Metal cluster expansion by addition of low valent group 14 reagents: III. Reaction of bis[bis(trimethylsilyl)methyl]tin with M3(CO)12 or its derivatives (M = Fe, Ru, or Os): the crystal and molecular structures of M3(μ-SnR2)2(CO)10(R = CH(SiMe3)2; M = Ru or Os)

The stannylene [SnR2] (R = CH(SiMe3)2) reacts in different ways with the three dodecacarbonyls of the iron triad: [Fe3(CO)12] gives [Fe2(CO)8(μ-SnR2)], [Ru3(CO)12] gives the planar pentametallic cluster [Ru3(CO)10(μ-SnR2)2], for which a full structural analysis is reported, while [Os3(CO)12] fails to react. Different products are also obtained from three nitrile derivatives: [Fe3-(CO)11(MeCN)] gives [Fe2(CO)6(μ-SnR2)2], which has a structure significantly different from that of known Fe2Sn2 clusters, [Ru3(CO)10(MeCN)2] gives the pentametallic cluster described above, while [Os3(CO)10(MeCN)2] gives the isostructural osmium analogue, which shows the unusual feature of a CO group bridging two osmium atoms.

Reaction of 2-methyl-1,4-naphthoquinone and its 3-substituted derivatives with active methylene group anions and with diazo compounds

The reaction of 2-chloro-3-methyl-1,4-naphthoquinone (3) with the anion of ethyl cyanoacetate led to a mixture of two epimeric fused-ring cyclopropane compounds, characterised as exo- and endo-1-cyano-1 -ethoxycarbonyl-1a-methyl-1a,7a-dihydro-1H-cyclopropa[b]naphthalene-2,7-dione (8) and (9). Various hydrolysis products of these were prepared and an X-ray crystallographic analysis was carried out on one of them, 1-carbamoyl-1 -carboxy-1a-methyl-1a,7a-dihydro-1H-cyclopropa[b]-naphthalene-2,7-dione (17). The reaction of 2-methyl-1,4-naphthoquinone (1) with ethyl diazoacetate gave a fused pyrazoline derivative, 3-ethoxycarbonyl-4-hydroxy-9a-methyl-1,9a-dihydro-benz[f]indazol-9-one (22), while reaction of 2-methyl-3-nitro-1,4-naphthoquinone (5) with diazomethane led to a fused Δ2-isoxazoline N-oxide, 3a-methyl-3,3a-dihydroisoxazolo[3,4-b]naphthalene-4,9-dione 1-oxide (26).

Alkenyl derivatives of the metals. Oxidative addition to intermediate tin(II) alkenyls; isolation, and crystal and molecular structure of n-butyltris(triphenylethenyl)tin(IV)

Reaction of tin(II) chloride with Li(CPhCPh2) at –78 °C in diethyl ether–hexane–tetrahydrofuran affords a deep red solution whose colour fades on warming, and which we believe contains the (unstable) first dialkenyltin(II) species. The latter survives long enough at low temperatures to undergo intermolecular oxidative addition, and one such adduct leads ultimately to the formation of Sn(CPhCPh2)3Bun, which has been fully characterised including a crystal and molecular structure study. The mechanism of formation of the final product has been examined and results are reported.

Molecular modelling studies of binding of DACD derivatives into G-Quadruplex DNA: comparison of force field and quantum polarized ligand docking methods

The DNA G-qadruplexes are one of the targets being actively explored for anti-cancer therapy by inhibiting them through small molecules. This computational study was conducted to predict the binding strengths and orientations of a set of novel dimethyl-amino-ethyl-acridine (DACA) analogues that are designed and synthesized in our laboratory, but did not diffract in Synchrotron light.Thecrystal structure of DNA G-Quadruplex(TGGGGT)4(PDB: 1O0K) was used as target for their binding properties in our studies.We used both the force field (FF) and QM/MM derived atomic charge schemes simultaneously for comparing the predictions of drug binding modes and their energetics. This study evaluates the comparative performance of fixed point charge based Glide XP docking and the quantum polarized ligand docking schemes. These results will provide insights on the effects of including or ignoring the drug-receptor interfacial polarization events in molecular docking simulations, which in turn, will aid the rational selection of computational methods at different levels of theory in future drug design programs. Plenty of molecular modelling tools and methods currently exist for modelling drug-receptor or protein-protein, or DNA-protein interactionssat different levels of complexities.Yet, the capasity of such tools to describevarious physico-chemical propertiesmore accuratelyis the next step ahead in currentresearch.Especially, the usage of most accurate methods in quantum mechanics(QM) is severely restricted by theirtedious nature. Though the usage of massively parallel super computing environments resulted in a tremendous improvement in molecular mechanics (MM) calculations like molecular dynamics,they are still capable of dealing with only a couple of tens to hundreds of atoms for QM methods. One such efficient strategy that utilizes thepowers of both MM and QM are the QM/MM hybrid methods. Lately, attempts have been directed towards the goal of deploying several different QM methods for betterment of force field based simulations, but with practical restrictions in place. One of such methods utilizes the inclusion of charge polarization events at the drug-receptor interface, that is not explicitly present in the MM FF.

Gaussian quadrature 4D-VAR

A new incremental four-dimensional variational (4D-Var) data assimilation algorithm is introduced. The algorithm does not require the computationally expensive integrations with the nonlinear model in the outer loops. Nonlinearity is accounted for by modifying the linearization trajectory of the observation operator based on integrations with the tangent linear (TL) model. This allows us to update the linearization trajectory of the observation operator in the inner loops at negligible computational cost. As a result the distinction between inner and outer loops is no longer necessary. The key idea on which the proposed 4D-Var method is based is that by using Gaussian quadrature it is possible to get an exact correspondence between the nonlinear time evolution of perturbations and the time evolution in the TL model. It is shown that J-point Gaussian quadrature can be used to derive the exact adjoint-based observation impact equations and furthermore that it is straightforward to account for the effect of multiple outer loops in these equations if the proposed 4D-Var method is used. The method is illustrated using a three-level quasi-geostrophic model and the Lorenz (1996) model.

Measures of observation impact in non-Gaussian data assimilation

ABSTRACT Non-Gaussian/non-linear data assimilation is becoming an increasingly important area of research in the Geosciences as the resolution and non-linearity of models are increased and more and more non-linear observation operators are being used. In this study, we look at the effect of relaxing the assumption of a Gaussian prior on the impact of observations within the data assimilation system. Three different measures of observation impact are studied: the sensitivity of the posterior mean to the observations, mutual information and relative entropy. The sensitivity of the posterior mean is derived analytically when the prior is modelled by a simplified Gaussian mixture and the observation errors are Gaussian. It is found that the sensitivity is a strong function of the value of the observation and proportional to the posterior variance. Similarly, relative entropy is found to be a strong function of the value of the observation. However, the errors in estimating these two measures using a Gaussian approximation to the prior can differ significantly. This hampers conclusions about the effect of the non-Gaussian prior on observation impact. Mutual information does not depend on the value of the observation and is seen to be close to its Gaussian approximation. These findings are illustrated with the particle filter applied to the Lorenz ’63 system. This article is concluded with a discussion of the appropriateness of these measures of observation impact for different situations.

Persistence of a wild type Escherichia coli and its multiple antibiotic-resistant (MAR) derivatives in the abattoir and on chilled pig carcasses

The aim of this study was to evaluate the ability of an Escherichia coli with the multiple antibiotic resistance (MAR) phenotype to withstand the stresses of slaughter compared to an isogenic progenitor strain. A wild type E. coli isolate (345-2RifC) of porcine origin was used to derive 3 isogenic MAR mutants. Escherichia coli 345-2RifC and its MAR derivatives were inoculated into separate groups of pigs. Once colonisation was established, the pigs were slaughtered and persistence of the E. coli strains in the abattoir environment and on the pig carcasses was monitored and compared. No significant difference (P>0.05) was detected between the shedding of the different E. coli strains from the live pigs. Both the parent strain and its MAR derivatives persisted in the abattoir environment, however the parent strain was recovered from 6 of the 13 locations sampled while the MAR derivatives were recovered from 11 of 13 and the number of MAR E. coil recovered was 10-fold higher than the parent strain at half of the locations. The parent strain was not recovered from any of the 6 chilled carcasses whereas the MAR derivatives were recovered from 3 out of 5 (P<0.001). This study demonstrates that the expression of MAR in 345-2RifC increased its ability to survive the stresses of the slaughter and chilling processes. Therefore in E. coli, MAR can give a selective advantage, compared to non-MAR strains, for persistence on chilled carcasses thereby facilitating transit of these strains through the food chain. (C) 2010 Elsevier B.V. All rights reserved.

Colonisation of the chicken caecum by afimbriate and aflagellate derivatives of Salmonella enterica serotype Enteritidis

A semi-quantitative cloacal-swab method was used as an indirect measure of caecal colonisation of one-day old and five-day old chicks after oral dosing with wild-type Salmonella enterica serovar Enteritidis PT4 and,genetically defined isogenic derivatives lacking the ability to elaborate flagella or fimbriae. Birds of both ages were readily and persistently colonised by all strains although there war a decline in shedding by the older birds after about 21 days. There were no significant differences in shedding of wild-type or mutants in single-dose experiments. In competition experiments, in which five-day old birds were dosed orally with wild-type and mutants together, shedding of non-motile derivatives was significantly lower than wild-type, At 35 days post infection, birds were sacrificed and direct counts of mutants and wild-type from each caecum were determined. Whilst there appeared to be poor correlation between direct counts and the indirect swab method, the overall trends shown by these methods of assessment indicated that flagella and not fimbriae were important in caecal colonisation in these models. Crown Copyright (C) 1999 Published by Elsevier Science B.V.

A Gaussian-mixture ensemble transform filter

We generalize the popular ensemble Kalman filter to an ensemble transform filter, in which the prior distribution can take the form of a Gaussian mixture or a Gaussian kernel density estimator. The design of the filter is based on a continuous formulation of the Bayesian filter analysis step. We call the new filter algorithm the ensemble Gaussian-mixture filter (EGMF). The EGMF is implemented for three simple test problems (Brownian dynamics in one dimension, Langevin dynamics in two dimensions and the three-dimensional Lorenz-63 model). It is demonstrated that the EGMF is capable of tracking systems with non-Gaussian uni- and multimodal ensemble distributions. Copyright © 2011 Royal Meteorological Society

Sparse polynomial approximation in positive order Sobolev spaces with bounded mixed derivatives and applications to elliptic problems with random loading

In the present paper we study the approximation of functions with bounded mixed derivatives by sparse tensor product polynomials in positive order tensor product Sobolev spaces. We introduce a new sparse polynomial approximation operator which exhibits optimal convergence properties in L2 and tensorized View the MathML source simultaneously on a standard k-dimensional cube. In the special case k=2 the suggested approximation operator is also optimal in L2 and tensorized H1 (without essential boundary conditions). This allows to construct an optimal sparse p-version FEM with sparse piecewise continuous polynomial splines, reducing the number of unknowns from O(p2), needed for the full tensor product computation, to View the MathML source, required for the suggested sparse technique, preserving the same optimal convergence rate in terms of p. We apply this result to an elliptic differential equation and an elliptic integral equation with random loading and compute the covariances of the solutions with View the MathML source unknowns. Several numerical examples support the theoretical estimates.

Fast identification algorithms for Gaussian process model

A class identification algorithms is introduced for Gaussian process(GP)models.The fundamental approach is to propose a new kernel function which leads to a covariance matrix with low rank,a property that is consequently exploited for computational efficiency for both model parameter estimation and model predictions.The objective of either maximizing the marginal likelihood or the Kullback–Leibler (K–L) divergence between the estimated output probability density function(pdf)and the true pdf has been used as respective cost functions.For each cost function,an efficient coordinate descent algorithm is proposed to estimate the kernel parameters using a one dimensional derivative free search, and noise variance using a fast gradient descent algorithm. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.

Commodity price dynamics and derivatives valuation: a review

This paper reviews extant research on commodity price dynamics and commodity derivatives pricing models. In the first half, we provide an overview of stylized facts of commodity price behavior that have been explored and documented in the theoretical and empirical literature. In the second half, we review existing derivatives pricing models and discuss how the peculiarities of commodity markets have been integrated in these models. We conclude the paper with a brief outlook on important research questions that need to be addressed in the future.

Observation impact in data assimilation: the effect of non-Gaussian observation error.

Data assimilation methods which avoid the assumption of Gaussian error statistics are being developed for geoscience applications. We investigate how the relaxation of the Gaussian assumption affects the impact observations have within the assimilation process. The effect of non-Gaussian observation error (described by the likelihood) is compared to previously published work studying the effect of a non-Gaussian prior. The observation impact is measured in three ways: the sensitivity of the analysis to the observations, the mutual information, and the relative entropy. These three measures have all been studied in the case of Gaussian data assimilation and, in this case, have a known analytical form. It is shown that the analysis sensitivity can also be derived analytically when at least one of the prior or likelihood is Gaussian. This derivation shows an interesting asymmetry in the relationship between analysis sensitivity and analysis error covariance when the two different sources of non-Gaussian structure are considered (likelihood vs. prior). This is illustrated for a simple scalar case and used to infer the effect of the non-Gaussian structure on mutual information and relative entropy, which are more natural choices of metric in non-Gaussian data assimilation. It is concluded that approximating non-Gaussian error distributions as Gaussian can give significantly erroneous estimates of observation impact. The degree of the error depends not only on the nature of the non-Gaussian structure, but also on the metric used to measure the observation impact and the source of the non-Gaussian structure.