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PubMed1995

Three-dimensional anatomical model-based segmentation of MR brain images through Principal Axes Registration.

Model-based segmentation and analysis of brain images depends on anatomical knowledge which may be derived from conventional atlases. Classical anatomical atlases are based on the rigid spatial distribution provided by a single cadaver. Their use to segment internal anatomical brain structures in a high-resolution MR brain image does not provide any knowledge about the subject variability, and therefore they are not very efficient in analysis. We present a method to develop three-dimensional computerized composite models of brain structures to build a computerized anatomical atlas. The composite models are developed using the real MR brain images of human subjects which are registered through the Principal Axes Transformation. The composite models provide probabilistic spatial distributions, which represent the variability of brain structures and can be easily updated for additional subjects. We demonstrate the use of such a composite model of ventricular structure to help segmentation of the ventricles and Cerebrospinal Fluid (CSF) of MR brain images. In this paper, a composite model of ventricles using a set of 22 human subjects is developed and used in a model-based segmentation of ventricles, sulci, and white matter lesions. To illustrate the clinical usefulness, automatic volumetric measurements on ventricular size and cortical atrophy for an additional eight alcoholics and 10 normal subjects were made. The volumetric quantitative results indicated regional brain atrophy in chronic alcoholics.

Alcoholism↗
PubMed1997

Efficient temporal probabilistic reasoning via context-sensitive model construction.

We present a language for representing context-sensitive temporal probabilistic knowledge. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a sound and complete algorithm for computing posterior probabilities of temporal queries, as well as an efficient implementation of the algorithm. Throughout we illustrate the approach with the problem of reasoning about the effects of medications and interventions on the state of a patient in cardiac arrest. We empirically evaluate the efficiency of our system by comparing its inference times on problems in this domain with those of standard Bayesian network representations of the problems.

Algorithms↗
PubMed1991

Causes versus enabling conditions.

People distinguish between a cause (e.g., a malfunctioning component in an airplane causing it to crash) and a condition (e.g., gravity) that merely enables the cause to yield its effect. This distinction cannot be explained by accounts of reasoning formulated purely in terms of necessity and sufficiency, because causes and enabling conditions hold the same logical relationship to the effect in those terms. Proposals to account for this apparent deviation from accounts based on necessity and sufficiency may be classified into three types. One approach explains the distinction in terms of an inferential rule based on the normality of the potential causal factors. Another approach explains the distinction in terms of the conversational principle of being informative to the inquirer given assumptions about his or her state of knowledge. The present paper evaluates variants of these two approaches, and presents our probabilistic contrast model, which takes a third approach. This approach explains the distinction between causes and enabling conditions by the covariation between potential causes and the effect in question over a focal set--a set of events implied by the context. Covariation is defined probabilistically, with necessity and sufficiency as extreme cases of the components defining contrasts. We report two experiments testing our model against variants of the normality and conversational views.

Adult↗
PubMed1977

Medium-dependent variation of deoxyribonucleic acid segregation in Escherichia coli.

The degree to which deoxyribonucleic acid segregates nonrandomly has been investigated for Escherichia coli B/r growing in different media. The degree of nonrandom segregation observed is dependent on the medium, with segregation becoming less random as the growth rate decreases. This indicates that there must be some varying probabilistic component to the segregation process. A probabilistic modification of the Pierucci-Zuchowski model is proposed as well as a probabilistic model, in which it is proposed that deoxyribonucleic acid strands segregate, with a probability greater than 0.5, in the same direction (toward the same pole) as at the previous cell division.

Cell Division↗
PubMed2004

A Matlab function to estimate choice model parameters from paired-comparison data.

Tversky (1972) has proposed a family of models for paired-comparison data that generalize the Bradley-Terry-Luce (BTL) model and can, therefore, apply to a diversity of situations in which the BTL model is doomed to fail. In this article, we present a Matlab function that makes it easy to specify any of these general models (EBA, Pretree, or BTL) and to estimate their parameters. The program eliminates the time-consuming task of constructing the likelihood function by hand for every single model. The usage of the program is illustrated by several examples. Features of the algorithm are outlined. The purpose of this article is to facilitate the use of probabilistic choice models in the analysis of data resulting from paired comparisons.

Algorithms↗
PubMed2006

Resolving neighborhood scale in air toxics modeling: a case study in Wilmington, CA.

Air quality modeling is useful for characterizing exposures to air pollutants. Whereas models typically provide results on regional scales, new concerns regarding the potential for differential exposures among racial/ethnic populations and income strata within communities are driving the need for increasingly refined modeling approaches. These approaches need to be capable of resolving concentrations on the scale of tens of meters, across modeling domains 10-100 km2 in size. One approach for refined air quality modeling is to combine Gaussian and regional photochemical grid models. In this paper, the authors demonstrate this approach on a case study of Wilmington, CA, focused on diesel exhaust particulate matter. Modeling results suggest that pollutant concentrations in the vicinity of emission sources are elevated, and, therefore, an understanding of local emission sources is necessary to generate credible modeling results. A probabilistic evaluation of the Gaussian model application indicated that spatial allocation, emission rates, and meteorological data are important contributors to input and parameter uncertainty in the model results. This uncertainty can be substantially reduced through the collection and integration of site-specific information about the location of emission sources and the activity and emission rates of key sources affecting model concentrations.

Air Pollutants↗
PubMed1990

Receptor-mediated cell attachment and detachment kinetics. II. Experimental model studies with the radial-flow detachment assay.

Quantitative information regarding the kinetics of receptor-mediated cell adhesion to a ligand-coated surface are crucial for understanding the role of certain key parameters in many physiological and biotechnology-related processes. Here, we use the probabilistic attachment and detachment models developed in the preceding paper to interpret transient data from well-defined experiments. These data are obtained with a simple model cell system that consists of receptor-coated latex beads (prototype cells) and a Radial-Flow Detachment Assay (RFDA) using a ligand-coated glass disc. The receptors and ligands used in this work are complementary antibodies. The beads enable us to examine transient behavior with particles that possess fairly uniform properties that can be varied systematically, and the RFDA is designed for direct observation of adhesion to the ligand-coated glass surface over a range of shear stresses. Our experiments focus on the effects of surface shear stress, receptor density, and ligand density. These data provide a crucial test of the probabilistic framework. We show that these data can be explained with the probabilistic analyses, whereas they cannot be readily interpreted on the basis of a deterministic analysis. In addition, we examine transient data on cell adhesion reported from other assays, demonstrating the consistency of these data with the predictions of the probabilistic models.

Biophysical Phenomena↗
PubMed1992

Intrinsic quantal variability due to stochastic properties of receptor-transmitter interactions.

Synaptic events at the neuromuscular junction are integer multiples of a quantum, the postsynaptic response to transmitter released from one presynaptic vesicle. At central synapses where quanta are small, it has been suggested they are invariant due to occupation of all postsynaptic receptors, a concept neglecting inherent fluctuations in channel behavior. If this did occur, the quantal release model would not apply there and could not be used to localize sites of synaptic modification. Monte Carlo simulations of quanta include transmitter diffusion and interactions with postsynaptic receptors that are treated probabilistically. These models suggest that when there are few postsynaptic channels available at a synapse, their stochastic behavior produces significant intrinsic variance in response amplitude and kinetics, and saturation does not occur. These results were confirmed by analysis of inhibitory quanta in embryonic and adult Mauthner cells involving a small and large number of channels, respectively. The findings apply to excitatory synapses as well.

Animals↗
PubMed1999

Mixtures of probabilistic principal component analyzers.

Principal component analysis (PCA) is one of the most popular techniques for processing, compressing, and visualizing data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a combination of local linear PCA projections. However, conventional PCA does not correspond to a probability density, and so there is no unique way to combine PCA models. Therefore, previous attempts to formulate mixture models for PCA have been ad hoc to some extent. In this article, PCA is formulated within a maximum likelihood framework, based on a specific form of gaussian latent variable model. This leads to a well-defined mixture model for probabilistic principal component analyzers, whose parameters can be determined using an expectation-maximization algorithm. We discuss the advantages of this model in the context of clustering, density modeling, and local dimensionality reduction, and we demonstrate its application to image compression and handwritten digit recognition.

Algorithms↗
PubMed2006

Comprehensive decision-analytic model and Bayesian value-of-information analysis: pentoxifylline in the treatment of chronic venous leg ulcers.

OBJECTIVE: To conduct a Bayesian value-of-information analysis of the cost effectiveness of pentoxifylline (vs placebo) as an adjunct to compression for venous leg ulcers. METHODS: A probabilistic Markov model was developed to estimate mean clinical benefits and costs associated with oral pentoxifylline (400mg three times daily) and placebo. Clinical data were obtained from a systematic review and synthesised using Bayesian methods. The decision uncertainty associated with the adoption of pentoxifylline as well as the maximum value associated with further research were estimated before and after the completion of the largest 'definitive' treatment trial. Resource use was obtained from a UK national audit and unit costs applied (pounds, 2004 values). RESULTS: The prior and posterior analyses suggest that pentoxifylline is a dominant therapy versus placebo. In the prior analysis, patients in the pentoxifylline group healed an average of 8.28 weeks quicker than patients in the placebo group (95% credibility interval [CI] 1.89, 14.56), had a 0.02 gain in QALYs (95% CI -0.12, 0.17) and an average reduction in cost of 153.4 pounds (95% CI -53.11, 354.9). Estimates of the uncertainty surrounding the cost effectiveness of pentoxifylline and the value of perfect information in both analyses did not suggest further research was justified. In the prior analysis, for willingness-to-pay values of 0 pounds, 100 pounds and 500 pounds per QALY gained, the estimated values of perfect information were 128,200 pounds, 127,100 pounds and 126,700 pounds, respectively. Incorporation of the information from the largest randomised controlled trial on pentoxifylline did improve the estimate of the clinical effect associated with this drug; however, the variation was not large enough to reverse either the decision regarding the dominance of pentoxifylline or the maximum value associated with further research. CONCLUSION: Bayesian value-of-information analysis represents a valuable tool for healthcare decision making. Had the results from this analysis been available before the largest trial was funded, a more efficient allocation of research and development resources could have been made.

Administration, Oral↗
PubMed2006

Workshop overview: reassessment of the cancer risk of dichloromethane in humans.

The U.S. Environmental Protection Agency (U.S. EPA) classifies dichloromethane (DCM) as a "probable human carcinogen," based upon its risk assessment conducted in the late 1980s (http://www.epa.gov/iris/subst/0070.htm). Since that time, cancer risk-assessment practices have evolved, leading to improved scientifically based methods for estimating risk and for illuminating as well as reducing residual uncertainties. A new physiologically based pharmacokinetic (PBPK) model has been developed, using data from human volunteers exposed to low DCM levels, that provides new information on the human to human variability in DCM metabolism and elimination (L. M. Sweeney et al., 2004, Toxicol. Lett. 154, 201-216). This information, along with data from other published human studies, has been used to develop a new cancer risk estimation model utilizing probabilistic methodology similar to that employed recently by U.S. EPA for other chemicals (ENVIRON Health Sciences Institute, 2005, Development of population cancer risk estimates for environmental exposure to dichloromethane using a physiologically based pharmacokinetic model. Final Report to Eastman Kodak Company). This article summarizes the deliberations of a scientific peer-review panel convened on 3 and 4 May 2005 at the CIIT Centers for Health Research in Research Triangle Park, North Carolina, to review the "state of the science" for DCM and to critically evaluate the new information for its utility in assessing potential human cancer risks from DCM exposure. The panel (Melvin E Andersen, CIIT Centers for Health Research, Research Triangle Park, NC 27709; A. John Bailer, Miami University, Scripps Gerontology Center, Oxford, OH 45056; Kenneth S. Crump, ENVIRON Health Sciences Institute, Ruston, LA 71270; Clifford R. Elcombe, University of Dundee, Biomedical Research Centre, Dundee DD1 9SY, United Kingdom; Linda S. Erdreich, Exponent, 420 Lexington Avenue, Suite 1740, New York, NY 10170; Jeffery W. Fisher, University of Georgia, Department of Environmental Health Science, Athens, GA 30602; David Gaylor, Gaylor and Associates, LLC, Eureka Springs, AR 72631; F Peter Guengerich, Vanderbilt University, Department of Biochemistry, Nashville, TN 37232; Kenneth Mundt, ENVIRON Health Sciences Institute, Amherst, MA 01004; Lorenz R Rhomberg, Gradient Corporation, Cambridge, MA 021138; Charles Timchalk, Pacific Northwest National Laboratory, Richland, WA 99352), chaired by M.E.A., was composed of experts in xenobiotic metabolism and carcinogenic mechanisms, PBPK modeling, epidemiology, biostatistics, and quantitative risk assessment. Observers included representatives from U.S. EPA, CIIT, and Eastman Kodak Company (Kodak), as well as several consultants to Kodak. The workshop was organized and sponsored by Kodak, which employs DCM as a solvent in the production of imaging materials. Overall, the panel concluded that the new models for DCM risk assessment were scientifically and technically sound and represented an advance over those employed in past assessments.

Carcinogens↗
PubMed1989

Mathematical models and individualized outcome estimates in multiple sclerosis.

There is an urgent need for individualized outcome estimates in multiple sclerosis (MS). This is a prerequisite for selecting appropriate therapies in a disease the outcome of which may vary between malignant and benign forms. This question was addressed by using probabilistic mathematical models. The disease course is described by movements of the patient's condition through well-defined disease states. The data used are based upon 278 reports of definite and probable MS cases collected over a 20-year period (1957-1976) at l'Hôpital Neurologique de Lyon. A Markov model was first prepared. However, it only provided an overall presentation of the disease which was not very meaningful for the individual patient, and limited studies of potential prognostic variables to dichotomous variables and univariate analyses. A stochastic survival model was then elaborated. It was complex at the theoretical level but in practice, easy to use. Personalized prognosis of a patient could be ascertained by combining several single potential prognostic variables and using their actual value in the case of quantitative variables. Although these models still need to be refined and have to be validated from the present state of the patients and other patient cohorts, they provide for the first time a global description of disease course likelihood and prognosis. The survival model may be adapted to each patient.

Age Factors↗
PubMed2007

Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA.

Probabilistic sensitivity analysis (PSA) is required to account for uncertainty in cost-effectiveness calculations arising from health economic models. The simplest way to perform PSA in practice is by Monte Carlo methods, which involves running the model many times using randomly sampled values of the model inputs. However, this can be impractical when the economic model takes appreciable amounts of time to run. This situation arises, in particular, for patient-level simulation models (also known as micro-simulation or individual-level simulation models), where a single run of the model simulates the health care of many thousands of individual patients. The large number of patients required in each run to achieve accurate estimation of cost-effectiveness means that only a relatively small number of runs is possible. For this reason, it is often said that PSA is not practical for patient-level models. We develop a way to reduce the computational burden of Monte Carlo PSA for patient-level models, based on the algebra of analysis of variance. Methods are presented to estimate the mean and variance of the model output, with formulae for determining optimal sample sizes. The methods are simple to apply and will typically reduce the computational demand very substantially.

Analysis of Variance↗
PubMed2000

Impact of measurement uncertainty in chemical quantities on environmental prognosis by geochemical transport modelling.

In Germany, geochemical modelling takes a strong position in two aspects of broader public interest. The first aspect is the safety assessment of a nuclear waste repository, the second is remediation of uranium mining areas. In both aspects, the application of geochemical modelling is stipulated by authorities. This situation results from the possibility to model highly complex situations by computers. The increase in computing power experienced in recent times now offers techniques to assess the sensitivity of modelling results to uncertain input data both in the thermodynamic data base and the site-specific field data. Both aspects are investigated by using Monte Carlo methods in combination with non-parametric statistics. A probabilistic geochemical modelling of uranium mill tailings leaching is demonstrated by application of TReaC modelling code using a simplified site model.

Environmental Monitoring↗
PubMed2004

Probabilistic space-time video modeling via piecewise GMM.

In this paper, we describe a statistical video representation and modeling scheme. Video representation schemes are needed to segment a video stream into meaningful video-objects, useful for later indexing and retrieval applications. In the proposed methodology, unsupervised clustering via Gaussian mixture modeling extracts coherent space-time regions in feature space, and corresponding coherent segments (video-regions) in the video content. A key feature of the system is the analysis of video input as a single entity as opposed to a sequence of separate frames. Space and time are treated uniformly. The probabilistic space-time video representation scheme is extended to a piecewise GMM framework in which a succession of GMMs are extracted for the video sequence, instead of a single global model for the entire sequence. The piecewise GMM framework allows for the analysis of extended video sequences and the description of nonlinear, nonconvex motion patterns. The extracted space-time regions allow for the detection and recognition of video events. Results of segmenting video content into static versus dynamic video regions and video content editing are presented.

Algorithms↗
PubMed2003

Derivation of an ambient water quality criterion for mercury: taking account of site-specific conditions.

Mercury is considered to be a serious risk to wildlife. As a result, the Great Lakes Water Quality Initiative and others have developed ambient water quality criteria (AWQC) for the protection of wildlife. These AWQC have been controversial, however, because the AWQC were single values that did not account for site-specific conditions, derivation of the AWQC relied on a single no-observed-adverse-effect level, and the AWQC had an unknown level of conservatism because of reliance on both average and conservative assumptions and uncertainty factors. Rather than develop a single-value AWQC for total mercury, we derived an AWQC model that explicitly incorporates factors controlling bioavailability, methylation rates, and bioaccumulation in the aquatic environment (e.g., pH, dissolved organic carbon). To derive our AWQC model, field data were collected from 31 lakes in Ontario and an additional 10 lakes in Nova Scotia (North America). In the field study, levels of total and methylmercury in water and fish as well as levels of key water quality variables were determined. We conducted multiple-regression analysis to derive a model that estimates mercury levels in prey of mink. Mink are very sensitive to mercury exposure. An independent dataset consisting of 51 water bodies in the United States was then used to confirm the validity and robustness of the AWQC model. Next, we combined the results of chronic-feeding studies with similar protocols and endpoints in a meta-analysis to derive a dose-response curve for mink exposed to mercury in the diet. In the final step, we used a probabilistic risk model to estimate the concentrations of methylmercury in water that would lead to levels in fish sufficient for a 10% probability of exceeding the lethal dose affecting 5% of the mink population. The result is an AWQC equation for mercury for the protection of wildlife that can be used with a variety of site-specific conditions.

Animals↗
PubMed1994

Probabilistic laser safety: ocular damage models for Q-switched neodymium and ruby lasers.

International standards for the protection of the eyes from the hazards of laser radiation define a nominal ocular hazard distance as a distance beyond which an individual would not be expected to suffer any adverse biological effects. The nominal ocular hazard distance, and any associated hazard zones, are calculated using a deterministic technique. This technique does not consider the likelihood that an eye will be irradiated, or the probability that if any eye is exposed, then some level of ocular damage will result. An alternative method of hazard assessment, without compromising safety, is to adopt a probabilistic approach in which the ultimate safety criterion is that the expectation of someone receiving ocular damage must be less than some acceptable risk level. An important element in this assessment is an ocular damage model, which predicts the probability with which a laser exposure will cause permanent eye damage. This paper describes a rationale for the use of the minimum ophthalmoscopically visible lesion as a threshold criterion for the development of ocular damage models. A brief review of existing ocular damage threshold data for Q-switched neodymium:YAG and ruby lasers is presented, and ocular damage models for these lasers systems are derived.

Eye Injuries↗
PubMed2004

Maximum likelihood estimation of oncogenetic tree models.

We present a new approach for modelling the dependences between genetic changes in human tumours. In solid tumours, data on genetic alterations are usually only available at a single point in time, allowing no direct insight into the sequential order of genetic events. In our approach, genetic tumour development and progression is assumed to follow a probabilistic tree model. We show how maximum likelihood estimation can be used to reconstruct a tree model for the dependences between genetic alterations in a given tumour type. We illustrate the use of the proposed method by applying it to cytogenetic data from 173 cases of clear cell renal cell carcinoma, arriving at a model for the karyotypic evolution of this tumour.

Carcinoma, Renal Cell↗