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PubMed1998

A mathematical evaluation of the multiple breath nitrogen washout (MBNW) technique and the multiple inert gas elimination technique (MIGET).

We consider two and 50 compartment lung models for use with two techniques used to investigate the efficiency of the lungs: the Multiple Breath Nitrogen Washout (MBNW) technique used for investigating the ventilation-volume distribution; and the Multiple Inert Gas Elimination Technique (MIGET) used for investigating the ventilation-perfusion distribution. In each of these techniques pulmonary respiratory gas exchange is described by conservation of mass equations which may be written in identical form, and in each the underlying distributions of ventilation to volume and ventilation to perfusion are assumed to be continuous functions (usually assumed to be a linear sum of log-normal distributions). The mathematical models used to describe the lung have predominantly used a collection of discrete compartments to approximate these continuous distributions. The most commonly used models have used one, two or 50 compartments. In this paper, we begin by showing that in the limit as the width of the peaks of the distribution tend to zero, the continuous distributions may be replaced by a single discrete compartment placed at each peak of the distribution. We investigate the various methods used previously for parameter recovery, and show that one commonly used method for the MBNW is not suitable and suggest a modification to this recovery technique. Using simulated error-free data, we show that both the two compartment model and the 50 compartment model contain information about the ventilation-volume (or ventilation-perfusion) distribution, and investigate the extent to which this information can be used to recover the parameters which define these distributions. We go on to use Monte-Carlo methods to investigate the stability of the recovery process.

Breath Tests↗
PubMed2005

An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem.

BACKGROUND: The protein folding problem is a fundamental problems in computational molecular biology and biochemical physics. Various optimisation methods have been applied to formulations of the ab-initio folding problem that are based on reduced models of protein structure, including Monte Carlo methods, Evolutionary Algorithms, Tabu Search and hybrid approaches. In our work, we have introduced an ant colony optimisation (ACO) algorithm to address the non-deterministic polynomial-time hard (NP-hard) combinatorial problem of predicting a protein's conformation from its amino acid sequence under a widely studied, conceptually simple model - the 2-dimensional (2D) and 3-dimensional (3D) hydrophobic-polar (HP) model. RESULTS: We present an improvement of our previous ACO algorithm for the 2D HP model and its extension to the 3D HP model. We show that this new algorithm, dubbed ACO-HPPFP-3, performs better than previous state-of-the-art algorithms on sequences whose native conformations do not contain structural nuclei (parts of the native fold that predominantly consist of local interactions) at the ends, but rather in the middle of the sequence, and that it generally finds a more diverse set of native conformations. CONCLUSIONS: The application of ACO to this bioinformatics problem compares favourably with specialised, state-of-the-art methods for the 2D and 3D HP protein folding problem; our empirical results indicate that our rather simple ACO algorithm scales worse with sequence length but usually finds a more diverse ensemble of native states. Therefore the development of ACO algorithms for more complex and realistic models of protein structure holds significant promise.

Algorithms↗
PubMed2004

Influence of multiple scattering and energy loss straggling on the absorbed dose distributions of therapeutic light ion beams: I. Analytical pencil beam model.

The lateral and longitudinal distributions of absorbed dose of broad and narrow light ion beams in water are investigated. An analytical algorithm based on the generalized Fermi-Eyges theory is developed and used to calculate the effects of multiple scattering and range straggling on the dose distribution of light ion beams in water. A first-order Gaussian multiple scattering and energy loss straggling approach is generally sufficiently accurate for describing the lateral and longitudinal spread of the Bragg peak and the associated energy deposition distribution of therapeutic light ion beams at ranges of clinical interest. Nuclear reactions are not taken into account in this study. The analytical algorithm given in the present study allows an accurate description of the radial spread and the range straggling of light ions traversing matter. A verification of this approach by comparing with experimental data, Monte Carlo methods and other analytical techniques will be presented in a forthcoming paper.

Algorithms↗
PubMed1999

Exact tests of goodness of fit of log-linear models for rates.

We propose Metropolis-Hastings sampling methods for estimating the exact conditional p-value for tests of goodness of fit of log-linear models for mortality rates and standardized mortality ratios. We focus on two-way tables, where the required conditional distribution is a multivariate noncentral hypergeometric distribution with known noncentrality parameter. Two examples are presented: a 2 x 3 table, where the exact results, obtained by enumeration, are available for comparison, and a 9 x 7 table, where Monte Carlo methods provide the only feasible approach for exact inference.

Biometry↗
PubMed2003

Preparation of (188) Re-labeled paper for treating skin cancer.

For homogeneous delivery of beta radiation to skin cancer, we developed a simple method for preparing (188) Re-labeled nitrocellulose paper. The homogeneity and stability of the labeled paper were investigated. Absorbed dose estimates were calculated using the Monte-Carlo method. A 74-MBq (188) Re-labeled paper with 1-cm diameter delivered 147.2 Gy up to 1-mm depth after 2-h irradiation. Animal experiments on tumor-bearing mice showed that 50 Gy is an adequate dose for treating skin cancer. Tumors disappeared 7 days after irradiation in all the groups irradiated by 50 or 100 Gy. The (188) Re-labeled paper provided a convenient, economical, effective, and non-invasive method of treating skin cancer.

Animals↗
PubMed2000

The use of Monte Carlo simulation to examine pharmacodynamic variance of drugs: fluoroquinolone pharmacodynamics against Streptococcus pneumoniae.

BACKGROUND: For fluoroquinolones, AUC:MIC ratios correlate with maximal bacterial eradication in in vitro models of infection and favorable cure rates in humans with respiratory tract infection. Inter-subject pharmacokinetic and MIC variability may impact the probability of attaining optimal AUC:MIC ratios and hence favorable clinical outcome. METHODS: Monte Carlo simulation was utilized to estimate the probability of attaining AUC:MIC ratios of 30, 40, 50, 60, 70, 80, 90, 100, 110 and 120 using AUC values from patients treated with either gatifloxacin or levofloxacin and microbiologic activity against S. pneumoniae observed in 1997 SENTRY Antimicrobial Surveillance Program. RESULTS: The probability curves for 5000 patient simulations were plotted. The median AUC:MIC ratios were 120 for gatifloxacin and 50.5 for levofloxacin. The probability of attaining AUC:MIC ratios of 30, 50, 70 and 100 for gatifloxacin were 94%, 86%, 78% and 62%, and for levofloxacin were 80%, 51%, 31% and 17%, respectively. CONCLUSION: Gatifloxacin has a higher probability of achieving target AUC:MIC ratios than levofloxacin. Monte Carlo simulation, using patient-based AUC and MIC distributions, may have implications for selection of optimal antibiotics for the empiric treatment of infections. Moreover, Monte Carlo simulation may have utility in the determination of MIC breakpoints.

Anti-Infective Agents↗
PubMed1987

Statistical methods for model discrimination. Applications to gating kinetics and permeation of the acetylcholine receptor channel.

Methods are described for discrimination of models of the gating kinetics and permeation of single ionic channels. Both maximum likelihood and regression procedures are discussed. In simple situations, where models are nested, standard hypothesis tests can be used. More commonly, however, non-nested models are of interest, and several procedures are described for model discrimination in these cases, including Monte Carlo methods, which allow the comparison of models at significance levels of choice. As an illustration, the methods are applied to single-channel data from acetylcholine receptor channels.

Ion Channels↗
PubMed1999

Ferrous sulphate gel dosimetry and MRI for proton beam dose measurements.

Ferrous sulphate gel dosimetry has the potential for measurement of absorbed dose distributions in proton therapy. The chemical properties of the gel are altered according to the radiation dose and these changes can be evaluated in three dimensions using MRI. The purpose of this work was to investigate the properties of a ferrous gel used with clinical proton beams. The gel was irradiated with both monoenergetic and range-modulated proton beams. It was then evaluated using MRI. The depth dose by means of the 1/T1 distribution was studied and compared with data from a plane-parallel plate ionization chamber. 1/T1 was shown to be proportional to the dose at a mean proton energy of approximately 90 MeV. The dose response was no different from that obtained using photon beams. However, on normalization at the entrance, the relative 1/T1 at the Bragg peak was 15-20% lower than the corresponding ionization chamber data for the monoenergetic proton beam. Better agreement was found for the modulated beam, but with significant differences close to the distal edge of the 1/T1 distribution. The change in sensitivity with depth was explained by means of a linear energy transfer dependence. This property was further studied using Monte Carlo methods.

Calibration↗
PubMed2000

Additive isotonic regression models in epidemiology.

Stone's method for assessing disease risk around a point source through isotonic regression is routinely used in spatial epidemiology. It is useful in situations where the relationship of risk with exposure (distance being commonly used as a surrogate variable) is assumed monotonic but otherwise of unknown form. This paper extends this method to non-spatial epidemiology, where typically two or more risk factors are present. The methodology described is based on the additive isotonic model approach of Bacchetti; versions appropriate to count (Poisson) data and case-control (binomial) data are described. In both cases, adjustment for covariates is incorporated, and a Monte Carlo method of hypothesis testing and interval estimation is presented. The methodology is illustrated through a case-control example concerning the analysis of the possible effect of preconceptional external ionizing radiation doses on the sex ratio at birth among children of fathers working at the Sellafield nuclear installation, Cumbria, U.K.

Case-Control Studies↗
PubMed1999

Determination of order parameters and correlation times in proteins: a comparison between Bayesian, Monte Carlo and simple graphical methods.

We describe a novel approach to deducing order parameters and correlation times in proteins using a Bayesian statistical method, and show how likelihood contours, P(tau,S), and confidence levels can be obtained. These results are then compared with those obtained from a simple graphical method, as well as those from Monte Carlo simulations. The Bayes approach has the advantage that it is simple and accurate. Unlike Monte Carlo methods, it gives useful contour plots of probability (also not provided by the simple graphical method), and provides likelihood/confidence information. In addition, the Bayesian approach gives results in very good agreement with those obtained from Monte Carlo simulations, and as such use of Bayesian statistical methods appears to have a promising future for studies of order and dynamics in macromolecules.

Bayes Theorem↗
PubMed2005

Optimal number of pinholes in multi-pinhole SPECT for mouse brain imaging--a simulation study.

This study simulates a multi-pinhole single-photon emission computed tomography (SPECT) system using the Monte Carlo method, and investigates different multi-pinhole designs for quantitative mouse brain imaging. Prior approaches investigating multi-pinhole SPECT were not often optimal, as the number and geometrical arrangement of pinholes were usually chosen empirically. The present study seeks to optimize the number of pinholes for a given pinhole arrangement, and also for the specific application of quantitative neuroreceptor binding in the mouse brain. An analytical Monte Carlo simulation based method was used to generate the projection data for various count levels. A three-dimensional ordered-subsets expectation-maximization algorithm was developed and used to reconstruct the images, incorporating a realistic pinhole model for resolution recovery and noise reduction. Although artefacts arising from overlapping projections could be a major problem in multi-pinhole reconstruction, the cold-rod phantom study showed minimal loss of spatial resolution in multi-pinhole systems, compared to a single-pinhole system with the same pinhole diameter. A quantitative study of neuroreceptor binding sites using a mouse brain phantom and low activity (37 MBq) showed that the multi-pinhole system outperformed the single-pinhole system by maintaining the mean and lowering the variance in the measured uptake ratio. Multi-pinhole collimation can be used to reduce the injected dose and thereby reduce the radiation exposure to the animal. Results also suggest that the nine-pinhole configuration shown in this paper is a good choice for mouse brain imaging.

Algorithms↗
PubMed2006

Approaches to calculating AAPM TG-43 brachytherapy dosimetry parameters for 137Cs, 125I, 192Ir, 103Pd, and 169Yb sources.

Underlying characteristics in brachytherapy dosimetry parameters for medical radionuclides 137Cs, 125I, 192Ir, 103Pd, and 169Yb were examined using Monte Carlo methods. Sources were modeled as unencapsulated point or line sources in liquid water to negate variations due to materials and construction. Importance of phantom size, mode of radiation transport physics--i.e., photon transport only or coupled photon:electron transport, phantom material, volume averaging, and Monte Carlo tally type were studied. For noninfinite media, g(r) was found to degrade as r approached R, the phantom radius. MCNP5 results were in agreement with those published using GEANT4. Brachytherapy dosimetry parameters calculated using coupled photon:electron radiation transport simulations did not differ significantly from those using photon transport only. Dose distributions from low-energy photon-emitting radionuclides 125I and 103Pd were sensitive to phantom material by upto a factor of 1.4 and 2.0, respectively, between tissue-equivalent materials and water at r =9 cm. In comparison, high-energy photons from 137Cs, 192Ir, and 169Yb demonstrated +/- 5% differences in dose distributions between water and tissue substitutes at r=20 cm. Similarly, volume-averaging effects were found to be more significant for low-energy radionuclides. When modeling line sources with L < or = 0.5 cm, the two-dimensional anisotropy function was largely within +/- 0.5% of unity for 137Cs, 125I, and 192Ir. However, an energy and geometry effect was noted for 103Pd and 169Yb, with Pd-103F(0.5,0 degrees)=l.05 and yb-169F(0.5,0 degrees)=0.98 for L=0.5 cm. Simulations of monoenergetic photons for L=0.5 cm produced energy-dependent variations in F(r, theta) having a maximum value at 10 keV, minimum at 50 keV, and approximately 1.0 for higher-energy photons up to 750 keV. Both the F6 cell heating and *F4 track-length estimators were employed to determine brachytherapy dosimetry parameters. F6 was found to be necessary for g(r), while both tallies provided equivalent results for F(r, theta).

Brachytherapy↗
PubMed2006

Gene-dropping vs. empirical variance estimation for allele-sharing linkage statistics.

In this study, we compare the statistical properties of a number of methods for estimating P-values for allele-sharing statistics in non-parametric linkage analysis. Some of the methods are based on the normality assumption, using different variance estimation methods, and others use simulation (gene-dropping) to find empirical distributions of the test statistics. For variance estimation methods, we consider the perfect variance approximation and two empirical variance estimates. The simulation-based methods are gene-dropping with and without conditioning on the observed founder alleles. We also consider the Kong and Cox linear and exponential models and a Monte Carlo method modified from a method for finding genome-wide significance levels. We discuss the analytical properties of these various P-value estimation methods and then present simulation results comparing them. Assuming that the sample sizes are large enough to justify a normality assumption for the linkage statistic, the best P-value estimation method depends to some extent on the (unknown) genetic model and on the types of pedigrees in the sample. If the sample sizes are not large enough to justify a normality assumption, then gene-dropping is the best choice. We discuss the differences between conditional and unconditional gene-dropping.

Alleles↗
PubMedDate not supplied

[A theoretical analysis of structural restructuring during formation of secondary RNA structures].

An improved method for predicting the RNA secondary structure is proposed. The process of self-organization of structure is considered as a Markov chain. The kinetic of secondary structure is analysed by the Monte-Carlo method. The topological compatibility of helices is discussed. On the base of analysis it follows that the dynamical process of secondary structure formation is so that it is impossible to define a static set of complementary pairs. The method was used for predicting the mRNA secondary structure of a series of recombinant plasmids, containing the cro gene. The observed variation in expression can be explained by secondary structure.

Computer Simulation↗
PubMed1992

Importance sampling. I. Computing multimodel p values in linkage analysis.

In linkage analysis, when the lod score is maximized over multiple genetic models, standard asymptotic approximation of the significance level does not apply. Monte Carlo methods can be used to estimate the p value, but procedures currently used are extremely inefficient. We propose a Monte Carlo procedure based on the concept of importance sampling, which can be thousands of times more efficient than current procedures. With a reasonable amount of computing time, extremely accurate estimates of the p values can be obtained. Both theoretical results and an example of maturity-onset diabetes of the young (MODY) are presented to illustrate the efficiency performance of our method. Relations between single-model and multimodel p values are explored. The new procedure is also used to investigate the performance of asymptotic approximations in a single model situation.

Diabetes Mellitus, Type 2↗
PubMedDate not supplied

Theoretical evaluation of dose distributions in water about models 6711 and 6702 125I seeds.

The distribution of absorbed dose about models 6711 and 6702 125I seeds in water has been calculated from first principles using the Monte Carlo method. Dose is calculated as a function of angle with respect to the transverse seed bisector for distances from the seed center ranging from 0.1 to 7.5 cm. The computed results are compared to measured data. A truncated Fourier series is used to describe the Monte Carlo data in terms of a small number of coefficients, facilitating accurate and efficient dose calculations for clinical treatment planning.

Brachytherapy↗
PubMedDate not supplied

Who's number one? The impact of variability on rankings based on public health indicators.

OBJECTIVE: Researchers, government, and the press often rank jurisdictions according to public health indicators; however, measures of uncertainty rarely accompany these comparisons. To demonstrate the variability associated with rankings that use public health measures, the authors examined the uncertainty associated with ranks based on three common methods used to derive public health indicators: age-adjustment, calculations based on census estimates, and calculations based on survey data. METHODS: The authors observed the effect of changing the standard population from the 1970 population to the 1997 population on rank-order lists of jurisdictions according to age-adjusted 1998 mortality rates. They used a Monte Carlo method to calculate confidence intervals (CIs) around ranks based on census estimates of 1998 infant mortality rates and based on 1999 Behavioral Risk Factor Surveillance System (BRFSS) survey data on the prevalence of hypertension. RESULTS: Changing the standard year from 1970 to 1997 resulted in a shift of at least three rank-order positions for seven states. Two states shifted five positions. CIs associated with ranking by infant mortality rates were broad, with a mean of 16 ranks. CIs around ranks for the prevalence of hypertension were also wide, with a mean of 18 ranks. CONCLUSION: While ranking based on public health indicators is an attractive and popular way of presenting public health data, caution and close examination of the underlying data are needed for proper interpretation. Alternative methods, such as longitudinal analysis or comparisons with standards, may prove more useful.

Age Factors↗