Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development

The application of liquid chromatography/mass spectrometry (LC/MS) followed by principal components analysis (PCA) has been successfully applied to the screening of rat urine following the administration of three candidate pharmaceuticals. With this methodology it was possible to differentiate the control samples from the dosed samples and to identify the components of the mass spectrum responsible for the separation. These data clearly show that LC/MS is a viable alternative, or complementary, technique to proton NMR for metabonomics applications in drug discovery and development.     

A mathematical model for pollution in a river and its remediation by aeration

We present a simple mathematical model for river pollution and investigate the effect of aeration on the degradation of pollutant. The model consists of a pair of coupled reaction–diffusion–advection equations for the pollutant and dissolved oxygen concentrations, respectively. The coupling of these equations occurs because of reactions between oxygen and pollutant to produce harmless compounds. Here we consider the steady-state case in one spatial dimension. For simplified cases the model is solved analytically. We also present a numerical approach to the solution in the general case. The extension to the transient spatial model is relatively straightforward. The study is motivated by the crucial problem of water pollution in many countries and specifically within the Tha Chin River in Thailand. For such real situations, simple models can provide decision support for planning restrictions to be imposed on farming and urban practices.     

Peak alignment of urine NMR spectra using fuzzy warping

Proton nuclear magnetic resonance (1H NMR) spectroscopic analysis of mixtures has been used extensively for a variety of applications ranging from the analysis of plant extracts, wine, and food to the evaluation of toxicity in animals. For example, NMR analysis of urine samples has been used extensively for biomarker discovery and, more simply, for the construction of classification models of toxicity, disease, and biochemical phenotype. However, NMR spectra of complex mixtures typically show unwanted local peak shifts caused by matrix and instrument variability, which must be compensated for prior to statistical analysis and interpretation of the data. One approach is to align the spectral peaks across the data set. An efficient and fast warping algorithm is required as the signals typically contain ca. 32,000-64,000 data points and there can be several thousand spectra in a data set. As demonstrated in our study, the iterative fuzzy warping algorithm fulfills these requirements and can be used on-line for an alignment of the NMR spectra. Correlation coefficients between the aligned and target spectra are used as the evaluation function for the algorithm, and its performance is compared with those of other published warping methods.     

Improving the equilibrium performance of active carbons for separation processes by co-adsorption with low pressure solvent: application to carbon capture

The equilibrium performance of a novel gas separation process described quite recently (Sweatman in Chem. Eng. Sci. 65:3907, 2010) called ‘pressure-swing wetting layer absorption’ here is investigated by means of molecular Monte-Carlo simulation. This process is very similar to pressure-swing adsorption except that solvent, in the form of low pressure vapour, is added to the gas to be separated in order to improve equilibrium performance. Earlier work, based on relatively simple density functional theory models, suggests that this process could be significantly more efficient than the analogous pressure-swing adsorption process when tetrahydrofuran (THF) is used as the solvent, although this conclusion is based only on equilibrium behaviour and does not take into account the effect of any dynamical processes. The aim of this work is to provide more detailed molecular simulation results to help understand this behaviour and guide experiments towards suitable solvents and conditions so that the process can experimentally tested. It is found that using acetonitrile as the solvent could be over nine times more effective than THF, which was modelled in previous work, for the particular carbon capture application studied here. These simulation results also demonstrate that, due to the effect of confinement on fluid structure, bulk solubility data cannot be used to reliably predict equilibrium performance in this context, and that the equilibrium performance is especially enhanced for pores that exhibit a bilayer phase transition.     

Variable order and stepsize in general linear methods

Numerical Algorithms - This paper describes the implementation of a class of IRKS methods (Wright 2002). These GLM algorithms are practical with reliable error estimators (Butcher and Podhaisky,...     

Gas adsorption in active carbons and the slit-pore model 1: Pure gas adsorption

We describe procedures based on the polydisperse independent ideal slit-pore model, Monte Carlo simulation and density functional theory (a 'slab-DFT') for predicting gas adsorption and adsorption heats in active carbons. A novel feature of this work is the calibration of gas-surface interactions to a high surface area carbon, rather than to a low surface area carbon as in all previous work. Our models are used to predict the adsorption of carbon dioxide, methane, nitrogen, and hydrogen up to 50 bar in several active carbons at a range of near-ambient temperatures based on an analysis of a single 293 K carbon dioxide adsorption isotherm. The results demonstrate that these models are useful for relatively simple gases at near-critical or supercritical temperatures.     

Weighted least-squares deconvolution method for discovery of group differences between complex biofluid 1H NMR spectra

Biomarker discovery through analysis of high-throughput NMR data is a challenging, time-consuming process due to the requirement of sophisticated, dataset specific preprocessing techniques and the inherent complexity of the data. Here, we demonstrate the use of weighted, constrained least-squares for fitting a linear mixture of reference standard data to complex urine NMR spectra as an automated way of utilizing current assignment knowledge and the ability to deconvolve confounded spectral regions. Following the least-squares fit, univariate statistics were used to identify metabolites associated with group differences. This method was evaluated through applications on simulated datasets and a murine diabetes dataset. Furthermore, we examined the differential ability of various weighting metrics to correctly identify discriminative markers. Our findings suggest that the weighted least-squares approach is effective for identifying biochemical discriminators of varying physiological states. Additionally, the superiority of specific weighting metrics is demonstrated in particular datasets. An additional strength of this methodology is the ability for individual investigators to couple this analysis with laboratory specific preprocessing techniques.     

Giant SALR cluster reproduction,with implications for their chemical evolution

ABSTRACT

Particles with SALR (short-range attraction and long-range repulsion) interactions are common to many physical systems, especially biological and soft matter, yet their behaviour is still not completely understood. Using Monte Carlo simulations and a thermodynamic model, it is shown here that giant SALR clusters can grow and reproduce in these fluids. Giant cluster growth and reproduction should therefore be common to a wide range of natural and synthetic systems under suitable conditions. If, in addition, cluster fitness selection occurs then chemical evolution of giant SALR cluster might be observed in suitable systems.     

Gas adsorption in active carbons and the slit-pore model 2: Mixture adsorption prediction with DFT and IAST

We use a fast density functional theory (a "slab-DFT") and the polydisperse independent ideal slit-pore model to predict gas mixture adsorption in active carbons. The DFT is parametrized by fitting to pure gas isotherms generated by Monte Carlo simulation of adsorption in model graphitic slit-pores. Accurate gas molecular models are used in our Monte Carlo simulations with gas-surface interactions calibrated to a high surface area carbon, rather than a low surface area carbon as in all previous work of this type, as described in part 1 of this work. We predict the adsorption of binary mixtures of carbon dioxide, methane, and nitrogen on two active carbons up to about 30 bar at near-ambient temperatures. We compare two sets of results; one set obtained using only the pure carbon dioxide adsorption isotherm as input to our pore characterization process, and the other obtained using both pure gas isotherms as input. We also compare these results with ideal adsorbed solution theory (IAST). We find that our methods are at least as accurate as IAST for these relatively simple gas mixtures and have the advantage of much greater versatility. We expect similar results for other active carbons and further performance gains for less ideal mixtures.     

The self-referential method combined with thermodynamic integration

The self-referential method [M. B. Sweatman, Phys. Rev. E 72, 016711 (2005)] for calculating the free energy of crystalline solids via molecular simulation is combined with thermodynamic integration to produce a technique that is convenient and efficient. Results are presented for the chemical potential of hard sphere and Lennard-Jones face centered cubic crystals that agree well with this previous work. For the small system sizes studied, this technique is about 100 times more efficient than the parameter hopping technique used previously.