X-ray crystallographic and theoretical studies of an anticonvulsant enaminone:methyl 4-(4'-bromophenyl)amino-6-methyl-2-oxocyclohex-3-en-1-oate

Objective: The aims of this study were to establish the structure of the potent anticonvulsant enaminone methyl 4-(4′-bromophenyl)amino-6-methyl-2- oxocyclohex-3-en-1-oate (E139), and to determine the energetically preferred conformation of the molecule, which is responsible for the biological activity. Materials and Methods: The structure of the molecule was determined by X-ray crystallography. Theoretical ab initio calculations with different basis sets were used to compare the energies of the different enantiomers and to other structurally related compounds. Results: The X-ray crystal structure revealed two independent molecules of E139, both with absolute configuration C11(S), C12(R), and their inverse. Ab initio calculations with the 6-31G, 3-21G and STO-3G basis sets confirmed that the C11(S), C12(R) enantiomer with both substituents equatorial had the lowest energy. Compared to relevant crystal structures, the geometry of the theoretical structures shows a longer C-N and shorter C=O distance with more cyclohexene ring puckering in the isolated molecule. Conclusion: Based on a pharmacophoric model it is suggested that the enaminone system HN-C=C-C=O and the 4-bromophenyl group in E139 are necessary to confer anticonvulsant property that could lead to the design of new and improved anticonvulsant agents. Copyright © 2003 S. Karger AG, Basel.

Robust automatic mapping algorithms in a network monitoring scenario

Automatically generating maps of a measured variable of interest can be problematic. In this work we focus on the monitoring network context where observations are collected and reported by a network of sensors, and are then transformed into interpolated maps for use in decision making. Using traditional geostatistical methods, estimating the covariance structure of data collected in an emergency situation can be difficult. Variogram determination, whether by method-of-moment estimators or by maximum likelihood, is very sensitive to extreme values. Even when a monitoring network is in a routine mode of operation, sensors can sporadically malfunction and report extreme values. If this extreme data destabilises the model, causing the covariance structure of the observed data to be incorrectly estimated, the generated maps will be of little value, and the uncertainty estimates in particular will be misleading. Marchant and Lark [2007] propose a REML estimator for the covariance, which is shown to work on small data sets with a manual selection of the damping parameter in the robust likelihood. We show how this can be extended to allow treatment of large data sets together with an automated approach to all parameter estimation. The projected process kriging framework of Ingram et al. [2007] is extended to allow the use of robust likelihood functions, including the two component Gaussian and the Huber function. We show how our algorithm is further refined to reduce the computational complexity while at the same time minimising any loss of information. To show the benefits of this method, we use data collected from radiation monitoring networks across Europe. We compare our results to those obtained from traditional kriging methodologies and include comparisons with Box-Cox transformations of the data. We discuss the issue of whether to treat or ignore extreme values, making the distinction between the robust methods which ignore outliers and transformation methods which treat them as part of the (transformed) process. Using a case study, based on an extreme radiological events over a large area, we show how radiation data collected from monitoring networks can be analysed automatically and then used to generate reliable maps to inform decision making. We show the limitations of the methods and discuss potential extensions to remedy these.

Fast pyrolysis oil fuel blend for marine vessels

The main driver for the investigation of fast pyrolysis oil marine fuel blends is EU directive 2012/33/EU which aims to cut the sulphur content of marine fuel and thereby reduce air pollution caused by marine vessels. The aim of this study was to investigate the miscibility of 3- and 4- component blends containing pyrolysis oil, 1-butanol, biodiesel (RME) and/or marine gas oil (MGO). The ideal blend would be a stable homogenous product with a minimum amount of butanol, whilst maximising the amount of pyrolysis oil. A successful blend would have properties suitable for use in marine engines. In order to successfully utilise a marine fuel blend in commercial vessels it should meet minimum specification requirements such as a flash point ≥60°C. Blends of pyrolysis oil, RME, MGO and 1-butanol were evaluated and characterised. The mixed blends were inspected after 48 hours for homogeneity and the results plotted on a tri-plot phase diagram. Homogenous samples were tested for water content, pH, acid number, viscosity and flash point as these give indicate a blend’s suitability for engine testing. The work forms part of the ReShip Project which is funded by Norwegian industry partners and the Research Council of Norway (The ENERGIX programme).

Organic reactions in ionic liquids; Ionic liquid-accelerated three-component reaction: a rapid one-pot synthesis of 3-alkyl-5-[(Z)-arylmethylidene]-1,3-thiazolidine-2,4-diones

A rapid one-pot synthesis of 3-alkyl-5-[(Z)-arylme­thylidene]-1,3-thiazolidine-2,4-dionesis described that occurs in recyclable ionic liquid [bmim]PF6 (1-butyl-3-methylimidazolium hexafluorophosphate).Significant rate enhancement and good selectivity have been observed.

Sequential, Bayesian geostatistics:a principled method for large data sets

The principled statistical application of Gaussian random field models used in geostatistics has historically been limited to data sets of a small size. This limitation is imposed by the requirement to store and invert the covariance matrix of all the samples to obtain a predictive distribution at unsampled locations, or to use likelihood-based covariance estimation. Various ad hoc approaches to solve this problem have been adopted, such as selecting a neighborhood region and/or a small number of observations to use in the kriging process, but these have no sound theoretical basis and it is unclear what information is being lost. In this article, we present a Bayesian method for estimating the posterior mean and covariance structures of a Gaussian random field using a sequential estimation algorithm. By imposing sparsity in a well-defined framework, the algorithm retains a subset of “basis vectors” that best represent the “true” posterior Gaussian random field model in the relative entropy sense. This allows a principled treatment of Gaussian random field models on very large data sets. The method is particularly appropriate when the Gaussian random field model is regarded as a latent variable model, which may be nonlinearly related to the observations. We show the application of the sequential, sparse Bayesian estimation in Gaussian random field models and discuss its merits and drawbacks.

Projected sequential Gaussian processes: flexible interpolation for large data sets

Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential framework for inference in such projected processes is presented, where the observations are considered one at a time. We introduce a C++ library for carrying out such projected, sequential estimation which adds several novel features. In particular we have incorporated the ability to use a generic observation operator, or sensor model, to permit data fusion. We can also cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the variogram parameters is based on maximum likelihood estimation. We illustrate the projected sequential method in application to synthetic and real data sets. We discuss the software implementation and suggest possible future extensions.