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PubMed1974

Derivation of a quantitative kinetic model for a visual pigment from observations of early receptor potential.

A "complete" and quantitative kinetic model for the states and transitions of the barnacle visual pigment in situ has been constructed from intracellular recordings of the early receptor potential responses to long light pulses. The model involves two stable and four thermolabile states and 10 photochemical, thermal, and metabolic transitions among them. The existence of each state and transition is demonstrated by qualitative examination of the response resulting from a carefully chosen experimental paradigm (combination of intensity, duration, and wavelength of adaptation and stimulation). Quantitative examination of the same responses determines all of the model transition rates, but only puts constraints on the state dipole moments. The latter are determined, and the former refined, by quantitative comparison of the predictions of the complete model with the responses to a set of paradigms chosen to involve as many states and transitions as possible. The fact that good fits can be obtained to these responses without further modification of the model supports its completeness.

Animals↗
PubMedDate not supplied

Accessible pool and system parameters: assumptions and models.

Quantitative assessment of substrate metabolism from in vivo tracer kinetic data requires a model of the system, i.e., a hypothesis on the structure and functioning of the system. Some fundamentals of modeling important for studying intermediary metabolism in the steady state will be discussed. Accessible pool and system parameters are defined. Although the calculation of accessible pool parameters is structure-free, that of system parameters requires the use of non-compartmental or compartmental structures. Assumptions, bases for choice, and relative merits of these two modeling strategies are discussed. Glucose and leucine metabolism serve as prototypes to illustrate the theoretical points.

Glucose↗
PubMed1993

A statistical test of compatibility of data sets to a common dose-response model.

Quantitative estimates of cancer risk generally involve low-dose extrapolation based on an exponential dose-response model for dichotomous response data. Frequently more than one data set is available. If a careful analysis of the biological issues indicates that more than one of the available data sets could be used in the quantitative estimate of cancer risk, it is reasonable to think of combining the data. Before combining data, however, it would be prudent to test whether the data sets are compatible with a common dose-response model. If they are not, it could be concluded that an underlying biological factor is responsible. If they are statistically compatible, the decision to combine data sets based on biological issues would be reinforced. A statistical test based on the generalized likelihood ratio method is proposed for evaluating the compatibility of different data sets with a common dose-response model. This method of constructing a statistical test and the associated asymptotic theory is consistent with the approach used by GLOBAL86 (R. B. Howe, K. S. Crump, and C. Van Landingham, GLOBAL86: A Computer Program to Extrapolate Quantal Animal Toxicity Data to Low Doses, K. S. Crump & Co., Ruston, LA, 1986) for estimating the confidence limits that are used as a basis for quantitative estimates.

Animals↗
PubMed2005

Quantitative assessment model for gastric cancer screening.

AIM: To set up a mathematic model for gastric cancer screening and to evaluate its function in mass screening for gastric cancer. METHODS: A case control study was carried on in 66 patients and 198 normal people, then the risk and protective factors of gastric cancer were determined, including heavy manual work, foods such as small yellow-fin tuna, dried small shrimps, squills, crabs, mothers suffering from gastric diseases, spouse alive, use of refrigerators and hot food, etc. According to some principles and methods of probability and fuzzy mathematics, a quantitative assessment model was established as follows: first, we selected some factors significant in statistics, and calculated weight coefficient for each one by two different methods; second, population space was divided into gastric cancer fuzzy subset and non gastric cancer fuzzy subset, then a mathematic model for each subset was established, we got a mathematic expression of attribute degree (AD). RESULTS: Based on the data of 63 patients and 693 normal people, AD of each subject was calculated. Considering the sensitivity and specificity, the thresholds of AD values calculated were configured with 0.20 and 0.17, respectively. According to these thresholds, the sensitivity and specificity of the quantitative model were about 69% and 63%. Moreover, statistical test showed that the identification outcomes of these two different calculation methods were identical (P>0.05). CONCLUSION: The validity of this method is satisfactory. It is convenient, feasible, economic and can be used to determine individual and population risks of gastric cancer.

Case-Control Studies↗
PubMedDate not supplied

Bulk flow model for multislice magnetic resonance imaging sequences with phantom validation.

A simple conceptual model for describing the effects of one-dimensional bulk flow on image intensities is presented and validated using a multislice imaging sequence specific to one vendor. The model allows quantitative estimates of echo amplitudes versus velocity by using the equation of motion to follow the pulse history of fluid volumes influenced by the readout pulse for any slice of interest. Each affected volume is divided into equal elemental components and the state of each component is computed at discrete times in a pulse position-timing diagram until readout occurs. The final amplitude for the composite volume is determined by the signed summation of each of the elemental components. Validation of the quantitative model was performed by imaging a rotating bulk flow phantom centered at each of the slices of a multislice, partial saturation, spin-echo sequence. Effects due to rf field inhomogeneities were normalized by dividing the results of the dynamic scans by the corresponding static one. The results presented here are relatively insensitive to odd-echo dephasing (even echo rephasing).

Humans↗
PubMed1999

Quantitative phase contrast images to quantitate flow in a rat model of microgravity.

A magnetic resonance angiographic (MRA) technique for noninvasive measurement of flow in the inferior vena cava (IVC) was used to study blood flow changes in a simulated microgravity model. Microgravity was simulated in adult male Fischer 344 rats (n = 12, with each rat acting as its own control) using a tail harness to elevate the hindquarters, producing a non-weight bearing hindlimb (NWH) model. Quantitative phase contrast images of flow within the IVC were obtained initially and after a 2-week NWH protocol. Inferior vena cava blood flow was determined by converting the intensity at the respective magnetic resonance pixels into a corresponding flow by Doppler techniques. Average values for flow determined with MR angiography were 351.8 (SEM = 49) mm3 x s(-1) initially and 524.5 (SEM = 46) mm3 x s(-1) after exposure to 2 weeks of the NWH protocol. Post 2-week NWH flow increased 49.1% over the initial NWH value. Using a paired t-test, a significant difference was found between the rats' IVC flow values in the initial and post-NWH groups (p < 0.004). The changes in IVC blood flow due to 45 degrees NWH may contribute to the overall changes observed in the cardiovascular system during simulated microgravity.

Animals↗
PubMedDate not supplied

Vascular endothelial growth factor (VEGF) expression regulates angiogenesis accompanying tumor growth in a peritoneal disseminated tumor model.

Quantitative analysis of the process of tumor angiogenesis was performed in a new animal model of tumor microcirculation, in which colon carcinoma cells were inoculated into the peritoneal cavity of rats. Time-dependent changes in the microvascular architecture of mesenteric microvessels of tumor-bearing rats were visualized using an intravital microscope. Simultaneously, the expression of vascular endothelial growth factor (VEGF) by the tumor cells and VEGF secretion into ascites were analyzed. The results showed that VEGF increases microvascular permeability and stimulates the growth of microvessels into the tumor and that the spatial and temporal concentration of VEGF is strongly correlated. Such a correlation was stronger in the early angiogenic stages of tumor growth than in the subsequently occurring multiple metastatic stage, when VEGF was still observed at a high level in tumor surroundings. Thus, VEGF is suggested to be primarily involved in the pathophysiological control of angiogenesis accompanying tumor progression.

Animals↗
PubMed1985

A covariance structure model for quantitative genetic research.

In this paper, we have presented a general covariance structure model for quantitative genetic research that incorporates measurement theory, components of variance, and regression theory for specification of structural relations among variance components. Each portion of the general model addresses specific issues important in delineating the genetic etiology of continuous traits. Through specification of a measurement model, genetic and environmental influences can be estimated independently of measurement error. Genetic and environmental sources of variance are defined from familial sampling designs through the components of variance model. Structural relationships among several traits at the genetic or environmental level can be specified through the regression model. The significance of parameters specified in each part of the model can be tested through model comparisons using a likelihood ratio chi-square test. We utilized the general model to test genetic covariance as a source of observed covariation of obesity and glucose tolerance in the Pima Indians. We initially applied the modeling approach using full-sib, half-sib sampling among the Pima with a second application including offspring as well as parental scores. Our initial applications of the methodology suggest that the covariance structure model can be a useful tool in genetic epidemiological research. Through model comparisons, our findings suggest that the association of glucose tolerance and obesity in the Pima is not due to a common set of genes but rather due to non-familial environmental influences. In conclusion, one task important for future research, as we see it, is the application of covariance structure models to other familial sampling designs and the evaluation of the usefulness of this approach through further applications to data.

Arizona↗
PubMed2000

[Identification of mixed major genes and polygenes inheritance model of quantitative traits by using DH or RIL population].

The accuracy of the mixed inheritance analysis of quantitative traits with larger experimental error could be improved while using DH or RIL population. The segregation analysis method of identifying mixed major genes and polygenes inheritance model, including linkage inheritance model, of quantitative traits by using DH or RIL population was developed in this paper. The method may be applied to identify the mixed major gene and polygenes inheritance model of quantitative traits, estimate genetic effects and variances of major genes and polygenes, and the recombination value while there is linkage between two major genes. Finally, an example was used to illuminate the above procedure.

Genetic Linkage↗
PubMed1991

Efficient computation of patterned covariance matrix mixed models in quantitative segregation analysis.

The use of patterned covariance matrices in forming pedigree-based mixed models for quantitative traits is discussed. It is suggested that patterned covariance matrix models provide intuitive, theoretically appealing, and flexible genetic modeling devices for pedigree data. It is suggested further that the very great computational burden assumed in the implementation of covariance matrix-dependent mixed models can be overcome through the use of recent architectural breakthroughs in computing machinery. A brief and nontechnical overview of these architectures is offered, as are numerical and timing studies on various aspects of their use in evaluating mixed models. As the kinds of computers discussed in this paper are becoming more prevalent and easier to access and use, it is emphasized that it behooves geneticists to consider their use to combat needless approximation and time constraints necessitated by smaller, scalar computation oriented, machines.

Algorithms↗
PubMed2003

Score tests for epistasis models on quantitative traits using general pedigree data.

Efficient score tests for a two-locus model for quantitative traits are proposed for general pedigrees. Two search strategies, simultaneous search and conditional search, are considered. These tests are attractive alternatives to the likelihood ratio test for their asymptotic equivalency to their respective likelihood ratio tests and efficiency in computation. The finite-sample properties of these tests are investigated through simulations. Extensions to more complicated models and to other search strategies are discussed.

Epistasis, Genetic↗
PubMed2006

Modeling visual attention.

Quantitative modeling of psychological data is both technically and mathematically challenging. The present article introduces a user friendly and flexible program package that enables quantitative fits of Bundesen's (1990) theory of visual attention to behavioral data from whole and partial report experiments. The program package is based on new computational formulas that are more general than previous ones and has already been used successfully in a number of neuropsychological investigations of attentional disorders, such as visual neglect and simultanagnosia. A clinical version of the program package is currently under development.

Attention↗
PubMed1999

Ligand efficacy and affinity in an interacting 7TM receptor model.

Quantitative understanding of the activation of G protein-coupled receptors is based mostly on some theoretical models that describe the interaction between ligand and protein partners and the activation process of the receptor. All of these models provide different definitions for observable affinity or efficacy. However, the property common to such parameters defined in the context of these models is that they are always independent of the concentration of the receptor molecule. This is based on the assumption that receptors do not interact with each other appreciably. In this article, experimental evidence for which this assumption does not seem to apply is discussed and an oligomerization model for seven-transmembrane-domain receptors that explains the relationship between receptor concentration, apparent affinity and efficacy is provided.

Humans↗
PubMed1989

The sieving of spheres during agarose gel electrophoresis: quantitation and modeling.

By use of agarose gel electrophoresis, the sieving of spherical particles in agarose gels has been quantitated and modeled for spheres with a radius (R) between 13.3 and 149 nm. For quantitation, the electrophoretic mobility has been determined as a function of agarose percentage (A). Because a previously used model of sieving [D. Rodbard and A. Chrambach (1970) Proc. Natl. Acad. Sci. USA 65, 970-977] was found incompatible with some of these data, alternative models have been tested. By use of an underivatized agarose, two models, both based on the assumption of a single effective pore radius (PE) for each A, were found to yield PE values that were independent of R and that were in agreement with values of PE obtained independently (PE = 118 nm X A-0.74): sieving by altered hydrodynamics in a cylindrical tube of radius, PE, and sieving by steric exclusion from a circular hole of radius, PE. The same analysis applied to a 6.5% hydroxyethylated commercial agarose yielded a steeper PE vs A plot and also agreement of the above two models with the data. The PE vs A plot was significantly altered by both further hydroxyethylation and factors that cause variation in the electro-osmosis found in commercial agarose.

Biopolymers↗
PubMed1974

Stereological analysis of the guinea pig pancreas. I. Analytical model and quantitative description of nonstimulated pancreatic exocrine cells.

A stereological model which provides detailed quantitative information on the structure of the fasted, nonstimulated gland has been developed for the guinea pig pancreas. The model consists of morphologically defined space and membrane compartments which were used to describe the general composition of the tissue and the specific components of exocrine cells. The results are presented, where appropriate, relative to a cubic centimeter of pancreas, a cubic centimeter of exocrine cell cytoplasm, and to the volume of an average exocrine cell. The exocrine cells, accounting for 82% of the pancreas volume, consisted of 54% cytoplasmic matrix, 22% rough-surfaced endoplasmic reticulum (RER), 8.3% nuclei, 8.1% mitochondria, 6.4% zymogen granules, and 0.7% condensing vacuoles. Their total membrane surface area was distributed as follows: 60% RER, 21% mitochondria, 9.9% Golgi apparatus, 4.8% plasma membranes, 2.6% zymogen granules, 1.8% plasma membrane vesicles, and 0.4% condensing vacuoles. The application of this model to the study of membrane movements associated with the secretory process is discussed within the framework of an analytical approach.

Analysis of Variance↗
PubMed2000

Improving diagnostic accuracy using a hierarchical neural network to model decision subtasks.

A number of quantitative models including linear discriminant analysis, logistic regression, k nearest neighbor, kernel density, recursive partitioning, and neural networks are being used in medical diagnostic support systems to assist human decision-makers in disease diagnosis. This research investigates the decision accuracy of neural network models for the differential diagnosis of six erythematous-squamous diseases. Conditions where a hierarchical neural network model can increase diagnostic accuracy by partitioning the decision domain into subtasks that are easier to learn are specifically addressed. Self-organizing maps (SOM) are used to portray the 34 feature variables in a two dimensional plot that maintains topological ordering. The SOM identifies five inconsistent cases that are likely sources of error for the quantitative decision models; the lower bound for the diagnostic decision error based on five errors is 0.0140. The traditional application of the quantitative models cited above results in diagnostic error levels substantially greater than this target level. A two-stage hierarchical neural network is designed by combining a multilayer perceptron first stage and a mixture-of-experts second stage. The second stage mixture-of-experts neural network learns a subtask of the diagnostic decision, the discrimination between seborrheic dermatitis and pityriasis rosea. The diagnostic accuracy of the two stage neural network approaches the target performance established from the SOM with an error rate of 0.0159.

Chronic Disease↗
PubMed1993

[Use of mathematical models for quantitative environmental health risk assessment].

Two groups of mathematical models used in the quantitative assessment of cancer risk resulting from exposure to chemical substances for identifying a dose-response relationship are presented. They are as follows: statistical and stochastic models or those biologically motivated. Among statistical models, the logit, probit and Weibull models were considered. Among those biologically motivated, a one-hit model was analysed with special reference to they way from assumptions of the carcinogenesis theory to obtaining a dose-response curve. The two remaining models--multi-hit and multi-stage are discussed very briefly. An example of the fitting dose-response curves to experimental data is presented.

Animals↗
PubMed2003

Quantitative predictive models for octanol-air partition coefficients of polybrominated diphenyl ethers at different temperatures.

Quantitative predictive models for octanol-air partition coefficients of polybrominated diphenyl ethers at different environmental temperatures (T) were developed. Partial least squares (PLS) regression was used for model development. A list of 18 theoretical molecular structural descriptors was screened by PLS analysis. The optimal model was selected from the one containing nine theoretical molecular descriptors and 1/T as predictor variables. The cross-validated Q(2)(cum) value for the optimal model is 0.975, indicating a good predictive ability and stability of the model. Intermolecular dispersive interactions play a leading role in governing the magnitude of logK(OA). The lower the E(LUMO) (the energy of the lowest unoccupied molecular orbital), the greater the intermolecular interactions between octanol and PCB molecules, and thus the greater the logK(OA) values.

Environmental Pollutants↗