Protonolysis of the Hg-C bond of chloromethylmercury and dimethylmercury. A DFT and QTAIM study

Possible mechanisms for degrading chloromethylmercury (CH(3)HgCl) and dimethylmercury [(CH3)2Hg] involving thiol and ammonium residues were investigated by DFT and atoms-in-molecules (QTAIM) calculations. Using H2S and HS- as models for thiol and thiolate groups RSH and RS-, respectively, we obtained transition states and energy barriers for possible decomposition routes to Hg(SH)2 based on a model proposed by Moore and Pitts (Moore, M. J.; Distefano, M. D.; Zydowsky, L. D.; Cummings, R. T.; Walsh, C. T. Acc. Chem. Res. 1990, 23, 301. Pitts, K. E.; Summers, A. O. Biochemistry 2002, 41, 10287). Demethylation was found to be a multistep process that involved initial substitution of Cl- by RS-. We found that successive coordination of Hg with thiolates leads to increased negative charge on the methyl group and facilitates the protonolysis of the Hg-C bond by H-SH. This was also found to be the case for (CH3)2Hg. We found that NH4(+) readily protonolyzes the Hg-C bond of these thiolate complexes, suggesting that ammonium residues of protonated amino acids might also act as effective proton donors.     

The Extraction of Polycyclic Aromatic Hydrocarbons from Atmospheric Particulate Matter Samples by Accelerated Solvent Extraction (ASE)

Abstract

The Accelerated solvent extraction (ASE) of PAHs (23 2- to 6-ring species) spiked onto glass fibre filters (GFFs) was studied as a function of variable extraction solvents, pressure, temperature and extraction times. Acceptable recoveries (85% ± 15%) were obtained for certain combinations of conditions and a tentative method (1500 psi, 150°C, 70:30 hexane:acetone mixture, 7 min heat-up time, 5 min static extraction time, 60% flush volume, 2 static cycles was selected for further testing. However, this method did not prove as effective as the traditional Soxhlet method of extraction when these parameters were used to extract native PAHs from ambient atmospheric particulate matter collected on a GFF by Integrated Atmospheric Deposition Network (IADN) sampling protocols. The extraction recovery study for spiked GFFs was repeated using slightly different extraction conditions: 2000 psi, 100°C, 70:30 hexane:acetone, 5 min heat-up time, 5 min static extraction time, 150% flush volume, 3 static cycles. When this method was applied to the extraction of native PAHs from ambient atmospheric particulate matter collected on GFFs, the results showed equivalent or better recoveries to that of the Soxhlet method. The total time of extraction was 25 min requiring only 30 mL of solvent. This ASE method is presently used to quantitatively determine PAHs in IADN particle-phase samples.     

Characterization and comparison of metal accumulation in two Escherichia coli strains expressing either CopA or MntA, heavy metal-transporting bacterial P-type adenosine triphosphatases

MntA from Lactobacillus plantarum and copA from Enterococcus hirae both encode membrane proteins that are members of the P-type family of adenosine triphosphatases (ATPases). Both transporters act as metal importers to take up nutritionally required substrates; MntA translocates Mn(II) and CopA translocates Cu(I). Both ATPases can also translocate secondary substrates, Cd(II) and Ag(I), respectively. Although functionally and sequentially similar, these ATPases differ in several key residues and in their membrane topologies. The bioaccumulation properties of these two proteins were examined by coexpressing the transporters with overexpressed metallothionein in Escherichia coli cells, a system that has previously shown high levels of substrate-specific uptake. Both strains exhibited rapid metal accumulation, both saturated at around 50 μM metal, and both displayed temperature-sensitive uptake. However, the transporters responded differently when external conditions were varied; MntA displayed increased sensitivity to ionic strength, while CopA was more pH sensitive and more inhibited by chelating agents. The differences in accumulation are likely owing to structural differences in the transmembrane region of these two ATPases.     

CanSAS‐IV

A two-day workshop on beamline integration and data formatting (HDF5/NeXus) of the EIGER detector was held in Baden, Switzerland, January 24–25, 2013. Its aim was to discuss the technical challenges inherent with the next generation of high-frame-rate, high-resolution X-ray imaging detectors, and specifically with the EIGER detector. EIGER is a photon-counting hybrid pixel detector developed at the Paul Scherrer Institute (PSI) and DECTRIS Ltd. With even higher spatial resolution and frame rates than its predecessor, the PILATUS detector, it will be able to continuously produce up to 3000 images per second. The corresponding extreme data rates generated by this and future detectors present a significant challenge for beamline integration of the detectors, for data handling by the users, and for data processing software. Efficient data flow, storage, and processing must be achieved to handle the huge data sets that will be produced in seconds by these devices.     

Sensitivity to point-spread function parameters in medical ultrasound image deconvolution

Clinical ultrasound images are often perceived as difficult to interpret due to image blurring and speckle inherent in the ultrasound imaging. But the image quality can be improved by deconvolution using an estimate of the point-spread function. However, it is difficult to obtain a sufficiently accurate estimate of the point-spread function in vivo because of the unknown properties of the soft tissue in clinical applications. Local variations in the speed of sound and attenuation change the pulse and beam shape. These in turn affect the point-spread function. The purpose and novelty of this paper is therefore to explore the sensitivity of a state-of-the-art deconvolution algorithm to uncertainty in the point-spread function. The point-spread function in our restoration algorithm is made shift invariant in the lateral dimension but shift dependent in the axial direction, and is modelled to match a 128-element 1D linear array often found in clinical use. We present simulated and in vitro sensitivity analyses of two-dimensional deconvolution while varying six parameters on which the point-spread function depends. Uncertainty in the ultrasound machine is analysed by varying the axial depths of lateral and elevational foci alongside height and width of transducer elements. Sensitivity to tissue influence is investigated by varying the speed of sound and frequency-dependent attenuation of the electro-mechanical impulse response. The results are analysed both quantitatively and in terms of the perceived image quality. First, the assessment of deconvolution using the logarithmic image amplitude is found to be a better indicator of the perceived improvement in the restoration. Secondly, the two most critical parameters for two-dimensional deconvolution are discovered to be the lateral focus and the speed of sound, because the success of deconvolution is perceived primarily in terms of deblurring. We also observed similar patterns for the simulation and in vitro experiment. Finally, we show that it is possible to restore in vivo ultrasound images using an assumed point-spread function and hence conclude that an exact point-spread function is not necessary for enhancing ultrasound image quality by deconvolution.     

Semi‐analytical method for departure point determination

A new method for departure point determination on Cartesian grids, the semi‐analytical upwind path line tracing (SUT) method, is presented and compared to two typical departure point determination methods used in semi‐Lagrangian advection schemes, the Euler method and the four‐step Runge–Kutta method. Rigorous comparisons of the three methods were conducted for a severely curving hypothetical flow field and for advective transport in the rotation of a Gaussian concentration hill. The SUT method was shown to have equivalent accuracy to the Runge–Kutta method but with significantly improved computational efficiency. Depending on the case being simulated, the SUT method provides either far greater or equivalent computational efficiency and more certain accuracy than the Euler method. Copyright © 2004 John Wiley & Sons, Ltd.     

Quantum Bayesian computation

Quantum Bayesian computation is an emerging field that levers the computational gains available from quantum computers. They promise to provide an exponential speed-up in Bayesian computation. Our article adds to the literature in three ways. First, we describe how quantum von Neumann measurement provides quantum versions of popular machine learning algorithms such as Markov chain Monte Carlo and deep learning that are fundamental to Bayesian learning. Second, we describe quantum data encoding methods needed to implement quantum machine learning including the counterparts to traditional feature extraction and kernel embeddings methods. Third, we show how quantum algorithms naturally calculate Bayesian quantities of interest such as posterior distributions and marginal likelihoods. Our goal then is to show how quantum algorithms solve statistical machine learning problems. On the theoretical side, we provide quantum versions of high dimensional regression, Gaussian processes and stochastic gradient descent. On the empirical side, we apply a quantum FFT algorithm to Chicago house price data. Finally, we conclude with directions for future research.     

Symmetry calculation for molecules and transition states

The symmetry of molecules and transition states of elementary reactions is an essential property with important implications for computational chemistry. The automated identification of symmetry by computers is a very useful tool for many applications, but often relies on the availability of three‐dimensional coordinates of the atoms in the molecule and hence becomes less useful when these coordinates are a priori unavailable. This article presents a new algorithm that identifies symmetry of molecules and transition states based on an augmented graph representation of the corresponding structures, in which both topology and the presence of stereocenters are accounted for. The automorphism group order of the graph associated with the molecule or transition state is used as a starting point. A novel concept of label‐stereoisomers, that is, stereoisomers that arise after labeling homomorph substituents in the original molecule so that they become distinguishable, is introduced and used to obtain the symmetry number. The algorithm is characterized by its generic nature and avoids the use of heuristic rules that would limit the applicability. The calculated symmetry numbers are in agreement with expected values for a large and diverse set of structures, ranging from asymmetric, small molecules such as fluorochlorobromomethane to highly symmetric structures found in drug discovery assays. The new algorithm opens up new possibilities for the fast screening of the degree of symmetry of large sets of molecules. © 2014 Wiley Periodicals, Inc.     

Investigation of confined ionic liquid in nanostructured materials by a combination of SANS, contrast-matching SANS, and nitrogen adsorption

Small-angle neutron scattering (SANS), contrast-matching SANS, and nitrogen adsorption have been utilized to investigate the confined ionic liquid (IL) [bmim][PF(6)] phase in ordered mesoporous silica MCM-41 and SBA-15. The results suggest that the pores of SBA-15 are completely filled with IL whereas a small fraction of the pore volume, the pore "core", of MCM-41 is empty. The contrast-matching SANS measurements confirm the enhanced solubility of water in IL. In addition, they provide strong evidence that water does not enter the empty pore core of MCM-41, possibly because of the preferred orientation of the IL molecules in the adsorbed layer.