door to a more comprehensive understanding of human functioning. The objective of our paper therefore is to explore how graphical models can be used in the study of ICF data for a range of applications. Methods We show the applicability of graphical models on ICF data for different tasks: Visualization of the dependence structure of the data set, dimension reduction and comparison of subpopulations. Moreover, we further developed and applied recent findings in causal inference using graphical models to estimate bounds on intervention effects in an observational study with many variables and without knowing the underlying causal structure. Results In each field, graphical models could be applied giving results of high face-validity. In particular, graphical models could be used for visualization of functioning in patients with spinal cord injury. The resulting graph consisted of several connected components which can be used for dimension reduction. Moreover, we found that the differences in the dependence structures between subpopulations were relevant and could be systematically analyzed using graphical models. Finally, when estimating bounds on causal effects of ICF categories on general health perceptions among patients with chronic health conditions, we found that the five ICF categories that showed the strongest effect were plausible. Conclusions Graphical Models are a flexible tool and lend themselves for a wide range of applications. In particular, studies involving ICF data seem to be suited for analysis using graphical models. PMID:20149230
Rational transfer function models for biofilm reactors
Wik, T.; Breitholtz, C.
1998-12-01
Design of controllers and optimization of plants using biofilm reactors often require dynamic models and efficient simulation methods. Standard model assumptions were used to derive nonrational transfer functions describing the fast dynamics of stirred-tank reactors with zero- or first-order reactions inside the biofilm. A method based on the location of singularities was used to derive rational transfer functions that approximate nonrational ones. These transfer functions can be used in efficient simulation routines and in standard methods of controller design. The order of the transfer functions can be chosen in a natural way, and changes in physical parameters may directly be related to changes in the transfer functions. Further, the mass balances used and, hence, the transfer functions, are applicable to catalytic reactors with porous catalysts as well. By applying the methods to a nitrifying trickling filter, reactor parameters are estimated from residence-time distributions and low-order rational transfer functions are achieved. Simulated effluent dynamics, using these transfer functions, agree closely with measurements.
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.
Functional Error Models to Accelerate Nested Sampling
NASA Astrophysics Data System (ADS)
Josset, L.; Elsheikh, A. H.; Demyanov, V.; Lunati, I.
2014-12-01
The main challenge in groundwater problems is the reliance on large numbers of unknown parameters with wide rage of associated uncertainties. To translate this uncertainty to quantities of interest (for instance the concentration of pollutant in a drinking well), a large number of forward flow simulations is required. To make the problem computationally tractable, Josset et al. (2013, 2014) introduced the concept of functional error models. It consists in two elements: a proxy model that is cheaper to evaluate than the full physics flow solver and an error model to account for the missing physics. The coupling of the proxy model and the error models provides reliable predictions that approximate the full physics model's responses. The error model is tailored to the problem at hand by building it for the question of interest. It follows a typical approach in machine learning where both the full physics and proxy models are evaluated for a training set (subset of realizations) and the set of responses is used to construct the error model using functional data analysis. Once the error model is devised, a prediction of the full physics response for a new geostatistical realization can be obtained by computing the proxy response and applying the error model. We propose the use of functional error models in a Bayesian inference context by combining it to the Nested Sampling (Skilling 2006; El Sheikh et al. 2013, 2014). Nested Sampling offers a mean to compute the Bayesian Evidence by transforming the multidimensional integral into a 1D integral. The algorithm is simple: starting with an active set of samples, at each iteration, the sample with the lowest likelihood is kept aside and replaced by a sample of higher likelihood. The main challenge is to find this sample of higher likelihood. We suggest a new approach: first the active set is sampled, both proxy and full physics models are run and the functional error model is build. Then, at each iteration of the Nested
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.
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. PMID:23936338
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.
MULTIVARIATE VARYING COEFFICIENT MODEL FOR FUNCTIONAL RESPONSES
Zhu, Hongtu; Li, Runze; Kong, Linglong
2012-01-01
Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment. PMID:23645942
MULTIVARIATE VARYING COEFFICIENT MODEL FOR FUNCTIONAL RESPONSES
Zhu, Hongtu; Li, Runze; Kong, Linglong
2012-01-01
Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment. PMID:12926711
MULTIVARIATE VARYING COEFFICIENT MODEL FOR FUNCTIONAL RESPONSES.
Zhu, Hongtu; Li, Runze; Kong, Linglong
2012-10-01
Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the local linear estimate of coefficient functions, as well as its asymptotic bias and variance, and then we derive asymptotic bias and mean integrated squared error of smoothed individual functions and their uniform convergence rate. We establish the uniform convergence rate of the estimated covariance function of the individual functions and its associated eigenvalue and eigenfunctions. We propose a global test for linear hypotheses of varying coefficient functions, and derive its asymptotic distribution under the null hypothesis. We also propose a simultaneous confidence band for each individual effect curve. We conduct Monte Carlo simulation to examine the finite-sample performance of the proposed procedures. We apply MVCM to investigate the development of white matter diffusivities along the genu tract of the corpus callosum in a clinical study of neurodevelopment.
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.
Physiology of female sexual function: animal models.
Giraldi, Annamaria; Marson, Lesley; Nappi, Rossella; Pfaus, James; Traish, Abdulmaged M; Vardi, Yoram; Goldstein, Irwin
2004-11-01
Data concerning the physiology of desire, arousal, and orgasm in women are limited because of ethical constraints. Aim. To gain knowledge of physiology of female sexual function through animal models. To provide state-of-the-art knowledge concerning female sexual function in animal models, representing the opinions of seven experts from five countries developed in a consensus process over a 2-year period. Expert opinion was based on the grading of evidence-based medical literature, widespread internal committee discussion, public presentation, and debate. Sexual desire may be considered as the presence of desire for, and fantasy about, sexual activity. Desire in animals can be inferred from certain appetitive behaviors that occur during copulation and from certain unconditioned copulatory measures. Proceptive behaviors are dependent in part on estrogen, progesterone, and drugs that bind to D1 dopamine receptors, adrenergic receptors, oxytocin receptors, opioid receptors, or gamma-amino butyric acid receptors. Peripheral arousal states are dependent on regulation of genital smooth muscle tone. Multiple neurotransmitters/mediators are involved including adrenergic, and nonadrenergic, noncholinergic agents such as vasoactive intestinal polypeptide, nitric oxide, neuropeptide Y, calcitonin gene-related peptide, and substance P. Sex steroid hormones, estrogens and androgens, are critical for structure and function of genital tissues including modulation of genital blood flow, lubrication, neurotransmitter function, smooth muscle contractility, mucification, and sex steroid receptor expression in genital tissues. Orgasm may be investigated by urethrogenital (UG) reflex, in which genital stimulation results in rhythmic contractions of striated perineal muscles and contractions of vagina, anus, and uterine smooth muscle. The UG reflex is generated by a multisegmental spinal pattern generator involving the coordination of sympathetic, parasympathetic, and somatic efferents
Mass functions in coupled dark energy models
Mainini, Roberto; Bonometto, Silvio
2006-08-15
We evaluate the mass function of virialized halos, by using Press and Schechter (PS) and/or Steth and Tormen (ST) expressions, for cosmologies where dark energy (DE) is due to a scalar self-interacting field, coupled with dark matter (DM). We keep to coupled DE (cDE) models known to fit linear observables. To implement the PS-ST approach, we start from reviewing and extending the results of a previous work on the growth of a spherical top-hat fluctuation in cDE models, confirming their most intriguing astrophysical feature, i.e. a significant baryon-DM segregation, occurring well before the onset of any hydrodynamical effect. Accordingly, the predicted mass function depends on how halo masses are measured. For any option, however, the coupling causes a distortion of the mass function, still at z=0. Furthermore, the z-dependence of cDE mass functions is mostly displaced, in respect to {lambda}CDM, in the opposite way of uncoupled dynamical DE. This is an aspect of the basic underlying result, that even a little DM-DE coupling induces relevant modifications in the nonlinear evolution. Therefore, without causing great shifts in linear astrophysical observables, the DM-baryon segregation induced by the coupling can have an impact on a number of cosmological problems, e.g., galaxy satellite abundance, spiral disk formation, apparent baryon shortage, entropy input in clusters, etc.
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.
Mouse models of p53 functions.
Lozano, Guillermina
2010-04-01
Studies in mice have yielded invaluable insight into our understanding of the p53 pathway. Mouse models with activated p53, no p53, and mutant p53 have queried the role of p53 in development and tumorigenesis. In these models, p53 is activated and stabilized via redundant posttranslational modifications. On activation, p53 initiates two major responses: inhibition of proliferation (via cell-cycle arrest, quiescence, senescence, and differentiation) and induction of apoptosis. Importantly, these responses are cell-type and tumor-type-specific. The analysis of mutant p53 alleles has established a gain-of-function role for p53 mutants in metastasis. The development of additional models that can precisely time the oncogenic events in single cells will provide further insight into the evolution of tumors, the importance of the stroma, and the cooperating events that lead to disruption of the p53 pathway. Ultimately, these models should serve to study the effects of novel drugs on tumor response as well as normal homeostasis.
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 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.
Genetically modified mouse models addressing gonadotropin function.
Ratner, Laura D; Rulli, Susana B; Huhtaniemi, Ilpo T
2014-03-01
The development of genetically modified animals has been useful to understand the mechanisms involved in the regulation of the gonadotropin function. It is well known that alterations in the secretion of a single hormone is capable of producing profound reproductive abnormalities. Human chorionic gonadotropin (hCG) is a glycoprotein hormone normally secreted by the human placenta, and structurally and functionally it is related to pituitary LH. LH and hCG bind to the same LH/hCG receptor, and hCG is often used as an analog of LH to boost gonadotropin action. There are many physiological and pathological conditions where LH/hCG levels and actions are elevated. In order to understand how elevated LH/hCG levels may impact on the hypothalamic-pituitary-gonadal axis we have developed a transgenic mouse model with chronic hCG hypersecretion. Female mice develop many gonadal and extragonadal phenotypes including obesity, infertility, hyperprolactinemia, and pituitary and mammary gland tumors. This article summarizes recent findings on the mechanisms involved in pituitary gland tumorigenesis and hyperprolactinemia in the female mice hypersecreting hCG, in particular the relationship of progesterone with the hyperprolactinemic condition of the model. In addition, we describe the role of hyperprolactinemia as the main cause of infertility and the phenotypic abnormalities in these mice, and the use of dopamine agonists bromocriptine and cabergoline to normalize these conditions. Copyright © 2014 Society for Biology of Reproduction & the Institute of Animal Reproduction and Food Research of Polish Academy of Sciences in Olsztyn. Published by Elsevier Urban & Partner Sp. z o.o. All rights reserved.
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.
Functional Validation of Heteromeric Kainate Receptor Models.
Paramo, Teresa; Brown, Patricia M G E; Musgaard, Maria; Bowie, Derek; Biggin, Philip C
2017-09-19
Kainate receptors require the presence of external ions for gating. Most work thus far has been performed on homomeric GluK2 but, in vivo, kainate receptors are likely heterotetramers. Agonists bind to the ligand-binding domain (LBD) which is arranged as a dimer of dimers as exemplified in homomeric structures, but no high-resolution structure currently exists of heteromeric kainate receptors. In a full-length heterotetramer, the LBDs could potentially be arranged either as a GluK2 homomer alongside a GluK5 homomer or as two GluK2/K5 heterodimers. We have constructed models of the LBD dimers based on the GluK2 LBD crystal structures and investigated their stability with molecular dynamics simulations. We have then used the models to make predictions about the functional behavior of the full-length GluK2/K5 receptor, which we confirmed via electrophysiological recordings. A key prediction and observation is that lithium ions bind to the dimer interface of GluK2/K5 heteromers and slow their desensitization. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.
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
A reversible functional sensory neuropathy model.
Danigo, Aurore; Magy, Laurent; Richard, Laurence; Sturtz, Franck; Funalot, Benoît; Demiot, Claire
2014-06-13
Small-fiber neuropathy was induced in young adult mice by intraperitoneal injection of resiniferatoxin (RTX), a TRPV1 agonist. At day 7, RTX induced significant thermal and mechanical hypoalgesia. At day 28, mechanical and thermal nociception were restored. No nerve degeneration in skin was observed and unmyelinated nerve fiber morphology and density in sciatic nerve were unchanged. At day 7, substance P (SP) was largely depleted in dorsal root ganglia (DRG) neurons, although calcitonin gene-related peptide (CGRP) was only moderately depleted. Three weeks after, SP and CGRP expression was restored in DRG neurons. At the same time, CGRP expression remained low in intraepidermal nerve fibers (IENFs) whereas SP expression had improved. In summary, RTX induced in our model a transient neuropeptide depletion in sensory neurons without nerve degeneration. We think this model is valuable as it brings the opportunity to study functional nerve changes in the very early phase of small fiber neuropathy. Moreover, it may represent a useful tool to study the mechanisms of action of therapeutic strategies to prevent sensory neuropathy of various origins. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
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.
Function modeling: improved raster analysis through delayed reading and function raster datasets
John S. Hogland; Nathaniel M. Anderson; J .Greg Jones
2013-01-01
Raster modeling is an integral component of spatial analysis. However, conventional raster modeling techniques can require a substantial amount of processing time and storage space, often limiting the types of analyses that can be performed. To address this issue, we have developed Function Modeling. Function Modeling is a new modeling framework that streamlines the...
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.
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.
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. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
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
Functional renormalization group approach to the Kraichnan model.
Pagani, Carlo
2015-09-01
We study the anomalous scaling of the structure functions of a scalar field advected by a random Gaussian velocity field, the Kraichnan model, by means of functional renormalization group techniques. We analyze the symmetries of the model and derive the leading correction to the structure functions considering the renormalization of composite operators and applying the operator product expansion.
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…
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.
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
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.
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.
Velocity autocorrelation functions in model liquid metals
NASA Technical Reports Server (NTRS)
Tsang, T.; Maclin, A. P.
1975-01-01
Starting from interatomic potentials and static radial distribution functions, a self-consistent iteration scheme has been used to calculate velocity autocorrelation functions in liquid metals. The interatomic forces are treated directly. The calculation bypasses the details of the many-body dynamics and it is not necessary to introduce any additional parameters. Several simplifications may be used without introducing appreciable deviations. The results are in good agreement with computer experiments on liquid sodium at 383 K, suggesting that the velocity autocorrelation function may be a simpler quantity than previously supposed.
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.
On the Potts Model Partition Function in an External Field
NASA Astrophysics Data System (ADS)
McDonald, Leslie M.; Moffatt, Iain
2012-03-01
We study the partition function of the Potts model in an external (magnetic) field, and its connections with the zero-field Potts model partition function. Using a deletion-contraction formulation for the partition function Z for this model, we show that it can be expanded in terms of the zero-field partition function. We also show that Z can be written as a sum over the spanning trees, and the spanning forests, of a graph G. Our results extend to Z the well-known spanning tree expansion for the zero-field partition function that arises though its connections with the Tutte polynomial.
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…
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…
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.
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.
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
Multiscale adaptive basis function modeling of spatiotemporal vectorcardiogram signals.
Gang Liu; Hui Yang
2013-03-01
Mathematical modeling of cardiac electrical signals facilitates the simulation of realistic cardiac electrical behaviors, the evaluation of algorithms, and the characterization of underlying space-time patterns. However, there are practical issues pertinent to model efficacy, robustness, and generality. This paper presents a multiscale adaptive basis function modeling approach to characterize not only temporal but also spatial behaviors of vectorcardiogram (VCG) signals. Model parameters are adaptively estimated by the "best matching" projections of VCG characteristic waves onto a dictionary of nonlinear basis functions. The model performance is experimentally evaluated with respect to the number of basis functions, different types of basis function (i.e., Gaussian, Mexican hat, customized wavelet, and Hermitian wavelets), and various cardiac conditions, including 80 healthy controls and different myocardial infarctions (i.e., 89 inferior, 77 anterior-septal, 56 inferior-lateral, 47 anterior, and 43 anterior-lateral). Multiway analysis of variance shows that the basis function and the model complexity have significant effects on model performances while cardiac conditions are not significant. The customized wavelet is found to be an optimal basis function for the modeling of spacetime VCG signals. The comparison of QT intervals shows small relative errors (<;5%) between model representations and realworld VCG signals when the model complexity is greater than 10. The proposed model shows great potentials to model space-time cardiac pathological behaviors and can lead to potential benefits in feature extraction, data compression, algorithm evaluation, and disease prognostics.
Generating functional for the Green’s functions of a two-flavor bosonic model
NASA Astrophysics Data System (ADS)
Blasone, M.; Jizba, P.; Smaldone, L.
2017-08-01
We examine a simple two-flavor scalar model with a non-diagonal mass matrix. We argue that the conventional definition of the QFT partition function does not allow to evaluate the Green’s functions with respect to the flavor vacuum as it favors only the mass vacuum. By using functional-integral techniques, we derive new generating functional for Green’s functions on the flavor vacuum.
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…
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…
Resnick, Barbara; Gruber-Baldini, Ann L; Hicks, Gregory; Ostir, Glen; Klinedinst, N Jennifer; Orwig, Denise; Magaziner, Jay
2016-07-01
Measurement of physical function post hip fracture has been conceptualized using multiple different measures. This study tested a comprehensive measurement model of physical function. This was a descriptive secondary data analysis including 168 men and 171 women post hip fracture. Using structural equation modeling, a measurement model of physical function which included grip strength, activities of daily living, instrumental activities of daily living, and performance was tested for fit at 2 and 12 months post hip fracture, and among male and female participants. Validity of the measurement model of physical function was evaluated based on how well the model explained physical activity, exercise, and social activities post hip fracture. The measurement model of physical function fit the data. The amount of variance the model or individual factors of the model explained varied depending on the activity. Decisions about the ideal way in which to measure physical function should be based on outcomes considered and participants. The measurement model of physical function is a reliable and valid method to comprehensively measure physical function across the hip fracture recovery trajectory. © 2015 Association of Rehabilitation Nurses.
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.
Correlation function of four spins in the percolation model
NASA Astrophysics Data System (ADS)
Dotsenko, Vladimir S.
2016-10-01
By using the Coulomb gas technics we calculate the four-spin correlation function in the percolation q → 1 limit of the Potts model. It is known that the four-point functions define the actual fusion rules of a particular model. In this respect, we find that fusion of two spins, of dimension Δσ =5/96, produce a new channel, in the 4-point function, which is due to the operator with dimension Δ = 5 / 8.
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
Technical Specifications for Spread Function Model.
1983-05-01
quency specular scatter off facets of a highly shadowed surface. At very high frequencies bubbles near the surface may play a role. (Such high...approximation leads to scattering off facets, controlled by the distribu- tion of surface slopes that the incident ray can "see" (i.e., including shadowing ...submodel must cover the limits from Rayleigh to Kirchoff and include shadowing . Brown’s composite surface model seems most promising, however some issues
Robust classification of functional and quantitative image data using functional mixed models.
Zhu, Hongxiao; Brown, Philip J; Morris, Jeffrey S
2012-12-01
This article introduces new methods for performing classification of complex, high-dimensional functional data using the functional mixed model (FMM) framework. The FMM relates a functional response to a set of predictors through functional fixed and random effects, which allows it to account for various factors and between-function correlations. The methods include training and prediction steps. In the training steps we train the FMM model by treating class designation as one of the fixed effects, and in the prediction steps we classify the new objects using posterior predictive probabilities of class. Through a Bayesian scheme, we are able to adjust for factors affecting both the functions and the class designations. While the methods can be used in any FMM framework, we provide details for two specific Bayesian approaches: the Gaussian, wavelet-based FMM (G-WFMM) and the robust, wavelet-based FMM (R-WFMM). Both methods perform modeling in the wavelet space, which yields parsimonious representations for the functions, and can naturally adapt to local features and complex nonstationarities in the functions. The R-WFMM allows potentially heavier tails for features of the functions indexed by particular wavelet coefficients, leading to a down-weighting of outliers that makes the method robust to outlying functions or regions of functions. The models are applied to a pancreatic cancer mass spectroscopy data set and compared with other recently developed functional classification methods. © 2012, The International Biometric Society.
Multiscale Modeling of Form and Function
Engler, Adam J.; Humbert, Patrick O.; Wehrle-Haller, Bernhard; Weaver, Valerie M.
2015-01-01
Topobiology posits that morphogenesis is driven by differential adhesive interactions among heterogeneous cell populations. This paradigm has been revised to include force-dependent molecular switches, cell and tissue tension, and reciprocal interactions with the microenvironment. It is now appreciated that tissue development is executed through conserved decision-making modules that operate on multiple length scales from the molecular and subcellular level through to the cell and tissue level and that these regulatory mechanisms specify cell and tissue fate by modifying the context of cellular signaling and gene expression. Here, we discuss the origin of these decision-making modules and illustrate how emergent properties of adhesion-directed multicellular structures sculpt the tissue, promote its functionality, and maintain its homeostasis through spatial segregation and organization of anchored proteins and secreted factors and through emergent properties of tissues, including tension fields and energy optimization. PMID:19359578
Wave-function model for the CP violation in mesons
NASA Astrophysics Data System (ADS)
Saberi Fathi, S. M.; Courbage, M.; Durt, T.
2017-10-01
In this paper, we propose a simple quantum model of the kaons decay providing an estimate of the CP symmetry violation parameter. We use the two-level Friedrich's Hamiltonian model to obtain a good quantitative agreement with the experimental estimate of the violation parameter for neutral kaons. A temporal wave-function approach, based on an analogy with spatial wave-functions, plays a crucial role in our model.
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. PMID:25852603
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.
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…
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…
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.
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…
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…
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.
Improved analyses using function datasets and statistical modeling
John S. Hogland; Nathaniel M. Anderson
2014-01-01
Raster modeling is an integral component of spatial analysis. However, conventional raster modeling techniques can require a substantial amount of processing time and storage space and have limited statistical functionality and machine learning algorithms. To address this issue, we developed a new modeling framework using C# and ArcObjects and integrated that framework...
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.
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.
Modeling functional neuroanatomy for an anatomy information system.
Niggemann, Jörg M; Gebert, Andreas; Schulz, Stefan
2008-01-01
Existing neuroanatomical ontologies, databases and information systems, such as the Foundational Model of Anatomy (FMA), represent outgoing connections from brain structures, but cannot represent the "internal wiring" of structures and as such, cannot distinguish between different independent connections from the same structure. Thus, a fundamental aspect of Neuroanatomy, the functional pathways and functional systems of the brain such as the pupillary light reflex system, is not adequately represented. This article identifies underlying anatomical objects which are the source of independent connections (collections of neurons) and uses these as basic building blocks to construct a model of functional neuroanatomy and its functional pathways. The basic representational elements of the model are unnamed groups of neurons or groups of neuron segments. These groups, their relations to each other, and the relations to the objects of macroscopic anatomy are defined. The resulting model can be incorporated into the FMA. The capabilities of the presented model are compared to the FMA and the Brain Architecture Management System (BAMS). Internal wiring as well as functional pathways can correctly be represented and tracked. This model bridges the gap between representations of single neurons and their parts on the one hand and representations of spatial brain structures and areas on the other hand. It is capable of drawing correct inferences on pathways in a nervous system. The object and relation definitions are related to the Open Biomedical Ontology effort and its relation ontology, so that this model can be further developed into an ontology of neuronal functional systems.
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.
A suggestion for computing objective function in model calibration
Wu, Yiping; Liu, Shuguang
2014-01-01
A parameter-optimization process (model calibration) is usually required for numerical model applications, which involves the use of an objective function to determine the model cost (model-data errors). The sum of square errors (SSR) has been widely adopted as the objective function in various optimization procedures. However, ‘square error’ calculation was found to be more sensitive to extreme or high values. Thus, we proposed that the sum of absolute errors (SAR) may be a better option than SSR for model calibration. To test this hypothesis, we used two case studies—a hydrological model calibration and a biogeochemical model calibration—to investigate the behavior of a group of potential objective functions: SSR, SAR, sum of squared relative deviation (SSRD), and sum of absolute relative deviation (SARD). Mathematical evaluation of model performance demonstrates that ‘absolute error’ (SAR and SARD) are superior to ‘square error’ (SSR and SSRD) in calculating objective function for model calibration, and SAR behaved the best (with the least error and highest efficiency). This study suggests that SSR might be overly used in real applications, and SAR may be a reasonable choice in common optimization implementations without emphasizing either high or low values (e.g., modeling for supporting resources management).
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
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.
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
Modeling of bacterial growth as a function of temperature.
Zwietering, M H; de Koos, J T; Hasenack, B E; de Witt, J C; van't Riet, K
1991-01-01
The temperature of chilled foods is a very important variable for microbial safety in a production and distribution chain. To predict the number of organisms as a function of temperature and time, it is essential to model the lag time, specific growth rate, and asymptote (growth yield) as a function of temperature. The objective of this research was to determine the suitability and usefulness of different models, either available from the literature or newly developed. The models were compared by using an F test, by which the lack of fit of the models was compared with the measuring error. From the results, a hyperbolic model was selected for the description of the lag time as a function of temperature. Modified forms of the Ratkowsky model were selected as the most suitable model for both the growth rate and the asymptote as a function of temperature. The selected models could be used to predict experimentally determined numbers of organisms as a function of temperature and time. PMID:2059034
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
Design, Fabrication, Characterization and Modeling of Integrated Functional Materials
2015-12-01
Award Number: W81XWH-10-2-0101 TITLE: Design, Fabrication, Characterization and Modeling of Integrated Functional Materials PRINCIPAL INVESTIGATOR...SUBTITLE Design, Fabrication, Characterization and Modeling of Integrated Functional Materials 5a. CONTRACT NUMBER W81XWH-10-2-0101 5c. PROGRAM...mobile refrigeration. These technological advances are critically dependent on the development of new and currently non-existing materials . This
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.
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.
Spin Structure Functions in a Covariant Spectator Quark Model
G. Ramalho, Franz Gross and M. T. Peña
2010-12-01
We apply the covariant spectator quark–diquark model, already probed in the description of the nucleon elastic form factors, to the calculation of the deep inelastic scattering (DIS) spin-independent and spin-dependent structure functions of the nucleon. The nucleon wave function is given by a combination of quark–diquark orbital states, corresponding to S, D and P-waves. A simple form for the quark distribution function associated to the P and D waves is tested.
Functional Renormalization Group Approach to the Sine-Gordon Model
Nagy, S.; Sailer, K.; Nandori, I.; Polonyi, J.
2009-06-19
The renormalization group flow is presented for the two-dimensional sine-Gordon model within the framework of the functional renormalization group method by including the wave-function renormalization constant. The Kosterlitz-Thouless-Berezinski type phase structure is recovered as the interpolating scaling law between two competing IR attractive area of the global renormalization group flow.
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
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.
NASA Astrophysics Data System (ADS)
Vannoni, Maurizio; Yang, Fan; Sinn, Harald
2015-01-01
An algorithm to solve the inverse problem of synchrotron radiation adaptive mirrors' tuning is presented. The influence functions are modeled and calculated for a generic bimorph mirror. An error function minimization method is used to simulate the correction of the surface figure of the mirror in some particular conditions. Possible applications to free-electron-laser mirror simulations are pointed out.
Comparison and Contrast of Two General Functional Regression Modeling Frameworks
Morris, Jeffrey S.
2017-01-01
In this article, Greven and Scheipl describe an impressively general framework for performing functional regression that builds upon the generalized additive modeling framework. Over the past number of years, my collaborators and I have also been developing a general framework for functional regression, functional mixed models, which shares many similarities with this framework, but has many differences as well. In this discussion, I compare and contrast these two frameworks, to hopefully illuminate characteristics of each, highlighting their respecitve strengths and weaknesses, and providing recommendations regarding the settings in which each approach might be preferable. PMID:28736502
Jellium-with-gap model applied to semilocal kinetic functionals
NASA Astrophysics Data System (ADS)
Constantin, Lucian A.; Fabiano, Eduardo; Śmiga, Szymon; Della Sala, Fabio
2017-03-01
We investigate a highly nonlocal generalization of the Lindhard function, given by the jellium-with-gap model. We find a band-gap-dependent gradient expansion of the kinetic energy, which performs noticeably well for large atoms. Using the static linear response theory and the simplest semilocal model for the local band gap, we derive a nonempirical generalized gradient approximation (GGA) of the kinetic energy. This GGA kinetic-energy functional is remarkably accurate for the description of weakly interacting molecular systems within the subsystem formulation of density functional theory.
Ab initio derivation of model energy density functionals
NASA Astrophysics Data System (ADS)
Dobaczewski, Jacek
2016-08-01
I propose a simple and manageable method that allows for deriving coupling constants of model energy density functionals (EDFs) directly from ab initio calculations performed for finite fermion systems. A proof-of-principle application allows for linking properties of finite nuclei, determined by using the nuclear nonlocal Gogny functional, to the coupling constants of the quasilocal Skyrme functional. The method does not rely on properties of infinite fermion systems but on the ab initio calculations in finite systems. It also allows for quantifying merits of different model EDFs in describing the ab initio results.
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
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.
Ensemble modeling with pedotransfer functions in the hydropedological context
USDA-ARS?s Scientific Manuscript database
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.
Single-index Varying Coefficient Model for Functional Responses
Luo, Xinchao; Zhu, Lixing
2016-01-01
Summary 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 paper 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 are 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. PMID:27061414
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.
Partially Linear Varying Coefficient Models Stratified by a Functional Covariate
Maity, Arnab; Huang, Jianhua Z.
2012-01-01
We consider the problem of estimation in semiparametric varying coefficient models where the covariate modifying the varying coefficients is functional and is modeled nonparametrically. We develop a kernel-based estimator of the nonparametric component and a profiling estimator of the parametric component of the model and derive their asymptotic properties. Specifically, we show the consistency of the nonparametric functional estimates and derive the asymptotic expansion of the estimates of the parametric component. We illustrate the performance of our methodology using a simulation study and a real data application. PMID:22904586
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
2017-01-04
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. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
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.
Density functionals and dimensional renormalization for an exactly solvable model
NASA Astrophysics Data System (ADS)
Kais, S.; Herschbach, D. R.; Handy, N. C.; Murray, C. W.; Laming, G. J.
1993-07-01
We treat an analytically solvable version of the ``Hooke's Law'' model for a two-electron atom, in which the electron-electron repulsion is Coulombic but the electron-nucleus attraction is replaced by a harmonic oscillator potential. Exact expressions are obtained for the ground-state wave function and electron density, the Hartree-Fock solution, the correlation energy, the Kohn-Sham orbital, and, by inversion, the exchange and correlation functionals. These functionals pertain to the ``intermediate'' density regime (rs≥1.4) for an electron gas. As a test of customary approximations employed in density functional theory, we compare our exact density, exchange, and correlation potentials and energies with results from two approximations. These use Becke's exchange functional and either the Lee-Yang-Parr or the Perdew correlation functional. Both approximations yield rather good results for the density and the exchange and correlation energies, but both deviate markedly from the exact exchange and correlation potentials. We also compare properties of the Hooke's Law model with those of two-electron atoms, including the large dimension limit. A renormalization procedure applied to this very simple limit yields correlation energies as good as those obtained from the approximate functionals, for both the model and actual atoms.
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.
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.
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.
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.
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
Towards refactoring the Molecular Function Ontology with a UML profile for function modeling.
Burek, Patryk; Loebe, Frank; Herre, Heinrich
2017-10-04
Gene Ontology (GO) is the largest resource for cataloging gene products. This resource grows steadily and, naturally, this growth raises issues regarding the structure of the ontology. Moreover, modeling and refactoring large ontologies such as GO is generally far from being simple, as a whole as well as when focusing on certain aspects or fragments. It seems that human-friendly graphical modeling languages such as the Unified Modeling Language (UML) could be helpful in connection with these tasks. We investigate the use of UML for making the structural organization of the Molecular Function Ontology (MFO), a sub-ontology of GO, more explicit. More precisely, we present a UML dialect, called the Function Modeling Language (FueL), which is suited for capturing functions in an ontologically founded way. FueL is equipped, among other features, with language elements that arise from studying patterns of subsumption between functions. We show how to use this UML dialect for capturing the structure of molecular functions. Furthermore, we propose and discuss some refactoring options concerning fragments of MFO. FueL enables the systematic, graphical representation of functions and their interrelations, including making information explicit that is currently either implicit in MFO or is mainly captured in textual descriptions. Moreover, the considered subsumption patterns lend themselves to the methodical analysis of refactoring options with respect to MFO. On this basis we argue that the approach can increase the comprehensibility of the structure of MFO for humans and can support communication, for example, during revision and further development.
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.
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.
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.
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.
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
The transverse momentum dependent distribution functions in the bag model
Avakian, Harut; Efremov, Anatoly; Schweitzer, Peter; Yuan, Feng
2010-01-29
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.
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.
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.
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
Enabling Cross-Discipline Collaboration Via a Functional Data Model
NASA Astrophysics Data System (ADS)
Lindholm, D. M.; Wilson, A.; Baltzer, T.
2016-12-01
Many research disciplines have very specialized data models that are used to express the detailed semantics that are meaningful to that community and easily utilized by their data analysis tools. While invaluable to members of that community, such expressive data structures and metadata are of little value to potential collaborators from other scientific disciplines. Many data interoperability efforts focus on the difficult task of computationally mapping concepts from one domain to another to facilitate discovery and use of data. Although these efforts are important and promising, we have found that a great deal of discovery and dataset understanding still happens at the level of less formal, personal communication. However, a significant barrier to inter-disciplinary data sharing that remains is one of data access.Scientists and data analysts continue to spend inordinate amounts of time simply trying to get data into their analysis tools. Providing data in a standard file format is often not sufficient since data can be structured in many ways. Adhering to more explicit community standards for data structure and metadata does little to help those in other communities.The Functional Data Model specializes the Relational Data Model (used by many database systems)by defining relations as functions between independent (domain) and dependent (codomain) variables. Given that arrays of data in many scientific data formats generally represent functionally related parameters (e.g. temperature as a function of space and time), the Functional Data Model is quite relevant for these datasets as well. The LaTiS software framework implements the Functional Data Model and provides a mechanism to expose an existing data source as a LaTiS dataset. LaTiS datasets can be manipulated using a Functional Algebra and output in any number of formats.LASP has successfully used the Functional Data Model and its implementation in the LaTiS software framework to bridge the gap between
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.
Characterization of a functional C3A liver spheroid model.
Gaskell, Harriet; Sharma, Parveen; Colley, Helen E; Murdoch, Craig; Williams, Dominic P; Webb, Steven D
2016-06-01
More predictive in vitro liver models are a critical requirement for preclinical screening of compounds demonstrating hepatotoxic liability. 3D liver spheroids have been shown to have an enhanced functional lifespan compared to 2D monocultures; however a detailed characterisation of spatiotemporal function and structure of spheroids still needs further attention before widespread use in industry. We have developed and characterized the structure and function of a 3D liver spheroid model formed from C3A hepatoma cells. Spheroids were viable and maintained a compact in vivo-like structure with zonation features for up to 32 days. MRP2 and Pgp transporters had polarised expression on the canalicular membrane of cells in the spheroids and were able to functionally transport CMFDA substrate into these canalicular structures. Spheroids expressed CYP2E1 and were able to synthesise and secrete albumin and urea to a higher degree than monolayer C3A cultures. Penetration of doxorubicin throughout the spheroid core was demonstrated. Spheroids showed increased susceptibility to hepatotoxins when compared to 2D cultures, with acetaminophen having an IC50 of 7.2 mM in spheroids compared to 33.8 mM in monolayer culture. To conclude, we developed an alternative method for creating C3A liver spheroids and demonstrated cellular polarisation and zonation, as well as superior liver-specific functionality and more sensitive toxicological response compared to standard 2D liver models, confirming a more in vivo-like liver model.
Forecasting pollen concentration by a two-step functional model.
Valderrama, Mariano J; Ocaña, Francisco A; Aguilera, Ana M; Ocaña-Peinado, Francisco M
2010-06-01
A functional regression model to forecast the cypress pollen concentration during a given time interval, considering the air temperature in a previous interval as the input, is derived by means of a two-step procedure. This estimation is carried out by functional principal component (FPC) analysis and the residual noise is also modeled by FPC regression, taking as the explicative process the pollen concentration during the earlier interval. The prediction performance is then tested on pollen data series recorded in Granada (Spain) over a period of 10 years.
Green function simulation of Hamiltonian lattice models with stochastic reconfiguration
NASA Astrophysics Data System (ADS)
Beccaria, M.
2000-03-01
We apply a recently proposed Green function Monte Carlo procedure to the study of Hamiltonian lattice gauge theories. This class of algorithms computes quantum vacuum expectation values by averaging over a set of suitable weighted random walkers. By means of a procedure called stochastic reconfiguration the long standing problem of keeping fixed the walker population without a priori knowledge of the ground state is completely solved. In the U(1)_2 model, which we choose as our theoretical laboratory, we evaluate the mean plaquette and the vacuum energy per plaquette. We find good agreement with previous works using model-dependent guiding functions for the random walkers.
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.
Gutzwiller wave function for a model of strongly interacting bosons
Krauth, W. ); Caffarel, M. Laboratoire Dynamique des Interactions Moleculaires, Universite Paris VI, F-75252 Paris CEDEX 05 ); Bouchaud, J. )
1992-02-01
We study a model of strongly interacting lattice bosons with a Gutzwiller-type wave function that contains only on-site correlations. The variational energy and the condensate fraction associated with the variational wave function are exactly evaluated for both finite and infinite systems and compared with exact quantum Monte Carlo results in two dimensions. This ansatz for the wave function gives the correct qualitative picture of the phase diagram of this system; at commensurate densities, this system enters a Mott-insulator phase for large values of the interaction.
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.
A model neural interface based on functional chemical stimulation.
Mehenti, Neville Z; Fishman, Harvey A; Bent, Stacey F
2007-08-01
While functional electrical stimulation has been applied to treat a variety of neurological disorders, it cannot mimic function that is primarily achieved using neurochemical means. In this work, we present a neurotransmitter-based prosthetic interface in the form of a flexible microdevice that selectively releases chemical pulses through an aperture in a polymer membrane. The release profiles through the aperture are controlled by microfluidic switching in an underlying channel network. The profiles have been characterized using fluorescence microscopy as a function of pulse duration and frequency. Hippocampal neurons were cultured on the microdevices and cell stimulation via glutamate delivery was detected using calcium imaging. The release properties could be tuned to repeatedly elicit discrete action potentials in cells seeded proximate to the aperture, including single cell stimulation at 2 Hz. This model neural interface based on functional chemical stimulation may provide the biomimetic platform necessary to restore physiological pathways and function that electrical stimulation cannot fundamentally address.
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
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.
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.
Selecting Bayesian priors for stochastic rates using extended functional models
NASA Astrophysics Data System (ADS)
Gibson, Gavin J.
2003-04-01
We propose an extension to the functional modelling methods described by Dawid and Stone (1982 Ann. Stat. 10 1119-38) that leads naturally to a method for selecting vague parameter priors for Bayesian analyses involving stochastic population models. Motivated by applications from quantum optics and epidemiology, we focus on analysing observed sequences of event times obeying a non-homogeneous Poisson process, although the techniques are more widely applicable. The extended functional modelling approach is illustrated for the particular case of Bayesian estimation of the death rate in the immigration-death model from observation of the death times only. It is shown that the prior selected naturally leads to a well defined posterior density for parameters and avoids some undesirable pathologies reported by Gibson and Renshaw (2001a Inverse Problems 17 455-66, 2001b Stat. Comput. 11 347-58) for the case of exponential priors. Some limitations of the approach are also discussed.
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
Regulator Loss Functions and Hierarchical Modeling for Safety Decision Making.
Hatfield, Laura A; Baugh, Christine M; Azzone, Vanessa; Normand, Sharon-Lise T
2017-07-01
Regulators must act to protect the public when evidence indicates safety problems with medical devices. This requires complex tradeoffs among risks and benefits, which conventional safety surveillance methods do not incorporate. To combine explicit regulator loss functions with statistical evidence on medical device safety signals to improve decision making. In the Hospital Cost and Utilization Project National Inpatient Sample, we select pediatric inpatient admissions and identify adverse medical device events (AMDEs). We fit hierarchical Bayesian models to the annual hospital-level AMDE rates, accounting for patient and hospital characteristics. These models produce expected AMDE rates (a safety target), against which we compare the observed rates in a test year to compute a safety signal. We specify a set of loss functions that quantify the costs and benefits of each action as a function of the safety signal. We integrate the loss functions over the posterior distribution of the safety signal to obtain the posterior (Bayes) risk; the preferred action has the smallest Bayes risk. Using simulation and an analysis of AMDE data, we compare our minimum-risk decisions to a conventional Z score approach for classifying safety signals. The 2 rules produced different actions for nearly half of hospitals (45%). In the simulation, decisions that minimize Bayes risk outperform Z score-based decisions, even when the loss functions or hierarchical models are misspecified. Our method is sensitive to the choice of loss functions; eliciting quantitative inputs to the loss functions from regulators is challenging. A decision-theoretic approach to acting on safety signals is potentially promising but requires careful specification of loss functions in consultation with subject matter experts.
Function-weighted frequency response function sensitivity method for analytical model updating
NASA Astrophysics Data System (ADS)
Lin, R. M.
2017-09-01
Since the frequency response function (FRF) sensitivity method was first proposed [26], it has since become a most powerful and practical method for analytical model updating. Nevertheless, the original formulation of the FRF sensitivity method does suffer the limitation that the initial analytical model to be updated should be reasonably close to the final updated model to be sought, due the assumed mathematical first order approximation implicit to most sensitivity based methods. Convergence to correct model is not guaranteed when large modelling errors exist and blind application often leads to optimal solutions which are truly sought. This paper seeks to examine all the important numerical characteristics of the original FRF sensitivity method including frequency data selection, numerical balance and convergence performance. To further improve the applicability of the method to cases of large modelling errors, a new novel function-weighted sensitivity method is developed. The new method has shown much superior performance on convergence even in the presence of large modelling errors. Extensive numerical case studies based on a mass-spring system and a GARTEUR structure have been conducted and very encouraging results have been achieved. Effect of measurement noise has been examined and the method works reasonably well in the presence of measurement uncertainties. The new method removes the restriction of modelling error magnitude being of second order in Euclidean norm as compared with that of system matrices, thereby making it a truly general method applicable to most practical model updating problems.
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.
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.
A new tropospheric mapping function based on ECMWF models
NASA Astrophysics Data System (ADS)
Biancale, Richard; Dupuy, Stephanie; Soudarin, Laurent
The tropospheric propagation delay of Earth-satellite tracking data (from electromagnetic or optical signals) is generally corrected in two steps: 1) computing the zenithal dry and wet delays at the station, 2) applying a mapping function to pull them down at the elevation needed. Considering that zenithal delays can be well computed from ground pressure, temperature and humidity data through hydrostatic theory, or can be integrated from ECMWF multiple layer models (for instance), or at least can be adjusted in orbit processing, we turned our attention more specifically to the validity of the mapping function. Starting on one hand from a few maps of the ECMWF meteorological model of pressure, temperature and humidity available each 6h in 91 isobaric layers we reconstructed first the dry and wet tropospheric delays at each grid point for several azimuth and elevation angles. On the other hand we computed the same delays from a Marini-type mapping function based on the integrated zenithal delays computed themselves from the same ECMWF models. An adequacy was searched between both approaches which led us to adjust all coefficients of the dry and wet mapping functions. We propose here to describe our approach and to present the dry and wet mapping functions obtained with some tests with real data.
The functional neuroanatomy of bipolar disorder: a consensus model
Strakowski, Stephen M; Adler, Caleb M; Almeida, Jorge; Altshuler, Lori L; Blumberg, Hilary P; Chang, Kiki D; DelBello, Melissa P; Frangou, Sophia; McIntosh, Andrew; Phillips, Mary L; Sussman, Jessika E; Townsend, Jennifer D
2013-01-01
Objectives Functional neuroimaging methods have proliferated in recent years, such that functional magnetic resonance imaging, in particular, is now widely used to study bipolar disorder. However, discrepant findings are common. A workgroup was organized by the Department of Psychiatry, University of Cincinnati (Cincinnati, OH, USA) to develop a consensus functional neuroanatomic model of bipolar I disorder based upon the participants’ work as well as that of others. Methods Representatives from several leading bipolar disorder neuroimaging groups were organized to present an overview of their areas of expertise as well as focused reviews of existing data. The workgroup then developed a consensus model of the functional neuroanatomy of bipolar disorder based upon these data. Results Among the participants, a general consensus emerged that bipolar I disorder arises from abnormalities in the structure and function of key emotional control networks in the human brain. Namely, disruption in early development (e.g., white matter connectivity, prefrontal pruning) within brain networks that modulate emotional behavior leads to decreased connectivity among ventral prefrontal networks and limbic brain regions, especially amygdala. This developmental failure to establish healthy ventral prefrontal–limbic modulation underlies the onset of mania and ultimately, with progressive changes throughout these networks over time and with affective episodes, a bipolar course of illness. Conclusions This model provides a potential substrate to guide future investigations and areas needing additional focus are identified. PMID:22631617
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.
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.
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…
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)
Dynamical response function of the disordered kinetic Ising model
NASA Astrophysics Data System (ADS)
Hinrichsen, Haye
2008-02-01
Recently Baumann et al (2007 Preprint 0709.3228v1) studied the phase-ordering kinetics of the two-dimensional Ising model for T
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.
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.
A Multi-Level Model of Moral Functioning Revisited
ERIC Educational Resources Information Center
Reed, Don Collins
2009-01-01
The model of moral functioning scaffolded in the 2008 "JME" Special Issue is here revisited in response to three papers criticising that volume. As guest editor of that Special Issue I have formulated the main body of this response, concerning the dynamic systems approach to moral development, the problem of moral relativism and the role of…
Adding ecosystem function to agent-based land use models
USDA-ARS?s Scientific Manuscript database
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. Biogeoche...
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.
Predicting functional brain ROIs via fiber shape models.
Zhang, Tuo; Guo, Lei; Li, Kaiming; Zhu, Dajing; Cui, Guangbin; Liu, Tianming
2011-01-01
Study of structural and functional connectivities of the human brain has received significant interest and effort recently. A fundamental question arises when attempting to measure the structural and/or functional connectivities of specific brain networks: how to best identify possible Regions of Interests (ROIs)? In this paper, we present a novel ROI prediction framework that localizes ROIs in individual brains based on learned fiber shape models from multimodal task-based fMRI and diffusion tensor imaging (DTI) data. In the training stage, ROIs are identified as activation peaks in task-based fMRI data. Then, shape models of white matter fibers emanating from these functional ROIs are learned. In addition, ROIs' location distribution model is learned to be used as an anatomical constraint. In the prediction stage, functional ROIs are predicted in individual brains based on DTI data. The ROI prediction is formulated and solved as an energy minimization problem, in which the two learned models are used as energy terms. Our experiment results show that the average ROI prediction error is 3.45 mm, in comparison with the benchmark data provided by working memory task-based fMRI. Promising results were also obtained on the ADNI-2 longitudinal DTI dataset.
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
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…
ERIC Educational Resources Information Center
Chang, Hua-Hua; Mazzeo, John
1994-01-01
This paper establishes the correspondence between an item response function and a unique set of item category response functions for the partial credit model and the graded response model. Theoretical and practical implications are discussed. (SLD)
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.
Penalty function model on the automatic testing of form errors
NASA Astrophysics Data System (ADS)
Xu, Xue-lin
1993-09-01
In this paper ,a principle and a way of searching form errors by using a penalty function model in dynamic measureement is introduced. Roundness and straightenss errors measurement for typical example are taken in paper ,it is grounded on relevant definitions of the Chinese National Standard(GB1 183 -80)to set up target function mm (x ,y) = Rmax (x , y)-Rmin (x ,y) , and to establish feasible collective "S" of desing the tolerance for variable x ,y . Namely (x ,y) E S is restraint condition. Is grounded on as started above,the model of punitive function and the punitive factors have been established. Then , an optimigation search of measurement process is undertaken. Then the points out of feasible collective can be rejected .Last the value form error is got. An example according to the theory can be run in a computer.
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
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.
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 function-structure model for NGF-activated TRK.
Cunningham, M E; Greene, L A
1998-01-01
Mechanisms regulating transit of receptor tyrosine kinases (RTKs) from inactive to active states are incompletely described, but require autophosphorylation of tyrosine(s) within a kinase domain 'activation loop'. Here, we employ functional biological assays with mutated TRK receptors to assess a 'switch' model for RTK activation. In this model: (i) ligand binding stimulates activation loop tyrosine phosphorylation; (ii) these phosphotyrosines form specific charge pairs with nearby basic residues; and (iii) the charge pairs stabilize a functionally active conformation in which the activation loop is restrained from blocking access to the kinase catalytic core. Our findings both support this model and identify residues that form specific charge pairs with each of the three TRK activation loop phosphotyrosines. PMID:9857185
Modeling of nanoscale liquid mixture transport by density functional hydrodynamics
NASA Astrophysics Data System (ADS)
Dinariev, Oleg Yu.; Evseev, Nikolay V.
2017-06-01
Modeling of multiphase compositional hydrodynamics at nanoscale is performed by means of density functional hydrodynamics (DFH). DFH is the method based on density functional theory and continuum mechanics. This method has been developed by the authors over 20 years and used for modeling in various multiphase hydrodynamic applications. In this paper, DFH was further extended to encompass phenomena inherent in liquids at nanoscale. The new DFH extension is based on the introduction of external potentials for chemical components. These potentials are localized in the vicinity of solid surfaces and take account of the van der Waals forces. A set of numerical examples, including disjoining pressure, film precursors, anomalous rheology, liquid in contact with heterogeneous surface, capillary condensation, and forward and reverse osmosis, is presented to demonstrate modeling capabilities.
Toward a multiscale modeling framework for understanding serotonergic function
Wong-Lin, KongFatt; Wang, Da-Hui; Moustafa, Ahmed A; Cohen, Jeremiah Y; Nakamura, Kae
2017-01-01
Despite its importance in regulating emotion and mental wellbeing, the complex structure and function of the serotonergic system present formidable challenges toward understanding its mechanisms. In this paper, we review studies investigating the interactions between serotonergic and related brain systems and their behavior at multiple scales, with a focus on biologically-based computational modeling. We first discuss serotonergic intracellular signaling and neuronal excitability, followed by neuronal circuit and systems levels. At each level of organization, we will discuss the experimental work accompanied by related computational modeling work. We then suggest that a multiscale modeling approach that integrates the various levels of neurobiological organization could potentially transform the way we understand the complex functions associated with serotonin. PMID:28417684
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.
Comparison of {gamma}Z-structure function models
Rislow, Benjamin C.
2013-11-01
The {gamma}Z-box is an important contribution to the proton's weak charge. The {gamma}Z-box is calculated dispersively and depends on {gamma}Z-structure functions, F{sub {gamma}Z1,2,3}(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 {gamma}Z-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.
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
Structure, function, and behaviour of computational models in systems biology
2013-01-01
Background Systems Biology develops computational models in order to understand biological phenomena. The increasing number and complexity of such “bio-models” necessitate computer support for the overall modelling task. Computer-aided modelling has to be based on a formal semantic description of bio-models. But, even if computational bio-models themselves are represented precisely in terms of mathematical expressions their full meaning is not yet formally specified and only described in natural language. Results We present a conceptual framework – the meaning facets – which can be used to rigorously specify the semantics of bio-models. A bio-model has a dual interpretation: On the one hand it is a mathematical expression which can be used in computational simulations (intrinsic meaning). On the other hand the model is related to the biological reality (extrinsic meaning). We show that in both cases this interpretation should be performed from three perspectives: the meaning of the model’s components (structure), the meaning of the model’s intended use (function), and the meaning of the model’s dynamics (behaviour). In order to demonstrate the strengths of the meaning facets framework we apply it to two semantically related models of the cell cycle. Thereby, we make use of existing approaches for computer representation of bio-models as much as possible and sketch the missing pieces. Conclusions The meaning facets framework provides a systematic in-depth approach to the semantics of bio-models. It can serve two important purposes: First, it specifies and structures the information which biologists have to take into account if they build, use and exchange models. Secondly, because it can be formalised, the framework is a solid foundation for any sort of computer support in bio-modelling. The proposed conceptual framework establishes a new methodology for modelling in Systems Biology and constitutes a basis for computer-aided collaborative research
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.
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,…
Function modeling improves the efficiency of spatial modeling using big data from remote sensing
John Hogland; Nathaniel Anderson
2017-01-01
Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have...
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,…
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
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.
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.
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.
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.
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.
Static longitudinal dielectric function of model molecular fluids
NASA Astrophysics Data System (ADS)
Raineri, Fernando O.; Resat, Haluk; Friedman, Harold L.
1992-02-01
The static longitudinal dielectric function ɛL(k) is calculated for several polar interaction site model (ISM) fluids for comparison with related models having arbitrary short-range interactions and a set of one or more lower-order multipole moments at the centers (ΩM models). The requisite averages over the ISM fluids are calculated by the extended reference interaction site method (XRISM) using site-site hypernetted chain (HNC)-like closures modified to reproduce the correct long-range behavior of the site-site pair correlation functions. They are compared with averages over the ΩM models under the RHNC theory taken from the literature or calculated under the mean spherical approximation. We find for fluids of strong enough polarity that ɛL(k) is negative over a finite range of k, the low end being in agreement with recent computer simulation studies of both ISM and ΩM polar fluids. However, we confirm that the expected large-k behavior ɛL(k)=1 governs the ISMs, but not the ΩM models. Based on an adaptation of the color charge-color field techniques of molecular dynamics, we develop the concept of the color longitudinal dielectric function; it provides useful information about the role of the spatial extent of the molecular charge distribution on the behavior of ɛL(k). The ISM fluids we have analyzed include dipolar dumbbells over a wide range of bond length and polarity as well as realistic interaction site models for water and methanol. For the methanol model, we compare our ɛL(k) with recent computer simulation results and find substantial agreement.
Multidimensional Model of Disability and Role Functioning in Rheumatoid Arthritis.
Ormseth, Sarah R; Draper, Taylor L; Irwin, Michael R; Weisman, Michael H; Aréchiga, Adam E; Hartoonian, Narineh; Bui, Thuy; Nicassio, Perry M
2015-12-01
To examine a model addressing the roles of rheumatoid arthritis (RA) disease burden, mood disturbance, and disability as determinants of impairments in role functioning. In a cross-sectional design, 103 RA patients recruited from the community to participate in a clinical trial completed assessments of self-assessed disease burden (total joint pain and disease activity), mood disturbance (Center for Epidemiological Studies Depression Scale depressed mood, somatic symptoms, lack of positive affect, and interpersonal problems), disability (Health Assessment Questionnaire disability index gross and fine motor), and role functioning (Short Form 36 health survey physical and social). Structural equation modeling (SEM) was used to examine direct and indirect mechanisms linking disease burden to role functioning. SEM results indicated that the model had excellent fit: S-Bχ(2)(30) = 38.59, P = 0.135; comparative fit index = 0.977, standardized root mean residual = 0.062, and root mean square error of approximation = 0.053. Mediational analyses demonstrated that, while disease burden was associated with poor role functioning, its effects were jointly mediated by mood disturbance and disability. After the effects of mood disturbance and disability were taken into account, the effect of disease burden on role functioning was not significant. The results indicate that mood disturbance and disability may serve as important pathways through which RA disease burden affects role functioning. Future longitudinal research is suggested to replicate these findings and further explore the mediational mechanisms examined in this study. © 2015, American College of Rheumatology.
Neural Network Hydrological Modelling: Linear Output Activation Functions?
NASA Astrophysics Data System (ADS)
Abrahart, R. J.; Dawson, C. W.
2005-12-01
The power to represent non-linear hydrological processes is of paramount importance in neural network hydrological modelling operations. The accepted wisdom requires non-polynomial activation functions to be incorporated in the hidden units such that a single tier of hidden units can thereafter be used to provide a 'universal approximation' to whatever particular hydrological mechanism or function is of interest to the modeller. The user can select from a set of default activation functions, or in certain software packages, is able to define their own function - the most popular options being logistic, sigmoid and hyperbolic tangent. If a unit does not transform its inputs it is said to possess a 'linear activation function' and a combination of linear activation functions will produce a linear solution; whereas the use of non-linear activation functions will produce non-linear solutions in which the principle of superposition does not hold. For hidden units, speed of learning and network complexities are important issues. For the output units, it is desirable to select an activation function that is suited to the distribution of the target values: e.g. binary targets (logistic); categorical targets (softmax); continuous-valued targets with a bounded range (logistic / tanh); positive target values with no known upper bound (exponential; but beware of overflow); continuous-valued targets with no known bounds (linear). It is also standard practice in most hydrological applications to use the default software settings and to insert a set of identical non-linear activation functions in the hidden layer and output layer processing units. Mixed combinations have nevertheless been reported in several hydrological modelling papers and the full ramifications of such activities requires further investigation and assessment i.e. non-linear activation functions in the hidden units connected to linear or clipped-linear activation functions in the output unit. There are two
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.
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
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
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
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.
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.
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
Pelagic functional group modeling: Progress, challenges and prospects
NASA Astrophysics Data System (ADS)
Hood, Raleigh R.; Laws, Edward A.; Armstrong, Robert A.; Bates, Nicholas R.; Brown, Christopher W.; Carlson, Craig A.; Chai, Fei; Doney, Scott C.; Falkowski, Paul G.; Feely, Richard A.; Friedrichs, Marjorie A. M.; Landry, Michael R.; Keith Moore, J.; Nelson, David M.; Richardson, Tammi L.; Salihoglu, Baris; Schartau, Markus; Toole, Dierdre A.; Wiggert, Jerry D.
2006-03-01
In this paper, we review the state of the art and major challenges in current efforts to incorporate biogeochemical functional groups into models that can be applied on basin-wide and global scales, with an emphasis on models that might ultimately be used to predict how biogeochemical cycles in the ocean will respond to global warming. We define the term "biogeochemical functional group" to refer to groups of organisms that mediate specific chemical reactions in the ocean. Thus, according to this definition, "functional groups" have no phylogenetic meaning—these are composed of many different species with common biogeochemical functions. Substantial progress has been made in the last decade toward quantifying the rates of these various functions and understanding the factors that control them. For some of these groups, we have developed fairly sophisticated models that incorporate this understanding, e.g. for diazotrophs (e.g. Trichodesmium), silica producers (diatoms) and calcifiers (e.g. coccolithophorids and specifically Emiliania huxleyi). However, current representations of nitrogen fixation and calcification are incomplete, i.e., based primarily upon models of Trichodesmium and E. huxleyi, respectively, and many important functional groups have not yet been considered in open-ocean biogeochemical models. Progress has been made over the last decade in efforts to simulate dimethylsulfide (DMS) production and cycling (i.e., by dinoflagellates and prymnesiophytes) and denitrification, but these efforts are still in their infancy, and many significant problems remain. One obvious gap is that virtually all functional group modeling efforts have focused on autotrophic microbes, while higher trophic levels have been completely ignored. It appears that in some cases (e.g., calcification), incorporating higher trophic levels may be essential not only for representing a particular biogeochemical reaction, but also for modeling export. Another serious problem is our
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.
Potts model partition functions on two families of fractal lattices
NASA Astrophysics Data System (ADS)
Gong, Helin; Jin, Xian'an
2014-11-01
The partition function of q-state Potts model, or equivalently the Tutte polynomial, is computationally intractable for regular lattices. The purpose of this paper is to compute partition functions of q-state Potts model on two families of fractal lattices. Based on their self-similar structures and by applying the subgraph-decomposition method, we divide their Tutte polynomials into two summands, and for each summand we obtain a recursive formula involving the other summand. As a result, the number of spanning trees and their asymptotic growth constants, and a lower bound of the number of connected spanning subgraphs or acyclic root-connected orientations for each of such two lattices are obtained.
Functional connectivity mapping using the ferromagnetic Potts spin model.
Stanberry, Larissa; Murua, Alejandro; Cordes, Dietmar
2008-04-01
An unsupervised stochastic clustering method based on the ferromagnetic Potts spin model is introduced as a powerful tool to determine functionally connected regions. The method provides an intuitively simple approach to clustering and makes no assumptions of the number of clusters in the data or their underlying distribution. The performance of the method and its dependence on the intrinsic parameters (size of the neighborhood, form of the interaction term, etc.) is investigated on the simulated data and real fMRI data acquired during a conventional periodic finger tapping task. The merits of incorporating Euclidean information into the connectivity analysis are discussed. The ability of the Potts model clustering to uncover the hidden structure in the complex data is demonstrated through its application to the resting-state data to determine functional connectivity networks of the anterior and posterior cingulate cortices for the group of nine healthy male subjects. (c) 2007 Wiley-Liss, Inc.
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.
[Trophic function of phytophagous rotifers (Rotatoria). Experiment and modelling].
Tiutiunov, Iu V; Titova, L I; Surkov, F A; Bakaeva, E N
2010-01-01
Three predator-dependent trophic functions have been fitted to experimental data on individual ration of two phytophagous rotifers (Brachionus calyciflorus and Philodina acuticornis) in laboratory monocultures of microalgae (Chlorella vulgaris Beyer, Scenedesmus quadricauda Brev. and Synechocistis sp.). The best fit was obtained with theoretical dependence proposed in that generalises the Arditi-Ginzburg ratio-dependent trophic function, approaching the Holling type II expression when either predator or prey density becomes low. At the same time the obtained results suggest that the original ratio-dependent function of Arditi-Ginzburg can be effectively used for modelling trophic interactions in the considered systems, because individual ration of rotifers is determined by the ratio of microalgae to consumer abundances.
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.
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
Multifractal electronic wave functions in the Anderson model of localization
Schreiber, M.; Grussbach, H. )
1992-06-20
In this paper, investigations of the multifractal properties of electronic wave functions in disordered samples are reviewed. The characteristic mass exponents of the multifractal measure, the generalized dimensions and the singularity spectra are discussed for typical cases. New results for large 3D systems are reported, suggesting that the multifractal properties at the mobility edge which separates localized and extended states are independent of the microscopic details of the model.
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
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.
A displaced-solvent functional analysis of model hydrophobic enclosures
Abel, Robert; Wang, Lingle; Friesner, Richard A.; Berne, B. J.
2010-01-01
Calculation of protein-ligand binding affinities continues to be a hotbed of research. Although many techniques for computing protein-ligand binding affinities have been introduced--ranging from computationally very expensive approaches, such as free energy perturbation (FEP) theory; to more approximate techniques, such as empirically derived scoring functions, which, although computationally efficient, lack a clear theoretical basis--there remains pressing need for more robust approaches. A recently introduced technique, the displaced-solvent functional (DSF) method, was developed to bridge the gap between the high accuracy of FEP and the computational efficiency of empirically derived scoring functions. In order to develop a set of reference data to test the DSF theory for calculating absolute protein-ligand binding affinities, we have pursued FEP theory calculations of the binding free energies of a methane ligand with 13 different model hydrophobic enclosures of varying hydrophobicity. The binding free energies of the methane ligand with the various hydrophobic enclosures were then recomputed by DSF theory and compared with the FEP reference data. We find that the DSF theory, which relies on no empirically tuned parameters, shows excellent quantitative agreement with the FEP. We also explored the ability of buried solvent accessible surface area and buried molecular surface area models to describe the relevant physics, and find the buried molecular surface area model to offer superior performance over this dataset. PMID:21135914
Optimizing Experimental Design for Comparing Models of Brain Function
Daunizeau, Jean; Preuschoff, Kerstin; Friston, Karl; Stephan, Klaas
2011-01-01
This article presents the first attempt to formalize the optimization of experimental design with the aim of comparing models of brain function based on neuroimaging data. We demonstrate our approach in the context of Dynamic Causal Modelling (DCM), which relates experimental manipulations to observed network dynamics (via hidden neuronal states) and provides an inference framework for selecting among candidate models. Here, we show how to optimize the sensitivity of model selection by choosing among experimental designs according to their respective model selection accuracy. Using Bayesian decision theory, we (i) derive the Laplace-Chernoff risk for model selection, (ii) disclose its relationship with classical design optimality criteria and (iii) assess its sensitivity to basic modelling assumptions. We then evaluate the approach when identifying brain networks using DCM. Monte-Carlo simulations and empirical analyses of fMRI data from a simple bimanual motor task in humans serve to demonstrate the relationship between network identification and the optimal experimental design. For example, we show that deciding whether there is a feedback connection requires shorter epoch durations, relative to asking whether there is experimentally induced change in a connection that is known to be present. Finally, we discuss limitations and potential extensions of this work. PMID:22125485
The stochastic resonance for the incidence function model of metapopulation
NASA Astrophysics Data System (ADS)
Li, Jiang-Cheng; Dong, Zhi-Wei; Zhou, Ruo-Wei; Li, Yun-Xian; Qian, Zhen-Wei
2017-06-01
A stochastic model with endogenous and exogenous periodicities is proposed in this paper on the basis of metapopulation dynamics to model the crop yield losses due to pests and diseases. The rationale is that crop yield losses occur because the physiology of the growing crop is negatively affected by pests and diseases in a dynamic way over time as crop both grows and develops. Metapopulation dynamics can thus be used to model the resultant crop yield losses. The stochastic metapopulation process is described by using the Simplified Incidence Function model (IFM). Compared to the original IFMs, endogenous and exogenous periodicities are considered in the proposed model to handle the cyclical patterns observed in pest infestations, diseases epidemics, and exogenous affecting factors such as temperature and rainfalls. Agricultural loss data in China are used to fit the proposed model. Experimental results demonstrate that: (1) Model with endogenous and exogenous periodicities is a better fit; (2) When the internal system fluctuations and external environmental fluctuations are negatively correlated, EIL or the cost of loss is monotonically increasing; when the internal system fluctuations and external environmental fluctuations are positively correlated, an outbreak of pests and diseases might occur; (3) If the internal system fluctuations and external environmental fluctuations are positively correlated, an optimal patch size can be identified which will greatly weaken the effects of external environmental influence and hence inhibit pest infestations and disease epidemics.
Linking geophysics and soil function modelling - biomass production
NASA Astrophysics Data System (ADS)
Krüger, J.; Franko, U.; Werban, U.; Fank, J.
2012-04-01
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 mentioned procedure with a focus on process modelling. It deals with the dynamics of soil water and the direct influence on crop biomass production. The new module PLUS extends CANDY to simulate crop biomass production based on environmental influences. A soil function modelling with an adapted model parameterisation based on data of ground penetration radar (GPR) and conductivity (EM38) was realized. This study shows an approach to handle heterogeneity of soil properties with geophysical data used for biomass production modelling. The Austrian field site Wagna is characterised by highly heterogenic soil with fluvioglacial gravel sediments. The variation of thickness of topsoil above a sandy subsoil with gravels strongly influences the soil water balance. EM38, mounted on a mobile platform, enables to rapidly scan large areas whereas GPR requires a greater logistical effort. However, GPR can detect exact soil horizon depth between topsoil and subsoil, the combination of both results in a detailed large scale soil map. The combined plot-specific GPR and field site EM38 measurements extends the soil input data and improves the model performance of CANDY PLUS for plant biomass production (Krüger et al. 2011). The example demonstrates how geophysics provides a surplus of data for agroecosystem modelling which identifies and contributes alternative options for agricultural management decisions. 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
Confronting species distribution model predictions with species functional traits.
Wittmann, Marion E; Barnes, Matthew A; Jerde, Christopher L; Jones, Lisa A; Lodge, David M
2016-02-01
Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day(-1). Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = -0.26, 95% CI (-0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.
Berto, Timothy C; Lehnert, Nicolai
2011-08-15
The role of NO and nitrite-bound methemoglobin (Hb(III)NO(2)(-)) in hypoxic signaling is highly controversial. One provoking possibility is that hemoglobin (Hb) functions as a nitrite anhydrase, producing N(2)O(3) (from nitrite) as an NO carrier. The ability of Hb to generate N(2)O(3) would provide an intriguing means of NO release from red blood cells. We have investigated this proposed new reactivity of Hb using density functional theory (DFT) calculations. For this purpose, models of the Hb/myoglobin (Mb) active site have been constructed. Our results show that the O-bound (nitrito) form of Hb/Mb(III)NO(2)(-) is essential for the formation of N(2)O(3). The formation and release of N(2)O(3) is shown to be energetically favorable by 1-3 kcal/mol, indicating that the anhydrase function of Hb/Mb is biologically feasible. © 2011 American Chemical Society
Biomechanical considerations in the modeling of muscle function.
Andrews, J G; Hay, J G
1983-09-01
The instantaneous functional role of a voluntary muscle in the neighborhood of a joint is often described in clinical terms (e.g. flexor; abductor; external rotator; agonist; contracting concentrically and isokinetically) that seen sufficiently explicit and clear in certain simple situations, but have not yet been carefully defined in precise biomechanical terminology for the general case. In order to describe the functional role of a voluntary muscle as its acts to change and/or maintain the configuration of a joint, it is necessary to make certain modeling assumptions. These include modeling the joint, modeling the muscle force line of action in the joint neighborhood, and establishing the location and orientation of the three joint axes for all possible joint configurations. Modeling the joint as a point leads to simple and sensible definitions which are consistent with clinical practice. The straight line model is most conveniently used to establish the muscle force line of action. A RHO coordinate system embedded in the distal joint segment with origin at the joint center point, and with intersecting axes coincident with the F/E, A/A and I/XR axes when the joint is in the anatomical position, is the joint coordinate system of choice to describe the turning effects of the muscle about the joint. Sensible and simple biomechanical definitions for clinical terms describing muscular contractions (i.e. concentric; eccentric; isometric; isokinetic; isotonic) were presented and appear to be relatively uncontroversial. Alternative biomechanical definitions for agonistic and antagonistic muscular activity were also presented, as were arguments for choosing a simple definition based on using the joint resultant moment as the criterion measure relative to which the individual muscle's moment about J should be compared. Biomechanical definitions for determining when a muscle functions as a joint flexor or extensor, abductor or adductor, and internal or external rotator
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.
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.
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.
Control of Weierstrass-Mandelbrot Function Model with Morlet Wavelets
NASA Astrophysics Data System (ADS)
Zhang, Li; Liu, Shutang; Yu, Chenglong
A Weierstrass-Mandelbrot function (WMF) model with Morlet wavelets is investigated. Its control relationships are derived quantitatively after proving the convergence of the controlled WMF model. Based on these relationships, it is shown that the scope of the WMF series increases with three parameters of the Morlet wavelets. But other parameters have opposite effect on the scope of the series. The results of simulated examples demonstrate the effectiveness of the control method. Moreover, two statistical characteristics of the series are obtained as the parameters change: One is multifractality of the series of the controlled WMF model, and the other is the Hurst exponent whose value stands for the long-time memory effect on the series.
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.
Model parameters for representative wetland plant functional groups
Williams, Amber S.; Kiniry, James R.; Mushet, David M.; Smith, Loren M.; McMurry, Scott T.; Attebury, Kelly; Lang, Megan; McCarty, Gregory W.; Shaffer, Jill A.; Effland, William R.; Johnson, Mari-Vaughn V.
2017-01-01
Wetlands provide a wide variety of ecosystem services including water quality remediation, biodiversity refugia, groundwater recharge, and floodwater storage. Realistic estimation of ecosystem service benefits associated with wetlands requires reasonable simulation of the hydrology of each site and realistic simulation of the upland and wetland plant growth cycles. Objectives of this study were to quantify leaf area index (LAI), light extinction coefficient (k), and plant nitrogen (N), phosphorus (P), and potassium (K) concentrations in natural stands of representative plant species for some major plant functional groups in the United States. Functional groups in this study were based on these parameters and plant growth types to enable process-based modeling. We collected data at four locations representing some of the main wetland regions of the United States. At each site, we collected on-the-ground measurements of fraction of light intercepted, LAI, and dry matter within the 2013–2015 growing seasons. Maximum LAI and k variables showed noticeable variations among sites and years, while overall averages and functional group averages give useful estimates for multisite simulation modeling. Variation within each species gives an indication of what can be expected in such natural ecosystems. For P and K, the concentrations from highest to lowest were spikerush (Eleocharis macrostachya), reed canary grass (Phalaris arundinacea), smartweed (Polygonum spp.), cattail (Typha spp.), and hardstem bulrush (Schoenoplectus acutus). Spikerush had the highest N concentration, followed by smartweed, bulrush, reed canary grass, and then cattail. These parameters will be useful for the actual wetland species measured and for the wetland plant functional groups they represent. These parameters and the associated process-based models offer promise as valuable tools for evaluating environmental benefits of wetlands and for evaluating impacts of various agronomic practices in
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
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
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
Allometric functional response model: body masses constrain interaction strengths.
Vucic-Pestic, Olivera; Rall, Björn C; Kalinkat, Gregor; Brose, Ulrich
2010-01-01
1. Functional responses quantify the per capita consumption rates of predators depending on prey density. The parameters of these nonlinear interaction strength models were recently used as successful proxies for predicting population dynamics, food-web topology and stability. 2. This study addressed systematic effects of predator and prey body masses on the functional response parameters handling time, instantaneous search coefficient (attack coefficient) and a scaling exponent converting type II into type III functional responses. To fully explore the possible combinations of predator and prey body masses, we studied the functional responses of 13 predator species (ground beetles and wolf spiders) on one small and one large prey resulting in 26 functional responses. 3. We found (i) a power-law decrease of handling time with predator mass with an exponent of -0.94; (ii) an increase of handling time with prey mass (power-law with an exponent of 0.83, but only three prey sizes were included); (iii) a hump-shaped relationship between instantaneous search coefficients and predator-prey body-mass ratios; and (iv) low scaling exponents for low predator-prey body mass ratios in contrast to high scaling exponents for high predator-prey body-mass ratios. 4. These scaling relationships suggest that nonlinear interaction strengths can be predicted by knowledge of predator and prey body masses. Our results imply that predators of intermediate size impose stronger per capita top-down interaction strengths on a prey than smaller or larger predators. Moreover, the stability of population and food-web dynamics should increase with increasing body-mass ratios in consequence of increases in the scaling exponents. 5. Integrating these scaling relationships into population models will allow predicting energy fluxes, food-web structures and the distribution of interaction strengths across food web links based on knowledge of the species' body masses.
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
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
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…
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.
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.
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.
Modelling rhythmic function in a musician post-stroke.
Wilson, Sarah J; Pressing, Jeffrey L; Wales, Roger J
2002-01-01
The aim of this study was to model the components of rhythmic function in a case (H.J.) of acquired rhythmic disturbance. H.J. is a right-handed, amateur male musician who acquired arrhythmia in the context of a global amusia after sustaining a right temporoparietal infarct. His rhythmic disturbance was analysed in relation to three independent components using an autoregressive extension of Wing and Kristofferson's model of rhythmic timing. This revealed preserved error-correction and motor implementation capacities, but a gross disturbance of H.J.'s central timing system ("cognitive clock"). It rendered him unable to generate a steady pulse, prevented adequate discrimination and reproduction of novel metrical rhythms, and partly contributed to bi-manual co-ordination difficulties in his instrumental performance. The findings are considered in relation to the essential components of the cognitive architecture of rhythmic function, and their respective cerebral lateralisation and localisation. Overall, the data suggested that the functioning of the right temporal auditory cortex is fundamental to 'keeping the beat' in music. The approach is presented as a new paradigm for future neuropsychological research examining rhythmic disturbances.
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.
Brunner, Claudia C; Kyprianou, Iacovos S
2013-10-21
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 R(2), 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
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
Skin barrier structure and function: the single gel phase model.
Norlén, L
2001-10-01
A new model for the structure and function of the mammalian skin barrier is postulated. It is proposed that the skin barrier, i.e., the intercellular lipid within the stratum corneum, exists as a single and coherent lamellar gel phase. This membrane structure is stabilized by the very particular lipid composition and lipid chain length distributions of the stratum corneum intercellular space and has virtually no phase boundaries. The intact, i.e., unperturbed, single and coherent lamellar gel phase is proposed to be mainly located at the lower half of stratum corneum. Further up, crystalline segregation and phase separation may occur as a result of the desquamation process. The single gel phase model differs significantly from earlier models in that it predicts that no phase separation, neither between liquid crystalline and gel phases nor between different crystalline phases with hexagonal and orthorhombic chain packing, respectively, is present in the unperturbed barrier structure. The new skin barrier model may explain: (i) the measured water permeability of stratum corneum; (ii) the particular lipid composition of the stratum corneum intercellular space; (iii) the absence of swelling of the stratum corneum intercellular lipid matrix upon hydration; and (iv) the simultaneous presence of hexagonal and orthorhombic hydrocarbon chain packing of the stratum corneum intercellular lipid matrix at physiologic temperatures. Further, the new model is consistent with skin barrier formation according to the membrane folding model of Norlén (2001). This new theoretical model could fully account for the extraordinary barrier capacity of mammalian skin and is hereafter referred to as the single gel phase model.
Secure and Resilient Functional Modeling for Navy Cyber-Physical Systems
2017-05-24
attacks. Pending. Functional Editor (SCCT) Started, on track. Started development of web -based editor for functional models. Designed database to...Functional Editor The development of the Functional Modeling Editor (FME) was started in this quarter by SCCT. The tool consists of a web -based editor for...functional models, which is paired with a database that is used to store these functional models. The web application is being developed in HTML5 and
Stress field models from Maxwell stress functions: southern California
NASA Astrophysics Data System (ADS)
Bird, Peter
2017-08-01
The lithospheric stress field is formally divided into three components: a standard pressure which is a function of elevation (only), a topographic stress anomaly (3-D tensor field) and a tectonic stress anomaly (3-D tensor field). The boundary between topographic and tectonic stress anomalies is somewhat arbitrary, and here is based on the modeling tools available. The topographic stress anomaly is computed by numerical convolution of density anomalies with three tensor Green's functions provided by Boussinesq, Cerruti and Mindlin. By assuming either a seismically estimated or isostatic Moho depth, and by using Poisson ratio of either 0.25 or 0.5, I obtain four alternative topographic stress models. The tectonic stress field, which satisfies the homogeneous quasi-static momentum equation, is obtained from particular second derivatives of Maxwell vector potential fields which are weighted sums of basis functions representing constant tectonic stress components, linearly varying tectonic stress components and tectonic stress components that vary harmonically in one, two and three dimensions. Boundary conditions include zero traction due to tectonic stress anomaly at sea level, and zero traction due to the total stress anomaly on model boundaries at depths within the asthenosphere. The total stress anomaly is fit by least squares to both World Stress Map data and to a previous faulted-lithosphere, realistic-rheology dynamic model of the region computed with finite-element program Shells. No conflict is seen between the two target data sets, and the best-fitting model (using an isostatic Moho and Poisson ratio 0.5) gives minimum directional misfits relative to both targets. Constraints of computer memory, execution time and ill-conditioning of the linear system (which requires damping) limit harmonically varying tectonic stress to no more than six cycles along each axis of the model. The primary limitation on close fitting is that the Shells model predicts very sharp
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.
AgBase: supporting functional modeling in agricultural organisms
McCarthy, Fiona M.; Gresham, Cathy R.; Buza, Teresia J.; Chouvarine, Philippe; Pillai, Lakshmi R.; Kumar, Ranjit; Ozkan, Seval; Wang, Hui; Manda, Prashanti; Arick, Tony; Bridges, Susan M.; Burgess, Shane C.
2011-01-01
AgBase (http://www.agbase.msstate.edu/) provides resources to facilitate modeling of functional genomics data and structural and functional annotation of agriculturally important animal, plant, microbe and parasite genomes. The website is redesigned to improve accessibility and ease of use, including improved search capabilities. Expanded capabilities include new dedicated pages for horse, cat, dog, cotton, rice and soybean. We currently provide 590 240 Gene Ontology (GO) annotations to 105 454 gene products in 64 different species, including GO annotations linked to transcripts represented on agricultural microarrays. For many of these arrays, this provides the only functional annotation available. GO annotations are available for download and we provide comprehensive, species-specific GO annotation files for 18 different organisms. The tools available at AgBase have been expanded and several existing tools improved based upon user feedback. One of seven new tools available at AgBase, GOModeler, supports hypothesis testing from functional genomics data. We host several associated databases and provide genome browsers for three agricultural pathogens. Moreover, we provide comprehensive training resources (including worked examples and tutorials) via links to Educational Resources at the AgBase website. PMID:21075795
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.
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
[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.
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.
A functional compartmental model of the Synechocystis PCC 6803 phycobilisome.
van Stokkum, Ivo H M; Gwizdala, Michal; Tian, Lijin; Snellenburg, Joris J; van Grondelle, Rienk; van Amerongen, Herbert; Berera, Rudi
2017-07-18
In the light-harvesting antenna of the Synechocystis PCC 6803 phycobilisome (PB), the core consists of three cylinders, each composed of four disks, whereas each of the six rods consists of up to three hexamers (Arteni et al., Biochim Biophys Acta 1787(4):272-279, 2009). The rods and core contain phycocyanin and allophycocyanin pigments, respectively. Together these pigments absorb light between 400 and 650 nm. Time-resolved difference absorption spectra from wild-type PB and rod mutants have been measured in different quenching and annihilation conditions. Based upon a global analysis of these data and of published time-resolved emission spectra, a functional compartmental model of the phycobilisome is proposed. The model describes all experiments with a common set of parameters. Three annihilation time constants are estimated, 3, 25, and 147 ps, which represent, respectively, intradisk, interdisk/intracylinder, and intercylinder annihilation. The species-associated difference absorption and emission spectra of two phycocyanin and two allophycocyanin pigments are consistently estimated, as well as all the excitation energy transfer rates. Thus, the wild-type PB containing 396 pigments can be described by a functional compartmental model of 22 compartments. When the interhexamer equilibration within a rod is not taken into account, this can be further simplified to ten compartments, which is the minimal model. In this model, the slowest excitation energy transfer rates are between the core cylinders (time constants 115-145 ps), and between the rods and the core (time constants 68-115 ps).
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
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.
Modeling the evolution of the cerebellum: from macroevolution to function.
Smaers, Jeroen B
2014-01-01
The purpose of this contribution is to explore how macroevolutionary studies of the cerebellum can contribute to theories on cerebellar function and connectivity. New approaches in modeling the evolution of biological traits have provided new insights in the evolutionary pathways that underlie cerebellar evolution. These approaches reveal patterns of coordinated size changes among brain structures across evolutionary time, demonstrate how particular lineages/species stand out, and what the rate and timing of neuroanatomical changes were in evolutionary history. Using these approaches, recent studies demonstrated that changes in the relative size of the posterior cerebellar cortex and associated cortical areas indicate taxonomic differences in great apes and humans. Considering comparative differences in behavioral capacity, macroevolutionary results are discussed in the context of theories on cerebellar function and learning. © 2014 Elsevier B.V. All rights reserved.
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.
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.
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.
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).
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.
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
2011-01-01
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. PMID:21923720
Predictions of Geospace Drivers By the Probability Distribution Function Model
NASA Astrophysics Data System (ADS)
Bussy-Virat, C.; Ridley, A. J.
2014-12-01
Geospace drivers like the solar wind speed, interplanetary magnetic field (IMF), and solar irradiance have a strong influence on the density of the thermosphere and the near-Earth space environment. This has important consequences on the drag on satellites that are in low orbit and therefore on their position. One of the basic problems with space weather prediction is that these drivers can only be measured about one hour before they affect the environment. In order to allow for adequate planning for some members of the commercial, military, or civilian communities, reliable long-term space weather forecasts are needed. The study presents a model for predicting geospace drivers up to five days in advance. This model uses the same general technique to predict the solar wind speed, the three components of the IMF, and the solar irradiance F10.7. For instance, it uses Probability distribution functions (PDFs) to relate the current solar wind speed and slope to the future solar wind speed, as well as the solar wind speed to the solar wind speed one solar rotation in the future. The PDF Model has been compared to other models for predictions of the speed. It has been found that it is better than using the current solar wind speed (i.e., persistence), and better than the Wang-Sheeley-Arge Model for prediction horizons of 24 hours. Once the drivers are predicted, and the uncertainty on the drivers are specified, the density in the thermosphere can be derived using various models of the thermosphere, such as the Global Ionosphere Thermosphere Model. In addition, uncertainties on the densities can be estimated, based on ensembles of simulations. From the density and uncertainty predictions, satellite positions, as well as the uncertainty in those positions can be estimated. These can assist operators in determining the probability of collisions between objects in low Earth orbit.
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.
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.
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
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.
Verbal Neuropsychological Functions in Aphasia: An Integrative Model.
Vigliecca, Nora Silvana; Báez, Sandra
2015-12-01
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 Aphasia Evaluation was used. A sample of 65 patients with left cerebral lesions, and two supplementary samples comprising 35 patients with right cerebral lesions and 30 healthy participants were studied. A model encompassing an all inclusive verbal organizer and two successive organizers was validated. The two last organizers were: three factors of comprehension, expression and a "complementary" verbal factor which included praxia, attention, and memory; followed by the individual (and correlated) factors of auditory comprehension, repetition, naming, speech, reading, writing, and the "complementary" factor. By following this approach all the patients fall inside the classification system; consequently, theoretical improvement is guaranteed.
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.
Alternate Function Bases for Global Scale Spectral General Circulation Models
NASA Astrophysics Data System (ADS)
Paldor, N.
2016-02-01
Solutions of the Shallow Water Equations (SWE) on a rotating sphere (AKA Laplace Tidal Equations) were recently derived for baroclinic and barotropic oceans. In baroclinic oceans where the speed of gravity waves is of the order of a few meters per second the solutions are very well approximated by Hermite Functions (i.e. Hermite Polynomials multiplied by a Gaussian "envelope") while in barotropic oceans where the speed of gravity waves is of the order of 100 m/s the eigenfunctions are Gegenbauer Functions i.e. Gengenbauer Polynomials multiplieג by an "envelope" which is a high power of cos(latitude). These two sets of functions approximate the solutions of the eigenvalue problem associated with zonally propagating wave solutions of the SWE and therefore they provide alternate bases to the Spherical Harmonics basis in spectral general circulation models. Our results in simulating exact solutions of the SWE demonstrate that in barotropic oceans at high wavenumbers numerical simulations by the Gegenbauer Functions are significantly more accurate than simulations by the Spherical Harmonic. In baroclinic oceans numerical simulations by the Hermite Harmonics are far more accurate than simulations by the Spherical Harmonics and time interval over which the latter simulations can be carried out is much shorter than by the former. Similar advantages of the new bases prevail in simulations of the nonlinear equations even though the base functions are not eigensolutions of the nonlinear SWE. Numerical simulations by the Gegenbauer Harmonics are more stable than those by the Spherical Harmonics even in baroclinic oceans where neither set of eigenfunctions is a solution of the associated eigenvalue problem. The poor performance of the Spherical Harmonics basis in baroclinic oceans is attributed to the fact that higher modes have increased resolution mainly near the poles while regular solutions decay with latitude over a scale proportional to the radius of deformation.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
Modelling soil functions and the impact of land use
NASA Astrophysics Data System (ADS)
Weller, Ulrich; Lang, Birgit; Rabot, Eva; Stößel, Bastian; Vogel, Hans-Jörg; Wiesmeier, Martin; Wollschläger, Ute
2017-04-01
One of the main tasks of the BonaRes working group is the evaluation of the impact of different management options on soil functions. These are defined as the production of biomass, storage of carbon and water, filtering of water and habitat for biological activity. In order to predict the impact of management decisions on these functions a comprehensive understanding of soil processes is inevitable. Therefore the modelling crew tries to identify the dominant properties in soil and their interactions. Focus is posed on components that have a meso scaled time horizon rather than short term state variables. Long term properties are considered as moderators for the interactions. The identification of the main interactions is based on a literature research where we try to evaluate the significant interactions and collect our knowledge base in a literature retrieval base. This can serve as a search tool for the whole scientific community to identify papers that have studied certain interactions on soil properties. Also, existing models, which typically act at a shorter time scale, serve as an analysis tool to quantify these interactions.
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.
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.
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
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
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
Melittin restores proteasome function in an animal model of ALS
2011-01-01
Amyotrophic lateral sclerosis (ALS) is a paralyzing disorder characterized by the progressive degeneration and death of motor neurons and occurs both as a sporadic and familial disease. Mutant SOD1 (mtSOD1) in motor neurons induces vulnerability to the disease through protein misfolding, mitochondrial dysfunction, oxidative damage, cytoskeletal abnormalities, defective axonal transport- and growth factor signaling, excitotoxicity, and neuro-inflammation. Melittin is a 26 amino acid protein and is one of the components of bee venom which is used in traditional Chinese medicine to inhibit of cancer cell proliferation and is known to have anti-inflammatory and anti-arthritic effects. The purpose of the present study was to determine if melittin could suppress motor neuron loss and protein misfolding in the hSOD1G93A mouse, which is commonly used as a model for inherited ALS. Meltittin was injected at the 'ZuSanLi' (ST36) acupuncture point in the hSOD1G93A animal model. Melittin-treated animals showed a decrease in the number of microglia and in the expression level of phospho-p38 in the spinal cord and brainstem. Interestingly, melittin treatment in symptomatic ALS animals improved motor function and reduced the level of neuron death in the spinal cord when compared to the control group. Furthermore, we found increased of α-synuclein modifications, such as phosphorylation or nitration, in both the brainstem and spinal cord in hSOD1G93A mice. However, melittin treatment reduced α-synuclein misfolding and restored the proteasomal activity in the brainstem and spinal cord of symptomatic hSOD1G93A transgenic mice. Our research suggests a potential functional link between melittin and the inhibition of neuroinflammation in an ALS animal model. PMID:21682930
Density Functional Theory Modeling of Ferrihydrite Nanoparticle Adsorption Behavior
NASA Astrophysics Data System (ADS)
Kubicki, J.
2016-12-01
Ferrihydrite is a critical substrate for adsorption of oxyanion species in the environment1. The nanoparticulate nature of ferrihydrite is inherent to its formation, and hence it has been called a "nano-mineral"2. The nano-scale size and unusual composition of ferrihydrite has made structural determination of this phase problematic. Michel et al.3 have proposed an atomic structure for ferrihydrite, but this model has been controversial4,5. Recent work has shown that the Michel et al.3 model structure may be reasonably accurate despite some deficiencies6-8. An alternative model has been proposed by Manceau9. This work utilizes density functional theory (DFT) calculations to model both the structure of ferrihydrite nanoparticles based on the Michel et al. 3 model as refined in Hiemstra8 and the modified akdalaite model of Manceau9. Adsorption energies of carbonate, phosphate, sulfate, chromate, arsenite and arsenate are calculated. Periodic projector-augmented planewave calculations were performed with the Vienna Ab-initio Simulation Package (VASP10) on an approximately 1.7 nm diameter Michel nanoparticle (Fe38O112H110) and on a 2 nm Manceau nanoparticle (Fe38O95H76). After energy minimization of the surface H and O atoms. The model will be used to assess the possible configurations of adsorbed oxyanions on the model nanoparticles. Brown G.E. Jr. and Calas G. (2012) Geochemical Perspectives, 1, 483-742. Hochella M.F. and Madden A.S. (2005) Elements, 1, 199-203. Michel, F.M., Ehm, L., Antao, S.M., Lee, P.L., Chupas, P.J., Liu, G., Strongin, D.R., Schoonen, M.A.A., Phillips, B.L., and Parise, J.B., 2007, Science, 316, 1726-1729. Rancourt, D.G., and Meunier, J.F., 2008, American Mineralogist, 93, 1412-1417. Manceau, A., 2011, American Mineralogist, 96, 521-533. Maillot, F., Morin, G., Wang, Y., Bonnin, D., Ildefonse, P., Chaneac, C., Calas, G., 2011, Geochimica et Cosmochimica Acta, 75, 2708-2720. Pinney, N., Kubicki, J.D., Middlemiss, D.S., Grey, C.P., and Morgan, D
A functional-structural modelling approach to autoregulation of nodulation.
Han, Liqi; Gresshoff, Peter M; Hanan, Jim
2011-04-01
Autoregulation of nodulation is a long-distance shoot-root signalling regulatory system that regulates nodule meristem proliferation in legume plants. However, due to the intricacy and subtleness of the signalling nature in plants, molecular and biochemical details underlying mechanisms of autoregulation of nodulation remain largely unknown. The purpose of this study is to use functional-structural plant modelling to investigate the complexity of this signalling system. There are two major challenges to be met: modelling the 3D architecture of legume roots with nodulation and co-ordinating signalling-developmental processes with various rates. Soybean (Glycine max) was chosen as the target legume. Its root system was observed to capture lateral root branching and nodule distribution patterns. L-studio, a software tool supporting context-sensitive L-system modelling, was used for the construction of the architectural model and integration with the internal signalling. A branching pattern with regular radial angles was found between soybean lateral roots, from which a root mapping method was developed to characterize the laterals. Nodules were mapped based on 'nodulation section' to reveal nodule distribution. A root elongation algorithm was then developed for simulation of root development. Based on the use of standard sub-modules, a synchronization algorithm was developed to co-ordinate multi-rate signalling and developmental processes. The modelling methods developed here not only allow recreation of legume root architecture with lateral branching and nodulation details, but also enable parameterization of internal signalling to produce different regulation results. This provides the basis for using virtual experiments to help in investigating the signalling mechanisms at work.
Animal Models to Study MicroRNA Function.
Pal, Arpita S; Kasinski, Andrea L
2017-01-01
The discovery of the microRNAs, lin-4 and let-7 as critical mediators of normal development in Caenorhabditis elegans and their conservation throughout evolution has spearheaded research toward identifying novel roles of microRNAs in other cellular processes. To accurately elucidate these fundamental functions, especially in the context of an intact organism, various microRNA transgenic models have been generated and evaluated. Transgenic C. elegans (worms), Drosophila melanogaster (flies), Danio rerio (zebrafish), and Mus musculus (mouse) have contributed immensely toward uncovering the roles of multiple microRNAs in cellular processes such as proliferation, differentiation, and apoptosis, pathways that are severely altered in human diseases such as cancer. The simple model organisms, C. elegans, D. melanogaster, and D. rerio, do not develop cancers but have proved to be convenient systesm in microRNA research, especially in characterizing the microRNA biogenesis machinery which is often dysregulated during human tumorigenesis. The microRNA-dependent events delineated via these simple in vivo systems have been further verified in vitro, and in more complex models of cancers, such as M. musculus. The focus of this review is to provide an overview of the important contributions made in the microRNA field using model organisms. The simple model systems provided the basis for the importance of microRNAs in normal cellular physiology, while the more complex animal systems provided evidence for the role of microRNAs dysregulation in cancers. Highlights include an overview of the various strategies used to generate transgenic organisms and a review of the use of transgenic mice for evaluating preclinical efficacy of microRNA-based cancer therapeutics. © 2017 Elsevier Inc. All rights reserved.
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.
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
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
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
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
Exact Potts model partition functions on ladder graphs
NASA Astrophysics Data System (ADS)
Shrock, Robert
2000-08-01
We present exact calculations of the partition function Z of the q-state Potts model and its generalization to real q, for arbitrary temperature on n-vertex ladder graphs, i.e., strips of the square lattice with width Ly=2 and arbitrary length Lx, with free, cyclic, and Möbius longitudinal boundary conditions. These partition functions are equivalent to Tutte/Whitney polynomials for these graphs. The free energy is calculated exactly for the infinite-length limit of these ladder graphs and the thermodynamics is discussed. By comparison with strip graphs of other widths, we analyze how the singularities at the zero-temperature critical point of the ferromagnet on infinite-length, finite-width strips depend on the width. We point out and study the following noncommutativity at certain special values q s: lim n→∞ limq→q s Z 1/n≠ limq→q s limn→∞ Z 1/n. It is shown that the Potts antiferromagnet on both the infinite-length line and ladder graphs with cyclic or Möbius boundary conditions exhibits a phase transition at finite temperature if 0< q<2, but with unphysical properties, including negative specific heat and non-existence, in the low-temperature phase, of an n→∞ limit for thermodynamic functions that is independent of boundary conditions. Considering the full generalization to arbitrary complex q and temperature, we determine the singular locus B in the corresponding C2 space, arising as the accumulation set of partition function zeros as n→∞. In particular, we study the connection with the T=0 limit of the Potts antiferromagnet where B reduces to the accumulation set of chromatic zeros. Certain properties of the complex-temperature phase diagrams are shown to exhibit close connections with those of the model on the square lattice, showing that exact solutions on infinite-length strips provide a way of gaining insight into these complex-temperature phase diagrams.
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
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…
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…
Towards a model independent approach to fragmentation functions
NASA Astrophysics Data System (ADS)
Christova, Ekaterina; Leader, Elliot
2009-01-01
We show that the difference cross sections in unpolarized semi-inclusive deep inelastic scattering e+N→e+h+X and pp hadron production p+p→h+X determine independently in a model independent way, in any order in QCD, the two fragmentation functions (FFs): Duh- hmacr and Ddh- hmacr , h=π±, K± or a sum over charged hadrons. If both K± and Ks0 are measured, then e+e-→K+X, e+N→e+K+X, and p+p→K+X present independent measurements of just one FF: Du-dK++K-. The above results allow one to test the existing parametrizations, obtained with various different assumptions about the FFs, and to test the Q2 evolution and factorization.
Insects as innovative models for functional studies of DNA methylation.
Lyko, Frank; Maleszka, Ryszard
2011-04-01
The emerging field of epigenomics has the potential to bridge the gap between static genomic sequences and complex phenotypes that arise from multigenic, nonlinear and often context-dependent interactions. However, this goal can only be achieved if easily manageable experimental systems are available in which changes in epigenomic settings can be evaluated in the context of the phenotype under investigation. Recent progress in the characterization of insect DNA methylation patterns enables evaluation of the extent to which epigenetic mechanisms contribute to complex phenotypes in easily accessible organisms whose relatively small genomes are not only sparingly methylated, but the methylated sites are also found almost exclusively in gene bodies. The implementation of insect models in the study of DNA methylation will accelerate progress in understanding the functional significance of this important epigenetic mechanism in controlling gene splicing, in environmentally driven reprogramming of gene expression and in adult brain plasticity. Copyright © 2011 Elsevier Ltd. All rights reserved.
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.
Iterated function system models in data analysis: detection and separation.
Alexander, Zachary; Meiss, James D; Bradley, Elizabeth; Garland, Joshua
2012-06-01
We investigate the use of iterated function system (IFS) models for data analysis. An IFS is a discrete-time dynamical system in which each time step corresponds to the application of one of the finite collection of maps. The maps, which represent distinct dynamical regimes, may be selected deterministically or stochastically. Given a time series from an IFS, our algorithm detects the sequence of regime switches under the assumption that each map is continuous. This method is tested on a simple example and an experimental computer performance data set. This methodology has a wide range of potential uses: from change-point detection in time-series data to the field of digital communications.
NASA Astrophysics Data System (ADS)
Bruntz, R. J.; Paxton, L. J.; Miller, E. S.; Bust, G. S.; Mayr, H. G.
2015-12-01
The Transfer Function Model (TFM) has been used in numerous studies to simulate gravity waves. In the TFM, the time dependence is formulated in terms of frequencies, and the horizontal wave pattern on the globe is formulated in terms of vector spherical harmonics. For a wide range of frequencies, the equations of mass, energy and momentum conservation are solved to compile a transfer function. The transfer function can then be easily combined with a time-dependent source whose spatial extent is also expressed in spherical harmonics, to produce a global atmospheric response, including gravity waves. This approach has significant benefits in that the solution is grid-independent (without any inherent limits on resolution), and the solutions do not suffer from singularities at the poles. We will show results from our simulations that couple the output of the TFM to an ionospheric model, to predict traveling ionospheric disturbances (TIDs) driven by the simulated gravity waves.
Efficient time-domain model of the graphene dielectric function
NASA Astrophysics Data System (ADS)
Prokopeva, Ludmila J.; Kildishev, Alexander V.
2013-09-01
A honey-comb monolayer lattice of carbon atoms, graphene, is not only ultra-thin, ultra-light, flexible and strong, but also highly conductive when doped and exhibits strong interaction with electromagnetic radiation in the spectral range from microwaves to the ultraviolet. Moreover, this interaction can be effectively controlled electrically. High flexibility and conductivity makes graphene an attractive material for numerous photonic applications requiring transparent conducting electrodes: touchscreens, liquid crystal displays, organic photovoltaic cells, and organic light-emitting diodes. Meanwhile, its tunability makes it desirable for optical modulators, tunable filters and polarizers. This paper deals with the basics of the time-domain modeling of the graphene dielectric function under a random-phase approximation. We focus at applicability of Padé approximants to the interband dielectric function (IDF) of single layer graphene. Our study is centered on the development of a two-critical points approximation (2CPA) of the IDF within a single-electron framework with negligible carrier scattering and a realistic range of chemical potential at room temperature. This development is successfully validated by comparing reflection and transmission spectra computed by a numerical method in time-domain versus semi-analytical calculations in frequency domain. Finally, we sum up our results - (1) high-quality approximation, (2) tunability, and (3) second-order accurate numerical FDTD implementation of the 2CPA of IDF demonstrated across the desired range of the chemical potential to temperature ratios (4 - 23). Finally, we put forward future directions for time-domain modeling of optical response of graphene with wide range of tunable and fabrication-dependent parameters, including other broadening factors and variations of temperature and chemical potentials.
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
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
A continuous function model for path prediction of entities
NASA Astrophysics Data System (ADS)
Nanda, S.; Pray, R.
2007-04-01
As militaries across the world continue to evolve, the roles of humans in various theatres of operation are being increasingly targeted by military planners for substitution with automation. Forward observation and direction of supporting arms to neutralize threats from dynamic adversaries is one such example. However, contemporary tracking and targeting systems are incapable of serving autonomously for they do not embody the sophisticated algorithms necessary to predict the future positions of adversaries with the accuracy offered by the cognitive and analytical abilities of human operators. The need for these systems to incorporate methods characterizing such intelligence is therefore compelling. In this paper, we present a novel technique to achieve this goal by modeling the path of an entity as a continuous polynomial function of multiple variables expressed as a Taylor series with a finite number of terms. We demonstrate the method for evaluating the coefficient of each term to define this function unambiguously for any given entity, and illustrate its use to determine the entity's position at any point in time in the future.
Homology Modeling: Generating Structural Models to Understand Protein Function and Mechanism
NASA Astrophysics Data System (ADS)
Ramachandran, Srinivas; Dokholyan, Nikolay V.
Geneticists and molecular and cell biologists routinely uncover new proteins important in specific biological processes/pathways. However, either the molecular functions or the functional mechanisms of many of these proteins are unclear due to a lack of knowledge of their atomic structures. Yet, determining experimental structures of many proteins presents technical challenges. The current methods for obtaining atomic-resolution structures of biomolecules (X-ray crystallography and NMR spectroscopy) require pure preparations of proteins at concentrations much higher than those at which the proteins exist in a physiological environment. Additionally, NMR has size limitations, with current technology limited to the determination of structures of proteins with masses of up to 15 kDa. Due to these reasons, atomic structures of many medically and biologically important proteins do not exist. However, the structures of these proteins are essential for several purposes, including in silico drug design [1], understanding the effects of disease mutations [2], and designing experiments to probe the functional mechanisms of proteins. Comparative modeling has gained importance as a tool for bridging the gap between sequence and structure space, allowing researchers to build structural models of proteins that are difficult to crystallize or for which structure determination by NMR spectroscopy is not tractable. Comparative modeling, or homology modeling, exploits the fact that two proteins whose sequences are evolutionarily connected display similar structural features [3]. Thus, the known structure of a protein (template) can be used to generate a molecular model of the protein (query) whose experimental structure is notknown.
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
Applying the luminosity function statistics in the fireshell model
NASA Astrophysics Data System (ADS)
Rangel Lemos, L. J.; Bianco, C. L.; Ruffini, R.
2015-12-01
The luminosity function (LF) statistics applied to the data of BATSE, GBM/Fermi and BAT/Swift is the theme approached in this work. The LF is a strong statistical tool to extract useful information from astrophysical samples, and the key point of this statistical analysis is in the detector sensitivity, where we have performed careful analysis. We applied the tool of the LF statistics to three GRB classes predicted by the Fireshell model. We produced, by LF statistics, predicted distributions of: peak ux N(Fph pk), redshift N(z) and peak luminosity N(Lpk) for the three GRB classes predicted by Fireshell model; we also used three GRB rates. We looked for differences among the distributions, and in fact we found. We performed a comparison between the distributions predicted and observed (with and without redshifts), where we had to build a list with 217 GRBs with known redshifts. Our goal is transform the GRBs in a standard candle, where a alternative is find a correlation between the isotropic luminosity and the Band peak spectral energy (Liso - Epk).
Electron Spectral Function for the t-J Model.
NASA Astrophysics Data System (ADS)
McMullen, T.; Gibbs, Zane P.; Bishop, Marilyn F.
1996-03-01
Computed electron spectral functions for the t-J model(Z. Wang, Y. Bang, and G. Kotliar, Phys. Rev. Lett. 67), 2733, (1991). are presented. Large-\\cal N expansion techniques are used in which all self energies are evaluated to leading order in 1/\\cal N. Slave bosons are employed to enforce the non-double-occupancy constraint. Results are given for several values of the exchange coupling J/t and doping δ, including those appropriate to cuprate superconductors. A sharp dispersive quasiparticle peak is found near the Fermi energy. Additional spectral weight occurs at lower frequencies, which arises from the form of the normal self-energy. This is qualitatively similar to the self-energy obtained by Kampf and Schrieffer(A. Kampf and J. R. Schrieffer, Phys. Rev. B 41), 6399 (1990). from a parameterized model of an electron coupled to antiferromagnetic spin fluctuations, which is characterized by shadow bands. The lower shadow band in their calculation is analogous to the lower band in our results.
Function of Hexagenia (Mayfly) Burrows: Fluid Model Suggests Bacterial Gardening
NASA Astrophysics Data System (ADS)
Traynham, B.; Furbish, D.; Miller, M.; White, D.
2006-12-01
Lake and stream bottoms experience an array of physical, chemical, and biological processes that create spatial variations both in the fluid column and in the sediment that provide a physical template for distinct niches. Burrowing insects are major ecological engineers of communities where they structure large areas of the benthic habitat through bioturbation and other activities including respiration, feeding, and defecation. The burrowing mayfly Hexagenia, when present in high densities, has a large impact on food-web dynamics and provides essential ecosystem services within the fluid column and benthic substrate, including sediment mixing, nutrient cycling, and ultimately, energy flow through the freshwater food web. It has long been recognized that particular benthic species are important in linking detrital energy resources to higher trophic levels and for determining how organic matter is processed in freshwater ecosystems; however, the unique contributions made by individual benthic species is largely absent from the literature. Here we present a model that describes the structure and function of a Hexagenia burrow. If testing supports this hypothesis, the model suggests that when high food concentration is available to Hexagenia, there exists a favorable tube length for harvesting bacteria that grow on the burrow walls. The burrow microhabitat created by Hexagenia serves as a case-study in understanding the influence of benthic burrowers on both energy flow through freshwater food webs and nutrient cycling.
Identification and functional modelling of DNA sequence elements of transcription.
Werner, T
2000-11-01
Identification of transcriptional elements in large sequences is a very difficult task, as individual transcription elements (eg transcription factor binding sites,TF-sites) are not clearly correlated with regions exerting transcription control. However, elucidation of the molecular organisation of genomic regions responsible for the control of gene expression is an essential part of the efforts to annotate the genomic sequences, especially within the Human Genome Project. The task for bioinformatics in this context is twofold. The first step required is the approximate localisation of regulatory sequences in large anonymous DNA sequences. Once those regions are located, the second task is the identification of individual transcriptional control elements and correlation of a subset of such elements with transcriptional functions. Part of this second task can be achieved by constructing organisational models of regulatory regions like promoters which can reveal elements important for a gene class or the coexpression of a set of genes. Comparative genomics in non-coding regions (eg phylogenetic footprinting) is a very promising approach that allows identification of potential new regulatory elements which may be used in modelling approaches.
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.
Bidirectional reflectance function in coastal waters: modeling and validation
NASA Astrophysics Data System (ADS)
Gilerson, Alex; Hlaing, Soe; Harmel, Tristan; Tonizzo, Alberto; Arnone, Robert; Weidemann, Alan; Ahmed, Samir
2011-11-01
The current operational algorithm for the correction of bidirectional effects from the satellite ocean color data is optimized for typical oceanic waters. However, versions of bidirectional reflectance correction algorithms, specifically tuned for typical coastal waters and other case 2 conditions, are particularly needed to improve the overall quality of those data. In order to analyze the bidirectional reflectance distribution function (BRDF) of case 2 waters, a dataset of typical remote sensing reflectances was generated through radiative transfer simulations for a large range of viewing and illumination geometries. Based on this simulated dataset, a case 2 water focused remote sensing reflectance model is proposed to correct above-water and satellite water leaving radiance data for bidirectional effects. The proposed model is first validated with a one year time series of in situ above-water measurements acquired by collocated multi- and hyperspectral radiometers which have different viewing geometries installed at the Long Island Sound Coastal Observatory (LISCO). Match-ups and intercomparisons performed on these concurrent measurements show that the proposed algorithm outperforms the algorithm currently in use at all wavelengths.
John S. Hogland; Nathaniel M. Anderson
2015-01-01
Raster modeling is an integral component of spatial analysis. However, conventional raster modeling techniques can require a substantial amount of processing time and storage space, often limiting the types of analyses that can be performed. To address this issue, we have developed Function Modeling. Function Modeling is a new modeling framework that streamlines the...
John S. Hogland; Nathaniel M. Anderson
2015-01-01
Raster modeling is an integral component of spatial analysis. However, conventional raster modeling techniques can require a substantial amount of processing time and storage space, often limiting the types of analyses that can be performed. To address this issue, we have developed Function Modeling. Function Modeling is a new modeling framework that...
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.
Passive Remote Sensing of Oceanic Whitecaps: Updated Geophysical Model Function
NASA Astrophysics Data System (ADS)
Anguelova, M. D.; Bettenhausen, M. H.; Johnston, W.; Gaiser, P. W.
2016-12-01
Many air-sea interaction processes are quantified in terms of whitecap fraction W because oceanic whitecaps are the most visible and direct way of observing breaking of wind waves in the open ocean. Enhanced by breaking waves, surface fluxes of momentum, heat, and mass are critical for ocean-atmosphere coupling and thus affect the accuracy of models used to forecast weather, predict storm intensification, and study climate change. Whitecap fraction has been traditionally measured from photographs or video images collected from towers, ships, and aircrafts. Satellite-based passive remote sensing of whitecap fraction is a recent development that allows long term, consistent observations of whitecapping on a global scale. The method relies on changes of ocean surface emissivity at microwave frequencies (e.g., 6 to 37 GHz) due to presence of sea foam on a rough sea surface. These changes at the ocean surface are observed from the satellite as brightness temperature TB. A year-long W database built with this algorithm has proven useful in analyzing and quantifying the variability of W, as well as estimating fluxes of CO2 and sea spray production. The algorithm to obtain W from satellite observations of TB was developed at the Naval Research Laboratory within the framework of WindSat mission. The W(TB) algorithm estimates W by minimizing the differences between measured and modeled TB data. A geophysical model function (GMF) calculates TB at the top of the atmosphere as contributions from the atmosphere and the ocean surface. The ocean surface emissivity combines the emissivity of rough sea surface and the emissivity of areas covered with foam. Wind speed and direction, sea surface temperature, water vapor, and cloud liquid water are inputs to the atmospheric, roughness and foam models comprising the GMF. The W(TB) algorithm has been recently updated to use new sources and products for the input variables. We present new version of the W(TB) algorithm that uses updated
Uterine glands: development, function and experimental model systems.
Cooke, Paul S; Spencer, Thomas E; Bartol, Frank F; Hayashi, Kanako
2013-09-01
Development of uterine glands (adenogenesis) in mammals typically begins during the early post-natal period and involves budding of nascent glands from the luminal epithelium and extensive cell proliferation in these structures as they grow into the surrounding stroma, elongate and mature. Uterine glands are essential for pregnancy, as demonstrated by the infertility that results from inhibiting the development of these glands through gene mutation or epigenetic strategies. Several genes, including forkhead box A2, beta-catenin and members of the Wnt and Hox gene families, are implicated in uterine gland development. Progestins inhibit uterine epithelial proliferation, and this has been employed as a strategy to develop a model in which progestin treatment of ewes for 8 weeks from birth produces infertile adults lacking uterine glands. More recently, mouse models have been developed in which neonatal progestin treatment was used to permanently inhibit adenogenesis and adult fertility. These studies revealed a narrow and well-defined window in which progestin treatments induced permanent infertility by impairing neonatal gland development and establishing endometrial changes that result in implantation defects. These model systems are being utilized to better understand the molecular mechanisms underlying uterine adenogenesis and endometrial function. The ability of neonatal progestin treatment in sheep and mice to produce infertility suggests that an approach of this kind may provide a contraceptive strategy with application in other species. Recent studies have defined the temporal patterns of adenogenesis in uteri of neonatal and juvenile dogs and work is underway to determine whether neonatal progestin or other steroid hormone treatments might be a viable contraceptive approach in this species.
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…
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…
Wightman function and vacuum fluctuations in higher dimensional brane models
NASA Astrophysics Data System (ADS)
Saharian, Aram A.
2006-02-01
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 AdSD1+1×Σ with a warped internal space Σ. 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 Σ 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(D1,1)×Σ. 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 Σ=S1 is considered, corresponding to the AdSD+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.
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
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.
Density functional theory modeling of multilayer "epitaxial" graphene oxide.
Zhou, Si; Bongiorno, Angelo
2014-11-18
CONSPECTUS: Graphene oxide (GO) is a complex material of both fundamental and applied interest. Elucidating the structure of GO is crucial to achieve control over its properties and technological applications. GO is a nonstoichiometric and hygroscopic material with a lamellar structure, and its physical chemical properties depend critically on synthesis procedures and postsynthesis treatments. Numerous efforts are in place to both understand and exploit this versatile layered carbon material. This Account reports on recent density functional theory (DFT) studies of "epitaxial" graphene oxide (hereafter EGO), a type of GO obtained by oxidation of graphene films grown epitaxially on silicon carbide. Here, we rely on selected X-ray photoelectron spectroscopy (XPS), infrared spectroscopy (IR), and X-ray diffraction (XRD) measurements of EGO, and we discuss in great detail how we utilized DFT-based techniques to project out from the experimental data basic atomistic information about the chemistry and structure of these films. This Account provides an example as to how DFT modeling can be used to elucidate complex materials such as GO from a limited set of experimental information. EGO exhibits a uniform layered structure, consisting of a stack of graphene planes hosting predominantly epoxide and hydroxyl groups, and water molecules intercalated between the oxidized carbon layers. Here, we first focus on XPS measurements of EGO, and we use DFT to generate realistic model structures, calculate core-level chemical shifts, and through the comparison with experiment, gain insight on the chemical composition and metastability characteristics of EGO. DFT calculations are then used to devise a simplistic but accurate simulation scheme to study thermodynamic and kinetic stability and to predict the intralayer structure of EGO films aged at room temperature. Our simulations show that aged EGO encompasses layers with nanosized oxidized domains presenting a high concentration of
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
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.
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
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…
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 modelling framework to simulate foliar fungal epidemics using functional-structural plant models.
Garin, Guillaume; Fournier, Christian; Andrieu, Bruno; Houlès, Vianney; Robert, Corinne; Pradal, Christophe
2014-09-01
Sustainable agriculture requires the identification of new, environmentally responsible strategies of crop protection. Modelling of pathosystems can allow a better understanding of the major interactions inside these dynamic systems and may lead to innovative protection strategies. In particular, functional-structural plant models (FSPMs) have been identified as a means to optimize the use of architecture-related traits. A current limitation lies in the inherent complexity of this type of modelling, and thus the purpose of this paper is to provide a framework to both extend and simplify the modelling of pathosystems using FSPMs. Different entities and interactions occurring in pathosystems were formalized in a conceptual model. A framework based on these concepts was then implemented within the open-source OpenAlea modelling platform, using the platform's general strategy of modelling plant-environment interactions and extending it to handle plant interactions with pathogens. New developments include a generic data structure for representing lesions and dispersal units, and a series of generic protocols to communicate with objects representing the canopy and its microenvironment in the OpenAlea platform. Another development is the addition of a library of elementary models involved in pathosystem modelling. Several plant and physical models are already available in OpenAlea and can be combined in models of pathosystems using this framework approach. Two contrasting pathosystems are implemented using the framework and illustrate its generic utility. Simulations demonstrate the framework's ability to simulate multiscaled interactions within pathosystems, and also show that models are modular components within the framework and can be extended. This is illustrated by testing the impact of canopy architectural traits on fungal dispersal. This study provides a framework for modelling a large number of pathosystems using FSPMs. This structure can accommodate both
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.
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
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.
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
A quantitative confidence signal detection model: 1. Fitting psychometric functions.
Yi, Yongwoo; Merfeld, Daniel M
2016-04-01
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. Copyright © 2016 the American Physiological Society.
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.
ERIC Educational Resources Information Center
Gericke, Niklas Markus; Hagberg, Mariana
2007-01-01
Models are often used when teaching science. In this paper historical models and students' ideas about genetics are compared. The historical development of the scientific idea of the gene and its function is described and categorized into five historical models of gene function. Differences and similarities between these historical models are made…
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…
Goldstein, Harvey; Leckie, George; Charlton, Christopher; Tilling, Kate; Browne, William J
2017-01-01
Aim To present a flexible model for repeated measures longitudinal growth data within individuals that allows trends over time to incorporate individual-specific random effects. These may reflect the timing of growth events and characterise within-individual variability which can be modelled as a function of age. Subjects and methods A Bayesian model is developed that includes random effects for the mean growth function, an individual age-alignment random effect and random effects for the within-individual variance function. This model is applied to data on boys' heights from the Edinburgh longitudinal growth study and to repeated weight measurements of a sample of pregnant women in the Avon Longitudinal Study of Parents and Children cohort. Results The mean age at which the growth curves for individual boys are aligned is 11.4 years, corresponding to the mean 'take off' age for pubertal growth. The within-individual variance (standard deviation) is found to decrease from 0.24 cm(2) (0.50 cm) at 9 years for the 'average' boy to 0.07 cm(2) (0.25 cm) at 16 years. Change in weight during pregnancy can be characterised by regression splines with random effects that include a large woman-specific random effect for the within-individual variation, which is also correlated with overall weight and weight gain. Conclusions The proposed model provides a useful extension to existing approaches, allowing considerable flexibility in describing within- and between-individual differences in growth patterns.
Şentürk, Damla; Dalrymple, Lorien S.; Nguyen, Danh V.
2014-01-01
Summary We propose functional linear models for zero-inflated count data with a focus on the functional hurdle and functional zero-inflated Poisson (ZIP) models. While 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 (PR), 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 two 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 (PCR). PMID:24942314
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.
Removing function model and experiments on ultrasonic polishing molding die
NASA Astrophysics Data System (ADS)
Huang, Qitai; Ni, Ying; Yu, Jingchi
2010-10-01
Low temperature glass molding technology is the main method on volume-producing high precision middle and small diameter optical cells in the future. While the accuracy of the molding die will effect the cell precision, so the high precision molding die development is one of the most important part of the low temperature glass molding technology. The molding die is manufactured from high rigid and crisp metal alloy, with the ultrasonic vibration character of high vibration frequency and concentrative energy distribution; abrasive particles will impact the rigid metal alloy surface with very high speed that will remove the material from the work piece. Ultrasonic can make the rigid metal alloy molding die controllable polishing and reduce the roughness and surface error. Different from other ultrasonic fabrication method, untouched ultrasonic polishing is applied on polish the molding die, that means the tool does not touch the work piece in the process of polishing. The abrasive particles vibrate around the balance position with high speed and frequency under the drive of ultrasonic vibration in the liquid medium and impact the workspace surface, the energy of abrasive particles come from ultrasonic vibration, while not from the direct hammer blow of the tool. So a nummular vibrator simple harmonic vibrates on an infinity plane surface is considered as a model of ultrasonic polishing working condition. According to Huygens theory the sound field distribution on a plane surface is analyzed and calculated, the tool removing function is also deduced from this distribution. Then the simple point ultrasonic polishing experiment is proceeded to certificate the theory validity.
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
Lawrence Livermore National Security Cost Model Functional Management Assessment
Tevis, J; Hirahara, J; Thomas, B; Mendez, M
2008-06-12
The scope of the Functional Management Assessment of the cost model included a review of the plan and progress of the Cost Model Review Team. The review focused on processes in place to ensure simplicity, compliance with cost accounting standards and indirect cost allocation methodology, and the change management plan. This was intended to be a high-level initial review in order to provide recommendations for a subsequent more comprehensive review. The single document reviewed by the team during the assessment was the Indirect Cost Recovery Model Review, which describes how the indirect rate restructure and new organizational structure have resulted in streamlined charging practices to better understand and strategically manage costs. ISSUE 1: The cost model focuses heavily on rate structure but not on cost management. Significant progress has been made to simplify the rate structure. The number of indirect rates has been reduced from 67 different indirect rates used under the prior contract to 32 rates in the first year of the LLNS contract, with a goal of further reduction to 16 for FY09. The reductions are being recommended by a broad-based Working Group driven by Lab leadership desiring a simplified rate structure that would make it easier to analyze the true cost of overhead, be viewed as equitable, and ensure appropriate use of Service, i.e., operations, Centers. This has been a real challenge due to the significant change in approach from one that previously involved a very complex rate structure. Under this prior approach, the goal was to manage the rates, and rates were established at very detailed levels that would 'shine the light' on pools of overhead costs. As long as rates stayed constant or declined, not as much attention tended to be given to them, particularly with so many pools to review (184 indirect rate pools in FY05). However, as difficult and important as simplifying the rate structure has been, the fundamental reason for the simplification
NASA Astrophysics Data System (ADS)
Menke, William
2017-04-01
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.
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.
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
ERIC Educational Resources Information Center
Bowman, Anita H.
A pentagonal model, based on the star model of function understanding of C. Janvier (1987), is presented as a framework for the design and interpretation of research in the area of learning the concept of mathematical function. The five vertices of the pentagon correspond to five common representations of mathematical function: (1) graph; (2)…
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
Multispecies probiotic protects gut barrier function in experimental models.
Nébot-Vivinus, Mylene; Harkat, Cherryl; Bzioueche, Hanene; Cartier, Christel; Plichon-Dainese, Raffaella; Moussa, Lara; Eutamene, Helene; Pishvaie, Dorsa; Holowacz, Sophie; Seyrig, Christian; Piche, Thierry; Theodorou, Vassilia
2014-06-14
To investigate the effect of the probiotic combination Lactibiane Tolerance(®) (LT) on epithelial barrier function in vitro and in vivo. The effect of the multispecies probiotic LT was assessed on several models of epithelial barrier function both in vitro (in basal and inflammatory conditions) and in vivo [visceral hypersensitivity induced by chronic stress or by colonic perfusion of a fecal supernatant (FSN) from patients with irritable bowel syndrome (IBS)]. In vitro, we measured the permeability of confluent T84 cell monolayers incubated with or without LT by evaluating the paracellular flux of macromolecules, in basal conditions and after stimulation with lipopolysaccharide (LPS) or with conditioned medium of colonic biopsies from IBS patients (IBS-CM). In vivo, male C57/Bl6 mice received orally NaCl or LT for 15 d and were submitted to water avoidance stress (WAS) before evaluating visceral sensitivity by measuring the myoelectrical activity of the abdominal muscle and the paracellular permeability with (51)Cr-EDTA. Permeability and sensitivity were also measured after colonic instillation of FSN. Tight-junctions were assessed by immunoblotting and TLR-4 expression was evaluated by immunohistochemistry Incubation of T84 cell monolayers with LT in basal conditions had no significant effect on permeability (P > 0.05 vs culture medium). By contrast, addition of LT bacterial bodies (LT) completely prevented the LPS-induced increase in paracellular permeability (P < 0.01 vs LPS 10 ng/mL (LPS 10); P < 0.01 vs LPS 100 ng/mL (LPS 100), P > 0.05 vs culture medium). The effect was dose dependent as addition of 10(9) LT bacterial bodies induced a stronger decrease in absorbance than 10(6) LT (10(9) LT + LPS 10: -20.1% ± 13.4, P < 0.01 vs LPS 10; 10(6) LT + LPS 10: -11.6% ± 6.2, P < 0.01 vs LPS 10; 10(9) LT + LPS 100: -14.4% ± 5.5, P < 0.01 vs LPS 100; 10(6) LT + LPS 100: -11.6% ± 7.3, P < 0.05 vs LPS 100). Moreover, the increase in paracellular permeability induced
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…
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…
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 ...
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 ...
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
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
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.
Fatigue Crack Growth Analysis Models for Functionally Graded Materials
Dag, Serkan; Yildirim, Bora; Sabuncuoglu, Baris
2008-02-15
The objective of this study is to develop crack growth analysis methods for functionally graded materials (FGMs) subjected to mode I cyclic loading. The study presents finite elements based computational procedures for both two and three dimensional problems to examine fatigue crack growth in functionally graded materials. Developed methods allow the computation of crack length and generation of crack front profile for a graded medium subjected to fluctuating stresses. The results presented for an elliptical crack embedded in a functionally graded medium, illustrate the competing effects of ellipse aspect ratio and material property gradation on the fatigue crack growth behavior.
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.
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.
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.
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.
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
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.
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. PMID:27303338
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.
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…
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…
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…
Implementation of Fixed-point Neuron Models with Threshold, Ramp and Sigmoid Activation Functions
NASA Astrophysics Data System (ADS)
Zhang, Lei
2017-07-01
This paper presents the hardware implementation of single-neuron models with three types of activation functions using fixed-point data format on Field Programmable Gate Arrays (FPGA). Activation function defines the transfer behavior of a neuron model and consequently the Artificial Neural Network (ANN) constructed using it. This paper compared single neuron models designed with bipolar ramp, threshold and sigmoid activation functions. It is also demonstrated that the FPGA hardware implementation performance can be significantly improved by using 16-bit fixed-point data format instead of 32-bit floating-point data format for the neuron model with sigmoid activation function.
A.V. Efremov, P. Schweitzer, O.V. Teryaev, P. Zavada
2011-03-01
We derive relations between transverse momentum dependent distribution functions (TMDs) and the usual parton distribution functions (PDFs) in the 3D covariant parton model, which follow from Lorentz invariance and the assumption of a rotationally symmetric distribution of parton momenta in the nucleon rest frame. Using the known PDFs f_1(x) and g_1(x) as input we predict the x- and pT-dependence of all twist-2 T-even TMDs.
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.
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.
A universal Model-R Coupler to facilitate the use of R functions for model calibration and analysis
Wu, Yiping; Liu, Shuguang; Yan, Wende
2014-01-01
Mathematical models are useful in various fields of science and engineering. However, it is a challenge to make a model utilize the open and growing functions (e.g., model inversion) on the R platform due to the requirement of accessing and revising the model's source code. To overcome this barrier, we developed a universal tool that aims to convert a model developed in any computer language to an R function using the template and instruction concept of the Parameter ESTimation program (PEST) and the operational structure of the R-Soil and Water Assessment Tool (R-SWAT). The developed tool (Model-R Coupler) is promising because users of any model can connect an external algorithm (written in R) with their model to implement various model behavior analyses (e.g., parameter optimization, sensitivity and uncertainty analysis, performance evaluation, and visualization) without accessing or modifying the model's source code.
Modelling graphene quantum dot functionalization via ethylene-dinitrobenzoyl
Noori, Keian; Giustino, Feliciano; Hübener, Hannes; Kymakis, Emmanuel
2016-03-21
Ethylene-dinitrobenzoyl (EDNB) linked to graphene oxide has been shown to improve the performance of graphene/polymer organic photovoltaics. Its binding conformation on graphene, however, is not yet clear, nor have its effects on work function and optical absorption been explored more generally for graphene quantum dots. In this report, we clarify the linkage of EDNB to GQDs from first principles and show that the binding of the molecule increases the work function of graphene, while simultaneously modifying its absorption in the ultraviolet region.
Substance-field Model for Functional Pneumatic Design
NASA Astrophysics Data System (ADS)
Xu, Z. G.; Yang, D. Y.; Shen, W. D.; Liu, T. T.
2017-07-01
The Substance-field analysis is put forward. The functional analysis method is studied to find out the problem, and an improved algorithm is put forward. The internal combustion engine is taken as an example, the harmful function is recognized, and is improved, i.e, high pressure gas is introduced to remove polluted air. The working principle of pneumatic engine is described, the thermodynamic engineering is analyzed, the energy release amounts are analyzed in the isothermal, polymorphism and adiabatic processes. It is concluded that, the isothermal process releases the most energy than the others. The expansion process of the pneumatic engine should be as close as possible to the isothermal process.
Peak functions for modeling high resolution soil profile data
USDA-ARS?s Scientific Manuscript database
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.
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.
Estimation of distribution function in bivariate competing risk models.
Sankaran, P G; Lawless, J F; Abraham, B; Antony, Ansa Alphonsa
2006-06-01
We consider lifetime data involving pairs of study individuals with more than one possible cause of failure for each individual. Non-parametric estimation of cause-specific distribution functions is considered under independent censoring. Properties of the estimators are discussed and an illustration of their application is given.
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 (...
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 (...
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.
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
Isabelle, Boulangeat; Pauline, Philippe; Sylvain, Abdulhak; Roland, Douzet; Luc, Garraud; Sébastien, Lavergne; Sandra, Lavorel; Jérémie, Van Es; Pascal, Vittoz; Wilfried, Thuiller
2012-11-01
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.
A functional circuit model of interaural time difference processing
McColgan, Thomas; Shah, Sahil; Köppl, Christine; Carr, Catherine
2014-01-01
Inputs from the two sides of the brain interact to create maps of interaural time difference (ITD) in the nucleus laminaris of birds. How inputs from each side are matched with high temporal precision in ITD-sensitive circuits is unknown, given the differences in input path lengths from each side. To understand this problem in birds, we modeled the geometry of the input axons and their corresponding conduction velocities and latencies. Consistent with existing physiological data, we assumed a common latency up to the border of nucleus laminaris. We analyzed two biological implementations of the model, the single ITD map in chickens and the multiple maps of ITD in barn owls. For binaural inputs, since ipsi- and contralateral initial common latencies were very similar, we could restrict adaptive regulation of conduction velocity to within the nucleus. Other model applications include the simultaneous derivation of multiple conduction velocities from one set of measurements and the demonstration that contours with the same ITD cannot be parallel to the border of nucleus laminaris in the owl. Physiological tests of the predictions of the model demonstrate its validity and robustness. This model may have relevance not only for auditory processing but also for other computational tasks that require adaptive regulation of conduction velocity. PMID:25185809
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.
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
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
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.
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…
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…
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
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.
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.
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.
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
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. Copyright © 2015. Published by Elsevier Inc.
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).
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
Inflorescence development in tomato: gene functions within a zigzag model
Périlleux, Claire; Lobet, Guillaume; Tocquin, Pierre
2014-01-01
Tomato is a major crop plant and several mutants have been selected for breeding but also for isolating important genes that regulate flowering and sympodial growth. Besides, current research in developmental biology aims at revealing mechanisms that account for diversity in inflorescence architectures. We therefore found timely to review the current knowledge of the genetic control of flowering in tomato and to integrate the emerging network into modeling attempts. We developed a kinetic model of the tomato inflorescence development where each meristem was represented by its “vegetativeness” (V), reflecting its maturation state toward flower initiation. The model followed simple rules: maturation proceeded continuously at the same rate in every meristem (dV); floral transition and floral commitment occurred at threshold levels of V; lateral meristems were initiated with a gain of V (ΔV) relative to the V level of the meristem from which they derived. This last rule created a link between successive meristems and gave to the model its zigzag shape. We next exploited the model to explore the diversity of morphotypes that could be generated by varying dV and ΔV and matched them with existing mutant phenotypes. This approach, focused on the development of the primary inflorescence, allowed us to elaborate on the genetic regulation of the kinetic model of inflorescence development. We propose that the lateral inflorescence meristem fate in tomato is more similar to an immature flower meristem than to the inflorescence meristem of Arabidopsis. In the last part of our paper, we extend our thought to spatial regulators that should be integrated in a next step for unraveling the relationships between the different meristems that participate to sympodial growth. PMID:24744766
Analytical YORP torques model with an improved temperature distribution function
NASA Astrophysics Data System (ADS)
Breiter, S.; Vokrouhlický, D.; Nesvorný, D.
2010-01-01
Previous models of the Yarkovsky-O'Keefe-Radzievskii-Paddack (YORP) effect relied either on the zero thermal conductivity assumption, or on the solutions of the heat conduction equations assuming an infinite body size. We present the first YORP solution accounting for a finite size and non-radial direction of the surface normal vectors in the temperature distribution. The new thermal model implies the dependence of the YORP effect in rotation rate on asteroids conductivity. It is shown that the effect on small objects does not scale as the inverse square of diameter, but rather as the first power of the inverse.
A note on partition functions of Gepner model orientifolds
NASA Astrophysics Data System (ADS)
Blumenhagen, Ralph; Weigand, Timo
2004-07-01
In this note we generalize the description of simple current extended Gepner model orientifolds as presented in arxiv:hep-th/0401148 on the case of even levels and non-trivial dressings of the parity transformation. We provide a comprehensive list of all the important ingredients for the construction of such orientifolds. Namely we present explicit expressions for the Klein-bottle, annulus and Möbius strip amplitudes and derive the general tadpole cancellation conditions. As an example we construct a supersymmetric Pati-Salam like model.
Diffusion kernel-based logistic regression models for protein function prediction.
Lee, Hyunju; Tu, Zhidong; Deng, Minghua; Sun, Fengzhu; Chen, Ting
2006-01-01
Assigning functions to unknown proteins is one of the most important problems in proteomics. Several approaches have used protein-protein interaction data to predict protein functions. We previously developed a Markov random field (MRF) based method to infer a protein's functions using protein-protein interaction data and the functional annotations of its protein interaction partners. In the original model, only direct interactions were considered and each function was considered separately. In this study, we develop a new model which extends direct interactions to all neighboring proteins, and one function to multiple functions. The goal is to understand a protein's function based on information on all the neighboring proteins in the interaction network. We first developed a novel kernel logistic regression (KLR) method based on diffusion kernels for protein interaction networks. The diffusion kernels provide means to incorporate all neighbors of proteins in the network. Second, we identified a set of functions that are highly correlated with the function of interest, referred to as the correlated functions, using the chi-square test. Third, the correlated functions were incorporated into our new KLR model. Fourth, we extended our model by incorporating multiple biological data sources such as protein domains, protein complexes, and gene expressions by converting them into networks. We showed that the KLR approach of incorporating all protein neighbors significantly improved the accuracy of protein function predictions over the MRF model. The incorporation of multiple data sets also improved prediction accuracy. The prediction accuracy is comparable to another protein function classifier based on the support vector machine (SVM), using a diffusion kernel. The advantages of the KLR model include its simplicity as well as its ability to explore the contribution of neighbors to the functions of proteins of interest.