The local electron attachment energy and the electrostatic potential as descriptors of surface-adsorbate interactions.

Two local reactivity descriptors computed by Kohn-Sham density functional theory (DFT) are used to predict and rationalize interactions of nucleophilic molecules (exemplified by CO and H2O) with transition metal (TM) and oxide surfaces. The descriptors are the electrostatic potential, VS(r), and the local electron attachment energy, ES(r), evaluated on surfaces defined by the 0.001 e Bohr-3 isodensity contour. These descriptors have previously shown excellent abilities to predict regioselectivity and rank molecular as well as nanoparticle reactivities and interaction affinities. In this study, we generalize the descriptors to fit into the framework of periodic DFT computations. We also demonstrate their capabilities to predict local surface propensity for interaction with Lewis bases. It is shown that ES(r) and VS(r) can rationalize the interaction behavior of TM oxides and of fcc TM surfaces, including low-index, stepped and kinked surfaces spanning a wide range of interaction sites with varied coordination environments. Broad future applicability in surface science is envisaged for the descriptors, including heterogeneous catalysis and electrochemistry.

Rigid-body motion correction of the liver in image reconstruction for golden-angle stack-of-stars DCE MRI.

PURPOSE: Respiratory motion can affect pharmacokinetic perfusion parameters quantified from liver dynamic contrast-enhanced MRI. Image registration can be used to align dynamic images after reconstruction. However, intra-image motion blur remains after alignment and can alter the shape of contrast-agent uptake curves. We introduce a method to correct for inter- and intra-image motion during image reconstruction. METHODS: Sixteen liver dynamic contrast-enhanced MRI examinations of nine subjects were performed using a golden-angle stack-of-stars sequence. For each examination, an image time series with high temporal resolution but severe streak artifacts was reconstructed. Images were aligned using region-limited rigid image registration within a region of interest covering the liver. The transformations resulting from alignment were used to correct raw data for motion by modulating and rotating acquired lines in k-space. The corrected data were then reconstructed using view sharing. RESULTS: Portal-venous input functions extracted from motion-corrected images had significantly greater peak signal enhancements (mean increase: 16%, t-test, P < 0.001) than those from images aligned using image registration after reconstruction. In addition, portal-venous perfusion maps estimated from motion-corrected images showed fewer artifacts close to the edge of the liver. CONCLUSIONS: Motion-corrected image reconstruction restores uptake curves distorted by motion. Motion correction also reduces motion artifacts in estimated perfusion parameter maps. Magn Reson Med 79:1345-1353, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Quantification of liver function by linearization of a two-compartment model of gadoxetic acid uptake using dynamic contrast-enhanced magnetic resonance imaging.

Dynamic gadoxetic acid-enhanced magnetic resonance imaging (MRI) allows the investigation of liver function through the observation of the perfusion and uptake of contrast agent in the parenchyma. Voxel-by-voxel quantification of the contrast uptake rate (k1 ) from dynamic gadoxetic acid-enhanced MRI through the standard dual-input, two-compartment model could be susceptible to overfitting of variance in the data. The aim of this study was to develop a linearized, but more robust, model. To evaluate the estimated k1 values using this linearized analysis, high-temporal-resolution gadoxetic acid-enhanced MRI scans were obtained in 13 examinations, and k1 maps were created using both models. Comparison of liver k1 values estimated from the two methods produced a median correlation coefficient of 0.91 across the 12 scans that could be used. Temporally sparse clinical MRI data with gadoxetic acid uptake were also employed to create k1 maps of 27 examinations using the linearized model. Of 20 scans, the created k1 maps were compared with overall liver function as measured by indocyanine green (ICG) retention, and yielded a correlation coefficient of 0.72. In the 27 k1 maps created via the linearized model, the mean liver k1 value was 3.93 ± 1.79 mL/100 mL/min, consistent with previous studies. The results indicate that the linearized model provides a simple and robust method for the assessment of the rate of contrast uptake that can be applied to both high-temporal-resolution dynamic contrast-enhanced MRI and typical clinical multiphase MRI data, and that correlates well with the results of both two-compartment analysis and independent whole liver function measurements.

Adsorption of Hydrogen Sulfide, Hydrosulfide and Sulfide at Cu(110) - Polarizability and Cooperativity Effects. First Stages of Formation of a Sulfide Layer.

Understanding the surface site preference for single adsorbates, the interactions between adsorbates, how these interactions affect surface site specificity in adsorption and perturb the electronic states of surfaces is important for rationalizing the structure of interfaces and the growth of surface products. Herein, using density functional theory (DFT) calculations, we investigated the adsorption of H2 S, HS and, S onto Cu(110). The surface site specificity observed for single adsorbates can be largely affected by the presence of other adsorbates, especially S that can affect the adsorption of other species even at distances of 13 Å. The large supercell employed with a surface periodicity of (6×6) allowed us to safely use the Helmholtz method for the determination of the dipole of the surface-adsorbate complex at low adsorbate coverages. We found that the surface perturbation induced by S can be explained by the charge transfer model, H2 S leads to a perturbation of the surface that arises mostly from Pauli exclusion effects, whereas HS shows a mix of charge transfer and Pauli exclusion effects. These effects have a large contribution to the long range adsorbate-adsorbate interactions observed. Further support for the long range adsorbate-adsorbate interactions are the values of the adsorption energies of adsorbate pairs that are larger than the sum of the adsorption energies of the single adsorbates that constitute the pair. This happens even for large distances and thus goes beyond the H-bond contribution for the H-bond capable adsorbate pairs. Exploiting this knowledge we investigated two models for describing the first stages of growth of a layer of S-atoms at the surface: the formation of islands versus the formation of more homogeneous surface distributions of S-atoms. We found that for coverages lower than 0.5 ML the S-atoms prefer to cluster as islands that evolve to stripes along the [1 1‾ 0] direction with increasing coverage. At 0.5 ML a homogeneous distribution of S-atoms becomes more stable than the formation of stripes. For the coverage equivalent to 1 ML, the formation of two half-monolayers of S-atoms that disrupt the Cu-Cu bonds between the first and second layer is more favorable than the formation of 1 ML homogeneous coverage of S-atoms. Here the S-Cu bond distances and geometries are reminiscent of pyrite, covellite, and to some extent chalcocite. The small energy difference of ≈0.1 eV that exists between this structure and the formation of 1 ML suggests that in a real system at finite temperature both structures may coexist leading to a structure with even lower symmetry.

σ-Holes and σ-lumps direct the Lewis basic and acidic interactions of noble metal nanoparticles: introducing regium bonds.

Using local DFT-based probes for electrostatic as well as charge transfer/polarization interactions, we are able to characterize Lewis basic and acidic sites on copper, silver and gold nanoparticles. The predictions obtained using the DFT-probes are compared to the interaction energies of the electron donating (CO, H2O, NH3 and H2S) and the electron accepting (BH3, BF3, HCl [H-down] and Na+) compounds. The probes include the local electron attachment energy [E(r)], the average local ionization energy [I(r)], and the electrostatic potential [V(r)] and are evaluated on isodensity surfaces located at distances corresponding to typical interaction distances. These probes have previously been successful in characterizing molecular interactions. Good correlations are found between Lewis acidity and maxima in V(r), appearing as a consequence of σ-holes, as well as minima in E(r), of the noble metal nanoparticles. Similarly are Lewis basic sites successfully described by surface minima in V(r) and I(r); the former are indicative of σ-lumps, i.e. regions of enhanced σ-density. The investigated probes are anticipated to function as reliable tools in nanoparticle reactivity and interaction characterization, and may act as suitable descriptors in large-scale screenings for materials of specific properties, e.g. in heterogeneous catalysis. Because of the similarity between the noble metal nanoparticle's interactions with Lewis bases and the concepts of halogen and hydrogen bonding, a new class of bonds is introduced - regium bonds - taking place between a σ-hole of a Cu, Ag or Au compound and an electron donor.

Molecular and dissociative adsorption of water and hydrogen sulfide at perfect and defective Cu(110) surfaces.

We performed a density functional theory (DFT) investigation of the molecular and dissociative adsorption of H2O and H2S at perfect and defective Cu(110) surfaces described using supercells with c(6 × 6) periodicity. The defective surface consists of a terrace surrounded by pits. We found considerable differences in adsorption modes and energies for H2O and H2S. At the defective Cu(110) surface, monomers of H2O and H2S preferentially adsorb at the terrace site and molecular adsorption of H2O is significantly more favorable than that of H2S. For dissociative adsorption however, the sulfur species are considerably more stable than the oxygen species. For monolayer (ML) coverages, there are small differences in the molecular adsorption energies for H2O and H2S. However, for the formation of 1 ML of HO and 1 ML of HS from 1 ML of H2O and 1 ML of H2S, respectively, with the release of H2(g), the differences are very large. The formation of 1 ML HO at the perfect Cu(110) surface is endoergic, while at the defective Cu(110) surface it is exoergic by -0.6 eV. For high coverages, H2S forms stacked half-monolayers that interact with each other via a complex hydrogen bond network with a strength per H2S molecule of -0.140 eV per H2S and -0.120 eV per H2S for H2S located in the underlayer and overlayer, respectively. The large distances between hydrogen bonded H2S molecules explain the preference for the formation of the two stacked half-monolayers of H2S instead of a single monolayer as it happens with H2O. Additionally, the formation of 1 ML of HS does not occur because of the spontaneous splitting of some H-S bonds resulting in surface bound HS and S and H2S molecules. Extensive surface reconstruction and relaxation accompanies adsorption of the sulfur adsorbates. Such reconstructions with outwards pull of Cu atoms can be at the origin of the weak adhesion of sulfide films that explains the release of CuS particles from copper sulfide films at copper surfaces. Overall, the surface defects here investigated induce non-linear effects in the molecular and dissociative adsorption energies of different O and S adsorbates.

Searching for the thermodynamic limit--a DFT study of the step-wise water oxidation of the bipyramidal Cu7 cluster.

Oxidative degradation of copper in aqueous environments is a major concern in areas such as catalysis, electronics and construction engineering. A particular challenge is to systematically investigate the details of this process for non-ideal copper surfaces and particles under the conditions found in most real applications. To this end, we have used hybrid density functional theory to study the oxidation of a Cu7 cluster in water solution. Especially, the role of a large water coverage is explored. This has resulted in the conclusion that, under atmospheric H2 pressures, the thermodynamically most favored state of degradation is achieved upon the generation of four H2 molecules (i.e. Cu7 + 8H2O → Cu7(OH)8 + 4H2) in both condensed and gas phases. This state corresponds to an average oxidation state below Cu(I). The calculations suggest that the oxidation reaction is slow at ambient temperatures with the water dissociation as the rate-limiting step. Our findings are expected to have implication for, among other areas, the copper catalyzed water-gas shift reaction, and for the general understanding of copper corrosion in aqueous environments.

Volumetric MRI with sparse sampling for MR-guided 3D motion tracking via sparse prior-augmented implicit neural representation learning.

BACKGROUND: Volumetric reconstruction of magnetic resonance imaging (MRI) from sparse samples is desirable for 3D motion tracking and promises to improve magnetic resonance (MR)-guided radiation treatment precision. Data-driven sparse MRI reconstruction, however, requires large-scale training datasets for prior learning, which is time-consuming and challenging to acquire in clinical settings. PURPOSE: To investigate volumetric reconstruction of MRI from sparse samples of two orthogonal slices aided by sparse priors of two static 3D MRI through implicit neural representation (NeRP) learning, in support of 3D motion tracking during MR-guided radiotherapy. METHODS: A multi-layer perceptron network was trained to parameterize the NeRP model of a patient-specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first training the network to learn the NeRP of two static 3D MRI with different breathing motion states, prior information of patient breathing motion was embedded into network weights through optimization. The prior information was then augmented from two motion states to 31 motion states by querying the optimized network at interpolated and extrapolated motion state coordinates. Starting from the prior-augmented NeRP model as an initialization point, we further trained the network to fit sparse samples of two orthogonal MRI slices and the final volumetric reconstruction was obtained by querying the trained network at 3D spatial locations. We evaluated the proposed method using 5-min volumetric MRI time series with 340 ms temporal resolution for seven abdominal patients with hepatocellular carcinoma, acquired using golden-angle radial MRI sequence and reconstructed through retrospective sorting. Two volumetric MRI with inhale and exhale states respectively were selected from the first 30 s of the time series for prior embedding and augmentation. The remaining 4.5-min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to two orthogonal slices and compared model-reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking. RESULTS: Across the seven patients evaluated, the peak signal-to-noise-ratio between model-reconstructed and ground truth MR images was 38.02 ± 2.60 dB and the structure similarity index measure was 0.98 ± 0.01. Throughout the 4.5-min time period, gross tumor volume (GTV) motion estimated by deforming a reference state MRI to model-reconstructed and ground truth MRI showed good consistency. The 95-percentile Hausdorff distance between GTV contours was 2.41 ± 0.77 mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1 mm in all three orthogonal directions. CONCLUSION: A prior-augmented NeRP model has been developed to reconstruct volumetric MRI from sparse samples of orthogonal cine slices. Only one exhale and one inhale 3D MRI were needed to train the model to learn prior information of patient breathing motion for sparse image reconstruction. The proposed model has the potential of supporting 3D motion tracking during MR-guided radiotherapy for improved treatment precision and promises a major simplification of the workflow by eliminating the need for large-scale training datasets.

Uncertainty estimation in dynamic contrast-enhanced MRI.

Using dynamic contrast-enhanced MRI (DCE-MRI), it is possible to estimate pharmacokinetic (PK) parameters that convey information about physiological properties, e.g., in tumors. In DCE-MRI, errors propagate in a nontrivial way to the PK parameters. We propose a method based on multivariate linear error propagation to calculate uncertainty maps for the PK parameters. Uncertainties in the PK parameters were investigated for the modified Kety model. The method was evaluated with Monte Carlo simulations and exemplified with in vivo brain tumor data. PK parameter uncertainties due to noise in dynamic data were accurately estimated. Noise with standard deviation up to 15% in the baseline signal and the baseline T1 map gave estimated uncertainties in good agreement with the Monte Carlo simulations. Good agreement was also found for up to 15% errors in the arterial input function amplitude. The method was less accurate for errors in the bolus arrival time with disagreements of 23%, 32%, and 29% for K(trans) , ve , and vp , respectively, when the standard deviation of the bolus arrival time error was 5.3 s. In conclusion, the proposed method provides efficient means for calculation of uncertainty maps, and it was applicable to a wide range of sources of uncertainty.

Mechanistic insights into copper-catalyzed Sonogashira-Hagihara-type cross-coupling reactions: sub-mol% catalyst loadings and ligand effects.

An efficient catalytic system for Sonogashira-Hagihara-type reactions displaying ligand acceleration in the copper-catalyzed formation of C(sp²)-C(sp) bonds is described. The structure of the ligand plays a key role for the coupling efficiency. Various copper sources show excellent catalytic activity, even in sub-mol% quantities. A wide variety of substituents is tolerated in the substrates. Mechanistic details have been revealed by kinetic measurements and DFT calculations.

Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information--potential application for MR-only radiotherapy and attenuation correction in positron emission tomography.

BACKGROUND: Estimation of computed tomography (CT) equivalent data, i.e. a substitute CT (s-CT), from magnetic resonance (MR) images is a prerequisite both for attenuation correction of positron emission tomography (PET) data acquired with a PET/MR scanner and for dose calculations in an MR-only radiotherapy workflow. It has previously been shown that it is possible to estimate Hounsfield numbers based on MR image intensities, using ultra short echo-time imaging and Gaussian mixture regression (GMR). In the present pilot study we investigate the possibility to also include spatial information in the GMR, with the aim to improve the quality of the s-CT. MATERIAL AND METHODS: MR and CT data for nine patients were used in the present study. For each patient, GMR models were created from the other eight patients, including either both UTE image intensities and spatial information on a voxel by voxel level, or only UTE image intensities. The models were used to create s-CT images for each respective patient. RESULTS: The inclusion of spatial information in the GMR model improved the accuracy of the estimated s-CT. The improvement was most pronounced in smaller, complicated anatomical regions as the inner ear and post-nasal cavities. CONCLUSIONS: This pilot study shows that inclusion of spatial information in GMR models to convert MR data to CT equivalent images is feasible. The accuracy of the s-CT is improved and the spatial information could make it possible to create a general model for the conversion applicable to the whole body.

Reactivity of metal oxide clusters with hydrogen peroxide and water--a DFT study evaluating the performance of different exchange-correlation functionals.

We have performed a density functional theory (DFT) investigation of the interactions of H2O2, H2O and HO radicals with clusters of ZrO2, TiO2 and Y2O3. Different modes of H2O adsorption onto the clusters were studied. In almost all the cases the dissociative adsorption is more exothermic than molecular adsorption. At the surfaces where H2O has undergone dissociative adsorption, the adsorption of H2O2 and the transition state for its decomposition are mediated by hydrogen bonding with the surface HO groups. Using the functionals B3LYP, B3LYP-D and M06 with clusters of 26 and 8 units of ZrO2, the M06 functional performed better than B3LYP in describing the reaction of decomposition of H2O2 and the adsorption of H2O. Additionally, we investigated clusters of the type (ZrO2)2, (TiO2)2 and (Y2O3) and the performance of the functionals B3LYP, B3LYP-D, B3LYP*, M06, M06-L, PBE0, PBE and PWPW91 in describing H2O2, H2O and HO˙ adsorption and the energy barrier for decomposition of H2O2. The trends obtained for HO˙ adsorption onto the clusters are discussed in terms of the ionization energy of the metal cation present in the oxide. In order to correctly account for the existence of an energy barrier for the decomposition of H2O2, the functional used must include Hartree-Fock exchange. Using minimal cluster models, the best performance in describing the energy barrier for H2O2 decomposition was obtained with the M06 and PBE0 functionals - the average absolute deviations from experiments are 6 kJ mol(-1) and 5 kJ mol(-1) respectively. With the M06 functional and a larger monoclinic (ZrO2)8 cluster model, the performance is in excellent agreement with experimental data. For the different oxides, PBE0 was found to be the most effective functional in terms of performance and computational time cost.

Evaluation of an attenuation correction method for PET/MR imaging of the head based on substitute CT images.

OBJECT: The aim of this study was to evaluate MR-based attenuation correction of PET emission data of the head, based on a previously described technique that calculates substitute CT (sCT) images from a set of MR images. MATERIALS AND METHODS: Images from eight patients, examined with (18)F-FLT PET/CT and MRI, were included. sCT images were calculated and co-registered to the corresponding CT images, and transferred to the PET/CT scanner for reconstruction. The new reconstructions were then compared with the originals. The effect of replacing bone with soft tissue in the sCT-images was also evaluated. RESULTS: The average relative difference between the sCT-corrected PET images and the CT-corrected PET images was 1.6% for the head and 1.9% for the brain. The average standard deviations of the relative differences within the head were relatively high, at 13.2%, primarily because of large differences in the nasal septa region. For the brain, the average standard deviation was lower, 4.1%. The global average difference in the head when replacing bone with soft tissue was 11%. CONCLUSION: The method presented here has a high rate of accuracy, but high-precision quantitative imaging of the nasal septa region is not possible at the moment.

Voxel-wise uncertainty in CT substitute derived from MRI.

PURPOSE: In an earlier work, we demonstrated that substitutes for CT images can be derived from MR images using ultrashort echo time (UTE) sequences, conventional T2 weighted sequences, and Gaussian mixture regression (GMR). In this study, we extend this work by analyzing the uncertainties associated with the GMR model and the information contributions from the individual imaging sequences. METHODS: An analytical expression for the voxel-wise conditional expected absolute deviation (EAD) in substitute CT (s-CT) images was derived. The expression depends only on MR images and can thus be calculated along with each s-CT image. The uncertainty measure was evaluated by comparing the EAD to the true mean absolute prediction deviation (MAPD) between the s-CT and CT images for 14 patients. Further, the influence of the different MR images included in the GMR model on the generated s-CTs was investigated by removing one or more images and evaluating the MAPD for a spectrum of predicted radiological densities. RESULTS: The largest EAD was predicted at air-soft tissue and bone-soft tissue interfaces. The EAD agreed with the MAPD in both these regions and in regions with lower EADs, such as the brain. Two of the MR images included in the GMR model were found to be mutually redundant for the purpose of s-CT generation. CONCLUSIONS: The presented uncertainty estimation method accurately predicts the voxel-wise MAPD in s-CT images. Also, the non-UTE sequence previously used in the model was found to be redundant.

Computational design of a Diels-Alderase from a thermophilic esterase: the importance of dynamics.

A novel computational Diels-Alderase design, based on a relatively rare form of carboxylesterase from Geobacillus stearothermophilus, is presented and theoretically evaluated. The structure was found by mining the PDB for a suitable oxyanion hole-containing structure, followed by a combinatorial approach to find suitable substrates and rational mutations. Four lead designs were selected and thoroughly modeled to obtain realistic estimates of substrate binding and prearrangement. Molecular dynamics simulations and DFT calculations were used to optimize and estimate binding affinity and activation energies. A large quantum chemical model was used to capture the salient interactions in the crucial transition state (TS). Our quantitative estimation of kinetic parameters was validated against four experimentally characterized Diels-Alderases with good results. The final designs in this work are predicted to have rate enhancements of ≈ 10(3)-10(6) and high predicted proficiencies. This work emphasizes the importance of considering protein dynamics in the design approach, and provides a quantitative estimate of the how the TS stabilization observed in most de novo and redesigned enzymes is decreased compared to a minimal, 'ideal' model. The presented design is highly interesting for further optimization and applications since it is based on a thermophilic enzyme (T (opt) = 70 °C).

Real time volumetric MRI for 3D motion tracking via geometry-informed deep learning.

PURPOSE: To develop a geometry-informed deep learning framework for volumetric MRI with sub-second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisition time. METHODS: A 2D-3D deep learning network with an explicitly defined geometry module that embeds geometric priors of the k-space encoding pattern was investigated, where a 2D generation network first augmented the sparsely sampled image dataset by generating new 2D representations of the underlying 3D subject. A geometry module then unfolded the 2D representations to the volumetric space. Finally, a 3D refinement network took the unfolded 3D data and outputted high-resolution volumetric images. Patient-specific models were trained for seven abdominal patients to reconstruct volumetric MRI from both orthogonal cine slices and sparse radial samples. To evaluate the robustness of the proposed method to longitudinal patient anatomy and position changes, we tested the trained model on separate datasets acquired more than one month later and evaluated 3D target motion tracking accuracy using the model-reconstructed images by deforming a reference MRI with gross tumor volume (GTV) contours to a 5-min time series of both ground truth and model-reconstructed volumetric images with a temporal resolution of 340 ms. RESULTS: Across the seven patients evaluated, the median distances between model-predicted and ground truth GTV centroids in the superior-inferior direction were 0.4 ± 0.3 mm and 0.5 ± 0.4 mm for cine and radial acquisitions, respectively. The 95-percentile Hausdorff distances between model-predicted and ground truth GTV contours were 4.7 ± 1.1 mm and 3.2 ± 1.5 mm for cine and radial acquisitions, which are of the same scale as cross-plane image resolution. CONCLUSION: Incorporating geometric priors into deep learning model enables volumetric imaging with high spatial and temporal resolution, which is particularly valuable for 3D motion tracking and has the potential of greatly improving MRI-guided radiotherapy precision.

CT substitute derived from MRI sequences with ultrashort echo time.

PURPOSE: Methods for deriving computed tomography (CT) equivalent information from MRI are needed for attenuation correction in PET/MRI applications, as well as for patient positioning and dose planning in MRI based radiation therapy workflows. This study presents a method for generating a drop in substitute for a CT image from a set of magnetic resonance (MR)images. METHODS: A Gaussian mixture regression model was used to link the voxel values in CT images to the voxel values in images from three MRI sequences: one T2 weighted 3D spin echo based sequence and two dual echo ultrashort echo time MRI sequences with different echo times and flip angles. The method used a training set of matched MR and CT data that after training was able to predict a substitute CT (s-CT) based entirely on the MR information for a new patient. Method validation was achieved using datasets covering the heads of five patients and applying leave-one-out cross-validation (LOOCV). During LOOCV, the model was estimated from the MR and CT data of four patients (training set) and applied to the MR data of the remaining patient (validation set) to generate an s-CT image. This procedure was repeated for all five training and validation data combinations. RESULTS: The mean absolute error for the CT number in the s-CT images was 137 HU. No large differences in method accuracy were noted for the different patients, indicating a robust method. The largest errors in the s-CT images were found at air-tissue and bone-tissue interfaces. The model accurately discriminated between air and bone, as well as between soft tissues and nonsoft tissues. CONCLUSIONS: The s-CT method has the potential to provide an accurate estimation of CT information without risk of geometrical inaccuracies as the model is voxel based. Therefore, s-CT images could be well suited as alternatives to CT images for dose planning in radiotherapy and attenuation correction in PET/MRI.

Theoretical insights into heme-catalyzed oxidation of cyclohexane to adipic acid.

Adipic acid is a key compound in the chemical industry, where it is mainly used in the production of polymers. The conventional process of its generation requires vast amounts of energy and, moreover, produces environmentally deleterious substances. Thus, there is interest in alternative ways to gain adequate amounts of adipic acid. Experimental reports on a one-pot iron-catalyzed conversion of cyclohexane to adipic acid motivated a theoretical investigation based on density functional theory calculations. The process investigated is interesting because it requires less energy than contemporary methods and does not produce environmentally harmful side products. The aim of the present contribution is to gain insight into the mechanism of the iron-catalyzed cyclohexane conversion to provide a basis for the further development of this process. The rate-limiting step of the process is discussed, but considering the accuracy of the calculations, it is difficult to ensure whether the rate-limiting step is in the substrate oxidation or in the generation of the catalytically active species. It is shown that the slowest step in the substrate oxidation is the conversion of cyclohexanol to cyclohexane-1,2-diol. Hydrogen-atom transfer from one of the OH groups of cyclohexane-1,2-diol makes the intradiol cleavage occur spontaneously.

Designing a new Diels-Alderase: a combinatorial, semirational approach including dynamic optimization.

A computationally inexpensive design strategy involving 'semirational' screening for enzymatic catalysis is presented. The protocol is based on well-established computational methods and represents a holistic approach to the catalytic process. The model reaction studied here is the Diels-Alder, for which a successful computational design has recently been published (Siegel, J. B. et al. Science 2010, 329, 309-313). While it is a leap forward in the field of computational design, the focus on designing only a small fraction of the active site gives little control over dynamics. Our approach explicitly incorporates mutagenesis and the analysis of binding events and transition states, and a promising enzyme-substrate candidate is generated with relatively little effort. We estimate catalytic rate accelerations of up to 105.

On the formation of hydrogen gas on copper in anoxic water.

Hydrogen gas has been detected in a closed system containing copper and pure anoxic water [P. Szakalos, G. Hultquist, and G. Wikmark, Electrochem. Solid-State Lett. 10, C63 (2007) and G. Hultquist, P. Szakalos, M. Graham, A. Belonoshko, G. Sproule, L. Grasjo, P. Dorogokupets, B. Danilov, T. Aastrup, G. Wikmark, G. Chuah, J. Eriksson, and A. Rosengren, Catal. Lett. 132, 311 (2009)]. Although bulk corrosion into any of the known phases of copper is thermodynamically forbidden, the present paper shows how surface reactions lead to the formation of hydrogen gas in limited amounts. While water cleavage on copper has been reported and investigated before, formation of molecular hydrogen at a single-crystal Cu[100] surface is here explored using density functional theory and transition state theory. It is found that although solvent catalysis seems possible, the fastest route to the formation of molecular hydrogen is the direct combination of hydrogen atoms on the copper surface. The activation free energy (ΔG(s)(‡)(f)) of hydrogen formation in condensed phase is 0.70 eV, which corresponds to a rate constant of 10 s(-1) at 298.15 K, i.e., a relatively rapid process. It is estimated that at least 2.4 ng hydrogen gas could form per cm(2) on a perfect copper surface.