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Results for “Monte Carlo Method”

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PubMed1998

Probing the conformation of bilirubins with monopropionic analogs: a biological, spectroscopic, and molecular modeling study.

The in vivo metabolism of a bilirubin analog substituted with a propionic acid chain in C8 (5) showed that it is excreted in bile conjugated with glucuronic acid, while a positional isomer substituted with a propionate in C7 (6) is excreted in bile without conjugation. A conformational analysis employing an optimized Monte Carlo method and a mixed Monte Carlo/stochastic dynamics reveals that isomer 5 adopts a 'ridge tile' conformation, stabilized by the presence of three intramolecular hydrogen bonds. On the contrary, isomer 6 exhibits a more closed structure, where impairment in the formation of at least one of the hydrogen bonds occurs. These theoretical predictions agree well with 1H NMR, UV-vis, and TLC data.

Bilirubin↗

PubMed2005

Calculations of dose distributions using a neural network model.

The main goal of external beam radiotherapy is the treatment of tumours, while sparing, as much as possible, surrounding healthy tissues. In order to master and optimize the dose distribution within the patient, dosimetric planning has to be carried out. Thus, for determining the most accurate dose distribution during treatment planning, a compromise must be found between the precision and the speed of calculation. Current techniques, using analytic methods, models and databases, are rapid but lack precision. Enhanced precision can be achieved by using calculation codes based, for example, on Monte Carlo methods. However, in spite of all efforts to optimize speed (methods and computer improvements), Monte Carlo based methods remain painfully slow. A newer way to handle all of these problems is to use a new approach in dosimetric calculation by employing neural networks. Neural networks (Wu and Zhu 2000 Phys. Med. Biol. 45 913-22) provide the advantages of those various approaches while avoiding their main inconveniences, i.e., time-consumption calculations. This permits us to obtain quick and accurate results during clinical treatment planning. Currently, results obtained for a single depth-dose calculation using a Monte Carlo based code (such as BEAM (Rogers et al 2003 NRCC Report PIRS-0509(A) rev G)) require hours of computing. By contrast, the practical use of neural networks (Mathieu et al 2003 Proceedings Journees Scientifiques Francophones, SFRP) provides almost instant results and quite low errors (less than 2%) for a two-dimensional dosimetric map.

Algorithms↗

PubMed2005

Computational methods in coupled electron-ion Monte Carlo simulations.

In the last few years, we have been developing a Monte Carlo simulation method to cope with systems of many electrons and ions in the Born-Oppenheimer approximation: the coupled electron-ion Monte Carlo method (CEIMC). Electronic properties in CEIMC are computed by quantum Monte Carlo rather than by density functional theory (DFT) based techniques. CEIMC can, in principle, overcome some of the limitations of the present DFT-based ab initio dynamical methods. The new method has recently been applied to high-pressure metallic hydrogen. Herein, we present a new sampling algorithm that we have developed in the framework of the reptation quantum Monte Carlo method chosen to sample the electronic degrees of freedom, thereby improving its efficiency. Moreover, we show herein that, at least for the case of metallic hydrogen, variational estimates of the electronic energies lead to an accurate sampling of the proton degrees of freedom.

Computer Simulation↗

PubMed2003

Power calculations for survival analyses via Monte Carlo estimation.

BACKGROUND: Power calculations can be a useful step in the design of epidemiologic studies. For occupational and environmental cohort studies, however, the calculation of statistical power has been difficult because researchers are often interested in situations where exposure assignment is time-dependent, and in research questions that pertain to cumulative exposure-mortality trends evaluated with statistical methods for survival analysis. These conditions are not easily accommodated by available software or published formulas for power calculation. METHODS: Monte Carlo methods can be used to estimate statistical power for survival analyses. Simple computer programs are presented to illustrate this approach. RESULTS: We show that, for the simple case of a randomized clinical trial involving a dichotomous exposure, the results of power calculations derived via this Monte Carlo approach conform to values derived using a previously published formula. We then illustrate how the Monte Carlo approach may be extended to obtain estimates of statistical power for analyses of cumulative exposure-mortality trends under conditions more typical of occupational cohort studies. CONCLUSIONS: The Monte Carlo approach provides a way to perform power calculations for a wide range of study conditions. The approach illustrated in this study should simplify the task of calculating power for survival analyses, particularly in epidemiologic research on occupational cohorts.

Epidemiologic Research Design↗

PubMed1999

Monte Carlo Markov chain methods for genome screening.

We used Monte Carlo Markov chain (MCMC) methods to analyze a quantitative trait, MAO level, and a discrete trait, Collaborative Study on the Genetics of Alcoholism (COGA) alcoholism. Segregation, linkage, and haplotype sharing were analyzed and effects of marker map features were examined. For MAO, modest signals were found on chromosomes 1 and 17 for raw data, and 15 for covariate-adjusted data. For alcoholism, a strong signal was found on chromosome 1 with modest signals on chromosomes 4 and 10.

Alcoholism↗

PubMed1992

Estimating effective population size from samples of sequences: a bootstrap Monte Carlo integration method.

We would like to use maximum likelihood to estimate parameters such as the effective population size N(e) or, if we do not know mutation rates, the product 4N(e) mu of mutation rate per site and effective population size. To compute the likelihood for a sample of unrecombined nucleotide sequences taken from a random-mating population it is necessary to sum over all genealogies that could have led to the sequences, computing for each one the probability that it would have yielded the sequences, and weighting each one by its prior probability. The genealogies vary in tree topology and in branch lengths. Although the likelihood and the prior are straightforward to compute, the summation over all genealogies seems at first sight hopelessly difficult. This paper reports that it is possible to carry out a Monte Carlo integration to evaluate the likelihoods approximately. The method uses bootstrap sampling of sites to create data sets for each of which a maximum likelihood tree is estimated. The resulting trees are assumed to be sampled from a distribution whose height is proportional to the likelihood surface for the full data. That it will be so is dependent on a theorem which is not proven, but seems likely to be true if the sequences are not short. One can use the resulting estimated likelihood curve to make a maximum likelihood estimate of the parameter of interest, N(e) or of 4N(e) mu. The method requires at least 100 times the computational effort required for estimation of a phylogeny by maximum likelihood, but is practical on today's work stations. The method does not at present have any way of dealing with recombination.

Base Sequence↗

PubMedDate not supplied

[Use of spheric micro lens elements for laser angioplasty].

The molding of laser energy by spherical microlens fiber tips applied to laser angioplasty has been experimentally calculated and investigated. The Monte-Carlo method has been proposed to simplify a calculation procedure. This has reduced the necessary amount of test light rays (250 versus 2100 in the former studies). A simplified calculation model (bicentric) is given, which replaces the Monte-Carlo method in many practical cases. The calculation data are in a good agreement with experimental findings.

Angioplasty, Laser↗

PubMed2005

Effect of uncertainties in agricultural working schedules and Monte-Carlo evaluation of the model input in basin-scale runoff model analysis of herbicides.

In the prediction of time-series concentrations of herbicides in river water with diffuse-pollution hydrological models, farming schedules (the dates of herbicide application and drainage of irrigation water from rice paddies) greatly affect the runoff behavior of the herbicides. For large catchments, obtaining precise data on farming schedules is impractical, and so the model input inevitably includes substantial uncertainty. This paper evaluates the effectiveness of using the Monte-Carlo method to generate sets of estimated farming schedules to use as input to a GIS-based basin-scale runoff model to predict the concentrations of paddy-farming herbicides in river water. The effects of using the Monte-Carlo method to compensate for uncertainty in the evaluated parameters for herbicide decomposition and sorption were also evaluated.

Acetanilides↗

PubMed2005

Asynchronous multicanonical basin hopping method and its application to cobalt nanoclusters.

The multicanonical basin hopping (MUBH) method, which uses a multicanonical weight in the basin hopping (BH) Monte Carlo method, was found to be very efficient for global optimization of large-scale systems such as Lennard-Jones clusters containing more than 150 atoms. We have implemented an asynchronous parallel version of the MUBH method using the message passing interface (MPI) to take advantage of the full usage of multiprocessors in either a homogeneous or heterogeneous computational environment. Based on the intrinsic properties of the Monte Carlo method, this MPI implementation used the task parallelism to minimize interthread data communication. For a Co nanocluster consisting of N atoms, we have applied the asynchronous multicanonical basin hopping (AMUBH) method (for 181 < N < or = 200), together with BH (for 2 < or = N < 150) and MUBH (for 150 < or = N < or = 180), to search for the molecular configuration of the global energy minimum. AMUBH becomes the only practical computational scheme for locating the energy minimum within realistic computational time for a relatively large cluster.

Journal Article↗

PubMed2006

Limb-size proportions in Australopithecus afarensis and Australopithecus africanus.

Previous analyses have suggested that Australopithecus africanus possessed more apelike limb proportions than Australopithecus afarensis. However, due to the errors involved in estimating limb length and body size, support for this conclusion has been limited. In this study, we use a new Monte Carlo method to (1) test the hypothesis that A. africanus had greater upper:lower limb-size proportions than A. afarensis and (2) assess the statistical significance of interspecific differences among these taxa, extant apes, and humans. Our Monte Carlo method imposes sampling constraints that reduce extant ape and human postcranial measurements to sample sizes comparable to the fossil samples. Next, composite ratios of fore- and hindlimb geometric means are calculated for resampled measurements from the fossils and comparative taxa. Mean composite ratios are statistically indistinguishable (alpha=0.05) from the actual ratios of extant individuals, indicating that this method conserves each sample's central tendency. When applied to the fossil samples, upper:lower limb-size proportions in A. afarensis are similar to those of humans (p=0.878) and are significantly different from all great ape proportions (p< or =0.034), while Australopithecus africanus is more similar to the apes (p> or =0.180) and significantly different from humans and A. afarensis (p< or =0.031). These results strongly support the hypothesis that A. africanus possessed more apelike limb-size proportions than A. afarensis, suggesting that A. africanus either evolved from a more postcranially primitive ancestor than A. afarensis or that the more apelike limb-size proportions of A. africanus were secondarily derived from an A. afarensis-like ancestor. Among the extant taxa, limb-size proportions correspond with observed levels of forelimb- and hindlimb-dominated positional behaviors. In conjunction with detailed anatomical features linked to arboreality, these results suggest that arboreal posture and locomotion may have been more important components of the A. africanus behavioral repertoire relative to that of A. afarensis.

Animals↗

PubMed1984

A simulation method for the study of laser transillumination of biological tissues.

The Monte-Carlo method is employed to simulate the illumination of a blood slab by a continuous laser. It is assumed that the geometry of the medium is bidimensional and that scattering or absorption takes place only when a photon strikes a red blood cell. The parameters involved in the calculations concern the photons free path lengths between two collisions, the scattering angles and the absorption probability at collision. These parameters are assessed according to experimental or literature data. Fortran programs allow the computation of diffuse and collimated transmittances (Td and Tc, respectively), of transmittance measured with an optic fiber Tf and of reflectance R. The variations of Tc and Tf with blood thickness are in accordance with established laws. Moreover, measured and computed reflectances change with hematocrit ratio in a similar way. This work demonstrates that the Monte-Carlo method is a simple reliable tool which can be used to provide a realistic model of laser penetration in complex biological structures. Moreover, this method will permit investigations in laser tomo-spectrometry by providing a useful simulation of the interaction of ultrashort light pulses with biological media.

Animals↗

PubMed2006

[Intra-operative stereotactic accuracy of computer-assisted robot orthopaedic trauma surgery planning system].

OBJECTIVE: To investigate the feasibility and accuracy of the software computer-assisted robot orthopaedic trauma surgery planning system (CAOTS). METHODS: The software CAOTS was developed and used on 85 cases of stereotactic operation, 24 model bones, 21 cadaveric extremity bones, and 40 patients. 307 groups of directional targets in these cases were randomly sampled. The distribution of error sources for evaluating system performance was embodied using Monte-Carlo method in order to derive the theoretic guarantees for further optimizing and enhancing the system performance, then the software SPSS 2.0 was used to analyze the errors. RESULTS: The statistical area of deviation number was 0.0408 +/- 0.4578 mm, corresponding to the result by Monte-Carlo method. Punching succeeded at the first run for all 307 cases without wrong locking and other clinical complications. CONCLUSION: Accurate and reliable, CAOTS improves the intra-operative navigation techniques and facilitates the orthopedists to perform operation.

Cadaver↗

PubMed2001

Conformation-family Monte Carlo: a new method for crystal structure prediction.

A new global optimization method, Conformation-family Monte Carlo, has been developed recently for searching the conformational space of macromolecules. In the present paper, we adapted this method for prediction of crystal structures of organic molecules without assuming any symmetry constraints except the number of molecules in the unit cell. This method maintains a database of low energy structures that are clustered into families. The structures in this database are improved iteratively by a Metropolis-type Monte Carlo procedure together with energy minimization, in which the search is biased toward the regions of the lowest energy families. The Conformation-family Monte Carlo method is applied to a set of nine rigid and flexible organic molecules by using two popular force fields, AMBER and W99. The method performed well for the rigid molecules and reasonably well for the molecules with torsional degrees of freedom.

Crystallization↗

PubMed1999

Mapping quantitative trait loci for complex binary traits in outbred populations.

Complex binary traits have a dichotomous phenotypic expression but do not show a simple Mendelian segregation ratio. These traits are considered to be jointly controlled by the actions of several genes and a random environmental effect. The binary phenotype and the underlying factor are assumed to be linked through a threshold model. The underlying factor, referred to as the liability, is treated as a regular but unobservable quantitative character. Mapping quantitative trait loci (QTL) can be performed directly on the liability. Methods of QTL mapping for the liability of a complex binary trait have been well developed in line-crossing experiments. However, such a method is not available in outbred populations which usually consist of many independent pedigrees (families). In this study, we develop a method to analyse jointly multiple families of an outbred population. The method is developed based on a fixed-model approach, i.e. the QTL effects, rather than the variance, are estimated and tested. After the test, the estimated effects are then converted into a single estimate of the QTL variance by taking into consideration errors in the estimated effects. The QTL effects and variance-covariance matrix of the estimates are obtained by a fast Fisher-scoring method. Monte Carlo simulations show that the method is not only powerful but also generates very accurate estimates of QTL variances.

Algorithms↗

PubMed2002

Deterministic calculations of photon spectra for clinical accelerator targets.

A method is proposed to compute photon energy spectra produced in clinical electron accelerator targets, based on the deterministic solution of the Boltzmann equation for coupled electron-photon transport in one-dimensional (1-D) slab geometry. It is shown that the deterministic method gives similar results as Monte Carlo calculations over the angular range of interest for therapy applications. Relative energy spectra computed by deterministic and 3-D Monte Carlo methods, respectively, are compared for several realistic target materials and different electron beams, and are found to give similar photon energy distributions and mean energies. The deterministic calculations typically require 1-2 mins of execution time on a Sun Workstation, compared to 2-36 h for the Monte Carlo runs.

Computers↗

PubMed2000

Projection of cancer risks from the Japanese atomic bomb survivors to the England and Wales population taking into account uncertainty in risk parameters.

Generalized relative risk models, with adjustments to the relative risk for time after exposure and age at exposure and incorporating a linear-quadratic dose response, were fitted to the latest (Life Span Study Report 12) Japanese atomic bomb survivor cancer mortality data using Bayesian Markov Chain Monte Carlo methods, taking account of random errors in the DS86 dose estimates. The resulting uncertainty distributions in the relative risk model parameters were used to derive uncertainties in population cancer risks for a current UK population. Following an assumed administered dose of 1 Sv, leukaemia mortality risks were estimated to be 1.93x10(-2) Sv(-1) (95% CI 1.14, 3.38), or 0.44 years of life lost Sv(-1) (95% CI 0.22, 0.94). Following an assumed administered dose of 1 Sv, solid cancer mortality risks were calculated to be 10.36x10(-2) Sv(-1) (95% CI 8.41, 12.42), or 1.38 years of life lost Sv(-1) (95% CI 1.11, 1.68). In general, solid cancer risks were very similar to those predicted by classical likelihood-based methods; however, leukaemia risks were somewhat higher, by 10-35%, than those predicted by classical likelihood-based methods. This is so in both cases, irrespective of whether or not adjustments are made in these likelihood-based fits for the effects of measurement errors, and the discrepancy for leukaemia tends to be greater at higher doses. Overall, cancer risks predicted by Bayesian Markov Chain Monte Carlo methods are similar to those derived by classical likelihood-based methods and which form the basis of established estimates of radiation-induced cancer risk.

Bayes Theorem↗

PubMed1994

Analysis of photon beam exit dose using photon point kernels.

The Monte Carlo method is used to analyse the dose fall-off at the exit surface of a megavoltage photon beam. The convolution/superposition method of dose calculation using Monte-Carlo-generated homogeneous photon kernels is shown to be in error for exit dose calculation. Instead, photon kernels that incorporate modelling of the exit surface were generated, also using Monte Carlo, to analyse the problem, and the calculated dose fall-off using these kernels agrees well with measured data. In addition, the physics underlying the characteristics of the dose fall-off is analysed based on complete Monte Carlo modelling. Practical improvements to the convolution/superposition method are suggested.

Algorithms↗

PubMed2001

Distinguishing migration from isolation: a Markov chain Monte Carlo approach.

A Markov chain Monte Carlo method for estimating the relative effects of migration and isolation on genetic diversity in a pair of populations from DNA sequence data is developed and tested using simulations. The two populations are assumed to be descended from a panmictic ancestral population at some time in the past and may (or may not) after that be connected by migration. The use of a Markov chain Monte Carlo method allows the joint estimation of multiple demographic parameters in either a Bayesian or a likelihood framework. The parameters estimated include the migration rate for each population, the time since the two populations diverged from a common ancestral population, and the relative size of each of the two current populations and of the common ancestral population. The results show that even a single nonrecombining genetic locus can provide substantial power to test the hypothesis of no ongoing migration and/or to test models of symmetric migration between the two populations. The use of the method is illustrated in an application to mitochondrial DNA sequence data from a fish species: the threespine stickleback (Gasterosteus aculeatus).

Animals↗