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At least 1,207 records · Page 67Linked to original sources
PubMed2004

Stochastic multitype epidemics in a community of households: estimation and form of optimal vaccination schemes.

This paper treats a stochastic model for an SIR (susceptible-->infective-->removed) multitype household epidemic. The community is assumed to be closed, individuals are of different types and each individual belongs to a household. Previously obtained probabilistic and inferential results for the model are used to derive the optimal vaccination scheme. By this is meant the scheme that vaccinates the fewest among all vaccination schemes that reduce the threshold parameter below 1. This is done for the situation where all model parameters are known and also for the case where parameters are estimated from an outbreak in the community prior to vaccination. It is shown that the algorithm which chooses vaccines sequentially, at each step selecting the individual which reduces the threshold parameter the most, is not in general an optimal scheme. As a consequence, explicit characterisation of the optimal scheme is only possible in certain special cases. Two different types of vaccine responses, leaky and all-or-nothing, are considered and compared for the problems mentioned above. The methods are illustrated with some numerical examples.

Algorithms↗
PubMed2006

A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays.

BACKGROUND: Uncertainty often affects molecular biology experiments and data for different reasons. Heterogeneity of gene or protein expression within the same tumor tissue is an example of biological uncertainty which should be taken into account when molecular markers are used in decision making. Tissue Microarray (TMA) experiments allow for large scale profiling of tissue biopsies, investigating protein patterns characterizing specific disease states. TMA studies deal with multiple sampling of the same patient, and therefore with multiple measurements of same protein target, to account for possible biological heterogeneity. The aim of this paper is to provide and validate a classification model taking into consideration the uncertainty associated with measuring replicate samples. RESULTS: We propose an extension of the well-known Naïve Bayes classifier, which accounts for biological heterogeneity in a probabilistic framework, relying on Bayesian hierarchical models. The model, which can be efficiently learned from the training dataset, exploits a closed-form of classification equation, thus providing no additional computational cost with respect to the standard Naïve Bayes classifier. We validated the approach on several simulated datasets comparing its performances with the Naïve Bayes classifier. Moreover, we demonstrated that explicitly dealing with heterogeneity can improve classification accuracy on a TMA prostate cancer dataset. CONCLUSION: The proposed Hierarchical Naïve Bayes classifier can be conveniently applied in problems where within sample heterogeneity must be taken into account, such as TMA experiments and biological contexts where several measurements (replicates) are available for the same biological sample. The performance of the new approach is better than the standard Naïve Bayes model, in particular when the within sample heterogeneity is different in the different classes.

Algorithms↗
PubMed1978

A review of parameter values used to assess the transport of plutonium, uranium, and thorium in terrestrial food chains.

A general methodology of predicting the food chain transport of atmospherically deposited radionuclides is reviewed with an emphasis on variation in parameter values important for realistic behavioral characterization of environmental releases of plutonium, uranium, and thorium. Parameters important to generic simulations of food chain transport, given a known constant deposition onto vegetation, include: fractional interception of particulates by vegetation, vegetation density, effective half-life of contamination on vegetation, soil-to-plant transfer factors, consumption rates by cattle and man, and transfer of nuclides from forage to meat and from forage to milk. Variation in these parameters, which has been encountered in field studies, is summarized. A partial reduction in the variation of predicted concentrations of actinides in foods can be accomplished by more accurately determining critical parameter values like fractional interception of deposition by vegetation, vegetation biomass, and the effective half-life of contamination on vegetation. Field research describing the site dependency and time dependency of probability density functions for model parameter values is needed to make probabilistic predictions concerning Pu, U, and Th transport in food chains and to reduce the uncertainty associated with model predictions and generic assessments of environmental impact.

Biological Transport↗
PubMed1999

Estimation of tumor stage and lymph node status in patients with colorectal adenocarcinoma using probabilistic neural networks and logistic regression.

Staging colorectal adenocarcinoma on the basis of biopsy specimens could identify patients who might benefit from neoadjuvant therapy without undergoing resection first. In this study, we evaluated the ability of artificial neural networks with genetic algorithms and multivariate logistic regression to predict the stage of 99 patients with primary colorectal adenocarcinoma by analyzing age, tumor grade, and immunoreactivity to p53 and bcl-2 with use of endoscopically obtained biopsy specimens. We correlated results with regional lymph node status and tumor stage, identified in subsequent colectomy specimens. bcl-2 and p53 protein expression were demonstrated by immunohistochemical methods, using formalin-fixed, paraffin-embedded biopsy tissues. Tumor grade was evaluated in hematoxylinand eosin-stained sections. Patients were divided into training (n = 75) and testing cases (n = 24). Several probabilistic neural networks with genetic algorithm models were trained, using the four prognostic features as input neurons and regional lymph node status or stage as output neurons. Data were analyzed with univariate statistics and multivariate logistic regression. The cases were divided into training (n = 40) and testing (n = 59). The best two models classified correctly the lymph node status of 20 of 24 test patients (specificity, 80%; sensitivity, 85%; positive predictive value, 86%) and the tumor stage of 21 of 24 test patients (specificity, 82%; sensitivity, 92%; positive predictive value, 85%), respectively. Tumor grade and p53 protein were statistically significant (P < .05) by analysis of variance for lymph node status and tumor stage. Logistic regression models with these two independent variables correctly estimated the probability of lymph node metastases in 44 of 59 test cases and the tumor stage of 43 of 59 test cases, respectively. Results indicated the usefulness of probabilistic neural networks in the population studied, but the findings should be validated with large groups of patients.

Adenocarcinoma↗
PubMed2005

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory.

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states-perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of "excess" zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to "excess" zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed-and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.

Accidents, Traffic↗
PubMed2006

Microheterogeneity of singlet oxygen distributions in irradiated humic acid solutions.

Singlet oxygen (1O2) is a highly reactive species formed through solar irradiation of organic matter in environmental waters. Implicated in a range of reactions, it has proven difficult to quantify its spatial distribution in natural waters. We assessed the microheterogeneous distribution of 1O2 in irradiated solutions containing chromophoric dissolved organic matter (CDOM) by using molecular probes of varying hydrophobicity. The apparent 1O2 concentrations ([1O2]app), measured by recently developed hydrophobic trap-and-trigger chemiluminescent probe molecules, were orders of magnitude higher than those measured by the conventional hydrophilic probe molecule furfuryl alcohol. The differential [1O2]app values measured by these probes reflect a steep concentration gradient between the CDOM macromolecules and the aqueous phase. A detailed kinetic model based on the data predicts probabilistic 1O2 distributions under different solvent conditions.

Humic Substances↗
PubMed2006

An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination.

An integrated fuzzy-stochastic risk assessment (IFSRA) approach was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with site conditions, environmental guidelines, and health impact criteria. The contaminant concentrations in groundwater predicted from a numerical model were associated with probabilistic uncertainties due to the randomness in modeling input parameters, while the consequences of contaminant concentrations violating relevant environmental quality guidelines and health evaluation criteria were linked with fuzzy uncertainties. The contaminant of interest in this study was xylene. The environmental quality guideline was divided into three different strictness categories: "loose", "medium" and "strict". The environmental-guideline-based risk (ER) and health risk (HR) due to xylene ingestion were systematically examined to obtain the general risk levels through a fuzzy rule base. The ER and HR risk levels were divided into five categories of "low", "low-to-medium", "medium", "medium-to-high" and "high", respectively. The general risk levels included six categories ranging from "low" to "very high". The fuzzy membership functions of the related fuzzy events and the fuzzy rule base were established based on a questionnaire survey. Thus the IFSRA integrated fuzzy logic, expert involvement, and stochastic simulation within a general framework. The robustness of the modeling processes was enhanced through the effective reflection of the two types of uncertainties as compared with the conventional risk assessment approaches. The developed IFSRA was applied to a petroleum-contaminated groundwater system in western Canada. Three scenarios with different environmental quality guidelines were analyzed, and reasonable results were obtained. The risk assessment approach developed in this study offers a unique tool for systematically quantifying various uncertainties in contaminated site management, and it also provides more realistic support for remediation-related decisions.

Fuzzy Logic↗
PubMed1996

Modelling ECG signals with hidden Markov models.

In this paper, we have studied the use of continuous probability density function hidden Markov models for the ECG signal analysis problem. Our previous work has focused on syntactic pattern recognition methods in signal processing. Hidden Markov model is basically a non-deterministic probabilistic finite state machine, which can be constructed inductively. It has been widely used in speech recognition and DNA modelling. We have found that hidden Markov models are very suitable for ECG recognition and analysis problems and that they are able to model accurately segmented ECG signals.

Artificial Intelligence↗
PubMed2006

The discovery of transcriptional modules by a two-stage matrix decomposition approach.

MOTIVATION: We address the problem of identifying gene transcriptional modules from gene expression data by proposing a new approach. Genes mostly interact with each other to form transcriptional modules for context-specific cellular activities or functions. Unraveling such transcriptional modules is important for understanding biological network, deciphering regulatory mechanisms and identifying biomarkers. METHOD: The proposed algorithm is based on two-stage matrix decomposition. We first model microarray data as non-linear mixtures and adopt the non-linear independent component analysis to reduce the non-linear distortion and separate the data into independent latent components. We then apply the probabilistic sparse matrix decomposition approach to model the 'hidden' expression profiles of genes across the independent latent components as linear weighted combinations of a small number of transcriptional regulator profiles. Finally, we propose a general scheme for identifying gene modules from the outcomes of the matrix decomposition. RESULTS: The proposed algorithm partitions genes into non-mutually exclusive transcriptional modules, independent from expression profile similarity measurement. The modules contain genes with not only similar but different expression patterns, and show the highest enrichment of biological functions in comparison with those by other methods. The usefulness of the algorithm was validated by a yeast microarray data analysis. AVAILABILITY: The software is available upon request to the authors.

Algorithms↗
PubMed2004

Standardized approach for developing probabilistic exposure factor distributions.

The effectiveness of a probabilistic risk assessment (PRA) depends on the quality and relevance of the output from exposure and risk models, which, in turn, depends on the critical inputs to the assessment. These critical inputs are often in the form of probabilistic exposure factor distributions that are derived for the given risk scenario. Deriving probabilistic distributions for model inputs can be time consuming and subjective. The absence of a standard approach for developing these distributions can result in PRAs that are inconsistent and difficult to review by regulatory agencies. We present an approach that reduces subjectivity in the distribution development process without limiting the flexibility needed to prepare relevant PRAs. The approach requires two steps. First, we analyze data pooled at a population scale to (i) identify the most robust demographic descriptors within the population for a given exposure factor, (ii) partition the data into subsets based on these variables, and (iii) construct archetypal distributions for each subpopulation. Second, we sample from these archetypal distributions according to site- or scenario-specific conditions to simulate exposure factor values and use these values to construct the scenario-specific input distribution. The archetypal distributions developed through Step 1 provide a consistent basis for developing scenario-specific distributions so risk assessors will not have to repeatedly collect and analyze raw data for each new assessment. We demonstrate the approach for two commonly used exposure factors--body weight (BW) and exposure duration (ED)--using data that are representative of the U.S. population. For these factors we provide a first set of subpopulation-based archetypal distributions and demonstrate methods for using these distributions to construct relevant scenario-specific probabilistic exposure factor distributions.

Adolescent↗
PubMed2003

How do people learn to allocate resources? Comparing two learning theories.

How do people learn to allocate resources? To answer this question, 2 major learning models are compared, each incorporating different learning principles. One is a global search model, which assumes that allocations are made probabilistically on the basis of expectations formed through the entire history of past decisions. The 2nd is a local adaptation model, which assumes that allocations are made by comparing the present decision with the most successful decision up to that point, ignoring all other past decisions. In 2 studies, participants repeatedly allocated a capital resource to 3 financial assets. Substantial learning effects occurred, although the optimal allocation was often not found. From the calibrated models of Study 1, a priori predictions were derived and tested in Study 2. This generalization test shows that the local adaptation model provides a better account of learning in resource allocations than the global search model.

Adult↗
PubMed2003

A causal-model theory of conceptual representation and categorization.

This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches.

Causality↗
PubMed1998

Modelling medical decisions in DynaMoL: a new general framework of dynamic decision analysis.

Dynamic decision analysis concerns decision problems in which both time and uncertainty are explicitly considered. We present a new dynamic decision analysis framework, called DynamoL, that supports graphical presentation of the decision factors in multiple perspectives. To alleviate the difficulty in assessing conditional probabilities over time in dynamic decision models, DynaMoL incorporates a Bayesian learning system to automatically learn the probabilistic parameters from large medical databases. We describe the DynaMoL modeling and learning architecture through a medical decision problem on the optimal follow-up schedule for patients after curative colorectal cancer surgery. We also show that the modeling experience and results indicate practical promise for the framework.

Artificial Intelligence↗
PubMed2004

Probabilistic cross-link analysis and experiment planning for high-throughput elucidation of protein structure.

Emerging high-throughput techniques for the characterization of protein and protein-complex structures yield noisy data with sparse information content, placing a significant burden on computation to properly interpret the experimental data. One such technique uses cross-linking (chemical or by cysteine oxidation) to confirm or select among proposed structural models (e.g., from fold recognition, ab initio prediction, or docking) by testing the consistency between cross-linking data and model geometry. This paper develops a probabilistic framework for analyzing the information content in cross-linking experiments, accounting for anticipated experimental error. This framework supports a mechanism for planning experiments to optimize the information gained. We evaluate potential experiment plans using explicit trade-offs among key properties of practical importance: discriminability, coverage, balance, ambiguity, and cost. We devise a greedy algorithm that considers those properties and, from a large number of combinatorial possibilities, rapidly selects sets of experiments expected to discriminate pairs of models efficiently. In an application to residue-specific chemical cross-linking, we demonstrate the ability of our approach to plan experiments effectively involving combinations of cross-linkers and introduced mutations. We also describe an experiment plan for the bacteriophage lambda Tfa chaperone protein in which we plan dicysteine mutants for discriminating threading models by disulfide formation. Preliminary results from a subset of the planned experiments are consistent and demonstrate the practicality of planning. Our methods provide the experimenter with a valuable tool (available from the authors) for understanding and optimizing cross-linking experiments.

Algorithms↗
PubMed1993

Comparison of three methods of estimating odds ratios from a job exposure matrix in occupational case-control studies.

A job exposure matrix consists of jobs on one axis and substances on the other, with the matrix elements describing the likelihood of an individual's exposure to a substance in a given job. This can be used in case-control studies to infer exposures of subjects whose jobs are known. The simplest form of job exposure matrix contains binary entries, but it is also possible to envisage continuous variables describing the probability of exposure in the job (probabilistic matrix). In such a case, the user has various options for transforming and analyzing the data, including the following: 1) transform to binary variables and analyze as conventional binary exposure variables; 2) leave as continuous variables and analyze using logistic regression; 3) leave as continuous variables and analyze using a linear model. Simulations were carried out to compare the ability of the three methods to estimate odds ratios under 36 experimental conditions. The linear model produced unbiased estimates, the logistic model produced somewhat biased estimates at high odds ratios, and the transformation to a binary variable produced systematically low estimates in most experimental circumstances. With the linear and logistic models, the odds ratio estimators had similar precision when the bias of the latter was not too great. The authors conclude that the linear model permits optimal use of a probabilistic matrix in an epidemiologic study and hope that these results will encourage the development of job exposure matrices containing probabilities rather than dichotomies.

Case-Control Studies↗
PubMed2002

Exposure chamber measurements of mass transfer and partitioning at the plant/air interface.

Dynamic measures of air and vegetation concentrations in an exposure chamber and a two-box mass balance model are used to quantify factors that control the rate and extent of chemical partitioning between vegetation and the atmosphere. A continuous stirred flow-through exposure chamber was used to investigate the gas-phase transfer of pollutants between air and plants. A probabilistic two-compartment mass balance model of plant/air exchange within the exposure chamber was developed and used with measured concentrations from the chamber to simultaneously evaluate partitioning (Kpa), overall mass transfer across the plant/air interface (Upa), and loss rates in the atmosphere (Ra) and aboveground vegetation (Rp). The approach is demonstrated using mature Capsicum annuum (bell pepper) plants exposed to phenanthrene (PH), anthracene (AN), fluoranthene (FL) and pyrene (PY). Measured values of log Kpa (V[air]/V[fresh plant]) were 5.7, 5.7, 6.0, and 6.2 for PH, AN, FL, and PY, respectively. Values of Upa (m d(-1)) under the conditions of this study ranged from 42 for PH to 119 for FL. After correcting for wall effects, the estimated reaction half-lives in air were 3, 9, and 25 h for AN, FL and PY. Reaction half-lives in the plant compartment were 17, 6, 17, and 5 d for PH, AN, FL, and PY, respectively. The combined use of exposure chamber measurements and models provides a robust tool for simultaneously measuring several different transfer factors that are important for modeling the uptake of pollutants into vegetation.

Atmosphere↗
PubMed2006

Toxicokinetics/toxicodynamics of arsenic for farmed juvenile milkfish Chanos chanos and human consumption risk in BFD-endemic area of Taiwan.

This paper presents a toxicokinetic/toxicodynamic analysis to appraise arsenic (As) bioaccumulation in farmed juvenile milkfish Chanos chanos at blackfoot disease (BFD)-endemic area in Taiwan, whereas probabilistic incremental lifetime cancer risk (ILCR) and hazard quotient (HQ) models are also employed to assess the range of exposures for the fishers and non-fishers who eat the contaminated fish. We conducted a 7-day exposure experiment to obtain toxicokinetic parameters, whereas a simple critical body burden toxicity model was verified with LC50(t) data obtained from a 7-day acute toxicity bioassay. Acute toxicity bioassay indicates that 96-h LC50 for juvenile milkfish exposed to As is 7.29 (95% CI: 3.10-10.47) mg l(-1). Our risk analysis for milkfish reared in BFD-endemic area indicates a low likelihood that survival is being affected by waterborne As. Human risk analysis demonstrates that 90%-tile probability exposure ILCRs for fishers in BFD-endemic area have orders of magnitude of 10(-3), indicating a high potential carcinogenic risk, whereas there is no significant cancer risk for non-fishers (ILCRs around 10(-5)). All predicted 90%-tiles of HQ are less than 1 for non-fishers, yet larger than 10 for fishers which indicate larger contributions from farmed milkfish consumptions. Sensitivity analysis indicates that to increase the accuracy of the results, efforts should focus on a better definition of probability distributions for milkfish daily consumption rate and As level in milkfish. Here we show that theoretical human health risks for consuming As-contaminated milkfish in the BFD-endemic area are alarming under a conservative condition based on a probabilistic risk assessment model.

Animals↗
PubMed1997

Biologically plausible neural computation.

The function of a neuron can be described simultaneously at several levels of abstraction. For instance, a spike train represents the result of a computation done by a single neuron with its inputs, but it also represents the result of a complex function realized by the network in which the neuron is embedded. When models of large parts of the brain are considered, it may be desirable to use computational modules operating at a very abstract level. However, it is shown here that abstract neural functions depend on detailed features of the single neuron model used in the network reproducing the abstract function. Examples are given of the multiplicative function, motion detection, short-term memory and timing. All these operations rely on one or another feature of the extended Leaky Integrate-and-Fire neuron used in this paper, e.g. probabilistic synapses, post-synaptic currents modelled with alpha functions or partial reset of the somatic membrane. Consequently it is suggested that neural modelling at an abstract level does not obviate the need for a clear statement on the nature of the underlying model of biological neuron. In that sense, not many abstract functions are convincingly grounded, not even the standard formal neurons used in most artificial neural networks.

Membrane Potentials↗