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At least 1,279 records · Page 71Linked to original sources
PubMed2005

A reliable probabilistic sleep stager based on a single EEG signal.

OBJECTIVE: We developed a probabilistic continuous sleep stager based on Hidden Markov models using only a single EEG signal. It offers the advantage of being objective by not relying on human scorers, having much finer temporal resolution (1s instead of 30s), and being based on solid probabilistic principles rather than a predefined set of rules (Rechtschaffen & Kales) METHODS AND MATERIAL: Sixty-eight whole night sleep recordings from two different sleep labs are analysed using Gaussian observation Hidden Markov models. RESULTS: Our unsupervised approach detects the cornerstones of human sleep (wakefulness, deep and REM sleep) with around 80% accuracy based on data from a single EEG channel. There are some difficulties in generalizing results across sleep labs. CONCLUSION: Using data from a single electrode is sufficient for reliable continuous sleep staging. Sleep recordings from different sleep labs are not directly comparable. Training of separate models for the sleep labs is necessary.

Adult↗
PubMed2005

Improved alignment of nucleosome DNA sequences using a mixture model.

DNA sequences that are present in nucleosomes have a preferential approximately 10 bp periodicity of certain dinucleotide signals, but the overall sequence similarity of the nucleosomal DNA is weak, and traditional multiple sequence alignment tools fail to yield meaningful alignments. We develop a mixture model that characterizes the known dinucleotide periodicity probabilistically to improve the alignment of nucleosomal DNAs. We assume that a periodic dinucleotide signal of any type emits according to a probability distribution around a series of 'hot spots' that are equally spaced along nucleosomal DNA with 10 bp period, but with a 1 bp phase shift across the middle of the nucleosome. We model the three statistically most significant dinucleotide signals, AA/TT, GC and TA, simultaneously, while allowing phase shifts between the signals. The alignment is obtained by maximizing the likelihood of both Watson and Crick strands simultaneously. The resulting alignment of 177 chicken nucleosomal DNA sequences revealed that all 10 distinct dinucleotides are periodic, however, with only two distinct phases and varying intensity. By Fourier analysis, we show that our new alignment has enhanced periodicity and sequence identity compared with center alignment. The significance of the nucleosomal DNA sequence alignment is evaluated by comparing it with that obtained using the same model on non-nucleosomal sequences.

Algorithms↗
PubMed2003

Mathematical model of diffusion-limited evolution of multiple gas bubbles in tissue.

Models of gas bubble dynamics employed in probabilistic analyses of decompression sickness incidence in man must be theoretically consistent and simple, if they are to yield useful results without requiring excessive computations. They are generally formulated in terms of ordinary differential equations that describe diffusion-limited gas exchange between a gas bubble and the extravascular tissue surrounding it. In our previous model (Ann. Biomed. Eng. 30: 232-246, 2002), we showed that with appropriate representation of sink pressures to account for gas loss or gain due to heterogeneous blood perfusion in the unstirred diffusion region around the bubble, diffusion-limited bubble growth in a tissue of finite volume can be simulated without postulating a boundary layer across which gas flux is discontinuous. However, interactions between two or more bubbles caused by competition for available gas cannot be considered in this model, because the diffusion region has a fixed volume with zero gas flux at its outer boundary. The present work extends the previous model to accommodate interactions among multiple bubbles by allowing the diffusion region volume of each bubble to vary during bubble evolution. For given decompression and tissue volume, bubble growth is sustained only if the bubble number density is below a certain maximum.

Air Pressure↗
PubMed2004

Propagation of uncertainty in hourly utility NOx emissions through a photochemical grid air quality model: a case study for the Charlotte, NC, modeling domain.

One of the major hypothesized sources of uncertainties in air quality model inputs is the emission inventory. A probabilistic hourly NOx emission inventory for 32 units of nine coal-fired power plants in the Charlotte domain for the year 1995 was propagated through the Multiscale Air Quality Simulation Platform (MAQSIP). The inventory was developed using time series techniques. Time series for a 4-d episode were simulated and propagated through the air quality model 50 times in order to represent the ranges of uncertainty in hourly emissions and predicted ozone levels. Intra-unit autocorrelation in emissions and inter-unit dependence were accounted for. The range of uncertainty in predicted ozone was greater when inter-unit dependence was included as compared to when units were treated as statistically independent. Uncertainties in maximum ozone hourly or 8-h concentrations at a specific location could be attributed to a specific power plant based upon regression analysis. Out of 3969 grid cells in the modeling domain, there were 43 and 1654 grid cells with a probability greater than 0.9 of exceeding a 1-h 120 ppb standard and an 8-h 80 ppb standard, respectively. The time series of predicted ozone values had similar autocorrelation as compared to monitored data. The implications of these results for air quality management are addressed.

Air Pollutants↗
PubMed2003

Issues of replicability in Monte Carlo modeling: a case study with a pesticide leaching model.

Sensitivity and uncertainty analyses based on Monte Carlo sampling were undertaken for various numbers of runs of the pesticide leaching model (PELMO). Analyses were repeated 10 times with different seed numbers. The ranking of PELMO input parameters according to their influence on predictions for leaching was stable for the most influential parameters. For less influential parameters, the sensitivity ranking was severely influenced by the seed number used. For uncertainty analyses, probabilities of exceeding a particular concentration were significantly influenced by the seed number used in the random sampling of values for the two parameters considered, even for those cases in which 5,000 model runs were undertaken (coefficient of variation of 10 replicated analyses, 5%). A decrease in the variability of exceedance probabilities could be achieved by further increasing the number of model runs. However, this may prove to be impractical when complex deterministic models with a relatively long running time are used. Attention should be paid to replicability aspects by modelers when devising their approach to assessing the uncertainty associated with the modeling and by decision makers when examining the results of probabilistic approaches.

Environmental Monitoring↗
PubMed2004

Stochastic modeling of T cell receptor gamma gene rearrangement.

The mechanisms controlling the recombination process of the gamma genes that encode the gamma chain of the antigen receptor of the gammadelta T lymphocytes are unclear. Based on experimental data on the recombination status of the two major TCR gamma genes expressed in V(gamma)4+ and V(gamma)1+ thymocytes, we tested the plausibility of three possible rearrangement mechanisms: (1) a time window mechanism according to which the two chromosomes are accessible to the recombination machinery during a defined period of time; (2) a feedback mechanism in which recombination stops shortly after the first in-frame rearrangement event anywhere in both chromosomes; and (3) a feedback mechanism with asynchronous chromosome accessibility, in which there is a first period when only one chromosome is accessible for recombination, followed by a second period when both chromosomes are accessible; shortly after the first in-frame rearrangement event, during any of these two periods, recombination will definitely stop. We model the time window mechanism using a pure probabilistic approach and the two feedback mechanisms using a continuous-time Markov chain formalism. We used maximum likelihood methodology to infer the goodness-of-fit of the models showing evidence for the last model, which best fits the data. Further analysis of this model suggests an evolutionary tradeoff between allelic and isotypic exclusion and the probability that a precursor differentiates into a mature gammadelta T lymphocyte.

Alleles↗
PubMed2005

Imaging the past: neural activation in frontal and temporal regions during regular and irregular past-tense processing.

This article presents fMRI evidence bearing on dual-mechanism versus connectionist theories of inflectional morphology. Ten participants were scanned at 4 Tesla as they covertly generated the past tenses of real and nonce (nonword) verbs presented auditorily. Regular past tenses (e.g., walked, wugged) and irregular past tenses (e.g., took, slept) produced similar patterns of activation in the posterior temporal lobe in both hemispheres. In contrast, there was greater activation in left and right inferior frontal gyrus for regular past tenses than for irregular past tenses. Similar previous results have been taken as evidence for the dual-mechanism theory of the past tense (Pinker & Ullman, 2002). However, additional analyses indicated that irregulars that were phonologically similar to regulars (e.g., slept, fled, sold) produced the same level of activation as did regulars, and significantly more activation than did irregulars that were not phonologically similar to regulars (e.g., took, gave). Thus, activation patterns were predicted by phonological characteristics of the past tense rather than by the rule-governed versus exception distinction that is central to the dual-mechanism framework. The results are consistent with a constraint satisfaction model in which phonological, semantic, and other probabilistic constraints jointly determine the past tense, with different degrees of involvement for different verbs.

Acoustic Stimulation↗
PubMed1997

Stochastic threshold characterization of the intensity of active channel dynamical action potential generation.

Stochastic threshold characterization of the intensity of active channel dynamical action potential generation. J. Neurophysiol. 78: 2616-2630, 1997. This paper develops a stochastic intensity description for action potential generation formulated in terms of stochastic processes, which are direct analogues of the physiological processes of the pre- and postsynaptic complex of the cochlear nerve: 1) neurotransmitter release is modeled as an inhomogeneous Poisson counting process with release intensity mu t, 2) the excitatory postsynaptic conductance (EPSC) process is modeled as a marked, linearly filtered Poisson process resulting from the linear superposition of standard shaped postsynaptic conductances of size G, and 3) action potential generation is modeled as resulting from the EPSC exceeding a random threshold determined by active channel dynamics of the Hodgkin-Huxley type. The random threshold is defined to be the least upper bound in the size of a standard-shaped neurotransmitter release injected at time t given the previous action potential time and the number of releases occurring in a short preconditioning time increment. The action potential process is modeled as a self-exciting point process with stochastic intensity resulting from the probability that the random threshold process crosses the threshold in some small time increment that is a function of time since previous action potential, release intensity, and the probability that a single synaptic event exceeds the stochastic threshold. The stochastic intensity model is consistent with a direct simulation of the nonlinear Hodgkin-Huxley differential equations over a variety of parameters for the vesicle release intensity, vesicle size, vesicle duration, and temperatures. Results are presented showing that the regularity properties seen in the vestibular primary afferent in the lizard, Calotes versicolor, associated with a slow-to-activate potassium channel resulting in a long afterhyperpolarization can be accommodated directly by the stochastic intensity description. The stimulus dependence of the model is attributed to synaptic transmission and the probabilistic nature to the threshold conductance process, which is dependent upon the EPSC process. The stochastic intensity is seen to have a form consistent with the phenomenologically based Siebert-Gaumond model, a stimulus-related function of time multiplied by a refractory-related function of time since previous action potential.

Action Potentials↗
PubMed2003

Linking atmospheric circulation to daily rainfall patterns across the Murrumbidgee River Basin.

The hydrological cycle in Australia covers an extraordinary range of climatic and hydrologic regimes. It is now widely accepted that Australian hydrology is significantly different from all other regions and continents with the partial exception of southern Africa. Rainfall variability is very high in almost all regions with respect to amount and the lengths of wet and dry spells. These factors are keys to the behaviour and health of Australian aquatic ecosystems and water resources. Thus assessment of how rainfall may change under a potential future climate is critical. For a case study of the Murrumbidgee River Basin (MRB), a statistical downscaling model that links broad scale atmospheric circulation to multi-site, daily precipitation is assessed using observed data. This model can be driven with climate model simulations to produce rainfall scenarios at the scale required by impacts models. These can then be used in probabilistic risk assessments of climate change impacts on river health. These issues will be discussed in the context of assessing the potential impacts of precipitation changes due to projected climate change on river health.

Australia↗
PubMed2004

Is cross-validation better than resubstitution for ranking genes?

MOTIVATION: Ranking gene feature sets is a key issue for both phenotype classification, for instance, tumor classification in a DNA microarray experiment, and prediction in the context of genetic regulatory networks. Two broad methods are available to estimate the error (misclassification rate) of a classifier. Resubstitution fits a single classifier to the data, and applies this classifier in turn to each data observation. Cross-validation (in leave-one-out form) removes each observation in turn, constructs the classifier, and then computes whether this leave-one-out classifier correctly classifies the deleted observation. Resubstitution typically underestimates classifier error, severely so in many cases. Cross-validation has the advantage of producing an effectively unbiased error estimate, but the estimate is highly variable. In many applications it is not the misclassification rate per se that is of interest, but rather the construction of gene sets that have the potential to classify or predict. Hence, one needs to rank feature sets based on their performance. RESULTS: A model-based approach is used to compare the ranking performances of resubstitution and cross-validation for classification based on real-valued feature sets and for prediction in the context of probabilistic Boolean networks (PBNs). For classification, a Gaussian model is considered, along with classification via linear discriminant analysis and the 3-nearest-neighbor classification rule. Prediction is examined in the steady-distribution of a PBN. Three metrics are proposed to compare feature-set ranking based on error estimation with ranking based on the true error, which is known owing to the model-based approach. In all cases, resubstitution is competitive with cross-validation relative to ranking accuracy. This is in addition to the enormous savings in computation time afforded by resubstitution.

Algorithms↗
PubMed2004

Probabilistic uncertainty analysis of the European Union system for the evaluation of substances multimedia regional distribution model.

The European Union System for the Evaluation of Substances (EUSES) is a computerized model system to facilitate and harmonize health and environmental risk assessment of previously notified and new substances. For calculation of regional background exposure, a multimedia distribution model is used. In the present study, the uncertainty of this regional model is analyzed. Environmental parameters were collected for North Rhine Westphalia (Germany), which resembles the standard region of EUSES. Probability distribution functions of various types (uniform, triangular, normal, log normal) depending on data availability were derived for environmental input parameters, including geometric parameters. Generic log-normal distribution functions with fixed standard deviations were chosen for solubility in air, water, and n-octanol as well as for degradation half-lives. Monte Carlo simulations were carried out for 10 reference substances having different properties. Contribution of environmental parameter uncertainty to total output uncertainties is higher than that of substance parameters. Range of output uncertainty, defined as the ratio of the logarithms of the 90th and 10th percentiles of the cumulative probability distribution function, shows an increase from air and water to soil. The highest-occurring range is 1.4 orders of magnitude, which means that total uncertainty of the regional model is relatively low and, usually, is lower than the range of measured values. The median of output probability distributions lies above the point estimate. Influence of input parameters was estimated as their rank correlation coefficients to output uncertainty. Substance and environmental parameters contribute differently to output variance depending on individual substance properties and environmental compartment. Hence, the present study underlines the need to perform uncertainty analyses instead of either using a set of simple rules or just looking at certain parameters.

Environment↗
PubMedDate not supplied

DIABNET: a qualitative model-based advisory system for therapy planning in gestational diabetes.

An intelligent decision support system for the analysis of home monitoring data and therapy planning in gestational diabetes is presented. The paper describes the integration of qualitative and quantitative reasoning modules within the DIABNET advisory system. The system kernel is a qualitative model of the physiological processes involved in the glucose metabolism of this type of diabetic patient. A causal probabilistic network (CPN) has been used to represent the qualitative model in order to manage uncertain and missing monitoring data. The DIABNET inputs are the patient's available ambulatory monitoring data, and the output is a dietary and insulin therapy adjustment that includes initiation of insulin therapy, quantitative insulin dose changes and qualitative diet and schedule modifications. Over periods of up to seven days, monitoring data are analysed by the CPN to detect any diet or insulin therapy items which may require modification. The qualitative insulin needs are translated into a quantitative proposal in line with the characteristics of the patient and the modification strategies usually used by doctors. The first evaluation of the system has been accomplished, the encouraging results of which are also presented.

Blood Glucose Self-Monitoring↗
PubMed2004

Maximum likelihood estimation of cascade point-process neural encoding models.

Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stage. We analyze the estimation of one such model for which this nonlinear step is implemented by a known parametric function; the assumption that this function is known speeds the estimation process considerably. We investigate the shape of the likelihood function for this type of model, give a simple condition on the nonlinearity ensuring that no non-global local maxima exist in the likelihood-leading, in turn, to efficient algorithms for the computation of the maximum likelihood estimator-and discuss the implications for the form of the allowed nonlinearities. Finally, we note some interesting connections between the likelihood-based estimators and the classical spike-triggered average estimator, discuss some useful extensions of the basic model structure, and provide two novel applications to physiological data.

Algorithms↗
PubMed2000

Subjective uncertainty, purposeful behavior, and theory of functional systems.

Neglect of probability prognosis orients the theory of functional systems to description of rigidly determined forms of behavior (congenital behavior, frontal syndrome, etc.). The authors suggest an original concept for analysis of behavior under conditions of subjective uncertainty based on probability estimations. It contains all classical components of a behavioral act model developed by P. K. Anokhin. The role of probability prognosis at the stages of making decision (purpose) and formation and realization of the program of actions is argued. Two new components are added to the classical model: memory buffer and system of making probabilistic decision for modifying the program of actions. Such an approach essentially extends the potentialities of using the theory of functional systems, making it usable not only for rigidly determined behavior but for behavior under conditions of subjective uncertainty.

Animals↗
PubMed1995

Clinical simulation using context-sensitive temporal probability models.

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 and an implemented algorithm (BNG) that generates Bayesian networks to compute the posterior probabilities of queries. We illustrate the use of the BNG system by applying it to the problem of modeling the effects of medications and other interventions on the condition of a patient in cardiac arrest.

Artificial Intelligence↗
PubMed2006

Regulation of class I major histocompatibility complex receptor expression in natural killer cells: one promoter is not enough!

The class I major histocompatibility complex (MHC) receptors expressed by natural killer (NK) cells play an important role in regulating their function. The number and type of inhibitory receptors expressed by NK cells must be tightly controlled in order to avoid the generation of dominantly inhibited NK cells. The selective stochastic expression of the class I MHC receptors generates a variegated NK cell population capable of discriminating subtle changes in MHC expression on potential target cells. The molecular mechanisms controlling the cell-specific and probabilistic expression of these receptors are without doubt very complex. The traditional approach of considering a core promoter modulated by upstream enhancer elements is likely too simplistic a paradigm to adequately explain the regulation of these genes, as well as other gene clusters that are not expressed in an 'all or none' fashion. Our studies on the regulation of the mouse Ly49 and human killer immunoglobulin-like receptor (KIR) clusters of class I MHC receptor genes have revealed the presence of multiple transcripts in both sense and antisense orientations. In both systems, an antisense promoter overlaps a promoter that produces sense transcripts, creating a bidirectional element. In the Ly49 genes, the competing promoters behave as probabilistic switches, and it is likely that the human bidirectional promoters will have a similar property. The antisense transcripts generated in the Ly49 genes are far removed from the promoter responsible for Ly49 expression in mature NK cells, whereas the antisense KIR transcripts detected are within the adult promoter region. This finding suggests that the mechanism of promoter regulation in the KIR genes may be quite different from that of the Ly49 genes. This review summarizes the current state of knowledge regarding class I MHC receptor gene regulation. The models proposed for the control of the probabilistic expression of the Ly49 and KIR genes are discussed in the context of current knowledge regarding the complex control of other well-studied gene clusters such as the beta-globin and cytokine clusters.

Animals↗
PubMed1998

Stochastic dose/risk assessment uncertainty and the concept of potential exposures.

Assessments of future potential doses and risks (e.g. in connection with radioactive waste disposal) can be associated with substantial uncertainties. These uncertainties are often incorporated into assessments using stochastic models, and give rise to doses characterised in probabilistic terms. Questions about how doses should be related to risks arise even in the case of deterministic dose assessments. However, results of a probabilistic dose assessment may lead to additional issues when interpreting them or attempting to convert them to risk for regulatory purposes. Some problems are shown to originate from the difference between the meaning of a probabilistic dose value and the definition of potential exposure. Others relate to the appropriate choice of, or approach to compliance with, a regulatory limit. A possible method for setting limits on probabilistic (potential) doses or risks is proposed, based on an accepted technique for determining detection limits.

Computer Simulation↗
PubMed2006

On the universal clustering under a broad class of loss functions.

Determination of the number of significant clusters in the sample represents a very important problem. It is expected that the outcome of clustering under a broad class of loss functions will be more stable if the correct number of clusters is used. In order to illustrate the model of universal clustering we consider 1) family of power loss functions in probabilistic space; 2) family of exponential loss functions in Euclidean space. The proposed model is general, and proved to be effective in application to the synthetic datasets.

Algorithms↗