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PubMed2005

Modeling salt-dependent proton binding by organic soils with the NICA-Donnan and Stockholm humic models.

Models are available for simulations of proton dissociation and cation binding by natural organic matter; two examples are the NICA-Donnan and Stockholm Humic (SHM) models. To model proton and metal binding, it is necessary to properly account for the ionic strength dependence of proton dissociation. In previous applications of the models for soils itwas assumed that the electrostatic interactions for solid-phase humic substances were the same as in solution; this assumption was recently challenged. Therefore, we reanalyzed previously published acid-base titrations of acid-washed Sphagnum peat, and we produced additional data sets for two Sphagnum peats and two Spodosol Oe horizons. For the soil suspensions, the original NICA-Donnan and SHM models, which were developed for dissolved humic substances, underestimated the observed salt dependence considerably. When a fixed Donnan volume of 1 L kg(-1) for humic substances in the solid phase was used, the NICA-Donnan model fits were much improved. Also for SHM, slight changes produced improved model fits. The models also produced acceptable simulations of the dissolved Ca, Mg, and Cd concentrations, provided that cation selectivitywas introduced. In conclusion, the proposed extensions to the NICA-Donnan and SHM models were shown to predict the salt dependence of solid-phase humic substances more satisfactorily than earlier model versions.

Humic Substances↗

PubMedDate not supplied

An individual modelling tool for consecutive clinical mastitis during the same lactation in dairy cows: a method based on a survival model.

The high number of clinical mastitis recurring within the same lactation in dairy cows constitutes one of the factors of overdispersion in standard Poisson models. Our method, based on biological parameters, i.e., recurrence hazard in relation to udder exogenous infection (Rex) or recurrence hazard and rate in relation to endogenous infection (Ren), produced a model capable of integrating a possible change of state in the udder after clinical mastitis. This model was based on a study of the time intervals between successive clinical episodes, both types of risk being considered in the form of a distribution mixture in the survival model. The modelling tool allowed to determine the factors that specifically act on either one of the potential risks and estimated the distribution of the number of clinical mastitis per lactation, as well as the distribution of when mastitis occurs. Estimation results obtained by this method in an experimental herd were compared with those from more classical models with or without random individual effects. The distribution of the number of mastitis per lactation estimated by our method was well-fitted to the data and the method identified variation factors which were relatively standard in this type of study: lactation number, lactation stage and calving month. Prediction results obtained in another experimental herd with more recent data without parameter re-estimation demonstrated the adequacy of the model in fitting observed data. This modelling method based on biological parameters in a mixture of survival distributions was interesting to model clinical mastitis recurring within the same lactation. However in the future it will also be important to integrate the possible relationship between successive lactations and to apply this model to other types of farming systems.

Animals↗

PubMed2006

Development of a dynamic aquatic model (DynA Model): estimating temporal emissions of DDT to Lake Maggiore (N. Italy).

BACKGROUND, AIMS AND SCOPE: Most existing models used to describe the fate of chemicals in surface water and sediment generally consider a 'static scenario', in which a contaminant is discharged at a constant rate and environmental input parameters do not change during the simulation time. This approach is not suitable in environmental scenarios characterized by daily or periodic changes of several input parameters. The aim of this study is to estimate approximate emissions of DDT lo Lake Maggiore using a new surface water model, (DynA Model) that describes the fate of a chemical in a dynamic scenario. METHODS: The model is developed on the grounds of an existing and validated model (QWASI). A numerical solution was adopted to build the fully dynamic version of the model. RESULTS AND DISCUSSION: The model was applied to Lake Maggiore emitting DDT at a constant rate until steady-state was reached. Emissions were stopped and later sporadic 'pulse' emissions were added. This was done to calculate the amount of DDT needed to simulate concentrations close to those measured in water and sediments. This allowed the evaluation of the order of magnitude of emissions. An uncertainty analysis for sediment resuspension was also performed, given the lack of measured resuspension rates. CONCLUSION: The model showed the time response of the Lake Maggiore system to varying emission scenarios and provided what are regarded as reasonable estimates of DDT emissions. The model demonstrated the importance of sediment-water exchange. RECOMMENDATION AND OUTLOOK: In order to better calculate DDT concentrations the model should be run with different discharge scenarios to clarify the time trends of concentrations, possibly with the use of different sets of measured data (such as biota and sediment deposition/resuspension rates).

DDT↗

PubMed1997

Model Predictive Impedance Control: A Model for Joint Movement.

Impedance control has been suggested as the strategy employed by the central nervous system to control human postures and movements. A realization of this strategy is presented that uses a model predictive control algorithm as a higher motor controller. External disturbances are explicitly included in the model. The combination of 3 key factors-joint impedance control, model predictive controller, and external disturbance input-forms the basis for the generality of this model. The model was applied to 3 different types of joint movements: a tracking movement with an unpredicted disturbance, a rhythmic movement, and an unstable biped model of human walking. Computer simulation results showed excellent performance of the model in all 3 cases for optimal values of active joint impedances and an exact match between the musculoskeletal system and the model internal to the model predictive controller. The controller was also able to maintain acceptable performance in the presence of a 25% mismatch between the musculoskeletal system and its internal model.

equilibrium-point hypothesis↗

PubMedDate not supplied

Modelling serial position curves with temporal distinctiveness.

We offer a critique of the temporal distinctiveness model of serial position effects (Nairne, Neath, Serra, & Byun, 1997). The temporal distinctiveness model combines a precise definition of stimulus distinctiveness with a memory perturbation process. The critique is empirically motivated-we show that with a more complete analysis, the temporal distinctiveness model does not adequately account for Nairne et al.'s experimental data. To better account for the data, we independently modified two components of Nairne et al.'s model: the mathematical form of the definition of temporal distinctiveness and the mathematical form of the mapping from distinctiveness to free-recall probabilities. Both of these modifications provided for better fits. Yet both Nairne et al.'s definition and our modified definition are fairly arbitrary. We show that a significant challenge to this approach is to find theoretically motivated constraints of the temporal distinctiveness model while providing for adequate fits to data.

Humans↗

PubMed2002

Modelling biological processes using workflow and Petri Net models.

MOTIVATION: Biological processes can be considered at many levels of detail, ranging from atomic mechanism to general processes such as cell division, cell adhesion or cell invasion. The experimental study of protein function and gene regulation typically provides information at many levels. The representation of hierarchical process knowledge in biology is therefore a major challenge for bioinformatics. To represent high-level processes in the context of their component functions, we have developed a graphical knowledge model for biological processes that supports methods for qualitative reasoning. RESULTS: We assessed eleven diverse models that were developed in the fields of software engineering, business, and biology, to evaluate their suitability for representing and simulating biological processes. Based on this assessment, we combined the best aspects of two models: Workflow/Petri Net and a biological concept model. The Workflow model can represent nesting and ordering of processes, the structural components that participate in the processes, and the roles that they play. It also maps to Petri Nets, which allow verification of formal properties and qualitative simulation. The biological concept model, TAMBIS, provides a framework for describing biological entities that can be mapped to the workflow model. We tested our model by representing malaria parasites invading host erythrocytes, and composed queries, in five general classes, to discover relationships among processes and structural components. We used reachability analysis to answer queries about the dynamic aspects of the model. AVAILABILITY: The model is available at http://smi.stanford.edu/projects/helix/pubs/process-model/.

Animals↗

PubMed2006

The SPECTRUM population model of the impact of screening and treatment on U.S. breast cancer trends from 1975 to 2000: principles and practice of the model methods.

OBJECTIVE: This stochastic simulation model was developed to estimate the impact of screening and treatment diffusion on U.S. breast cancer mortality between 1975 and 2000. MODELING APPROACH: We use an event-driven continuous-time state transition model. Women who are destined to develop breast cancer may be screen detected, present with symptoms, or die of other causes before cancer is diagnosed. At presentation, the cancer has a stage assigned on the basis of mode of detection. Cancers are assumed to be estrogen receptor (ER) positive or negative. Data on screening and treatment diffusion are based on national datasets; other parameters are based on a synthesis of the evidence available in the literature. MODEL METHODS: The model is calibrated to predict incidence and stage distribution (in situ, local, regional, and distant). Other than screening or treatment, background events that affect mortality are not explicitly modeled but are captured in the deviation between model projections of mortality trends and actual trends. We assume that: 1) tumors progress more slowly in older age groups, 2) screen- and clinically detected disease have the same survival conditional on age and stage, 3) women do not die of breast cancer within the "lead time" period, 4) screening benefits are captured by shifts in stage at diagnosis, 4) tamoxifen benefits only ER-positive women, and 5) preclinical sojourn time and dwell times in each of the clinical stages are stochastically independent. MODEL RESULTS: Dissemination of screening and therapeutic advances had a substantial impact on mortality trends. We estimate that, by the year 2000, diffusion of screening lowered mortality by 12.4% and treatment improvements and dissemination lowered mortality by 14.6%. CONCLUSIONS: Models such as this one can be useful to translate clinical trial findings to general populations. This model can also be used inform policy debates about how to best achieve targeted reductions in breast cancer morbidity and mortality.

Adult↗

PubMed1993

Interpreting model coefficients when the true model form is unknown.

In this paper, we critically examine mathematical modeling. We outline the major assumptions required by modeling methods used in epidemiology and discuss in detail one fundamental assumption that is usually violated in epidemiologic studies: the assumption that the structural model form is correctly specified. We apply concepts from the econometrics literature to examine how epidemiologic inference may be affected when the structural model form is incorrectly specified. Because the structural model is almost always misspecified in practice, tests and confidence intervals for model coefficients do not refer to "true population parameters" in the ordinary sense. Rather, these statistics concern parameters that depend on features of study design, as well as the effects under study. In cohort studies analyzed with multiplicative rate models, model parameters are interpretable as approximations to log standardized rate ratios; unfortunately, such interpretations are not as accurate for other models and designs. We therefore conclude that model coefficients can serve as reasonable effect summaries in some, but not all, situations.

Bias↗

PubMed2006

Model-based RSA of a femoral hip stem using surface and geometrical shape models.

Roentgen stereophotogrammetry (RSA) is a highly accurate three-dimensional measuring technique for assessing micromotion of orthopaedic implants. A drawback is that markers have to be attached to the implant. Model-based techniques have been developed to prevent using special marked implants. We compared two model-based RSA methods with standard marker-based RSA techniques. The first model-based RSA method used surface models, and the second method used elementary geometrical shape (EGS) models. We used a commercially available stem to perform experiments with a phantom as well as reanalysis of patient RSA radiographs. The data from the phantom experiment indicated the accuracy and precision of the elementary geometrical shape model-based RSA method is equal to marker-based RSA. For model-based RSA using surface models, the accuracy is equal to the accuracy of marker-based RSA, but its precision is worse. We found no difference in accuracy and precision between the two model-based RSA techniques in clinical data. For this particular hip stem, EGS model-based RSA is a good alternative for marker-based RSA.

Arthroplasty, Replacement, Hip↗

PubMed2006

Semiparametric models for missing covariate and response data in regression models.

We consider a class of semiparametric models for the covariate distribution and missing data mechanism for missing covariate and/or response data for general classes of regression models including generalized linear models and generalized linear mixed models. Ignorable and nonignorable missing covariate and/or response data are considered. The proposed semiparametric model can be viewed as a sensitivity analysis for model misspecification of the missing covariate distribution and/or missing data mechanism. The semiparametric model consists of a generalized additive model (GAM) for the covariate distribution and/or missing data mechanism. Penalized regression splines are used to express the GAMs as a generalized linear mixed effects model, in which the variance of the corresponding random effects provides an intuitive index for choosing between the semiparametric and parametric model. Maximum likelihood estimates are then obtained via the EM algorithm. Simulations are given to demonstrate the methodology, and a real data set from a melanoma cancer clinical trial is analyzed using the proposed methods.

Algorithms↗

PubMedDate not supplied

System dynamics modeling of transboundary systems: the bear river basin model.

System dynamics is a computer-aided approach to evaluating the interrelationships of different components and activities within complex systems. Recently, system dynamics models have been developed in areas such as policy design, biological and medical modeling, energy and the environmental analysis, and in various other areas in the natural and social sciences. The Idaho National Engineering and Environmental Laboratory, a multipurpose national laboratory managed by the Department of Energy, has developed a system dynamics model in order to evaluate its utility for modeling large complex hydrological systems. We modeled the Bear River basin, a transboundary basin that includes portions of Idaho, Utah, and Wyoming. We found that system dynamics modeling is very useful for integrating surface water and ground water data and for simulating the interactions between these sources within a given basin. In addition, we also found that system dynamics modeling is useful for integrating complex hydrologic data with other information (e.g., policy, regulatory, and management criteria) to produce a decision support system. Such decision support systems can allow managers and stakeholders to better visualize the key hydrologic elements and management constraints in the basin, which enables them to better understand the system via the simulation of multiple "what-if" scenarios. Although system dynamics models can be developed to conduct traditional hydraulic/hydrologic surface water or ground water modeling, we believe that their strength lies in their ability to quickly evaluate trends and cause-effect relationships in large-scale hydrological systems, for integrating disparate data, for incorporating output from traditional hydraulic/hydrologic models, and for integration of interdisciplinary data, information, and criteria to support better management decisions.

Computer Simulation↗

PubMed2002

Sound propagation in concentrated emulsions: comparison of coupled phase model and core-shell model.

The predictions of two models of sound propagation in concentrated emulsions are compared with experimental measurements of ultrasonic velocity and attenuation in emulsions with volume fractions up to 0.7. The core-shell model includes irreversible heat transfer, viscoinertial forces, and multiple scattering [McClements et al., J. Acoust. Soc. Am. 105, 915-918 (1999)]. This model accounts for the effect of thermal interactions between neighboring particles by introducing an effective medium, and is valid for all volume fractions. The coupled phase model includes irreversible heat transfer and viscoinertial forces, and also is valid for all volume fractions, since it is derived from volume-averaged balance equations [J. M. Evans and K. Attenborough, J. Acoust. Soc. Am. 102, 278-282 (1997)]. This model has a significantly simpler formulation than the core-shell model and does not require the assumption of an effective medium. The coupled phase model is shown to be a good approximation to the core-shell model when the acoustic radius is small. Despite the fact that it does not include thermal interactions, the coupled phase model is shown to give at least as good agreement as the core-shell model with the experimental data, for all volume fractions, as long as the acoustic radius is less than 0.01.

Emulsions↗

PubMed2000

Control variables in mechanical muscle models: a mini-review and a new model.

A new mechanical model of isolated muscle is proposed in which spring with variable slack length is the force-generating element. Based on the review of experimental studies in isolated muscle, it is suggested that spring slack length Xo is the control variable in the model and is a function of motor unit firing rate. In the presence of sensory feedback, the Sliding Spring model is equivalent to the Rack and Pinion model. However, sensory feedback is essential in the Rack and Pinion model but complementary in the Sliding Spring model. How the new control variable in the model of isolated muscle affects the interpretation of control processes up the motor system hierarchy is discussed in light of certain controversies associated with the Lambda and Alpha models of control of movement. It is argued that the Sliding Spring model of isolated muscle can be used as a basis for developing models of control of movement.

Biomechanical Phenomena↗

PubMed2006

The two-state dimer receptor model: a general model for receptor dimers.

Nonlinear Scatchard plots are often found for agonist binding to G-protein-coupled receptors. Because there is clear evidence of receptor dimerization, these nonlinear Scatchard plots can reflect cooperativity on agonist binding to the two binding sites in the dimer. According to this, the "two-state dimer receptor model" has been recently derived. In this article, the performance of the model has been analyzed in fitting data of agonist binding to A(1) adenosine receptors, which are an example of receptor displaying concave downward Scatchard plots. Analysis of agonist/antagonist competition data for dopamine D(1) receptors using the two-state dimer receptor model has also been performed. Although fitting to the two-state dimer receptor model was similar to the fitting to the "two-independent-site receptor model", the former is simpler, and a discrimination test selects the two-state dimer receptor model as the best. This model was also very robust in fitting data of estrogen binding to the estrogen receptor, for which Scatchard plots are concave upward. On the one hand, the model would predict the already demonstrated existence of estrogen receptor dimers. On the other hand, the model would predict that concave upward Scatchard plots reflect positive cooperativity, which can be neither predicted nor explained by assuming the existence of two different affinity states. In summary, the two-state dimer receptor model is good for fitting data of binding to dimeric receptors displaying either linear, concave upward, or concave downward Scatchard plots.

Adenosine A1 Receptor Agonists↗

PubMed2000

Comparison of a rabbit model of bacterial endocarditis and an in vitro infection model with simulated endocardial vegetations.

Animal models are commonly used to determine the efficacy of various antimicrobial agents for treatment of bacterial endocarditis. Previously we have utilized an in vitro infection model, which incorporates simulated endocardial vegetations (SEVs) to evaluate the pharmacodynamics of various antibiotics. In the present study, we compared four experimental rabbit endocarditis protocols to an in vitro infection model in an effort to determine if these models are comparable. We have evaluated the activity of clinafloxacin, trovafloxacin, sparfloxacin, and ciprofloxacin in rabbit models against Staphylococcus aureus and Enterococcus spp. In vitro models were performed simulating the antibiotic pharmacokinetics obtained in the in vivo studies. Models were dosed the same as rabbit models, and SEVs were evaluated at the same time the rabbit vegetations were examined. Clinafloxacin and trovafloxacin were evaluated against methicillin-susceptible (MSSA1199) and -resistant (MRSA494) strains of S. aureus. Ciprofloxacin was studied against MSSA1199 and MSSA487. Sparfloxacin and clinafloxacin were evaluated against Enterococcus faecium SF2149 and Enterococcus faecalis WH245, respectively. We found that reductions in SEV bacterial density obtained in the in vitro model were similar to those obtained in rabbit vegetations, indicating that the SEV model may be a valuable tool for assessing antibiotic potential in the treatment of bacterial endocarditis.

Animal Testing Alternatives↗

PubMed1992

The in vivo orthodontic banding model for vital teeth and the in situ orthodontic banding model for hard-tissue slabs.

This paper presents the orthodontic banding model for vital teeth and the orthodontic in situ model for slabs of enamel, root surface, dentin, or other mineralized tissues such as shark enamel. The model for vital teeth is an in vivo model, since a crevice for plaque accumulation is created behind orthodontic bands on the buccal enamel surfaces of teeth in situ. Visible white-spot lesions are usually seen after a four-week banding period in the absence of fluoride. The microbiological flora developed behind the bands shows a similarity to that of natural caries. Microradiographic data show that the initial lesion is a softening of the enamel surface. Later, a subsurface lesion develops. A modification of the model has been developed for the use of slabs of mineralized tissues. In this model, slabs are mounted on a removable appliance. The slabs are covered with orthodontic banding material for plaque accumulation. Lesion development in enamel in the two model systems is almost identical. The benefit of the in vivo model is that caries development can be studied on vital teeth in young individuals. The model is independent of the patient's cooperation. No special diet is required, e.g., no sucrose rinsing. In the in situ model, slabs could be examined after one study period and then replaced for another period.

Dental Caries↗

PubMed2003

Modelling of cardiovascular system: development of a hybrid (numerical-physical) model.

Physical models of the circulation are used for research, training and for testing of implantable active and passive circulatory prosthetic and assistance devices. However, in comparison with numerical models, they are rigid and expensive. To overcome these limitations, we have developed a model of the circulation based on the merging of a lumped parameter physical model into a numerical one (producing therefore a hybrid). The physical model is limited to the barest essentials and, in this application, developed to test the principle, it is a windkessel representing the systemic arterial tree. The lumped parameters numerical model was developed in LabVIEW environment and represents pulmonary and systemic circulation (except the systemic arterial tree). Based on the equivalence between hydraulic and electrical circuits, this prototype was developed connecting the numerical model to an electrical circuit--the physical model. This specific solution is valid mainly educationally but permits the development of software and the verification of preliminary results without using cumbersome hydraulic circuits. The interfaces between numerical and electrical circuits are set up by a voltage controlled current generator and a voltage controlled voltage generator. The behavior of the model is analyzed based on the ventricular pressure-volume loops and on the time course of arterial and ventricular pressures and flow in different circulatory conditions. The model can represent hemodynamic relationships in different ventricular and circulatory conditions.

Coronary Circulation↗

PubMed2006

Environmental modelling of use of treated organic waste on agricultural land: a comparison of existing models for life cycle assessment of waste systems.

Modelling of environmental impacts from the application of treated organic municipal solid waste (MSW) in agriculture differs widely between different models for environmental assessment of waste systems. In this comparative study five models were examined concerning quantification and impact assessment of environmental effects from land application of treated organic MSW: DST (Decision Support Tool, USA), IWM (Integrated Waste Management, U.K.), THE IFEU PROJECT (Germany), ORWARE (ORganic WAste REsearch, Sweden) and EASEWASTE (Environmental Assessment of Solid Waste Systems and Technologies, Denmark). DST and IWM are life cycle inventory (LCI) models, thus not performing actual impact assessment. The DST model includes only one water emission (biological oxygen demand) from compost leaching in the results and IWM considers only air emissions from avoided production of commercial fertilizers. THE IFEU PROJECT, ORWARE and EASEWASTE are life cycle assessment (LCA) models containing more detailed land application modules. A case study estimating the environmental impacts from land application of 1 ton of composted source sorted organic household waste was performed to compare the results from the different models and investigate the origin of any difference in type or magnitude of the results. The contributions from the LCI models were limited and did not depend on waste composition or local agricultural conditions. The three LCA models use the same overall approach for quantifying the impacts of the system. However, due to slightly different assumptions, quantification methods and environmental impact assessment, the obtained results varied clearly between the models. Furthermore, local conditions (e.g. soil type, farm type, climate and legal regulation) and waste composition strongly influenced the results of the environmental assessment.

Agriculture↗