The Wheal Jane wetlands model for bioremediation of acid mine drainage

Acid mine drainage (AMD) is a widespread environmental problem associated with both working and abandoned mining operations. As part of an overall strategy to determine a long-term treatment option for AMD, a pilot passive treatment plant was constructed in 1994 at Wheal Jane Mine in Cornwall, UK. The plant consists of three separate systems, each containing aerobic reed beds, anaerobic cell and rock filters, and represents the largest European experimental facility of its kind. The systems only differ by the type of pretreatment utilised to increase the pH of the influent minewater (pH <4): lime dosed (LD), anoxic limestone drain (ALD) and lime free (LF), which receives no form of pretreatment. Historical data (1994-1997) indicate median Fe reduction between 55% and 92%, sulphate removal in the range of 3-38% and removal of target metals (cadmium, copper and zinc) below detection limits, depending on pretreatment and flow rates through the system. A new model to simulate the processes and dynamics of the wetlands systems is described, as well as the application of the model to experimental data collected at the pilot plant. The model is process based, and utilises reaction kinetic approaches based on experimental microbial techniques rather than an equilibrium approach to metal precipitation. The model is dynamic and utilises numerical integration routines to solve a set of differential equations that describe the behaviour of 20 variables over the 17 pilot plant cells on a daily basis. The model outputs at each cell boundary are evaluated and compared with the measured data, and the model is demonstrated to provide a good representation of the complex behaviour of the wetland system for a wide range of variables. (C) 2004 Elsevier B.V/ All rights reserved.

A mathematical model of the in vitro keratinocyte response to chromium and nickel exposure

A mathematical model describing the main mechanistic processes involved in keratinocyte response to chromium and nickel has been developed and compared to experimental in vitro data. Accounting for the interactions between the metal ions and the keratinocytes, the law of mass action was used to generate ordinary differential equations which predict the time evolution and ion concentration dependency of keratinocyte viability, the amount of metal associated with the keratinocytes and the release of cytokines by the keratinocytes. Good agreement between model predictions and existing experimental data of these endpoints was observed, supporting the use of this model to explore physiochemical parameters that influence the toxicological response of keratinocytes to these two metals.

Mathematical model for low density lipoprotein (LDL) endocytosis by hepatocytes

Individuals with elevated levels of plasma low density lipoprotein (LDL) cholesterol (LDL-C) are considered to be at risk of developing coronary heart disease. LDL particles are removed from the blood by a process known as receptor-mediated endocytosis, which occurs mainly in the liver. A series of classical experiments delineated the major steps in the endocytotic process; apolipoprotein B-100 present on LDL particles binds to a specific receptor (LDL receptor, LDL-R) in specialized areas of the cell surface called clathrin-coated pits. The pit comprising the LDL-LDL-R complex is internalized forming a cytoplasmic endosome. Fusion of the endosome with a lysosome leads to degradation of the LDL into its constituent parts (that is, cholesterol, fatty acids, and amino acids), which are released for reuse by the cell, or are excreted. In this paper, we formulate a mathematical model of LDL endocytosis, consisting of a system of ordinary differential equations. We validate our model against existing in vitro experimental data, and we use it to explore differences in system behavior when a single bolus of extracellular LDL is supplied to cells, compared to when a continuous supply of LDL particles is available. Whereas the former situation is common to in vitro experimental systems, the latter better reflects the in vivo situation. We use asymptotic analysis and numerical simulations to study the longtime behavior of model solutions. The implications of model-derived insights for experimental design are discussed.

Estimation of Hidden Markov Models parameters using differential evolution

Hidden Markov Models (HMMs) have been successfully applied to different modelling and classification problems from different areas over the recent years. An important step in using HMMs is the initialisation of the parameters of the model as the subsequent learning of HMM’s parameters will be dependent on these values. This initialisation should take into account the knowledge about the addressed problem and also optimisation techniques to estimate the best initial parameters given a cost function, and consequently, to estimate the best log-likelihood. This paper proposes the initialisation of Hidden Markov Models parameters using the optimisation algorithm Differential Evolution with the aim to obtain the best log-likelihood.

Multiplexed quantitative proteomics using differential metabolic 15N-labeling

There are several advantages of using metabolic labeling in quantitative proteomics. The early pooling of samples compared to post-labeling methods eliminates errors from different sample processing, protein extraction and enzymatic digestion. Metabolic labeling is also highly efficient and relatively inexpensive compared to commercial labeling reagents. However, methods for multiplexed quantitation in the MS-domain (or ‘non-isobaric’ methods), suffer from signal dilution at higher degrees of multiplexing, as the MS/MS signal for peptide identification is lower given the same amount of peptide loaded onto the column or injected into the mass spectrometer. This may partly be overcome by mixing the samples at non-uniform ratios, for instance by increasing the fraction of unlabeled proteins. We have developed an algorithm for arbitrary degrees of nonisobaric multiplexing for relative protein abundance measurements. We have used metabolic labeling with different levels of 15N, but the algorithm is in principle applicable to any isotope or combination of isotopes. Ion trap mass spectrometers are fast and suitable for LC-MS/MS and peptide identification. However, they cannot resolve overlapping isotopic envelopes from different peptides, which makes them less suitable for MS-based quantitation. Fourier-transform ion cyclotron resonance (FTICR) mass spectrometry is less suitable for LC-MS/MS, but provides the resolving power required to resolve overlapping isotopic envelopes. We therefore combined ion trap LC-MS/MS for peptide identification with FTICR LC-MS for quantitation using chromatographic alignment. We applied the method in a heat shock study in a plant model system (A. thaliana) and compared the results with gene expression data from similar experiments in literature.

Differential effects of overexpression of ERalpha and ERbeta in MCF10A immortalised, non-transformed human breast epithelail cells

Background: Cellular effects of oestrogen are mediated by two intracellular receptors ERα and ERβ. However, to compare responses mediated through these two receptors, experimental models are needed where ERα and ERβ are individually stably overexpressed in the same cell type. Methods: We compared the effects of stable overexpression of ERα and ERβ in the MCF10A cell line, which is an immortalised but non-transformed breast epithelial cell line without high endogenous ER expression. Results: Clones of MCF10A cells were characterised which stably overexpressed ERα (10A-ERα2, 10A-ERα13) or which stably overexpressed ERβ (10A-ERβ12, 10A-ERβ15). Overexpression of either ERα or ERβ allowed induction of an oestrogen-regulated ERE-LUC reporter gene by oestradiol which was not found in the untransfected cells. Oestradiol also increased proliferation of 10A-ERα13 and 10A-ERβ12 cells, but not untransfected cells, by 1.3-fold over 7 days. The phytoestrogen, genistein, which is reported to bind more strongly to ERβ than to ERα, could induce luciferase gene expression from an ERE-LUC reporter gene at concentrations of 10−6 M and 10−5 M but only in the clones overexpressing ERβ and not in those overexpressing ERα. Clone 10A-ERβ12 also yielded growth stimulation with 10-6 M genistein. Finally, the overexpression of ERα, but not ERβ, gave rise to increased growth in semi-solid methocel suspension culture in the presence of 70 nM oestradiol, suggesting that overexpression of ERα, but not ERβ, produces characteristics of a transformed phenotype. Conclusions: This provides a model system to compare effects of oestradiol with other oestrogenic ligands in cells stably overexpressing individually ERα or ERβ.

Applying the extended Kalman filter to systems described by nonlinear differential-algebraic equations

This paper describes a method for the state estimation of nonlinear systems described by a class of differential-algebraic equation models using the extended Kalman filter. The method involves the use of a time-varying linearisation of a semi-explicit index one differential-algebraic equation. The estimation technique consists of a simplified extended Kalman filter that is integrated with the differential-algebraic equation model. The paper describes a simulation study using a model of a batch chemical reactor. It also reports a study based on experimental data obtained from a mixing process, where the model of the system is solved using the sequential modular method and the estimation involves a bank of extended Kalman filters.

Optimal control of nonlinear systems represented by differential algebraic equations

An iterative procedure is described for solving nonlinear optimal control problems subject to differential algebraic equations. The procedure iterates on an integrated modified simplified model based problem with parameter updating in such a manner that the correct solution of the original nonlinear problem is achieved.

Optimal control of nonlinear differential algebraic equation systems

A novel iterative procedure is described for solving nonlinear optimal control problems subject to differential algebraic equations. The procedure iterates on an integrated modified linear quadratic model based problem with parameter updating in such a manner that the correct solution of the original non-linear problem is achieved. The resulting algorithm has a particular advantage in that the solution is achieved without the need to solve the differential algebraic equations . Convergence aspects are discussed and a simulation example is described which illustrates the performance of the technique. 1. Introduction When modelling industrial processes often the resulting equations consist of coupled differential and algebraic equations (DAEs). In many situations these equations are nonlinear and cannot readily be directly reduced to ordinary differential equations.

A model for exchange rates with crawling bands: an application to the Colombian peso

This paper builds upon previous research on currency bands, and provides a model for the Colombian peso. Stochastic differential equations are combined with information related to the Colombian currency band to estimate competing models of the behaviour of the Colombian peso within the limits of the currency band. The resulting moments of the density function for the simulated returns describe adequately most of the characteristics of the sample returns data. The factor included to account for the intra-marginal intervention performed to drive the rate towards the Central Parity accounts only for 6.5% of the daily change, which supports the argument that intervention, if performed by the Central Bank, it is not directed to push the currency towards the limits. Moreover, the credibility of the Colombian Central Bank, Banco de la República’s ability to defend the band seems low.

Superensembles of linear viscoelastic models of polymer melts

The idea of incorporating multiple models of linear rheology into a superensemble, to forge a consensus forecast from the individual model predictions, is investigated. The relative importance of the individual models in the so-called multimodel superensemble (MMSE) was inferred by evaluating their performance on a set of experimental training data, via nonlinear regression. The predictive ability of the MMSE model was tested by comparing its predictions on test data that were similar (in-sample) and dissimilar (out-of-sample) to the training data used in the calibration. For the in-sample forecasts, we found that the MMSE model easily outperformed the best constituent model. The presence of good individual models greatly enhanced the MMSE forecast, while the presence of some bad models in the superensemble also improved the MMSE forecast modestly. While the performance of the MMSE model on the out-of-sample training data was not as spectacular, it demonstrated the robustness of this approach.

Advances in sequential data assimilation and numerical weather forecasting: an Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic Ensemble Square Root Filters, and a test of a new time stepping scheme in an atmospheric model

This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn’t represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion in the mean value of the function. Using statistical significance tests both at the local and field level, it is shown that the climatology of the SPEEDY model is not modified by the changed time stepping scheme; hence, no retuning of the parameterizations is required. It is found the accuracy of the medium-term forecasts is increased by using the RAW filter.

Lexical access across languages: a multinomial model of auditory distraction

Recall in many types of verbal memory task is reliably disrupted by the presence of auditory distracters, with verbal distracters frequently proving the most disruptive (Beaman, 2005). A multinomial processing tree model (Schweickert, 1993) is applied to the effects on free recall of background speech from a known or an unknown language. The model reproduces the free recall curve and the impact on memory of verbal distracters for which a lexical entry exists (i.e., verbal items from a known language). The effects of semantic relatedness of distracters within a language is found to depend upon a redintegrative factor thought to reflect the contribution of the speech-production system. The differential impacts of known and unknown languages cannot be accounted for in this way, but the same effects of distraction are observed amongst bilinguals, regardless of distracter-language.

Correction of atmospheric delay effects in radar interferometry using a nested mesoscale atmospheric model

The temporal variability of the atmosphere through which radio waves pass in the technique of differential radar interferometry can seriously limit the accuracy with which the method can measure surface motion. A forward, nested mesoscale model of the atmosphere can be used to simulate the variable water content along the radar path and the resultant phase delays. Using this approach we demonstrate how to correct an interferogram of Mount Etna in Sicily associated with an eruption in 2004-5. The regional mesoscale model (Unified Model) used to simulate the atmosphere at higher resolutions consists of four nested domains increasing in resolution (12, 4, 1, 0.3 km), sitting within the analysis version of a global numerical model that is used to initiate the simulation. Using the high resolution 3D model output we compute the surface pressure, temperature and the water vapour, liquid and solid water contents, enabling the dominant hydrostatic and wet delays to be calculated at specific times corresponding to the acquisition of the radar data. We can also simulate the second-order delay effects due to liquid water and ice.

Interpreting Differential Temperature Trends at the Surface and in the Lower Troposphere

Estimated global-scale temperature trends at Earth's surface (as recorded by thermometers) and in the lower troposphere (as monitored by satellites) diverge by up to 0.14°C per decade over the period 1979 to 1998. Accounting for differences in the spatial coverage of satellite and surface measurements reduces this differential, but still leaves a statistically significant residual of roughly 0.1°C per decade. Natural internal climate variability alone, as simulated in three state-of-the-art coupled atmosphere-ocean models, cannot completely explain this residual trend difference. A model forced by a combination of anthropogenic factors and volcanic aerosols yields surface-troposphere temperature trend differences closest to those observed.