Comparison of chromosomal aberrations frequency and polymorphism of GSTs genes in workers occupationally exposed to cytostatics or anaesthetics

Authors compared the incidence of chromosomal aberrations (CAs) of workers occupationally exposed to cytostatics (group EXP1) or anaesthetics (group EXP2) in relationship to polymorphism of GSTM1, GSTP1 and GSTT1 genes. The cytogenetic analysis for chromosomal aberrations frequency and for polymorphisms of genes the PCR and PCR-RFLP method were used. Statistically higher frequency of total CAs was detected in both exposed groups: group EXP1 1.90±1.34%; Mann-Whitney U-test, p=0.001; group EXP2 2.53±1.46%, p=0.0008) as compared to control (1.26±0.93%). In group EXP2 was detected statistically higher frequency of aberrations CSA-type as compared to CTA-type. In xenobiotic metabolizing genes for GST higher frequency of total CAs and constituent types chromatid-type aberrations (CTAs) and chromosome-type aberrations (CSAs) of genes GSTM1 and GSTT1 with null genotype was detected. Statistically significant difference was detected only in CSA-type of aberrations in GSTT1 gene. In gene GSTP1 was not detected any difference in frequency of aberrations in presence of the variant allele. Presented results point out importance of individual susceptibility in evaluation of genotoxic agents of anaesthetics or cytostatics

Diffusion Monte Carlo Study of Para -Diiodobenzene Polymorphism Revisited

We revisit our investigation of the diffusion Monte Carlo (DMC) simulation of p-DIB molecular crystal polymorphism. [J. Phys. Chem. Lett. 2010, 1, 1789-1794] We perform, for the first time, a rigorous study of finite-size effects and choice of nodal surface on the prediction of polymorph stability in molecular crystals using fixed-node DMC. Our calculations are the largest which are currently feasible using the resources of the K computer and provide insights into the formidable challenge of predicting such properties from first principles. In particular, we show that finite-size effects can influence the trial nodal surface of a small (1×1×1) simulation cell considerably. We therefore repeated our DMC simulations with a 1×3×3 simulation cell, which is the largest such calculation to date. We used a DFT nodal surface generated with the PBE functional and we accumulated statistical samples with ∼6.4×105 core-hours for each polymorph. Our final results predict a polymorph stability consistent with experiment, but indicate that results in our previous paper were somewhat fortuitous. We analyze the finite-size errors using model periodic Coulomb (MPC) interactions and kinetic energy corrections, according to the CCMH scheme of Chiesa, Ceperley, Martin, and Holzmann. We investigate the dependence of the finite-size errors on different aspect ratios of the simulation cell (k-mesh convergence) in order to understand how to choose an appropriate ratio for the DMC calculations. Even in the most expensive simulations currently possible, we show that the finite size errors in the DMC total energies are far larger than the energy difference between the two polymorphs, although error cancellation means that the polymorph prediction is accurate. Finally, we found that the T-move scheme is essential for these massive DMC simulations in order to circumvent population explosions and large time-step biases.Chemistry and Chemical Biolog

Measuring and Benchmarking Corporate Environmental Performance

Part 7: Analytics and VisualizationInternational audienceCorporate environmental performance is discussed in this paper. The aim of the paper is to propose a framework for an environmental performance benchmarking model. Corporate environmental performance is measured by key performance indicators (KPIs): Emissions of Greenhouse Gases, Water Consumption, Waste Production and Gross Value Added. Performance is benchmarked against the production frontier estimated by Data Envelopment Analysis. The environmental performance benchmarking model was created and tested on real corporate data. The model determines relative corporate environmental performance, identifies weaknesses in performance and quantifies performance gaps