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PubMed2005

An anatomically constrained, stochastic model of eye movement control in reading.

This article presents SERIF, a new model of eye movement control in reading that integrates an established stochastic model of saccade latencies (LATER; R. H. S. Carpenter, 1981) with a fundamental anatomical constraint on reading: the vertically split fovea and the initial projection of information in either visual field to the contralateral hemisphere. The novel features of the model are its simulation of saccade latencies as a race between two stochastic rise-to-threshold LATER units and its probabilistic selection of the target for the next saccade. The model generates simulated eye movement behavior that exhibits important characteristics of actual eye movements made during reading; specifically, simulations produce realistic saccade target distributions and replicate a number of critical reading phenomena, including the effects of word frequency on fixation durations, the inverted optimal viewing position effect, the trade-off between first and second fixation durations of refixated words, and the dependence of parafoveal preview benefit on eccentricity.

Eye Movements↗

PubMed2026

TPMM: three-component posterior mixture model enables robust inverton detection in low-depth metagenomes and suggests potential viral invertons.

SUMMARY: Bacterial phase variation enables reversible, locus-specific phenotypic switching, often driven by DNA inversion (invertons). To identify these events, researchers commonly rely on sequencing reads that provide orientation-specific support. Metagenomic sequencing, which captures total genetic material independent of cultivation, offers a powerful platform for the comprehensive study of invertons. However, computational inverton calling from metagenomic data is difficult at low sequencing depth: hard read-support cutoffs can miss true events, while sequence-only predictors lack read-backed interpretability and uncertainty quantification. To address this, we present TPMM, a three-component posterior mixture model for inverton calling in metagenomic data. TPMM explicitly incorporates sequencing depth to formulate inverton detection as a probabilistic mixture problem. Starting from candidates flanked by inverted repeats, the model classifies the candidates into noise, low-probability, or high-probability inversion signals using read evidence. Finally, TPMM assigns posterior probabilities as soft labels and applies cumulative Bayesian False Discovery Rate control to robustly identify true invertons. On two real gut metagenomic datasets, TPMM agrees well with PhaseFinder at high depth but recovers substantially more invertons under systematic downsampling, demonstrating superior performance in sparse-data regimes. We further examine potential reversible inversion elements in viral genomes and provide supporting analyses, suggesting a broader scope for inversion-mediated regulation. AVAILABILITY: The source code of TPMM is available via: https://github.com/KennyxxD/TPMM.

Metagenomics↗

PubMed1999

Nature versus nurture in T cell cytokine production.

Both extrinsic and intrinsic factors influence the development of cytokine expression patterns in T lymphocytes. The models proposed to accommodate these factors are often separated into two types: deterministic (or instructional) and probabilistic (or stochastic). In this review we compare these two types of models and examine how they account for different stages of T cell cytokine responses to antigen stimulation. We conclude by showing how a reconciliation of the two types of models may be possible, perhaps through the regulatory potential of heritable epigenetic mechanisms. This type of reconciliation may open up new avenues for manipulating T cell cytokine expression and redirecting immune responses.

Animals↗

PubMed2003

Simulating convergent extension by way of anisotropic differential adhesion.

Simulations using the Extended Potts Model suggest that anisotropic differential adhesion can account for convergent extension, as observed during embryonic development of the frog Xenopus laevis for example. During gastrulation in these frogs, convergent extension produces longitudinal tissue growth from latitudinal elongation and migration of aligned constituent cells. The Extended Potts Model employs clustered points on a grid to represent subdivided cells with probabilistic displacement of cell boundaries such that small changes in energy drive gradual tissue development. For modeling convergent extension, simulations include anisotropic differential adhesion: the degree of attachment between adjacent elongated cells depends on their relative orientation. Without considering additional mechanisms, simulations based on anisotropic differential adhesion reproduce the hallmark stages of convergent extension in the correct sequence, with random fluctuations as sufficient impetus for cell reorganization.

Animals↗

PubMed2006

Modeling the health care costs of geriatric inpatients.

This paper extends a method for modeling the survival of patients in hospitals to allow the expected cost to be estimated for the patients' accumulated duration of time in care. An extension of Bayesian network (BN) theory has previously been developed to model patients' survival time in hospitals with respect to the graphical and probabilistic representation of the interrelationships between the patients' clinical variables. Unlike previous BN techniques, this extended model can accommodate continuous times that are skewed in nature. This paper presents the theory behind such an approach and extends it by attaching a cost variable to the survival times, enabling the costing and efficient management of groups of patients in hospitals. An application of the model is illustrated by considering a group of 4260 patients admitted into the geriatric department of a U.K. hospital between 1994-1997. Results are derived for the distribution for their length of stay in the hospital and associated costs. The model's practical use is highlighted by illustrating how hospital managers could benefit using such a method for investigating the influence of future decisions and policy changes on the hospital's expenditure.

Computer Simulation↗

PubMed1998

Modeling the Disordered Dense Phase in the Packing of Binary Mixtures of Spheres.

A new, probabilistic approach is applied to the case of dense random packings of binary mixtures of spheres, assuming gapless packing. The model describes correctly the dependence of the porosity of the packing on mixture composition and size ratio for the disordered dense "phase" of the binary mixture. The volumes of the voids are calculated accurately by means of a Monte Carlo integration instead of being evaluated by kissing spheres. The results, in term of the porosity/sphere-fraction relationship, are compared with those of a previous model, also based on the gapless packing approximation. Copyright 1998 Academic Press.

Journal Article↗

PubMed2002

What is the relevance of obtaining multiple blood samples for culture? A comprehensive model to optimize the strategy for diagnosing bacteremia.

Through a heuristic and probabilistic approach, we evaluated blood culture operating characteristics (sensitivity, specificity, and predictive values) as a function of several pretest parameters, together with their variability. On the basis of a meta-analysis of quantitative data from the literature, a model was developed and an estimation of the operating characteristics through numerical simulations (Monte Carlo method) was performed. The model evaluates the influence of ordering and drawing parameters on the ability of blood culture to distinguish bacteremic from nonbacteremic patients, regardless of the causative species. By considering the total blood volume to be cultured (six 5-10-mL bottles), results were found to confirm the current guidelines. On the basis of this hypothesis, the results, together with an analysis of the literature, failed to show any benefit of a strategy that involves obtaining multiple samples. The best strategy when performing blood culture is to obtain blood for 6 bottles (for a total volume of 35-42 mL), preferably at the same time.

Bacteremia↗

PubMed1994

The prisoner's dilemma without synchrony.

There are many situations in which biological organisms cooperate despite obvious incentives to do otherwise. Such situations are commonly modelled by using a paradigm known as the prisoner's dilemma. In this way cooperative behaviour has previously been shown to emerge in a model population of strategies. If players can make probabilistic choices, taking into account their co-player's previous action, a strategy known as 'generous tit for tat' dominates the long-term behaviour of such a population. If they can also take into account their own previous action, a strategy of 'win stay, lose shift' dominates instead. These models assumed that participants make their decisions in synchrony, which seems improbable in many biological situations. Here we show that the timing of decisions is critical in determining which strategy emerges in the long run. If individuals make their decisions at different times, neither of the above strategies survives given the usual payoffs. In the former case, generous tit for tat succumbs to inveterate defectors, and in the latter a new strategy takes over. This 'firm but fair' strategy is retaliatory yet highly cooperative. In particular, continued exploitation of a sucker is no longer a successful behaviour.

Animals↗

PubMed2005

Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra.

Recently the National Institute for Clinical Excellence (NICE) updated its methods guidance for technology assessment. One aspect of the new guidance is to require the use of probabilistic sensitivity analysis with all cost-effectiveness models submitted to the Institute. The purpose of this paper is to place the NICE guidance on dealing with uncertainty into a broader context of the requirements for decision making; to explain the general approach that was taken in its development; and to address each of the issues which have been raised in the debate about the role of probabilistic sensitivity analysis in general. The most appropriate starting point for developing guidance is to establish what is required for decision making. On the basis of these requirements, the methods and framework of analysis which can best meet these needs can then be identified. It will be argued that the guidance on dealing with uncertainty and, in particular, the requirement for probabilistic sensitivity analysis, is justified by the requirements of the type of decisions that NICE is asked to make. Given this foundation, the main issues and criticisms raised during and after the consultation process are reviewed. Finally, some of the methodological challenges posed by the need fully to characterise decision uncertainty and to inform the research agenda will be identified and discussed.

Cost-Benefit Analysis↗

PubMed2004

Fuzzy simulation of pharmacokinetic models: case study of whole body physiologically based model of diazepam.

The aim of the present study is to develop and implement a methodology that accounts for parameter variability and uncertainty in the presence of qualitative and semi-quantitative information (fuzzy simulations) as well as when some parameters are better quantitatively defined than others (fuzzy-probabilistic approach). The fuzzy simulations method consists of (i) representing parameter uncertainty and variability by fuzzy numbers and (ii) simulating predictions by solving the pharmacokinetic model. The fuzzy-probabilistic approach includes an additional transformation between fuzzy numbers and probability density functions. To illustrate the proposed method a diazepam WBPBPK model was used where the information for hepatic intrinsic clearance determined by in vitro-in vivo scaling was semi-quantitative. The predicted concentration time profiles were compared with those resulting from a Monte Carlo simulation. Fuzzy simulations can be used as an alternative to Monte Carlo simulation.

Animals↗

PubMedDate not supplied

Analyzing functional brain images in a probabilistic atlas: a validation of subvolume thresholding.

PURPOSE: The development of structural probabilistic brain atlases provides the framework for new analytic methods capable of combining anatomic information with the statistical mapping of functional brain data. Approaches for statistical mapping that utilize information about the anatomic variability and registration errors of a population within the Talairach atlas space will enhance our understanding of the interplay between human brain structure and function. METHOD: We present a subvolume thresholding (SVT) method for analyzing positron emission tomography (PET) and single photon emission CT data and determining separately the statistical significance of the effects of motor stimulation on brain perfusion. Incorporation of a priori anatomical information into the functional SVT model is achieved by selecting a proper anatomically partitioned probabilistic atlas for the data. We use a general Gaussian random field model to account for the intrinsic differences in intensity distribution across brain regions related to the physiology of brain activation, attenuation effects, dead time, and other corrections in PET imaging and data reconstruction. RESULTS: H2(15)O PET scans were acquired from six normal subjects under two different activation paradigms: left-hand and right-hand finger-tracking task with visual stimulus. Regional region-of-interest and local (voxel) group differences between the left and right motor tasks were obtained using nonparametric stochastic variance estimates. As expected from our simple finger movement paradigm, significant activation (z = 6.7) was identified in the left motor cortex for the right movement task and significant activation (z = 6.3) for the left movement task in the right motor cortex. CONCLUSION: We propose, test, and validate a probabilistic SVT method for mapping statistical variability between groups in subtraction paradigm studies of functional brain data. This method incorporates knowledge of, and controls for, anatomic variability contained in modern human brain probabilistic atlases in functional statistical mapping of the brain.

Adult↗

PubMed2002

Testing time-predictable earthquake recurrence by direct measurement of strain accumulation and release.

Probabilistic estimates of earthquake hazard use various models for the temporal distribution of earthquakes, including the 'time-predictable' recurrence model formulated by Shimazaki and Nakata (which incorporates the concept of elastic rebound described as early as 1910 by H. F. Reid). This model states that an earthquake occurs when the fault recovers the stress relieved in the most recent earthquake. Unlike time-independent models (for example, Poisson probability), the time-predictable model is thought to encompass some of the physics behind the earthquake cycle, in that earthquake probability increases with time. The time-predictable model is therefore often preferred when adequate data are available, and it is incorporated in hazard predictions for many earthquake-prone regions, including northern California, southern California, New Zealand and Japan. Here we show that the model fails in what should be an ideal locale for its application -- Parkfield, California. We estimate rigorous bounds on the predicted recurrence time of the magnitude approximately 6 1966 Parkfield earthquake through inversion of geodetic measurements and we show that, according to the time-predictable model, another earthquake should have occurred by 1987. The model's poor performance in a relatively simple tectonic setting does not bode well for its successful application to the many areas of the world characterized by complex fault interactions.

Journal Article↗

PubMed2000

[Decision analysis in radiology using Markov models].

Markov models (Multistate transition models) are mathematical tools to simulate a cohort of individuals followed over time to assess the prognosis resulting from different strategies. They are applied on the assumption that persons are in one of a finite number of states of health (Markov states). Each condition is given a transition probability as well as an incremental value. Probabilities may be chosen constant or varying over time due to predefined rules. Time horizon is divided into equal increments (Markov cycles). The model calculates quality-adjusted life expectancy employing real-life units and values and summing up the length of time spent in each health state adjusted for objective outcomes and subjective appraisal. This sort of modeling prognosis for a given patient is analogous to utility in common decision trees. Markov models can be evaluated by matrix algebra, probabilistic cohort simulation and Monte Carlo simulation. They have been applied to assess the relative benefits and risks of a limited number of diagnostic and therapeutic procedures in radiology. More interventions should be submitted to Markov analyses in order to elucidate their cost-effectiveness.

Cost-Benefit Analysis↗

PubMed2026

LAML-Pro: joint maximum likelihood inference of cell genotypes and cell lineage trees.

MOTIVATION: Recent dynamic lineage tracing technologies use genome editing to induce heritable mutations, or edits, that accumulate across successive cell divisions. These edits are measured using single-cell sequencing or imaging, providing data to reconstruct cell lineages at single-cell resolution. Current computational approaches to infer cell lineage trees, or phylogenies, from these data perform two separate steps: (i) Identify each cell's edits (genotype) from the raw sequencing or imaging data; (ii) Infer a cell lineage tree from the cell genotypes. However, genotyping cells is an inexact process and genotype errors can yield an inaccurate lineage tree. For example, using fluorescence based-imaging to measure edits results in a high fraction (≈25%-50%) of uncertain or erroneous genotypes. RESULTS: We introduce Lineage Analysis via Maximum Likelihood with PRobabilistic Observations (LAML-Pro), an algorithm that jointly infers cell genotypes and a cell lineage tree. LAML-Pro is based on the Probabilistic Mixed-type Missing Observation (PMMO) model, which we derive to describe both the genome editing and genotype observation processes. LAML-Pro constructs lineage trees from thousands of cells in under an hour by leveraging the sparsity of transitions under the PMMO model. On simulated data, we demonstrate that LAML-Pro corrects genotype errors and infers substantially more accurate trees than existing methods which are vulnerable to genotype errors. Applied to data from two recent imaging-based lineage tracing systems, LAML-Pro reduces genotype errors by 5-fold and produces more spatially coherent lineage trees compared to existing methods. AVAILABILITY AND IMPLEMENTATION: LAML-Pro is implemented in C++ and is available as both a command-line interface and as a Python library at: github.com/raphael-group/LAML-Pro.

Cell Lineage↗

PubMedDate not supplied

Late-training amnesic deficits in probabilistic category learning: a neurocomputational analysis.

Building upon earlier behavioral models of animal and human learning, we explore how a psychobiological model of animal conditioning can be applied to amnesic category learning. In particular, we show that the late-training deficit found in Knowlton, Squire, and Gluck's 1994 study of amnesic category learning can be understood as a natural consequence of Gluck and Myers's (1993) theory of hippocampal-region function, a theory that has heretofore been applied only to studies of animal learning. When applied to Knowlton et al.'s category learning task, Gluck and Myers's model assumes that the hippocampal region induces new stimulus representations over multiple training trials that reflect stimulus-stimulus regularities in the training set. As such, the model expects an advantage for control subjects over hippocampal-damaged amnesic patients only later in training when control subjects have developed new hippocampal-dependent stimulus representations; in contrast, both groups are expected to show equivalent performance early in training. A potentially analogous early/late distinction is described for animal studies of stimulus generalization. Our analyses suggest that careful comparisons between early and late-training differences in learning may be an important factor in understanding amnesia and the neural bases of both animal and human learning.

Amnesia↗

PubMed1997

Multiwavelength anomalous diffraction of sulfite reductase hemoprotein: making the most of MAD data.

The structure of the 60 kDa E. coli sulfite reductase hemoprotein (SiRHP) was determined by using multiwavelength anomalous diffraction (MAD) to exploit the relatively small anomalous signals produced near the Fe K absorption edge from the protein's native Fe(4)S(4) cluster and siroheme Fe atom. Because of systematic measurement error, generation of useful MAD data required rejection of outlying intensity observations that were only identified by careful manual scrutiny of the observed intensities and single parameter scaling among wedges of diffraction data. The key steps for obtaining effective phases were local anisotropic scaling between Bijvoet pairs and among wavelengths, extraction of phase information from unmerged observations, and refinement of the anomalous scattering model. Important factors for positioning the anomalous scattering model included removal of aberrant coefficients from Patterson syntheses, positional refinement of the Fe positions against MAD-derived normal-scattering amplitudes, and systematic searches of cluster orientation that attempted to optimize agreement between observed and calculated MAD intensities. To obtain MAD phases for reflections that were underdetermined for least-squares methods, parameters necessary for defining phase-probability distributions had to be estimated from the anomalous scattering model. The MAD phase distributions, when combined probabilistically with otherwise insufficient MIR phase information, led to the determination of the SiRHP structure. The techniques developed and lessons learned from the SiRHP MAD experiment should be applicable to the design of MAD experiments on other macromolecules.

Journal Article↗

PubMed1999

Dynamic updating in DIAS-NIDDM and DIAS causal probabilistic networks.

Diabetes advisory system (DIAS) is a decision support system, which has been developed to provide advice on the amount of insulin injected by subjects with insulin-dependent diabetes mellitus (IDDM). DIAS employs a temporal causal probabilistic network (CPN) to implement a stochastic model of carbohydrate metabolism. The CPN network has recently been extended to provide also advice to subjects with noninsulin-dependent diabetes mellitus (NIDDM). However, due to increased complexity and size of the extended CPN the calculations became unfeasible. The CPN network was, therefore, simplified and a novel approach employed to generate conditional probability tables. The principles of dynamic CPN's were adopted and, in combination with the method of conditioning, learning, and forecasting, were implemented in a time- and memory-efficient way. An evaluation using experimental data was carried out to compare the original and revised DIAS implementations employing data collected by patients with IDDM, and to assess the a posteriori identifiability of model parameters in patients with NIDDM.

Adult↗

PubMed1979

Role of nuclear size in cell growth initiation.

Swiss 3T3 cells arrested in B0 (quiescent state) by reducing serum content of the medium all contain the same amount of DNA but vary in nuclear volume over approximately a twofold range. By use of flow microfluorimetry, scatterplots of nuclear volume versus DNA content were obtained in intervals after serum stimulation. The earliest cells to enter DNA synthesis were those with the largest nuclei, whereas cells with the smallest nuclei were among the latest. Regulation of cellular transit from G0 to the S phase was therefore, at least in part, deterministic, since all G0 cells did not have equal probabilities of entry into S at a given moment. All cells having the same nuclear volume did not initiate DNA synthesis at the same moment; therefore, factors other than nuclear volume must also influence this timing. Nuclear volume correlated with the maximum rate at which cells could enter S. The kinetic model of the cell cycle postulating a probabilistic event as solely responsible for entry into S thus appears too simple.

Animals↗