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PubMed2003

A substructure approach for the midfrequency vibration of stochastic systems.

A novel substructure coupling technique based on the proper orthogonal decomposition method is presented for the midfrequency range vibration of linear dynamical systems with parameter uncertainty. For a given frequency band, the methodology permits the derivation of an adaptive basis for each subsystem and the construction of a reduced-order model of the global structure. The formulation is directed toward the efficient probabilistic characterization of model-based predictions in the framework of a stochastic finite element method. The efficiency of the substructure method has been contrasted both from the viewpoint of adopting free-free and fixed-fixed substructure proper orthogonal modes in order to arrive at a reduced subsystem model. The distinction as well as similarity of the present methodology with the component mode synthesis is also pointed out The proper orthogonal modes are obtained from both frequency- and time-domain approaches, and their suitability is discussed in relation to the behavior of a specific system. The substructure approach elegantly integrates with a version of stochastic finite elements based on orthogonal decompositions and projections of stochastic processes.

Journal Article↗

PubMed1981

The evolution of cooperation.

Cooperation in organisms, whether bacteria or primates, has been a difficulty for evolutionary theory since Darwin. On the assumption that interactions between pairs of individuals occur on a probabilistic basis, a model is developed based on the concept of an evolutionarily stable strategy in the context of the Prisoner's Dilemma game. Deductions from the model, and the results of a computer tournament show how cooperation based on reciprocity can get started in an asocial world, can thrive while interacting with a wide range of other strategies, and can resist invasion once fully established. Potential applications include specific aspects of territoriality, mating, and disease.

Animals↗

PubMed2005

Microbial risk indicators of early childhood caries.

The aim of this study was to use molecular identification methods, such as 16S RNA gene sequence and reverse-capture checkerboard hybridization, for identification of the bacteria associated with dental caries and with dental health in a subset of 204 twins aged 1.5 to 7 years old. A total of 448 plaque samples (118 collected from caries-free subjects and 330 from caries-active subjects) were used for analysis. We compared the bacteria found in biofilms of children exhibiting severe dental caries, with different degrees of lesion severity, with those found in biofilms of caries-free children. A panel of 82 bacterial species was selected, and a PCR-based reverse-capture checkerboard method was used for detection. A simple univariate test was used to determine the overabundance and underabundance of bacterial species in the diseased and in the healthy groups. Features identified with this univariate test were used to construct a probabilistic disease prediction model. Furthermore, a method for the analysis of global patterns of gene expression was performed to permit simultaneous analysis of the abundance of significant species by allowing cross-bacterial comparisons of abundance profiles between caries-active and caries-free subjects. Our results suggested that global patterns of microbial abundance in this population are very distinctive. The top bacterial species found to be overabundant in the caries-active group were Actinomyces sp. strain B19SC, Streptococcus mutans, and Lactobacillus spp., which exhibited an inverse relationship to beneficial bacterial species, such as Streptococcus parasanguinis, Abiotrophia defectiva, Streptococcus mitis, Streptococcus oralis, and Streptococcus sanguinis.

Child↗

PubMed2005

Vestige: maximum likelihood phylogenetic footprinting.

BACKGROUND: Phylogenetic footprinting is the identification of functional regions of DNA by their evolutionary conservation. This is achieved by comparing orthologous regions from multiple species and identifying the DNA regions that have diverged less than neutral DNA. Vestige is a phylogenetic footprinting package built on the PyEvolve toolkit that uses probabilistic molecular evolutionary modelling to represent aspects of sequence evolution, including the conventional divergence measure employed by other footprinting approaches. In addition to measuring the divergence, Vestige allows the expansion of the definition of a phylogenetic footprint to include variation in the distribution of any molecular evolutionary processes. This is achieved by displaying the distribution of model parameters that represent partitions of molecular evolutionary substitutions. Examination of the spatial incidence of these effects across regions of the genome can identify DNA segments that differ in the nature of the evolutionary process. RESULTS: Vestige was applied to a reference dataset of the SCL locus from four species and provided clear identification of the known conserved regions in this dataset. To demonstrate the flexibility to use diverse models of molecular evolution and dissect the nature of the evolutionary process Vestige was used to footprint the Ka/Ks ratio in primate BRCA1 with a codon model of evolution. Two regions of putative adaptive evolution were identified illustrating the ability of Vestige to represent the spatial distribution of distinct molecular evolutionary processes. CONCLUSION: Vestige provides a flexible, open platform for phylogenetic footprinting. Underpinned by the PyEvolve toolkit, Vestige provides a framework for visualising the signatures of evolutionary processes across the genome of numerous organisms simultaneously. By exploiting the maximum-likelihood statistical framework, the complex interplay between mutational processes, DNA repair and selection can be evaluated both spatially (along a sequence alignment) and temporally (for each branch of the tree) providing visual indicators to the attributes and functions of DNA sequences.

Algorithms↗

PubMed2004

Nonparametric Methods in Reliability.

Probabilistic and statistical models for the occurrence of a recurrent event over time are described. These models have applicability in the reliability, engineering, biomedical and other areas where a series of events occurs for an experimental unit as time progresses. Nonparametric inference methods, in particular, the estimation of a relevant distribution function, are described.

Journal Article↗

PubMed1999

Public health consequences of global climate change in the United States--some regions may suffer disproportionately.

Current risk assessments of the likely regional health impacts of global climate change (GCC) are hindered by two factors. First, dose-response relationships between weather parameters and many of the likely health effects have not been developed, and second, reliable estimates of future regional climates across the United States are still beyond the scope of current modeling efforts. Consequently, probabilistic risk estimates of most of the likely regional health impacts of GCC have such a high degree of uncertainty that their usefulness to health officials dealing with regional issues is very limited. With the numerous pressures on today's health care systems, it is understandable that the possible consequences of GCC have received scant attention from regional health care decision makers. Indeed, the consensus among this community appears to be that any increases in health effects associated with GCC will be easily handled by the current health care system. However, such a position may be naive as the potential exists that an unequal distribution of such effects could overwhelm some regions, whereas others may feel little or no impact. This review of the likely regional impacts of GCC has been structured as a semianalytical look at this issue of distributional effects. Because of the lack of dose-response information and reliable estimates of future regional climates, however, it takes a historical perspective. That is, it assumes that the quality and quantity of health risks a region faces under GCC will be directly related to its recent history of health risks from warm weather/climate-related diseases as well as to the size, characteristics, and distribution of the sensitive subpopulations currently residing within its borders. The approach is semiquantitative; however, it uses national data gathered on a regional level and as such should only be used to generate a hypothesis rather than test it. When applied to the United States, its outcome leads to the hypothesis that if indeed history repeats itself, some states or regions may be more greatly affected by GCC than others, not only because historically they are more prone to summer weather/climate-related diseases, but also because they contain a greater proportion of the sensitive subpopulations in the United States.

Air Pollutants↗

PubMedDate not supplied

[Clustering patterns of behavioral risk factors linked to chronic disease among young adults in two localities in Bogota, Colombia: importance of sex differences].

BACKGROUND: The characterization of clustering behavioral risk factors may be used as a guideline for interventions aimed at preventing chronic diseases. This study determined the clustering patterns of some behavioral risk factors in young adults aged 18 to 29 years and established the factors associated with having two or more of them. METHODS: Patterns of clustering by gender were established in four behavioral risk factors (low consumption of fruits and vegetables, physical inactivity in leisure time, current tobacco consumption and acute alcohol consumption), in 1465 young adults participants through a multistage probabilistic sample. Regression models identified the sociodemografic variables associated with having two or more of the aforementioned behavioral risk factors. RESULTS: Having one, 32.9% two and 17.7% three or four. Acute alcohol consumption was the risk factor most frequent in the combined risk factor patterns among males; physical inactivity during leisure time being the most frequent among females. Among the females, having two or more behavioral risk factors was linked to be separated or divorced, this having been linked to work having been the main activity over the past 30 days among males. CONCLUSIONS: The combinations of behavioral risk factors studied and the factors associated with clustering show different patterns among males and females. These findings stressed the need of designing interventions sensitive to gender differences.

Adolescent↗

PubMed2006

Recovery of forest structure and spectral properties after selective logging in lowland Bolivia.

Effective monitoring of selective logging from remotely sensed data requires an understanding of the spatial and temporal thresholds that constrain the utility of those data, as well as the structural and ecological characteristics of forest disturbances that are responsible for those constraints. Here we assess those thresholds and characteristics within the context of selective logging in the Bolivian Amazon. Our study combined field measurements of the spatial and temporal dynamics of felling gaps and skid trails ranging from <1 to 19 months following reduced-impact logging in a forest in lowland Bolivia with remote-sensing measurements from simultaneous monthly ASTER satellite overpasses. A probabilistic spectral mixture model (AutoMCU) was used to derive per-pixel fractional cover estimates of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil. Results were compared with the normalized difference in vegetation index (NDVI). The forest studied had considerably lower basal area and harvest volumes than logged sites in the Brazilian Amazon where similar remote-sensing analyses have been performed. Nonetheless, individual felling-gap area was positively correlated with canopy openness, percentage liana coverage, rates of vegetation regrowth, and height of remnant NPV. Both liana growth and NPV occurred primarily in the crown zone of the felling gap, whereas exposed soil was limited to the trunk zone of the gap. In felling gaps >400 m2, NDVI, and the PV and NPV fractions, were distinguishable from unlogged forest values for up to six months after logging; felling gaps <400 m2 were distinguishable for up to three months after harvest, but we were entirely unable to distinguish skid trails from our analysis of the spectral data.

Bolivia↗

PubMed1999

Application of risk analysis and geographic information system technologies to the prevention of diarrheal diseases in Nigeria.

Among the poor in developing countries, up to 20% of an infant's life experience may include diarrhea. This problem is spatially related to the lack of potable water at different sites. This project used risk analysis (RA) methods and geographic information system (GIS) technologies to evaluate the health impact of water source. Maps of Imo State, Nigeria were converted into digital form using ARC/INFO GIS software, and the resulting coverages included geology, hydrology, towns, and villages. A total of 11,537 diarrheal cases were reported. Thirty-nine water sources were evaluated. A computer modeling approach called probabilistic layer analysis (PLA) spatially displayed the water source at layers of geology, hydrology, population, environmental pollution, and electricity according to a color-coded five-point ranking. The water sources were categorized into A, B, and C based on the cumulative scores < 10 for A, 10-19 for B, and > 19 for C. T-test showed revealed significant differences in diarrheal disease incidence between categories A, B, and C with mean +/- SEM values of 1.612 +/- 0.325, 6.257 +/- 0.408, and 15.608 +/- 2.151, respectively. The differences were significant between categories A and B (P = 0.0000022), A and C (P = 0.0000188), and B and C (P = 0.0011348). The PLA enabled estimation of the probability of the risk of diarrheal diseases occurring at each layer and solutions to eliminate these risks.

Adolescent↗

PubMed2000

Return on investment (ROI) analysis in the face of uncertainty

Calculation of return on investment should be as easy as knowing the cost and the benefit and doing the math. Unfortunately in medical informatics rarely do we know the benefit with absolute certainty. Often the benefit is a combination of "fuzzy" ill defined concepts. In order to support the ROI analysis for medical information systems we developed a probabilistic time varying model that allows each of the fuzzy concepts of savings to be expressed as a likelihood distribution rather than a fixed value. The model and an interactive simulator were created using the graphical programming language G (National Instruments, Dallas,TX). This tools allows rapid prototyping and drag and drop ease of customizing the model to the particular clinical setting. The resulting ROI is expressed in terms of likelihoods, potential risk and confidence intervals providing a unique view of the financial analysis not typically available. In this paper an example is illustrated for perioperative information systems.

Journal Article↗

PubMed2004

[Clinical reasoning in neurology].

There is much neurological literature on clinical, pathological, technical aspects of diagnostic or therapeutic aspects, however there are few articles about clinical-neurological reasoning. This paper analyzes the psychological processes by which neurologists solve complicated diagnostic problems. Clinicians can use a variety of reasoning, such as model (pattern), exhaustive, probabilistic, deterministic (categorical), and hypothetical-deductive reasoning mechanisms. The neurologist must be aware of what kind of reasoning is being used at a given time as well as the errors that can occur. Most of these errors are found in the probabilistic setting and can be grouped into three categories: representativeness errors, availability or adjustment and anchoring biases. Most of them occur due to an inadequate intuitive judgement by the physician to handle the probabilities. There are other sources of error, related with the diagnostic process or with the environmental circumstances in which they develop. The most important ones as well as their theoretical and practical implications are described. In the evidence based medicine era, it is important for the clinical neurologists to be aware of this terminology and concepts for an adequate practice of neurology.

Decision Making↗

PubMedDate not supplied

Decompression tables and dive-outcome data: graphical analysis.

We compare outcomes of experimental air dives with prescriptions for ascent given by various air decompression tables. Among experimental dives compiled in the U.S. Navy Decompression Database, many profiles that resulted in decompression sickness (DCS) have longer total decompression times (TDTs, defined as times spent at decompression stops plus time to travel from depth to the surface) than profiles prescribed by the U.S. Navy table; thus, the divers developed DCS despite spending more time at stops than the table requires. The same is true to a lesser extent for the table used by the Canadian forces. A few DCS cases occurred in profiles having longer TDTs than those of the VVal-18 table and a table prepared at the University of Pennsylvania. The TDTs for 2.2% risk according to the probabilistic NMRI'98 Model are often far longer than TDTs of experimental dives that resulted in DCS. This analysis dramatizes the large differences among alternative decompression instructions and illustrates how the U.S. Navy table provides too little time at stops when bottom times are long.

Decompression↗

PubMed2005

A combined approach for locating box H/ACA snoRNAs in the human genome.

A novel combined method for locating box H/ACA small nucleolar RNAs (snoRNAs) is described, together with a software tool. The method adopts both a probabilistic hidden Markov model (HMM) and a minimum free energy (MFE) rule, and filters possible candidate box H/ACA snoRNAs obtained from genomic DNA sequences. With our novel method 12 known box H/ACA snoRNAs, and one strong candidate were identified in 30 nucleolar protein genomic sequences.

Algorithms↗

PubMedDate not supplied

Modeled estimates of chlorpyrifos exposure and dose for the Minnesota and Arizona NHEXAS populations.

This paper presents a probabilistic, multimedia, multipathway exposure model and assessment for chlorpyrifos developed as part of the National Human Exposure Assessment Survey (NHEXAS). The model was constructed using available information prior to completion of the NHEXAS study. It simulates the distribution of daily aggregate and pathway-specific chlorpyrifos absorbed dose in the general population of the State of Arizona (AZ) and in children aged 3-12 years residing in Minneapolis-St. Paul, Minnesota (MSP). Pathways included were inhalation of indoor and outdoor air, dietary ingestion, non-dietary ingestion of dust and soil, and dermal contact with dust and soil. Probability distributions for model input parameters were derived from the available literature, and input values were chosen to represent chlorpyrifos concentrations and demographics in AZ and MSP to the extent possible. When the NHEXAS AZ and MSP data become available, they can be compared to the distributions derived in this and other prototype modeling assessments to test the adequacy of this pre-NHEXAS model assessment. Although pathway-specific absorbed dose estimates differed between AZ and MSP due to differences in model inputs between simulated adults and children, the aggregate model results and general findings for simulated AZ and MSP populations were similar. The major route of chlorpyrifos intake was food ingestion, followed by indoor air inhalation. Two-stage Monte Carlo simulation was used to derive estimates of both inter-individual variability and uncertainty in the estimated distributions. The variability in the model results reflects the difference in activity patterns, exposure factors, and concentrations contacted by individuals during their daily activities. Based on the coefficient of variation, indoor air inhalation and dust ingestion were most variable relative to the mean, primarily because of variability in concentrations due to use or no-use of pesticides. Uncertainty analyses indicated a factor of 10-30 for uncertainty of model predictions of 10th, 50th, and 90th percentiles. The greatest source of uncertainty in the model stems from the definition of no household pesticide use as no use in the past year. Because chlorpyrifos persists in the residential environment for longer than a year, the modeled estimates are likely to be low. More information on pesticide usage and environmental concentrations measured at different post-application times is needed to refine and evaluate this and other pesticide exposure models.

Age Distribution↗

PubMed2003

A probabilistic method to detect regulatory modules.

MOTIVATION: The discovery of cis-regulatory modules in metazoan genomes is crucial for understanding the connection between genes and organism diversity. RESULTS: We develop a computational method that uses Hidden Markov Models and an Expectation Maximization algorithm to detect such modules, given the weight matrices of a set of transcription factors known to work together. Two novel features of our probabilistic model are: (i) correlations between binding sites, known to be required for module activity, are exploited, and (ii) phylogenetic comparisons among sequences from multiple species are made to highlight a regulatory module. The novel features are shown to improve detection of modules, in experiments on synthetic as well as biological data.

Algorithms↗

PubMedDate not supplied

A population-based exposure model for benzene.

A model of daily-average inhalation exposures and total-absorbed doses of benzene to members of large populations was developed as part of a series of multimedia exposure and absorbed dose models. The benzene exposure and dose model is based upon probabilistic rather than sequential simulation of time-activity patterns, a simpler approach to modeling personal benzene exposures than other existing models. An important innovation of the benzene model is the incorporation of an anthropometric module for generating correlated exposure factors used to estimate absorbed doses occurring from inhalation, ingestion, and dermal absorption of benzene. A preliminary validation exercise indicates that the benzene model produces reasonable estimates of the distribution of benzene personal air concentrations expected for a large population. Uncertainty about specific percentiles of the predicted distributions of personal air concentrations was found to be dominated by uncertainty about microenvironmental benzene concentrations rather than time-activity patterns, and uncertainty about total absorbed doses was dominated by a lack of knowledge about the true absorption coefficient for benzene in the lung rather than knowledge gaps about microenvironmental concentrations or intake rates. The results of this modeling effort have implications for environmental control decisions, including evaluation of source control options, characterization of population and individual risk, and allocation of resources for future studies.

Air Pollution↗

PubMed2006

Identification and separation of DNA mixtures using peak area information.

We introduce a new methodology, based upon probabilistic expert systems, for analysing forensic identification problems involving DNA mixture traces using quantitative peak area information. Peak area is modelled with conditional Gaussian distributions. The expert system can be used for ascertaining whether individuals, whose profiles have been measured, have contributed to the mixture. It can also be used to predict DNA profiles of unknown contributors by separating the mixture into its individual components. The potential of our probabilistic methodology is illustrated on case data examples and compared with alternative approaches. The advantages are that identification and separation issues can be handled in a unified way within a single probabilistic model and the uncertainty associated with the analysis is quantified. Further work, required to bring the methodology to a point where it could be applied to the routine analysis of casework, is discussed.

DNA↗

PubMed2005

Spatiotemporal video segmentation based on graphical models.

This paper proposes a probabilistic framework for spatiotemporal segmentation of video sequences. Motion information, boundary information from intensity segmentation, and spatial connectivity of segmentation are unified in the video segmentation process by means of graphical models. A Bayesian network is presented to model interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notion of the Markov random field is used to encourage the formation of continuous regions. Given consecutive frames, the conditional joint probability density of the three fields is maximized in an iterative way. To effectively utilize boundary information from the intensity segmentation, distance transformation is employed in local objective functions. Experimental results show that the method is robust and generates spatiotemporally coherent segmentation results. Moreover, the proposed video segmentation approach can be viewed as the compromise of previous motion based approaches and region merging approaches.

Algorithms↗