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At least 325 records · Page 18Linked to original sources

PubMed2005

Calibration models for the vinyl acetate concentration in ethylene-vinyl acetate copolymers and its on-line monitoring by near-infrared spectroscopy and chemometrics: use of band shifts associated with variations in the vinyl acetate concentration to improve the models.

The present study investigates calibration models for the vinyl acetate (VA) concentration in ethylene-vinyl acetate (EVA) copolymers and its on-line monitoring by near-infrared (NIR) spectroscopy and chemometrics. The key point in the present study is to make use of band shifts associated with concentration changes in the vinyl acetate (VA) for the improvement of the models. NIR spectra of EVA in melt and solid states were measured by a Fourier transform near-infrared (FT-NIR) on-line monitoring system and a FT-NIR laboratory system. Some of the bands in the NIR spectra for both states show significant shifts with the variations in the VA concentration. The peak shifts induced by the VA concentration changes are larger in the solid-state EVA than those in the melt-state EVA. We have developed calibration models for the VA concentration in the solid-state EVA and investigated how to improve the calibration models. The factor analysis of partial least squares (PLS) regression has suggested that the wavenumber shifts caused by the VA concentration changes affect the calibration models for the VA concentration in EVA. From the analysis, it has been proposed that the wavenumbers in the spectrum of one sample in nine EVA samples (VA concentration range: 0-41.1%) are shifted for the improvement of the calibration models, and the effects of the proposed method have been confirmed by using the PLS calibration models for the VA concentration in the solid EVA samples. As the next step, the effects of the wavenumber shift method have been explored for the calibration models for the VA concentration in the melt-state EVA. After that, the discrimination method using the score plots of PLS and the application sequence for the on-line monitoring to use the proposed wavenumber shift method were studied. The simulation results using the discrimination and wavenumber shift methods have shown that those methods are very effective to improve the predicted values of the calibration models for the on-line monitoring of the VA concentration in the melt-state EVA.

Algorithms↗

PubMed2005

Modelling the treated course of schizophrenia: development of a discrete event simulation model.

In schizophrenia, modelling techniques may be needed to estimate the long-term costs and effects of new interventions. However, it seems that a simple direct link between symptoms and costs does not exist. Decisions about whether a patient will be hospitalized or admitted to a different healthcare setting are based not only on symptoms but also on social and environmental factors. This paper describes the development of a model to assess the dependencies between a broad range of parameters in the treatment of schizophrenia. In particular, the model attempts to incorporate social and environmental factors into the decision-making process for the prescription of new drugs to patients. The model was used to analyse the potential benefits of improving compliance with medication by 20% in patients in the UK. A discrete event simulation (DES) model was developed, to describe a cohort of schizophrenia patients with multiple psychotic episodes. The model takes into account the patient's sex, disease severity, potential risk of harm to self and society, and social and environmental factors. Other variables that change over time include the number of psychiatric consultations, the presence of psychotic episodes, symptoms, treatments, compliance, side-effects, the lack of ability to take care of him/herself, care setting and risk of harm. Outcomes are costs, psychotic episodes and symptoms. Univariate and multivariate sensitivity analyses were performed. Direct medical costs were considered (year of costing 2002), applying a 6.0% discount rate for costs and a 1.5% discount rate for outcome. The timeframe of the model is 5 years. When 50% of the decisions about the patient care setting are based on symptoms, a 20% increase in compliance was estimated to save 16,147 pounds and to avoid 0.55 psychotic episodes per patient over 5 years. Sensitivity analysis showed that the costs savings associated with increased compliance are robust over a range of variations in parameters. DES offers a flexible structure for modelling a disease, taking into account how a patient's history affects the course of the disease over time. This approach is particularly pertinent to schizophrenia, in which treatment decisions are complex. The model shows that better compliance increases the time between relapses, decreases the symptom score, and reduces the requirement for treatment in an intensive patient care setting, leading to cost savings. The extent of the cost savings depends on the relative importance of symptoms and of social and environmental factors in these decisions.

Adult↗

PubMed2006

Integrated modelling for river basin management: the influence of temporal and spatial scale in economic models of water allocation.

The increasing use of integrated optimization or simulation models to guide river basin management has placed greater attention on the roles that the temporal and spatial scale of each model play in determining a model's suitability and effectiveness. This is especially the case in "economic" models that incorporate monetary incentives and the optimizing behaviour of economic agents to address decisions about the sources and levels of consumptive and non-consumptive water usage within the basin. With respect to spatial scale, models that aggregate behaviour over entire river basins may prove useful for examining inter-sectoral allocations of water, but are unlikely to provide useful information about how these water allocations influence-and are influenced by-choices of crops or of technologies in irrigation, for example. With respect to temporal scale, very short-run models can illustrate options for water management within an irrigation season should unforeseen water surpluses or deficits arise. Conversely, long-run models can allow adjustment time for investments in machinery, infrastructure and changes in land uses and cropping patterns. The basin management alternatives and choices generated by models on each scale are likely to vary considerably. The paper provides specific illustrative examples from recent models of Alberta's Bow River Basin.

Alberta↗

PubMedDate not supplied

Application of multiple modelling to hyperthermia estimation: reducing the effects of model mismatch.

Multiple model estimation is a viable technique for dealing with the spatial perfusion model mismatch associated with hyperthermia dosimetry. Using multiple models, spatial discrimination can be obtained without increasing the number of unknown perfusion zones. Two multiple model estimators based on the extended Kalman filter (EKF) are designed and compared with two EKFs based on single models having greater perfusion zone segmentation. Results given here indicate that multiple modelling is advantageous when the number of thermal sensors is insufficient for convergence of single model estimators having greater perfusion zone segmentation. In situations where sufficient measured outputs exist for greater unknown perfusion parameter estimation, the multiple model estimators and the single model estimators yield equivalent results.

Body Temperature↗

PubMed2005

Alzheimer's disease and models of computation: imaging, classification, and neural models.

Prediction or early-stage diagnosis of Alzheimer's disease (AD) requires a comprehensive understanding of the underlying mechanisms of the disease and its progression. Researchers in this area have approached the problem from multiple directions by attempting to develop (a) neurological (neurobiological and neurochemical) models, (b) analytical models for anatomical and functional brain images, (c) analytical feature extraction models for electroencephalograms (EEGs), (d) classification models for positive identification of AD, and (e) neural models of memory and memory impairment in AD. This article presents a state-of-the-art review of research performed on computational modeling of AD and its markers. The review covers the following approaches: computer imaging, classification models, connectionist neural models, and biophysical neural models. It is concluded that a mixture of markers and a combination of novel computational techniques such as neural computing, chaos theory, and wavelets can increase the accuracy of algorithms for automated detection and diagnosis of AD.

Alzheimer Disease↗

PubMed1999

Generating correlation matrices with model error for simulation studies in factor analysis: a combination of the Tucker-Koopman-Linn model and Wijsman's algorithm.

Most simulation studies in factor analysis follow a process of constructing population correlation matrices from the common-factor model and generating sample correlation matrices from the population matrices. In the common-factor model, the population correlation matrix is perfectly fit by the model's containing common and unique factors. However, since no mathematical model accounts exactly for the real-world phenomena that it is intended to represent, the Tucker-Koopman-Linn model (1969) is more realistic for generating correlation matrices than the conventional common-factor model because the former incorporates model error. In this paper, a procedure for generating population and sample correlation matrices with model error by combining the Tucker-Koopman-Linn model and Wijsman's algorithm (1959) is presented. The SAS/IML program for generating correlation matrices is described, and an example is also provided.

Algorithms↗

PubMed1999

On the dangers of averaging across observers when comparing decision bound models and generalized context models of categorization.

Averaging across observers is common in psychological research. Often, averaging reduces the measurement error and, thus, does not affect the inference drawn about the behavior of individuals. However, in other situations, averaging alters the structure of the data qualitatively, leading to an incorrect inference about the behavior of individuals. In this research, the influence of averaging across observers on the fits of decision bound models (Ashby, 1992a) and generalized context models (GCM; Nosofsky, 1986) was investigated through Monte Carlo simulation of a variety of categorization conditions, perceptual representations, and individual difference assumptions and in an experiment. The results suggest that (1) averaging has little effect when the GCM is the correct model, (2) averaging often improves the fit of the GCM and worsens the fit of the decision bound model when the decision bound model is the correct model, (3) the GCM is quite flexible and, under many conditions, can mimic the predictions of the decision bound model, whereas the decision bound model is generally unable to mimic the predictions of the GCM, (4) the validity of the decision bound model's perceptual representation assumption can have a large effect on the inference drawn about the form of the decision bound, and (5) the experiment supported the claim that averaging improves the fit of the GCM. These results underscore the importance of performing single-observer analysis if one is interested in understanding the categorization performance of individuals.

Bias↗

PubMed1999

Experimental models of Parkinson's disease: insights from many models.

Toxin-induced and genetic experimental models have been invaluable in investigating idiopathic Parkinson's disease (PD). The neurotoxins--reserpine, 6-hydroxydopamine (6-OHDA), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), and methamphetamine--have been used to develop parkinsonian models in a wide variety of species. Both 6-OHDA and MPTP can replicate the neurochemical, morphologic, and behavioral changes seen in human disease. The unilateral 6-OHDA rat model is an excellent model for testing and determining modes of action of new pharmacologic compounds. The nonhuman primate MPTP-induced parkinsonian model has behavioral features that best approximate idiopathic PD. These induced and genetic models have been used to study the pathophysiology of the degenerating nigrostriatal system and to evaluate novel therapeutic strategies. Important differences within these models provide insights into various aspects of the dopaminergic phenotype and its role as a target in disease. These models provide an avenue to evaluate many anti-parkinsonian compounds, such as levodopa, which was first evaluated in an animal model and is the gold standard of parkinsonian treatment today.

1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine↗

PubMed2003

[Department of a finite element model of the Head Model of HYBRID III Dummy with the Human Mandible].

OBJECTIVE: To analyze the biomechanics of impact injury in the condition of the simulate impact on mandible. METHODS: A finite element model of human mandible was developed from the CT scan images by the technologies of three-dimensional reconstruction, image processing and meshing. The mandible model was connected to a modified head model of HYBRID III dummy with joint according to the anatomic structure and mechanical characteristics of the temporomandibular joint. RESULTS: A finite element model of human head with true anatomic structure mandible has been developed. This model has been validated with the cadaver test results. The higher stress was showed in the condyle rejoins and coracoid in the model when mandible was in impact simulation. CONCLUSIONS: This model can be used to research the mechanism of craniofacial blunt-impact injury and assess the injury severity. The model of HYBRID III dummy with the human mandible was helpful for the boundary design of mandibular model in the impact simulations.

Biomechanical Phenomena↗

PubMed1985

Stochastic model of human granulocyte-macrophage progenitor cell proliferation and differentiation. I. Setting up the model.

A mathematical model is constructed for the proliferation and differentiation of granulocyte-macrophage progenitor cells in response to the specific proliferation/differentiation stimulus granulocyte-macrophage colony stimulating activity (gm-CSA). The major objective of this model was to test an earlier conceptual model in which we proposed that clone size potential and sensitivity to gm-CSA are functional properties of granulocyte-macrophage progenitor cells, properties that gradually change as the cells differentiate down the granulocyte-monocyte pathway. Another aim was to provide a tool for further analysis of the regulation of granulopoiesis and granulocyte-macrophage progenitor cell proliferation and differentiation. To formulate and then test the mathematical model, available experimental data were divided, one part being used to construct the model, and the other, the results of different and independent experiments, being used for model validation. This report describes the mathematical model and the estimation of model parameters using in vitro experimental results on the relationship between clone number and gm-CSA concentration and on the clone size distributions obtained under conditions of maximal stimulation by gm-CSA. The accompanying article shows how the model was then tested using data from three other types of experiment.

Cell Differentiation↗

PubMed1996

Risk modeling in acute renal failure requiring dialysis: the introduction of a new model.

Predicting patient outcome in acute renal failure has become increasingly important as technology advances and ethical questions arise concerning life supporting therapies. We propose a new model which uses mortality as an endpoint and may be applied to the acute renal failure patient in the ICU setting who requires dialysis. This model is based on our ICU acute renal failure registry and has been prospectively validated for our institution. Our registry for the purposes of developing this model consists of data from 512 ICU patients requiring acute dialysis from 1988 until 1992. The model was developed by testing a variety of potential risk factors for mortality in a univariate analysis (Student's t-test and Chi square), and those factors found to be significant (p < 0.05) were subsequently tested in a multivariate fashion. The factors found significant included male gender, respiratory failure requiring intubation, hematologic dysfunction (platelet count < 50,000, leukocyte count < 2,500, or bleeding diathesis), bilirubin < 2.0 mg/dl, the absence of surgery, serum creatinine on the first dialysis treatment day, an increasing number of failed organ systems, and an increased BUN from the time of admission. Weights are assigned to each variable based on the odds ratio, and a score is generated with a range of 0 to 20. The initial data for the registry demonstrates good fit using the Hosmer and Lemeshow goodness-of-fit table. The model is next validated in 88 patients from 1993 through February 1994, then prospectively tested in 35 additional patients using a standard data collection form, and the model continues to demonstrate good fit. Although this model has been prospectively validated at our institution, this model or any such predictive model should be used with caution if not independently validated at any institution which proposes its use.

Acute Kidney Injury↗

PubMed1997

Large scale protein modelling and model repository.

Knowledge-based molecular modelling of proteins has proven useful in many instances including the rational design of mutagenesis experiments, but it has been generally limited by the availability of expensive computer hardware and software. To overcome these limitations, we have developed the SWISS-MODEL server for automated knowledge-based protein modelling. The SWISS-MODEL server uses the Brookhaven Protein Data Bank as a source of structural information and automatically generates protein models for sequences which share significant similarities with at least one protein of known 3D-structure. We now use the software framework of the server to generate large collections of protein models. To store these models, we have established the SWISS-MODEL Repository, a new database for protein models generated by theoretical approaches. This repository is directly integrated with SWISS-PROT and other databases through the ExPASy World-Wide Web server (URL is http:(/)/www.expasy.ch).

Amino Acid Sequence↗

PubMed1998

Anticonvulsant drug effects in the direct cortical ramp-stimulation model in rats: comparison with conventional seizure models.

A modified cortical ramp stimulation (CRS) model has been developed allowing repeated determinations of seizure threshold at short time intervals in individual rats without inducing postictal threshold increases. Anticonvulsant potency of the standard antiepileptic drugs carbamazepine, phenytoin, phenobarbital, valproate, diazepam and ethosuximide in the CRS model was compared with respective drug potencies in two more traditional seizure models with transcorneal stimulus application, i.e., the minimal electroshock seizure threshold (minEST) and the maximal electroshock seizure threshold (maxEST). In the CRS model, two different types of threshold were determined, the threshold for localized seizures (TLS) and the threshold for generalized seizures (TGS). When screw electrodes were implanted over the primary motor cortex, TLS was characterized by unilateral forelimb clonus, tonic abduction of contralateral forelimb, and head adversion. When ramp-shaped stimulation was continued above the TLS current, bilateral clonic forelimb seizures with loss of posture developed, which was defined as TGS. In contrast to TLS, TGS could not be repeatedly determined at short time intervals because of postictal threshold increase. TLS was dose-dependently increased by carbamazepine, phenobarbital, valproate and diazepam, although phenytoin showed a truncated dose-response, and ethosuximide was ineffective. In comparison to TLS, drug-induced increases in TGS were more marked. All drugs dose-dependently increased minEST and, except ethosuximide, maxEST. For comparison of drug potencies, doses increasing seizure thresholds by 20 or 50% were calculated from dose-response curves. Respective comparisons showed marked differences in drug potencies between models, indicating that the CRS method presents a model of another, more pharmacoresistant seizure type than seizure types induced in traditional models, such as transcorneal electroshock. Based on the location of electrodes in the frontal neocortex, the characteristic seizure pattern, and the low pharmacological sensitivity of the seizures to standard antiepileptics, the modified CRS model most likely represents a new model of localization-related seizures occurring in frontal lobe epilepsy and may thus be used in the search for novel drugs with higher efficacy against this difficult-to-treat type of epilepsy.

Animals↗

PubMed1995

Simulation of micropopulations in epidemiology: tutorial. 4. Evaluations of simulation models. A series of tutorials illustrated by coronary heart disease models.

The first tutorial in this series (Ackerman E: Simulation of micropopulations in epidemiology: Tutorial 1. Simulation: an introduction, Int J Biomed Comput, 36 (1994) 229-238) introduces the general approach of simulation of micropopulation models of coronary disease using Monte Carlo techniques. The modeling process includes the selection of functional forms to represent the probability of state transfers. Alternative functional forms are described in the second tutorial (Ackerman E: Simulation of micropopulations in epidemiology: Tutorial 2. Analytic forms of event probabilities, Int J Biomed Comput, 37 (1994) 139-149). The third tutorial (Ackerman E: Simulation of micropopulations in epidemiology: Tutorial 3. Simulation model evaluation methods, Int J Biomed Comput, 37 (1994) 195-204) considers the actual risk factors used in the models and the compartmentalization of the population. However, that tutorial emphasizes the methods of evaluating different models of coronary heart disease. In the current tutorial, the evaluation methods are applied to the models previously introduced. Essential to this is the concept of degrees of freedom. Limitations of that concept are discussed with specific reference to models of coronary heart disease. The estimation of optimal values for the risk coefficients is considered further. The various tests of appropriateness are applied to specific models and the role of sensitivity analysis is further illustrated. Discussions of intervention strategies and their simulation is deferred to the following tutorial.

Adult↗

PubMedDate not supplied