MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test

Insulin sensitivity and pancreatic responsivity are the two main factors controlling glucose tolerance. We have proposed a method for measuring these two factors, using computer analysis of a frequently-sampled intravenous glucose tolerance test (FSIGT). This 'minimal modelling approach' fits two mathematical models with FSIGT glucose and insulin data: one of glucose disappearance and one of insulin kinetics. MINMOD is the computer program which identifies the model parameters for each individual. A nonlinear least squares estimation technique is used, employing a gradient-type of estimation algorithm, and the first derivatives (not known analytically) are computed according to the 'sensitivity approach'. The program yields the parameter estimates and the precision of their estimation. From the model parameters, it is possible to extract four indices: SG, the ability of glucose per se to enhance its own disappearance at basal insulin, SI, the tissue insulin sensitivity index, phi 1, first phase pancreatic responsivity, and phi 2, second phase pancreatic responsivity. These four characteristic parameters have been shown to represent an integrated metabolic portrait of a single individual.     

Application of the SAAM modeling program to minimal model analysis of intravenous glucose tolerance test data

The minimal model approach to analysis of intravenous glucose tolerance tests (IVGTT) yields estimates of parameters representing insulin sensitivity, glucose-mediated glucose disposal and pancreatic responsiveness. The precision of these estimates can deteriorate if the glucose and insulin data lack well-defined structure or freedom from data noise (random error). The precision of parameter estimates can be enhanced if data sets from two or more IVGTTs, obtained under different experimental conditions in the same subject, are analysed together in one data file. Following initial fitting using CONSAM, the conversational version of the modeling program SAAM, those parameters whose estimates remain at the same value under the different experimental conditions are constrained. This effectively reduces the number of adjustable parameters, and their estimates can then be fine-tuned with enhanced precision using the batch version of SAAM.     

Coifes—an efficient structural graphics program using the hidden line technique

The theory of the hidden-line, or opaque-imaging problem for polyhedra is discussed, with particular emphasis on its application to finite-element structures. Documentation is provided for the program COIFES, which is a versatile implementation of a fast algorithm for removing hidden lines from objects consisting of mixed sets of non-intersecting polyhedra and polygons.     

A MATLAB program for estimation of unsaturated hydraulic soil parameters using an infiltrometer technique

We combined an inverse routine for assessing the hydraulic soil parameters of the Campbell/Mualem model with the power series solution developed by Philip for describing one-dimensional vertical infiltration into a homogenous soil. We based the estimation routine on a proposed measurement procedure especially suitable for early-time infiltrometer experiments where the flow can be considered as one-dimensional. The routine requires input of the initial soil water content and cumulative infiltration in two experiments with different pressures at the upper boundary and/or initial conditions. An independent measurement of the soil water content at saturation may reduce the uncertainty of estimated parameters. Response surfaces of the objective function were analysed. Scenarios for various soils and conditions, using numerically generated synthetic cumulative infiltration data with normally distributed errors, show promising results for finding the true values of the optimized parameters. We also investigated the effects of measurement frequency for the cumulative infiltration and errors in water content determinations.     

A program for the optimization of cyclosporine therapy using population kinetics modeling

Cyclosporine is one of the most widely used immunosuppressive agents in organ transplantation. Due to large inter- and intra-individual variations, its behavior in the specific patient is still difficult to predict. Dosage optimization is thus mainly performed on a trial-and-error basis. In this paper, we present a new program based on the population kinetics approach, which was designed to help physicians in the difficult task of adjusting patient specific cyclosporine dosing regimens. Dose optimization is carried out by model simulation, using a two-compartment mathematical model of cyclosporine kinetics to predict the drug behavior in the patient. Two of the model parameters are assumed from the literature, the other two are estimated from the patient data through a Bayesian estimation procedure. Previous information needed by the Bayesian algorithm is derived by a population analysis, performed beforehand and based on a nonlinear mixed effect model. A user-friendly graphical interface written in Delphi under Windows makes the program easily accessible to physicians. A preliminary retrospective validation of the program, performed on data from 18 renal transplanted patients, yielded very satisfactory results.     

Fisk: A FORTRAN program to estimate the mode of (hornblende-) biotite gneiss and amphibolite from chemical analyses

FISK is a FORTRAN program that calculates the mode of biotite gneiss and amphibolite using the chemical analyses and the estimated An-content of plagioclase. A correction for accessory minerals is included. The program prints out the chemical analysis and the chemical composition of the calculated minerals.     

A computer program to estimate the parameters of covariate dependent higher order Markov model

This paper presents a computer program developed in S-plus to estimate the parameters of covariate dependent higher order Markov Chain and related tests. The program can be applied for two states Markov Chain with any order and any number of covariates depending on the PC capabilities. The program provides the maximum likelihood estimates of the parameters, together with their estimated standard error, t-value and significance level. It also produces the test results for likelihood ratio and model chi-square. To illustrate the program we have used a longitudinal data set on maternal morbidity of rural women in Bangladesh. The occurrences of haemorrhage, convulsion, or fits at different follow-ups were used as outcome variable. Economic status, wanted pregnancy, ages at marriage, and education of women were used as covariates.     

A method to estimate the parameters of an ARMA model

A method of estimating the parameters of an ARMA model is proposed. In the transient case, we show that the formulas recently given by Li and Dickinson [7] for the AR parameters are easily obtained by considering the formulation of the one-step predictor. In the steady-state case, we can get separately the AR parameters and the MA ones.     

A simple single layer model to estimate transpiration from vegetation using multi-spectral and meteorological data

A methodology is developed here to model evapotranspiration (λE_c ) from the canopy layer over large areas by combining satellite and ground measurements of biophysical and meteorological variables. The model developed here follows the energy balance approach, where λE_c is estimated as a residual when the net radiation (Rn), sensible heat flux (H) and ground flux (G) are known. Multi-spectral measurements from the NOAA Advanced Very High Resolution Radiometer (AVHRR) were used along with routine meteorological measurements made on the ground to estimate components of the energy balance. The upwelling long wave radiation, and H from the canopy layer were modelled using the canopy temperature, obtained from a linear relation between the Normalized Difference Vegetation Index (NDVI) and surface temperature. This method separates flux measurements from the canopy and bare soil without the need for a complex two layer model. From theoretical analysis of canopy reflectance, leaf area, and canopy resistance, a model is developed to scale the transpiration estimates from the full canopy to give an area averaged estimate from the mean NDVI of the study area. The model was tested using data collected from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), and the results show that the modelled values of total surface evapotranspiration from the soil and canopy layers vary from the ground measurements by less than 9 per cent.     

A technique to estimate topsoil thickness in arid and semi-arid areas of north-eastern Jordan using synthetic aperture radar data

A Japanese Earth Resources Satellite (JERS)-1 L-band synthetic aperture radar (SAR) dataset was used for estimating topsoil thickness, of different types, in arid and semi-arid areas in north-eastern Jordan. In this research, the relationship between remotely sensed data, backscattering coefficient and the thickness of topsoil was investigated. Based on the dielectric constant properties of the topsoil samples, the relationship between the backscattering coefficient and the topsoil thickness was obtained by developing a multilayer modelling analysis. Using this model, the topsoil thickness had been estimated by means of the derived backscattering coefficients from JERS-1 SAR image. The estimated thickness values of the different topsoil types were found to be comparable with field ground data. The estimated minimum thickness for hardpan topsoil is 55?cm; Qaa topsoil, 74?cm; and topsoil of herbaceous area, 46?cm, while the estimated maximum thickness is more than 98?cm, more than 100?cm, and 82?cm, respectively. Ground data, on the other hand, revealed the minimum thickness for hardpan topsoil to be 50?cm; Qaa topsoil, 70?cm; and topsoil of herbaceous area, 40?cm, while the maximum thickness is more than 120?cm, more than 100?cm, and 80?cm, respectively.     

A fast MATLAB program to estimate the multifractal spectrum of multidimensional data: Application to fractures

A MATLAB^® program based on the Hou algorithm for estimation of fractal dimension and multifractal spectrum of fractures is presented. The program performance was tested with many synthetical fractals and field data. Interpolation and sampling effects on the fractal dimension and multifractal spectrum estimation were also studied. Some common problems related to the fractal dimension and multifractal spectrum are also discussed.     

Validation of multibody modeling and simulation using an instrumented bicycle: from the computer to the road

This paper describes the use of an instrumented bicycle and its computational model for teaching multibody dynamics. The presented approach employs the Whipple model for the kinematic and inverse dynamic simulation of a bicycle ride using as an input three generalized coordinates registered with digital sensors. During the experimental phase, students ride the instrumented bicycle to collect the necessary sensor data. The kinematic and inverse dynamic simulations based on these signals provide a full picture of the motion of the system in different positions and at a range of velocities and accelerations. In addition, they estimate the traction, control, and tire-to-road contact forces during the ride. To validate the simulated results, the simulated velocity and accelerations are compared with the data acquired with an inertial measurement unit (IMU) installed on the bicycle. The paper describes the experimental setup of the instrumented bicycle, enabling readers to build the very same system for their own educational use. The instrumented bicycle system is based on open-source software and as much as possible on open hardware.     

A simulation program for the sensitivity and linearity ofpiezoresistive pressure sensors

A simulation program is developed which is capable of calculating the output responses of piezoresistive pressure sensors as a function of pressure and temperature. Analytical models based on small and large deflection theories have been applied to predict the sensitivity and linearity of pressure sensors. Surface-micromachined diaphragms with square or circular shapes, fabricated by a low pressure chemical vapor deposition sealing process, are designed and tested to verify the program. They are made of polysilicon and have a standard width (diameter) of 100 μm and thickness from 1.5 to 2.2 μm. Various parameters of the piezoresistive sensing resistors, including length, orientation, and dopant concentration, have been derived and constructed on top of the diaphragms. For a 100-μm-wide 2-μm-thick square-shape pressure sensor, calculated and experimental results show that int sensitivity of 0.24 mV/V/(Ibf/in²) is achieved. Experimentally, non a maximum linearity error of ±0.1% full-scale span) is found out on a 100-μm-wide 2.2-μm-thick square-shape pressure sensor. Both sensitivity and linearity are characterized by the diaphragm thickness and the length of the sensing resistors     

A computer program to estimate power and relative efficiency to assess multiplicative interactions in flexibly matched case-control studies

Matching on one of two possibly interacting exposures can increase power and efficiency to estimate multiplicative interactions in case-control studies. We recently introduced the concept of flexible matching strategies with varying proportions of a dichotomous matching factor among controls to further increase power and efficiency. In order to facilitate the application of this concept, we developed a computer program which provides estimates of power and efficiency varying the proportion of the matching factor in controls over all possible values from 1 to 99%. The program allows one to estimate the effect of frequency matching on power and efficiency to study multiplicative interactions and to assess the optimal prevalence of the matching factor in selected controls for a given scenario which often differs from the prevalence in cases (aimed at in traditional 'fixed' frequency matching). Our program will strongly facilitate assessing the benefits of matching and flexible matching strategies in case-control studies addressing multiplicative interactions, including gene-environment interactions.     

Agent-based modeling to estimate exposures to urban air pollution from transportation: Exposure disparities and impacts of high-resolution data

Better understanding of the complex links between urban transportation, land use, air quality, and population exposure is needed to improve urban sustainability. A goal of this study was to develop an exposure modeling framework that integrates agent-based activity and travel simulation with air pollution modeling for Tampa, Florida. We aimed to characterize exposure and exposure inequality for traffic-related air pollution, and to investigate the impacts of high-resolution information on estimated exposure. To do these, we developed and applied a modeling framework that combines the DaySim activity-based travel demand model, the MATSim dynamic traffic assignment model, the MOVES mobile source emissions estimator, and the R-LINE dispersion model. Resulting spatiotemporal distributions of daily individual human activity and pollutant concentration were matched to analyze population and subgroup exposure to oxides of nitrogen (NO_x) from passenger car travel for an average winter day in 2010. Four scenarios using data with different spatiotemporal resolutions were considered: a) high resolution for both activities and concentrations, b) low resolution for both activities and concentrations, c) high resolution for activities, but low resolution for concentrations, and d) vice versa. For the high-resolution scenario, the mean daily population exposure concentration of NO_x from passenger cars was 10.2 μg/m³; individual exposure concentrations ranged from 0.2 to 145 μg/m³. Subgroup mean exposure was higher than the population mean for individuals living below-poverty (by ~16%), those with daily travel time over one hour (8%), adults aged 19–45 (7%), blacks (6%), Hispanics (4%), Asians (2%), combined other non-white races (2%), people from middle income households (2%), and residents of urban areas (2%). The subgroup inequality index (a measure of disparity) largely increased with concentration up to the 90th percentile level for these groups. At higher levels, disparities increased sharply for individuals from below poverty households, blacks, and Hispanics. Low-resolution simulation of both activities and concentrations decreased the exposure estimates by 10% on average, with differences ranging from eight times higher to ~90% lower.     

Insulin sensitivity in type 2 diabetes: univariate and multivariate techniques to derive estimates of insulin sensitivity from the insulin modified intravenous glucose tolerance test (FSIGT)

The use of univariate and multivariate techniques to derive estimates of insulin sensitivity from the insulin modified FSIGT were investigated in 12 Type 2 diabetic subjects aged (mean+/-S.D.) 59+/-9.5 years and BMI 28.1+/-2.2 kg m(-2), who underwent both a FSIGT and an isoglycemic hyperinsulinemic clamp. Reproducibility of the FSIGT was tested in four patients on three separate occasions. FSIGT data were assessed by both univariate and multivariate techniques. The sensitivity index for the FSIGT ranged from 0.162 to 3.292 (mean 1.378) x 10(-4) x l min(-1) mU(-1) for the univariate approach and from 0.163 to 2.727 (mean 1.378) x 10(-4) x l min(-1) mU(-1) for the multivariate method. Mean S(Iclamp) was 44.41 x 10(-4) x l(-2) min(-1) x mU(-1) (range 22.0-77.92). The correlation of the insulin sensitivity indices between the clamp and the FSIGT was 0.51 (P=0.056) for the univariate and 0.67 (P=0.017) for the multivariate analyses. Repeated FSIGTs showed a lower variability for the multivariate than for the standard approach.