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Results for “Monte Carlo Method”

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PubMed2002

Response of silicon diode dosemeters to scattered radiation from megavoltage photon beams.

Dose response effects of diodes due to the high atomic number of silicon relative to water are investigated. While quality chances in the primary component of a megavoltage beam with depth are minimal. Compton scattered photons are shown to have a substantial effect on the quality leading to their enhanced absorption in silicon via the photoelectric effect. Monte Carlo methods were used to study and model this phenomenon. Measurements of dose rate, depth and field size dependence are examined for commercially available diode detectors and ionisation chambers.

Monte Carlo Method↗

PubMed2003

Multicanonical chain-growth algorithm.

We present a temperature-independent Monte Carlo method for the determination of the density of states of lattice proteins that combines the fast ground-state search strategy of the new pruned-enriched Rosenbluth chain-growth method and multicanonical reweighting for sampling the complete energy space. Since the density of states contains all energetic information of a statistical system, we can directly calculate the mean energy, specific heat, Helmholtz free energy, and entropy for all temperatures. We apply this method to lattice proteins consisting of hydrophobic and polar monomers, and for the examples of sequences considered, we identify the transitions between native, globule, and random coil states. Since no special properties of heteropolymers are involved in this algorithm, the method applies to polymer models as well.

Algorithms↗

PubMed1983

The validity of Monte Carlo simulation in studies of scattered radiation in diagnostic radiology.

A computer program using Monte Carlo methods has been developed for the simulation of photon scattering in tissue-equivalent phantoms for incident x-rays in the diagnostic energy range. The study verified that the sampling schemes used in the program can produce random samples according to the theoretical probability distribution functions which describe the photon-scattering process. The Monte Carlo program was applied to determinations of the scatter fractions and edge responses for various phantom sizes, X-ray energies, and recording systems. These quantities were also measured experimentally under comparable imaging conditions. Excellent agreement was obtained between the predicted and experimental results. This investigation established the validity of our Monte Carlo calculations for studies of the physical characteristics of scattered radiation in diagnostic radiology.

Computers↗

PubMed2006

Segregation analysis of a complex quantitative trait: approaches for identifying influential data points.

BACKGROUND/AIMS: Complex traits pose a particular challenge to standard methods for segregation analysis (SA), and for such traits it is difficult to assess the ability of complex SA (CSA) to approximate the true mode of inheritance. Here we use an oligogenic Bayesian Markov chain Monte Carlo method for SA (OSA) to verify results from a single-locus likelihood-based CSA for data on a quantitative measure of reading ability. METHODS: We compared the profile likelihood from CSA, maximized over the trait allele frequency, to the posterior distribution of genotype effects from OSA to explore differences in the overall parameter estimates from SA on the original phenotype data and the same data Winsorized to reduce the potential influence of three outlying data points. RESULTS: Bayesian OSA revealed two modes of inheritance, one of which coincided with the QTL model from CSA. Winsorizing abolished the model originally estimated by CSA; both CSA and OSA identified only the second OSA model. CONCLUSION: Differences between the results from the two methods alerted us to the presence of influential data points, and identified the QTL model best supported by the data. Thus, the Bayesian OSA proved a valuable tool for assessing and verifying inheritance models from CSA.

Alleles↗

PubMed1995

The spectrum and angular distribution of x rays scattered from a water phantom.

To calculate the response of an image receptor to the x rays emerging from a scattering medium, it is necessary to know the x-ray spectrum and intensity as a function of the angle of incidence on the receptor. To permit this calculation for any x-ray spectrum incident on a medium, these functions must be known for monoenergetic x rays. For monoenergetic x rays in the range 20-70 keV we have measured with a high-purity germanium detector the spectrum and intensity of x rays emitted from a water phantom at angles of 0 degree-50 degrees to the direction of the primary beam. The spectrum and intensity of emitted x rays have also been calculated by the Monte Carlo method. At small exit angles, most of the x rays have energies close to the incident energy. As the exit angle increases, the fraction of multiply scattered x rays increases. At very large exit angles, the dominant feature of the spectrum is the peak due to these multiply scattered x rays. For small scattering angles the Monte Carlo calculations are in good agreement with the measurements over the range of energies. For large scattering angeles the scattered photon fluence predicted by Monte Carlo modeling is consistently lower than the measurement in the region just below the full energy peak. The cause of the discrepancies is not fully understood, but cannot be accounted for by Compton broadening alone. An alternate approach to model incoherent scattering is proposed.

Humans↗

PubMed2000

Permutation tests for joinpoint regression with applications to cancer rates.

The identification of changes in the recent trend is an important issue in the analysis of cancer mortality and incidence data. We apply a joinpoint regression model to describe such continuous changes and use the grid-search method to fit the regression function with unknown joinpoints assuming constant variance and uncorrelated errors. We find the number of significant joinpoints by performing several permutation tests, each of which has a correct significance level asymptotically. Each p-value is found using Monte Carlo methods, and the overall asymptotic significance level is maintained through a Bonferroni correction. These tests are extended to the situation with non-constant variance to handle rates with Poisson variation and possibly autocorrelated errors. The performance of these tests are studied via simulations and the tests are applied to U.S. prostate cancer incidence and mortality rates.

Algorithms↗

PubMed1997

Biospecific polymers: recognition of phosphorylated polystyrene derivatives by anti-DNA antibodies.

The recognition of DNA-like phosphorylated polymers by anti-DNA antibodies from the plasma of systemic lupus erythematosus patients was evidenced a few years ago by our research group. However, the radioimmunological Farr assay used for the assessment of anti-DNA antibodies adsorption was not sensitive enough to give accurate results, particularly in the case of weak levels of antibodies. An alternative method based on the use of radiolabelled species was set up in order to check the validity of previous results. Polystyrene resins with different levels in phosphate groups substitution were assessed with regard to their interactions with anti-DNA antibodies. Results show that the anti-DNA antibodies affinity is dependent on the composition of the polymers and reaches a maximum for a composition of 17.5-22.5 mol of phosphorus per 100 mol of monomeric units. This composition corresponds to the DNA-like polymer previously described. A computer-assisted method was used in order to have an insight into the structure of the active sites responsible for the DNA-like behaviour of this polymer. Numerical simulations of the phosphorylation reaction were performed using a Monte Carlo method, taking the structure predictions and the environment of the phosphorylated units into account. A number of thus generated virtual polymers correlated with the experimental results of the adsorption of anti-DNA antibodies. The chemical structure of the active site was determined by computations introducing selected hypotheses on the structure of the phosphorylated units. Moreover, since the number of active sites is directly related to the number of adsorbed anti-DNA antibodies in the experimental results, the most probable structure of the active sites is proposed and compared to a fragment of DNA. Conclusions are that the distances between the phosphate groups in the active sites of the DNA-like polymer and in the DNA fragment are similar. Optimal conditions for the purification of SLE sera by highly specific liquid chromatography using phosphorylated polystyrene resins of precise compositions as stationary phases can thus be envisaged, as well as a new method for the detection of anti-DNA antibodies.

Adsorption↗

PubMed2002

Assessment of calibration parameters for an aerial gamma spectrometry system using Monte-Carlo technique.

During a radiation emergency subsequent to a nuclear accident or weapon fallout, quick assessment of the ground contamination and the resulting exposure is of prime importance in planning and execution of effective counter measures. For an online assessment of ground contamination, it is essential to calibrate the detector system for several parameters viz. the source energy, source deployment matrix, the flight altitude and position above the contaminated surface. This article discusses the methodology to predict all the necessary parameters like photon fluence at various altitudes, the photo-peak counts in different energy windows, Air to Ground Correlation Factors IAGCF) and the dose rate at any height due to air scattered gamma ray photons. The methodology includes generation of theoretically simulated gamma spectra at a required detector position for a given source distribution on the ground using Monte-Carlo method provided by general purpose coupled neutron/photon transport code (MCNP CCC-200). Thus generated gamma spectra are analyzed to arrive at the required parameters mentioned above.

Air Pollution, Radioactive↗

PubMed1978

Computer simulation of the solvent structure around biological macromolecules.

The structure and energetics of the solvent, both ordered and disordered, in a small peptide crystal and in the triclinic lysozyme crystal have been simulated using the Monte Carlo method. The results are in good agreement with the experimental data and provide insight into the role of solvent structure around biological macromolecules in solution.

Chemical Phenomena↗

PubMed2002

Standards for reproducible access flow measurements.

Practical techniques for determining the access flow rate during hemodialysis are reviewed and compared; all are based on indicator dilution principles. The precision of duplicate determinations of the access flow rate in close succession during a given treatment session for all techniques is good; the coefficients of variation vary from 5 to 12%. Access flow measurements during an entire treatment session or from one treatment to another may be considerably more variable since changes in systemic hemodynamics can alter the access flow rate. The influence of variability in access flow measurements on tests for access surveillance is illustrated by data simulations using the Monte Carlo method. Improved approaches for normalizing access flow measurements to changing hemodynamic conditions are desirable to permit more accurate predictions of access stenosis and thrombosis.

Blood Flow Velocity↗

PubMed2002

Use of the most likely failure point method for risk estimation and risk uncertainty analysis.

The most likely failure point (MLFP) method, developed within the field of structural reliability analysis (where it is known as the FORM/SORM method) is a technique for estimating the risk (probability) that a calculated quantity Q exceeds a set limit Q(lim) when some or all of the inputs to the calculation are uncertain. It can be used as an efficient stand-alone method for this type of risk calculation. However, for application within the field of toxic hazards, it is proposed as a means for performing sensitivity analyses, possibly in parallel with a risk calculation carried out by conventional methods. The basis of the method is outlined and its use is demonstrated by means of an example calculation of the risk arising from an installation containing chlorine. The calculation uses, as a consequence model, commercial software for the prediction of dense gas transport. The risk estimate is shown to be acceptably close to that obtained by the Monte Carlo method. The use of a proposed screening procedure utilising the sensitivity formulas that the method provides, in order to identify the most significant uncertainties, is demonstrated. The identification of a single set of input values containing sufficient information to summarise (at least approximately) the entire risk analysis is considered to be an important feature of the method and is proposed as the basis of a means for assessing the validity of the consequence model.

Forecasting↗

PubMed2002

Nonequilibrium phase transitions in a model for the origin of life.

The requisites for the persistence of small colonies of self-replicating molecules living in a two-dimensional lattice are investigated analytically in the infinite diffusion or mean-field limit and through Monte Carlo simulations in the position-fixed or contact process limit. The molecules are modeled by hypercyclic replicators A that are capable of replicating via binary fission A+E-->2A with production rate s, as well as via catalytically assisted replication 2A+E-->3A with rate c. In addition, a molecule can degrade into its source materials E with rate gamma. In the asymptotic regime, the system can be characterized by the presence (active phase) and the absence (empty phase) of replicators in the lattice. In both diffusion regimes, we find that for small values of the ratio c/gamma these phases are separated by a second-order phase transition that is in the universality class of the directed percolation, while for small values of s/gamma the phase transition is of first order. Furthermore, we show the suitability of the dynamic Monte Carlo method, which is based on the analysis of the spreading behavior of a few active cells in the center of an otherwise infinite empty lattice, to address the problem of the emergence of replicators. Rather surprisingly, we show that this method allows an unambiguous identification of the order of the nonequilibrium phase transition.

Biophysical Phenomena↗

PubMed2002

Margins for translational and rotational uncertainties: a probability-based approach.

PURPOSE: To define margins for systematic rotations and translations, based on known statistical distributions of these deviations. METHODS AND MATERIALS: The confidence interval-based expansion method for translations, known as the "rolling ball algorithm," was extended to include rotations. This new method, which we call the Rotational and Translational Confidence Limit (RTCL) method, is exact for a point with arbitrary rotations and translations or for a finite shape with rotations only. The method was compared with two existing expansion methods: a rolling ball algorithm without rotations, and a convolution (blurring) method which included rotations. On the basis of these methods, planning target volumes (PTVs, expanded clinical target volumes [CTVs]) were constructed for a number of shapes (a sphere, a sphere with an extension, and three prostate cases), and evaluated in several ways by means of a Monte Carlo method. The accuracy of each method was measured by determining the probability of finding the CTV completely inside the PTV (P(CTVinPTV)), using parameters that yield a 90% probability for a sphere-shaped CTV without rotations. Furthermore, with the expansion parameters adjusted to give an equal P(CTVinPTV) for all methods, PTV volumes were compared. RESULTS: With the expansion algorithm parameters chosen to yield P(CTVinPTV) = 90% for a sphere, an average P(CTVinPTV) of 84%, 57%, and 46% was obtained for the other shapes, using the RTCL method, coverage probability, and rolling ball, respectively. With the parameters adjusted to yield an equal P(CTVinPTV) for all methods, the PTV volume was on average 8% larger for the coverage probability method and 15% larger for the rolling ball algorithm compared to the RTCL method. CONCLUSION: The RTCL method provides an accurate way to include the effects of systematic rotations in the margin. Compared to other algorithms, the method is less sensitive to the shape of the CTV, and, given a fixed probability of finding the CTV inside the PTV, a smaller PTV volume can be obtained.

Algorithms↗

PubMed1989

Using permutation tests and bootstrap confidence limits to analyze repeated events data from clinical trials.

In clinical trials comparing treatments for superficial bladder cancer, patients are at risk of repeated recurrences of their disease. Statistical methods of analyzing such data are required. This article presents a nonparametric approach. A statistical test to compare the recurrence or tumor rates in two treatment groups, using the randomization distribution, is described. Confidence intervals for the rate ratio are determined from the bootstrap distribution. The implementation of both requires Monte Carlo methods. Computer simulations support the use of these nonparametric methods when there are more than 60 recurrences in each treatment group. An example illustrating their use is given. The strategy adopted for analysis of these data could be applied to other clinical trials where standard methodology is inappropriate.

Biometry↗

PubMedDate not supplied

Determining the permeability of fractured rocks based on joint mapping.

A typical fractured rock mass is intersected by several sets of discontinuities, which provide the main flowpath for ground water. Due to the limitations of data obtained by conventional field measurements, it is often difficult to estimate the anisotropic permeability tensor associated with the joints existing in the rock mass. For that reason, determining permeability tensors for fractured rocks is an important topic in rock mass hydraulics. Based on field surveys, joint parameters can be analyzed by using probabilistic and statistical tools, and three-dimensional mapping of the jointed rock mass. Through analysis of a single joint's hydraulic characteristics, the principal value of the permeability tensor for the jointed rock mass can be determined by using Monte Carlo methods and the searching percolation trace method, which is developed in this paper. The study reports on practical examples demonstrating that results from the methods discussed in this paper are in agreement with those from field hydrogeological surveys and measurements.

Data Collection↗

PubMed2002

Modeling multivariate survival data by a semiparametric random effects proportional odds model.

In this article, the focus is on the analysis of multivariate survival time data with various types of dependence structures. Examples of multivariate survival data include clustered data and repeated measurements from the same subject, such as the interrecurrence times of cancer tumors. A random effect semiparametric proportional odds model is proposed as an alternative to the proportional hazards model. The distribution of the random effects is assumed to be multivariate normal and the random effect is assumed to act additively to the baseline log-odds function. This class of models, which includes the usual shared random effects model, the additive variance components model, and the dynamic random effects model as special cases, is highly flexible and is capable of modeling a wide range of multivariate survival data. A unified estimation procedure is proposed to estimate the regression and dependence parameters simultaneously by means of a marginal-likelihood approach. Unlike the fully parametric case, the regression parameter estimate is not sensitive to the choice of correlation structure of the random effects. The marginal likelihood is approximated by the Monte Carlo method. Simulation studies are carried out to investigate the performance of the proposed method. The proposed method is applied to two well-known data sets, including clustered data and recurrent event times data.

Animals↗

PubMed1992

Development of an extended simulated annealing method: application to the modeling of complementary determining regions of immunoglobulins.

An extended simulated annealing process (ESAP) has been developed in order to obtain an ensemble of conformations of a peptide segment from a protein fluctuating at a given temperature. The annealing process was performed with a fast Monte Carlo method using the scaled collective variables developed by Noguti and Go. The system was divided into two parts: one consists of one or more peptide segments and is flexible around the main-chain and side-chain torsional angles; the other represents the rest of the molecule and was maintained fixed at the atomic positions determined by x-ray experiments. The target function included the nonbonding atomic interactions and a distance function to anchor the N and C terminal ends of each segment to the fixed part. Three systems of complementary determining regions (CDR) of antibodies were tested and compared to x-ray data: L2 loop (7 residues) of the light chain of lambda-type Bence-Jones protein, H1 and the H2 loops (14 residues) of McPC603, and H1 and H2 loops (12 residues) of HyHEL-5. Each state of CDR conformations was characterized at room temperature by the average of their coordinates (average conformation) and the internal energy. With a limited number of annealing processes (10), starting from the extended conformation, we have obtained states with conformations close to the observed x-ray structures, from 1.1 to 1.7 A root mean square deviation (rmsd) of main-chain atoms depending on the system. These states were identical or within 0.25 A rmsd of those of lowest internal energy. For unknown CDR structures the criteria of lowest internal energies from ESAP can be used to predict hypervariable loop structures in antibodies with an accuracy comparable to other methods.

Amino Acid Sequence↗

PubMed2001

Inferring the trajectory of genetic variance in the course of artificial selection.

A method is proposed to infer genetic parameters within a cohort, using data from all individuals in an experiment. An application is the study of changes in additive genetic variance over generations, employing data from all generations. Inferences about the genetic variance in a given generation are based on its marginal posterior distribution, estimated via Markov chain Monte Carlo methods. As defined, the additive genetic variance within the group is directly related to the amount of selection response to be expected if parents are chosen within the group. Results from a simulated selection experiment are used to illustrate properties of the method. Four sets of data are analysed: directional selection with and without environmental trend, and random selection, with and without environmental trend. In all cases, posterior credibility intervals of size 95% assign relatively high density to values of the additive genetic variance and heritability in the neighbourhood of the true values. Properties and generalizations of the method are discussed.

Animals↗