Monte Carlo Study of Structural and Thermodynamic Properties of Liquid Chloroform Using a Five Site Model

A five site potential model combining Lennard–Jones plus Coulomb potential functions has been developed for chloroform molecule. The partial charges needed for Coulombic interactions were derived using the chelpg procedure implemented in the gaussian 92 program. These calculations were performed at the MP2 level with MC-311G* basis set for Cl and 6-311G** for C and H atoms. The parameters for the Lennard–Jones potentials were optimized to reproduce experimental values for the density and enthalpy of vaporization of the pure liquid at 298 K and 1 atm. The statistical mechanics calculations were performed with the Monte Carlo method in the isothermic and isobaric (NpT) ensemble. Besides the values obtained for density, ρ, and molar enthalpy of vaporization at constant pressure, Δ H_V, for liquid chloroform, results for molar volume, V_m, molar heat capacity, C_p, isobaric thermal expansivity, α_p, and isothermal compressibility, κ_T, for this pure liquid are also in very good agreement with experimental observations. Size effects on the values of thermodynamic properties were investigated. The potential model was also tested by computing the free energy for solvating one chloroform molecule into its own liquid at 298 K using a statistical perturbation approach. The result obtained compares well with the experimental value. Site–site pair correlation functions were calculated and are in good accordance with theoretical results available in the literature. Dipole–dipole correlation functions for the present five site model were also calculated at different carbon–carbon distances. These correlations were compared to those obtained using the four site model reported in the literature. An investigation of the solvent dependence of the relative free energy for cis/trans conversion of a hypothetical solute in TIP4P water and chloroform was accomplished. The results show strong interaction of water and chloroform molecules with the gauche conformer. The value obtained for the free energy barrier for cis/trans rotation in TIP4P water is higher than that for chloroform. This result is in agreement with the continuous theory for solvation as the conformer with higher dipole moment is more favoured by the solvent with higher dieletric constant. The results also show an increase in entropy as the solute goes from the cis to the trans geometry and this result is more appreciable in the aqueous solution. This revised version was published online in June 2006 with corrections to the Cover Date.     

Homology modeling by the ICM method

Five models have been built by the ICM method for the Comparative Modeling section of the Meeting on the Critical Assessment of Techniques for Protein Structure Prediction. The targets have homologous proteins with known three-dimensional structure with sequence identity ranging from 25 to 77%. After alignment of the target sequence with the related three-dimensional structure, the modeling procedure consists of two subproblems: side-chain prediction and loop prediction. The ICM method approaches these problems with the following steps: (1) a starting model is created based on the homologous structure with the conserved portion fixed and the noncon-served portion having standard covalent geometry and free torsion angles; (2) the Biased Probability Monte Carlo (BPMC) procedure is applied to search the subspaces of either all the nonconservative side-chain torsion angles or torsion angles in a loop backbone and surrounding side chains. A special algorithm was designed to generate low-energy loop deformations. The BPMC procedure globally optimizes the energy function consisting of ECEPP/3 and solvation energy terms. Comparison of the predictions with the NMR or crystallographic solutions reveals a high proportion of correctly predicted side chains. The loops were not correctly predicted because imprinted distortions of the backbone increased the energy of the near-native conformation and thus made the solution unrecognizable. Interestingly, the energy terms were found to be reliable and the sampling of conformational space sufficient. The implications of this finding for the strategies of future comparative modeling are discussed. © 1995 Wiley-Liss, Inc.     

Building proteins from C alpha coordinates using the dihedral probability grid Monte Carlo method.

Dihedral probability grid Monte Carlo (DPG-MC) is a general-purpose method of conformational sampling that can be applied to many problems in peptide and protein modeling. Here we present the DPG-MC method and apply it to predicting complete protein structures from C alpha coordinates. This is useful in such endeavors as homology modeling, protein structure prediction from lattice simulations, or fitting protein structures to X-ray crystallographic data. It also serves as an example of how DPG-MC can be applied to systems with geometric constraints. The conformational propensities for individual residues are used to guide conformational searches as the protein is built from the amino-terminus to the carboxyl-terminus. Results for a number of proteins show that both the backbone and side chain can be accurately modeled using DPG-MC. Backbone atoms are generally predicted with RMS errors of about 0.5 A (compared to X-ray crystal structure coordinates) and all atoms are predicted to an RMS error of 1.7 A or better.     

Macrophage function in tumor-bearing mice: dissociation of phagocytic and chemotactic responsiveness

Infusion of CBA mice with lymphoid cells from the H-2 compatible but Mls-antigen incompatible C3H × CBA hybrid results in a specifically reduced capacity of the recipients lymphocytes to react in the MLC against C3H-cells. Although this reduction is immunologically specific the results of this investigation have shown that such mice exhibit a strongly reduced capacity to produce humoral antibodies against heterologous erythrocytes and a T-cell independent antigen (PVP).     

Dual-energy contrast-enhanced digital mammography: Glandular dose estimation using a Monte Carlo code and voxel phantom

PurposeTo estimate the mean glandular dose of contrast enhanced digital mammography, using the EGSnrc Monte Carlo code and female adult voxel phantom.MethodsAutomatic exposure control of full field digital mammography system was used for the selection of the X-ray spectrum and the exposure settings for dual energy imaging. Measurements of the air-kerma and of the half value layers were performed and a Monte Carlo simulation of the digital mammography system was used to compute the mean glandular dose, for breast phantoms of various thicknesses, glandularities and for different X-ray spectra (low and high energy).ResultsFor breast phantoms of 2.0–8.0 cm thick and 0.1–100% glandular fraction, CC view acquisition, from AEC settings, can result in a mean glandular dose of 0.450 ± 0.022 mGy −2.575 ± 0.033 mGy for low energy images and 0.061 ± 0.021 mGy – 0.232 ± 0.033 mGy for high energy images. In MLO view acquisition mean glandular dose values ranged between 0.488 ± 0.007 mGy – 2.080 ± 0.021 mGy for low energy images and 0.065 ± 0.012 mGy – 0.215 ± 0.010 mGy for high energy images.ConclusionThe low kV part of contrast enhanced digital mammography is the main contributor to total mean glandular breast dose. The results of this study can be used to provide an estimated mean glandular dose for individual cases.     

A comparison of five distance‐based methods for spatial pattern analysis

Abstract. The behaviour of five statistics (extensions of Pielou's, Clark and Evansapos;, Pollard's, Johnson & Zimmer's, and Eberhardt's statistics, which are denoted as P_i, C_e, P_o, J_z and E_b respectively) that involve the distance from a random point to its jth nearest neighbour were examined against several alternative patterns (lattice‐based regular, inhomogeneous random, and Poisson cluster patterns) through Monte Carlo simulation to test their powers to detect patterns. The powers of all the five statistics increase as distance order j increases against inhomogeneous random pattern. They decrease for P_i and C_e and increase for P_o, J_z, and E_b against regular and Poisson cluster patterns. P_o, J_z, and E_b can reach high powers with the third or higher order distances in most cases. However, P_o is recommended because no extra information is needed, it can reach a high power with the second or third distance even though the sample size is not large in most cases, and the test can be performed with an approximate χ² distribution associated with it. When a regular pattern is expected, J_z is recommended because it is more sensitive to lattice‐based regular pattern than P_o and E_b, especially for the first distance. However, simulation tests should be used because the speed of convergence of J_z to normal distribution is very slow.     

A study on the minimum number of loci required for genetic evaluation using a finite locus model

For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees, genetic evaluations obtained by best linear prediction (BLP) were compared to genetic evaluations obtained by BP. For BLP evaluation, used here as the standard of comparison, only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation, the gene frequencies and genotypic effects, which completely specify the required distribution, were derived such that the genotypic mean, the additive variance, and the dominance variance were the same as in the simulation model. For lowly heritable traits, evaluations obtained by BP under models with up to three loci closely matched the evaluations obtained by BLP for both purebred and crossbred data. For highly heritable traits, models with up to six loci were needed to match the evaluations obtained by BLP.     

Monte Carlo study on the secondary cancer risk estimations for patients undergoing prostate radiotherapy: A humanoid phantom study

AimThe aim of this study was to estimate the secondary malignancy risk from the radiation in FFB prostate linac-based radiotherapy for different organs of the patient.BackgroundRadiation therapy is one of the main procedures of cancer treatment. However, the application the radiation may impose dose to organs of the patient which can be the cause of some malignancies.Materials and methodsMonte Carlo (MC) simulation was used to calculate radiation doses to patient organs in 18 MV linear accelerator (linac) based radiotherapy. A humanoid MC phantom was used to calculate the equivalent dose s for different organs and probability of secondary cancer, fatal and nonfatal risk, and other risks and parameters related to megavoltage radiation therapy. In out-of-field radiation calculation, it could be seen that neutrons imparted a higher dose to distant organs, and the dose to surrounding organs was mainly due to absorbed scattered photons and electron contamination.ResultsOur results showed that the bladder and skin with 54.89 × 10⁻³ mSv/Gy and 46.09 × 10⁻³ mSv/Gy, respectively, absorbed the highest equivalent dose s from photoneutrons, while a lower dose was absorbed by the lung at 3.42 × 10⁻³ mSv/Gy. The large intestine and bladder absorbed 55.00 × 10⁻³ mSv/Gy and 49.08 × 10⁻³, respectively, which were the highest equivalent dose s due to photons. The brain absorbed the lowest out-of-field dose, at 1.87 × 10⁻³ mSv/Gy.ConclusionsWe concluded that secondary neutron portion was higher than other radiation. Then, we recommended more attention to neutrons in the radiation protection in linac based high energy radiotherapy.     

Simulation of dose distribution and secondary particle production in proton therapy of brain tumor

AimThe aim of this study is simulation of the proton depth-dose distribution and dose evaluation of secondary particles in proton therapy of brain tumor using the GEANT4 and FLUKA Monte Carlo codes.BackgroundProton therapy is a treatment method for variety of tumors such as brain tumor. The most important feature of high energy proton beams is the energy deposition as a Bragg curve and the possibility of creating the spread out Bragg peak (SOBP) for full coverage of the tumor.Materials and methodsA spherical tumor with the radius of 1 cm in the brain is considered. A SNYDER head phantom has been irradiated with 30−130 MeV proton beam energy. A PMMA modulator wheel is used for covering the tumor. The simulations are performed using the GEANT4 and FLUKA codes.ResultsUsing a modulator wheel, the Spread Out Bragg Peak longitudinally and laterally covers the tumor. Flux and absorbed dose of secondary particles produced by nuclear interactions of protons with elements in the head are considerably small compared to protons.ConclusionsUsing 76.85 MeV proton beam and a modulator wheel, the tumor can be treated accurately in the 3-D, so that the distribution of proton dose in the surrounding tissues is very low. The results show that more than 99% of the total dose of secondary particles and protons is absorbed in the tumor.