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Results for “Monte Carlo Method”

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PubMed1998

Monte Carlo based method for conversion of in-situ gamma ray spectra obtained with a portable Ge detector to an incident photon flux energy distribution.

A Monte Carlo based method for the conversion of an in-situ gamma-ray spectrum obtained with a portable Ge detector to photon flux energy distribution is proposed. The spectrum is first stripped of the partial absorption and cosmic-ray events leaving only the events corresponding to the full absorption of a gamma ray. Applying to the resulting spectrum the full absorption efficiency curve of the detector determined by calibrated point sources and Monte Carlo simulations, the photon flux energy distribution is deduced. The events corresponding to partial absorption in the detector are determined by Monte Carlo simulations for different incident photon energies and angles using the CERN's GEANT library. Using the detector's characteristics given by the manufacturer as input it is impossible to reproduce experimental spectra obtained with point sources. A transition zone of increasing charge collection efficiency has to be introduced in the simulation geometry, after the inactive Ge layer, in order to obtain good agreement between the simulated and experimental spectra. The functional form of the charge collection efficiency is deduced from a diffusion model.

Barium Radioisotopes↗

PubMed1992

A multiple-start Monte Carlo docking method.

We present a method to search for possible binding modes of molecular fragments at a specific site of a potential drug target of known structure. Our method is based on a Monte Carlo (MC) algorithm applied to the translational and rotational degrees of freedom of the probe fragment. Starting from a randomly generated initial configuration, favorable binding modes are generated using a two-step process. An MC run is first performed in which the energy in the Metropolis algorithm is substituted by a score function that measures the average distance of the probe to the target surface. This has the effect of making buried probes move toward the target surface and also allows enhanced sampling of deep pockets. In a second MC run, a pairwise atom potential function is used, and the temperature parameter is slowly lowered during the run (Simulated Annealing). We repeat this procedure starting from a large number of different randomly generated initial configurations in order to find all energetically favorable docking modes in a specified region around the target. We test this method using two inhibitor-receptor systems: Streptomyces griseus proteinase B in complex with the third domain of the ovomucoid inhibitor from turkey, and dihydrofolate reductase from E. coli in complex with methotrexate. The method could consistently reproduce the complex found in the crystal structure searching from random initial positions in cubes ranging from 25 A to 50 A about the binding site. In the case of SGPB, we were also successful in docking to the native structure. In addition, we were successful in docking small probes in a search that included the entire protein surface.

Algorithms↗

PubMed2005

A Monte Carlo based method to estimate radiation dose from multidetector CT (MDCT): cylindrical and anthropomorphic phantoms.

The purpose of this work was to extend the verification of Monte Carlo based methods for estimating radiation dose in computed tomography (CT) exams beyond a single CT scanner to a multidetector CT (MDCT) scanner, and from cylindrical CTDI phantom measurements to both cylindrical and physical anthropomorphic phantoms. Both cylindrical and physical anthropomorphic phantoms were scanned on an MDCT under the specified conditions. A pencil ionization chamber was used to record exposure for the cylindrical phantom, while MOSFET (metal oxide semiconductor field effect transistor) detectors were used to record exposure at the surface of the anthropomorphic phantom. Reference measurements were made in air at isocentre using the pencil ionization chamber under the specified conditions. Detailed Monte Carlo models were developed for the MDCT scanner to describe the x-ray source (spectra, bowtie filter, etc) and geometry factors (distance from focal spot to isocentre, source movement due to axial or helical scanning, etc). Models for the cylindrical (CTDI) phantoms were available from the previous work. For the anthropomorphic phantom, CT image data were used to create a detailed voxelized model of the phantom's geometry. Anthropomorphic phantom material compositions were provided by the manufacturer. A simulation of the physical scan was performed using the mathematical models of the scanner, phantom and specified scan parameters. Tallies were recorded at specific voxel locations corresponding to the MOSFET physical measurements. Simulations of air scans were performed to obtain normalization factors to convert results to absolute dose values. For the CTDI body (32 cm) phantom, measurements and simulation results agreed to within 3.5% across all conditions. For the anthropomorphic phantom, measured surface dose values from a contiguous axial scan showed significant variation and ranged from 8 mGy/100 mAs to 16 mGy/100 mAs. Results from helical scans of overlapping pitch (0.9375) and extended pitch (1.375) were also obtained. Comparisons between the MOSFET measurements and the absolute dose value derived from the Monte Carlo simulations demonstrate agreement in terms of absolute dose values as well as the spatially varying characteristics. This work demonstrates the ability to extend models from a single detector scanner using cylindrical phantoms to an MDCT scanner using both cylindrical and anthropomorphic phantoms. Future work will be extended to voxelized patient models of different sizes and to other MDCT scanners.

Body Burden↗

PubMed2004

[Cluster analysis in geographical epidemiology: the use of several statistical methods and comparison of their results].

BACKGROUND: The increasing interest in environmental epidemiology has been followed by the development of many statistical tests for detecting disease clustering near a point source. The objectives of this study were to compare several tests to detect disease clustering, among which modelisation using Markov Chain Monte Carlo methods. METHODS: We compared six statistical methods for detecting disease clustering of bladder cancer around an industrial centre of Isère (France) for the period 1983-1997: Stone's test, score test, and two log-linear modelisations (with and without corrections for extra-Poisson variations) using two ways of parameters estimation (maximum likelihood and Markov Chain Monte Carlo methods). RESULTS: The results of the Stone test and the score test are not in favour of a higher risk of bladder cancer around the considered point source. The conclusions brought by the log linear modelisations are the same, but the results obtained using the Markov Chain Monte Carlo Method are very dependant of prior distributions determined for the different parameters. CONCLUSION: Markov Chain Monte Carlo methods, which allow taking into account complex geographical effects, seem well adapted to cluster analysis in geographical epidemiology. However, they remain difficult to implement.

Adolescent↗

PubMed2002

Direct Monte Carlo simulation methods for nonreacting and reacting systems at fixed total internal energy or enthalpy.

A Monte Carlo computer simulation method is presented for directly performing property predictions for fluid systems at fixed total internal energy, U, or enthalpy, H, using a molecular-level system model. The method is applicable to both nonreacting and reacting systems. Potential applications are to (1) adiabatic flash (Joule-Thomson expansion) calculations for nonreacting pure fluids and mixtures at fixed (H,P), where P is the pressure; and (2) adiabatic (flame-temperature) calculations at fixed (U,V) or (H,P), where V is the system volume. The details of the method are presented. The method is compared with existing related simulation methodologies for nonreacting systems, one of which addresses the problem involving fixing portions of U or of H, and one of which solves the problem at fixed H considered here by means of an indirect approach. We illustrate the method by an adiabatic calculation involving the ammonia synthesis reaction.

Journal Article↗

PubMed1998

Kinked-helices model of the nicotinic acetylcholine receptor ion channel and its complexes with blockers: simulation by the Monte Carlo minimization method.

A model of the nicotinic acetylcholine receptor ion channel was elaborated based on the data from electron microscopy, affinity labeling, cysteine scanning, mutagenesis studies, and channel blockade. A restrained Monte Carlo minimization method was used for the calculations. Five identical M2 segments (the sequence EKMTLSISVL10LALTVFLLVI20V) were arranged in five-helix bundles with various geometrical profiles of the pore. For each bundle, energy profiles for chlorpromazine, QX-222, pentamethonium, and other blocking drugs pulled through the pore were calculated. An optimal model obtained allows all of the blockers free access to the pore, but retards them at the rings of residues known to contribute to the corresponding binding sites. In this model, M2 helices are necessarily kinked. They come into contact with each other at the cytoplasmic end but diverge at the synaptic end, where N-termini of M1 segments may contribute to the pore. The kinks disengage alpha-helical H-bonds between Ala12 and Ser8. The uncoupled lone electron pairs of Ser8 carbonyl oxygens protrude into the pore, forming a hydrophilic ring that may be important for the permeation of cations. A split network of H-bonds provides a flexibility to the chains Val9-Ala12, the numerous conformations of which form only two or three intrasegment H-bonds. The cross-ectional dimensions of the interface between the flexible chains vary essentially at the level of Leu11. We suggest that conformational transitions in the chains Val9-Ala12 are responsible for the channel gating, whereas rotations of more stable alpha-helical parts of M2 segments may be necessary to transfer the channel in the desensitized state.

Amino Acid Sequence↗

PubMed1997

Propagation of fluorescent light.

BACKGROUND AND OBJECTIVE: In general, the remitted fluorescence spectrum is affected by the scattering and absorption properties of tissue. Other important factors are boundary conditions, geometry of the tissue sample, and the quantum yield of tissue fluorophores. Each of these factors is examined through a series of Monte Carlo simulations. STUDY DESIGN/MATERIALS AND METHODS: Monte Carlo modeling is used to simulate the propagation of excitation light and the resulting fluorescence. Remitted fluorescence is determined for semi-infinite single and multiple layer geometries and for cubic geometries representing small tissue samples. Monte Carlo results are compared to approximations obtained with a heuristic model. RESULTS: Remitted fluorescence as a function of (1) the depth of fluorescence generation and (2) radial escape position is presented for semi-infinite single and multiple layer geometries. Fluorescence from a small tissue sample is simulated in terms of a cubic geometry, and losses from the sides and bottom are presented as a function of cube dimensions in terms of optical depth of the excitation wavelength. Monte Carlo results for a homogeneous semi-infinite layer are compared to a simple, fast heuristic model. CONCLUSION: Both Monte Carlo simulations and the heuristic model clearly detail the volume of tissue interrogated by fluorescence. Since approximately 35-40% of the remitted fluorescence is due to photons originally directed away from the surface, distal layers affect the remitted fluorescence. Fluorescence spectra from small biopsy samples may not produce the correct line shape owing to wavelength dependent losses.

Aorta↗

PubMed2000

Removing the effect of statistical uncertainty on dose-volume histograms from Monte Carlo dose calculations.

Dose-volume histograms (DVHs) of the dose distributions calculated by the Monte Carlo method contain statistical uncertainties. The Monte Carlo DVH can be considered as blurred from the noiseless DVH by the statistical uncertainty. The focus of the present work is on the removal of the statistical uncertainty effect on the Monte Carlo DVHs and the reconstruction of the noiseless DVHs. We first study the effect of statistical uncertainty. It is found that the steeper the DVH, the more significant the effect. For typical critical structure DVHs the effect is usually negligible. For the target DVHs the effect could be clinically significant, depending on the value of uncertainty and the slope of the DVH. We then propose an iterative reconstruction algorithm. Using the DVHs and statistical uncertainties from the Monte Carlo simulations, we are able to reconstruct the noiseless DVHs. A hypothetical example and a number of clinical cases have been used to test the proposed algorithm. For each clinical case, two Monte Carlo simulations (denoted A and B) were performed. Simulation A has very large statistical uncertainties (about 10% of dose in the target volume) while simulation B has very small uncertainties (about 1%). DVHs from simulation B were used to approximate the noiseless DVHs. Using the proposed algorithm, the effect of statistical uncertainty can be removed from the DVHs of simulation A. The reconstructed DVHs were in good agreement with the DVHs from simulation B. The proposed approach is expected to be useful in removing the blurring effect on a quickly calculated Monte Carlo DVH when performing the iterative forward treatment planning.

Algorithms↗

PubMed2003

A computational study of regioselectivity in a cyclodextrin-mediated Diels-Alder reaction: revelation of site selectivity and the importance of shallow binding and multiple binding modes.

The use of a cyclodextrin.Diels-Alder transition structure complex (CD.TS) as a model in molecular dynamics simulations has enabled us to gain insight into the controlling factors in the cyclodextrin-mediated Diels-Alder reaction of methyl-p-benzoquinone with isoprene. MD simulations were carried out with multiple binding configurations of the CD.TS (TS=meta-TS, para-TS) complexes at the top and bottom rims of beta-CD. We discovered that i) only shallow binding with the CD is necessary for the regioselectivity, and multiple binding geometries are possible; ii) the narrow bottom rim, with the primary hydroxyl groups, of the CD binds both regio-TSs better than at the wider top rim (secondary hydroxyl groups), which was unexpected from the perspective of shape complementarity that governs the stability of most CD.guest complexes. Overall, the bottom rim of the CD exhibits higher regioisomer discrimination for the meta-TS; iii) structural clustering analyses of the CD.TS configurations (sampled during MD simulations) have enabled us to evaluate the binding energies of the different binding configurations. The result indicates that there is a direct correlation between meta-product selectivity and a higher number of binding configurations favoring the formation of the CD.meta-TS complex. The main forces of stabilization in the CD.TS complexes are the van der Waals interactions when the TS is bound at the top rim. At the bottom rim, closer contacts between polar functional groups of the TS and CD have increased the importance of electrostatic interactions. We found that van der Waals, solvation, and torsional forces are less favorable for complexation at the bottom rim; however, this is compensated by large favorable electrostatic interactions. With insights obtained from the study of CD.TS complexes and MD simulations of the modified heptakis-[6-O-(2-hydroxy)propyl]-beta-CD, we were able to explain why a low selectivity was observed when the Diels-Alder reaction was carried out in this modified CD. Two types of search method [Monte Carlo and multiple minimum (MCMM) and molecular dynamics (MD)] to explore and evaluate the different possible binding geometries of the TS within beta-CD, were discussed.

Chemical Phenomena↗

PubMed2004

Statistics of single-molecule measurements: applications in flow-cytometry sizing of DNA fragments.

BACKGROUND: The measurement of physical properties from single molecules has been demonstrated. However, the majority of single-molecule studies report values based on relatively large data sets (e.g., N > 50). While there are studies that report physical quantities based on small sample sets, there has not been a detailed statistical analysis relating sample size to the reliability of derived parameters. METHODS: Monte Carlo simulations and multinomial analysis, dependent on quantifiable experimental parameters, were used to determine the minimum number of single-molecule measurements required to produce an accurate estimate of a population mean. Simulation results were applied to the fluorescence-based sizing of DNA fragments by ultrasensitive flow cytometry (FCM). RESULTS: Our simulations show, for an analytical technique with a 10% CV, that the average of as few as five single-molecule measurements would provide a mean value within one SD of the population mean. Additional simulations determined the number of measurements required to obtain the desired number of replicates for each subpopulation within a mixture. Application of these results to flow cytometry data for lambda/HindIII and S. aureus Mu50/SmaI DNA digests produced accurate DNA fingerprints from as few as 98 single-molecule measurements. CONCLUSIONS: A surprisingly small number of single-molecule measurements are required to obtain a mean measurement descriptive of a normally-distributed parent population.

Bacteriophage lambda↗

PubMed1995

Steps to optimize transscleral photocoagulation.

BACKGROUND: In transscleral photocoagulation, the desired effect is coagulation of parts of the ciliary body or of the peripheral retina. However, the application is often limited by the unwanted effect of coagulation of the sclera. To reduce this effect, the ratio of incident radiation flux to radiation flux transported through the sclera (and able to coagulate the target tissue) should be minimized by the incident beam characteristics. METHODS: Monte Carlo simulations for the radiation transport problem of multiple scattering in the sclera were used to calculate the ratio of transported to incident radiation for different parameter settings of beam diameters, optical thicknesses of the sclera and beam angles. To verify the theoretical calculations, an simple optical device utilizing a bulb instead of a laser source was constructed and applied to enucleated porcine eyes. RESULTS: The theoretical calculations showed that the ratio of incident to transported radiation flux can typically be decreased by a factor of three by increasing the beam radius from 0.35 mm (as used in state-of-the-art laser devices) to 2 mm. This was confirmed by the experiments. Coagulations of the ciliary body or of the peripheral retina were possible with power densities an order of magnitude below the values normally applied with laser sources. CONCLUSION: To improve transscleral photocoagulation, beam diameters should be increased.

Animals↗

PubMed2004

The pulse in reflectance pulse oximetry: modeling and experimental studies.

OBJECTIVE: Reflectance pulse oximetry permits the use of alternative monitoring sites such as the face or torso, and is the approach commonly employed in fetal pulse oximetry systems. The purpose of this study is to investigate the impact of assumptions about the nature of arterial pulsatility on the calibration of such systems. METHODS: Monte Carlo simulations of reflectance pulse oximetry were run on a six-layer tissue model, varying depth and magnitude of the arterial pulse. SpO2 readings on and off the femoral artery obtained during desaturation studies in newborn piglets were compared to predictions. Results. Monte Carlo simulation results clarified the difference between deep and shallow pulsatility found with photon diffusion models, agreeing with earlier in vivo observations. Significant overestimation of SpO2 <75% and slight underestimation >75% is expected if a sensor is placed on a highly pulsatile site. The on- and off-artery SpO2 readings recorded during desaturation in the newborn piglet follow the model predictions. CONCLUSIONS: The sensitivity of reflectance pulse oximetry calibration to the depth and magnitude of arterial pulsatility reinforces the observation that monitoring site selection is of importance in optimizing reflectance pulse oximetry performance, particularly fetal pulse oximetry. Sites with palpable pulsatility should be avoided.

Animals↗

PubMed1999

Dynamics and thermodynamics of beta-hairpin assembly: insights from various simulation techniques.

Small peptides that might have some features of globular proteins can provide important insights into the protein folding problem. Two simulation methods, Monte Carlo Dynamics (MCD), based on the Metropolis sampling scheme, and Entropy Sampling Monte Carlo (ESMC), were applied in a study of a high-resolution lattice model of the C-terminal fragment of the B1 domain of protein G. The results provide a detailed description of folding dynamics and thermodynamics and agree with recent experimental findings (. Nature. 390:196-197). In particular, it was found that the folding is cooperative and has features of an all-or-none transition. Hairpin assembly is usually initiated by turn formation; however, hydrophobic collapse, followed by the system rearrangement, was also observed. The denatured state exhibits a substantial amount of fluctuating helical conformations, despite the strong beta-type secondary structure propensities encoded in the sequence.

Amino Acid Sequence↗

PubMed1997

Proper receiver operating characteristic analysis: the bigamma model.

RATIONALE AND OBJECTIVES: The standard binormal model is the most commonly used model for fitting receiver operating characteristic rating data; however, it sometimes produces inappropriate fits that cross the chance line with degenerate data sets. The authors proposed and evaluated a proper constant-shape bigamma model to handle binormal degeneracy. METHODS: Monte Carlo samples were generated from both a standard binormal population model and a proper constant-shape bigamma model in a series of Monte Carlo studies. RESULTS: The results confirm that the standard binormal model is robust in large samples with no degenerate data sets and that the standard binormal model is not robust in small samples because of degenerate data sets. CONCLUSION: A proper constant-shape bigamma model seems to solve the problem of degeneracy without inappropriate chance line crossings. The bigamma fitting model outperformed the standard binormal fitting model in small samples and gave similar results in large samples.

Decision Making↗

PubMed1999

The effect of collinearity on parameter estimates in nonlinear mixed effect models.

PURPOSE: To demonstrate how correlations among predictor variables in a population pharmacokinetic model affect the ability to discern which covariates should enter into the structural pharmacokinetic model. METHODS: Monte Carlo simulation was used to generate multiple-dose concentration-time data similar to that seen in a Phase III clinical trial. The drugs' pharmacokinetics were dependent on two covariates. Five data sets were simulated with increasing correlation between the two covariates. All data sets were analyzed using NONMEM both with and without inclusion of the covariates in the structural pharmacokinetic model. Summary measures for ill-conditioning and sensitivity analysis were used to examine how increasing correlation among covariates affects the accuracy and precision of the parameter estimates. RESULTS: When covariates were included in the structural pharmacokinetic model and the correlation between covariates increased, the standard error of the parameter estimates increased and the value of parameter estimates themselves became increasingly biased. When the correlation between predictor variables was 0.75, the standard errors of the parameter estimates were too large to declare statistical significance. CONCLUSIONS: Correlations among predictor variables greater than 0.5 when entered into the model simultaneously should be a warning to researchers because the (1) the accuracy of the parameter estimates themselves may be biased and (2) the precision of the estimates may be inflated due to ill-conditioning.

Clinical Trials, Phase III as Topic↗

PubMed2005

Modeling transmission of methicillin-resistant Staphylococcus aureus among patients admitted to a hospital.

OBJECTIVE: To determine the impact of the screening test, nursing workload, handwashing rates, and dependence of handwashing on risk level of patient visit on methicillin-resistant Staphylococcus aureus (MRSA) transmission among hospitalized patients. SETTING: General medical ward. METHODS: Monte Carlo simulation was used to model MRSA transmission (median rate per 1,000 patient-days). Visits by healthcare workers (HCWs) to patients were simulated, and MRSA was assumed to be transmitted among patients via HCWs. RESULTS: The transmission rate was reduced from 0.89 to 0.56 by the combination of increasing the sensitivity of the screening test from 80% to 99% and being able to report results in 1 day instead of 4 days. Reducing the patient-to-nurse ratio from 4.3 in the day and 6.8 at night to 3.8 and 5.7, respectively, reduced the number of nosocomial infections from 0.89 to 0.85; reducing the ratio to 1 and 1, respectively, further reduced the number of nosocomial infections to 0.32. Increases in handwashing rates by 0%, 10%, and 20% for high-risk visits yielded reductions in nosocomial infections similar to those yielded by increases in handwashing rates for all visits (0.89, 0.36, and 0.24, respectively). Screening all patients for MRSA at admission reduced the transmission rate to 0.81 per 1,000 patient-days from 1.37 if no patients were screened. CONCLUSION: Within the ranges of parameters studied, the most effective strategies for reducing the rate of MRSA transmission were increasing the handwashing rates for visits involving contact with skin or bodily fluid and screening patients for MRSA at admission.

Cost-Benefit Analysis↗

PubMed2006

Quantifying the impact of survivor treatment bias in observational studies.

RATIONALE: Observational cohort studies are frequently used to measure the impact of therapies on the time to a particular outcome. Treatment often has a time-variant nature since it is frequently initiated at varying times during a patient's follow-up. Studies in the medical literature frequently ignore the time-dependent nature of treatment exposure. Survivor treatment bias can arise when the time dependent nature of treatment exposure is ignored since patients who survived to receive treatment may be healthier than patients who died prior to receipt of treatment. AIMS AND OBJECTIVES: The objective of the current study was to explicitly quantify the magnitude of survivor-treatment bias. METHODS: Monte Carlo simulations using parameters obtained from an analysis of patients admitted to hospital with a diagnosis of acute myocardial infarction in Ontario, Canada. RESULTS AND CONCLUSIONS: When the true treatment was null (hazard ratio of 1), estimated treatment effects varied from a 4% reduction in mortality to a reduction in mortality of 27% when the time varying nature of the treatment was ignored. Furthermore, survivor-treatment bias increased as the time required foe exposed patients to receive treatment increased. Similarly, survivor treatment bias was amplified as exposure was defined to be exposure at any time prior to mortality compared to exposure within a fixed time interval starting at the time origin. Ignoring the time-dependent nature of treatment results in overly optimistic estimates of treatment effects. Depending on the period required for patients to initiate therapy, treatments with no effect on survival can appear to be strongly associated with improved survival. The current study is the first to explicitly quantify the magnitude of bias that results from ignoring the time-varying nature of treatment exposure in survival studies.

Adrenergic beta-Antagonists↗

PubMed1996

Modeling DNA in aqueous solutions: theoretical and computer simulation studies on the ion atmosphere of DNA.

This article provides a review of current theoretical and computational studies of the ion atmosphere of DNA as related to issues of both structure and function. Manning's elementary yet elegant concept of "counterion condensation" is revisited and shown to be well supported by current state-of-the-art molecular simulations. Studies of the ion atmosphere problem based on continuum electrostatics, integral equation methods, Monte Carlo simulation, molecular dynamics, and Brownian dynamics are considered. Grand canonical Monte Carlo and non-linear Poisson Boltzmann studies have recently focussed on the determination and significance of the index of non-ideality in solution known as the "preferential interaction coefficient," for which the relevant current literature is cited. The review concludes with a survey of applications to ligand binding problems involving drug-DNA and protein-DNA interactions.

Computer Simulation↗