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Space mediates coexistence of females and hermaphrodites.

In gynodioecious populations of flowering plants females and hermaphrodites coexist. Gynodioecy is widespread and occurs in both asexual and sexual species but does not admit a satisfactory explanation from classical sex ratio theory. In sexual populations male fertility restoring genes have evolved to counter non-nuclear male sterility mutations. In pseudogamous asexual populations pollen retention and increased self-fertilization can make male sterility costly. Both of these mechanisms can promote coexistence. However, it remains unclear how either of these mechanisms could evolve if coexistence was not initially possible. In the absence of these adaptations non-spatial models predict that females either fail to invade hermaphrodite populations or else displace them until pollen shortage drives the population to extinction. We develop a pair approximation to a probabilistic cellular automata model in which females and hermaphrodites interact on a regular lattice. The model features independent pollination and colonization processes which take place on different timescales. The timescale separation is exploited to obtain, with perturbation methods, a more manageable aggregated pair approximation. We present both the mean field model which recreates the classical non-spatial predictions and the pair approximation, which strikingly predicts different invasion criteria and coexistence under a wide range of parameters. The pair approximation is shown to correspond well qualitatively with simulation behaviour.

Models, Biological↗

Clonal evolution of stem and differentiated cells can be predicted by integrating cell-intrinsic and -extrinsic parameters.

Stem cells and their derivatives represent a renewable source of cells for therapeutic applications. However, the inability to quantitatively integrate and exploit the effects of multiple parameters on the fate of stem cells limits their use in clinical applications. To address this, we developed a computational model that combines probabilistic, individual-cell and deterministic cell-population parameters to simultaneously calculate the specific effects of exogenous and endogenous factors on the overall population-dynamics behaviour. The model tracks the progeny trajectory of individual cells over several generations as a threshold function of ligand-receptor signalling interactions. Simulations in silico were validated against an Oct 4-promoter-driven green-fluorescent-protein-expressing murine embryonic stem cell line, and used to understand the effects of key parameters on the clonal evolution of stem versus differentiated cells in this system. Our approach demonstrated the ability to distinguish between individual-cell and population-averaged parameters with respect to their effects on governing dynamic behaviour. Moreover, we could discriminate between digital versus graded regulation of the Oct 4 transcription factor in accounting for experimental observations. Finally, we showed that our approach could be generalized to other stem-cell systems, in particular the previously characterized intestinal crypt system, in elucidating relative contributions of stem and progenitor cells to population output. On the basis of all these results, we believe that our iterative experimental and computational approach has been found to be useful for the study of various stem-cell systems.

Animals↗

Measurement-related problems in functional assessment.

Occupational therapists, like other rehabilitation professionals, have accepted ordinal raw scores as a sufficient basis for developing evaluation tools. This paper summarizes problems commonly found in evaluation methods based on summing ordinal raw item scores and demonstrates how Rasch measurement models provide a solution to the construction of calibrated (linear) measures. Rasch measurement models are contrasted with Steven's lax definition of measurement and Guttman's unreasonably rigid requirements. The simple Rasch model is a probabilistic formulation of the fundamental requirements for additive linear measurement. This formulation retains Guttman's concept of order, but construes it probabilistically, making it neither too lax (random) nor too rigid. When a measure is based on a theory of what counts as an observation of more or less of something, Rasch measurement models are useful for constructing valid measures.

Activities of Daily Living↗

Transcriptional regulation of protein complexes within and across species.

Yeast two-hybrid and coimmunoprecipitation experiments have defined large-scale protein-protein interaction networks for many model species. Separately, systematic chromatin immunoprecipitation experiments have enabled the assembly of large networks of transcriptional regulatory interactions. To investigate the functional interplay between these two interaction types, we combined both within a probabilistic framework that models the cell as a network of transcription factors regulating protein complexes. This framework identified 72 putative coregulated complexes in yeast and allowed the prediction of 120 previously uncharacterized transcriptional interactions. Several predictions were tested by new microarray profiles, yielding a confirmation rate (58%) comparable with that of direct immunoprecipitation experiments. Furthermore, we extended our framework to a cross-species setting, identifying 24 coregulated complexes that were conserved between yeast and fly. Analyses of these conserved complexes revealed different conservation levels of their regulators and provided suggestive evidence that protein-protein interaction networks may evolve more slowly than transcriptional interaction networks. Our results demonstrate how multiple molecular interaction types can be integrated toward a global wiring diagram of the cell, and they provide insights into the evolutionary dynamics of protein complex regulation.

Animals↗

Regional radiation risk and vulnerability assessment by integration of mathematical modelling and GIS analysis.

The Kola Peninsula, Russian Arctic exceeds all other regions in the world in the number of nuclear reactors. The study was aimed at estimating possible radiation risks to the population in the Nordic countries in case of a severe accident in the Kola Peninsula. Two approaches were tested: (1) probabilistic analysis of modelled possible pathways of radionuclide transport and precipitation and (2) deterministic approach (case studies) for most possible or worst-case scenarios of modelled transport and deposition of radionuclides. For the general population, Finland is at most risk with respect to the Kola nuclear power plant (NPP) because of (a) relatively high population density or proximity to the radiation-risk sites and (b) rather high probability of an airflow trajectory there and precipitation. After considering the critical group, northern counties in Norway, Finland and Sweden appear to be most vulnerable. The case scenarios demonstrate that population in many counties in each country, both near and far away from a nuclear site, might be subject to high risk depending on the meteorological situation.

Air Movements↗

Improving decisionmaking processes with the fuzzy logic approach in the epidemiology of sleep disorders.

Epidemiological studies can provide information not only on specific diagnostic entities but also on their underlying symptomatic constellations. For this purpose, an expert system was developed for the assessment of sleep disorders and endowed with the fuzzy logic capabilities necessary to determine the degree to which a given symptom corresponds to a specific diagnosis. Uncertainty is inherent in fields such as sleep medicine and psychiatry, and becomes evident in clinical practice at the stages of data collection and diagnostic formulation, when the clinician must determine whether a symptom is present and must choose from several diagnostic possibilities. The process involves a considerable degree of subjectivity on the part of the patient in trying to describe his or her symptoms, and of the clinician whose final diagnosis will depend on his or her clinical experience and interpretation of what is normal and what is pathological. Inferential models of the probabilistic or fuzzy logic type take into account such uncertainty. The Sleep-Eval system has been used in epidemiological and clinical studies involving 34,044 interviews collected by close to 300 interviewers. The diagnostic potential of these models is illustrated using data collected in an epidemiological study of the noninstitutionalized general population of Italy and underlines the advantages and limits of the binary, bayesian, and fuzzy logic methods and analyses.

Diagnosis, Computer-Assisted↗

DIAS-NIDDM--a model-based decision support system for insulin dose adjustment in insulin-treated subjects with NIDDM.

A decision support system has been developed, Diabetes Insulin Advisory System for patients with non-insulin dependent diabetes mellitus (DIAS-NIDDM), assisting in the adjustment of insulin doses in insulin-treated subjects. DIAS-NIDDM uses a causal probabilistic network (CPN) model of carbohydrate metabolism to make stochastic predictions of blood glucose (BG) excursions. The CPN model is an extension of an existing model with an added component representing endogenous insulin secretion. A linear relationship between BG and insulin concentration due to BG stimulated insulin secretion is assumed. Model parameters (pancreatic sensitivity, insulin sensitivity, and time-to-peak of NPH insulin) are estimated by Bayesian probability updating from patient's specific data (food intake, insulin doses, BG measurements) recorded over a period of 4 days. The estimated parameters allow the system to be potentially used as a diagnostic tool to identify abnormalities of carbohydrate metabolism: impaired insulin secretion, insulin resistance and the severity of the impairments. DIAS-NIDDM was used to predict patient-specific BG profiles and advise on insulin doses during a pilot study in eight patients with NIDDM of whom five were treated with insulin. Compared to the administered insulin amount, daily insulin amount advised by DIAS-NIDDM was similar (within 4 U) in three patients, higher by 20% (19 U) in one patient and lower by 40% (18 U) and 50% (11 U) in two patients, respectively. The inter-day coefficient of variation of the daily insulin advice suggests that, at least according to DIAS-NIDDM criteria, day-to-day adjustment of insulin doses is necessary to maintain optimum control.

Computer Simulation↗

Conformational subspace in simulation of early-stage protein folding.

A probability calculus was used to simulate the early stages of protein folding in ab initio structure prediction. The probabilities of particular phi and psi angles for each of 20 amino acids as they occur in crystal forms of proteins were used to calculate the amount of information necessary for the occurrence of given phi and psi angles to be predicted. It was found that the amount of information needed to predict phi and psi angles with 5 degrees precision is much higher than the amount of information actually carried by individual amino acids in the polypeptide chain. To handle this problem, a limited conformational space for the preliminary search for optimal polypeptide structure is proposed based on a simplified geometrical model of the polypeptide chain and on the probability calculus. These two models, geometric and probabilistic, based on different sources, yield a common conclusion concerning how a limited conformational space can represent an early stage of polypeptide chain-folding simulation. The ribonuclease molecule was used to test the limited conformational space as a tool for modeling early-stage folding.

Amino Acids↗

A probabilistic description of radioactive contamination: a multivariate model.

A multivariate discrete probability model is used to facilitate the description of gamma-ray spectroscopic data obtained from radioactively contaminated territory, east of the former Semipalatinsk nuclear test site in Kazakhstan. Two possible estimators of probabilities of interest have been considered: maximum likelihood and unbiased estimators. We show that unbiased estimators are much easier to compute. The model was used in two variants: (i) several radionuclides in spatially independent measurements, (ii) a single radionuclide in spatially dependent measurements. We show that, in both cases, it is important to take into account the correlation for the accurate evaluation of probabilities of interest.

Gamma Rays↗

Probabilistic approach to the Bak-Sneppen model.

We study here the Bak-Sneppen model, a prototype model for the study of self-organized criticality. In this model several species interact and undergo extinction with a power-law distribution of activity bursts. Species are defined through their "fitness" whose distribution in the system is uniform above a certain threshold. Run time statistics is introduced for the analysis of the dynamics in order to explain the peculiar properties of the model. This approach based on conditional probability theory, takes into account the correlations due to memory effects. In this way, we may compute analytically the value of the fitness threshold with the desired precision. This represents a substantial improvement with respect to the traditional mean field approach.

Journal Article↗

A probabilistic similarity metric for Medline records: a model for author name disambiguation.

We present a model for automatically generating training sets and estimating the probability that a pair of Medline records sharing a last and first name initial are authored by the same individual, based on shared title words, journal name, co-authors, medical subject headings, language, and affiliation, as well as distinctive features of the name itself (i.e., presence of middle initial, suffix, and prevalence in Medline).

Authorship↗

Limitations of the continuum assumption in cancellous bone.

Most existing stress analyses of the skeleton which consider cancellous bone assume that it can be modelled as a continuum. In this paper we develop a criterion for the validity of this assumption. The limitations of the continuum assumption appear in two areas: near biologic interfaces, and in areas of large stress gradients. These limitations are explored using a probabilistic line scanning model for density measurement, resulting in an estimate of density accuracy as a function of line length which is experimentally verified. Within three to five trabeculae of an interface, a continuum model is suspect. When results as predicted using continuum analyses vary by more than 20-30% over a distance spanning three to five trabeculae, the results are suspect.

Biomechanical Phenomena↗

Mixed Markov models.

Markov random fields can encode complex probabilistic relationships involving multiple variables and admit efficient procedures for probabilistic inference. However, from a knowledge engineering point of view, these models suffer from a serious limitation. The graph of a Markov field must connect all pairs of variables that are conditionally dependent even for a single choice of values of the other variables. This makes it hard to encode interactions that occur only in a certain context and are absent in all others. Furthermore, the requirement that two variables be connected unless always conditionally independent may lead to excessively dense graphs, obscuring the independencies present among the variables and leading to computationally prohibitive inference algorithms. Mumford [Mumford, D. (1996) in ICIAM 95, eds. Kirchgassner, K., Marenholtz, O. & Mennicken, R. (Akademie Verlag, Berlin), pp. -->233-256-->] proposed an alternative modeling framework where the graph need not be rigid and completely determined a priori. Mixed Markov models contain node-valued random variables that, when instantiated, augment the graph by a set of transient edges. A single joint probability distribution relates the values of regular and node-valued variables. In this article, we study the analytical and computational properties of mixed Markov models. In particular, we show that positive mixed models have a local Markov property that is equivalent to their global factorization. We also describe a computationally efficient procedure for answering probabilistic queries in mixed Markov models.

Journal Article↗

Simulation of human hypnograms using a Markov chain model.

A Markov chain model has been proposed as a mechanism that generates human sleep stages. A method for estimating the parameters of the model, i.e., the transition probabilities (rates) between sleep stages, has been introduced and applied to 95 hypnograms taken from 23 subjects. The rates characterize interindividual differences and nightly variations of the sleep mechanism, related to sleep-onset behavior, to the decreasing amount of slow wave sleep in the course of the night, and to the REM-NREM periodicity. The model simulates both probabilistic and the above-mentioned predictable dynamics of sleep, but only if these time-varying, individual rates are applied.

Humans↗

A decision support model and analysis for hospital administrators when choosing future strategies of their hospitals.

How to allocate a hospital's finite resources to health care is an extremely important problem for hospital management. This paper shows what kind of business strategies a given hospital should consider, and present a model to support such decision making. From the model, a hospital can decide how much to emphasize different tasks in order to set a given direction for the future. We used nonlinear discriminant functions to derive a decision support model, based on findings from 831 hospital directors. This model provides a probabilistic basis for deciding how much a given hospital should emphasize any of a number of different strategies.

Decision Support Systems, Management↗

A revised probabilistic estimate of the maternal methyl mercury intake dose corresponding to a measured cord blood mercury concentration.

In 2001, the U.S. Environmental Protection Agency (EPA) adopted a revised reference dose (RfD) for methyl mercury (MeHg) of 0.1 microg/kg/day. The RfD is based on neurologic developmental effects measured in children associated with exposure in utero to MeHg from the maternal diet. The RfD derivation proceeded from a point of departure based on measured concentration of mercury in fetal cord blood (micrograms per liter). The RfD, however, is a maternal dose (micrograms per kilogram per day). Reconstruction of the maternal dose corresponding to this cord blood concentration, including the variability around this estimate, is a critical step in the RfD derivation. The dose reconstruction employed by the U.S. EPA using the one-compartment pharmacokinetic model contains two areas of significant uncertainty: It does not directly account for the influence of the ratio of cord blood: maternal blood Hg concentration, and it does not resolve uncertainty regarding the most appropriate central tendency estimates for pregnancy and third-trimester-specific model parameters. A probabilistic reassessment of this dose reconstruction was undertaken to address these areas of uncertainty and generally to reconsider the specification of model input parameters. On the basis of a thorough review of the literature and recalculation of the one-compartment model including sensitivity analyses, I estimated that the 95th and 99th percentiles (i.e., the lower 5th and 1st percentiles) of the maternal intake dose corresponding to a fetal cord blood Hg concentration of 58 microg/L are 0.3 and 0.2 microg/kg/day, respectively. For the 99th percentile, this is half the value previously estimated by the U.S. EPA.

Environmental Monitoring↗

Using Bayesian inference to perform meta-analysis.

Bayesian modeling offers an elegant approach to meta-analysis that efficiently incorporates all sources of variability and relevant quantifiable external information. It provides a more informative summary of the likely value of parameters after observing the data than do non-Bayesian approaches. This leads to direct probabilistic inference about model parameters such as the average treatment effect, the between-study variance, and individual study treatment effects. The latter are weighted averages of the common mean and individual study means with weights reflecting the amount of information provided by each study relative to the others. Homogeneity among these posterior study estimates indicates that pooling these studies is appropriate; heterogeneity suggests that some cause of between-study variation should be explored. The author describes the construction of such models and shows how to use them to estimate a common mean and regression slopes. Two examples illustrate the additional inferences available with the Bayesian methodology.

Angiotensin-Converting Enzyme Inhibitors↗