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At least 145 records · Page 8Linked to original sources
PubMed2005

Anaerobic digestion: a new model for plant-wide wastewater treatment process modelling.

This paper describes a new anaerobic digestion model for wastewater treatment systems (MantisAD). The model has been developed specifically for plant-wide wastewater treatment modelling. That is, rather than modelling nitrogen as a series of fractions of other carbonaceous state variables, this new model includes six dedicated nitrogen state variables. This structure makes this new anaerobic model easier to incorporate into plant-wide models by simplifying the aerobic/anaerobic model interfaces. The model is complete and initial success with the model has been achieved. A comprehensive description of the model including a Petersen Matrix representation of the model is included as is a case study in which the model is applied to full-scale anaerobic digesters.

Anaerobiosis↗
PubMedDate not supplied

The parameter conversion from the Kedem-Katchalsky model into the two-parameter model.

Cryopreservation is an important process for preserving cells and tissues. The process itself can, however, cause damage to cells and tissues. During addition and removal of a cryoprotective agent (CPA), cells are subjected to imbalanced osmotic pressures between the intracellular and extracellular solutions. Cells can be injured if these shrinkage and swelling events are beyond their tolerable limits. The characteristics of the cell volume changes during these processes depend on the types of CPA, the methods of adding and removing of the CPA and the permeability of cells to CPA and water. The typical models of this transportation are the two-parameter (2-p) model and the Kedem-Katchalsky (K-K) model. The K-K model is more general than the 2-p model. However, there is evidence that in many cases water and CPAs do not permeate through common pathways, hence the use of the reflection coefficient in the K-K model may be unnecessary and in some cases it may create conceptual errors. Therefore, the 2-p model is more suitable for use as a transport model than the K-K model. The aim of this study is to use the values of the K-K model parameters from data in the literature to calculate the corresponding parameters for the 2-p model. The results from these simulations demonstrate that the cell volume changes during CPA addition and removal from the K-K model can be closely estimated by the 2-p model.

Animals↗
PubMed2006

The time-profile of cell growth in fission yeast: model selection criteria favoring bilinear models over exponential ones.

BACKGROUND: There is considerable controversy concerning the exact growth profile of size parameters during the cell cycle. Linear, exponential and bilinear models are commonly considered, and the same model may not apply for all species. Selection of the most adequate model to describe a given data-set requires the use of quantitative model selection criteria, such as the partial (sequential) F-test, the Akaike information criterion and the Schwarz Bayesian information criterion, which are suitable for comparing differently parameterized models in terms of the quality and robustness of the fit but have not yet been used in cell growth-profile studies. RESULTS: Length increase data from representative individual fission yeast (Schizosaccharomyces pombe) cells measured on time-lapse films have been reanalyzed using these model selection criteria. To fit the data, an extended version of a recently introduced linearized biexponential (LinBiExp) model was developed, which makes possible a smooth, continuously differentiable transition between two linear segments and, hence, allows fully parametrized bilinear fittings. Despite relatively small differences, essentially all the quantitative selection criteria considered here indicated that the bilinear model was somewhat more adequate than the exponential model for fitting these fission yeast data. CONCLUSION: A general quantitative framework was introduced to judge the adequacy of bilinear versus exponential models in the description of growth time-profiles. For single cell growth, because of the relatively limited data-range, the statistical evidence is not strong enough to favor one model clearly over the other and to settle the bilinear versus exponential dispute. Nevertheless, for the present individual cell growth data for fission yeast, the bilinear model seems more adequate according to all metrics, especially in the case of wee1Delta cells.

Biophysics↗
PubMed2006

Inference for a family of survival models encompassing the proportional hazards and proportional odds models.

For survival data regression, the Cox proportional hazards model is the most popular model, but in certain situations the Cox model is inappropriate. Various authors have proposed the proportional odds model as an alternative. Yang and Prentice recently presented a number of easily implemented estimators for the proportional odds model. Here we show how to extend the methods of Yang and Prentice to a family of survival models that includes the proportional hazards model and proportional odds model as special cases. The model is defined in terms of a Box-Cox transformation of the survival function, indexed by a transformation parameter rho. This model has been discussed by other authors, and is related to the Harrington-Fleming G(rho) family of tests and to frailty models. We discuss inference for the case where rho is known and the case where rho must be estimated. We present a simulation study of a pseudo-likelihood estimator and a martingale residual estimator. We find that the methods perform reasonably. We apply our model to a real data set.

Chlamydia Infections↗
PubMed1999

Improved modeling of side-chains in proteins with rotamer-based methods: a flexible rotamer model.

Side-chain modeling has a widespread application in many current methods for protein tertiary structure determination, prediction, and design. Of the existing side-chain modeling methods, rotamer-based methods are the fastest and most efficient. Classically, a rotamer is conceived as a single, rigid conformation of an amino acid sidechain. Here, we present a flexible rotamer model in which a rotamer is a continuous ensemble of conformations that cluster around the classic rigid rotamer. We have developed a thermodynamically based method for calculating effective energies for the flexible rotamer. These energies have a one-to-one correspondence with the potential energies of the rigid rotamer. Therefore, the flexible rotamer model is completely general and may be used with any rotamer-based method in substitution of the rigid rotamer model. We have compared the performance of the flexible and rigid rotamer models with one side-chain modeling method in particular (the self-consistent mean field theory method) on a set of 20 high quality crystallographic protein structures. For the flexible rotamer model, we obtained average predictions of 85.8% for chi1, 76.5% for chi1+2 and 1.34 A for root-mean-square deviation (RMSD); the corresponding values for core residues were 93.0%, 87.7% and 0.70 A, respectively. These values represent improvements of 7.3% for chi1, 8.1% for chi1+2 and 0.23 A for RMSD over the predictions obtained with the rigid rotamer model under otherwise identical conditions; the corresponding improvements for core residues were 6.9%, 10.5% and 0.43 A, respectively. We found that the predictions obtained with the flexible rotamer model were also significantly better than those obtained for the same set of proteins with another state-of-the-art side-chain placement method in the literature, especially for core residues. The flexible rotamer model represents a considerable improvement over the classic rigid rotamer model. It can, therefore, be used with considerable advantage in all rotamer-based methods commonly applied to protein tertiary structure determination, prediction, and design and also in predictions of free energies in mutational studies.

Amino Acids↗
PubMed2003

Crystallization kinetics of amorphous nifedipine studied by model-fitting and model-free approaches.

The crystallization of amorphous nifedipine was studied using hot-stage microscopy (HSM), powder X-ray diffractometry (PXRD), and differential scanning calorimetry (DSC). The kinetic data obtained from DSC studies under isothermal and nonisothermal conditions were examined using both model-fitting and model-free approaches. Evaluation of 16 different models showed that model A4 (Avrami-Erofeev, n = 4) to be most appropriate for crystallization in the conversion range 0.05-0.80. This choice was based on the goodness of fit, the residual plots, and the guidance provided by the model-free approach. The model-free approach indicated that the activation energy decreases slightly as the crystallization proceeds. This variation of the activation energy with the extent of conversion determines the range of conversion over which a model can be fit, and the magnitude of the activation energy helps in the selection of the best model. The model-free approach gives much better predictions than the model of best fit and allows the experimental kinetic function to be numerically evaluated. At the early stage (alpha = 0-0.6), the numerically reconstructed model is almost identical to A4, but gradually approaches A3 (Avrami-Erofeev, n = 3) as the crystallization progresses (alpha = 0.6-0.8) and deviates from both models near the end of the reaction. This behavior may be explained by the relative contributions of nucleation and crystal growth at different stages of the reaction.

Calorimetry, Differential Scanning↗
PubMed2006

Development of a recursive finite difference pharmacokinetic model from an exponential model: application to a propofol bolus.

Propofol is commonly administered, as a single bolus dose, for the induction of general anesthesia. The purpose of this study was to mathematically assess the ability to model propofol induction-dose serum levels with a recursive finite difference equation (RFDE). Using data obtained from a prior published study, propofol induction pharmacokinetics were accurately modeled, on a subject-specific basis, with a third-order homogeneous finite difference equation with constant coefficients: P((k + 3)) = AP((k + 2)) + BP((k + 1)) + CP((k)). Furthermore, each RFDE model is derived directly from the coefficients of a traditional three-compartment pharmacokinectic exponential equation. Based on this study, third-order RFDE models can have identical accuracy as three-compartment exponential models. In this particular application, it should be noted that each RFDE model required only three coefficients whereas each exponential model required six. Also, there was overall less patient-to-patient variability of the coefficients of the RFDE models. In general, it appears that RFDE models uniquely allow for predicting subsequent drug levels from preexisting ones. However, RFDE models require initial conditions whereas exponential models do not. Additional studies and applications of exponentially-derived RFDE pharmacokinetic models may be warranted.

Adult↗
PubMed1996

A community model of ciliate Tetrahymena and bacteria E. coli: Part I. individual-based models of tetrahymena and E. coli populations.

The dynamics of a microbial community consisting of a eucaryotic ciliate Tetrahymena pyriformis and procaryotic Escherichia coli in a batch culture is explored by employing an individual-based approach. In this portion of the article, Part I, population models are presented. Because both models are individual-based, models of individual organisms are developed prior to construction of the population models. The individual models use an energy budget method in which growth depends on energy gain from feeding and energy sinks such as maintenance and reproduction. These models are not limited by simplifying assumptions about constant yield, constant energy sinks and Monod growth kinetics as are traditional models of microbal organisms. Population models are generated from individual models by creating distinct individual types and assigning to each type the number of real individuals they represent. A population is a compilation of individual types that vary in a phase of cell cycle and physiological parameters such as filtering rate for ciliates and maximum anabolic rate for bacteria. An advantage of the developed models is that they realistically describe the growth of the individual cells feeding on resource which varies in density and composition. Part II, the core of the project, integrates models into a dynamic microbial community and provides model analysis based upon available data.

Animals↗
PubMed2006

On the relationship of sufficient component cause models with potential outcome (counterfactual) models.

The sufficient-component cause (SSC) model provides a useful, concise method to conceptualize and organize ideas concerning biologic effects and interactions of multiple factors. Another type of model, the potential outcome or counterfactual models, has also proved useful in epidemiology, providing insights into definitions of effects, exchangeability and confounding, and selection bias. Prior work has shown important links between these two types of models. Here, we show additional connections between the two types of models. We first review basic concepts for both the SSC model and the potential outcome models. We then indicate additional similarities between them, and derive a quantitative link. Our results show that the SSC model actually corresponds to a unique response pattern in a particular potential outcome model. Recognition of the links between the two models should allow greater insight into use of each model, and allow the strengths of both models to be used jointly.

Causality↗
PubMed2004

A new generic sub-model for radionuclide fixation in large catchments from continuous and single-pulse fallouts, as used in a river model.

This paper presents a new general sub-model for fixation in catchment areas to be used within the framework of a river model for substances such as radionuclides and metals from continuous and single-pulse fallouts. The model has been critically tested using data from 27 European river sites covering a very wide geographical area and contaminated by radiocesium and radiostrontium from the Chernobyl accident and from the nuclear weapons tests (NWT fallout). This modelling approach gives radionuclide concentrations in water (total, dissolved and particulate phases) at defined sites on a monthly basis. The overall river model is based on processes in the upstream river stretch and in the catchment area. The catchment area is differentiated into inflow (approximately dry land) areas and outflow (approximately wetland) areas. The model has a general structure, which can be used for all radionuclides or substances. It is simple to apply in practice since all driving variables may be readily accessed from maps and standard monitoring programs. The driving variables are: latitude, altitude, catchment area, mean annual precipitation and fallout. Note that for large catchments, this model does not require data on the characteristic soil type or the percentage of outflow areas (wet lands) in the catchment, as in most previous models, since in practice it is very difficult to obtain reliable data on characteristic soil type or percentage of outflow areas, especially in large and topographically complex catchments. Modelled values have been compared to empirical data from rivers sites covering a wide domain (catchment areas from 3000 to 3,000,000 km2, precipitation from 400 to 1700 mm/year; fallouts from 1600 to 280,000 Bq/m2; altitudes from 0 to 1000 m.a.s.l. and latitudes from 41 degrees to 72 degrees N). The river model with its sub-model for fixation predicts close to the uncertainty factors given by the empirical data, which have been shown to be about a factor of 1.6 for 137Cs and a factor of 2.2 for 90Sr in river water. The obtained characteristic uncertainty factors for 137Cs from the Chernobyl fallout is 2.4, for 137Cs from the NWT fallout it is 1.3 and for the 90Sr results from the NWT fallout it is 3 using the new model.

Catchment Area, Health↗
PubMed2004

Testing the behaviour of different kinetic models for uptake/release of radionuclides between water and sediments when implemented in a marine dispersion model.

Three kinetic models for adsorption/release of (137)Cs between water and sediments have been tested when they are included in a previously validated dispersion model of the English Channel. Radionuclides are released to the Channel from La Hague nuclear fuel reprocessing plant (France). The kinetic models are a 1-step model consisting of a single reversible reaction, a 2-step model consisting of two consecutive reversible reactions and an irreversible model consisting of three parallel reactions: two reversible and one irreversible. The models have been tested under three typical situations that correspond to the source terms that can generally be found: instantaneous release, continuous release and redissolution of radionuclides from contaminated sediments. Differences between the models become more evident when contact times between water and sediments are larger (continuous release) and in the case of redissolution from sediments. Time scales for the redissolution process are rather different between the three models. The 1-step model produces a redissolution that is too fast when compared with experimental evidence. The irreversible model requires that saturation effects of the irreversible phase are included. Probably, the 2-step model represents the best compromise between ease and level of detail of the description of sorption/release processes.

Adsorption↗
PubMed2004

Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests.

Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects of the selection of substitution models in phylogenetics from a theoretical, philosophical and practical point of view, and summarize this comparison in table format. We argue that the most commonly implemented model selection approach, the hierarchical likelihood ratio test, is not the optimal strategy for model selection in phylogenetics, and that approaches like the Akaike Information Criterion (AIC) and Bayesian methods offer important advantages. In particular, the latter two methods are able to simultaneously compare multiple nested or nonnested models, assess model selection uncertainty, and allow for the estimation of phylogenies and model parameters using all available models (model-averaged inference or multimodel inference). We also describe how the relative importance of the different parameters included in substitution models can be depicted. To illustrate some of these points, we have applied AIC-based model averaging to 37 mitochondrial DNA sequences from the subgenus Ohomopterus(genus Carabus) ground beetles described by Sota and Vogler (2001).

Animals↗
PubMed2005

Predictive modeling of total healthcare costs using pharmacy claims data: a comparison of alternative econometric cost modeling techniques.

OBJECTIVE: We sought to evaluate several statistical modeling approaches in predicting prospective total annual health costs (medical plus pharmacy) of health plan participants using Pharmacy Health Dimensions (PHD), a pharmacy claims-based risk index. METHODS: We undertook a 2-year (baseline year/follow-up year) longitudinal analysis of integrated medical and pharmacy claims. Included were plan participants younger than 65 years of age with continuous medical and pharmacy coverage (n = 344,832). PHD drug categories, age, gender, and pharmacy costs were derived across the baseline year. Annual total health costs were calculated for each plan participant in follow-up year. Models examined included ordinary least squares (OLS) regression, log-transformed OLS regression with smearing estimator, and 3 two-part models using OLS regression, log-OLS regression with smearing estimator, and generalized linear modeling (GLM), respectively. A 10% random sample was withheld for model validation, which was assessed via adjusted r, mean absolute prediction error, specificity, and positive predictive value. RESULTS: Most PHD drug categories were significant independent predictors of total costs. Among models tested, the OLS model had the lowest mean absolute prediction error and highest adjusted r. The log-OLS and 2-part log-OLS models did not predict costs accurately as the result of issues of log-scale heteroscedasticity. The 2-part model using GLM had lower adjusted r but similar performance in other assessment measures compared with the OLS or 2-part OLS models. CONCLUSION: The PHD system derived solely from pharmacy claims data can be used to predict future total health costs. Using PHD with a simple OLS model may provide similar predictive accuracy in comparison to more advanced econometric models.

Adolescent↗
PubMed2005

Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models.

Constructing maps of dry deposition pollution levels is vital for air quality management, and presents statistical problems typical of many environmental and spatial applications. Ideally, such maps would be based on a dense network of monitoring stations, but this does not exist. Instead, there are two main sources of information for dry deposition levels in the United States: one is pollution measurements at a sparse set of about 50 monitoring stations called CASTNet, and the other is the output of the regional scale air quality models, called Models-3. A related problem is the evaluation of these numerical models for air quality applications, which is crucial for control strategy selection. We develop formal methods for combining sources of information with different spatial resolutions and for the evaluation of numerical models. We specify a simple model for both the Models-3 output and the CASTNet observations in terms of the unobserved ground truth, and we estimate the model in a Bayesian way. This provides improved spatial prediction via the posterior distribution of the ground truth, allows us to validate Models-3 via the posterior predictive distribution of the CASTNet observations, and enables us to remove the bias in the Models-3 output. We apply our methods to data on SO2 concentrations, and we obtain high-resolution SO2 distributions by combining observed data with model output. We also conclude that the numerical models perform worse in areas closer to power plants, where the SO2 values are overestimated by the models.

Air Pollution↗
PubMed2004

Investigating the mechanics of multimedia box models: how to explain differences between models in terms of mass fluxes?

The simple mathematical structure of multimedia fate models makes it possible to change the process descriptions and geometry of such models relatively easily. With different versions of a model, the effect of a process or compartment that is included in one version of the model but excluded in another version can be investigated. Here, a new method for performing such a model comparison in a quantitative way is presented. Based on the mass balances for the two model versions, it can be shown that, for a compartment contained in both model versions, the difference between a chemical's concentrations in this compartment is related directly to the difference in those mass fluxes that have different rate constants in the two models. Moreover, it is possible to identify the contributions to the concentration difference that stem from individual mass fluxes so that the concentration difference can be tracked back to specific differences in the process descriptions of the two models. This flux analysis method is illustrated with two versions of a unit-world model, one with and one without a vegetation compartment. With DDT and six polychlorinated biphenyl (PCB) congeners as example chemicals, the differences of the chemicals' concentrations in air and soil caused by the vegetation compartment are explained by using the flux analysis method. The future potential of the method for comparing not only versions of the same model but also models of different structure is discussed.

DDT↗
PubMed2006

Modelling of wastewater treatment plants--how far shall we go with sophisticated modelling tools?

Several levels of complexity are available for modelling of wastewater treatment plants. Modelling local effects rely on computational fluid dynamics (CFD) approaches whereas activated sludge models (ASM) represent the global methodology. By applying both modelling approaches to pilot plant and full scale systems, this paper evaluates the value of each method and especially their potential combination. Model structure identification for ASM is discussed based on a full-scale closed loop oxidation ditch modelling. It is illustrated how and for what circumstances information obtained via CFD (computational fluid dynamics) analysis, residence time distribution (RTD) and other experimental means can be used. Furthermore, CFD analysis of the multiphase flow mechanisms is employed to obtain a correct description of the oxygenation capacity of the system studied, including an easy implementation of this information in the classical ASM modelling (e.g. oxygen transfer). The combination of CFD and activated sludge modelling of wastewater treatment processes is applied to three reactor configurations, a perfectly mixed reactor, a pilot scale activated sludge basin (ASB) and a real scale ASB. The application of the biological models to the CFD model is validated against experimentation for the pilot scale ASB and against a classical global ASM model response. A first step in the evaluation of the potential of the combined CFD-ASM model is performed using a full scale oxidation ditch system as testing scenario.

Models, Theoretical↗